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Water SA

On-line version ISSN 1816-7950
Print version ISSN 0378-4738

Water SA vol.39 n.1 Pretoria Jan. 2013










Laboratory method design for investigating the phytoremediation of polluted water



DM JacklinI; IC BrinkII; J de WaalI, *

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




The performance of plants to remove, remediate or immobilise environmental contaminants in a growth matrix through natural biological, chemical or physical activities was studied in a laboratory phytoremediation system. This study aimed to develop a novel phytoremediation system capable of investigating the remediation of agricultural pollutants by individual and multiple plant species. The designed system analysed community phytoremediation by uniquely implementing multiple plant species within the same growth silo, with indigenous and alien assemblages compared to establish community performance, highlighting the importance of biodiversity in plant assemblages. The constructed system successfully analysed the phytoremediatory capabilities of plant species within the critically endangered Renosterveld vegetation type, with unvegetated soil controls included to illustrate the pollutant removal efficiency of plants only. Growth silos were constructed from PVC piping and irrigated with drippers from a submersible pump. Eighteen different plant species were included in the experiment, i.e., 14 indigenous species, 3 invasive alien plant (IAP) species, and Palmiet. Five agricultural pollutant parameters were analysed, i.e., for fertilizers NH3-N, NO3-N and PO43-P and for herbicide contamination using two glyphosate concentrations. The growth silos and unvegetated soil control were irrigated using a pollutant-municipal water solution at 3-day intervals. The multiple plants per silo design approach seeks to contribute to the limited literature pertaining heterogeneity importance, by comparing the pollutant removal performance of plant assemblages. Community comparison further investigated the biofilter implementation potential of indigenous South African plants as an alternative to their more invasive alien counterparts, adding to the knowledge base of plant-based phytoremediation by indigenous South African plant species. The laboratory phytoremediation system successfully measured the agricultural pollutant removal performance of individual plants and vegetative communities, with soil remediation influence acknowledged. The proposed system is a simple and inexpensive method for obtaining the plant-based biofiltration efficiency of individual and multiple plant species.

Keywords: experimental design, phytoremediation, water quality, bioremediation




Globally, terrestrial surface and groundwaters are affected by pollution from a range of industrial and agricultural activities (Schoumans et al., 2014). In particular, fertilizers and pesticides, derived from various agricultural practices, lead to the degradation of both surface and groundwater (Barcelo, 1997; Donoso et al., 1999; Lam et al., 2010). Diffuse water pollution from agricultural applications carry an immense cost to society, including environmental and ecosystem damage, loss of aquaculture and fisheries income and increased treatment costs for drinking water (Norse et al., 2001; Norse, 2005; Smith and Siciliano, 2015). Nitrogen (N) and phosphorus (P) fertilizers, and glyphosate-based (C3H8NO5P) herbicides, can cause nutrient loading and widespread water quality degradation to both surface and groundwater (Schachtschneider et al., 2010; Hashemi et al., 2016). However, vegetation buffers between agricultural fields and watercourses can potentially slow the migration of chemicals, thus limiting agricultural pollutants into adjacent waterways (Campbell, 1999; Beltrano et al., 2013). Due to the hazardous effects of agricultural pollutants on the environment, specifically non-point source aquatic ecosystem pollution, techniques to reduce nutrient and herbicide discharge must be developed (Tesfamariam et al., 2009; Schoumans et al., 2014).

In developing countries phytoremediation has become a technology of choice for remediation projects, due to cost-effectiveness and implementation ease (Terry and Banuelos, 1999). The technique additionally takes into account the probable end-use of the site once it has been remediated (Pilon-Smits, 2005). Conventional approaches to remediation often produce infertile soil by destroying the microenvironment (Kennen and Kirkwood, 2015). Additionally, knowledge regarding the phytoremediatory capabilities of individual plant species is limited. Since plants and soils respond differently when exposed to polluted water, it is essential to determine the independent remediation efficiencies of these media. For effective phytoremediation, the implemented system needs to be capable of remediating pollutants without displaying ecosystem invasive properties. It is for this reason that the phytoremediatory capabilities of individual plant species and a vegetative community as a whole need to be studied.

