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vol.45 número4Evaluation of acidogenic sludge from anaerobic reactors running at low solids retention times to reduce sludge generation and enhance biogas productionThe investigation into the adsorption removal of ammonium by natural and modified zeolites: kinetics, isotherms, and thermodynamics índice de autoresíndice de assuntospesquisa de artigos
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Water SA

versão On-line ISSN 1816-7950
versão impressa ISSN 0378-4738

Water SA vol.45 no.4 Pretoria Out. 2019

http://dx.doi.org/10.17159/wsa/2019.v45.i4.7545 

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Received 5 December 2017
Accepted in revised form 23 September 2019

 

 

* Corresponding author, email: Ayanda.Shabalala@ump.ac.za

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RESEARCH PAPERS

 

The investigation into the adsorption removal of ammonium by natural and modified zeolites: kinetics, isotherms, and thermodynamics

 

 

Min PanI; Mingchuan ZhangII; Xuehua ZouIII; Xuetong ZhaoI; Tianran DengI; Tong ChenI; Xiaoming HuangI, III, *

IKey Laboratory of Environmental Biotechnology (XMUT), Fujian Province University, School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen, 361024, China
IICollege of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China
IIILaboratory of Nanomineralogy and Environmental Material, School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, 230009, China

 

 


ABSTRACT

The objectives of this study were to modify Chinese natural zeolite by NaCl and to investigate its suitability as a low-cost clay adsorbent to remove ammonium from aqueous solution. The effect of pH on ammonium removal was investigated by batch experiments. The findings indicated that pH has a significant effect on the removal of ammonium by M-Zeo and maximum adsorption occured at pH 8. Ion exchange dominated the ammonium adsorption process at neutral pH, with the order of exchange selectivity being Na+ > Ca2+ > K+ > Mg2+. The Freundlich model provided a better description of the adsorption process than the Langmuir model. The maximum ammonium adsorption capacity was 17.83 mg/g for M-Zeo at 293K. Considering the adsorption isotherms and thermodynamic studies, the adsorption of ammonium by M-Zeo was endothermic and spontaneous chemisorption. Kinetic studies indicated that the adsorption of ammonium onto M-Zeo is well fitted by the pseudo-second-order kinetic model. Ea in the Arrhenius equation suggested the adsorption of ammonium on M-Zeo was a fast and diffusion-controlled process. The regeneration rate was 90.61% after 5 cycles. The removal of ammonium from real wastewater was carried out, and the removal efficiency was up to 99.13%. Thus, due to its cost-effectiveness and high adsorption capacity, M-Zeo has potential for use in ammonium removal from aqueous solutions.

Keywords: zeolite, sodium chloride modified, adsorbent, regeneration, wastewater


 

 

INTRODUCTION

Nitrogen compounds are nutrients and are essential to all forms of life. In surface water, concentrations of ammonium nitrogen (NH4+-N) exceeding 0.3-0.5 mg/L (eutrophication) can promote the growth of algae and decrease the dissolved oxygen required for aquatic life (Hussain et al., 2007). With increased awareness and understanding of the deleterious effects of nitrogen, authorities have introduced stringent laws to restrict nitrogen discharges from both wastewater treatment facilities and other point-source contributors (Karapinar, 2009). Thus, efficient removal of ammonium has gained greater attention in water and wastewater treatment.

Traditionally, ammonium removal is achieved by a typical biological nitrogen removal (BNR) process, where NH4+ is transformed to nitrite/nitrate in the nitrification process, and then nitrite/nitrate is finally transformed to nitrogen gas through the denitrification process (Pan et al., 2015; Pan et al., 2017). However, biological systems confront great challenges in full-scale treatment plants and water bodies with low ammonium concentrations (< 5 mg/L). As an alternative to biological treatment, NH4+ removal by ion exchange and adsorption is of great importance for nutrient removal/recycling operations (Karapinar, 2009).

Considering the benefits of low-cost and high-safety ion exchange, zeolite has been shown to be an abundant cation exchange material and is economically used in water and wastewater treatment (Widiastuti et al., 2011; Kolakovic et al., 2014; Onyango et al., 2011). Zeolites are hydrated aluminosilicates with symmetrically stacked alumina- and silica-tetrahedrals, which results in an open and stable three-dimensional honeycomb structure possessing high cation exchange capacity, cation selectivity, higher void volume and great affinity for NH4+ (Huang et al., 2010). However, the NH4+ removal capacity of natural zeolite varies with the source of the zeolite, and the location within a particular deposit (Daramola et al., 2012; Zhao et al., 2010).

Malekian et al. (2011) reported the maximum NH4+ exchange capacity of natural Iranian zeolite to be 11.31 mg/g. Sarioglu (2005) found the maximum NH4+ exchange capacity of natural Turkish zeolite to be 25.93 mg/g. It seems that natural zeolite from different origins show different characteristics (Sarioglu, 2005). Therefore, each specific zeolite is required to be studied individually (Alshameri et al., 2014).

Additionally, the ion exchange selectivity of zeolite was reported to follow Cs+> Rb+> K+> NH4+> Ba2+> Sr2+> Na+> Ca2+> Fe3+> Al3+> Mg2+> Li+ (Lin et al., 2013). Natural zeolite generally has a high Si/Al ratio and contains quite a few impurities, which greatly reduce its cation exchange capacity (Wang, Lin, and Pang, 2008). In order to enhance the NH4+ adsorption capacity of zeolite, several modification methods have been applied, including microwave pre-treatment, NaOH, HCl, and NaCl solution treatment, magnetic material application, silicate-carbon solution treatment, among others (Lei, Li, and Zhang, 2008; Li et al., 2011; Liu et al., 2013). Many studies have proved that natural zeolite treated by NaOH solution could transform low-grade natural materials to high capacity cation exchangers (Wang, Lin, and Pang, 2008). However, in many of those studies, the long time of conversion, high temperature, complex operation process, and a significant amount of residual raw impurities have limited modified zeolite application in NH4+ adsorption. NaCl-modified zeolite is a common and cheap method and the adsorbent was easily obtained. NaCl modification effectively increased ammonium adsorption capacity by increasing the Na contents in zeolite and by modifying the surface morphology to enhance film mass transfer rate (Lin et al., 2013).

Although a large number of studies related to the removal of ammonium by using types of zeolites have been reported in the literature, zeolites from different locations with special physical and chemical properties require individual investigation (Alshameri et al., 2014; Huang et al., 2010; Malekian et al., 2011; Sarioglu, 2005). The mineral reserve of clinoptilolite in Xuancheng is abundant, and clinoptilolite has significant performance in adsorption of ammonium from aqueous solution. Zeolite modified by sodium chloride solution can have a greatly increased adsorption capacity for ammonium. Therefore, it is important to study the property of ammonium adsorption for natural zeolite (N-Zeo) and NaCl-modified zeolite (M-Zeo). The objectives of this study were: (i) to prepare M-Zeo and systematically investigate its application for NH4+ removal from aqueous solution; (ii) to elucidate the effects of environmental conditions, including pH, initial concentration of ammonium and temperature on the adsorption of ammonium to M-Zeo by batch experiments; (iii) to reveal the exchange selectivity of Na+, Ca2+, K+, Mg2+ contained in zeolite for ammonium; (iv) to study the adsorptive mechanism of NH4+ on M-Zeo through adsorption isotherms, thermodynamic and kinetic models; (v) to discuss the rate-controlled process of NH4+ adsorption onto M-Zeo according to the apparent activation energy; (vi) to investigate the treatment of real wastewater containing ammonium by M-Zeo. The aim of this paper is to evaluate the suitability of M-Zeo as an efficient and low-cost clay adsorbent for adsorption of ammonium from aqueous solution and wastewater in environmental clean-up, and the Arrhenius formula was employed to reveal the rate-controlled process of ammonium adsorption onto M-Zeo

 

MATERIALS AND METHODS

Materials

Natural zeolite (N-Zeo) used in the experiments was obtained from Xuancheng in Anhui Province, China, which was ground and selected for particle sizes of 45-74 µm. Due to adsorbents with smaller particle size and larger specific surface area showing higher adsorption performance, zeolite powder in the particle size of 45-74µm was used in this study. Zeolite samples (25 g) were dispersed in 500 mL of 2 mol/L NaCl solution by magnetic stirring for 24 h; the concentration of NaCl used followed Lin et al. (2013). Then the mixtures were centrifuged, washed 5 times with deionized water, and dried at 105°C for 12 h. The obtained NaCl-modified zeolite (M-Zeo) was finally ground and screened though a 200 mesh sieve (74 µm).

Stock ammonium solution (10000 mg NH4+-N/L) was prepared by dissolving 38.207 g NH4Cl into 1 L deionized water. All working solutions were prepared by diluting this stock solution with deionized water.

Batch adsorption experiments

To investigate the impact of pH values on the adsorption capacity of ammonium, natural and modified zeolite were tested. Ammonium solutions (25 mL, 1000 mg/L) were added into 150 mL conical flasks with stoppers, and the pH of solutions was adjusted to 5, 6, 7, 8, and 9 by adding 0.1-1 M NaOH solution and 0.1-1 M HCl solution. After adding 0.5 g of adsorbent, the flasks were stirred at 200 r/min in thermostatic shakers for 24 h at 293 K. After the mixture was centrifuged, the supernatant was filtered through a 0.45 µm membrane filter prior to the determination of ammonium concentrations. The equilibrium adsorptive capacity was calculated by Eq. 1:

where qt is the adsorptive capacity at time t, mg/g; C0 is the initial concentration of ammonium in the solution, mg/L; Ct is the concentration of ammonium in the solution at time t, mg/L; V is the volume of the solution, L; and W is the mass of the adsorbent, g.

Adsorption isotherms for ammonium were carried out in thermostatic shakers for 24 h at desired temperatures (293, 303, 313 K). Adsorbents (0.5 g) were mixed with ammonium solutions (25 mL) at different initial concentrations ranging from 5 to 1 000 mg/L (5, 10, 25, 50, 100, 200, 500, 800, 1 000 mg/L) at pH 8. The order of exchange selectivity was evaluated by examining the concentrations of cations.

Adsorption kinetics for ammonium were evaluated at pH 8 and at an ambient temperature of 293 K. Adsorbents (0.5 g) were added to ammonium solutions (25 mL) with an initial concentration of 1 000 mg/L. Samples withdrawn at different time intervals of 0.25, 0.5, 1, 2, 3, 4, 8 and 12 h were analysed for ammonium concentration.

The regeneration study was performed by evaluating the effect of regeneration cycles on the ammonium adsorptive capacity at pH 8 and at an ambient temperature of 293 K. Adsorbents (0.5 g) were added in 25 mL of 1 000 mg/L ammonium solutions. The adsorbents were collected after adsorption and regenerated by 250 mL of 2 mol/L NaCl solution; the concentration of NaCl was consistent with the concentration used in the absorbent preparation. Then, the zeolites were washed by deionized water and centrifuged several times. The regenerated zeolite was reused for adsorption of ammonium from aqueous solution.

Analysis methods

Ammonium concentrations in liquid samples were analysed by spectrophotometry with a spectrophotometer (V-1100D, Mapada Co., Shanghai, China). The concentrations of Na+, K+, Ca2+ and Mg2+ in solution were measured by atomic absorption spectroscopy (AAS-6300C, Shimadzu, Japan). Elemental compositions of M-Zeo were determined using X-ray fluorescence (XRF) (XRF-1800, Shimadzu, Japan). Mineral phases were identified by X-ray diffraction (XRD) using a D/max-RB powder diffraction meter (Rigaku, Japan), with a Cu-target operated at 40 kV, 100 mA. The XRD patterns were taken in the range of 4-70° at a scan rate of 4°/min, which were analysed using the software (Search-Match) by comparing the experimental data with those included in the Joint Committee of Powder Diffraction Standards (JCPDSs) database.

 

RESULTS AND DISCUSSION

Characterization

XRD patterns of N-Zeo and M-Zeo are illustrated in Fig. 1. Diffraction patterns at 2θ = 9.88, 11.22, 17.34, 22.74, 26.12, 29.06, and 32° are identified as clinoptilolite when compared with the standard database. The characteristic peaks of quartz can be found at 26.7 and 50.1°. The intensity of quartz became weaker after modification. The XRD spectra of M-Zeo showed no significant differences from N-Zeo, indicating that the main mineral phases of zeolite were not changed after modification.

 

 

XRF was applied to analyse the elemental compositions of N-Zeo and M-Zeo, presented as percentage of element in the highest oxidation state (Table 1). It can be clearly seen that the contents of the exchangeable cations such as K+, Ca2+, and Mg2+ in the M-Zeo were decreased, while the amount of Na+ was increased significantly. The result suggested that Ca2+, K+, and Mg2+ were replaced by Na+, which can be used to remove NH4+ in ion-exchange applications.

Effect of pH

Figure 2a shows the adsorption of ammonium onto N-Zeo and M-Zeo as a function of initial pH. In acidic condition, increasing pH favoured both N-Zeo and M-Zeo adsorption of ammonium. In basic conditions (pH 8 to 10), reduced adsorption capacity with increasing pH is seen in Fig. 2a, leading to the highest adsorption capacities of 11.39 and 17.77 mg/g on N-Zeo and M-Zeo, respectively, at pH 8. This clearly implies that ammonium adsorption onto both N-Zeo and M-Zeo was pH dependent. The dominant mechanism of ammonium adsorption onto N-Zeo and M-Zeo was assumed to be ion-exchange between cations (Na+, K+, Ca2+, Mg2+, et al.) on the adsorbent surface and ammonium in the solution. As shown in Fig. 2b, ammonium existed in the form of NH4+ in aqueous solution at pH 2-8, and as NH3 at pH 10-13. The species of ammonium were converted from NH4+ to NH3 when pH was between 8 and 10. At pH < 8, ammonium adsorption increased with increasing pH, principally being attributed to the decline in competing hydrogen ions, and with cation exchange being the dominant mechanism (Liu et al., 2013). At pH > 8, ammonium removal decreased with increasing pH, likely owing to the conversion of NH4+ into NH3 in alkaline solution. Thus, molecule adsorption was the main mechanism for ammonium removal, which resulted in the reduction of ion-exchange potential. This observation correlated with findings reported in the literature (He et al., 2016; Lin et al., 2013). Therefore, the optimum pH of M-Zeo for adsorption of ammonium is that of a neutral solution.

 

 

Ion-exchange adsorption

The ion exchange process between the zeolite frame and aqueous ammonium solution can be expressed by Eq. 2 (Lin et al., 2013):

where M represents the loosely held cations in zeolite and n is the number of electric charges. Assuming M in zeolite are Na+, K+, Ca2+ and Mg2+, the ion exchange capacity (IEC) can be calculated as the sum of exchange cations as follows:

As shown in Fig. 3, the adsorption capacity of ammonium onto M-Zeo increased significantly at different initial ammonium ion concentrations, while the equivalent concentrations of Mg2+, K+, Na+ and Ca2+ released into the solution increased significantly. The IEC for the sum of the four cations was a little lower than the ammonium adsorption capacity at equilibrium, indicating that ion exchange is predominant in the adsorption of ammonium by zeolite. The extra amount of ammonium adsorption on M-Zeo is ascribed to electrostatic attraction between negative charges on the adsorbent surface and NH4+ (Alshameri, Ibrahim, et al., 2014).

 

 

When the initial ammonium concentration rose to 25 mg/L, K+ started to be released from zeolite. Mg2+ appeared in the aqueous solution after the initial ammonium concentration rose to 50 mg/L. Na+ was the dominant cation exchanged with ammonia under an initial ammonia concentration of less than 100 mg/L, while Ca2+ was the dominant cation exchanged with ammonia under an initial ammonia concentration higher than 100 mg/L. Thus, the effect of the metal ions on ammonium adsorption to zeolite suggests an order of preference of Na+ > Ca2+ > K+ > Mg2+. A similar result was reported by other researchers (Lin et al., 2013). A slightl difference in the order was determined as Na+> K+> Ca2+ > Mg2+ by other researchers (Lei et al., 2008; Watanabe et al., 2007).

Adsorption isotherms

The adsorption isotherms of ammonium on zeolite were fitted by two typical models, Langmuir and Freundlich, as Eqs 4 and 5 (Liu et al., 2013; Langmuir, 1918):

where Ce is the equilibrium concentration (mg/L) in the solution; qe is the adsorption capacity on adsorbent (mg/g); qm refers to the maximum adsorption capacity at monolayer coverage (mg/g). The values of k (L/mg) and Kf (mg/g) are the Langmuir and Freundlich adsorption constants, respectively. 1/n is a constant relating to adsorption intensity or surface heterogeneity.

 

 

The relative parameters (qm, k, Kf and 1/n) were calculated from the slope and intercept of the linear plots based on the Langmuir and Freundlich adsorption isotherms. As the correlation coefficient of the Freundlich model (R2 > 0.9905) was higher than that of the Langmuir model (R2 < 0.9815), the Freundlich model was suggested to better fit ammonium sorption onto both N-Zeo and M-Zeo. This indicated that adsorption occurred on a structurally heterogeneous adsorbent (Pan et al., 2017). The maximum adsorption capacity on a monomolecular layer of M-Zeo was estimated to be 17.83 mg/g at 293 K, which is higher than that found by other researchers. For example, Mazloomi and Jalali (2016) found the maximum adsorption of ammonium by Iranian zeolite to be 10.08 mg/g. Saltalı et al. (2007) reported that the adsorption capacity of ammonium by natural Turkish zeolite was 9.64 mg/g at 294 K (Saltali et al., 2007). It has also been reported that the maximum adsorption of ammonium using a salt-activated Chinese (Hulaodu) zeolite was 9.52 mg/g (Alshameri et al., 2014). Meanwhile the ammonium exchange capacity for natural and modified Yemeni zeolites were 11.18 mg/g and 8.29 mg/g, respectively (Alshameri et al., 2014). The constant of 1/n for the Freundlich model is related to the adsorption intensity, which varies with the heterogeneity of materials. The values of 1/n were lower than 0.52 in this study, which suggests that the adsorption of ammonium on N-Zeo and M-Zeo was highly favourable (Table 2).

The Dubinin-Redushckevich (D-R) isotherm was also employed to reveal the type of adsorption (physical adsorption or chemical adsorption) (Mazloomi and Jalali, 2016). The D-R equation has the linear form:

where qm is the D-R adsorption capacity (mol/g); β is the constant of the adsorption energy (mol2/J2), related to the average energy of adsorption per mole of the sorbate as it is transferred to the surface of the solid from infinite distance in the solution; ε is Polanyi potential, which is described as:

where T is the absolute temperature (K) and R is the gas constant (8.314 J/mol·K).

Moreover, the mean energy of adsorption E (kJ/mol) can be calculated from the D-R parameter β using the following formula:

As seen in Table 2, the correlation coefficients of the D-R model for ammonium sorption on N-Zeo and M-Zeo were higher than 0.977, suggesting the D-R model was acceptably applied to fit the experimental data in this study. The relative parameters (β and qm) were calculated from the slope and intercept of Eq. 6. The value of mean energy of adsorption E is in the range of 1-8 kJ/mol and 8-16 kJ/mol for physical and chemical adsorption, respectively. In this study, the E values of ammonium adsorption on N-Zeo and M-Zeo were in the range of 8-16 kJ/mol, indicating that the adsorption process was essentially chemisorption (Table 2).

Thermodynamic parameters

The thermodynamic parameters can be calculated from the temperature-dependent adsorption isotherms based on Eqs 9-11:

where Kd is the distribution coefficient, mL/g; ΔG0 is the change of Gibbs energy, kJ/mol. The values of enthalpy (ΔH0) and entropy (ΔS0) can be obtained by the slope and intercept of the plot of lnKd versus 1/T (Fig. 5). The values of Kd, ΔG0, ΔH0 and ΔS0 are summarized in Table 3. Negative values of ΔG0 and positive values of ΔH0 were found, which reveals that the processes of ammonium adsorption on N-Zeo and M-Zeo were endothermic, feasible and spontaneous. The change of entropy (ΔS0) was 0.032 and 0.038 kJ/(mol·K) for the adsorption of ammonium on N-Zeo and M-Zeo, respectively. The positive values of ΔS0 suggested that the randomness increased during the removal of ammonium ions from aqueous solution onto N-Zeo and M-Zeo.

 

 

Adsorption kinetics

The adsorption kinetics of ammonium on M-Zeo and N-Zeo was simulated by four typical kinetic models. The kinetic equations, including pseudo first-order model, pseudo second-order model, Elovich model and intraparticle diffusion model, are described as follows (Huang et al., 2015; Malekian et al., 2011; Yang et al., 2015):

where qt is the adsorbed amount at time t, mg/g; qe is the adsorption amount at equilibrium, mg/g; k1 is the rate constant of pseudo first-order adsorption, g/(mg·h); k2 is the rate constant of pseudo second-order adsorption, g/(mg·h); the parameter ae is the initial adsorption rate, mg/(g·h), and be is related to extent of surface coverage and activation energy for chemisorption, g/mg; k3 is the intraparticle diffusion rate constant, mg/(g·h0.5).

Table 2 tabulates the relative parameters calculated from these four kinetic models. Constants k1 and k2 were respectively determined from the slope of the line obtained by plotting ln(qe-qt) versus t in the pseudo-first-order model and the intercept of the line by plotting t/qt versus t in the pseudo-second-order model, while the initial adsorption rate ae was determined from the intercept of the line obtained by plotting qt versus ln t in the Elovich equation. The intraparticle diffusion rate constant k3 was calculated from the slope of the line obtained by plotting qt and t0.5. The correlation coefficient for the pseudo-second-order model is the highest among the four kinetic models, revealing that the pseudo-second-order model best describes the adsorption kinetics of ammonium onto M-Zeo, and that chemisorption dominates in the adsorption process (Huang et al., 2017; Liao et al., 2012). This conclusion matched the fitting results from the D-R isotherm. Moreover, , the theoretically adsorbed amount at equilibrium (16.34 mg/g) obtained from the pseudo-second-order model was much closer to the adsorbed amount at equilibrium obtained from experiment (17.83 mg/g) than that obtained from the other models. Due to the high correlation coefficient (>0.99), the Elovich equation was also found to be suitable to describe the second-order kinetic, assuming that the actual solid surfaces are energetically heterogeneous (Mezenner and Bensmaili, 2009). The initial adsorption rate ae was 49 166 mg/(g·h) for ammonium adsorption onto M-Zeo, which was much higher than 80.27 mg/(g·h) for ammonium adsorption onto N-Zeo. The intraparticle diffusion model is assumed to be the sole rate-controlling step if the regression of qt versus t0.5 is linear and the plots pass through the origin (Huang et al., 2010). The fitting results show that the regression was linear, but the plot did not pass through the origin. As seen in Fig. 6d, the ammonium adsorption onto N-Zeo and M-Zeo involved two steps and presented a multilinearity. Therefore, the adsorption processes of ammonium onto N-Zeo and M-Zeo can be divided into two steps. The first, fast step was mainly contributed by boundary layer diffusion or macro-pore diffusion. The second, gradual step was attributed to intraparticle diffusion or micro-pore diffusion (Pan et al., 2017; Widiastuti et al., 2011).

The apparent activation energy

The linear form of the Arrhenius equation can be expressed as the following formula:

where k is the rate constant of pseudo second-order adsorption; Ea is the apparent activation energy, J/mol; R is the gas constant, 8.314 J/mol·K; T is the absolute temperature (K).

Comparison of the outputs of adsorption kinetics models suggested that the adsorption process of ammonium onto N-Zeo and M-Zeo was in accordance with the pseudo-second order model. Thus, the apparent activation energy can be determined by the slope of the line plotting ln k (k2 in Table 4) versus 1/T according to the Arrhenius equation. Chen (2016) stated that the reaction rate would be fast if Ea is less than 40 kJ/mol at room temperature, and rather slow if Ea is greater than 120 kJ/mol (Chen et al., 2016). Moreover, adsorption would be a diffusion-controlled process if the Ea is less than 25-30 kJ/mol (Lazaridis and Asouhidou, 2003; Mezenner and Bensmaili, 2009). In the present study, Ea was 13.37 kJ/mol for ammonium adsorption onto N-Zeo and ٩.٨٤ kJ/mol for ammonium adsorption onto M-Zeo, which was less than 25 kJ/mol. Thus, the adsorption of ammonium on M-Zeo was a fast and diffusion-controlled process and its reaction rate was much higher than the adsorption of ammonium on N-Zeo.

 

 

Regeneration and treatment of real wastewater

Fig. 8 shows the regeneration of M-Zeo after adsorbing ammonium. After being regenerated after the first cycle, the adsorption capacity of ammonium onto M-Zeo was slightly decreased from 17.36 mg/g to 16.77 mg/g. After 5 cycles in regeneration, the adsorption capacity had dropped to 15.73 mg/g and the regeneration ratio was up to 90.61%. A similar result was reported by Ji et al. (2007). Treatment of real wastewater by adsorbents could evaluate if the adsorbents can be used for environmental purification (Kostic et al., 2017; Kostic et al., 2018). In this study, sewage, which was collected from a domestic wastewater pipe at Xiamen University of Technology campus, with an initial ammonium concentration of 14.92 mg/L was employed to estimate the possibility of M-Zeo being used for real wastewater treatment. Under the condition of a pH value of 8.5 for the campus sewage, the final concentration of ammonium was 0.13 mg/L, indicating a removal efficiency for ammonium of 99.13%. Given the high removal efficiency and regeneration ratio of M-Zeo, it can be considered as a promising adsorbent in the preconcentration and removal of ammonium from aqueous solutions in environmental clean-ups.

 

 

CONCLUSIONS

The synthetic M-Zeo was successfully prepared from N-Zeo by dispersing into NaCl solution and drying at 105°C. The adsorption isotherms and kinetics of ammonium by M-Zeo could be satisfactorily simulated by the Freundlich model and the pseudo-second-order model, respectively. Thermodynamic parameters indicated that the adsorption process of ammonium onto M-Zeo was endothermic and spontaneous. The fitting results of the D-R isotherm determined that ammonium removal by M-Zeo was chemisorption. According to the intraparticle diffusion model, ammonium adsorption onto M-Zeo involved two adsorption steps: (i) boundary layer diffusion or macro-pore diffusion, and (ii) intraparticle diffusion or micro-pore diffusion. The Ea in the Arrhenius equation suggested that the adsorption of ammonium on M-Zeo was a fast and diffusion-controlled process. The findings of this study suggest that the low cost, high adsorption capacity and good regeneration performance of M-Zeo indicate that it is a promising adsorbent to be widely utilized for ammonium removal from aqueous solution.

 

ACKOWLEDGEMENTS

The authors would like to express their gratitude for the financial support provided by the Natural Science Foundation of Fujian Province, China (2016J05140), the Open Research Fund Program from Key Laboratory of Environmental Biotechnology (XMUT), Fujian Province University (EBL2018004), the Scientific Research Project of Xiamen Overseas Talents (201631402), Scientific Climbing Program of Xiamen University of Technology (XPDKQ18031) and the Science and Technology Project of Longyan City (2017LY63).

 

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Received 16 September 2018
Accepted in revised form 27 September 2019

 

 

* Corresponding author, email: huangxman@vip.sina.com

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RESEARCH PAPERS

 

Comparison of refined and non-refined wastewater effect on wheat seed germination and growth under drought

 

 

Hassan Heidari*; Saman Moradi

Department of Agronomy and Plant Breeding, Faculty of Agricultural Science and Engineering, Razi University, Kermanshah, Iran

 

 


ABSTRACT

Wastewater has attracted special attention as a possible source of irrigation. The present study aimed to compare the effect of refined and non-refined wastewater on wheat seed germination and growth under induced drought conditions in laboratory and pot experiments. The laboratory experiment included the iso-osmotic potentials of 0.275, 0.4, and 0.47 MPa of polyethylene glycol (PEG, as a drought factor) and wastewater. In addition, the pot experiment included a wastewater factor (i.e., tap water, 100% refined wastewater, 50% refined wastewater + 50% non-refined wastewater, and 100% non-refined wastewater) and a drought factor (i.e., an irrigation interval of two and three days as normal and drought conditions, respectively). The results demonstrated that the drought related to PEG did not reduce seed germination while wastewater decreased seed germination. Further, an osmotic potential of 0.47 MPa resulted in the highest and lowest radicle length in both wastewater and PEG, respectively. The results also revealed that caulicle length and seed vigour were decreased by PEG as the osmotic potential increased while no significant difference was observed between wastewater treatments and distilled water (control). Based on the results, an irrigation interval of 3 days with 100% non-refined wastewater produced the highest chlorophyll content and 100% refined and 100% non-refined wastewater produced a larger leaf area compared to the control. Furthermore, drought with wastewater application increased specific leaf weight whereas it reduced the total biomass compared to control (i.e., tap water with an irrigation interval of 2 days), except for 100% non-refined wastewater. Therefore, wastewater application compensates for the adverse effect of drought due to nutrient addition.

Keywords: chlorophyll, drought, refined wastewater, seed vigour


 

 

INTRODUCTION

Drought is considered to be one of the most important factors which limits crop production across the globe. Agricultural production should be increased to meet global population demands (Tilman et al., 2011). Irrigation is regarded as one of the methods which effectively increases crop production while water use efficiency typically remains stable under different water amounts (Curt et al., 1995). According to Mustafa Tahir et al. (2014), shortening the irrigation interval at the critical periods of growth can lead to an increase in plant height and forage yield for oats.

Chlorophyll concentration is a key factor in photosynthesis rate (Ghosh et al., 2004), which increases under drought since leaf area reduces and the leaf becomes thicker, leading to an increase in chlorophyll concentration (Barraclough and Kate, 2001).

Wastewater (e.g., domestic and industrial types) can be used for crop irrigation. In addition, it has been found to increase plant height and biomass in wheat (Pandey and Singh, 2015). According to Ashraf and Ali (2007), seed germination is one of the sensitive stages in plant growth and, as an index of plant sensitivity to contamination, has attracted the attention of different studies. Wastewater contains salts and heavy metals. Li et al. (2005) reported that an increase in heavy metal concentration caused a decline in seed germination percentage. The highest wheat seed germination was recorded at 25% effluent concentration when compared to a variety of other concentrations (e.g., 0%, 25%, 50%, 75%, and 100%) of effluents from a textile and sugar factory (Nandal et al., 2017). Seedling root and shoot growth decreased relative to a control when applying effluents from a pharmaceutical and battery industry at various irrigation intervals (Raju et al., 2015).

However, various studies have reported different adverse effects of salinity and drought. Both salinity and drought reduce coleoptile and root length, as well as the fresh and dry weight of the root and coleoptiles in wheat (Jovovic et al., 2018). The drought and salinity in these studies resulted from polyethylene glycol and NaCl, respectively, and had no significant effect on seed germination percentage, germination rate, or seedling shoot and root weight in wheat, compared to the control (Mohammadi and Dargahi, 2015). Farmers may use wastewater for irrigation, in refined or non-refined form, from different sources including a domestic source. Further, physical and chemical processes may be utilized for treating the wastewater (Mlakar et al., 2017). The application of non-refined wastewater for irrigation can inhibit plant growth through cell division, due to sticky and lagging chromosomes (Sik et al., 2009), and irrigating plants with non-refined wastewater may cause disease if directly applied by a person. Conversely, several other studies have indicated that non-refined wastewater can promote plant growth (Moradi et al., 2016; Khaleel et al., 2013).

It is not clear whether refined wastewater can have the same promoting effect as non-refined wastewater. Furthermore, wastewater refinement is a costly process and its efficiency needs evaluation. Therefore, the current study sought to compare physical, chemical, and biological traits of refined and non-refined wastewater and their effects on seed germination and early plant growth in wheat under drought.

 

MATERIALS AND METHODS

Laboratory experiment

A laboratory experiment was conducted at Physiology Laboratory, the College of Agricultural Science and Engineering, Razi University, during 2014. Based on the aim of the study, the treatments encompassed the iso-osmotic potentials of 0.275, 0.4, and 0.47 MPa (equal to 100% refined wastewater, 50% refined wastewater + 50% non-refined wastewater, and 100% non-refined wastewater, respectively), of polyethylene glycol (PEG, as a drought factor) and refined or non-refined wastewater. Table 1 demonstrates some of the parameters of refined and non-refined wastewater. PEG solutions were prepared according to Michel and Kaufmann's formula (1973):

 

 

 

where: Ys, C, and T demonstrate the osmotic potential, the concentration of PEG in gkg1 H2O, and the temperature in degrees Celsius, respectively. The osmotic potentials of wastewater were measured using an osmometer and distilled water was utilized as a control. Therefore, the experiment comprised 7 treatments (i.e., one control and 0.275, 0.4, and 0.47 MPa of polyethylene glycol and wastewater). The study was conducted as a completely randomized design with 3 replications. Ghareso is a river in Kermanshah and the wastewaters from industrial and domestic sources are discharged into this river. Additionally, a wastewater treatment plant exists in Kermanshah to purify part of the effluents. In general, the purification process occurs in 3 steps: physical purification, which encompasses the construction of overflow, screens, the initial sedimentation ponds, the secondary sedimentation ponds, and the pump house of the sludge; chemical purification, which involves activated sludge and chlorination pools; and, finally, biological treatment, which involves biological reactors (Iran's Environmental Health, 2019). Wastewater samples were collected from Kermanshah wastewater treatment plant with an output of 60 000 m3day1 (Ghamarnia et al., 2014), which serves 400 000 persons.

To assess the effect of wastewater and drought on seed germination, the seeds of wheat (Triticum aestivum cv. Sirwan) were placed on filter paper in Petri dishes and then 6 mL of the prepared solution was added to each Petri dish. Next, these dishes were kept in a germinator for a week, after which several parameters were measured: seed germination percentage, caulicle length, radicle length, radicle to caulicle ratio, and seed vigour. Seed vigour was calculated by Heidari's (2013) equation.