The purpose of this paper is to demonstrate the design and construction of a laboratory phytoremediation system, capable of establishing the performance of individual plant species and vegetative communities, by investigating agricultural pollutant remediation. In assessing the remediation performance of multiple plants per growth silo, the important role of biodiversity in vegetative assemblages is highlighted. The efficacy of the proposed laboratory method design is tested by comparing the pollutant extraction capabilities of individual, multiple-indigenous and alien wetland plant species commonly used for phytoremediation.



Experimental design

To evaluate the pollutant extraction capabilities of plant species, the system was required to integrate 5 influent pollutants across multiple growth silos whilst guaranteeing uniform standardized influent irrigation throughout. Growth silos were constructed from polyvinyl chloride (PVC) piping, each containing a threaded slit drainage pipe that protruded from the sealed base of each silo - enabling effluent collection into sampling containers directly below. Silos for individual plants and larger growth silos for multiple species (indigenous and alien) within a singular silo were constructed from PVC, to test the overall contribution of phytoremediation (Fig. 1). The larger silos accommodated 4 plant species within each silo. Voids were cut along the length of the silos for the establishment of plants at different intervals. At each void a plant species was introduced. The quantity per plant species introduced depended on the surface cover associated with that species, for instance, quantity of grasses to be introduced was greater than the quantity of sedges. For each growing compartment (void) a roof-like structure was inserted, ensuring soil stability and preventing collapsed media and pollutants cascading onto and potentially harming the plants.

An equivalent growth medium volume between different silo sizes ensured that the effect of degradation and adsorption by soil was consistent. It is important to acknowledge the remediation effect of soil media within the different silo sizes, thus soil controls associated with the different silo sizes were included.

Soil growth volume calculation

For the individual plant per silo experiment, the silo dimensions were selected to represent a growth volume (V) capable of supporting rhizosphere processes and plant root growth. Considering that only a portion of the silo is used as growth medium and the rest as a layer of natural filtration, the selected growth medium height for individual species per silo was 30 cm (Fig. 2).

The selected soil growth volume for all plants was standardized at 2 548.46 cm3 (Fig. 2). Growth volume for each plant species was consistent throughout. The soil control volume of the multiple plant species silo combined the soil growth volume of the individual plant species per silo, adapted to represent a combination of four species. As voids were created to allow for efficient plant growth, the growth volumes were adjusted to accommodate the areas lost by the voids.

Drainage layers

Paired drainage layers comprising of coarse sand and gravel were added below the soil growth, to cover the drainage pipe. These drainage layers prevented sedimentation within the slits of the drainage pipe, preventing clogging of the effluent runoff. The thickness of the drainage layers was comparable with previous urban drainage studies (Bratieres et al., 2008; Read et al., 2008; Milandri et al., 2012)Australia, to test the performance of stormwater biofilters for the removal of sediment, nitrogen and phosphorus. The aim of the study was to provide guidance on the optimal design for reliable treatment performance. A variety of factors were tested, using 125 large columns: plant species, filter media, filter depth, filter area and pollutant inflow concentration. The results demonstrate that vegetation selection is critical to performance for nitrogen removal (e.g. Carex appressa and Melaleuca ericifolia performed significantly better than other tested species.

Soil utilized as growth medium

The soil growth medium was selected to reflect the natural conditions for plant root growth and pollutant adsorption. The use of the soil type which the plants under study are naturally accustomed to alleviates stress during plant extraction and transplantation. For similar studies it is recommended that soil be included which is associated with the plant species under study, as rhizosphere-condition familiarity would minimise the acclimatisation period (Bunt, 2012).


An automated irrigation system was installed to ensure a consistent irrigation regime with frequency of 72 h. The system contained three submersible pumps, one for each of the three treatments (fertilizer, herbicide and municipal control), submersed within their respective storage containers (Fig. 3). The capacity of each container was 45 L; with each container fitted with an external clear pipe marked to indicate the volume of the solution within. The municipal control container was fitted to a municipal tap to refill the water volume as the submersible pump transported solution to the system. The capacity within the container was controlled by a domestic toilet flow inlet control valve connected to a float ball, to ensure a constant water level. Each submersible pump transferred the water from their respective storage containers using 15 mm irrigation pipes attached to 35 treatment silos via drippers; each pipe was fitted with an Emjay filter to remove any material that may impede the flow.