Pot experiment

An outdoor pot experiment was conducted at the College of Agricultural Science and Engineering, Razi University, in 2014. The experiment was conducted as a factorial arrangement based on a randomized complete block design with 3 replicates. One factor was wastewater (i.e., tap water, 100% refined wastewater, 50% refined wastewater + 50% non-refined wastewater, and 100% non-refined wastewater). The other factor included irrigation intervals (2 and 3 days) which were determined by a pre-experiment. In the pre-experiment, the irrigation intervals of 2 and 3 days were determined as well-watered and drought treatments, respectively, based on soil factors and plant symptoms. At each irrigation event, the soil surface was gradually watered to ensure the soil was totally wet. Then, watering was stopped when the pot soil started to drain and the seeds of the wheat (Triticum aestivum cv. Sirwan) were sown in pots (7 cm in diameter and 7.5 cm in height) filled by field soil. The experiment lasted 21 days, after which related parameters were estimated: leaf chlorophyll content, plant height, leaf area, stem fresh and dry weight, leaf fresh and dry weight, leaf to stem ratio, total biomass, and specific leaf weight. Specific leaf weight was calculated by dividing leaf dry weight by leaf area. A SPAD (soil plant analytical development) device was used to determine the index of leaf chlorophyll (Bail et al., 2005). Finally, leaf and stem samples were dried in an oven at 70°C for 24 h in order to calculate their dry weight.

Data analysis

Data were analysed by SAS software and the means were compared by applying Duncan's multiple range test at a probability level of 5%.

 

RESULTS

Water quality

The effluent quality was improved by the wastewater treatment plant (Table 1). For example, heavy metals, as well as some essential nutrients for plant growth, such as nitrogen and phosphorus, were reduced by wastewater treatment. Fe increase could be attributed to FeCl3 for coagulation. Mn increase could be due to the lack of subsurface oxygen. pH increase could be attributed to liming.

Laboratory experiment

Figure 1 illustrates the influence of polyethylene glycol (PEG) iso-osmotic potentials and wastewater on the seed germination traits of wheat. Drought related to PEG fails to reduce seed germination while wastewater decreases it (Fig. 1a). Further, germination reduction by wastewater is initiated by the lowest osmotic potential. The osmotic potential of 0.47 MPa for the wastewater and PEG resulted in the highest and lowest radicle length, respectively (Fig. 1b). An increase in osmotic potential leads to a decrease in the caulicle length with PEG, whereas no significant difference is observed between wastewater treatments and distilled water as control (Fig. 1c). With regard to the radicle to caulicle ratio, there is also no significant difference between wastewater treatments and distilled water (Fig. 1d). Furthermore, seed vigour decreases with PEG through increasing the osmotic potential, while wastewater treatments and distilled water demonstrate no significant difference in terms of seed vigour (Fig. 1e).

 





 

Pot experiment

The effect of irrigation interval and wastewater on early growth traits in wheat is displayed in Fig. 2. The irrigation interval of 3 days with 100% non-refined wastewater produces the highest chlorophyll content. More precisely, drought reduces leaf area while increasing leaf thickness (specific leaf weight), leading to an increase in chlorophyll concentration which is confirmed by the results of the current experiment (Fig. 2a). Under the irrigation interval of 2 days, leaf fresh weight increases for all wastewater treatments whereas under the irrigation interval of 3 days, only 100% non-refined wastewater results in an increase in leaf fresh weight (Fig. 2b). Drought reduces leaf dry weight (Fig. 2c). Additionally, 100% refined wastewater produces the highest stem dry weight. A remarkable reduction of some nutrients while improving some physiochemical properties of wastewater (e.g., electrical conductivity) in 100% refined wastewater results in increasing the stem dry weight (Fig. 2d). In addition, 100% refined wastewater with an irrigation interval of 2 days reveals higher plant height compared to the control, namely, tap water with an irrigation interval of 2 days. By increasing the irrigation interval, the height of the plant irrigated by 100% non-refined wastewater shows no reduction (Fig. 2e), which is likely due to the positive effect of wastewater on plant growth. Further, 100% refined and 100% non-refined wastewater produce a higher leaf area compared to the control (Fig. 2f). The lowest and highest leaf to stem ratio, among the treatments, is observed for 100% refined wastewater with an irrigation interval of 2 and 3 days, respectively (Fig. 2g). On the other hand, drought increases leaf to stem ratio under 100% refined wastewater. This suggests that stem elongation initiation is postponed by drought. Additionally, drought with wastewater application leads to an increase in specific leaf weight compared to the control (Fig. 2h), and finally, drought reduces total biomass compared to the control, except in the case of 100% wastewater (Fig. 2i).

 

DISCUSSION

Laboratory experiment

The reduction in seed germination by wastewater can be attributed to salinity and heavy metal stresses, which is confirmed by the data in Table 1. Further, higher electrical conductivity and some inhibiting elements, including Na, in the wastewater are the main reasons for such a reduction in seed germination. Apparently, salinity stress had a negative effect on germination while some nutrients promoted radicle and caulicle length after germination in the present study. However, these nutrients demonstrated their effect only at high concentration. The adverse effect of osmotic potential was compensated for by the positive effect of wastewater due to nutrients (Li et al., 2005). Raju et al. (2015) also reported a decline in wheat seed germination due to effluent from a pharmaceutical and battery industry, which is in line with the results of the current study. Furthermore, some other studies found that seed priming by sodium compounds such as sodium silicate (Hameed et al., 2013) and sodium nitroprusside (Ali et al., 2017) enhance seedling root and shoot growth in wheat due to a reduction in the oxidative stress of salinity. This corroborates the results of the present study. Contrary to the results of the current study, Sayar et al. (2010) concluded that drought stress resulting from polyethylene glycol (PEG) had a higher adverse effect on germination percentage in wheat compared to NaCl-related salinity. Additionally, PEG-induced drought stress had an even more negative effect on radicle to caulicle ratio compared to wastewater (Fig. 1d). Essential nutrients available in wastewater reduced the negative effects of drought. Saidi et al. (2010) reported that root weight increases in wheat seedlings under increasing drought. Seed treated with wastewater represented higher seed vigour under the osmotic potential of 0.4 and 0.47 MPa, compared to the seed treated with PEG (Fig. 1e). The results of another study indicated that NaCl had a more adverse effect on germination compared to drought (Al-taisan, 2010) while other studies have reported that drought had a more adverse effect on germination compared to NaCl (e.g., Okcu et al., 2005; Rahimi et al., 2006).

Pot experiment

Heidari et al. (2011) found an increase in the chlorophyll content of water-stressed plants while Abdalla and El-khoshiban (2007) reported that drought reduced the chlorophyll content. This contradiction can be attributed to stress severity. In other words, chlorophyll is destroyed under severe drought which leads to a decrease in its content. In this respect, the effect of wastewater should be considered as well. Wastewater contains essential nutrients, including nitrogen, for plant growth and chlorophyll formation, and chlorophyll content, as one of source strength components, is affected by wastewater (Ghosh et al., 2004). In addition, 100% non-refined wastewater contains more nutrients than tap water and refined wastewater (Table 1). Leaf fresh weight mainly relies on leaf moisture and its trend indicates that wastewater salts may be absorbed by plant tissues. Further, salts accumulate water, thus the leaf becomes succulent (Sen and Rajpurohit, 2012) and drought reduces leaf dry weight. In the present study, the comparison of leaf dry and fresh weight revealed that wastewater increased leaf moisture (Fig. 2). According to Alyemeny (1998), a reduction in leaf biomass helps the plant to tolerate water deficit, and plant height reduction as a result of drought is considered as an adaptation response of plants in order to reduce transpiration (Karam et al., 2003). Furthermore, drought decreases cell size and internode length (Ludlow et al., 1990). However, based on the results of the current study, drought did not reduce leaf area for the 100% non-refined wastewater treatment (Fig. 2). A larger amount of nutrients such as nitrogen and phosphorus in 100% non-refined wastewater plays a role in chlorophyll production and increasing leaf area, compared to refined wastewater. Additionally, drought diminishes cell elongation and division, and thus reduces leaf area. Other studies, including Karam et al. (2003), have reported leaf area reduction due to drought as well. In addition, the results of the present study confirm that drought reduces the leaf area and increases the leaf thickness, leading to an increase in specific leaf weight (Fig. 2). The results further indicate that wastewater can promote plant growth by increasing the leaf thickness in order to capture more radiant energy. Water deficit leads to various morpho-physiological changes in the plants. For example, leaf growth is inhibited first, before photosynthesis and respiration, when there is a reduction in water potential, and soil water deficit reduces xylem sap movement toward the leaves (Liptay et al., 1998).

 

CONCLUSIONS

In general, an osmotic potential of 0.47 MPa of wastewater, resulted in the lowest seed germination percentage. Further, wastewater at low water potential failed to reduce seed germination traits such as seed vigour, while using polyethylene glycol to create a low osmotic potential decreased these traits. It is likely that the positive physiochemical properties of wastewater, including nitrogen content, acted to promote plant growth. Furthermore, plants irrigated with 100% refined wastewater under the well-watered condition produced the highest total biomass, since 100% refined wastewater contained growth-promoting ingredients such as nitrogen and phosphorus. Based on the results, drought reduced different growth parameters such as leaf and stem dry weight whereas wastewater application compensated for the negative effect of drought. Finally, drought decreased total biomass compared to control, except in the case of the 100% wastewater treatments. Therefore, irrigation of wheat with wastewater is recommended after germination instead of at the germination stage, and irrigation with refined wastewater can promote plant growth.

 

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Received 18 March 2018
Accepted in revised form 20 September 2019

 

 

* Corresponding author, email: heidari1383@gmail.com

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RESEARCH PAPERS

 

Modelling maize grain yield and nitrate leaching from sludge-amended soils across agro-ecological zones: A case study from South Africa

 

 

ZM Ogbazghi; EH Tesfamariam*; JG Annandale

Department of Plant and Soil Sciences, University of Pretoria, Private Bag: X20, Hatfield, 0028, Pretoria, South Africa

 

 


ABSTRACT

When applying municipal sludge according to crop N requirements, the primary aim should be optimizing sludge application rates in order to maximize crop yield and minimize environmental impacts through nitrate leaching. Nitrate leaching and subsequent groundwater contamination is potentially one of the most important factors limiting the long-term viability of sludge application to agricultural soils. This study assessed maize grain yield and potential nitrate leaching from sludge-amended soils, using the SWB-Sci model, based on crop nitrogen requirements and inorganic fertilizer. The following hypotheses were tested using the SWB-Sci model and 20 years of measured weather data for 4 of the 6 South African agro-ecological zones. Under dryland maize cropping, grain yield and nitrate leaching from sludge-amended soils compared to inorganic fertilizer: (1) will remain the same across agro-ecological zones and sites, (2) will not vary across seasons at a specific site, and (3) will not vary across soil textures. Model simulations showed that annual maize grain yield and nitrate leaching varied significantly (P > 0.05) across the four agro-ecological zones, both for sludge-amended and inorganic fertilizer amended soils. The annual maize grain yield and nitrate leaching from sludge-amended soils were 12.6 tha1 and 32.7 kgNO3-Nha1 compared to 10.2 tha1 and 43.2 kgNO3-Nha1 for inorganic fertilizer in the super-humid zone. Similarly, maize grain yield and nitrate leaching varied significantly across seasons and soil textures for both sludge and inorganic fertilizer amended soils. However, nitrate losses were lower from sludge-amended soils (2.3-8.2%) compared to inorganic fertilizer (11.1-26.7%) across all zones in South Africa. Therefore, sludge applied according to crop N requirements has a lower environmental impact from nitrate leaching than commercial inorganic fertilizer. Further validation of these findings is recommended, using field studies, and monitoring potential P accumulation for soils that received sludge according to crop N requirements.

Keywords: sewage sludge, inorganic fertilizer, nitrate leaching, maize yield, agro-ecological zones, SWB-Sci


 

 

INTRODUCTION

Generally, biosolids are applied to agricultural lands based on the N requirement of crops (USEPA, 2012; Alvarez-Campos, 2019). This is a common practice, especially on soils with high P-fixing abilities. When applying sludge according to crop N requirements, the primary aim should be optimizing sludge application rates in order to maximize crop yield and minimize environmental impacts through nitrate leaching. Unlike inorganic fertilizer, a large fraction of the N from sludge is in organic form and is gradually released to plant in available forms of N (NH4+ and NO3-). Consequently, nitrate leaching from sludge-amended agricultural lands is expected to be minimal compared to soils which receive similar amounts of inorganic fertilizer (Tesfamariam et al., 2015).

Previous studies by Tesfamariam (2009) and Kayikcioglu and Delibacak (2018) have shown that there is a linear relationship between sludge application rate and maize, oat, and weeping lovegrass yield, until a point of diminishing return, which is often linked to the availability of water. This direct relationship was mainly due to an increase in plant-available N from sludge, which is considered the key element for dry matter production (Miles and Manson, 2000). An increase in sludge application rate may, however, lead to excessive nitrate leaching, especially when the rate of N release exceeds crop uptake (Tesfamariam et al., 2015; Paramashivam et al., 2017).

According to Ogbazghi et al. (2016), NO3-N leaching and subsequent groundwater contamination is a potential concern with the long-term sustainability of uncontrolled biosolid application to agricultural lands. Nitrate leaching is controlled by soil water dynamics and is a function of the nitrate concentration in soil solution (Ogbazghi et al., 2016; Zhao et al., 2019). Soil water dynamics is influenced by soil properties such as texture and structure, as well as by the availability of water through irrigation and/or rainfall. Nitrate leaching from agricultural lands is a result of a complex interaction between N transformation processes, soil water dynamics, and soil characteristics. Henceforth, the need for decision support tools is becoming quite important due to the ever-increasing concerns over environmental pollution associated with the use of organic and inorganic fertilizers (Tesfamariam et al., 2015). Several N computer models, as decision support tools, have been developed with varying levels of complexity depending on their purpose (Banger et al., 2017).

The SWB-Sci model is a mechanistic soil-water balance (Annandale et al., 2000), crop growth/irrigation scheduling (Annandale et al., 2000, 2003), N (Tesfamariam, 2009; Ogbazghi et al., 2016) and phosphorus (P) (Van der Laan et al., 2010) model. It has been successfully calibrated and validated for N dynamics in sludge-amended soils planted to maize, oats and weeping love grass, both under dryland and irrigated conditions (Tesfamariam et al., 2015).

The objective of this study was to assess maize grain yield and potential nitrate leaching using the SWB-Sci model from soils amended with sludge, based on crop N requirements adjusted for several South African agro-ecological zones. To achieve this, the following hypotheses were tested using the SWB-Sci model and 20 years of measured weather data for 4 of the 6 South African agro-ecological zones. Under dryland maize cropping, maize grain yield and nitrate leaching from sludge-amended soils compared with inorganic fertilizer: (i) will remain the same across agro-ecological zones and sites; (ii) will not vary across seasons at a specific site; and (iii) will not vary across soil textures.

 

MATERIALS AND METHODS

Model description

The SWB-Sci model is a mechanistic crop growth, irrigation scheduling, salt, N and P balance model. It is a generic one-dimensional, daily time-step model that uses soil, weather and crop units to mechanistically carry out crop growth, soil-water and salt balances, as well as nitrogen cycle simulations. A detailed description of the crop growth, irrigation scheduling, salt, and water balance modules of the SWB-Sci model is not presented in this paper, and can be found in Annandale et al. (2000).

The Nitrogen module of the SWB-Sci model follows a similar approach to that of the Cropping Systems Simulation Model (CropSyst) (Stöckle et al., 2003). The nitrogen balance in the SWB-Sci module includes nitrogen transformations (mineralisation, nitrification, denitrification and ammonia volatilisation), ammonium sorption, nitrogen transport and crop nitrogen uptake. The model simulates ammonium sorption using the approach presented by Stöckle and Campbell (1989), while symbiotic N fixation is simulated after the approaches of Bouniols et al. (1991). Crop nitrogen uptake is modelled using a modified version of the Godwin and Jones (1991) approach, where crop nitrogen uptake is determined as the lesser of crop nitrogen demand and potential nitrogen uptake (Stöckle et al., 1994). A detailed description of the N module, including the major nitrogen transformation processes, can be found in Stöckle et al. (2003).

Model parameterization

Soil

Four major soil textural classes (clay, clay loam, sandy clay loam and sandy loam) were selected from our database to investigate maize crop yield and potential nitrate leaching from sludge-amended soils that received sludge based on crop N requirements adjusted for each agro-ecological zone, using inorganic fertilizer as a benchmark. Selected physical and chemical properties of the four soil textural classes used for model simulation are presented in Table 1.

Sludge

The sludge used for simulations in this study was anaerobically digested and dried on conventional concrete beds. The sludge was digested to 33% volatile suspended solids (VSS) destruction under mesophilic conditions. The retention time was 15 days in primary and 2 days in secondary digesters. Sludge properties required for model parameterization to run scenario simulations are presented in Table 2.

 

 

Inorganic fertilizer

The amount and timing of inorganic fertilizer application for this modelling work was based on the Fertilizer Handbook (FSSA, 2007) to meet the target yield for each selected site. The fertilizer was applied at planting and top dressed 5 weeks later according to the FSSA (2007) recommendations presented in Table 3. Limestone ammonium nitrate (LAN) with 28% N content was used as a source of nitrogen to meet the crop N requirement in this study.

 

 

Crop

Maize was selected as test crop because it is one of the most widely cultivated crops across the globe and accounts for 51% of the cultivated land in South Africa (FAO, 2005). A well-studied maize cultivar, PAN 6966, was selected and certain crop model parameters are presented in Table 4.

 

 

Study site

Scenario simulations were run using the SWB-Sci model for 4 of the 6 major agro-ecological zones of South Africa (Table 5, Column 1) to predict maize grain yield and nitrate leaching from sludge-amended soils using inorganic fertilizer as price benchmark.

The potential yield of maize for the representative sites and inorganic fertilizer recommendations for each site were obtained from FSSA Guidelines (2007) and Du Plessis (2003) (Table 5). Sludge application/recommendation rate was estimated based on the annual sludge N release rates adjusted to match the crop N requirements (Table 5, Column 6) using the SWB-Sci model (Ogbazghi et al., 2015).

Long-term weather records for the selected sites within each agro-ecological zone were obtained from the South African Weather Service (SAWS) for 1993-2013. SAWS collates, maintains and runs a quality control process of South Africa's meteorological and climatological data and related information. This archived data consists of daily rainfall values since 1936 as well as mean hourly and daily data of wind direction, wind speed, temperature, humidity, pressure and sunshine since 1950. Two sites, Nelspruit and Port Alfred, were exceptions, since data were available only for 2002-2013. The annual rainfall figures (1993-2013) of the selected sites are presented in Table 6.

Simulation and statistical analyses conducted

Simulations of 80 scenarios were done based on fully factorial combinations of 4 agro-ecological zones with 3 sites for the semi-arid, sub-humid and humid zones and one for the super-humid zone, and 4 soil textures. Each scenario was run for 20 years of simulation time. The numbers of years were used as replicates, except in testing Hypothesis 2 (that under dryland maize cropping, nitrate leaching and maize grain yield from sludge-amended soils compared with inorganic fertilizer will not vary across seasons at a specific site), where the number of years was used as the main effect. Statistical analyses were done using general linear model (GLM) procedures of Windows SAS version 9.4 (SAS Institute, 2012).

 

RESULTS AND DISCUSSION

Maize grain yield and nitrate leaching across South African agro-ecological zones and sites

Model scenario simulation was carried out to predict maize grain yield and nitrate leaching from sludge-amended soils across 5 of the 6 South African agro-ecological zones. Findings from these model simulation results are presented in the following sections.

Maize grain yield from sludge-amended soils and inorganic fertilizer

Maize grain yield varied significantly across the 4 agro-ecological zones for both sludge and inorganic fertilizer-amended soils (Fig. 1). Generally, maize yield was higher from sludge-amended soils than lands receiving inorganic fertilizer (Fig. 1). The highest average grain yield of 12.6 tha1 was predicted for the super-humid zone (Nelspruit) under sludge treatment, while lowest yield of 4.1 tha1 was recorded in the semi-arid zone of Bloemfontein under inorganic fertilizer application. Generally, the predicted yield for the sites was within the ranges reported by FSSA (2007).

 

 

The mean annual maize grain yield varied significantly (P < 0.05) between sites within an agro-ecological zone (Fig. 2). In the sub-humid zone, maize grain yield in Johannesburg was 20% higher under sludge-amended and 32% higher under inorganic fertilizer-amended soils than for Bethlehem. Similarly, in the semi-arid zone, maize grain yield in Rustenburg was 25% higher under sludge-amended and 20% higher under inorganic fertilizer-amended soils than in Bloemfontein. In the humid zone, maize grain yield in Durban was 20% higher under sludge-amended and 28% higher under inorganic fertilizer-amended soils than in East London (Fig. 2). These variations are attributed to the differences in rainfall and temperature between sites, which affected dry matter production and grain yield.

It was apparent from the simulations that maize grain yield from sludge-amended soils varied significantly (P < 0.05) across agro-ecological zones and sites compared with inorganic fertilizer. Maize grain yield was higher from sludge-amended soils than inorganic fertilizer, indicating the agronomic benefits of sewage sludge over inorganic fertilizer.

Nitrate leaching from sludge-amended soils and inorganic fertilizer

Henceforth, the simulation findings showed that nitrate leaching varied significantly (P < 0.05) across agro-ecological zones for both inorganic fertilizer and sludge-amended soils (Fig. 3). Cumulative annual nitrate leaching varied from 11.2 kgNO3-Nha1 (semi-arid) to 43.2 kgNO3-Nha1 (super-humid) for inorganic fertilizer-amended soils and from 5.6 kgNO3-Nha1 (semi-arid) to 32.7 kgNO3-Nha1 (super-humid) for sludge-amended soils. Generally, nitrate leaching within each agro-ecological zone was significantly higher (P < 0.05) from inorganic fertilizer-amended soils than sludge-amended soils (Fig. 3).

Simulations also showed that nitrate leaching varied between sites within an agro-ecological zone (Fig. 4). For instance, in the semi-arid zone, leaching was higher in Rustenburg (8.1 kgNO3-Nha1) than in Bloemfontein (4.1 kgNO3-Nha1) and Polokwane (5.6 kgNO3-Nha1); in the sub-humid zone, leaching was higher in Johannesburg (34.2 kgNO3-Nha1) than in Bethlehem (14.2 kg NO3-N ha1) and Port Alfred (8.3 kgNO3-Nha1); and in the humid zone, leaching was higher in Durban (40.2 kgNO3-Nha1) than in East London (13.2 kgNO3-Nha1) (Fig. 4).

The variation in nitrate leaching between agro-ecological zones generally follows a similar pattern to the rainfall for both inorganic fertilizer and sludge-amended soils. This concurs with previous findings that reported a direct relationship between water availability and nitrate leaching (Tesfamariam et al., 2015; Holland et al., 2018). Similarly, the difference in nitrate leaching between sites within an agro-ecological zone was attributed mainly to the variation in rainfall amount and distribution. For instance, in the semi-arid zone annual rainfall was 75 mm higher in Rustenburg than Bloemfontein; in the sub-humid zone, rainfall was 80 mm and 60 mm higher in Johannesburg than Bethlehem and Port Alfred; and in the humid zone, rainfall was 150 mm and 134 mm higher in Durban than East London and Cape Town (Tables 5 and 6).

This significant variation between sludge application and inorganic fertilizer was attributed mainly to the form of N. A large fraction of the N in sludge (> 70%) is organic, which is released gradually to plant-available form. In contrast, N in inorganic fertilizers is all inorganic and is potentially leachable under excessive rainfall.

Nitrate losses are low from sludge-amended soils compared with the conventional agronomic use of inorganic fertilizer. For instance, in the semi-arid zone of Rustenburg, only 2.3% of the organic N that is added with sludge was leached, compared with 11.1% for inorganic fertilizer. Similarly, in the sub-humid zone in Johannesburg, 6.2% of the organic N was leached from sludge compared with 28.5%. In the humid zone of Durban 7.9% of the organic N was leached from sludge compared with 26.7%. In the super-humid zone of Nelspruit, only 8.2% of the organic N that is added with sludge was leached as nitrate, compared with 25.4% of inorganic fertilizer. Therefore, using sludge in agricultural lands has a low risk of nitrate leaching compared with inorganic fertilizer. Therefore, the hypothesis that 'under dryland maize cropping, annual maize grain yield and nitrate leaching from sludge-amended soils will remain the same as inorganic fertilizer-amended soil across agro-ecological zones and sites' is not accepted.

 

NITRATE LEACHING AND MAIZE YIELD ACROSS SEASONS

Model scenario simulation was carried out to predict maize grain yield and nitrate leaching from sludge-amended soils across seasons within a site. Findings from these model simulation results are presented in the following sections.

Maize grain yield over years within a site

Model simulations were conducted for over 20 years on 2 selected sites, namely, Johannesburg (sub-humid zone) and Durban (humid zone), to assess maize grain yield from sludge and inorganic fertilizer-amended soils. Maize grain yield varied significantly over years, both for inorganic fertilizer and sludge-treated soils (Fig. 5). Generally, maize grain yield was higher for soils fertilized with sludge than inorganic fertilizer in both Johannesburg and Durban. For instance, in the sub-humid zone of Johannesburg, maize grain yield from sludge-amended soil was predicted to be 10-15% higher than soils amended with inorganic fertilizer (Fig. 5a). Similarly, in the humid zone of Durban, maize grain yield was 15-20% higher in sludge-amended soils than those fertilized with inorganic fertilizer over 20 years (Fig. 5b).

The difference in maize grain yield between years was statistically significant (P < 0.05) when the difference in rain amount between years exceeded 241 mm (semi-arid zone), 362 mm (sub-humid zone), and 429 mm (humid zone). This event happened in 2 of the 20 years (2002 and 2004) of model simulation for both Johannesburg and Durban. Nitrate leaching in these 2 years was also lower compared with other years (Fig. 6). The low rainfall events of 2002 and 2004 led to low grain yield and nitrate leaching, because there is a direct relationship between water availability and maize grain yield (Nilahyane et al., 2019) as well as between high rainfall events and nitrate leaching (Holland et al., 2018). It is well documented that the presence of water plays a critical role in both nutrient uptake by plants and release of nutrients as plant-available inorganic forms from organic nutrient sources (Guntiňas et al., 2012, Ogbazghi et al., 2015).

Nitrate leaching over years within a site

Nitrate leaching varied significantly (P < 0.05) over the years under both sludge and inorganic fertilizer-amended soils (Fig. 6a and 6b). Nitrate leaching remained significantly higher under inorganic fertilizer-amended soils than those that received sludge. For instance, in the sub-humid zone of Johannesburg, the mean annual nitrate leaching from inorganic fertilizer was 15% higher than with sludge-treated soils (Fig. 6a). Similarly, in the humid zone of Durban, the mean annual nitrate leaching from inorganic fertilizer-treated soil was 20% higher than for sludge-treated soil (Fig. 6b). Similar to maize grain yield, the significant difference (P < 0.05) in nitrate leaching between years was observed in 2 of the 20 years.

The observed increase in nitrate leaching as the rainfall increased is attributed to the increase in the mobility of nitrate within the profile, at a relatively faster rate than the rate of uptake by plants. Such interactive effects of both rainfall and leaching on uptake of N by plants are well documented (Banger et al., 2018). Holland et al. (2018) and Ogbazghi et al. (2016) reported a direct relationship between nitrate leaching and soil water availability. Therefore, the hypothesis that 'under dryland maize cropping, annual maize grain yield and nitrate leaching will not vary across years both from sludge and inorganic fertilizer-amended soils' is not accepted.

Maize grain yield and nitrate leaching across soil textures

Maize grain yield and nitrate leaching varied significantly (P < 0.05) between soil textures for both sludge and inorganic fertilizer-amended soils (Fig. 7a and 7b). Maize grain yield was predicted to be higher for clay loam and sandy clay loam soils than for clay and sandy loam soils (Fig. 7a). It was also apparent that maize grain yield was higher from sludge-amended than inorganic fertilizer-amended soil across soil textures (Fig. 7a). This is mainly due to the higher nitrate leaching from inorganic fertilizer compared with sludge-amended soil (Fig. 7b).

Nitrate leaching from inorganic fertilizer-amended soils was generally higher than from sludge-amended soils (Fig. 7b). Nitrate leaching was lower in clay and clay loam soils than in sandy clay loam and sandy loam soils in both sludge and inorganic fertiliser soils. This agrees with the general literature in which nitrate leaching from sand-dominated soils is reported to be higher (Elasbah et al., 2019; Fang and Su, 2019). Therefore, the hypothesis that 'under dryland maize cropping, annual maize grain yield and nitrate leaching will remain similar across soil textures' is not accepted.

 

CONCLUSIONS

Predicted maize grain yield and nitrate leaching varied significantly across 4 agro-ecological zones, for both sludge-amended and inorganic fertilizer-amended soils. Similarly, maize grain yield and nitrate leaching, were predicted to vary significantly across seasons and soil textures for both the sludge- and inorganic fertilizer-amended soils. However, nitrate leaching losses were lower from sludge-amended soils compared with those receiving inorganic fertilizer across all agro-ecological zones. Predicted maize grain yield was higher from sludge-amended soils than for inorganically fertilized crops, while nitrate leaching was higher with inorganic fertilizer than with sludge, indicating the agronomic and environmental benefits of municipal sludge over inorganic fertilizer. Further validation of these findings using field experiments and monitoring potential P accumulation for soils that received sludge according to crop N requirements is recommended.

 

ACKNOWLEDGMENTS

The authors would like to express their appreciation and gratitude to the Water Research Commission of South Africa (WRC) and East Rand Water Care Works (ERWAT) for the funding without which this study would have been but a dream. The authors would like to further express their gratitude to the Agro-Climatology Unit of the South African Agricultural Research Council - Soil, Climate, and Water for providing us with the long-term weather data for the study sites.

 

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Received 4 April 2018
Accepted in revised form 20 September 2019

 

 

* Corresponding author, email: eyob.tesfamariam@up.ac.za

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RESEARCH PAPERS

 

Assessing the influence of DEM source on derived streamline and catchment boundary accuracy

 

 

Zama Eric MashimbyeI, II, *; Willem Petrus De ClercqI; Adriaan Van NiekerkII

IDepartment of Soil Science, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
IIDepartment of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa

 

 


ABSTRACT

Accurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived mainly from free DEMs, it is essential to investigate the accuracy of these parameters. This study compared the spatial accuracy of streamlines and catchment boundaries derived from available digital elevation models in South Africa. Two versions of Stellenbosch University DEMs (SUDEM5 and DEMSA2), the second version of the 30 m advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM2), the 30 and 90 m shuttle radar topography mission (SRTM30 and SRTM90 DEM), and the 90 m Water Research Commission DEM (WRC DEM) were considered. As a reference, a 1 m GEOEYE DEM was generated from GeoEye stereo images. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module. A reference catchment boundary was generated from the GEOEYE DEM and verified during field visits. Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. Visual inspection, as well as quantitative measures such as correctness index, mean absolute error, root mean squares error and figure of merit index were used to validate the results. The study affirmed that high resolution (<30 m) DEMs produce more accurate parameters and that DEM source and resampling techniques also play a role. However, if high resolution DEMs are not available, the 30 m SRTM DEM is recommended as its vertical accuracy was relatively high and the quality of the streamlines and catchment boundary was good. In addition, it was found that the novel Euclidean distance-based MAE and RMSE proposed in this study to compare reference and DEM-extracted raster datasets of different resolutions is a more reliable indicator of geometrical accuracy than the correctness and figure of merit indices.

Keywords: hydrology, catchment delineation, digital elevation model, correctness index, figure of merit index, Euclidean distance index


 

 

INTRODUCTION

Digital elevation model (DEM) derived catchment boundaries, sub-basins and streamlines play an important role in hydrological studies (Li and Wong, 2010; Martz and De Jong, 1998; O'Callaghan and Mark, 1984; Renssen and Knoop, 2000; Turcotte et al., 2001; Vogt et al., 2003). The availability of good quality DEMs makes it possible to carry out hydrological and geomorphological analyses on regional or national levels (Moore and Wilson, 1992; Thomas et al., 2014). DEMs are offered at a variety of resolutions ranging from very high (0.1-5 m) to low (1 km) (Behrens et al., 2010; Tarekegn et al., 2010). Very high resolution (VHR) DEMs, as derived from airborne light detection and ranging (LiDAR) data, are often only available for small areas, particularly in developing countries where this technology is still prohibitively expensive. Consequently, freely available near-global DEMs are frequently used for hydrological studies at national or regional scales.

Various studies have investigated the value of DEMs for hydrological analysis. For instance, Weepener et al. (2012) developed a hydrologically improved DEM for South Africa from the SRTM90 DEM using 20 m 1:50 000 contours and ASTER GDEM data. They found that useful river lines and catchment boundaries can be delineated from the hydrologically improved SRTM90 DEM. Li and Wong (2010) compared stream networks extracted from the national elevation dataset (NED), SRTM90 DEM and LiDAR with stream networks extracted from the national hydrography dataset (NHD). They also compared flood simulations using the stream networks delineated from the different DEMs and concluded that higher-resolution DEMs can derive more accurate river networks, but that the spatial resolution of a DEM only has a minor effect on flood simulation results. Callow et al. (2007) evaluated the effect of commonly used hydrological correction methods (stream burning, Agree, ANUDEM v4.6.3 and ANUDEM v5.1) on the overall nature of a DEM. They found that different methods produce non-convergent results for catchment boundaries, stream position and length, and that these techniques differentially compromise secondary terrain analysis. Their study also concluded that, while hydrological correction methods successfully improved the calculation of the catchment area, stream position and length, they increased catchment slope.

DEMs invariably contain errors, most of which can be attributed to the data source, methods, topography complexity and spatial resolution (Aguilar et al., 2005; Kinsey-Henderson and Wilkinson, 2013; Mukherjee et al., 2011; Mukherjee et al., 2013; Thomas et al., 2014; Rodriguez et al., 2005). It has also been reported that the accuracy of a DEM is dependent on its application (Sharma and Tiwari, 2014; Sharma et al., 2010). Kensey-Henderson and Wilkinson (2013) compared DEMs derived from synthetic aperture radar (SAR) data and DEMs interpolated from topographical data for slope gradient and soil erosion estimation in low relief areas. They evaluated the magnitude of error in DEM slope and erosion estimates using the Revised Universal Soil Loss Equation. They determined that the SRTM DEMs provided more accurate estimates of slope gradient and erosion in low relief areas.

Frey and Paul (2012) investigated the suitability of the SRTM90 DEM and the ASTER GDEM for the compilation of glacier-specific topographic parameters in Switzerland. Comparing the delineated parameters with those derived from the Swiss national DEM (DHM25), they concluded that, although the SRTM90 DEM yielded slightly more accurate results, both DEMs were suitable for the compilation of topographic parameters in glacier inventories.

Evidently, the freely available medium (90 m) and high resolution (30 m) near-global DEMs have opened up many possibilities for hydrological analyses, especially at national and regional scales (De Clercq et al., 2013; Wang et al., 2011; Weepener et al., 2012; Sharma and Tiwari, 2014). Researchers frequently use these DEMs for hydrological studies, mainly because they are freely available (De Clercq et al., 2013; Gichamo et al., 2012; Wang et al., 2011; Weepener et al., 2012). However, little attention has been paid to the quality of the products that are derived from these DEMs. Given their popularity, it is important to assess the accuracy of the derived hydrological parameters so that uncertainties can be considered in the interpretation of hydrological analysis results. For South Africa, in addition to the freely available global DEMs (for example, SRTM and ASTER GDEM), a hydrologically improved Water Research Commission (WRC) DEM is available. While these DEMs are widely used to derive hydrological parameters, the accuracy of the resultant parameters has not been evaluated. This study investigated the validity of hydrological parameters derived from these freely available DEMs. The spatial accuracy of catchment boundaries and streamlines derived from a total of 7 DEMs that are available at national level in South Africa was evaluated. In addition, this study investigated a novel Euclidean distance-based technique for validating the geometric accuracy of DEM derived streamlines and catchment boundaries using root mean squares error and mean absolute error.

 

MATERIAL AND METHODS

The study site

The study area is the Sandspruit catchment, a subcatchment of the Berg River in the Western Cape Province, South Africa (Fig. 1). The catchment is located in a winter rainfall region, and the mean annual rainfall is about 400 mm (Flügel, 1995). The study area is 152 km2 in size and has a gently hilly topography. The geology of the Sandspruit catchment is mainly Malmesbury shales, even though there are smaller occurrences of fine sediment, silcrete-ferricrete, greenstone, quartzite and granite. An opencast mine is located in the south-eastern part of the catchment. While the catchment is largely used for dryland cultivation of winter wheat, canola and pasture are also cultivated, and a small proportion of the catchment is covered by natural vegetation.

Datasets used

The datasets used in this study included trig beacon heights, field survey points, satellite and aerial imagery, DEMs, reference streamlines and a reference catchment boundary. Each of these datasets is described in the following subsections.

Trig beacons and field survey points

A combination of trig beacons and GPS field survey points were used to validate the vertical accuracy of the DEMs. Trig beacons covering the Sandspruit catchment, established by the South African Chief Directorate of National Geo-spatial information (CDNGI) and their coordinates (including ground height), were obtained from the Centre for Geographical Analysis (CGA) at Stellenbosch University. GPS field survey points were measured using a survey grade Trimble Differential GPS. The GPS points were differentially corrected to improve their accuracy to about 10 cm. A total of 38 points (6 trig beacons and 32 GPS points) were used as reference points to validate the DEMs.

Satellite and aerial imagery

GeoEye stereo-images were acquired from Geo Data Design. The 0.4 m resolution images were captured in July 2011, a period in the year when crops in the study area were still at seedling height. Vegetation would therefore have had a minimal effect on photogrammetrically extracted heights.

Very high-resolution (0.5 m) orthorectified digital aerial images covering the study area were sourced from CDNGI (http://www.ngi.gov.za). The images were acquired in 2007 and were used as spatial reference during the orthorectification of GeoEye stereo images.

DEMs

The DEMs considered in this study were the SRTM90 DEM, SRTM30 DEM, ASTER GDEM2, two versions of Stellenbosch University's digital elevation model (SUDEM) (SUDEM5 and the digital elevation model of South Africa), the 90 m Water Research Commission DEM (WRC DEM) and a 1 m DEM generated from GeoEye images (GEOEYE DEM).

The SRTM90 DEM was completed in 2000 and provides the first medium-resolution DEM data at near-global scale (Farr and Kobrick, 2001; Li and Wong, 2010). The SRTM90 DEM has an absolute vertical error of less than 16 m and an absolute horizontal accuracy of 20 m (Farr, 2000; Mulder et al., 2011; Van Niekerk, 2008). According to the Consultative Group on International Agricultural Research Consortium for Spatial Information (CGIAR-CSI, 2011), the SRTM DEM data have been processed to fill data voids and can be used by a wide range of potential users.

The SRTM30 DEM is a near-global DEM that comprises a combination of data from the Shuttle Radar Topography Mission flown in February 2000 and the United States Geological Survey's GTOPO30 data set (USGS, 2016).

The ASTER GDEM was developed jointly by the Ministry of Economy, Trade and Industry (METI) of Japan and the United States National Aeronautics and Space Administration (NASA). The second version of ASTER GDEM (GDEM2) was released in October 2011 (ASTER GDEM Validation Team, 2011) with the inclusion of 26 000 additional scenes to improve coverage. The new version uses a smaller correlation kernel to yield higher spatial resolution, and water masking was also enhanced. ASTER GDEM2 was validated by comparing it to the absolute geodetic references over the conterminous United States (CONUS), the national elevation grids over the US and Japan, the SRTM 1 arc-second DEM over the US and 20 sites around the globe, and global space-borne laser altimeter data. The vertical and horizontal accuracy of the GDEM2 is estimated at 17 m and 71 m, respectively (ASTER GDEM Validation Team, 2011; Mukherjee et al., 2013).

The SUDEM, developed by the Centre for Geographical Analysis (CGA) at Stellenbosch University, is a commercially available product. As of 2015, four products that involve various levels of processing were produced (Van Niekerk, 2016). The 5 m resolution SUDEM5 was generated by fusing the 30 m SRTM DEM with the so called 'Level 1 product'. The Level 1 product (5 m spatial resolution) was interpolated from large (1:10 000) and smaller (1:50 000) scale contours and spot-height data (Van Niekerk, 2016). Smaller-scale contours were only used in areas where large-scale data were not available. Using LiDAR data as reference, the SUDEM5 product was estimated to have a mean absolute error (MAE) of 2.2 m (Van Niekerk, 2016). The 2 m digital elevation model of South Africa (DEMSA2) is a digital surface model (DSM) that is available at 2 m resolution. This DEM was extracted from 0.5 m resolution CDNGI stereo aerial photography (Van Niekerk, 2016). Based on surveyed reference points, the MAE of DEMSA2 product is estimated to be 0.35 m (Van Niekerk, 2016). The SUDEM and DEMSA2 products were considered in this study as they are the only very high resolution DEMs available nationally in South Africa.

The Water Research Commission's digital elevation model (WRC DEM) was developed by the Agricultural Research Council (ARC) for the WRC (Weepener et al., 2012). This DEM was interpolated from the SRTM90 DEM. The SRTM voids were filled with elevation values interpolated from 20 m (1:50 000 scale) vertical interval contours obtained from CDNGI. The resulting DEM was hydrologically corrected by filling sinks and depressions. The vertical accuracy of the WRC DEM was determined to be less than 5 m.

The GEOEYE DEM was created from GeoEye stereo images acquired in July 2011 using the rational polynomial coefficients (RPC) model in the LPS module of ERDAS Imagine software (www.intergraph.com). The GEOEYE DEM was extracted at 1 m horizontal intervals and was validated using reference points (trigonometric beacons) in the Sandspruit catchment. A MAE of 0.70 m was recorded. The GEOEYE DEM was used to delineate a reference catchment boundary. The reference catchment boundary was extracted using the Arc Hydro module in ArcGIS 10.

Reference catchment boundary and reference streamlines

Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. The reference streamlines were visually compared to the 1:50 000 national riverlines dataset. It was found that, although the two datasets were geometrically aligned, the 1:50 000 streamlines were much more generalised and contained many topological errors (e.g. gaps).

The reference catchment boundary, generated from the 1 m resolution GEOEYE DEM, was used to validate the lower resolution DEM-delineated catchment boundaries. The reference catchment boundary was validated during several field visits and by visual inspection in ERDAS Stereo Analyst (www.intergraph.com).

Delineation of catchment boundaries and streamlines from DEMs

The Arc Hydro extension for ArcGIS software was used to delineate the Sandspruit catchment boundaries and streamlines from the DEMs. All the datasets were projected to the Universal Transverse Mercator (UTM) coordinate system (Zone 34S). Catchment boundaries and streamlines were extracted at the native resolution of the DEMs. Additionally, the DEMs were resampled to the resolution of the coarsest DEMs (90 m) to allow comparison without the effect of spatial resolution. The threshold for stream delineation was set at 1% of the maximum flow accumulation, as recommended by Arc Hydro's rule of thumb for stream delineation from DEMs (Merwade, 2012; Tarboton, 2003). The GEOEYE DEM was used to calculate reference flow accumulation thresholds for the other DEMs at their respective resolutions. For catchment boundary delineation, outlet (pour) points were selected at the same position. A stream network was extracted from the GEOEYE DEM to enable comparison with previous studies conducted with very high resolution (VHR) DEMs (Li and Wong, 2010). Catchment boundaries and streamlines extracted from all the DEMs were converted to raster datasets using the Feature to Raster tool in ArcGIS 10.1, and the cell size was set to 5 m for comparison purposes. Cells representing boundaries or streamlines (using the GRID_CODE ID of the feature dataset generated by Arc Hydro) were allocated values of 1. All other cells were defined as having no values (i.e. NODATA). Separate raster datasets were created for catchment boundaries and streamlines.

Validation

Vertical accuracy of the DEMs

The vertical accuracies of the DEMs were determined using the absolute and relative mean error (MAE), absolute and relative root mean squares error (RMSE) and 90th percentile, based on a combination of trig beacons and differentially corrected GPS points as a reference. RMSE, MAE and 90th percentile are metrics based on reference values commonly used to determine the accuracy of a DEM (Rawat et al., 2019). The MAE and RMSE were calculated based on Eqs 1 and 2:

where Xi is the elevation of a DEM at point i, Yi is the reference elevation at point i, and n is the number of samples. According to Rawat et al. (2019), RMSE varies with the variability within the distribution of error magnitudes, square root of the number of errors and the magnitude of MAE. MAE is a more natural measure of average error and, unlike RMSE, is unambiguous (Rawat et al., 2019). Lower RMSE and MAE values show good accuracy. The 90th percentile error reveals the value below which 90% of the errors fall.

DEM delineated catchment boundaries and streamlines

The catchment boundaries and streamlines extracted from the DEMs were visually compared to the reference datasets. Four measures, namely the correctness index (Cr), figure of merit index (FMI), MAE and RMSE were used to quantitatively evaluate continuous delineated catchment boundaries and stream networks. The Cr and FMI were introduced by Li and Wong (2010) to validate stream networks extracted from DEMs, while MAE and RMSE are proposed in this study as additional measures of spatial agreement.

The Cr compares two sets of raster cells (A and B), which represent DEM-extracted and reference raster datasets, respectively (Li and Wong, 2010). The Cr is calculated by Eq. 3 below:

where NB is the number of cells representing the reference raster and N(AB) is the number of cells of the DEM-extracted raster. Index values range between 0 and 1, and indicate the proportion of the reference raster that is correctly represented by the extracted raster (Li and Wong, 2010). A high correctness index value indicates a high accuracy of extracted streams.

According to Li and Wong (2010), Cr does not reflect how well the extracted raster (representing stream networks in their case) can reproduce the entire actual raster, and they assert that the FMI offers a better solution. The FMI is the ratio of the intersection of the observed change and predicted change to the union of observed change and predicted change (Pontius et al., 2008; Perica and Foufoula-Georgiou, 1996). FMI is computed by Eq. 4 below:

where N(AB) is the number of unique cells found in rasters A and B and N(AB) is the total number of cells found in both A and B (overlapping cells are only counted once). FMI values range between 0 and 1, and a higher FMI value indicates a higher overlap between the two raster datasets, therefore high accuracy.

Euclidean distance (ED) is calculated from the centre of the reference raster cell to the centre of the extracted raster cell. Figure 2 depicts how ED is calculated for streamlines. MAE and RMSE consider the offset (ED) between cells in the reference raster and the closest cell in the candidate raster. The sum of the offsets was used to calculate MAE and RMSE using Eqs 1 and 2. Relatively low MAE and RMSE values indicate a high accuracy of DEM-extracted raster datasets. RMSE is considered a better indicator of accuracy as it is more sensitive to outliers than MAE, but it is often useful to interpret these measures in combination. Large differences between MAE and RMSE are indicative of high variances in individual errors (i.e. outliers).

 

 

RESULTS

Vertical accuracy of the DEMs

The descriptive and accuracy statistics of all the DEMs used in this study are given in Table 1 and Fig. 3. The DEMs show disparities in how they represent the character of the study area, as depicted by the variances in the different descriptive and accuracy measures recorded (Table 1, Fig. 3). ASTER GDEM2 shows the highest bias followed by the SRTM30 and SRTM90, GEOEYE DEM, WRC DEM, DEMSA2 and SUDEM5 (Fig. 3). The absolute and relative MAE and RMSE are <1.54 m for DEMSA2, <3.28 for SUDEM5, <3.75 for GEOEYE DEM, <6.99 for SRTM30 DEM, <7.14 for WRC DEM, <12.35 for ASTER GDEM2 and >13.03 for SRTM90 DEM, respectively. Regarding the 90th percentile, 90% of elevation values fall below 2.17 (DEMSA2), 2.28 (SUDEM5), 3.09 (GEOEYE DEM), 7.37 (SRTM30 DEM), 9.05 (WRC DEM), 12.3 (ASTER GDEM2) and 12.99 (SRTM90), respectively. It is obvious that DEMSA2 is the most accurate (vertically), followed by SUDEM5, GEOEYE DEM, SRTM30 DEM, WRC DEM, ASTER GDEM2 and SRTM90 DEM.

DEM-delineated catchment boundaries

Based on visual assessment, the catchment boundaries extracted from all the DEMs seem relatively accurate compared to the reference catchment boundary (see Fig. 4a-f). It appears that SUDEM5 and DEMSA2 delineated very accurate catchment boundaries (Fig. 4e, f). The catchment boundaries delineated from these DEMs show small discrepancies with the reference catchment boundary. While the catchment boundaries delineated from the SRTM30 DEM and WRC DEM also appear to be visually accurate, the discrepancies of the catchment boundaries delineated from these DEMs seem slightly greater than those of the SUDEM5 and DEMSA2 (Fig. 4b, d). The WRC DEM appears to delineate a better boundary than the SRTM30 DEM at the area occupied by a mine on the south-eastern part of the Sandspruit catchment (Fig. 4b, d). The ASTER GDEM2 and SRTM90 DEM delineated catchment boundaries show visibly larger discrepancies with the reference catchment boundary (Fig. 4a, c). While the ASTER GDEM2 shows a large discrepancy with the reference catchment boundary in the middle of the eastern part of the catchment, the SRTM90 DEM catchment boundary shows a larger discrepancy compared to the reference boundary in the vicinity of the mine at the south-eastern part of the catchment and at the outflow of the catchment at the north-eastern part. The SRTM90 DEM appears to overestimate the catchment boundary in the south-eastern parts, but performs better than the ASTER DEM2 in delineating the eastern boundary (Fig. 4c). The ASTER GDEM2 slightly underestimates the catchment boundary at the south-eastern part of the catchment and is also unable to correctly delineate the eastern boundary (Fig. 4c).

Regarding the accuracy measures at the supply resolution of the DEMs, DEMSA2 yielded the lowest RMSE and MAE, followed by SUDEM5, SRTM30 DEM, ASTER GDEM2, WRC DEM and the SRTM90 DEM (Fig. 5). The Cr and FMI ratios for SUDEM5 and DEMSA2 are all at near-maximum values (Cr = FMI = 0.99). Whereas the Cr values for SRTM30 DEM and WRC DEM are near maximum and equal (Cr = 0.99), the FMI ratio for SRTM30 DEM is slightly higher than that of WRC DEM (Fig. 5). The ASTER GDEM2 recorded the lowest Cr and FMI values. From these results, it is clear that DEMSA2 delineated the most accurate catchment boundary followed by the SUDEM5 (Fig. 5). Figure 5 indicates that the SRTM30 DEM yields a more accurate catchment boundary in comparison to the ASTER GDEM2. When comparing the medium resolution (MR) DEMs, it is clear that the WRC DEM delineated a more accurate boundary than the SRTM90 DEM (Fig. 5). While the vertical accuracy of DEMSA2, SUDEM5, SRTM30 DEM and SRTM90 DEM is in line with the accuracy of the delineated catchment boundary, this is not the case for ASTER GDEM2 and WRC DEM. Although the WRC DEM yielded a better vertical accuracy than the ASTER GDEM2, the ASTER GDEM2 delineated a slightly more accurate catchment boundary than the WRC DEM (Table 1 and Fig. 3, Fig. 5). Based on the differences between RMSE and MAE, it is obvious that VHR DEMs yielded lower variations in individual errors and that accuracy decreased as resolution decreased (Fig. 5). As can be seen in Fig. 5, the variation in individual errors for catchment delineation increases with an increase in the spatial resolution of the DEMs.

For catchment delineation performed when all DEMs are resampled to MR, SRTM30 DEM records the lowest RMSE, followed by DEMSA2, WRC DEM, SUDEM5, ASTER GDEM2 and SRTM90 DEM (Fig. 6). With regard to MAE, WRC DEM yields the lowest MAE values, followed by DEMSA2, SUDEM5, SRTM30 DEM, ASTER GDEM2 and SRTM90 DEM (Fig. 6). Similarly, lower Cr and FMI values for catchment delineation are seen when the DEMs are resampled to MR (Fig. 6). For the VHR DEMs, DEMSA2 delineates a more accurate catchment boundary than SUDEM5 as was the case at supply resolutions. A similar trend is observed for the HR DEMs and the MR DEMs where SRTM 30 DEM delineates a better boundary than ASTER GDEM2. It is obvious from Fig. 6 that the variations in individual errors for the VHR and HR DEMs for catchment boundary delineation increase when they are resampled to MR. The variations in individual errors for SRTM30 DEM and WRC DEM are lower than those of very high resolution DEMs (DEMSA2 and SUDEM5) when they are resampled to MR.

DEM-extracted streamlines

Streamlines extracted from the DEMs are depicted in Fig. 7a-g. Visually, the streamlines appear to align well with the reference streamlines, although some misalignments for the different DEMs are apparent in certain areas. An in-depth view of a selected area around the mid-northern part of the catchment reveal larger discrepancies for the GEOEYE DEM, ASTER GDEM2 and the SRTM30 DEM (Fig. 7a, b and c). The visual discrepancies of the streams delineated from the SRTM90 DEM, WRC DEM and SUDEM5 appear to be smaller than those of GEOEYE DEM, ASTER GDEM2 and the SRTM30 DEM. Visually, the DEMSA2 streams align very well with the reference streamlines (Fig. 7g).

At DEM supply resolution, GEOEYE DEM recorded the longest streamlines, followed by SUDEM5, DEMSA2, ASTER GDEM2, SRTM30 DEM, WRC DEM and SRTM90 DEM (Fig. 8). The RMSE and MAE values for delineated streamlines for all the DEMs are similar. Similarly, the variation of individual errors for stream delineation for the DEMs is similar for all the DEMs (Fig. 8). Cr and FMI values for all DEMs are low. VHR DEMs recorded slightly larger Cr and FMI values.

 

 

Regarding delineation when the finer resolution DEMs are resampled to MR, the total lengths of extracted streamlines are generally shorter for all DEMs, with the exception of ASTER GDEM2. Contrary to streamline lengths extracted at supply resolution, ASTER GDEM recorded the longest streamlines, followed by DEMSA2, GEOEYE DEM, SUDEM5, WRC DEM, SRTM30 DEM and SRTM90 DEM. As at DEM supply resolution, RMSE and MAE for delineated streamlines are similar for all the DEMs (Fig. 9). It does not seem that the geometric accuracy of the DEM extracted streamlines is in line with the vertical accuracy of the DEMs (Table 1 and Fig. 3, Fig. 9).

 

 

DISCUSSION

All the DEMs used in this study show reliable vertical accuracies. The overall vertical accuracies of DEMSA2 and SUDEM5 are slightly lower than reported by Van Niekerk (2016). This is likely due to the use of a combination of differentially corrected GPS points and trig beacon heights in this study, whereas LiDAR data were used by Van Niekerk (2016). Also, validation data used in this study were mainly biased along the main channel in the catchment. Although the surveyed points were mainly measured on areas without vegetation, it is likely that riparian vegetation could have influenced the accuracy as it could have been included in the pixels. The vertical accuracies of SRTM DEM and ASTER GDEM2 are higher than reported in product specifications. While the absolute vertical accuracy of SRTM DEM and ASTER GDEM2 were reported to be around 16 m and 17 m, respectively, all vertical accuracy measures used in this study yielded accuracy values <14 m for both DEMs. These results are consistent with previous findings where Elkhrachy (2018) reported absolute vertical accuracies of 5.94 and 5.07 m for the SRTM30 DEM and ASTER GDEM2, respectively. Also, Patel et al. (2016) recorded absolute RMSE values of 3.72 and 6.03 m for the SRTM30 DEM and ASTER GDEM2, respectively. The vertical accuracy of WRC DEM is consistent with what was reported by Weepener et al. (2012). The vertical accuracy of GEOEYE DEM is slightly less than that of SUDEM5 and DEMSA2. Occurrence of vegetation could have influenced the accuracy of the GEOEYE DEMs since the survey was conducted in August 2017 whilst the stereo images used to create the DEM were captured in July 2011. Areas without vegetation along the stream could easily have been vegetated by the time the imagery was taken.

Reliable catchment boundaries were delineated from VHR to medium-resolution DEMs investigated in this study when carried out at supply resolutions. Based on the assessment indicators, the VHR DEMs yielded more accurate catchment boundaries followed by high-resolution and medium-resolution DEMs at supply resolution. DEMSA2 demonstrates superiority over all the DEMs for catchment delineation, while the SUDEM5 also records a relatively accurate catchment boundary at supply resolution. While SRTM30 DEM yielded a more accurate catchment boundary than ASTER GDEM2 at supply resolution for the HR DEMs, the WRC DEM recorded a more reliable boundary in comparison to the SRTM90 DEM for MR DEMs. DEM resolution does not appear to play any role when catchment boundaries are extracted at medium resolution for all the DEMs. These findings are consistent with previous studies that demonstrated that the outputs of hydrological modelling are not influenced by DEM resolution alone (Tan et al., 2015; Wang et al., 2015; Wang et al., 2011; Wu et al., 2008; Chaplot, 2005; Wolock and Price, 1994), as the DEM source (Wang, Yang and Yao, 2011; Li and Wong, 2010) and resampling technique (Wu et al., 2008) also play a role in the accuracy of delineated hydrological parameters. According to Woodrow et al. (2016), Zhang et al. (2008), Garbrecht and Martz (2000) and Walker and Willgoose (1999), the source of data used to generate a DEM is the main factor determining the spatial and horizontal detail of a DEM. DEMs derived from contours and spot heights are known to be generalized and are unlikely to contain sufficient detail in areas where the horizontal contour interval is larger than the DEM resolution (Vaze et al., 2010).

In this study, a catchment with relatively moderate terrain was chosen to assess the quality of the derived datasets. It is expected that the quality of the SUDEM5 products will improve as terrain complexity increases, as they are largely unaffected by distortions caused by view angle and vegetation cover. Contours are also more densely distributed in areas of complex terrain, which means that interpolated elevations are generally more accurate in such areas. The better performance of the SRTM 30 DEM in comparison to ASTER GDEM for catchment delineation is in line with the findings of Li et al. (2013) who investigated the impact of resolution and DEM source based on ASTER GDEM and SRTM90 DEM and found that SRTM DEM performed better than ASTER GDEM, irrespective of the course grid size. Also, Zhang et al. (2008) evaluated SRTM, NED and LiDAR DEMs at three spatial resolutions (4, 10 and 30 m) in simulating hydrologic responses. They concluded that a 10 m LiDAR DEM recorded the best results.

With regard to delineation performed when VHR and HR DEMs are resampled to MR, the accuracy of the catchment boundaries decreases substantially. This is likely due to the resampling technique. Le Coz et al. (2009) used 6 resampling techniques to aggregate the SRTM DEM from 0.09 to 10 km. They found that mean and median resampling techniques yielded smoother relief while maximum and nearest neighbour produced rougher relief, which resulted in overestimation of the surface area of floodplains. A nearest neighbour resampling techniques was used in this study.

Similar to catchment boundary delineation, reliable streamlines were extracted from all DEMs used in this study. The accuracy of streamlines extracted from all the DEMs appears to be similar irrespective of resolution and the vertical accuracy of DEMs. While the differences in the accuracy measures are slight, they do not seem to be in line with the resolutions and vertical accuracies of the DEMs. This is in contrast to Charrier and Li (2012), who found that the offset from the reference tends to continuously increase as DEM resolution decreases. Vogt et al. (2003) also demonstrated that the quality of DEM-derived river networks is limited by the spatial resolution and vertical accuracy of the underlying DEMs. However, our study is in support of Charrier and Li (2012) with respect to the length of streamlines decreasing with decreasing DEM resolutions, and that the mean offset is mainly less than the cell size of the DEMs. In this study, the offset is similar for all the DEMs. It is likely that terrain complexity affects the delineation of streamlines in the current study. The studied catchment has moderate terrain. For streamline delineation at HR, the SRTM30 DEM performed better compared to the ASTER GDEM2. According to Tarekegn et al. (2010), ASTER-based DEMs are relatively accurate in near-flat and smoothly-sloped areas, but they are characterised by large errors in areas covered by forest, snow, steep cliffs and deep valleys. The catchment area in this study is generally flat and clear of tall vegetation, which would have been beneficial to the ASTER GDEM. However, the results of this study show that the SRTM DEMs performed better than the ASTER GDEM for the derivation of topographic indices (Frey and Paul, 2012).

Regarding streamline extraction at MR, the WRC DEM showed a slight improvement over the SRTM90 DEM. This is in line with Callow et al. (2007) who concluded that hydrologically corrected DEMs resulted in an improved calculation of the catchment area, stream position and length as compared to unmodified DEMs. Although the differences in accuracy measures were marginal, it appears that the positional accuracy of streams stay relatively similar when VHR and HR DEMs are up-sampled to MR. However, total extracted streamline length decreased when the VHR DEMs were up-resampled to MR, and the decrease was more than 9%.

The Cr and FMI ratios calculated for the SRTM90 DEM at 5 m cell size in this study are comparable to those reported by Li and Wong (2010), who recorded Cr and FMI ratios of about 0.03 and 0.01, respectively, for the SRTM90 DEM.

While the Cr for the 1 m GEOEYE DEM in this study is slightly lower than that of the 2 m LiDAR DEM at 5 m cell size resolution used by Li and Wong (2010), the 1 m GEOEYE DEM yielded a higher FMI than their LiDAR DEM. The 2 m DEMSA2 and 5 m SUDEM5 yielded higher Cr and FMI values at 5 m cell size in comparison to the 2 m LiDAR DEM in Li and Wong (2010). However, the Cr and FMI ratios are not good indicators of accuracy when DEMs of different resolutions are compared. Instead, Euclidean distance based MAE and RMSE measures are recommended as they are less sensitive to resolution differences. The positional accuracy of DEMSA2 streamlines is comparable to those of WRC DEM despite its lower resolution. This can partly be attributed to VHR resolution DEMs being more sensitive to topographic features and, in the case of DEMSA2 land cover features (e.g. vegetation growing in the river-bed), it can cause inaccuracies in the extracted streamlines.

 

CONCLUSIONS

This study investigated the utility of DEMs for extracting two hydrological parameters, namely, catchment boundaries and streamlines. The accuracy of these hydrological parameters extracted from two VHR DEMs (DEMSA2 and SUDEM5), three freely available HR global DEMs (30 m ASTER GDEM2 and SRTM DEMs) and two MR global DEMs (90 m WRC DEM and SRTM DEMs) were compared. The study affirmed that the higher resolution DEMs generally produce more accurate parameters (only with respect to catchment boundaries in this study), but that other factors such as source data, resampling technique, terrain complexity and interpolation algorithm also play a role. It is also evident from the results that, of the HR DEMs considered in this study, the SRTM30 DEM produced more satisfactory catchment hydrological parameters than the ASTER GDEM2. Regarding the MR DEMs, the WRC DEM yielded consistently more accurate catchment boundaries and streamlines than the SRTM90 DEM. When the VHR and HR DEMs were resampled to MR, the HR DEMs generated less accurate catchment boundaries.

The ED-based MAE and RMSE proposed in this study can be reliably used to compare reference and DEM-extracted raster datasets of different resolutions and are generally better indicators of geometrical accuracy than the Cr and FMI ratios. The MAE and RMSE values are more intuitive because they provide a quantitative measure of the ED between the generated and reference features. The Cr and FMI ratios are unitless, which makes comparisons difficult. The difference between the MAE and RMSE values can also be used as an indicator of consistency (i.e. impact of outliers).

Despite the relatively lower accuracies of the streamlines and catchment boundaries derived from the high- and medium-resolution DEMs considered, the quality of these datasets seems to be acceptable but depends on the application and scope of assessment. It is critical that the uncertainties in the derived products are taken into consideration when these are used for hydrological analyses. Large errors in streamlines and catchment boundaries can have a significant impact on some applications. Hydrologic modelling, in particular, requires accurate channel and catchment morphology data; large offsets in stream centre lines and catchment boundaries will have a negative impact on flow prediction accuracies. DEM-derived streamlines are also increasingly being used in automated topographical and land cover mapping. Errors in streamlines derived from DEMs will be propagated to these datasets, particularly at large mapping scales.

From the results presented in this paper, it is clear that VHR DEMs should be used at supply resolution to delineate catchment boundaries and streamlines, if available/affordable. Caution should be exercised when using hydrological parameters extracted from up-sampled VHR DEMs, particularly catchment boundaries and total streamline lengths, as these can be highly inaccurate. Also, it does not seem that there is a significant effect on the geometrical accuracy of extracted streamlines when finer resolution DEMs are resampled to MR.

Of the available DEMs covering South Africa, the DEMSA2 is the most suitable product for delineating detailed catchment boundaries. The hydrological parameters from the SUDEM5 are also relatively accurate. As stated earlier, these DEMs should be used at supply resolution for accurate catchment boundary delineation. It does not seem that up-sampling VHR and HR DEMs to medium resolution has a substantial effect on the positional accuracy of delineated streamlines.

Regarding freely available DEMs for delineating catchment boundaries and streamlines, the SRTM30 DEM is recommended. This DEM generated superior catchment boundaries in comparison to the other freely available DEMs (namely, WRC DEM, ASTER GDEM2, WRC DEM and SRTM90 DEM).

More research is, however, needed to evaluate how the different DEMs will perform in landscapes with complex terrain and land cover.

 

ACKNOWLEDGEMENTS

The Water Research Commission, National Research Foundation and the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW), are acknowledged for funding this work. The Chief Directorate National GeoSpatial Information of the Department of Rural Development and Land Reform in Mowbray is thanked for providing the digital aerial images. The Stellenbosch University Centre for Geographical Analysis is thanked for providing VHR DEMs. Our appreciation is given to Chris Hacking at Stellenbosch University for collecting field survey data. We extend our gratitude to Mrs Helene Van Niekerk for editing the manuscript.

 

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Received 16 September 2016
Accepted in revised form 20 September 2019

 

 

* Corresponding author, email: ericm@sun.ac.za

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RESEARCH PAPERS

 

Assessment of straight and meandering furrow irrigation strategies under different inflow rates

 

 

S Sayari; M Rahimpour*; M Zounemat-Kermani

Water Engineering Department, Shahid Bahonar University of Kerman, Kerman, 76169-133, Iran

 

 


ABSTRACT

This paper reports the effect of straight furrow (SF) and meandering furrow (MF) irrigation strategies, as well as inflow rate, on infiltration and hydraulic parameters including advance time, recession time, and runoff hydrograph. The performance of SF and MF irrigation in terms of runoff ratio, deep percolation, and application efficiency was evaluated in 6 furrow fields at Shahid Bahonar University of Kerman, Iran. The required data were collected from the farm, consisting of free drainage furrows with length 70 m, top width 0.8 m, depth 0.25 m, and slope 0.2%. The advance and recession times were significantly longer in MF than SF irrigation. The infiltration was estimated by Lewis-Kostiakov equation. The infiltration coefficients were calculated: The values of k were higher and of a were lower in MF furrows than in SF furrows. The average runoff ratio and application efficiency for the SF irrigation events were 50.53% and 49.07%, respectively, while those of the MF irrigation events were 7.04% and 52.94%, respectively. Based on the results, the velocity of water advance in MF irrigation is decreased and, thus, the runoff, erosion losses, mass of fertilizer lost and surface water contamination were reduced. Using a lower inflow rate and appropriate irrigation time leads to better management outcomes in irrigation systems.