Added storage tanks were constructed for the herbicide and fertilizer containers. Two 70 L storage tanks were included in the study to increase the mixed herbicide and fertilizer solution capacities. The tanks were placed above the experimental set-up on scaffolding, to allow transport of fluid to the 45 L containers below, containing the submersible pumps, by gravitational flow. The solution was transported to the submersible pump containers by 15 mm irrigation lines, controlled with internal valves, with the ability to impede the flow when maintenance on the submersible pump containers was required.

The capacity of the submersible pump containers was controlled by attaching the irrigation inflow, from the storage tanks above to a domestic toilet flow inlet control valve. The valves were connected to float balls. This ensured consistent irrigation of solution volume into the growth silos. Drippers of dissimilar irrigation rates were used for the different silo sizes, 870 mL/h and 2 070 mL/h for the smaller individual plant species silos and the larger multiple plant species silos, respectively.

Treatments consisted of municipal tap water (as the control), a fertilizer solution and a herbicide solution. The solutions were mixed prior to each treatment cycle. Each container housed an additional submersible pump, to prevent stagnation and ensure complete dispersion of nutrients and chemicals. After transplantation, the plants received municipal tap water for 6 months, this period allowed the plants to acclimatize to their new growing conditions. The process was mitigated by establishing similar environmental growing conditions, with the utilization of soil excavated from the selected field study site. The duration of the acclimatization period granted the species sufficient time to develop into stronger, more mature plants and, if any adverse impacts resulting from plant transplantation and translocation were experienced, time to recuperate.


An indoor laboratory phytoremediation system is prone to irregular natural light, limiting uniform plant growth, thus artificial lighting was provided to produce a uniform distribution. The lighting was distributed by fluorescent tubes via Osram Biolux lamps due to their wavelength distribution comparable to sunlight (Osram, 2018). Eight 58W Biolux tubes were mounted throughout the system, placed at specific locations to ensure uniform light distribution. The fluorescent lights were controlled by a mechanical timer, switching the lights on and off according to a programmable schedule. The timer was programmed to display light between 05:30 and 20:00, to reflect natural growing conditions.

Plants for phytoremediation

Plant species vary with regard to their pollutant removal abilities, with the most effective plant species characterised by long roots, deep root depth, and heavy root mass (Read et al., 2010). The introduction of certain plant species for phyto-extraction may, however, pose a set of alien invasive problems, necessitating investigation into the removal efficiencies of plant species indigenous to contaminated areas (Schachtschneider et al., 2010; Leguizamo et al., 2017). For this reason, indigenous South African plant species were included for plant-based biofilter investigation. Various phytotechnologies utilize different plant properties and typically implement different plant species for each scenario. Properties that have been accepted as advantageous to phytoremediation are: fast growing, high biomass, competitive and high tolerance to pollution (Pilon-Smits, 2005).

The pollutant removal efficiency of indigenous plant species and invasive alien plant (IAP) and Palmiet species (commonly used in phytoremediation) were investigated and compared. A sample of indigenous plants displaying non-invasive properties, and being potentially capable of remediating pollutants with either matching or superior efficacy to the IAP, and Palmiet species are tested. The implementation of these species as phyto-extractors, rather than their potentially invasive counterparts, benefit biodiversity conservation initiatives.

Plant collection and transplantation

The plant species were all either removed from areas where they naturally occur, from drainage canals and catchments, or sourced from nurseries in the Western Cape, South Africa. During the transplantation process, special attention was given to remove all visible foreign organic matter from the soil. This limited the contribution of any external factors during the phytoremediation process, ensuring equal conditions throughout the growth silos. Immediately following transplantation, the specimens received municipal tap water irrigation for 6 months, allowing time to mature and adjust to growing conditions. Thereafter, the plants received standardized contaminated fertilizer and herbicide water treatments. From Fig. 4, the individual plant species experiment and alien vs. indigenous community comparison experiment layout is depicted in the constructed laboratory system. Species locality was selected to minimise the effect of a dominant neighbour, creating a canopy and hindering light distribution, further inhibiting uniform growth.