Keywords: meandering furrow irrigation, straight furrow irrigation, advance and recession time, runoff ratio, irrigation performance


 

 

INTRODUCTION

Furrow irrigation is the most common method of surface irrigation because of its low cost and energy. However, these systems are usually associated with low application efficiency and high labour requirements for land levelling and setting up the system (Spaskhah and Shaabani, 2007). In Iran, agricultural water uses over 90% of the water supply (Mergen, 2014). The application efficiency of surface irrigation is low and a large volume of water is lost. Improving application efficiency can reduce water application without affecting productivity. Several researchers have made recommendations to improve irrigation performance (Moravejalahkami, 2012; Reddy et al., 2013) or for the use of alternate furrow irrigation to increase productivity (Barios-Masias and Jackson, 2016; Mintesinot et al., 2004; Siyal et al., 2016; Xiao et al., 2015). One of the solutions to this problem is using innovative and high-tech irrigation methods (drip, sprinkler, etc.). However, farmers in Iran or other developing countries often refuse to use these methods because of the high cost of set-up, performance, and maintenance.

In many countries, farmers cover the furrows and canals with straw to minimize water velocity and soil erosion in the first irrigation (Roldán-Cañas et al., 2015). Some farmers in Iran use meandering furrow (MF) irrigation. In MF irrigation, compared to straight furrow (SF) irrigation, water flows in a furrow that has a meandering path and, therefore, the velocity of water advance decreases, leading to a higher irrigation efficiency. In addition, decreasing the flow velocity will increase infiltration volume and decrease runoff and erosion losses (Mostafazadeh-Fard et al., 2010; Soroush et al., 2012). Distribution uniformity and application efficiency are affected by the furrow inflow rate, especially as the inflow is reduced (Alazba, 1999; Gharbi et al., 1993; Gillies et al., 2007). Prediction of the values of infiltration parameters is required to design surface irrigation (Spaskhah and Afshar-Chamanabad, 2002; Zerhun et al., 1996). The very small changes in the inflow rate could have a considerable impact on infiltration parameters (McClymont and Smith, 1996).

Mostafazadeh-Fard and Moravejalahkami (2006) studied the performance of snake-shaped furrow irrigation. For this purpose, they used three experimental farms with different soil textures and field slopes. The results showed that by increasing the slope and keeping the other parameters the same, the application efficiency of the snake-shaped furrow irrigation increases, but the application efficiency of the straight furrow irrigation decreases. Sepaskhah and Shaabani (2007) studied infiltration parameters, flow hydraulics, and geometric parameters in an anguiform furrow, and compared these parameters with those of straight furrow irrigation. Compared to the straight furrow irrigation, the recession time and infiltration rate were higher in the anguiform furrow. According to Mostafazadeh-Fard et al. (2010), erosion and runoff are lower in MF than SF irrigation. Soroush et al. (2012) investigated the influence of the meandering and standard furrow on distribution uniformity and fertilizer losses. The results showed that the mass of nitrogen losses is notably less for meandering than standard furrow irrigation because of the lower runoff from MF irrigation. Roldán-Cañas et al. (2015) studied MF irrigation in an experimental field in Bolivia. Ten irrigation events were evaluated by measuring advance and recession times, inflow, and runoff rate. The results revealed that the furrow irrigation system was poorly managed and performed poorly.

In surface irrigation, run-off losses lead to soil erosion which can be damaging because it results in the loss of productive soil on the farm (Lehrsch et al., 2014), especially when the soil is bare or plant growth is in its early stages, or in fields that slope steeply. In deep percolation losses, soil erosion decreases, and part of the applied irrigation water is unreachable. Deep percolation results in the transport of dissolved salts from the root zone. Therefore, it is useful for saline soils (Letey et al., 2011). Water for irrigation is a major limitation to agricultural production in many countries. In Iran, 90% of water use is for agriculture. Therefore, the management of water consumption is important. When the soil is saline, leaching by irrigation water is vital. The type of soil and slope of the field can be important for choosing the type of furrow irrigation (SF or MF).

The primary objective of this study was to describe, characterize and evaluate meandering furrow irrigation by conducting irrigation field experiments at different inflow rates, and to compare the results with that of standard furrow irrigation. To this end, the operating and performance variables were measured by field monitoring of irrigation events.

 

MATERIALS AND METHODS

The furrows (straight and meandering) were constructed in the agricultural farm of Shahid Bahonar University of Kerman (SBUK), Iran. The research farm is located in Southeast Kerman (57°10´E, 30°20´N) on sandy loam soil, at 1 750 m amsl. The field had been prepared for planting tomatoes, but the experiments were performed on bare soil. Soil properties of the research farm are presented in Table 1. The experiment was laid out using a complete randomized design with 3 replications. During the field experiments, advance times, recession times and runoff were measured. The experiments were conducted on furrows of 70 m length and 0.75 m width. Inflow and outflow discharge values were measured by a V-notch weir and 1-inch Parshall flume, respectively. For the MF irrigation method, the width of each bend was 4 m. The longitudinal slope of both furrows was 0.2% and the lateral slope in MF was zero. Twenty-four stations were marked along the length of the furrows, and the advance and recession times were measured at each station by recording when the water reached a station and when it disappeared from it. Figure 1 presents an overview of the experimental furrows.

 

 

Data were collected from the first irrigation in each furrow. Inflow rates were determined by control valves connected to a concrete pipe at the upstream end of the field. Furrow cross-section parameters are based on the furrow geometry equation:

where A is the cross-sectional area, T is the top width, y is the furrow depth, and R is the hydraulic radius, presented in Table 2 as measured before the first irrigation.

 

 

Infiltration parameters can be estimated by the observed advance data (Elliott et al., 1983; McClymont and Smith, 1996) or by a combination of advance and runoff data (Gillies and Smith, 2005; Scaloppi et al., 1995). The two-point method computes infiltration parameters with the measured advanced data (Gillies and Smith, 2005; Gillies et al., 2007; Guardo et al., 2000). Infiltration in the furrow was computed by the Lewis-Kostiakov equation Z = kτa + f0τ, where Z is the water infiltrated volume per unit length of the furrow, τ is the intake opportunity time, f0 is the final infiltration rate, and k and a are empirical parameters. The advance equation can be used to calculate infiltration parameters a and k in furrow irrigation as follows (Walker, 1989):

where x is the advance distance, tadv is the advance time, and p and r are advance parameters. Walker and Skogerboe (1987) combined Lewis-Kostiakov, advance, and water balance equations and obtained the following equation:

where Qin is the inflow rate; A0 is the water cross-sectional area upstream of the furrow calculated from A0 = (Qinn/(ρ1S1/2))1/ρ2 (Walker, 1989), n is the Manning coefficient, ρ1 and ρ2 are the furrow geometrical parameters, σy is the surface storage water profile shape factor assumed to be 0.75, and σz is the infiltrated water profile shape factor computed by the following equation (Walker, 1989):

where a is the exponent of the Lewis-Kostiakov equation and r is the advance curve parameter. Instead of using the two-point method, all the data from the stations have to be used to estimate k and a for each irrigation, since the slope and inflow rate are different throughout the furrow length. Therefore, Eq. 2 can be rewritten as follows (Sepaskhah and Shaabani, 2007):

The infiltration of the Lewis-Kostiakov equation can be calculated from Eq. 5. The values of Vx1 (the Vx parameter at 1 min interval) and a are estimated by regression analysis for Vx and t. Then, the k parameter is computed from K = Vx1/σz.

The infiltration parameters and properties of the experi-mental furrow for each irrigation event are shown in Table 3.

The application efficiency (Ea) for each experiment was computed by Eq. 6:

where Vin is the total volume of water applied at each irrigation; Vreq is the water volume required in the soil profile; zreq is the soil moisture extracted by the crop between irrigations; L is furrow length; w is furrow spacing; tco is cut-off time; and Qo is inflow rate. The amount of water required in the root zone was assumed to be 50 mm for all experiments.

Runoff ratio was calculated from the following equation:

where Vinf is the infiltrated volume. Deep percolation (Dp) is the percentage of the infiltrated water that is unreachable for the plants and penetrates to the lower depths. Dp was obtained from Eq. 8.

 

RESULTS AND DISCUSSION

The advance and recession curves for both irrigation methods are presented in Figs 2 to 4. The advance times in the irrigation events S1, S2, S3, M1, M2, and M3 were 40.25, 23.16, 19.38, 63.36, 35.28, and 15.83 min, respectively. It can be observed that the advance time was significantly greater in MF than SF. In irrigation events M1 and M2, the advance time was, respectively, 57% and 52% larger than its counterpart in irrigation events S1 and S2. These results indicate that the flow velocity was lower in MF than in SF.

 

 

 

 

 

 

While the flow rate increased from 0.6 to 1.2 Ls1, the advance time was reduced 42% and 44% for SF and MF irrigations, respectively. By varying the flow rate from 0.6 to 2.4 Ls1 for SF irrigation and from 1.2 to 3.6 Ls1 for MF irrigation, the advance time decreased 51% and 55%, respectively. The impact of changing the flow rate is almost the same for both irrigation methods, although the reduction of advance time is slightly greater in MF irrigation than SF irrigation.

The recession time was greater for MF than SF because of the higher water storage at the stations. During irrigation events M1 and M2, the recession time was 17% longer than its counterpart in irrigation events S1 and S2. The disappearance of water in the recession phase of MF irrigation is mostly because of the infiltration and not because of the outflow downstream of the furrow. The advance and recession curves were almost parallel for both furrow irrigation methods, which shows uniform infiltration throughout the furrows. As can be seen from Figs 2 and 3, advance times decrease when inflow rate increases and the difference in advance times between the two irrigation methods also decreases.

The values of inflow during irrigation events were 0.6, 1.2, 2.4, and 3.6 Ls1. The runoff hydrographs of each irrigation event are presented in Figs 5-7. Runoff was notably less in MF than SF irrigation. As water flows in the MF, the direction of flow changes along the furrow, leading to an increase in the wetted perimeter and the infiltration depth. Furthermore, because of the lower velocity in MF irrigation, soil erosion is less compared to SF irrigation. The volumes of inflow and outflow during each irrigation event are shown in Table 3.

 

 

 

 

 

 

The flow rate and depth of water are affected by the basic infiltration rate (Sepaskhah and Afshar-Chamanabad, 2002), as confirmed by the findings of the present study (Table 3). According to Table 3, the basic infiltration rate for the same inflow is greater in MF than SF irrigation due to a higher wetted perimeter and flow depth. The parameters of the Kostiakov-Lewis equation for each experiment are presented in Table 3. In MF irrigation, the value of k is higher and the value of a is lower than in SF irrigation.

The values of k in MF irrigation with inflow rates of 0.6 and 1.2 Ls1 are 18% and 67% higher than that for SF irrigation. By increasing the flow rate, the value of k is also increased in MF irrigation. The values of a decreased 18% and 46% for inflow rates of 0.6 and 1.2 Ls1, respectively, in MF compared to SF irrigation. The runoff percentage, deep percolation, and application efficiency for each irrigation event are given in Table 4.

For MF irrigation (irrigation events M1 and M2), the runoff losses are 11.55-21.86 times lower than they are in SF irrigation (irrigation events S1 and S2). When the inflow rate was increased, the runoff also increased in SF irrigation. The deep percolation losses are significantly greater in MF than SF irrigation. MF irrigation can be recommended for plants with a deep root zone, heavy textured soil, and sloping fields.

The water application efficiency ranged from 25.66% (irrigation event S3) to 82.15% (irrigation event M1). In both methods, the lowest application efficiency values occurred with high inflow rates (irrigation events S3 and M3). In contrast, the highest application efficiency values occurred with low inflow rates. The application efficiency of MF irrigation is slightly greater than that of SF irrigation. According to Table 4, the difference in advance time between the two methods is not significant at the 5% level in S3 and M3 irrigation events. Despite the inflow rate in the M3 irrigation event being higher than in S3, the advance times are 95% similar. The table also shows that there is no difference in outflow volume between S1 and M3 irrigation events. The inflow rate in the M3 irrigation event is 6 times higher than for the S1 irrigation event and the outflow volumes are the same due to the fact that meandering furrow irrigation reduces the velocity of flow throughout the furrow and increases infiltrated water volume.

Batista et al. (2012) and Roldán-Cañas et al. (2013) reported that lower runoff losses and high application efficiency could be achieved by using low inflow rates. The values of the exponents and coefficients of the advance equations are presented in Table 5. These parameters are varied for straight and meandering furrows since the hydraulic condition is different in these furrows. These results also indicate that the flow velocity is lower in MF than SF.

 

 

Irrigation events must be carefully implemented to achieve high efficiency. The MF system often requires a much greater labour input for construction and deep percolation in sandy soil is very high. Therefore, the type of soil and available labour are important factors in choosing the MF system. Harvesting requires a labour-intensive method in the MF system and harvest machines are not able to move easily in MF furrows. Therefore, further research is recommended into conducting, managing and harvesting with meandering furrow irrigation.

 

CONCLUSION

Field irrigation events were undertaken to evaluate the impact of MF and SF irrigation on performance and hydraulic and infiltration parameters. Advance and recession times were considerably greater in MF irrigation than SF irrigation. The average infiltrated water was lower in SF irrigation than MF irrigation. The recession times in MF irrigation were higher because of greater water storage in upstream stations. The parameters of the advance equation were estimated for MF and SF furrow irrigation and the results showed that the velocity of water advance was reduced in MF irrigation; therefore, runoff and erosion losses were also reduced. The disappearance of water in the recession phase would mostly be due to infiltration in MF irrigation and runoff in SF irrigation. The basic infiltration rate in the meandering furrow is higher than in the ordinary furrow. The coefficient of the infiltration equation, k, was higher and the exponent a was lower in the MF irrigation than in the SF irrigation. The application efficiency was better in the MF irrigation event with the inflow rate of 0.6 Ls1 compared with other irrigation events. Therefore, selecting MF irrigation, reducing the inflow rate, and choosing an appropriate cut-off time can lead to improved irrigation efficiency. MF irrigation can be a viable alternative to expensive irrigation systems such as sprinkler or trickle irrigation.

 

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Received 8 June 2018
Accepted in revised form 23 September 2019

 

 

* Corresponding author, email: Rahimpour@uk.ac.ir

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RESEARCH PAPERS

 

An evaluation of the primary South African standard and guideline for the provision of water for firefighting

 

 

CB Mac Bean; AA Ilemobade*

School of Civil and Environmental Engineering, University of the Witwatersrand, Johannesburg, Private Bag 3, WITS 2050, South Africa

 

 


ABSTRACT

In South Africa, as is mostly the norm globally, national legislation and guidelines specify that potable water distribution networks maintain the capacity to provide specified quantities of water for firefighting. This paper addresses the question: is the South African standard and guideline pertaining to fire-flow provision appropriate for firefighting and do these ensure the most efficient balance between providing sufficient fire protection and promoting sustainable water use? In answering this question, this study: (i) reviewed national and international design standards and guidelines; and (ii) captured and analysed 10 years of billable fire incident reports representing 3 859 fire events within the City of Johannesburg. Highlights from the study include: inconsistencies in categories when comparing the SANS 10090 and The Red Book fire tables and violations (in The Red Book) of stipulated Minimum Fire Flows; over the 10 year period, 75% of fire incidents within the City of Johannesburg were extinguished using less than 6.6 kL of water - less than the capacity (6.9 kL) of the City's conventional pumping tanker during the period; 99.9% of fire incidents within the City were quenched using an average fire flow rate of less than 1 200 L/min, which is the minimum hydrant flow rate for the lowest fire risk category in SANS 10090; and peak fire occurrence did not correspond with typical peak residential water use. Recommendations are proffered in respect of the above.

Keywords: firefighting water standard and guideline


 

 

INTRODUCTION

Water conservation has become a priority for many water-scarce countries around the world, including South Africa. With the majority of potable water supply to the public being delivered via municipal water systems, it is critical that these systems be designed, constructed and maintained in such a manner that they promote the most efficient use of water. Inseparable from the topic of efficient potable water use, is the efficient or inefficient provision of potable water for firefighting and the impact that this provision has on water distribution systems (WDSs) and water conservation.

A global consensus on the ideal design philosophy for providing water for firefighting remains to be established. Likewise, civil infrastructure standards linked to firefighting remain diverse and widely debated, as engineers face the challenge of balancing firefighting water provision against efficient water use. It is due to this complex trade-off between ensuring adequate supply of water for firefighting and minimizing water use that further research into this topic is critically needed.

In South Africa, the national standard (SANS 10090; SABS, 2003) and guideline (DHS and CSIR, 2019) recommend that potable water distribution mains have and maintain the capacity, both in flow (L/min) and pressure head (m), to provide specified quantities of water for firefighting purposes. As a result, a dominant design constraint on WDSs is providing for fire flow, which is defined as the rate of flow of water required by the firefighting service for the extinguishing of fires (SABS, 2003).

Since fire flows significantly influence the sizing of a reticulated network, it is important that these requirements are defined as accurately as possible. It is interesting to note that the demand for water supplies during firefighting is believed, by some, to be historically based on instinct and was strongly characterised by what was available rather than a technical analysis of what was needed (Law and Beever, 1995; Davis, 2000).

The condition of infrastructure, development of firefighting technologies and techniques, and the growth in fire safety awareness have all progressed with time and evolved dramatically since 1966 when the national codes for the provision of water for firefighting in South Africa were first published. Therefore, Van Zyl and Haarhoff (1997) recommend that the provision and requirements for fire flows be amended to reflect present conditions and technologies.

Objectives

This study addresses two objectives:

To present an analysis of international and South African design standards and guidelines pertaining to water provision for firefighting

To present actual fire flow data recorded in the City of Johannesburg, to compare this data with the primary South African standard and guideline values, and to make recommendations to guide future revisions of the primary South African design standard and guideline for the provision of water for firefighting

South African standards and guidelines for fire flows

Guidelines are intended to assist decision-making, whereas standards are enforceable absolute limits (Schlotfeldt, 1995 cited in CSIR, 2005). The national standard (also termed 'code') for the provision of water for firefighting in South Africa titled 'Community protection against fire' was first published in 1966 (SABS 090) and revised in 1972 (SABS, 1972). This code was compiled with the assistance of organizations from the UK, USA, Canada, New Zealand and Germany (Van Zyl and Haarhoff, 1997). A notable feature of this code is the absence of minimum water pressures for both water provision and the pumping ability of response units during a firefighting event. A summary of fire flow values within the most recent edition of this standard (i.e. SANS 10090: SABS, 2003) is shown in Table 1.

A separate national code, SANS 10252-1:2016 (SABS, 2016) titled 'Water supply and drainage for buildings Part 1: Water supply installations for buildings' addresses design pressures and flows for fire installations. A minimum provision of 30 L/min per fire hose reel and 1 200 L/min per hydrant is stipulated without any reference to fire risk categories. This code neither refers to SANS 10090 nor provides as much detail as it does. However, SANS 10252-1 stipulates that a minimum pressure of 300 kPa must be maintained in hoses and hydrants.

Another industry-recognised reference that provides guidance on firefighting is the recently released Red Book (DHS and CSIR, 2019) titled The Neighbourhood Planning and Design Guide (Red Book): Creating Sustainable Human Settlement. The Red Book (DHS and CSIR, 2019) is an updated version of the CSIR (2005) Guidelines for Human Settlement Planning and Design. In contrast to the CSIR (2005) firefighting guidelines, The Red Book (DHS and CSIR, 2019) references the SABS 10090 (2003) code. The Red Book's fire flow values are presented in Table 2.

A notable distinction between SANS 10090 (SABS, 2003) and The Red Book (DHS and CSIR, 2019), apart from the different values they stipulate, is their differing fire risk categories. The Red Book presents a single set of fire risk categories to which all its various recommendations are related. SANS 10090, however, presents two categories. The first is titled, 'Fire Risk Category', and the second, which is a subset of the first, is titled, 'Possible Fire Sizes'. The 'Possible Fire Sizes' category is used exclusively to determine 'Minimum Fire Flow' rates. An adverse consequence of having two categories in the SANS 10090 is that the Minimum Fire Flow and the Minimum Hydrant Flow are determined from different Fire Risk Categories, despite the fact that both are within the same table and connected. By way of example, an affluent residential area (Category C) where houses are spaced further than 30 m apart (Category D1) would have SANS 10090 recommend two Minimum Hydrant Flows of 2 000 L/min (Category C) and 1 200 L/min (Category D1) and two Minimum Fire Flows of 6 000 L/min (Category C) and 1 900 L/min (Category D1).

In addition to the above matter, some violations arise when employing Minimum Fire Flow values from The Red Book. The Red Book, which is a guideline that is intended to assist decision-making, should not, without reasonable justification, violate standards (in this instance, SANS 10090), which present enforceable absolute minimum limits (Schlotfeldt, 1995 cited in CSIR, 2005). All fire flow values in The Red Book are less than the values stipulated in SANS 10090 for similar fire risk categories. An example of this violation is seen in the first two fire risk categories in both documents - The Red Book recommends a fire flow of 6 ٠٠٠ L/min for the 'high risk' category and 3 ٠٠٠ L/min for the 'moderate risk 1' category; SANS 10090, on the other hand, stipulates 13 000 L/min and 9 000 L/min, respectively, for Categories A and B. This paper recommends a uniform category for both documents and Minimum Fire Flows that are consistent with analysed data.

International standards and guidelines for fire flows

Across the world, many methods have been developed to calculate fire flows. These methods generally form the basis on which fire protection codes, such as those discussed above, are established. These methods regulate the design of various WDS features such as:

Spacing of fire hydrants

Minimum size of reticulation pipes

Minimum flow rates and pressures

Storage requirements and flow durations

In a report conducted by The Fire Protection Research Foundation, titled 'Evaluation of fire flow methodologies', 16 fire flow calculation methods were evaluated. The methods identified were from the USA, UK, France, Germany, the Netherlands, New Zealand, and Canada. Eleven of the methods address pre-incident infrastructure/building planning (see (a) below) and five are best suited for on-scene firefighting (see (b) below) (Benfer and Scheffey, 2014):

(a) Infrastructure/building planning: These methods are necessarily predictive in nature, are more complicated and involve several steps and multiple calculations. Typical variables accounted for include: building construction, occupancy, fire size, heat release and sprinkler contribution. The inclusion of a variety of variables enables adjustments to be made to the building type or protection features (e.g. adding a sprinkler system) in order to reduce the fire flow.

(b) On-scene firefighting: These methods, by comparison, are much simpler, allowing fire fighters on the scene to assess whether they need more hose lines or apparatus to fight the fire. They typically consist of one equation with one independent variable - either the volume or area of the fire.

The 16 fire flow calculation methods were simulated for two differently sized non-residential buildings and two differently sized single-family residential buildings. Their study included both sprinklered and non-sprinklered calculations. Figure 1 shows the fire flow requirements for a non-sprinklered, non-residential building of 50 000 ft2 (4 645 m2).

To compare the primary South African fire flow standard (SANS 10090:2003, SABS, 2003) and guideline (The Red Book, 2019) with the 16 shown on Fig. 1, the Minimum Fire Flow requirements for a simlar structure, as defined in SANS 10090 and The Red Book, are superimposed on the results presented in the figure. It is important to note that the SANS 10090 fire flow values presented in Figs 1 and 2 do not explicitly deal with a single incident. However, as expressed in SANS 10090 (SABS, 2003) clause 11.4.1: 'The required fire flow should be available to the firefighting team on arrival at the fire.' It is thus assumed that the comparison made below is fair. Where applicable, both the Minimum Fire Flow and Minimum Hydrant Flow for SANS 10090 and The Red Book are shown in Figs 1 and 2.

Employing the SANS 10090 (2003) 'Possible Fire Sizes' category 'Non-residential buildings with divisions not greater than 5 000 m2 (53 800 ft2)', a Minimum Fire Flow of 13 000 L/min is required (Fig. 1). In the figure, the SANS 10090 requirement falls within the ranges of the ISO, IFC/NFPA 1, and the IWUIC Building Planning methods but is, for several of the other methods, several orders of magnitude lower. The Minimum Fire Flow requirement in The Red Book for the 'high risk' category (which is a similar category to the SANS 10090 category above) is 6 ٠٠٠ L/min. The corresponding Minimum Hydrant Flow for SANS 10090 and The Red Book are ٢ ٠٠٠ L/min and 1 500 L/min, respectively.

It is seen from Fig. 1 that the range of possible fire flows is large, not only when comparing the various methods, but also within some ranges. The FEDG and PAS 4509 methods have the largest ranges. Furthermore, as can be seen in Fig. 1, the Building Planning methods tend to recommend fire flows that are higher than the on-scene methods. many of the on-scene firefighting methods do not incorporate sprinkler protection systems in their calculations (Benfer and Scheffey, 2014).

Figure 2 shows the sprinklered and non-sprinklered fire flow values for a residential building of 3 500 ft2 (325 m2).

Employing the SANS 10090 Risk Category D1 - 'Houses > 30 m apart', a Minimum Fire Flow of 1 900 L/min and a Minimum Hydrant Flow of ١ ٢٠٠ L/min are required. Employing The Red Book 'low risk' category, the Fire Flow and the Minimum Hydrant Flow are each 900 L/min.

For residential buildings fitted with sprinklers, it is worth noting that 12 out of the 18 Benfer and Scheffey (2014) methodologies shown on Fig. 2 required the same fire flow as non-sprinkler fitted buildings. However, as seen in Fig 1 and 2, Minimum Fire Flow requirements vary greatly across many countries.

 

METHODS

Fire incident reports within the City of Johannesburg

A fire incident report (or call slip) is a physical document that is filled out and submitted to the central Emergency Management Services (EMS) headquarters after each fire incident attended to by the fire brigade. Only billable (i.e. incidents that the fire department charges the property owner for services rendered) fire incident reports are digitally captured in spreadsheets by the EMS. Billable fire incident reports are best explained by sections 10.1 and 10.2 (Fees) of the Fire Brigade Services Act No. 99 (RSA, 1987). These sections (listed below) outline the basis on which the local fire department may charge for services rendered:

(1) A controlling authority may, subject to any condition contemplated in section 11(2)(a), determine the fees payable by a person on whose behalf the service of the controlling authority is applied-

(a) for the attendance of the service;

(b) for the use of the service and equipment; or

(c) for any material consumed.

(2) A person on whose behalf, in the opinion of the chief fire officer concerned, a service of a controlling authority has been employed, may in writing be assessed by that chief fire officer for the payment of the fees referred to in subsection (1) or any portion thereof.

It is the above billable incidents that have been consolidated and presented in this paper. In these reports, details captured include the duration of the call out, the quantity of water used, and the appliances used during the incident (see an example in Table 3). On-site calculations are carried out to estimate the total fire flow volume released during the incident. These calculations account for water obtained from fire trucks, water tankers, and hydrants.

The volume or flow rate is determined by reading the meters installed on each appliance. In this paper, the fire incident reports discussed are for fire incidents within the City of Johannesburg only and for the past 10 years (1 January 2006 to 30 September 2017).

Table 3 shows an example fire incident report which was used for invoicing purposes. In the table, it can be seen that 4 different stations responded to one emergency and a total of 30 kL of water was used for firefighting. The Malvern unit was on-site for the longest duration - 2 h 27 min. It is assumed that fire flow rate extracted from the municipal network was constant over the on-site firefighting duration. While this assumption produces an average flow rate per incident (Eq. 1), it underestimates the peak firefighting flow (data which were not, and currently are not, recorded).

From 1 January 2006 to 30 September 2017, there were 4 556 billable firefighting incident reports recorded in the City of Johannesburg. Of this number, 697 were recorded as incidents that did not require municipal water and, therefore, the analysis below was based on 3 859 billable water use incidents. This dataset excludes all non-billable fire incidents including informal settlements, veld/grass and car/motorcycle fires.

 

RESULTS AND DISCUSSION

The scatter plot shown in Fig. 3 shows the magnitude and distribution of the 3 859 fire flow volumes recorded from 1 January 2006 to 30 September 2017. Figure 4 shows the magnitude and distribution of the 3 859 fire flow rates from 1 January 2006 to 30 September 2017. To gauge the validity of the incidents with large fire flow volumes (> 300 kL) and fire flow rates (> 1 000 L/min), fire event characteristics (such as duration, number of responding stations, presence of fire safety officials, and fire location) were individually examined. From this exercise, the fire incident circled in Figs 3 and 4 was identified as a likely data capture error because it did not bear the same characteristics as the other large fire volume incidents. The largest (800 kL) fire flow volume in the dataset was responded to by 6 different fire stations, lasted over 15 h and had fire safety officials present.

 

 

Figure 4 also includes the SANS 10090 (2003) standard and The Red Book (DHS and CSIR, 2019) guideline values for Fire Flow as well as the Minimum Hydrant Flow for the different fire risk categories. An assumption made in the below analysis is that the recorded firefighting flows extracted from the municipal network or fire equipment were what was required to fight the fires. None of the 3 859 billable fire incident reports indicate otherwise.

Figure 4 reveals that over the 10-year period, not a single fire incident in the City of Johannesburg recorded an average flow rate greater than 6 000 L/min. This implies that over the 10-year period, no incident can be classified as a SANS 10090 Category A, B or C nor The Red Book 'high risk' category fire. During the 10-year period, only 2 incidents recorded average flow rates greater than 2 000 L/min. Three incidents recorded average flow rates greater than 1 500 L/min. The vast majority of average flow rates fell below both the SANS 10090 Minimum Hydrant Flow for Categories A, B, C and D and The Red Book Minimum Hydrant Flow for 'high risk' and 'moderate risk' categories.

Figure 5 shows that 75% of fire incidents were extinguished using less than 6.6 kL of water -this volume is less than the capacity (6.9 kL) of a conventional pumping tanker within the City of Johannesburg's fleet purchased in 2003. This means that over the study's 10-year period, 75% of fire incidents in the City of Johannesburg could have been extinguished without the use of municipal fire hydrants if a pumping tanker with a full tank of water was dispatched. The below quote from Myburgh and Jacobs (2014 p.11) confirms similar results obtained for 3 municipal areas in the Western Cape: 'only 8.6% of all fires were extinguished using water from the WDS by connecting firefighting equipment to a fire hydrant at the time of the fire. Most fires were extinguished by means of water ejected from a pre-filled tanker vehicle disconnected from the WDS at the time of fighting the fire.'

 

 

Figure 6 presents the cumulative probability plot of average flow rates, with the SANS 10090 and The Red Book values superimposed. The figure shows that 99.9% of all fire incidents within the City during the designated period resulted in an average fire flow rate less than 1 200 L/min, which equals the lowest of the Minimum Hydrant Flow rates for SANS 10090 (i.e. Category D). Likewise, 99.7% of all fire incidents resulted in an average fire flow rate less than 900 L/min, which equals the Minimuim Hydrant Flow rate for The Red Book's lowest fire risk category (i.e. low risk). These findings suggest that there is scope to reduce the current Minimum Fire Flows especially in low risk categories whilst maintaining adequate levels of safety. Because of the need to fight low probability but high consequence fires in moderate- to high-risk fire category areas, the authors caution on the application of the above statement to these areas.

To better understand intra-day and intra-year firefighting trends, Fig. 7 shows, over an average month, the average volume of water used to extinguish fires in relation to the frequency of fire occurrence while Fig. 8 shows the frequency of occurrence of fires and residential water use over a typical day. In Fig. 7, the green bar chart shows the average number of fire incidents occurring each month while the blue bar chart shows the average fire flow volume per incident for each month over the period 1 January 2006 to 30 September 2017. An expected seasonal trend is observed with regard to frequency of fire occurrence, with a notable rise in incidents from June to October, which are typically dry and low-rainfall months in Johannesburg. While average fire flow volumes range between 7 to 12 kL per incident, there is no observable seasonal trend. These trends imply that, while the frequency of fire occurrence is strongly related to climatic conditions, the volume of water used to quench fires, and by implication, the size of the fires, is not a function of climatic conditions within the City of Johannesburg. As a consequence, seasonal peak factors for fire flows may not be necessary when incorporating the provision for water for firefighting in the design of municipal mains within the City of Johannesburg or other metropolitan municipalities with similar fire flow and climatic conditions.

 

 

 

 

Figure 8 displays the occurrence of incidents throughout the course of a day, averaged over the period 1 January 2006 to 30 September 2017. The green graph shows the percentage distribution of fire incident start times. In Fig. 8, three peaks (at 01:00, 15:00 and 20:00) are observed. The highest of the three was at 01:00 - this represents 230 (5.9%) fire incidents. The blue graph shows a typical diurnal residential water use pattern published by Van Zyl (1996) (cited in Scheepers, 2012). The water use pattern shows the primary peak residential demand occurring at 06:00 while the secondary peak demand occurs between 16:00 and 17:00. When compared to the start times of fires within the City of Johannesburg, it is observed that the start times of peak fires do not correspond with peak residential water demand periods. The inverse is the case - the lowest observed start times of fires were during peak demand periods. This finding may therefore provide motivation to further investigate the recommendation to cater for both instantaneous peak demand and fire demand during WDS design as recommended by The Red Book (DHS and CSIR, 2019: J.3.2.2) i.e.: 'Conveyance infrastructure should have sufficient capacity for peak demand conditions and fire-flow requirements, in accordance with the design guidelines in this document' and (CSIR, 2005: volume 2, Chapter 9, page 27): 'The nominal capacity of the duty pump should be equivalent to the sum of the instantaneous peak demand and the fire demand (obtained from the section on provision of water for firefighting), or the instantaneous peak demand plus an allowance of 20%, whichever is the greater.'

 

CONCLUSIONS AND RECOMMENDATIONS

The key results and recommendations arising from the two objectives addressed in this study are presented below:

Objective 1: To present an analysis of international and South African design standards and guidelines pertaining to water provision for firefighting

o A review of national and international standards and guidelines for water provision for firefighting are presented in the text. A notable distinction between the SANS 10090 (SABS, 2003) standard and The Red Book (DHS and CSIR, 2019) guideline, apart from the different values they recommend for Fire Flow and Minimum Hydrant Flow, is their differing fire risk categories. The Red Book presents a single set of fire risk categories while SANS 10090 presents two fire risk categories which, in certain instances, do not recommend consistent fire flow values for the same category.

o In addition, The Red Book, which is a guideline, in all instances, violates the Minimum Fire Flows in SANS 10090, which is a standard that stipulates minimum acceptable values.

o It is therefore a recommendation of this paper that the SANS 10090 fire risk categories (A, B, C, D and E) be revised. As a result of their simplicity and recent revision, The Red Book (DHS and CSIR, 2019) fire risk categories may be adopted in the recommended revision of the SANS 10090 fire risk categories.

Objective 2: To present actual fire flow data recorded in the City of Johannesburg, to compare this data with the primary South African standard and guideline values, and to make recommendations to guide future revisions to the primary South African design standard and guideline for the provision of water for firefighting.

o Fire incident reports were obtained from the City of Johannesburg's EMS for the period 1 January 2006 to 30 September 2017. These reports show that the majority of average fire flow rates fell below both the SANS 10090 (2003) and The Red Book (DHS and CSIR, 2019) Minimum Hydrant Flow for all its categories. The below highlights are evidence of this:

Almost all (99.9%) fire incidents recorded an average fire flow rate less than 1 200 L/min - the lowest Minimum Hydrant Flow rate for the SANS 10090 Categories

Similarly, 99.7% of all fire incidents recorded an average extracted fire flow rate less than 900 L/min - the lowest Minimuim Hydrant Flow rate for The Red Book's categories

o A second finding from the analysis of fire incident reports was that 75% of fire incidents were extinguished using less than 6.6 kL of water and thus could have been extinguished using one of the City of Johannesburg's conventional pumping tankers which have a capacity of 6.9 kL. This, by implication, means that 75% of fire incidents within the City could have been extinguished without the use of municipal fire hydrants if a suitable tanker with a full tank of water was available.

o A third highlight was that, while the frequency of fire occurrence was strongly related to climatic conditions, the volume of water used to quench the fires was not a function of climatic conditions

o A fourth highlight was that the start times of peak fires did not correspond with peak residential water use periods within the City of Johannesburg over the 10-year period. The inverse was however the case - the lowest observed fire incidents occurred during peak demand periods

Based on the above findings, and the assumption that the results from this study can be generically applied, the following recommendations can be made:

A Minimum Hydrant Flow of 1 200 L/min is recommended for all SANS 10090 and The Red Book Categories. SANS 10252-1:2012 (SABS, 2012) stipulates the same value.

To improve the efficiency of firefighting within the City of Johannesburg, especially considering the potential devastation that could occur due to increasing instances of water cuts and low pressures (Kahanji et al., 2019), EMS should focus on acquiring pumping appliances with sufficient capacity and volume (minimum of 6.6 kL) to extinguish fires.

Based on the findings of this study, future research may investigate:

The need for seasonal peak factors when incorporating the provision for water for firefighting in the design of municipal mains

Catering for both instantaneous peak demand and fire demand during WDS design as recommended by The Red Book (DHS and CSIR, 2019)

Understanding the change in rate of water use during a fire event

 

ACKNOWLEDGEMENTS

The participation and data provided by the City of Johannesburg's Emergency Management Services personnel are gratefully acknowledged. Also gratefully acknowledged is the postgraduate funding and support by Mott MacDonald.

 

REFERENCES

BENFER M and SCHEFFEY J (2014) Evaluation of Fire Flow Methodologies. The Fire Protection Research Foundation, Quincy, Massachusetts. https://doi.org/10.1007/978-1-4939-2889-7        [ Links ]

CSIR (2005) Guidelines for Human Settlement Planning and Design. Red Book. CSIR Building and Construction Technology, Pretoria.         [ Links ]

DHS and CSIR (Department of Human Settlements, South Africa and Centre for Scientific and Industrial Research (2019) The Neighbourhood Planning and Design Guide (Red Book): Creating Sustainable Human Settlement. DHS and CSIR, Pretoria. ISBN 978-0-6399283-2-6.         [ Links ]

DAVIS SK (2000) A review of fire fighting water requirements. ME thesis, University of Canterbury.         [ Links ]

KAHANJI C, WALLS RS and CICIONE A (2019) Fire spread analysis for the 2017 Imizamo Yethu informal settlement conflagration in South Africa. Int. J. Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2019.101146        [ Links ]

LAW M and BEEVER P (1995) Magic numbers and golden rules. Fire Saf. Sci. 4 79-84. https://doi.org/10.3801/IAFSS.FSS.4-79        [ Links ]

MYBURGH HM and JACOBS HE (2014), Water for firefighting in five South African towns. Water SA 40 (1) 11-17. http://dx.doi.org/10.4314/wsa.v40i1.2        [ Links ]

RSA (Republic of South Africa) (1987) Fire Brigade Services Act. Act No. 99 of 1987. Government Gazette 11006, 23 October 1987, Issue 99.         [ Links ]

SABS (South African Bureau of Standards) (1972) SABS 090:1972. Community protection against fire. SABS, Pretoria.         [ Links ]

SABS (South African Bureau of Standards) (2003) SANS 10090:2003 South African National Standard. Community protection against fire. Standards South Africa (a division of SABS), Pretoria. ISBN 0-626-14666-6        [ Links ]

SABS (South African Bureau of Standards) (2012) SANS 10252-1:2012. South African National Standard. Water supply and drainage for buildings Part 1: Water supply installations for buildings. SABS Standards Division, Pretoria.         [ Links ]

SCHEEPERS HM (2012) Deriving peak factors for residential indoor water demand by means of a probability based end-use model. Master of Science in Engineering, Stellenbosch University.         [ Links ]

SCHLOTFELDT CJ (1995) The "Red Book": Background, purpose and way forward. Paper presented at Red Book Workshops. February and March, Bloemfontein, Cape Town, Pretoria, Durban and Port Elizabeth.         [ Links ]

VAN ZYL JE (1996) Peak factors in municipal water reticulation networks. Proceedings of the Water Institution of South Africa Biennial Conference and Exhibition, 20-23 May 1996, Port Elizabeth, South Africa.         [ Links ]

VAN ZYL JE and HAARHOFF J (1997) South African fire water guidelines and their impact on water supply system cost. J. S. Afr. Inst. Civ. Eng. 39 (1) 16-22.         [ Links ]

 

 

Received 23 August 2018
Accepted in revised form 26 September 2019

 

 

* Corresponding author, email: adesola.ilemobade@wits.ac.za

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RESEARCH PAPERS

 

Environmental life cycle assessment for potable water production - a case study of seawater desalination and mine-water reclamation in South Africa

 

 

T GogaI; E FriedrichI, *; CA BuckleyII

ICivil Engineering Programme, School of Engineering, University of KwaZulu-Natal, Durban, 4041, South Africa
IIPollution Research Group, Chemical Engineering Programme, School of Engineering, University of KwaZulu-Natal, Durban, 4041, South Africa

 

 


ABSTRACT

Water is becoming a scarce resource in many parts of South Africa and, therefore, numerous plans are being put in place to satisfy the increased urban demand for this resource. Two of the methods currently considered are desalination of seawater and reuse of mine-affected water based on the use of reverse osmosis (RO) membranes. Due to their high energy consumption and associated environmental impacts, these methods have been under scrutinity and, therefore, an LCA was undertaken for both methods. To allow comparison between the two, the functional unit of 1 kL of potable water was specified. Design data were collected for both the construction and operation phases of the plants while SimaPro was used as the LCA analysis software with the application of the ReCiPe Midpoint method. The results indicate that the operation phase carried a greater environmental burden than the materials required for the infrastructure. In particular, electricity production and consumption is responsible for the majority of environmental impacts that stem from the respective plants. The total energy consumption of the proposed desalination plant is 3.69 kWh/kL and 2.16 kWh/kL for the mine-water reclamation plant. This results in 4.17 kg CO2 eq/kL being emitted for the desalination plant and 2.44 kg CO2 eq/kL for the mine-affected plant. A further analysis indicated that replacing South African electricity with photovoltaic (solar) and wind power has the potential to bring significant environmental benefits. The integration of these renewable energy systems with desalination and membrane treatment of mine-affected water has been proven to reduce environmental burdens to levels associated with conventional water technologies powered by the current electricity mix.

Keywords: LCA, water treatment, mine water, desalination


 

 

INTRODUCTION

Water is regarded as one of the most precious and critical resources worldwide. In South Africa, the scarcity of water presents various challenges, mainly relating to efficient development, management and utilisation (Knüppe, 2011). To overcome these obstacles and ensure that South Africa has sufficient water supply, various water treatment techniques have been explored. As is the case with all industrial processes, there are substantial environmental impacts that occur from the construction of the infrastructure, through to commissioning, operation and decommissioning. In order to effectively evaluate the environmental burden of each water treatment system as well as its associated processes, a life cycle assessment (LCA) can be utilised. The use of such a sustainability tool provides a true reflection of the product's life cycle from 'cradle to grave' by systematically quantifying the amount of energy used, the consumption of raw materials, emissions to the atmosphere as well as the amount of waste generated (ISO, 2006).

The use of LCA as an assessment tool to gauge the environmental impacts of water technologies has been increasingly used since the late 1990s. Loubet et al. (2014) and Buckley et al. (2011) present a comprehensive review of the applications of environmental LCAs in the water industry both internationally and locally. Assessments have been successfully conducted locally for conventional technologies utilised in potable water and wastewater treatment plants (Friedrich et al., 2007); however, there are only two local studies researching membrane processes. Internationally, such membrane processes have been the focus of many LCAs, with Meijers et al. (1998) starting this trend. Zhou et al. (2014) present an extensive review of the international studies employing LCAs for desalination and they include more than 30 individual research papers. Locally, there are only two such investigations (Friedrich, 2002; Ras and Von Blottnitz, 2012) that employ LCA for the use of membranes in the treatment of water. However, there are no such investigations for the local desalination of seawater or reclamation of mine-affected water. Therefore, this paper aims to satisfy this need by investigating the environmental burdens associated with membrane-based treatment processes.

This study compared two water treatment processes in South Africa to produce potable water. The first study is based on a proposed desalination plant that will be installed by Umgeni Water. During the feasibility study phase, it was determined that the plant should be located on the South Coast of KwaZulu-Natal and will be designed to produce a total of 150 ML/d of potable water (Umgeni Water, 2015a). The second study revolves around a water treatment process in Mpumalanga that treats mine-affected water to potable water standards. The plant is currently treating 15 ML/d of raw water via two processing trains (Golder Associates Africa, 2012). Both plants make use of membrane technologies to achieve the desired separation. Currently, these alternative sources of water and associated technologies are in rare use (DWA, 2013). However, considering the increasing demand for a limited resource, such operations will become more widespread. Thus, it is imperative to shape the design process for future projects from the outset, so as to reach the best outcome locally. The findings from this study will provide guidance regarding focus areas to guide this process.

The LCA process consists of 4 phases, namely, goal and scope definition, inventory analysis, impact assessment and interpretation (ISO, 2006). The first stage set the aims of the study and provided an outline of the functional unit, assumptions made and data requirements. The next stage consisted of the gathering of data which was used as inputs into SimaPro which was the selected LCA software. A series of scores for the various environmental impacts were obtained which provided an indication of the environmental contribution of the process parameters. Recommendations based on these results were then proposed.

 

CASE STUDIES

The first case study centred around a proposed desalination plant in the Southern eThekwini area that makes use of RO technology. The second case study focused on a mine-water reclamation process in Mpumalanga that was designed using both UF and RO.

Desalination plant in eThekwini Municipality

To determine the feasibility of constructing a large-scale desalination plant, an investigation by Umgeni Water was initiated by undertaking a desalination pre-feasibility study. After much consultation, a revised strategy was adopted where the detailed feasibility study would consider the option of a 150 ML/d plant situated on both the North and South Coast (Meier, 2012). A diagram of the desalination process highlighting the key components is presented in Fig. 1. In general, the desalination plants at the selected locations would include the following key components (Umgeni Water, 2015a):

Offshore open intake and discharge pipeline with diffusers

Pipeline and structures conveying intake water to the desalination plant

Pre-treatment facilities

Reverse osmosis systems equipped with energy recovery devices

Post-treatment systems for re-mineralization and disinfection

Water storage tanks and pump stations

Electrical substations connected to power grid

The desalination process centres around the RO system. It is recommended that the RO system consists of 16 seawater reverse osmosis (SWRO) trains with one high-pressure feed pump. This system must be designed to meet the specified product water quality and possess a certain degree of flexibility to accommodate potential increase in production or future changes in membrane technology (Umgeni Water, 2015a). Approximately 40-50% of the energy requirements for desalination are contained within the concentrate produced by the RO process. In order to optimise the energy consumption of the system, this energy can be recovered and reused by installing energy-recovery devices. It is noted in the Feasibility Report that the payback period of equipment costs for installation of these devices through energy savings is usually less than 5 years. Thus, the consulting engineers have suggested the addition of 16 pressure exchange recovery systems - one per SWRO train (Umgeni Water, 2015a).

The mine-water reclamation plant in Mpumalanga

Various coal mines in Gauteng and Mpumalanga have been in existence for a substantial period of time. In order to allow safe access to the coal reserves, water is pumped away from active areas and stored in previously mined underground cavities. The objective of the proposed Mine Water Reclamation Scheme (MWRS) was to abstract and treat the accumulated mine-water in order to increase the potable water supply and allow mining to occur within areas that were previously flooded (Golder Associates Africa, 2012).

It was proposed that the project will involve the construction and operation of the MWRS which would consist of mine-water abstraction points and delivery pipelines, a mine-water storage dam, a water treatment plant (WTP), sludge and brine ponds (for WTP waste), treated water supply pipelines and support infrastructure such as powerlines and access roads (Golder Associates Africa, 2012). The WTP would comprise of a raw water pond, pre-treatment and UF facilities as well as a two-stage RO system. It was envisaged that the project will be carried out in three phases with the aim of abstracting and treating a total of 45 ML/d. At this stage, Phase 1 of the plant has been successfully completed which processes 15 ML/d of contaminated mine-water (Golder Associates Africa, 2012).

The mine-water reclamation process commences with the pumping of the mine-affected water through deep bed up-flow (DUP) filters and treatment with the addition of several chemical compounds (Prentec, 2013). The water then flows through the first stage of UF and RO. The reject flow from this first stage then flows through a secondary treatment phase. At present, the product water from both stages is collected and then discharged into a river. All process units are housed in customised modules and integrated with process, mechanical, electrical and control components for full functionality and ease of design (Prentec, 2013). It is envisaged that future uses of this treated water would include the mine's internal use (4 ML/d), the proposed power plant (1.2-1.7 ML/d) and possible potable water supply to the surrounding communities (Golder Associates Africa, 2012).

The design for the mine-water reuse plant makes extensive use of membranes with two stages of UF and RO. The primary UF module consists of polyvinylidene fluoride (PVDF) membranes with 0.08 μm pore size (Hydranautics, 2016). Stage 1 of RO is configured into two banks of spiral-wound elements with polyamide thin-film composite membranes with a 75-80% recovery (Dow Filmtec, 2015). The secondary treatment stage is designed to effectively recover water from a saline solution. Stage 2 of UF utilises 1.5 mm membranes with an inside-out configuration to reduce the potential for scaling (Prentec, 2013). The modified polyethersulphone (PES) membrane material is resistant to fouling while the large 1.5 mm size allows for a more effective cleaning process (Prentec, 2013). The second stage of RO comprises of three banks of membrane elements with a higher feed pressure than the first stage (Prentec, 2013).

 

METHODOLOGY

For this investigation an LCA methodological approach as defined by ISO 14040 (2006) was undertaken and the four major steps (goal and scope definition, inventory analysis, impact assessment and interpretation) were followed.

Goal and scope definition

The main goal of this study was to quantify the overall environmental impact of each of the selected cases of membrane water treatment processes with the generation of local LCA data. The intended audience for this study is broad and includes environmental and operational managers in the water sector. It is envisaged that government authorities who are responsible for investigating environmental processes could also gain insight from the findings of such a research project.

The purpose of defining the scope is to provide sufficient detail regarding the object of the LCA study. This should be completed in conjunction with the goal definition (European Commission, 2010). The items that need to be considered include the product system demarcated by the system boundaries, the selected function and functional unit, data requirements and assumptions and limitations made during the course of the study.

The systems under consideration are the two processes for the production of potable water. The first process under review was the desalination of seawater while the second process focuses on the reclamation of mine-affected water. For both processes, the construction and operation phase were considered as the decommissioning phase was considered negligible based on the findings of Friedrich (2001) and Raluy et al. (2005). Figure 2 depicts the stages in the LCA with the black box depicting the system boundary.

 

 

The function for both systems is identical, i.e., to produce potable water of a certain quality. The functional unit for this study was 1 kL of water at the specified standard for potable water produced over the lifespan of each process unit. The selection of this particular functional unit enabled a reference to which all inputs and outputs are related. It should be noted that this functional unit was chosen due to the demand for potable water. The mine-affected water would have had to be treated as per current South African guidelines before being released into the environment. However, the quality would have been required to meet a much lower standard as compared to that of potable water. For seawater there is no need for treatment in the absence of the potable water demand. For the purpose of this study the potential treatment of mine-affected water in the absence of potable water demand was neglected, as in reality the membrane processes would not have been employed if not for the need for potable water quality.

Data quality requirements are a general indication of the characteristics of the data for the study. For both case studies, data that were directly obtained from the feasibility and design reports were preferable. Such data included the consumption of electricity and chemicals. For process flows within the system that were not available, mass balances were employed. When direct data was unavailable, as was the case for the construction of civil engineering structures, calculations based on technical literature were utilised. Several calculations were often undertaken and the highest values, representing a worst-case scenario, were used for purposes of the study. Decisions regarding materials of construction as well as equipment types were based on case studies of similar water treatment processes. The geographical area for data gathering was South Africa. Within the SimaPro databases, South African data were only available for national electricity and mined coal that was used as filter media. For the remainder of the inputs, European or global figures were utilised.

Limitations to a certain extent were to be expected, considering the task of accounting for all inputs and outputs of the system. In general, data were found to be sparse and lacking which is often the case for LCAs, but even more so for industries based in South Africa. One problem that was encountered was that data were considered to be confidential and thus were not easily accessible. This was the case for both case studies and lengthy negotiations had to occur before any exchange of information happened. Agreements between Umgeni Water, Prentec and the consulting engineers had to be made in order to obtain certain process details. Another reason for the lack of data can be attributed in part to the fact that the desalination plant was still in the early design phases. Thus, some information, such as the weights of certain pumps, was unavailable. As a result, information from design specification sheets for similar pumps had to be used as inputs for the calculations. For the mine-water reclamation plant, design data rather than operational data had to be utilised. This was due to changes in the feed quality of the source water which affected the operation of the plant.

A set of assumptions had to be made in order to bridge data gaps. For certain inputs that were based on international data, it was assumed that the technology and equipment utilised will perform in a similar manner to what is used in South Africa. Where the material of construction was unspecified for components such as the filter cells, various literature sources were perused and the most common materials were selected for the purpose of calculation. In other instances, super duplex stainless steel was chosen as the construction material of choice for any equipment that is in contact with the ocean water. It was also assumed that both plants will be operational for the entire year, i.e. 365 days with no allowance for shutdown periods. This was to account for the worst-case scenario.

Inventory analysis

As the second stage of the LCA process, the inventory analysis consisted of collecting environmentally relevant data as well as formulating equations in order to quantify the flows into and out of the system. For this particular study, the process of data collection and compilation was the most work-intensive and time-consuming activity.

Data collection for the desalination plant

The procedure for data collection started off with a compilation of a process flow diagram (PFD) which highlighted the significant flows and operations within the system. From this point, a spreadsheet was drawn up which included material and energy inputs and outputs for each unit operation. For the construction phase, four major components were analysed: civil engineering structures, pipes, pumps, filters and membranes. Civil engineering structures consisted of fixtures such as tanks, pillars and filter cells. The weights of these constituents were generally not stated and had to be calculated based on available dimensions provided in the feasibility reports. In the event that the material of construction was not specified, technical literature was used to select the most appropriate building material.

In the case of pipes, all pipes were specified to be constructed of high density polyethylene (HDPE) due to its higher durability, non-corrosive nature and lower construction and maintenance costs compared to other materials. The mass of these pipes was calculated by firstly calculating volume of a hollow cylinder (which represents a pipe) using the inside and outside diameters, subtracting the volume of the inner from the outer and using the density to obtain the mass. The second method used a HDPE pipe brochure to obtain the mass of the pipe based on the outer diameter and standard dimension ratio (SDR) class which were stated in the Pipelines and Pump Stations Report (Umgeni Water, 2015b). The higher figure was then utilised in subsequent calculations.

Pumps are a fundamental part of the infrastructure of any plant and the design for the proposed desalination plant was no different. For the intake pumps, options were provided for various pump models in the above-mentioned report that detailed pipe specifications (Umgeni Water, 2015b). The mass was then obtained from locating the pump specification sheets for the selected model. For other pumps where model numbers were unavailable, the installed motor size and pumping capacity which was provided in the Desalination Options and Feasibility Report (Umgeni Water, 2015a) were used as guidelines to select an appropriate pump. The masses of the respective pumps were taken as a single entity inclusive of parts such as motors, gears, bearings, casings, etc. This was due to difficulty experienced in obtaining these figures. The Feasibility Report also detailed that the pumps be constructed of super duplex stainless steel. As such a material was not available on the SimaPro database, steel which had a high chromite content (±25%) was selected.

For the production of potable water, the main operational inputs into the system were the energy consumed, the chemicals utilised and the filter media, as displayed in Table 1. There were a range of chemicals used in the production process with the majority being used in the pre-treatment and post-treatment phase. Chemicals were used for various purposes: disinfection, RO membrane cleaning, chlorination and re-mineralisation. The utilisation of chemicals was stated in terms of milligrams per litre of water (mg/L) with the majority of the chemicals specified. For the chemicals that were not categorically stated, such as the coagulant and antiscalant, research was undertaken to determine the most suitable chemical for the application. Table 1 provides a summary of the chemical usage for the desalination process. The first and second column lists the chemicals mentioned in the Umgeni Water report as well as the unit operation. The last column states the chemical that was utilised in SimaPro based on technical literature.

 

 

From the literature review, it is evident that the electricity requirement has always been one of the determining factors regarding life cycle assessment results. For the purposes of this study, this information was available in the Feasibility Report and was expressed in terms of kWh/m3 (Umgeni Water, 2015a). As electricity is such a fundamental element, it was imperative that a consistent and representative life cycle inventory (LCI) of electricity supply was utilised. The latest version of ecoinvent (version 3) offers new LCI data of power supply in 71 geographical locations which includes South Africa (Paul Scherrer Institute, 2012). Thus, this inventory set was utilised for this study to account for the South African energy mix.

The filtration step forms part of the pre-treatment phase to protect the RO membranes further on in the process. As mentioned in the previous sub-section, dual media filters were specified in both stages of pre-treatment containing silica sand, anthracite and garnet. SimaPro contains data for coal from extraction to point of sale in South Africa. This local dataset was utilised to represent the media layers for the filters.

Data collection for the mine-water reclamation plant

This procedure commenced with an initial meeting with the senior process engineer from Prentec. An overview of the treatment process was provided together with several process and instrumentation diagrams (PIDs) as well as schedules for power use. This was followed by the compilation of the spreadsheet that segregated the design data per unit operation and then further into the construction and operation phases. Average design feed flows into each sub-operation were stated in the design reports.

The major constituents of the construction phase for this case study comprised of components such as civil engineering structures, frames of the modules, grating, pipes, pumps, filters and membranes. The civil engineering structures for this case study consisted of tanks and filter cells. Design sheets for the various tanks provided dimensions of the tanks such as the diameters and heights. This was utilised to calculate the circumference and thereafter the number of panels that make up the wall of the tank. Together with the dimensions of the panel, the weight of the walls and base of the tank can be calculated. From the design sheets, the material of construction for the base and walls was reinforced concrete to withstand pressures of 25 and 30 MPa, respectively. With respect to working life, the senior process engineer was consulted and agreed that a reasonable working life for tanks would be 50 years.

For this case study, the mass of components, such as the frames of the skid, grating and pipes, were obtained courtesy of the personnel from the company involved. A 3D model of the plant, which collates the total mass of each skid, was utilised, from which masses of the individual items were extracted. These data are extremely accurate as pipe mass would include the mass of all lengths of pipe including all bends and tees. The frames and grating that form part of the skid are constructed of carbon steel and galvanised steel, respectively, while the pipes are either assembled from PVC or stainless steel. As advised by the design engineer, the working life of the frames, grating and stainless steel pipes was taken as 25 years while the PVC pipes were assumed to last 20 years.

With respect to the pumps, product names were provided in the design proposal report. As the majority of the pumps were manufactured by Grundfos, the product centre on the Grundfos website was perused. As all the pumps were classified as 'end suction close coupled' (NB range), the pump catalogue was browsed by pump design to locate the masses of the specific pumps. As in the initial case study, the masses of the respective pumps were taken as a single entity inclusive of all internal mechanical parts. The working life of all pumps was stated as 7 years.

The water treatment process for mine-affected water consists of two stages of treatment. Thus, there were two stages of reverse osmosis and two stages of ultrafiltration (UF). For the RO membranes, the masses of 8" spiral wound membranes were obtained from the Dow website while the design sheets for the glass-reinforced plastic vessels were provided. According to the engineer as well as figures from technical literature, the membranes which are constructed of polysulphone would last an average of 5 years while the working life of the outer shells was noted as 20 years. With respect to the UF membranes, product data sheets for the weight of membranes were located on the supplier's website. These were constructed of polyvinyl chloride (PVC) with the same working life as the RO membranes.

The three components in the operation phase include the chemical use within the process and the energy consumption as well as the filter media used. There were numerous chemicals used in the treatment process to satisfy various objectives. Amongst them were coagulants, biocides, antiscalants, chemically enhanced backwash (CEB) and clean-inplace (CIP) chemicals. The major chemical constituents used were phosphoric acid as an antiscalant, ferric chloride as a coagulant and secondary antiscalant, sodium hydroxide, sodium hypochlorite and hydrochloric acid as CEB and CIP chemicals. The average concentrations in terms of ppm for each chemical were provided.

From the literature, it was evident that energy was of utmost importance. Thus, a concerted effort was made to obtain an accurate portrayal of the electricity consumption within the process. The power used by each unit operation expressed as kilowatts was obtained from a design schedule. Together with the design feed rate into each area, the electricity requirement in terms of kWh/m3 was calculated. As with the first case study, the South African electricity (medium voltage) dataset in SimaPro was utilised.

To fulfil the purpose of pre-treatment, DUP filters were employed prior to the membrane treatment stages. The filter media consisted of two layers: silica sand and magnetite. As in the case of the desalination process, the local data available in SimaPro were utilised to represent both media layers for the filtration process.

Input into SimaPro

For an effective assessment, all data had to be scaled down in accordance with the functional unit. Thus, all material data were expressed in terms of kg/kL potable water, energy inputs as kWh/kL potable water and chemicals used as mg/kL potable water. Once this information was in the relevant format, it could be used as inputs into the SimaPro LCA Software. Within the SimaPro programme, new projects depicting both processes were created. In addition, each unit operation was developed as an individual process together with the appropriate inputs and outputs.

The result of the inventory analysis was the generation of an inventory table. This is as a result of the 'analyse' function used in SimaPro which, through a reduced matrix, calculates the system inventory by constructing the process network and tracing the movement of materials from one stage to another. The software presents the table as a single list that is itemised alphabetically. This list is used as an input into the following phase, the impact assessment phase, which seeks to understand the contribution of the various processes to the overall environmental burden.

Impact assessment

The impact assessment phase aims to establish a link between the product system and potential environmental impacts. To achieve this objective, inventory information is related to relevant impact categories and indicators. Furthermore, this phase provides a basis for the next stage, i.e., life cycle interpretation.

The ISO 14042 (2000) document stated that there are three compulsory steps that need to be completed: selection and definition of impact categories, classification and characterisation. In addition, there are several optional elements that can be used, dependent on the goal and scope of the study: normalisation, grouping, weighting and data quality analysis. For the purposes of this study, the three mandatory elements were deemed sufficient and were thus performed for the system. The optional steps were excluded to avoid introducing a degree of subjectivity to the study.

The SimaPro 8.1.1.16 Software contains various impact assessment methods. For the study, the ReCiPe Midpoint Method was used. At this level, 18 impact categories are defined: climate change, human toxicity, ionising radiation, photochemical oxidant formation, particulate matter formation, terrestrial acidification, ozone depletion, terrestrial ecotoxicity, agricultural land occupation, urban land occupation, natural land transformation, marine ecotoxicity, marine eutrophication, fresh water eutrophication, fresh water ecotoxicity, fossil fuel depletion, minerals depletion and fresh water depletion. The full set of 18 impact categories has been used due to availability and completeness.

Interpretation

As the final stage in the LCA study, the interpretation phase aims to analyse the results from the previous phase and draw appropriate conclusions and recommendations.

For the interpretation phase, one kL (m3) of potable water for distribution was analysed by the ReCiPe method. As data were collected pertaining to the construction and operation phases, it was decided to firstly segregate the environmental impacts in terms of these two phases. This was possible through the 'analyse groups' function in SimaPro which provides the user with an opportunity to select and compare the impact of various operations or inputs in terms of the available categories. The results were presented in a tabular format which provided an overview of the contributions of the individual sub-processes. In addition, each impact category was also examined in greater detail with the results depicted in a network diagram which produces a visual representation of each input's contribution to the overall impact of the process.

 

RESULTS

The analysis of the results obtained from the study form part of the final phase of the LCA methodology which is Life Cycle Interpretation. The resulting evaluation would be utilized to reach suitable conclusions and provide relevant recommendations.

Results for the desalination process

Figure 3 illustrates the contribution of the various inputs to the relevant impact categories that are pre-defined in the software package as part of the ReCiPe impact assessment method. From the diagram, it is evident that electricity has an overwhelming burden contribution in 11 of 18 of the categories, such as climate change and terrestrial acidification. However, in other categories such as water, ozone and minerals depletion, the contribution of electricity is much lower (approximately 40 to 50%) and as low as 25% for agricultural land occupation, with the chemical usage becoming more prominent. It is also interesting to note that the infrastructure carries a relatively insignificant burden compared to the other two inputs.

 

 

For the mine-water reclamation process (Fig. 4), electricity consumption has the greatest contribution to the bulk of the impacts. In the case of impact categories such as climate change and terrestrial acidification, the energy usage is responsible for greater than 95% of the overall impact. In the case of other impacts, e.g., ozone and metal depletion, chemical consumption within the water treatment process carries a much more significant environmental burden as it accounts for approximately half of the total impact. The results also indicate that chemical usage had a positive environmental impact on agricultural land occupation. This is due to the use of two chemicals, ferric chloride and hydrochloric acid, attributed to the manufacture of chlorine and associated use of wood chips. Unlike the operation phase, the environmental impact of the infrastructure phase, which encompasses materials used in the construction of the plant, is relatively less significant.

 

 

Desalination and mine-water reclamation environmental scores

Once the results for the individual categories for each case study had been analysed, a comparison between the two water treatment processes was made. Table 2 provides a summary of the total environmental impacts. The figures highlight the fact that the desalination process carries a much higher overall burden compared to the mine-water reclamation process. For the bulk of the categories such as climate change and terrestrial acidification, desalination displays scores that are approximately double the impact associated with mine-water reclamation. There are two categories where mine-water reclamation performs worse than desalination: water and metal depletion. This can be attributed to the process of extracting the groundwater from abandoned mines, thereby adversely affecting the water and metal content in the surrounding environment. Furthermore, the significant depletion of water for the mine-water reclamation process is mainly due to the source water. In the case of desalination, the impact assessment method utilized does not consider water depletion of seawater. Hence, the figure for overall water depletion for desalination is significantly lower then that for mine-water reclamation. These assumptions mirror the water situation in the country - the scarcity of fresh water in general, and the abundance of seawater for costal cities in South Africa.

 

 

Although a comparison between the water treatment processes can be made, there are associated challenges and difficulties as the operations differ in many respects. Table 3 highlights a few of the significant differences between the two processes. The ISO 14040 (2006) document states that the results of various LCA studies can only be directly compared if the assumptions and context of each study are the same.

Both the water treatment processes discussed in the study are secondary processes which are necessary to implement due to the scarcity of water. In addition, there are also practical considerations that need to be taken into account prior to the design of these plants. Due to its feed source, the desalination plant has to be constructed in coastal areas with close proximity to seawater. As the mine reclamation plant will have to treat accumulated water from previously mined areas, the plant will reside close to the mines. The feed water quality for mine-water reclamation is also a significant factor due to variances in source water. This occurs as a result of different minerals being mined as well as the age of the mine. The quality of the mine-water to be treated is the main factor why the results for the current mine-water cannot be generalised to other mine-affected waters from other locations and/or mines.

Energy requirements and the potential for improvement

Energy consumption is an important cost and environmental factor for all water treatment processes, and especially for desalination. Therefore, benchmarking energy consumption is of value. In South Africa, it has been proposed that electricity consumption be used as a crude environmental indicator for the performance of urban water systems (Friedrich et al., 2009). In terms of energy requirements, seawater membrane desalination with energy recovery devices generally consumes about 3.5-4.5 kWh/m3 of electricity according to Vince et al. (2008) and between 4 to 6 kWh/m3 according to Abdelkareem et al. (2017). The desalination of brackish water needs about 1.5 to 2.5 kWh/m3 (Abdelkareem et al., 2017). The local desalination process investigated in this study should need a total of 3.69 kWh/m3 which is within the above-mentioned ranges. Furthermore, it is lower than the stated electricity consumption of the three operational desalination plants in South Africa: the Sedgefield Plant (3.97 kWh/m3), the Albany Coast Plant (4.52 kWh/m3) and the Mossel Bay Plant (4.39 kWh/m3) (Turner et al., 2015). However, this figure has to be validated and monitored once the desalination plant will become operational.

A further assessment regarding energy consumption was undertaken comparing the environmental impacts of alternative water treatment plants to a conventional water treatment plant in South Africa. At the outset, it must be acknowledged that the energy requirement for desalination and mine-water reclamation is much greater than other water treatment technologies. When considering the conventional treatment of raw wastewater in the local context, the Wiggins Waterworks had the highest electricity consumption per kilolitre of water produced at 0.10 kWh/m3 which represents the worst-case scenario for the eThekwini Municipality (Friedrich et al., 2009). The total energy consumption of the proposed desalination plant is 3.69 kWh/m3 and 2.16 kWh/m3 for the mine-water reclamation plant. Taking the above two figures for energy usage and associating them with characterisation factors for climate change due to the use of renewable energy sources (sun and wind) yields the results in Table 4. In addition, the South African 2030 electricity mix as described in the Integrated Resource Plan - Scenario IRP 1 (DoE) 2018) was modelled to represent a realistic situation for the future energy requirement of both water treatment plants. This scenario assumes that by 2030 the South African electricity mix will be from coal (64%), wind (13%), solar energy (8%), nuclear energy (4%), hydro energy (3%), gas (1%) and others (5%). It is the most conservative scenario presented in the planned integrated resource plan for South Africa (DoE, 2018). Although less than 1% of the RO desalination plants worldwide are currently powered by renewable sources, their use is predicted to increase in the future (Abdelkareem et al., 2017).

The figures in Table 4 demonstrate that desalination using wind and solar power has the potential to produce GHG emissions in the range of 0.07-0.28 kg CO2 eq/kL potable water. The release of GHG emissions for the mine-water reclamation plant will be even lower with emissions between 0.04 and 0.16 kg CO2 eq/kL potable water. These figures are comparable to the emissions of 0.08-0.11 kg CO2 eq/kL water, which would be released from the conventional water treatment processes employed at Durban Heights and Wiggins Waterworks. These are the two most important Umgeni Water potable water plants in the eThekwini Municipality (core city Durban).

The figures in Table 4 for renewable energy sources represent an ideal best-case scenario which in reality cannot be achieved by membrane plants of the sizes of those investigated in this study. Large RO plants usualy need storage and backup and, therefore, practically cannot rely 100% on renewable energy. However, in the literature a series of authors (Biswas et al., 2009; Stokes et al., 2009 and Shahabi et al., 2014) have used 100% wind energy and 100% solar energy for similar studies involving RO in order to gauge a best-case scenario. From an implementation and practical point of view, Chew and Ng (2019) also used 100% solar power in this manner for a small pilot plant for ultra-filtration membranes to be used for rural (off-grid) water supply in Malaysia. In the South African context this is an unrealistic best-case scenario which can only be theoretically considered for plants of the size of those investigated in this study.

The South African 2030 electricity mix (64% coal, wind 13%, solar 8%, nuclear 4%, hydro 3%, gas 1% and other 5%) was modelled to represent a realistic situation for the future energy requirement of membrane-based plants in South Africa. Results indicate that that desalination using the predicted 2030 energy mix has the potential to produce 1.16 kg CO2 eq/ kL potable water. The release of GHG emissions for the mine-water reclamation plant will be even lower at 0.678 kg CO2 eq/kL potable water. This shows that a reduction in coal-fired energy coupled with an increase in the use of renewable energy sources has the potential to decrease the carbon footprint of water treatment plants that use alternative feed sources. These are more realistic calculations for the local context.

The results presented in Table 4 are in line with similar results from other studies. For example, Raluy et al. (2004) reported that a reduction of greater than 35% is expected, dependent on the origin of the energy, i.e., cogeneration, internal combustion engine or a combined cycle (Raluy et al., 2004). In a follow-up report, Raluy et al. (2005) has reported that, for a desalination plant using an energy source that is based on combustion of fossil fuel, carbon dioxide emissions of 1.78 kg/m3 of desalted water and NOx emissions of 4.05 g/m3 of desalted water were released. Together with the integration of the RO system with photovoltaic energy, CO2 emissions were reduced to between 0.6 and 0.9 kg/m3 eq. and NOx emissions to between 1.8 and 2.1 g/m3 (Raluy et al., 2005). A further decrease in environmental impact is achieved when wind energy is utilised in conjunction with the desalination plant with CO2 emissions of 0.1 kg/m3 eq and NOx emissions of 0.4 g/m3 (Raluy et al., 2005). Biwas (2009) reports for Western Australia a potential reduction of CO2 emissions from 3.80 kg/ m3 (electricity mix) to 0.32 kg/ m3 (100% wind power) due to the replacement of conventional electricity by wind power. Stokes and Horvath (2009) show a similar trend for California where CO2 emissions were potentially reduced from 3.95 kg/ m3 to 0.72 kg/ m3 by replacing the US average electricity mix with 100% photovoltaic electricity. Thus, a substantial reduction in emission of GHG is theoretically possible by substituting fossil fuels with renewable energy sources. Another emerging renewable energy source not investigated in this research but appropriate for the desalination plant is the energy from ocean waves (Leijon and Boström, 2017).

Other available options to reduce the energy usage of a membrane-based water treatment plant include the implementation of a hybrid system which incorporates both brackish and seawater elements as well as a two-pass NF system (Veerapaneni et al., 2007 and Long, 2008). Investigation into innovative material based membranes that reduce fouling are also currently underway (Subramani et al., 2011). In addition, emerging technologies such as forward osmosis, ion concentration polarization and capacitive deionization technology are all advancements in the pipeline that could potentially have a positive impact on energy consumption (Elimelech and Phillip, 2011 and Subramani et al., 2011).

Chemical requirements and the potential for improvement

Chemical production and use also carries a significant burden for both case studies. In particular, the chemicals for post-treatment (lime and carbon dioxide) in the desalination process and for pre-treatment (ferric chloride as coagulant, secondary antiscalant and biocide) in the mine-water reclamation process appear in the modelling process as the chemicals with the highest impacts. This is in line with results reported by Vince et al. (2008). Large doses of chemicals are necessary in order to adjust the alkalinity of the demineralised water to potable water quality standards. For the selected desalination process, lime and CO2 release the highest amount of GHG emissions after electricity use. These results necessitate an investigation into alternate chemicals as well as permeate blending of the product water with other mineralised water sources in order to decrease chemical use.

Vince et al. (2008) carried out an analysis centred around various water treatment processes for particular local conditions. One of the conclusions that was reached pertains to the detrimental effect of coagulant production and use (Vince et al., 2008). As is evident from the treatment of mine-water, the usage of coagulant depends on the concentration of organic and suspended matter in the source water. Vince et al. (2008) goes on to state that the production of a kilogram of ferric chloride has an impact on ozone depletion that is equal to the impact of 35 kg of aluminium sulphate. This brings to light the fact that the choice of similar chemicals may result in vastly different impacts. For the second case study, ferric chloride is responsible for 35% of the total potential for ozone depletion of the system. Thus, it is recommended that the production process of ferric chloride be investigated together with the consideration of other coagulants. In addition, it has been proven by Al-Mashharawi et al. (2012) that the use of low-pressure membranes in the pre-treatment phase has the capacity to reduce the use of coagulants. This should be also considered for the local desalination case study.

As far as the toxicity of discharged concentrate is concerned, Mezher et al. (2011) mentions that the overall temperature, density and total dissolved salts (TDS) of the discharge are of importance as they could potentially cause damage to the aquatic ecosystem. Increased temperature could have detrimental consequences while a rise in specific gravity would cause the contents of the reject stream to sink. The quantity of dissolved solids also increases with an increase in plant recovery. These factors need to be considered when debating the release of any concentrate into large bodies of water.

 

CONCLUSIONS

The results indicate that the operational phase is the predominant stage responsible for the majority of the environmental impacts attributed to both systems. Within this stage, the energy consumption is generally the greatest contributor, with chemical use representing the second-highest environmental burden. A detailed investigation of both water treatment processes reveals that the desalination process has a greater overall environmental impact than the mine-water reuse process, mainly due to the increased energy requirements. As the results indicate that plant impacts are highly dependent on the electricity supply source, further investigations of the substitution of fossil fuel-based energy with renewable energy were undertaken. It was calculated that the use of solar or wind energy could significantly reduce the climate change effect (i.e. reduce GHG emissions) of using seawater and mine-affected water to levels that are comparable to conventional water treatment processes currently employed in the eThekwini Municipality. Other technological developments should also be considered to reduce the energy and chemical usage of the system and can bring environmental improvments, in particular for the desalination plant as it is still in the planning process. In particular for this plant a pre-treatment stage and an overall optimization with regard to chemical usage should be investigated, as well as replacing chemicals with high environmental burdens. For long-term future developments of the RO processes for the production of potable water, the use of alternative sources of energy (solar and wind) should be promoted.

 

ACKNOWLEDGEMENTS

The authors would like to acknowledge the Water Research Commission for sponsorship of this study and Umgeni Water as well as Prentec for the provision of information.

 

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Received 26 April 2018
Accepted in revised form 25 September 2019

 

 

* Corresponding author, email: Friedriche@ukzn.ac.za

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SHORT COMMUNICATION

 

The rate of release of Cry1Ab protein from Bt maize leaves into water

 

 

Amy du Pisanie; Louis du Preez; Johnnie van den Berg; Rialet Pieters*

Unit for Environmental Sciences and Management, North-West University, Potchefstroom, 2531, South Africa

 

 


ABSTRACT

Transgenic Bt maize plants are genetically modified to contain genes of Bacillus thuringiensis that encode for δ-endotoxins (Cry1Ab protein) that have insecticidal properties. These endotoxins target certain lepidopteran pests of maize. There are several entry routes by which Cry proteins enter the aquatic ecosystem in which aquatic organisms are exposed to these proteins. The main route is through plant debris such as leaves, stalks and postharvest detritus that are transported by means of run-off, rain and wind. While several studies have been conducted on the fate of Cry1Ab protein in terrestrial ecosystems, little is known of the release rates of Cry1Ab proteins from maize plant tissues that end up in aquatic ecosystems. In this study, leaves of Bt-maize and its isoline were submerged in containers filled with deionised or borehole water for a period of 16 days, and kept at 3 different temperatures (10±1, 21±1 and 30±1°C). Samples were collected at 1, 2, 4, 8, 16, 24, 48, 96, 192 and 384 h post submersion and analysed for Cry protein content using enzyme-linked immunosorbent assay (ELISA). The release of Cry protein from submerged maize leaves was influenced by temperature, and duration of immersion. An increase in Cry protein levels in the water was observed from the first hour onwards in both water types until the end of the experiment. The highest concentration of Cry protein was found at 30°C. This study showed that temperature and time period influence the release rate of Cry proteins from dried leaf matter into the aquatic environment.

Keywords: aquatic ecosystem, arthropods, GM crops, environment, risk assessment


 

 

INTRODUCTION

Genetically modified (GM) transgenic Bt maize plants contain genes of the soil bacterium Bacillus thuringiensis that encode for δ-endotoxins (Cry1Ab protein) that have insecticidal properties. These endotoxins target certain lepidopteran pests of maize (Betz et al., 2000; Baumgarte and Tebbe, 2005; Bravo et al., 2007; Chambers et al., 2010). Production of Cry endotoxins by GM plants confers pesticidal properties to these plants, for example, Bt maize, of which various maize hybrids express different Cry proteins such as Cry1Ab, Cry1Ac and Cry1F, that are toxic for specific insect species (Baumgarte and Tebbe, 2005; Bravo et al., 2007; Swan et al., 2009). While GM Bt maize that expresses only Cry1Ab protein has been planted in South Africa from 1998 onwards, Bt maize hybrids that express both Cry1.105 and Cry2Ab2 proteins started to replace these hybrids from 2012 onwards. South Africa is currently the 9th largest producer of Bt maize in the world, with approximately 1.9 million ha planted to Bt crops in 2017 (ISAAA, 2017). The most Bt maize planted per country in 2017 was the 29.4 million ha in the USA and almost 15 million ha in Brazil (second-most). Globally, a combined surface area of 101 million ha is planted to Bt cotton, Bt maize and Bt soybean (ISAAA, 2017).

Between 65 and 75% of the maize planted annually in South Africa is Bt maize (Masehela et al., 2016), which is planted for control of the maize stem borer complex that consists of Busseola fusca (Lepidoptera: Noctuidae), Chilo partellus (Lepidoptera: Crambidae) and Sesamia calamistis (Lepidoptera: Noctuidae) (Van den Berg and Van Wyk, 2007; Van den Berg et al., 2013). The high adoption rates for Bt crops in the world are ascribed to the many benefits they provide to farmers. Reduced use of chemical insecticides, increased target specificity and ease of crop management are widely reported to be the drivers of these high levels of adoption (Raybould and Quemada, 2010; Kruger et al., 2009; Brookes and Barfoot, 2014). However, these crops have largely not been approved for cultivation in Europe and most countries in Africa. The benefits these crops hold are strongly debated, leading to controversies regarding environmental safety and effects on biodiversity (Naranjo, 2009; Lövei and Arpaia, 2005) and especially aspects around aquatic ecosystem health (Venter and Bøhn, 2016). It is especially with regards to aquatic environments where risk assessments are lacking (Carstens et al., 2012). Despite growing recognition that aquatic ecosystems near agricultural fields receive significant amounts of run-off and crop residues that contain these toxins (Böttger et al., 2015; Li et al., 2013), environmental risk assessments tend to neglect aquatic ecosystems as a relevant context for assessing the potential risks associated with GM crops.

A critical evaluation of the fate of Cry proteins is imperative in order to gain an accurate characterization of exposure levels to sensitive species for risk assessments. Arthropods that feed on Bt crop tissue ingest Bt protoxins which are then activated before they can have an effect on susceptible individuals (Broderick et al., 2006). The gut protease of the target pest species cleaves the protoxin producing an active toxin which binds to the mid-gut epithelial cells, creating pores in the cell membrane. This causes immobilization of the gut, lyses of epithelial cells and ultimately death due to both septicaemia and starvation (Broderick et al., 2006).

Direct exposure to Cry proteins takes place when transgenic crop residues are consumed by organisms (Kratz et al., 2010), while indirect exposure may be through leaching of protein from crop plant tissue into the aquatic environment, with possible adverse effects on exposed organisms (Li et al., 2007; Venter and Bøhn, 2016). There are several routes through which Cry proteins enter the aquatic ecosystem in which aquatic organisms are exposed to these proteins through different exposure pathways (Carstens et al., 2012). Rosi-Marshall et al. (2007) indicated the main entry route into aquatic systems to be through plant debris (which includes pollen, leaves, crop dust, stalks and postharvest detritus) that are transported by means of run-off, rain and wind. The leaching of Cry proteins from plant tissue can be influenced by temperature, plant tissue type, sediment composition and the presence of microbes (Li et al., 2007).

The presence and persistence of Cry1Ab protein in the environment has been studied by several authors. Bøhn et al. (2008) showed that Cry1Ab protein is present in maize grain at a concentration of approximately 67 ng/g tissue, and 2 530 ng/g in leaves (Holderbaum et al. 2015), and for stacked events the concentration can be much higher. The Bt toxin load in pollen of 'Smartstax' can be 100 to 200 ng/mg pollen dry mass (Phillips, 2008; Stillwell and Silvanovich, 2008). A study by Tank et al. (2010) indicated that the presence of Cry1Ab protein in natural streams containing maize detritus was above the minimum detection limit of 6 ng/L in 23% of the sites sampled.

In terrestrial ecosystems the concentration of Bt toxins in soil depends on soil type (Palm et al., 1994). Higher clay and organic matter results in stronger toxin binding, making extraction difficult (Palm et al., 1994). For example, 27% and 60% B. thuringiensis var. kurstaki (Btk) toxins were recovered from soil by Palm et al. (1994), with high and low clay-organic matter content, respectively. The degradation rate of Cry1Ab proteins in different soil types (differed in texture but not in silt or pH) showed that carbon dioxide (CO2) production in soil was initially high and then declined over a period of 135 days, indicating that the Cry protein was used by microorganisms as a growth substrate (Valldor et al., 2015). The latter study also reported that the low levels of Cry1Ab in soil indicate that these proteins mineralize faster due to microbial degradation (Valldor et al., 2015). Cry toxin may also remain active in the soil where it binds quickly and tightly to humic acid and clays (Saxena et al., 1999; Carstens et al., 2012). Though bound, the Cry toxin maintains its insecticidal characteristic and, since it is bound to soil particles, is protected from degradation. Depending on the soil type, the toxin can persist for at least 234 days (Saxena et al., 1999).

Using an enzyme-linked immunosorbent assay (ELISA) to determine Cry1Ab levels, Zwahlen et al. (2003) studied how long the toxin remains in the plant tissue when left on field after harvest and over different periods of the year. They reported the degradation rate of Cry protein to be temperature dependent and reduced at lower soil temperatures. Feng et al. (2011) also reported that soil temperature had a significant effect on the degradation of Cry1Ab protein, but that pH had no obvious effect.

Jensen et al. (2010) reported that Cry1Ab protein present inside maize leaves lost its bioactivity after 2 weeks of immersion in water. However, they did not quantify the level of Cry1 proteins either in the plant tissue or aquatic medium, which made it difficult to determine whether the protein in the plant tissue degraded or leached out into the water. Another study reported a decline of Cry3Bb1 content in maize tissue following water immersion (Prihoda and Coats, 2008).

Cry proteins enter aquatic systems by leaching into the soil from plant material (Victorov, 2011). Leaf detritus are also left on fields to provide nutrition and as animal feed, while at the same time Bt proteins can leach into the ground and make its way to nearby streams or water bodies (Swan et al., 2009; Chambers et al., 2010; Carstens et al., 2012). Large amounts of maize debris have been reported to end up in water systems over very short periods of time (<7 days) (Victorov, 2011; Venter and Bøhn, 2016), which may lead to sudden increases in Bt protein concentration in such aquatic systems. The amount of Bt maize debris in water streams has been reported to correlate with the amount of Cry protein found in stream water (Tank et al., 2010). Whiting et al. (2014) reported high concentrations (33 ng/L) of Cry1Ab protein in run-off water sampled in maize fields. Shogren et al. (2019) found that Cry1Ab proteins are removed from the water column in riverine systems either via sorption of the protein to the biofilm or by biological removal thereof.

This study investigated the release rate of the Cry1Ab protein from water-submerged Bt maize leaves, with a specific focus on different periods of immersion, temperature and type of water. These results will provide insight into how the immediate environment influences the extent of Cry protein release into aquatic systems, and will provide useful information for use in the design of laboratory bioassays in which potential non-target effects of Cry proteins are studied in aquatic environments. This study was undertaken as a pilot study in order to glean necessary information to allow planning of more comprehensive studies.

 

MATERIALS AND METHODS

The experiment consisted of 12 treatments, each replicated 3 times. Dried maize leaf tissue was exposed to different water types and different temperatures. The treatments were as follows: Bt maize leaf tissue in either borehole water or deionised water, maintained at 3 different temperatures. The control treatment consisted of non-Bt maize leaves exposed to similar water and temperature treatments.

Maize leaves were collected from Bt and non-Bt plants grown under the same field conditions. The leaves were removed from the stems just before flowering (7 weeks after seedling emergence) and dried under natural conditions for 5 weeks in a well-ventilated plant growth tunnel. Maize hybrids DKC 7815B (MON810) and CRN 3505 were used as the Bt and non-Bt iso-hybrid, respectively.

The leaves were cut into 9 cm long pieces after which infusions were prepared by submerging 24 g of maize leaves into 1 L of water in glass containers. After putting the leaf tissue into the water, the glass containers were kept at 3 different temperatures (10±1oC, 21±1oC and 30±1oC) for the duration of the experiment. The beakers were covered to limit evaporation, but not sealed airtight. Gas exchange could still take place.

Water samples were taken from each treatment at the following time intervals after submersion of leaf tissue: 1, 2, 4, 8, 16, 24, 48, 96, 192, and 384 h. Each sample consisted of 9 mL infusion, made up by three 3 mL sub-samples taken from each container at the respective time intervals. All samples were immediately frozen at 80°C until assays were performed. Once all the samples were acquired, analyses were done to determine the concentration of Cry1Ab protein content.

ELISA analysis

The ELISA procedure used was similar to that described by Strain et al. (2014), although the high amount of proteins present in our samples precluded the necessity to concentrate the samples. Analyses were done following the manufacturer's instructions and using a commercially available ELISA kit (EnviroLogixQuantiPlate assay Kit for Cry1Ab/Cry1Ac).

The ELISA was carried out as described by the product manufacturer. Briefly, 50 µL enzyme conjugate was added to each well which was followed by 50 µL sample, or positive control or Cry1Ab analytical standard and incubated for 2 h at room temperature. The commercial kit does not include known concentrations of Cry1Ab protein to create a calibration curve. Lyophilised, activated Cry1Ab toxin prepared from Cry1Ab protoxin was acquired from Marianne Pusztai-Carey at the Department of Biochemistry, Case Western University, Cleveland, Ohio. The protoxin from B. thuringiensis subsp. kurstaki HD-1 was expressed as a single gene product in Escherichia coli, cleaved with trypsin and deionised by high-pressure liquid chromatography (HPCL). The lyophilised powder was dissolved in Tris-EDTA (Sigma 93302) at pH = 4. Two 12-point calibration series were created independently with known Cry1A concentrations ranging between 0.03 ng/mL and 3.5 ng/mL and run on each plate. This series was optimised for the capabilities of the commercial ELISA kit and the plate reader used to quantify absorbance. This meant that dilutions were made of selected samples if their initial protein concentration caused the maximum absorbency of the plate reader. After incubation the conjugate was removed and washed with phosphate-buffered saline before adding the substrate. The stop solution was added after 30 min and concentrations of Bt Cry1Ab were determined by subtracting optical density values read at 650 nm (reference wavelength) and 450 nm on a Berthold Tristar LB941 plate reader.

The water samples were allowed to thaw at room temperature. Strain et al. (2014) found that samples stored above freezing point (4 and 23°C) had low recoveries, but that there was no significant difference in the recoveries between the two sub-zero temperatures (20 and 80oC). Should the samples not be analysed immediately, they suggested that the samples be frozen at either of the latter two temperatures.

The entire ELISA protocol was repeated 3 times for each sample. Data are graphically presented to indicate the concentration of Cry1Ab protein in water over time (Fig. 1). Dilution factors were taken into consideration and corrections were made for the gradual decrease of water volume due to the consecutive sampling.

 


 

A sample was considered a non-detect (ND) if the optical density value was below that of the blank plus 3 times the standard deviation. The limit of detection (LOD) and limit of quantification (LOQ) were determined using regression analysis of the calibration curves where LOD = 3Sb/b and LOQ = 10Sb/b (Sb = slope uncertainty and b = slope). The LOD was 0.13 ± 0.05 ng/mL and the LOQ was 1.06 ± 0.85 ng/mL.

Statistical analysis

The normality of the data distribution was investigated with the Shapiro-Wilk test to decide on the application of one-way analysis of variance (ANOVA) (if the dataset was normally distributed) or Kruskal-Wallis ANOVA if the dataset was non-normally distributed. IBM's SPSS software package was used for the calculations.

 

RESULTS AND DISCUSSION

There were no detectable levels of Cry proteins in any of the water samples at the start of the experiment before leaf matter was added. The Cry1Ab protein concentration in the control treatments (non-Bt iso-hybrid) was very low with the highest concentration in deionised water and borehole water being 0.13 ng/mL and 0.10 ng/mL, respectively, which were <LOQ (Data not shown). These levels might be ascribed to light absorption in the relevant nanometer range caused by other dissolved compounds that leached out of the plant material or that could also have had an affinity for the antibody on the plates. Nevertheless, these levels were lower than the limit of quantification.

The Cry1Ab concentrations in deionised water and borehole water at the three different temperatures are presented in Figs 1A and 1B, respectively. This study indicated that Cry protein released from Bt maize leaves is more pronounced at higher ambient temperatures. Both infusions at 10°C had lower concentrations of Cry1Ab proteins, varying between 11 and 15 ng/mL up to 16 days after the exposure commenced. Exposure of submerged Bt maize leaves under an ambient temperature of 30oC resulted in the highest Cry concentration throughout the experiment. The infusions exposed to 30°C had the highest Cry1Ab concentrations after 16 days, ranging between 39.8 ng/mL (borehole water) and 54.8 ng/mL (deionised water), with those exposed at room temperature having concentrations between 16.3 and 23.8 ng/mL (Fig. 1A). For the 10°C treatment, the maximum Cry protein concentrations after 16 days of leaf exposure were 14.8 ng/mL in borehole water and 10.5 ng/mL in deionised water.

The general tendency in all treatments was a slow increase in Cry1Ab concentration over time until the end of the 16-day period. No significant difference in Cry protein concentrations between the two types of water was found. Compared to the two lower temperature treatments, a marked increase in Cry1Ab protein concentration in both water treatments over the latter half of the experiment (192 h onwards) was observed in water kept at 30°C. The concentrations of Cry1Ab protein in the two types of water kept at 30°C were approximately 3.4- and 1.7-fold higher at the end of the exposure period, compared to the other temperature treatments. It would be expected that the highest concentration of Cry1Ab in the water would be reached later at lower temperatures when compared to higher temperatures. When comparing the entire infusion period, there was a statistically significant difference between Bt protein released into the water at 10°C and 30°C and between 21°C and 30°C, but not between 10°C and 21°C for both the water types (Kruskal-Wallis; p < 0.05). A study by Tank et al. (2010) indicated that the maximum concentration of Cry1Ab protein recorded in natural streams containing maize detritus was 32 ng/L (Tank et al., 2010), much lower than those recorded in this study. Carstens et al. (2012) reported that aquatic organisms in a pond or ditch with maize detritus could be exposed to a maximum concentration of 22.5-1 125 ng/mL or 0.67-33ng/mL of Bt protein, respectively, in the worst-case scenario assumptions for the risk assessment they did. The range of concentrations of Cry proteins recorded after 16 days in the two water types used in this study (Fig. 1A and 1B) ranged between 23.8 and 54.8ng/mL. Comparing this to an approximate 14.7 x 106 µg/mL that could potentially be present in the initial 24 g leaf material (we did not quantify the Cry1Ab contents in the leaf material in this study, but Andreassen et al. (2015) reported 612.51 ng Cry1Ab in 1 mg Mon810 leaf), what seems to be leaching out in these 16 days is but a tiny fraction of the potentially available Cry1Ab. While these concentrations are in the lower range of that predicted by Carstens et al. (2012) for a pond system, they are much higher than that reported by Tank et al. (2010) in natural streams. The methods described in this study can therefore be used as a guideline in planning of risk assessment studies on aquatic organisms, since this study gives an indication of the exposure scenarios that can be developed using these methods.

Strain and Lydy (2015) found that exposure time played a significant role in the release rate of the Cry1Ab proteins into the surrounding environment. They observed that the Cry proteins leached out of the leaf tissues at a rapid rate, peaking within a day or two (depending on the temperature) and then declining. This observation is not supported by our findings, where there was a gradual increase in Cry concentrations after 48 h. Strain and Lydy (2015) also reported higher concentrations of the Bt protein in colder temperatures than warmer temperatures, which is also opposite to the findings we report here. However, the experimental set-up used by Strain and Lydy (2015) was different to ours. The one difference that might explain the decline of Bt proteins from the water in their experiment is the presence of sediment in their microcosm, whereas sediment was absent from our experimental set-up. It is known that Cry proteins strongly adsorb to surface active particles of clay and organic matter in soils (Stotzky, 2005; Mueting et al., 2014), but in the absence of sediment, the Cry protein concentration increased up until the termination of our experiment at the end of Day 16.

The presence of sediment in the Strain and Lydy (2015) microcosm could possibly also explain the higher concentrations reported in the colder water (4°C). In contrast, we report higher concentrations for Cry protein at the higher temperatures and we did not add sediment. The temperatures in the current study were 10°C, 21°C and 30°C, while Strain and Lydy (2015) evaluated the influence of a similar temperature range (4°C, 23°C and 37°C). They added 10 dry leaf disks of 1.8 cm diameter to 75 mL water on top of 5 g sediment, whereas we added strips of dry leaf to the equivalent of 24 g to 1 L of water. Although we cannot definitively compare the leaf mass used in the two experiments, it is clear from the reported levels that the leaf mass:water ratio resulted in two orders of magnitude lower Cry concentrations in this study compared to that reported by Strain and Lydy (2015). Both Strain and Lydy (2015) and this study made use of unsterilised water (and unsterilised sediment in the case of Strain and Lydy (2015)), and although they reported no significant difference between the Cry levels in the unsterilised and sterilised aquatic environments, we postulate that the soil bacteria in the sediment could have metabolised the Cry proteins (Valldor et al., 2015). Furthermore, at higher temperatures higher metabolic rates may lead to decreased Cry protein levels. Because the present study was conducted without sediment, it is likely that there would have been less bacterial activity in our experimental set-up, explaining the steady increase in Cry levels over time. The plant material would also degrade quicker at higher temperatures, releasing the Cry protein and, in the absence of adsorbing sediment, contributing to the higher Cry levels at higher temperatures in this study.

No studies that can inform risk assessments regarding the effect of Bt maize on aquatic ecosystems have been done in South Africa. It is important that future studies address the possible effects of Cry proteins on non-target species that are closely related to the target pests of Cry proteins. Although the target Lepidoptera species of the Cry1Ab protein are all crop pests, off-target effects on other lepidopteran species may result if susceptible and closely related species ingest such proteins. Although Lepidoptera are characteristically terrestrial, the Pyralidae family includes several species with truly aquatic larvae (Gerber and Gabriel, 2002). The Pyralidae family also includes several maize and sugarcane pests which are susceptible to Cry1Ab protein, for example, Eldana saccharina (Walker) (Keeping et al. 2007) and Chilo partellus (Swinhoe) in South Africa (Van Rensburg, 1999).

 

CONCLUSIONS

Our data showed that accumulation of Cry1Ab protein released by Bt maize leaves is influenced by temperature and that the concentration of Cry proteins may increase over time. This study also quantified levels of Cry protein present in water that contains Bt maize leaf tissue in the absence of confounding factors such as sediment (and its associated microbial activity). These factors should be considered during risk assessment studies with aquatic organisms. The characterization of exposure of aquatic organisms along with the known specificity of the insecticidal trait, linked to the ecology of non-target species present in that habitat (in particular those closely related to Lepidoptera or other target groups), will contribute to improved risk assessment studies on aquatic environments.

 

AUTHOR CONTRIBUTIONS

Amy du Pisanie performed all experimental and analytical procedures, and prepared the first draft of the manuscript for submission. LH. du Preez and J van den Berg were supervisors of the study and R Pieters assisted with the ELISA and writing of the manuscript.

 

SOURCES OF FUNDING

This study was funded by Biosafety South Africa (Project BS08-001) and we accordingly give due acknowledgment.

 

CONFLICTS OF INTEREST

The authors have no conflicts of interest to declare.

 

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Received 22 May 2018
Accepted in revised form 20 September 2019

 

 

* Corresponding author, email: rialet.pieters@nwu.ac.za

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SHORT COMMUNICATION

 

Detection of selected agricultural pesticides in river and tap water in Letsitele, Lomati and Vals-Renoster catchments, South Africa

 

 

M MacheteI, *; JM ShadungII

ICollege of Agriculture and Environmental Sciences, University of South Africa (UNISA), Science Campus, Florida Park, South Africa
IICouncil for Scientific and Industrial Research (CSIR), Natural Resources & the Environment, PO Box 395, Pretoria, 0001, South Africa

 

 


ABSTRACT

This paper presents the levels of detection of selected agricultural pesticides in river and tap water in the Letsitele, Lomati and Vals-Renoster catchments, South Africa. Agriculture plays a major role in the development of communities through job creation and poverty eradication. However, exposure to agricultural pesticides can result in serious human health and environmental effects. This study, therefore, identified critical areas where specific pesticides might result in high environmental and human health risks. Three water catchment areas, namely, Letsitele, Lomati and Vals-Renoster, were identified for raw and tap water analysis. The results confirmed the presence of selected agricultural pesticides: atrazine, terbuthylazine, imidacloprid, metolachlor, simazine and alachlor. Although low concentrations of most of these pesticides were detected, pesticides such as atrazine, alachlor and simazine are known for endocrine disruption. A critical finding of this study is the detection of these pesticides in tap water (drinking water) of a primary school in the Lomati catchment. This reveals a high exposure potential for human health. It is thus recommended that further research be conducted to determine the potential health risks associated with these pesticides among vulnerable communities, through epidemiological studies.

Keywords: pesticides, endocrine disruptors, environmental health risks, agriculture, water quality


 

 

INTRODUCTION

South Africa has the largest agricultural pesticide market in the sub-Saharan Africa region (Naidoo and Buckley, 2003). The South African National Department of Agriculture, Forestry and Fisheries (DAFF, 2010; 2013) registered a variety of pesticides for agricultural use during the years 2010 and 2013. Dabrowski (2015a) confirmed that more than 3 000 pesticides were registered for use by the agricultural sector in South Africa in 2015. Pesticides are one of the many technologies commonly used to improve agricultural production in South Africa (Dabrowski, 2015a). In an earlier study, Maharaj (2005) raised concern about the increasing trend of pesticide use in South African agricultural production and the number of pesticides used. The primary concern of Maharaj (2005) was the deterioration in the chemical water quality of South Africa's rivers.

Agriculture is one of the key sectors of the South African economy that is relied upon for addressing the country's 26.6% and 39% unemployment and poverty rates, respectively (StatsSA, 2016). In the light of the current levels of unemployment, poverty and inequality, cessation of pesticide use is not considered an option. In contrast to the economic potential of rapid growth of the agricultural sector, the use of most agricultural pesticides presents both chronic and acute environmental and human health risks, particularly for farming communities that are vulnerable due to living in close proximity to pesticide use.

Machete (2017) points out that organic and inorganic elements or compounds of agricultural pesticides are capable of moving from a point of application into non-target environments, particularly in surface and groundwater resources. In an earlier study, Dabrowski and De Klerk (2013) found the presence of agricultural pesticides in the water resources bordering intensely farmed areas, and detected pesticides in ground, surface and drinking water. Various studies have highlighted the persistent nature, mobility and potential environmental risks associated with a number of commonly used agricultural pesticides (Schulz, 2001; Footprint, 2006; Dabrowski, 2015a; Machete, 2017).

Numerous chronic and acute environmental health risks are associated with agricultural pesticide exposure. Footprint (2006) and Schulz (2001) concur that environmental exposure to agricultural pesticides is responsible for many incidences of toxicity to aquatic organisms. For instance, chlorpyrifos and endosulfan have been detected in environmental water samples at levels that may be toxic to fish and other macro-invertebrates (Dabrowski et al., 2014). According to Hallenbeck and Cunningham-Burns (2011), agricultural pesticides contain chemical elements or compounds that are carcinogenic, teratogenic, mutagenic and endocrine disruptive to living organisms (including human beings).

In developing countries, the use of agricultural pesticides has been associated with a myriad of detrimental effects on female health (London et al., 2002). In South Africa, the consequence of pesticide exposure has been related to several human health effects such as endocrine disruption, among others (Aneck-Hahn et al., 2007; English et al., 2012). In light of the seriousness of the environmental health risks associated with agricultural pesticide exposure, this study aimed to determine if agricultural pesticides could be detected in river and tap water in Letsitele, Lomati and Vals-Renoster catchments, South Africa.

 

MATERIALS AND METHODS

This study was conducted in three farmed water catchment areas, namely, Letsitele, Lomati, and Vals-Renoster. The three catchment areas were selected due to suspected potentially high pesticide contamination.

Letsitele catchment

The Letsitele River is located in Limpopo. The river drains the Wolkberg Mountains and flows in a north-easterly direction towards the town, Letsitele, where it flows into the Letaba River. The upper reaches of the catchment mainly comprise of commercial forestry. The river passes through intensively cultivated commercial agricultural land where avocados, citrus fruit and mangoes are produced. Human settlements with domestic livestock and communal gardens occur in close proximity to these commercial agricultural areas. The towns of Khujwana and Mogoboya are adjacent to the Letsitele River near fruit orchards. The upper sections of the catchment are characterised by relatively steep slopes, resulting in a relatively high runoff potential for pesticides. The main soil type is a sandy clay loam, which has a relatively high percentage of sand and is thus susceptible to leaching agrochemicals (Dabrowski, 2015a). This study identified these fruit orchards as potential sources for pesticide spray drift towards human settlements and water sources. Thus, the catchment area and specific sites in it were selected for the purposes of tracing potential endocrine-disrupting chemicals (EDCs) in the domestic water supply.

Lomati catchment

The Lomati River is located in the eastern part of Mpumalanga, north of Eswatini and south of Mozambique. Sugarcane production is the primary agricultural activity undertaken in the surrounding areas, with a smaller proportion contributed by maize, mangoes, bananas and wheat production. The Lomati River originates in Eswatini and flows into Driekoppies Dam and then into the Komati River. Agricultural activities are generally undertaken on low to medium gradient slopes of relatively sandy soil. The mean annual precipitation for the Lomati catchment is 880 mm (Deksissa et al., 2003), with most rainfall occurring in the summer months between November and March.

Vals and Renoster catchments

The Vals River flows in a westerly direction from upstream of Kroonstad past Khotsong and Bothaville before entering the Vaal River. Kroonstad is located midway along the river between VL3 and VL2 and this section of the river is affected by a number of industries and point-source pollution impacts (e.g. sewage outfalls). The catchment of the Renoster River is also an intensive agricultural area, and few large towns are situated along the length of the river. The river flows northwards, entering the Vaal River upstream of its confluence with the Vals River. Relatively few large towns are located along the length of the river (Dabrowski, 2015b). Agricultural activity in the Vals and Renoster catchments consists of intensive maize production along with sorghum and sunflower production. The catchment area is predominately agricultural and is particularly flat in the lower reaches. It has a high proportion of sandy soils which are associated with increased pesticide leaching potential (Dabrowski, 2015b).

Sampling

A total of 14 water sampling points were selected across the three catchments (Table 1).

Three samples were collected from Letsitele, five from Lomati and six from ValsRenoster (see Table 1). Four seasonal sampling sessions were conducted for each sampling point.

Selection of pesticides for analysis

Information on pesticide use per crop at a national scale (DAFF, 2013) was used to prioritise specific pesticides for testing and analysis in the 14 samples collected. Quarterly raw and tap water samples were collected and analysed to assess the presence of pesticides and their seasonal variation, if any. All raw water samples were collected in 1 L amber glass bottles at approximately 50 cm below the water surface for raw water sampling. All raw and tap water samples were transported on ice at 4°C until delivered to an accredited laboratory. The pesticide crop prioritisation matrix identifies atrazine, terbuthylazine, imidacloprid, metolachlor, simazine and alachlor as relatively high national priority pesticides used in agriculture. Atrazine, alachlor and simazine are among the 12 known endocrine disrupting pesticides (Footprint, 2006; McKinlay et al., 2008).

Quantitative analysis of GC-MS

Liquid-to-liquid extraction was used to extract samples, which involved collecting the organic layer; adjusting the pH; shaking and filtering. The elution was rotary evaporated to the required final volume of 0.5 mL and analysed on capillary column GC-MSD for organophosphates (following the method of Dabrowski et al., 2014).

 

RESULTS AND DISCUSSION

Letsitele catchment

Five EDC pesticides were present at the Letsitele catchment sites (1) upstream (LT3), (2) tap at a public primary school (LT4) and (3) downstream (LT9), as presented in Fig. 1.

The results show a high detection level of diphenylamine, imazalil, thiabendazole, imidacloprid and propiconazole in November 2011. Much higher concentrations of imazalil, thiabendazole and diphenylamine were observed, with propiconazole and imidacloprid occurring at lower concentrations. EDCs were detected in the tap water (drinking water) from a borehole at a primary school in Letsitele Furthermore, a much higher concentration of diphenylamine, imizalil and thiabendazole was detected in November 2011, with propiconazole consistently detected in the tap water at the primary school in almost all seasons, with the exception of the August and November 2012 sampling seasons. The results also show higher EDC concentration in ground water than in surface water, as seen for LT3 and LT4 samples across seasons. Consistent detection of higher levels of imidacloprid at LT9 in all seasons were also noted, as compared to its absence in LT3.

Lomati catchment

Based on the type of agricultural production and potential EDC pesticides used in the catchment area, the concentrations (μg/L) of selected pesticides were analysed in water samples collected from selected sites in the Lomati catchment area (Fig. 2).

The results show no detected EDCs in the June 2012 sampling season. However, during the September and December 2012 and March 2013 sampling seasons, atrazine, imidacloprid, terbuthylazine and carbofuran were detected at varying concentrations. Atrazine was detected in all samples, despite being at low concentrations. It was noted that atrazine concentration was at higher levels in NK4 during September and December 2012 than for all other sampling sites. All the above-mentioned five pesticides were detected at NK3, NK3-tap, NK4 and NK6 sampling sites. Higher concentrations of pesticides were consistently detected at NK4 throughout the sampling period when compared to the other sites. It is also noted that NK5 was the least contaminated of all sites. However, the presence of EDCs in the NK3 tap water is of serious concern, as it represents direct human exposure to the pesticides, in particular schoolchildren in the school where the tap is located and is used for drinking water supply.

Vals-Renoster water catchment

Presence and concentrations of atrazine, alachlor, imadaclopid and terbuthylazine (μg/L) were analysed in water samples collected from selected sites in the Vals and Renoster catchments, based on the prevailing agricultural production practices and types of pesticides used (Fig. 3).

The results confirm the presence of atrazine, alachlor, imadaclopid and terbuthylazine in different samples in all seasons in the Vals and Renoster catchments. Although not all samples had all five EDCs detected in equal frequencies and concentrations, all EDCs were detected at one or more sampling sites during each sampling season. In January and April 2013 (rainy season), the concentration levels showed an increase for atrazine, simazine and terbuthylazine. The samples from VL2 and VL3 showed consistently high concentrations of simazine, atrazine and terbuthylazine in January and April 2013, followed by RN3 in January 2013 and VL3 in April 2013. The detection and concentrations indicate a relationship between the rainy season and leaching of EDC pesticides, as found in previous studies (Schulz, 2001; Dabrowski and Schulz, 2003; Dabrowski, 2015b).

 

CONCLUSION

This study confirmed traces of various EDC pesticides in raw and drinking water in the Letsitele, Lomati and Vals-Renoster catchment areas. Traces of pesticides in borehole and reticulated tap water samples were confirmed in Nkomati and Letsitele, respectively. The pesticides investigated are known EDCs, and were found at high concentrations in some instances. Wu et al. (2009) found that exposure to atrazine concentrations as low as 0.1 ppb can alter the sex characteristics of male frogs, resulting in male frogs with female sex characteristics, hermaphroditism and the presence of eggs in male frog testes. The current study found much higher concentrations of atrazine and other EDCs in drinking water in this study, which presents a much higher potential of exposure and the possibility of a myriad of effects on humans and the environment (fauna and flora). The (ATSDR, 2003), found that atrazine can cause liver, kidney and heart damage in animals, and could possibly cause cancer in humans. Maternal exposure to atrazine has been associated with low birth weights, heart, urinary and limb defects in humans. Wu et al. (2009) report that atrazine exposure can lead to adverse reproductive effects in animals and humans, even at low levels of exposure. When exposure coincides with the development of the brain and reproductive organs, the effects may be even more severe. Also of great concern is the potential for atrazine to act synergistically with other pesticides to increase their toxic effects. Fitzmayer et al. (1982) and Garry (2004) confirmed the acute toxicity effects of simazine and other EDCs on human health, particularly in children. Further studies are recommended, including epidemiological investigations to establish the prevalence of environmental health risks and specifically to establish a cause-effect relationship between human exposure to the studied pesticides and potential environmental health risks highlighted in other studies. Finally, this study also highlights the question of the efficacy of existing water treatment technologies in the study areas, due to their inability to completely eliminate EDCs during water treatment processes. This suggests the need for water treatment in the indicated areas to be investigated.

 

ACKNOWLEDGEMENTS

This paper has emerged from a research project funded by the Water Research Commission (Report No. 1956/1/15), the Council for Scientific and Industrial Research (CSIR), under the Natural Resources and the Environment Business Unit. Data analysis, presentation of results, write-up and editing of this paper was sponsored by Ga-Machete Guesthouses.

 

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Received 15 June 2017
Accepted in revised form 4 October 2019

 

 

* Corresponding author, email: mtec.dr@gmail.com

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RESEARCH PAPERS

 

Drivers and barriers to sustainable fisheries in two peri-urban impoundments in Zimbabwe

 

 

Beaven UteteI, III, *; Crispen PhiriII; Tosan B FregeneIII

IDepartment of Wildlife Ecology and Conservation, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
IIDepartment of Freshwater and Fishery Science, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
IIIDepartment of Aquaculture and Fisheries Management, University of Ibadan, Oyo State, Nigeria

 

 


ABSTRACT

Fisheries sustainability is categorised through four conceptual pillars: ecological, economic, and social, including cultural and institutional. Much work on fisheries sustainability has been done in marine fisheries relative to inland fisheries. Two inland peri-urban impoundments, Chivero and Manyame in Zimbabwe, support numerous small-scale fisheries; however, environmental and socioeconomic variables threaten the sustainability of the fisheries. This study aimed to identify and contextualise drivers and barriers to sustainability of small-scale fisheries in these two peri-urban impoundments. We applied three frameworks, Fishery Performance Indicators, Community-Based Fishery Indicators and FAO Small-Scale Fisheries Indicators, to identify and contextualise the drivers and barriers. Quantitative and qualitative methods were used to collect data from fishers in the two impoundments. A structured questionnaire was administered to 115 fishers in 23 fishing companies operating in the two lakes. Fisheries income and revenue as well as food security are key drivers. Lack of post-harvest equipment, volatile fish markets, water quality and quantity deterioration and fish stock decreases are key barriers to sustainability of fisheries in the two impoundments. There are subtle differences in the extent and impact of the drivers and barriers of fisheries sustainability in the two lakes. The differences relate to the uniqueness of the aquatic habitats, social constructs and fisheries operational frameworks in each lake. This suggests a need to assess fisheries sustainability using an integrated bottom-up approach starting from individual fisheries < community fisheries < global/generic fisheries.

Keywords: peri-urban fisheries, food security, water resource conservation, water pollution, sustainability


 

 

INTRODUCTION

Small-scale inland fisheries provide essential ecosystem services comprising food, nutrient cycling in water, employment and income to millions of people, and generate foreign currency for developing countries such as Zimbabwe in Sub-Saharan Africa (Allison et al., 2009; Marshall, 2011; FAO, 2013, 2014a,b,c, 2016; Bartley et al., 2015). Inland fisheries support numerous livelihoods and enhance the socio-economic development of urban and rural areas in Africa (Allan, 2005; WorldFish, 2009; AU-IBAR, 2012; De Graaf and Garibaldi, 2014). However, the sustainability of inland fisheries is threatened by a plethora of factors, including aquatic environmental degradation (Welcomme et al., 2009; Cooke et al., 2012), overexploitation and overfishing (FAO, 2015; 2016) and market volatility of fish prices (Taylor et al., 2007; Vörösmarty et al., 2010). The dynamics in other factors such as geographical expansion, fishing capacity-building, natural variability and climate change further threaten the viability of inland fisheries (Béné, 2003; Allison and Horemans, 2006; Garcia and Rosenberg, 2010). Hence it is imperative to effectively monitor and assess the state of freshwater inland fisheries (FAO, 2016).

Within Africa, contemporary fisheries assessment and management remain heavily dominated by the ecological aspects and to a lesser extent the economic aspects (Stephenson et al., 2018). No comprehensive and holistic frameworks exist to integrate and evaluate various other aspects such as the cultural, political, institutional and social elements threatening the sustainability of small-scale inland capture fisheries (Bond and Morrison-Saunders, 2011; FAO, 2016; Thompson and Stephenson, 2016). The challenge in evaluating the sustainability of fisheries is largely driven by a lack of reliable and consistent fisheries statistics (Cooke et al., 2013), and non-cogent classification of small-scale fisheries into urban, rural and peri-urban sets, and this tends to complicate effective management of small-scale inland fisheries (Béné et al., 2003; Kebe and Tallec, 2006; FAO, 2010; Bartley et al., 2015). As a result of lack of a clear assessment framework for sustainability and reliable statistics, small-scale inland fisheries are excluded from national and regional economic planning initiatives and are notably absent in the Sustainable Development Goals (Bartley et al., 2015, FAO, 2015; Link et al., 2017; Stephenson et al., 2018). Clearly, there is a need for a framework that assesses the ecological, economic, institutional and social elements or indicators of fisheries to evaluate their sustainability (Stephenson et al., 2018).

Two contiguous peri-urban eutrophic lakes, Chivero and Manyame, in Zimbabwe support numerous fisheries and provide vital ecosystems services such as potable water and habitat for aquatic organisms (Marshall, 2011). The viability and sustainability of the fisheries in the two impoundments is threatened by a number of factors such as water pollution, climate change, over-abstraction of the water and competing water withdrawing activities (Magadza, 2011; Mhlanga and Mhlanga, 2013). Despite their ecological and socioeconomic significance, fisheries in urban and peri-urban impoundments such as Chivero and Manyame are not often a national priority and are undervalued and largely overlooked in Zimbabwe (FAO, 2015; Kupaza et al., 2015). This study aimed to identify and contextualise drivers and barriers to sustainability of small-scale fisheries in these two peri-urban impoundments. Three indicators - Fishery Performance Indicators (FPI) described by Anderson et al. (2015), Community-Based Fishery Indicators (CFI) following Boyd and Charles (2006) and FAO Small-Scale Fisheries Indicators (FSSFI) (FAO, 2015) - shown in Table 1, were used to evaluate the drivers and barriers threatening the sustainability of fisheries and viability of fishing livelihoods in Lakes Chivero and Manyame, Zimbabwe.

 

MATERIAL AND METHODS

Study area

Lakes Chivero and Manyame (Fig. 1) are two peri-urban impoundments located about 30-40 km south-east of Harare, the capital city of Zimbabwe (Magadza, 2003). Morphometrically, Lake Chivero has a capacity of 247 181 × 106 m3, a mean depth of 9.4 m and a surface area of 2 630 ha with a retention time of 1.1 years. Lake Manyame has a surface area of 8 100 ha at full capacity, when its maximum and mean depths are 23 m and 5.6 m, respectively, with an estimated mean retention time of 0.7 years (Marshall, 2011; Utete et al., 2018). The impoundments were constructed in 1952 and 1976, respectively, to mainly provide potable water to Harare (then Salisbury City). However, other uses such as water abstraction for irrigation, small-scale and subsistence fishing and recreational activities such as boating, angling and birdwatching have evolved over the years (Marshall, 2011).

With regard to small-scale fishing, there are a total of 23 fishing cooperatives (12 in Lake Chivero and 11 in Lake Manyame). Each fishing cooperative consists of at least 8-11 members who contribute fishing gear (boats and nets), labour and start-up capital, as well as paying the permit fees to National Parks (Utete et al., 2018). The fisheries from Lake Chivero have been estimated to catch an annual yield of 250 kgh-1yr-1 fish (Marshall, 2011), though there is no clear estimate of catches in Lake Manyame (Utete et al., 2018). The allowed mesh sizes in the two lakes range from 26 mm to 152 mm (1-6'') with 12.5 mm (0.5'') increments (Marshall, 2011). Fish catches have been declining in the two lakes, thus threatening the sustainability of the fisheries and the livelihoods of the fishers (Utete et al., 2018). Hence there is a need for an integrated framework to assess the sustainability of the fisheries, not only from a stock assessment perspective but using a holistic lens approach encompassing other covariates such as water pollution, post-harvest considerations, market accessibility as well as demographic aspects (FAO, 2015).

Data collection

We employed a mix of quantitative and qualitative methods to collect data from fishers in 2017. A structured questionnaire was administered to 115 fishers in 23 fishing cooperatives operating in Lakes Chivero and Manyame. Focus group discussions and key informant interviews were done on active men and women fishers, and Parks officials responsible for Lakes Chivero and Manyame, in order to have an in-depth qualitative cross-validation of the drivers and challenges affecting their livelihoods. From 115 questionnaires administered, 87 fully completed ones were used for further data analysis.

Description of the indicator frameworks and their assessment in fisheries

Three indicators, Fishery Performance Indicators (FPI) after Anderson et al. (2015), Community-Based Fishery Indicators (CFI) by Boyd and Charles (2006) and FAO Small-Scale Fisheries Indicators (FSSFI) frameworks, summarised in Table 1, were used to evaluate the drivers and barriers threatening the sustainability of fisheries and viability of fishing livelihoods in Lakes Chivero and Manyame, Zimbabwe. Conceptually, separating measures of performance, the FPI uses 68 individual outcome metrics - coded on a 1 to 5 scale based on expert assessment to facilitate application to data-poor fisheries and sectors - that can be partitioned into sector-based or triple-bottom-line sustainability-based interpretative indicators conveniently classified into ecology, economic, social and community aspects (Anderson et al., 2015). For any given fishery or cooperative, the respondents, who were mostly fishers, were asked a raft of closed and open-ended questions which are broadly classified into ecology, economic, social and institutional aspects. Every recurring theme or response is scored on a scale of 1 to 5 and the results are collated as a mean for each theme in the main classification scheme. The frequency of recurrence of any theme implies its significance as an aspect to measure the sustainability of the fishery (Chu et al., 2017).

The Community-Based Fishery Indicators (CFI) by Boyd and Charles (2006) presupposes a collection of fisheries in a water body to be a fishing community. Then it assesses a wide array of aspects ranging from the fishing fleets or vessels, nets, gender composition and roles, access to financial capital, fishery training facilities and opportunities afforded to the fishing community. It also assesses on a thematic basis the perceptions of the fishing community on fish stocks, depletion levels, and environmental issues such as water pollution and climate change (Boyd and Charles, 2006). Frequently recurring or generic issues across the fishing community are broadly categorised into ecological, social, economic and institutional categories (Boyd and Charles, 2006; FAO, 2015). The idea is not to weigh the significance of the factors but to get a holistic perspective from the fishing communities who are on the ground on the topical or recurring factors affecting their viability and livelihoods (FAO, 2015). This enables broad policy considerations rather than narrow quantified (weighed) aspects which tend to shift over temporal scales in fishing communities as the aquatic environment is largely dynamic (Bartley et al., 2015).

The FAO Small-Scale Fisheries Indicators (FSSFI) target mainly small-scale fishers who are considered as self-employed (FAO, 2013). The main factors considered are the food security and nutrition levels of the fishers, largely driven by fish stock dynamics, water quality concerns, climate change, changes in fishing regulations, fish poaching and competition for fishing zones (FAO, 2015). FSSI tends to evaluate the ecological elements of the fishing business on a Likert scale of 1-4 or 1-5, based on the mean responses or perceptions of the fishers themselves. In line with other fishery indicators, the most recurrent themes for any given fishery are allocated high scores 4 or 5 on the scale and are the key factors to be considered in fisheries management and ensuring and evaluating sustainability of small-scale inland fisheries (Stephenson et al., 2018).

Comparative analysis of the perceptions of fishers

After identification of the most recurrent factors influencing sustainability of small-scale fisheries in the two lakes using the three indicator frameworks, we further comparatively tested for the significance of differences in the perceptions of fishers in Lakes Chivero and Manyame towards the types of drivers and barriers affecting their livelihoods. For clarity, the mean perceptions are derived from the Likert Scale of: 1 = strongly disagree, 2 = disagree, 3 = agree, and 4 = strongly agree. Fishers in the two lakes were used as the test groups. After testing for normality using the Kolmogorov-Smirnov test, the data were found to be non-parametric, and thus we used descriptive and multivariate inferential statistics. We treated the two lakes as categorical groups, and since our perceptions were on a Likert scale of 1-4 we applied the 2-way contingency Chi square (χ2) test of homogeneity for comparison at the 5% level using the SPSS 21 version. The same test was also used to assess for differences in the livelihood strategies of the respondents between the two lakes.

 

RESULTS

Demography and livelihood strategies of fishers in the two impoundments

The basic demography of the fisheries in Lakes Chivero (males = 34, females = 1) and Manyame (males = 45, female = 7) was skewed towards males. The majority (n = 13; 37%) of the fishers were primarily involved in fishing and farming, and the rest (n = 8; 23%) were fish traders in Lake Chivero (Table 2). In Lake Manyame, the majority (n = 32; 62%) of the fishers were directly and actively involved in fishing, whereas a sizeable portion (n = 10; 19%) of the fishers were also into fish marketing and 10% (n = 5) were engaged in trading and repairing of fishing gear such as nets and boats. The rest of the respondents (n = 5; 10%) were into farming as an alternative livelihood strategy in Lake Manyame (Table 2). Statistical analysis indicated a significant difference (χ2 (3) = 14.749; p < 0.05) in the actual livelihood strategies between the two lakes. For Lake Manyame, the hierarchy for the livelihood strategies was: fishing > trading > farming > others, whilst for Lake Chivero it was: farming > fishing > trading > others.

 

 

Drivers and barriers of fisheries sustainability in Lakes Chivero and Manyame based on the fisheries viability indicator frameworks

From the three indicator frameworks applied for the Lakes Chivero and Manyame, the FPI framework identified ecological elements such as depleting fish catches/stocks, deteriorating water quality, dynamic land-use patterns and climate change as barriers threatening the sustainability of the fisheries. Economic barriers such as volatile markets, poor post-harvest preservation infrastructure, expensive annual fishing permits and unskilled fish processing workers also threaten sustainability of small-scale fisheries in both lakes. Social factors such as rapid fisheries managerial and casual labour turnovers, as well as lack of cross linkages with communities and unguaranteed short-term fisheries careers, act as barriers to the viability and sustainability of fisheries in the two lakes. Institutional or governance issues such as inaccessible fishing and harvesting rights are barriers to the sustainability of fisheries and livelihoods of fishers in Lakes Chivero and Manyame in Zimbabwe. Lack of collective bargaining action, for instance towards accessing loans and capital to maintain fisheries viability, is a barrier to sustainability of the fisheries. Inadequate participation of the community and exclusion of females in fisheries governance is a barrier to the sustainability of fisheries in the two lakes as indicated by the FPI (Table 3).

The CFI framework identified reductions in fish biodiversity, deterioration in the quality of lake habitat, decreases in fishable areas and drawdown zones as well as reductions in the catch of targeted species and an increase in by-catch wastes as ecological barriers to the sustainability of fishing communities in Lakes Chivero and Manyame. Fluctuations in the income value of fish harvests/catches, low market incentives for fisheries and fish which tend to be seasonal, as well as the low credit worthiness and natural capital are economic barriers to fishing-dependent communities in both lakes (Table 3). Dynamics in the demographic structure of fishing communities as well as access to fishing-related education and contribution and cooperation of marginal groups such as females and youths are some of the social factors affecting the two fishing communities (Table 3). Lack of an integrated approach towards research and management of fisheries resources are barriers towards sustainability of fishing communities (Table 3).

Using the FSSFI framework, aquatic habitat pollution and degradation are the main ecological barriers for sustainability of fisheries in the two lakes (Table 3). Volatile income from fish sales is the main economic barrier for sustainability of the small-scale fisheries in Lakes Chivero and Manyame (Table 3). Failure to uphold or respect cultures and ensuring gender equality and equity are social barriers for sustainable small-scale fisheries in the FSSFI framework (Table 2). Within the governance and institutional elements, lack of transparency of fisheries management, effective implementation of fishing rules and unclear access regulations are key barriers for sustainable fisheries in Lakes Chivero and Manyame (Table 3).

An evaluative summary of the three fisheries indicators show that fisheries-related income, employment, level of education of the fishers, and guaranteed food security and nutrition from fisheries, as well as accessible fish markets, are the key drivers for the sustainability of small-scale inland fisheries in the two lakes. Other key driving factors for sustainable fisheries comprise the level of fishing technology, such as fishing gear, boats and suitable post-harvest facilities, available (Table 3).

Demography and comparative analysis of perceptions of fishers in the two impoundments

The basic demography of the fisheries in Lakes Chivero (males = 34, females = 1) and Manyame (males = 45, female = 7) was skewed towards males with a large proportion 91% (n = 79) and the rest 9% (n = 8) were females.

The drivers and barriers facing small-scale inland fisheries suggested by peri-urban fishers and as indicated by the three frameworks were added and the results are summarised in Table 4. Among the identified drivers in sustainability of fisheries there were significant differences (χ2 (3), p < 0.05 in the educational levels towards fishing-related issues among fishers in Lakes Chivero and Manyame (Table 4). There is a significant difference (χ2 (3), p < 0.05) in the importance of fish harvest incomes as a driver for fishing among fishers between the two lakes. There is a significant difference (χ2 (3), p < 0.05) in the fishers' perceptions towards the benefit of fish catches and by-catch wastes for food security between the two lakes (Table 4). Fishers in both lakes do not differ (χ2 (3),p > 0.05) in their perceptions towards the availability and dynamics of the fish market as a driver of sustainability of fisheries (Table 4).

 

 

Fishers in Lakes Chivero and Manyame concur (χ2 (3),p > 0.05) that declines in peri-urban fish stocks, reductions in water quality and quantity as well as climate change, gender disproportion, poor networking among fishers, and obsolete post-harvest equipment and infrastructure, as well as inaccessible and low financial capital, are the main barriers to sustainability of fisheries (Table 4). Most fishers in both lakes consider bay accessibility / fishing zone restrictions / prohibitions as barriers for their fishing business. However, fishers from both lakes differ significantly in their perceptions of the barrier role played by reduced fishing bay accessibility on fish catches (χ2(3), p < 0.05) and the lack of legal frameworks (χ2(3), p < 0.05) in the sustainability of fisheries in the two lakes (Table 4).

 

DISCUSSION

The main aim of the study was to identify and contextualise drivers and barriers to sustainability of small-scale fisheries in two peri-urban impoundments Chivero and Manyame in Zimbabwe. Three indicators: Fishery Performance Indicator (FPI), Community-Based Fishery Indicators (CFI) and FAO Small-Scale Fisheries Indicator (FSSFI) frameworks were used to evaluate the drivers and barriers affecting the sustainability of fisheries, and viability of fishing-dependent livelihoods in the two lakes.

Results of the study indicated the need for a consistent income, food security, and food nutrition as the main drivers of small-scale inland fisheries in Lakes Chivero and Manyame. Accessible fish markets and the educational levels of the fishers are also key drivers for the sustainability of small-scale inland fisheries in the two lakes. These findings resonate with research by Allison and Ellis (2001); Béné (2003) and FAO (2015), which reflect that small-scale inland fisheries serve multiple purposes, although food security, food nutrition and an income to alleviate poverty form the main basis for continued operations. The educational levels of fishers are key drivers of fisheries sustainability as they infer a capacity to: undergo fisheries and water resource conservation training, adopt new fishing methods and adapt to new post-harvest technologies (Fregene, 2002).

The three fisheries indicator assessment frameworks revealed almost similar ecological, economic, social and institutional barriers to the sustainability of the peri-urban fisheries in Lakes Chivero and Manyame. The FPI, in particular, indicated ecological barriers such as depleting fish stocks, water pollution, climate change and dynamics in land use patterns in the catchment as the main barriers to the sustainability of small-scale fisheries. The CFI indicated similar results with the FPI as it identified declines in targeted fish stocks and biodiversity as the key barriers to the sustainability of small-scale fisheries. The FSSFI indicated aquatic environmental degradation and practice of unsustainable fishing methods as the key ecological barriers to the sustainability of fisheries in Lakes Chivero and Manyame. The ecological barriers identified in this study resonate with generic ecological hazards threatening the sustainability of most marine, coastal and inland fisheries. Béné (2003); Béné et al. (2009); Allison et al. (2005, 2009); Marshall (2011); Kolding and Van Zwieten (2012); FAO (2012, 2016) largely attribute key ecological elements such as unfishable drawdown zones, depleting fish stocks and poor water quality and erratic water level fluctuations as barriers to the sustainability of inland fisheries in Benin, Chad, Cameroon, Zimbabwe, Niger and Malawi. For this study, the deteriorating water quality standards, declining fish stocks and biodiversity in the two peri-urban impoundments are owed to the transboundary (complex mixture of urban and rural characteristics) nature of the catchment areas, punctuated by a lack of clear demarcation and improper water and land resource governance (Nhapi and Gijzen, 2004; Khan et al., 2013).

The three indicator frameworks reflected economic barriers, such as poor post-harvest infrastructure, low recapitalisation and creditworthiness, volatile fish markets and subdued seasonal prices, as key threats to the sustainability of the fisheries in both lakes. Seasonal fluctuations in fish stocks tend to affect the fish prices at the fish markets in the two lakes, with prices relatively higher in the winter season (Seijo et al., 1998; FAO, 2015; Mhlanga and Mhlanga, 2013; Kupaza et al., 2015). The lack of efficient and climate-smart fishing and post-harvest technologies in small-scale inland fisheries affects fishing effort, fish catches and the subsequent market prices (Fregene, 2002). This economic barrier leads to price disparities in small-scale inland fisheries which tend to lead fishers to overexploit the fisheries resources using high fishing effort and inefficient gear (Béné, 2003; 2009).

The demographic distribution of the fishers showed male dominance in the two impoundments. All three fisheries assessment frameworks indicated social barriers such as disproportionate gender consideration, where females are marginalised and perform peripheral and fringe post-harvest roles, including fish gutting, gleaning, and cleaning in fisheries. Undefined and peripheral roles for women, even though they may be as educated as men in leadership positions, in the peri-urban fisheries ensures male domination of the industry (Matsue et al., 2014). The peri-urban nature of the two impoundments implies that women and the youths who are equally affected by poverty would have been attracted to fishing as an alternative source of income to alleviate poverty (Nelson et al., 2008; Khan et al., 2013; FAO, 2016). However, the FSSFI framework indicated a strict adherence to cultural values within the fishing communities, where some fisheries do not deliberately employ women for cultural, ethical and inferred hygienic reasons, which are largely mythical as stated by Allison et al. (2009) Béné (2009) and Matsue et al., (2014). This tends to marginalise women from lucrative fishing operations and economically disempowers them and discourages females from considering fishing as a career in both impoundments (Matsue et al., 2014).

From a governance and institutional perspective, the three frameworks showed that barriers such as restricted access to lucrative fishing zones, expensive annual fishing permits, low consideration for fisheries project management and extension services, fishing land access, rules, laws, and regulation awareness, as well as water conservation education and awareness, affect the sustainability of fisheries in both impoundments. Lack of transparency in fisheries operations is a highlighted barrier indicated by the FSSI, threatening the sustainability of small-scale inland fisheries. Lack of training and extension services may hinder adoption and transfer of fishing technologies to peri-urban fisherfolks and threatens the sustainability of individual small-scale inland fisheries (Adelekan and Fregene, 2015). Even more so, the peri-urban fisheries have poor social organisational networks, limited access to financial capital, rely on obsolete equipment and hardly have legal representation, and this threatens their sustainability (Béné, 2009). In most cases, institutional elements of fisheries are neglected and lead to their non-consideration in economic planning and governance processes (Fregene, 2002; Béné, 2009, Welcomme et al., 2010; Bartley et al., 2015; Link et al., 2017; Stephen et al., 2018).

There are subtle differences in the key drivers and barriers reflected by the three indicator frameworks. This is because the three fisheries viability assessment frameworks lack conceptual coherence and often neglect to incorporate important aspects of the fishery system. In fact most fisheries sustainability assessment frameworks tend to consider individual fisheries, and are dimensional with much focus on fish stock assessment (Béné, 2009; Bartley et al., 2015), water and habitat dynamics (Marshall, 2011; Tendaupenyu, 2012, Nyarumbu and Magadza, 2016), impacts on fishing-dependent livelihoods (Garba, 1997; Allison et al., 2005; Salmi, 2005; Mhlanga and Mhlanga, 2013) or currently the effects of climate change (Brander, 2010; Welcomme et al., 2010; FAO, 2012; 2016; Wichelns, 2017). This suggests a need to assess fisheries sustainability using a bottom-up approach starting from individual fisheries < community fisheries < global/generic fisheries (Seijo et al., 1998; FAO, 2012; 2015). Inclusion of cultural, social, governance, ecological and economic aspects will lead to a holistic assessment of the sustainability of fisheries.

Comparative assessment of the perceptions of the fishers in Lakes Chivero and Manyame towards the drivers and barriers to the sustainability of fisheries reveal significant differences in perspectives towards drivers, such as the effect of educational levels, alternative income strategies adopted, food security of fishing livelihoods, and livestock owned. Some fishers consider the individuals' educational level to be irrelevant as a motivational factor driving fishing activities. Rather, fishing is viewed as a physical activity needing minimal cognitive effort. Fregene (2002) argues that such an attitude hinders the smooth operation and uptake of fisheries extension and management training services in most small-scale inland fisheries in Sub-Saharan Africa. Fishers from the two lakes differ significantly (p < 0.05) in their perspectives towards the need to earn an income from fishing being a driver of sustainability of fisheries. Rather, they relate the need to maximise profits with overexploitation and overfishing of the fisheries resources, often using unregulated gear in the two lakes. Thus, fishers in the two lakes tend to use illegal gear in order to maximise catches, resulting in overfishing which in the long-term threatens the sustainability of the fisheries and livelihoods of fishers themselves (see Tweddle et al., 2015; Irvine et al., 2018).

The significant differences in fishers' perceptions towards a need for food security as a driver of fisheries sustainability in the two lakes indicates that fishers have different motivational factors for continuing with fishing as a livelihood strategy. Fishers significantly disagree that owning livestock such as cattle and goats is a driver of fisheries sustainability in the two lakes. This partly reflects the peri-urban nature of the two lakes, and proximity of Lake Chivero to the main capital city of Harare, relative to Lake Manyame. Fishers in Lake Chivero adopt a more urban lifestyle and tend to alternatively go into non-fishing-related income activities such as trinket trading, tobacco marketing and formal jobs (FAO, 2013; Kupaza et al., 2015). Fishers in Lake Manyame adopt a more rural lifestyle with agriculture and livestock ranching as an alternative livelihood. Non-significant differences in perceptions towards most of the barriers to the sustainability of the fisheries between fishers in the two lakes indicated the universal nature of challenges facing small-scale inland fisheries, such as poor water quality and quantity (FAO, 2015), depleting fish stocks (Welcomme et al., 2010; Tweddle et al., 2015), climate change (Brander, 2010), gender disparity (Matsue et al., 2014), low capital and poor post-harvest technology (Fregene, 2002) and peri-urbanisation (Khan et al., 2013; Nagendra and Ostrom, 2014).

 

CONCLUSIONS AND RECOMMENDATIONS

The main drivers for sustainability of fisheries include the need for a consistent income, food security and food nutrition in Lakes Chivero and Manyame. The applied fisheries sustainability assessment indices; the FPI, CFI and FSSFI, indicated similar barriers threatening the viability of the small-scale inland fisheries. However, the significant differences in the perceptions of small-scale inland fishers towards the barriers and drivers of fisheries between the two lakes shows the inherent uniqueness of individual fisheries and fishers. Thus, in order to guarantee the sustainability of the fisheries in the two peri-urban lakes, there is a need to consider a bottom-up approach incorporating the concerns of individual fisheries which then feeds into community fisheries and can inform global fisheries aspects. Even more so, future studies of fisheries may exploit the integrated application of a raft of fisheries assessment frameworks for effective evaluation of their sustainability in the face of ecological, economic, social and institutional threats.

 

ACKNOWLEDGEMENTS

Our special thanks go to Mr Newman Songore at Lake Chivero Fisheries Research Station and all the Parks staff for assistance with the field logistics. We thank the Graduate Studies Office at Chinhoyi University of Technology for funding this research under Grant PG 4299. This research was also supported by funding from the Department for International Development (DFID) under the Climate Impact Research Capacity and Leadership Enhancement (CIRCLE) programme, implemented by the African Academy of Sciences (AAS) and the Association of Commonwealth Universities (ACU). We also thank the two anonymous reviewers who made this manuscript readable.

 

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Received 9 August 2018
Accepted in revised form 26 September 2019

 

 

* Corresponding author, email: mkaiyo@gmail.com

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RESEARCH PAPERS

 

Quality of water recovered by treating acid mine drainage using pervious concrete adsorbent

 

 

AN ShabalalaI, II, *; SO EkoluI

IDepartment of Civil Engineering Science, University of Johannesburg, PO Box 524, Auckland Park 2006, South Africa
IIUniversity of Mpumalanga, P/Bag X 11283, Mbombela 1200, South Africa

 

 


ABSTRACT

In this paper, a batch experiment was conducted to evaluate the water quality obtained from using pervious concrete (PERVC) technology to treat acid mine drainage (AMD). The study proposes an innovative application of PERVC as a permeable reactive barrier liner in evaporation ponds. The effectiveness of PERVC adsorbent in removing heavy metals was compared with that of zero-valent iron (ZVI) of particle size 1.0 to 1.8 mm. The AMD used in the study was obtained from abandoned gold and coal mines. PERVC mixtures consisted of granite aggregate and ordinary Portland cement CEM I 52.5R (CEM I) or CEM I containing Class F 30% fly ash (30%FA) as a cement replacement material. ZVI was prepared from a mixture of silica sand and iron grit of specific sizes. PERVC and ZVI media were used to conduct batch reactor tests with AMD, for a period of 43 days at a ratio of 1 L of reactive material to 3 L of AMD. The quality of treated AMD was compared against effluent discharge standards. The contaminants Al, Fe and Zn were effectively removed by both PERVC and ZVI. Also, both adsorbents reduced Ni, Co and Cu to levels below those measured in raw AMD. However, PERVC was more effective in removing Mn and Mg while ZVI was ineffective. Although PERVC removed more heavy metals and with greater efficiency than ZVI, the PERVC-treated water showed high pH levels and exhibited elevated Cr6+ concentrations, owing to leaching from the cement and fly ash materials used in PERVC mixtures.

Keywords: Pervious concrete, zero-valent iron, acid mine drainage, batch test, permeable reactive barrier


 

 

INTRODUCTION

Water preservation, recycling and reuse is quickly becoming inevitable as urbanisation and growth of the human population continues to stretch the demands on water availability in various nations. Water in some countries is quite a scarce commodity. Southern Africa is among the known water-stressed regions, amongst others such as the Middle East, China, etc. (Jobson, 1999; Procházka et al., 2018). It is estimated that 40% of the world population may be living in water-scarce or -stressed countries within the next 50 years (Bichai et al., 2016). A critical strategy for future water security lies in development of a portfolio of supply sources, including water recycling. A common source which is already widely employed in several countries is reuse of treated municipal wastewater. Another potential resource for water recovery is acid mine drainage (AMD).

AMD typically occurs in abandoned mining sites rich in pyrites which are typically found embedded in mineral ore sources. Upon extraction of minerals during a mining activity, the pyrites are left exposed to atmospheric conditions within the mined rock sources or tailings. Under these exposure conditions, pyrites undergo oxidation forming acidic water discharge. Similarly, acid sulphate soils contain sulphidic materials which typically result in acidic water run-off, i.e., AMD (Igarashi and Oyama, 1999; Testa et al., 2013; Komnitsas et al., 1995; Fitzpatrick, 2003). AMD dissolves acid-soluble heavy metals from tailings and deposits the contaminants through a variety of mechanisms, including precipitation and surface sorption onto soils and water courses, endangering the ecological systems, and plant and aquatic life (Fripp et al., 2000).

A simplified Eq. 1 gives the pyritic oxidation reaction leading to AMD formation (Kefeni et al., 2015; Ford, 2003; Akcil and Koldas, 2006; Petrik el al., 2006). The presence of some bacterial species, especially Thiobacillus ferrooxidans, is known to remarkably oxidize iron and sulphur in pyrites, typically at a low pH < 3.5 (Igarashi and Oyama, 1999; Testa et al., 2013; Komnitsas et al., 1995; Blowes et al., 2003; Younger, 2004).

AMD emanates from its source which may be an underground or open-cast mine, then flows to the surrounding environment that may include soil, wetlands, water courses or water bodies. AMD is typically characterised by acidity and high concentrations of heavy metals. As a result of its chemical composition, it tends to be highly aggressive to the natural ecosystem. It pollutes wetlands, lakes, rivers, etc., usually destroying aquatic life and rendering these water resources unsuitable for human or animal consumption and for agricultural uses. Also, AMD contamination strangulates animal and plant life, and renders barren even soils that were naturally fertile (Fripp et al., 2000; Ochieng et al., 2010). The acidic nature of AMD causes corrosion of infrastructure used in dams, bridges, water pumping and supply, amongst others (Gitari et al., 2008; Pagnanelli et al., 2009; Offeddu et al., 2015; Macías et al., 2012a). Figure 1 shows an AMD source in a South African open-cast mine. Crystallised metal and/or sulphate mineral salts can be seen deposited at the soil surface, following evaporation of AMD-contaminated seepage water in the soils (Antivachis et al., 2016; Harris et al., 2003). The dam in Fig. 1 may also be considered as an evaporation pond, which serves as the AMD receptor prior to effluent discharge into the river downstream.

 

 

Sustainable treatment of acid mine drainage

Active treatment of AMD, by dosing with lime or other chemicals, is presently the most commonly used technique. However, this method has major disadvantages, including the formation of sludge which itself has to be disposed of, the high cost of chemicals, labour and equipment maintenance (Hengen et al., 2014). These treatment costs can be so high as to be non-sustainable in the long-term, as commonly seen in some developing countries.

Passive treatment systems, such as the wetland system and permeable reactive barriers (PRBs), are considered to be among the most sustainable options as they do not require continuous chemical inputs, nor do they involve high maintenance. PRBs have emerged as one of the most promising passive systems for treatment of contaminated groundwater (Phillips, 2009; Thiruvenkatachari et al., 2008; Amos and Younger, 2003; Komnitsas et al., 2006). It is a cost-effective technology that could be used to treat groundwater with an underground PRB or to treat surface water with a PRB liner in facultative evaporation ponds. The latter innovation is the preoccupation of the present paper. A typical PRB consists of a trench or wall filled with granular material which is sufficiently permeable to allow passage of groundwater through it, as determined by the natural groundwater flow regime.

Various types of reactive materials have been studied for potential use in PRBs. The most common of them is zero-valent iron (ZVI) as indicated by various studies (Cundy et al., 2008; Suponik and Blanco, 2014; Moraci and Calabró, 2010; Gusmão et al., 2004; Cantrell et al., 1995; Komnitsas et al., 2006). Others, including activated carbon, zeolites, peat, sawdust, oxygen-releasing compounds, etc., have also been used and evaluated (Thiruvenkatachari et al., 2008; Obiri-Nyarko et al., 2014). Alkaline materials such as limestone, hydrated or slaked lime and dolomite are commonly used to treat groundwater that is contaminated by AMD. These materials have been shown to effectively remove divalent and trivalent metal cations such as copper, cadmium, lead and zinc from solution (Wang et al., 2016; Gitari et al., 2008; Pagnanelli et al., 2009; Offeddu et al., 2015; Macías et al., 2012a).

Several recent pioneering studies (Shabalala et al., 2017; Solpuker et al., 2014; Ekolu et al., 2016a; Shabalala, 2013) have shown pervious concrete (PERVC) technology to be an effective system for polluted water remediation. Ekolu and Bitandi (2018) showed PERVC to also possess greater treatment longevity, of about twice that of ZVI. PERVC is a mixture of single size coarse aggregate, Portland cement, water, and little to no sand. It is typically used to drain stormwater run-off from the streets, parking lots, driveways, and walkways. Porous pavements are known to reduce surface run-off and to minimize stormwater accumulation during a rain event in urbanised areas. Studies show that PERVC can also function as a pollution sink for run-off, owing to its particle retention capacity through filtration (Ekolu et al., 2014a and Solpuker et al., 2014). The high porosity of PERVC leads to good water infiltration and air exchange rates (Scholz and Grabowiecki, 2007).

Objectives

It has been shown that ordinary evaporation ponds hardly improve the quality of contaminated mine water (Mapanda et al., 2007). However, they provide effective interception points that can be exploited to employ AMD treatment, for example, by introducing alkaline materials and sulphate-reducing bacteria (SRB) using limestone, manure, etc. (Barnhisel et al., 2000; Macías et al., 2012b; Metesh et al., 1998).

This paper proposes an innovative application of PERVC as a PRB liner in evaporation ponds, for recovery of water from AMD. To the best knowledge of the authors, the proposed use is the first of such PERVC application. Accordingly, a batch reactor experiment was conducted to evaluate the water quality obtained by using PERVC made using Portland cement of grade CEM I 52.5R (CEM I) or CEM I/FA mixture containing 30% FA (30%FA) as a cement replacement material. Comparisons were then made on treatability of AMD using PERVC versus using ZVI as adsorbents. The measurements conducted on water include physico-chemical parameters, changes in water quality due to the various treatments, adsorption parameters, and removal efficiency. The quality of treated water was evaluated against the United States Environmental Protection Act (USEPA, 1986) and South Africa's National Water Act (RSA, 1999) being the standards for effluent disposal to the environment.

 

EXPERIMENTAL STUDY

Configuration

The experiment comprised batch tests conducted on AMD using PERVC and ZVI adsorbents. The batch reactor set-up depicts a configuration of PERVC-PRB liner in a facultative evaporation pond or dam, as illustrated in Fig. 2a. Often, these ponds are trapezoidal or rectangular-shaped, clay-lined trenches that serve as receptors of contaminated mine water seepage. From these ponds, the effluent may be discharged into the adjacent natural water body or stream. The present study proposes to provide a PERVC-PRB liner upon the walls of evaporation ponds. AMD undergoes treatment as it passes through the PERVC-PRB lining. As shown in previous studies (Ekolu et al., 2016a), PERVC is highly porous and has high hydraulic conductivity that allows uninhibited flow of water through its pore network, as also depicted in Fig. 2b (Yang and Jiang, 2003). As water percolates through the pore network of the PERVC liner, it comes in contact with highly alkaline cement paste in the concrete matrix. This paste neutralises the AMD by raising its pH, in turn leading to precipitation of dissolved heavy metals from the polluted mine-water (Shabalala et al., 2017; Ekolu and Bitandi, 2018).

Acid mine drainage and reactive media

The AMD types used in the study were obtained from abandoned gold and coal mines, anonymously designated as WZ and TDB, respectively. AMD was collected from field sources using high density polyethylene containers and transported to the laboratory for use in the experiments. As already mentioned, the reactive media comprising PERVC and ZVI were used. PERVC was made using constituents consisting of Portland cement CEM I 52.5R with or without 30% fly ash (FA), and 6.7 mm granite aggregate. In an earlier study (Ekolu et al., 2014b), it was shown that FA rapidly neutralises AMD, attaining maximum pH within 10 to 15 min.

The chemical compositions of the cementitious materials used are given in an associated paper (Shabalala et al., 2017) and repeated in Table 1 for convenience. Evidently, the FA used was of Class F category (ASTM C 618, 2015). The granite aggregate used was selected following an earlier study, which involved aggregates of different types and sizes (Ekolu et al., 2016a).

Also given in Shabalala et al. (2017) are mixture details, including the mix design, mixing and casting procedures for the 100 mm PERVC cubes used. The mixes were designated as CEM1 for the PERVC made of ordinary Portland cement, and 30%FA for PERVC containing 30% FA as a partial cement replacement material. Incorporation of 30% FA into the concrete mixture provides effective resistance to potential acid attack by AMD (Ekolu et al., 2016b; Shabalala et al., 2017).

The composition of ZVI was 80.6% Fe2O3, 0.72% MnO, 0.24% Al2O3, 0.19% Cr2O3, 0.03% MgO, 0.02%ZnO and trace elements. Evidently, the ZVI had a high iron content. The density of ZVI is 7 800 kg/m3, while its specific surface area is typically 1.0 to 2.0 m2/g. In PERVC, the hardened cement paste (HCP) forms a coating on aggregate particles and reacts with AMD (Fig. 2). The density of HCP is 1 900-1 950 kg/m2 and its Brunauer-Emmet-Teller (BET) specific surface area is 30 to 100 m2/g (Hunt, 1966; Thomas et al., 1998; Ekolu and Bitandi, 2018).

Commercially available ZVI material supplied by B.V. Boksburg (Pty) Ltd, was used in the study. In preparing the ZVI-sand mixture, standard 100 mm cube moulds were filled with equal proportions of fine silica sand of size range 0.4 to 0.85 mm, coarse silica sand of size range 0.8 to 1.8 mm, fine ZVI grade GH 80 of size range 0.18 to 0.42 mm and coarse ZVI grade GH 18 of size range 1.0 to 1.4 mm. The fine particles of ZVI result in low porosity and low permeability, making it vulnerable to fast clogging. By incorporating sand into ZVI, the mixture attains increased porosity and higher permeability for better hydraulic conductivity and reduced clogging (Bartzas and Komnitsas, 2010).

Batch reactor experiment

In the batch reactor set-up, 1 L cube of CEM 1, 1 L cube of 30%FA and -1 L of ZVI-sand mixture, were each placed in a 4 L plastic container; 3 L of WZ or TDB were added to each container. Table 2 gives the quantities of constituents used in the batch set-up. Vadapalli et al. (2008) observed that active treatment and neutralization of AMD to circumneutral or alkaline pH was optimized when the ratio of AMD to reactive media was maintained at 3:1 by volume. Accordingly, a ratio of 1 L of reactive material to 3 L of AMD was used in the present study. Containers were tightly closed to ensure no evaporation took place. During the first 10 days, aqueous samples of 200 mL were collected once a day and stored at room temperature. Thereafter, the sampling frequency was decreased to once a week. The experiment was conducted continuously for a period of 43 days.

 

 

Measurements and analyses

Measurement of pH was conducted using the MP-103 microprocessor-based pH/mV/Temp tester. pH tests were done immediately upon collection of aqueous samples from batch tests. The pH electrode was calibrated using standard NIST - traceable pH 2.0, 4.0, 7.0 and 10.0 buffers. Samples of treated AMD were collected into 220 mL plastic vials, stored at 4°C and analysed for Al, Fe, Zn, Mn, Na, Mg, K, Ca, Mn, Fe, Co, Ni and Cu. The Perkin Elmer SCIEX (Concord, Ontario, Canada) ELAN 6000 inductively coupled plasma mass spectrometer (Perkin Elmer, 2003) was employed for the water analyses. SO4 concentration was determined using ion chromatography, Dionex QIC-IC.

Adsorption capabilities of the reactive media were assessed based on retention parameters consisting of the amount of metal adsorbed (qe) in mg/g, contaminant removal efficiency (RE%), partition (also referred to as adsorption or distribution) coefficient (Kd) in mL/g. Eqs. 2 to 4 give the expressions used to calculate these parameters. +

where Co is the initial concentration of the contaminant in AMD (mg/L), Ce is equilibrium concentration of the contaminant (mg/L), V is volume (L), m is mass of the reactive material or adsorbent (g).

 

RESULTS AND DISCUSSION

The subsequent sections give the results obtained upon AMD treatment using PERVC and ZVI. The two AMD types used in the present study had different elemental compositions and acidity levels with pH values of 4.15 and 5.79 for WZ and TDB, respectively. Chemical analyses of WZ samples showed high metal concentrations of Ca (582 mg/L), Mg (170 mg/L) , Na (139 mg/L), Mn (131 mg/L), Fe (12 mg/L) and Al (3 mg/L), while TDB also had high contents of Ca (470 mg/L), Mg (214 mg/L), Na (3 061 mg/L), Fe (9 mg/L) and Al (6 mg/L). Both, the WZ and TDB had high SO4 concentrations of 1 123 and 2 870 mg/L, respectively.

Figures 3 to 9 show the pH results and the changes in concentrations of heavy metals, with duration of the treatment. These results are discussed comparing the treatability of AMD using PERVC relative to using ZVI.

 

 

 


 

 

 

 

 

 

 

 



 

 


 

pH change

During the batch reactor experiments, the pH values of raw AMD increased from 4.15 or 5.79 before treatment to pH = 6 to 8 for ZVI and pH = 9 to 12 for PERVC after treatment, as seen in Fig. 3. For both reactive media, a rapid increase of pH was observed within the first 24 hours of the experiment. For a given reactive material, the treated TDB always gave pH levels that were 1 to 2 points higher than the corresponding values for WZ. The high pH values observed in PERVC-treated AMD are related to dissolution of portlandite from the cementitious matrix, which adds alkalinity to the system (Chandrappa and Biligiri, 2016). In the experiments conducted using ZVI, the oxidation of ZVI to ferrous and ferric iron caused the increase in pH. As already indicated, lower final pH values were attained for acidic AMD water samples that were treated using ZVI as compared to those that were treated using PERVC.

Effect of using plain pervious concrete

Figure 4 presents the changes in concentrations of Al, Fe and Mn during 43 days of the batch tests. The neutralising capacity of PERVC is attributed to the large quantity of portlandite phase which adds alkalinity to the solution. CEM I effectively removed Al, Fe, Zn and Mn from both WZ and TDB with efficiency levels of 98% to 100%. In all the treated AMD samples, i.e., WZ-CEM1, TDB-CEM1, WZ-30%FA, TDB-30%FA, WZ-ZVI and TDB-ZVI, there was generally no consistent decrease in the concentration of sulphate, as seen in Fig. 5. It can be concluded that none of the reactive media were successful in removing sulphate. While most metals precipitate out of solution at high pH, sulphate remains in solution and does not precipitate since its stability is not pH dependent. However, some sulphate may be removed by PERVC as gypsum precipitate (Shabalala et al., 2017). Treatment methods such as microbial remediation of AMD using SRB, filtration, electrocoagulation, adsorption and ion exchange are considered as promising alternatives for sulphate removal (Fernando et al., 2018).

It can be seen in Figs 6 and 7 that the concentrations of Pb, Zn, Ni, Co and Cu decreased as the pH of the solution increased. Precipitation of metal hydroxides and oxides may explain the observed reductions in concentrations of these contaminants (Aube, 2004; Seneviratne, 2007). The Ni, Cu, Pb and Zn metals may have precipitated as Ni(OH)2, Cu(OH)2, Pb(OH)2 and Zn(OH)2, respectively. The removal of cobalt is probably due to its adsorption onto, or co-precipitation with, iron and aluminium hydroxides or hydrosulphates. At pH values between 8 and 9, Ni is adsorbed onto calcite in solution (Kefeni et al., 2015).

Effect of using pervious concrete mixtures containing fly ash

Major reductions in concentrations of most metals were observed for WZ-30%FA and TDB-30%FA as shown in Figs 4, 6 and 7. The 30% FA adsorbent removed 99% of Al, reducing it from 3 mg/L in raw WZ to 0.07 mg/L in WZ-30%FA, and from 6 mg/L in raw TDB to 0.05 mg/L in TDB-30%FA (Fig. 4a). The observed reductions of Al concentration in WZ-30%FA and in TDB-30%FA may have resulted through the formation of amorphous Al(OH)3 (Komnitsas et al., 2004). As pH increases, Fe3+ precipitates to form amorphous ferric hydroxides and oxyhydroxides, which explains the complete removal of iron from WZ-30%FA and TDB-30%FA.

The concentrations of Pb, Zn, Ni, Co and Cu in raw AMD were generally low and decreased to undetectable levels after PERVC or ZVI treatment. Removal of Ni can be attributed to its precipitation as Ni(OH)2 and possible adsorption on the precipitating amorphous Al and Fe-oxyhydroxides. Cu tends to precipitate as cupric and cuprous fernite and may be adsorbed onto the surface of FA at pH values between 5 and 6. Zn co-precipitates with Si that is solubilised from FA and forms willemite (Vadapalli et al., 2008).

Effect of using zero-valent iron

When raw AMD was treated using ZVI, the concentrations of most metals measured in the batch tests decreased, as seen in Figs. 4, 6 and 7. Al removal levels were 82% and 97% for WZ-ZVI and TDB-ZVI, respectively. In Fig. 4c, the reduction of Mn concentration from 107 mg/L in raw WZ to 63 mg/L in WZ-ZVI, and from 20 mg/L in raw TDB to 2 mg/L in TDB-ZVI, may be attributed to its precipitation as Mn(OH)2 at alkaline or neutral pH.

Concentrations of Pb, Zn, Ni, Co and Cu were maintained at low values following ZVI treatment, as seen in Figs. 6 and 7. When Fe0 is oxidised to Fe2+ then to Fe3+, various iron corrosion products Fe(OH)2, FeOOH, Fe(OH)3 may form (Schwertmann and Murad, 1983), as shown in Eqs 5 to 7

Metals in cationic forms may be sorbed onto these iron corrosion products (Lindsay et al., 2008; Hashim et al., 2011; Bartzas and Komnitsas, 2010). Thus, it is likely that the main processes for Ni (II), Co (II), Cu (II) and Zn (II) removal are their adsorption onto the surface of iron corrosion products. Ni, Co and Zn may also be precipitated as metal hydroxides.

Alkali metal changes for treatments done using pervious concrete and ZVI adsorbents

Figure 8a shows that the K concentration levels remained elevated in both the PERVC (CEM I, 30%FA)-treated and the ZVI-treated AMD water. Also, there were no significant reductions in Ca and Mg concentrations of the ZVI-treated AMD, as seen in Figs. 8b and 8c. Interestingly, high removal of Mg was achieved in AMD samples that were treated using PERVC but the ZVI-treated samples showed very low Mg removal. The PERVC's Mg removal levels for WZ and TDB were, respectively, 96% and 99%, while ZVI gave corresponding removal levels of 12% and 16%. Mg removal by PERVC was observed to be optimal at a pH range of 9 to 11 and may be attributed to the formation of brucite and hydrotalcite in solution (Solpuker et al., 2014).

Removal efficiencies

The metal removal efficiency levels were calculated as summarised in Table 3. Average equilibrium concentrations of each contaminant over the period 10 to 43 days were calculated and used to determine its proportional decrease or increase relative to its initial level in raw AMD. Al, Fe, Zn and Pb had zero or undetectable concentrations after treatment with CEM I or 30%FA. For the purpose of conducting calculations, the equilibrium concentrations of these contaminants were assumed to be 0.01 m

As seen in Table 3, the Al, Fe, Ni, Co, Pb and Zn were successfully removed by all the reactive media (CEM 1, 30%FA, ZVI), with removal efficiency levels of up to 100%. The removal efficiency levels for Al, Mn, Mg and Cu were greater when AMD was treated using CEM I or 30%FA relative to the treatment with ZVI. For instance, 91% to 100% of Mn and Mg in WZ or TDB were removed by CEM I or 30%FA, yet ZVI treatment correspondingly achieved a low 44% to 58% removal of Mn and even lower 12% to 16% removal of Mg. Clearly, the ZVI adsorbent was ineffective while PERVC was very effective in removing both Mn and Mg from raw AMD.

A comparison is given in Fig. 9 showing the equilibrium concentrations of the major contaminants in AMD before and after treatment. It is clear from Fig. 9(a) that the major heavy metals present in AMD were completely removed or reduced to negligible concentrations when treated using CEM I or 30%FA. The contaminants removed by CEM I or 30%FA include Mn and Mg. The ZVI also removed most heavy metals except Mg and Mn. The inability of ZVI to remove these two contaminants is attributed to the lower pH, of 6 to 8, attainable through ZVI treatment, while CEM I or 30%FA attained a pH of 9 to 11, which is the range for precipitation of Mn and Mg.

Since sulphate removal is not pH dependent, none of the media effectively removed or reduced SO4 concentrations. It is notable in Fig. 9b that the concentration of SO4 increased following AMD treatment using each of the adsorbents. The ZVI treatment gave greater increase in the SO4 concentrations compared to CEM I and 30%FA treatments, while the latter showed a slightly higher SO4 increase than the former.

Retention properties of reactive media

Results showing the retention characteristics of CEM I, 30%FA and ZVI are given in Table 4 for the various heavy metals. For each type of AMD, the uptake of heavy metals (qe) was similar for both PERVC media i.e. CEM I and 30%FA. It can also be observed that ZVI had a similar metal uptake as PERVC, except for the metals Mn and Mg where the uptake by ZVI was quite low. For WZ, the uptake of Mn or Mg by PERVC was in the range 67 to 95 mg/g which is much higher than the 11 to 32 mg/g uptake by ZVI. Similarly for TDB, the Mn or Mg uptake of 11 to 125 mg/g by PERVC is much higher compared with 6 to 20 mg/g uptake by ZVI. These results are consistent with the inability of ZVI to significantly remove Mn and Mg, while PERVC adsorbents were effective in removing these contaminants, as discussed earlier. PERVC adsorbents also showed higher uptake of metals from TDB relative to their corresponding uptake from WZ. These observations underscore the relative ease of metal release by TDB as opposed to WZ which appears to be more difficult to treat.

 

 

The adsorption coefficient Kd gives the proportion of metal concentration sorbed by the reactive media relative to the concentration left dissolved in solution, as expressed in Eq. 4. CEM I and 30%FA were generally more effective sorbents compared to ZVI. For instance, ZVI showed little to no sorption of Mn and Mg giving Kd = 0.11 to 0.78 mL/g in TDB, compared to the corresponding 85 to 586 mL/g for PERVC. It is, however, notable that sorption of Mn by 30%FA was quite diminished in WZ unlike in TDB where higher sorption was observed. However, sorption of Mn in WZ by CEM I was also high. This observation may be related to the dilution effect of using FA as a partial replacement material in Portland cement.

Evaluation of treated water quality

The contaminant concentrations in AMD before and after treatment with CEM I, 30%FA and ZVI were compared with the limits specified in USEPA (1986) and RSA (1999) standards for pollutant discharge to the environment. Table 5 gives comparisons for the various contaminants in the raw AMD, treated WZ, and treated TDB. It may be noted that the standard limits given in USEPA (1986) and RSA (1999) are the requirements for discharge of pollutants to a water resource.

As shown in the table, both the raw WZ and raw TDB fail, for almost all the contaminants, to meet the standard requirements for pollutant discharge into a water resource. Treatment of both AMD types using ZVI reduces the concentration levels of contaminants to limits generally meeting the USEPA (1986) and NWA (1999) criteria for discharge of treated AMD to the environment, with the exception of Mn. Treatment of AMD using CEM I or 30%FA leads to lower heavy metal concentrations relative to using ZVI; however, the PERVC-treated AMD water exhibits undesirably high pH levels and elevated Cr6+ concentrations (Table 5). It is known that both acidity and high alkalinity of water inhibit microbial growth. A circumneutral pH range, typically 6.5 to 7.5, is essential for sustenance of microbial activity and the ecosystem, generally.

Cr6+ is known to be carcinogenic (Zhitkovich, 2011; WHO, 2003). Both CEM I and 30%FA materials do release Cr6+ into treated water, leading to concentration elevation beyond the maximum limits of 0.10 and 0.05 mg/L specified in USEPA (1986) and NWA (1999), respectively.

Also, all the reactive media resulted in elevation of SO4 concentration in the treated AMD, but there is no specified SO4 limit given in USEPA (1986) and NWA (1999) for pollutant discharge to water bodies. The concentrations of most contaminants in CEM I-treated or 30%FA-treated water also meet the specified limits for drinking water standards (SANS 241: SABS, 2015), except for Na, SO4, Cr6+ and the high pH of 11. The ZVI-treated AMD water also fails to meet the drinking water limits for Na, SO4, Mg and Mn (Table 5).

 

CONCLUSIONS

In this study, the resulting water quality obtained from treating acid mine drainage using pervious concrete or zero-valent iron was compared against water standards for discharge of effluents to the environment. Based on findings from the investigation, the following conclusions are drawn:

(a) In both of the AMD treatments done using pervious concrete and zero-valent iron, a rapid increase in pH was observed during the first 24 h of the experiment. For pervious concrete treatment, a maximum pH of 9 to 12 was attained as compared to 6 to 8 obtained after treatment of acid mine drainage using zero-valent iron.

(b) The removal efficiency levels for Al, Fe, Zn, Mn, Mg, Ca, and Cu were 93 to 100% when acid mine drainage was treated using pervious concrete as compared to the corresponding 12 to 99% for the treatment done using zero-valent iron. Mn, Mg and Cu exhibited the lowest removal levels, of 44, 12, 70%, respectively, obtained upon treatment of acid mine drainage using zero-valent iron. After treatment of acid mine drainage using pervious concrete or zero-valent iron, the equilibrium concentration of SO4 was always higher than that in raw acid mine drainage. For both the pervious concrete and zero-valent iron adsorbents, the Ni, Co and Cu in the treated mine drainage were maintained at levels below those in raw acid mine drainage.

(c) The main process responsible for heavy metal removal when raw acid mine drainage was treated using zero-valent iron is the adsorption of precipitates onto the surface of iron corrosion products. However, the removal mechanism associated with the use of pervious concrete to treat acid mine drainage is not fully understood; further research is needed.

(d) Pervious concrete mixtures were found to be better sorbents than zero-valent iron, as indicated by comparison of metal uptake and adsorption coefficients for the different contaminants.

(e) Acid mine drainage treatment using zero-valent iron produces water that generally meets the standard criteria for pollutant disposal to the environment. Treatment of acid mine drainage using pervious concrete containing cement with or without fly ash, gave better water quality than the treatment done using zero-valent iron. However, the AMD water that was treated using pervious concrete failed to meet the limits applicable for discharge of effluent into a water resource, mainly due to the resulting elevated Cr6+ and high pH levels of the treated water. These issues need to be resolved to allow potential practical use of pervious concrete in water treatment applications. Further investigations are ongoing to improve the pervious concrete treatment system.

 

ACKNOWLEDGEMENTS

This paper is based on acid mine drainage research project at the University of Johannesburg, partly funded by the National Research Foundation (NRF) of South Africa, IPRR Grant No. 96800 and the University of Mpumalanga (UMP). The authors are grateful for the financial support given.

 

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Received 5 December 2017
Accepted in revised form 23 September 2019

 

 

* Corresponding author, email: Ayanda.Shabalala@ump.ac.za

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