The dosing concentrations of nitrogen (N) and phosphorus (P), viz. 46.376 mg/L and 17.391 mg/L, were selected as a result of recommendations from the Department of Agriculture, Forestry and Fisheries (DAFF) for site-specific agricultural practices affecting the watercourses under study (DAFF, 1996). Three analytical grade compounds were used to create a fertilizer mixture similar to the recommended products. In commercial fertilizers ammonia (NH4) and nitrate (NO3) is generally the source of N with phosphate (PO43) the source of P. N and P are represented by NH4Cl + KNO3 and K2HPO4 respectively. The concentration of the analytical grade compound is calculated from the initial 46.376 mg/L N and 17.391 mg/L P.



A glyphosate-based herbicide was selected to represent the agricultural herbicide pollutant on a basis of relevance, as a result of its popularity in the agricultural sector. The agricultural practices under study apply Springbok 360 SL, a product of Arysta LifeScience, prior to planting crops and after a rainfall event. Two glyphosate concentrations were selected for this study, viz. 0.7 mg/L glyphosate and 225 mg/L glyphosate; representing a non-toxic contamination to aquatic ecosystems and a worst-case scenario acute contamination, respectively.

Contamination treatment

After the initial 6-month water irrigation, the pollutant treatments commenced. The irrigation regime, every 3 days, was based on the saturation and permeability of the growth silos. A dosage of 0.653 L/3-day and 1.553 L/3-day, for the individual plant species per growth silos and multiple plant species per growth silos, respectively, was regarded as the optimum volume and rate for irrigation. Every 10 days the influent solutions were drained and replaced with a fresh mixture of pollutants, thus hindering the effect of pollutant degradation in the storage tanks.

Sampling process

Samples were collected on 5 occasions during the study. The first round of sampling was for the purpose of examining the baseline nutrient concentrations. This determined the nutrient concentrations within the growth silos prior to treatment. The baseline determination allowed for precise comparison between influent and effluent water. The percentage removal by all specimens was compared as influent concentrations were premixed to known standardized concentrations and baseline concentrations were known. Sampling intervals ensured sufficient time for previous dosage solutions to percolate through the column, ensuring influent removal analyses were not duplicated over the duration of the experiment. Sampling time correlated with the proposed irrigation schedule for agricultural activities applicable where the plants are distributed (DAFF, 2016).

Treated effluent water was collected with collection containers directly below the drainage pipes of each growth silo. Two plants per species received treatment, establishing experimental duplication and reducing outlier influence. The effluent solutions were collected in 90 mL specimen containers, with twin plant species' effluent solutions mixed post effluent collection.


In order to evaluate the efficacy of the experiment's pollutant removal, various water quality parameters were measured throughout the experiment. These include pH, dissolved oxygen (DO), electrical conductivity (EC), nitrogen in ammonia (NH3-N), nitrogen in nitrate (NO3-N), phosphorus in orthophosphate/soluble reactive phosphorus (PO43-P/SRP) and glyphosate (C3H8NO5P).

The pH, DO and EC were measured using the HQ440d Benchtop Multi-Parameter Meter manufactured by Hach. The NH3-N, NO3-N and PO43-P concentrations were measured colorimetrically using the DR3900 Benchtop Spectrophotometer and their associated TNTplus test kits. For glyphosate analysis, the acuity ultra-performance liquid chromatography (UPLC) was coupled to a Xevo Triple Quadrupole Tandem Mass Spectrometer (MS/MS) and used for high-resolution UPLC-MS/MS analysis (Waters, 2018). Glyphosate was further separated by multiple reactions monitoring (MRM) using electrospray ionisation in a positive mode.



The experimental design allows for the comparison of chemical removal in vegetated silos and that of the soil medium control. This allows for the determination of the relative chemical absorption by both the plant and soil components. The system further indicated that there is potential to rather integrate the indigenous plant species as an alternative to their alien counterparts currently used in local and international constructed wetlands, SuDS and biofiltration systems.

In evaluating removal efficiencies (the difference between the influent and effluent concentrations), baseline concentration values need to be known. The baseline values indicate the initial nutrient content within the growth silos prior to the addition of pollutants. Without this information, one cannot deduce the removal efficiencies of the system. The initial baseline concentration of every growth silo was measured before contaminants were added to the system. Baseline concentrations were deducted from the effluent concentrations to allow for the calculation of percentage removal for each sampling round. The following equation was used:


Influent conc. = Influent concentration (mg/L)

Effluent conc. = Effluent concentration (mg/L)

Baseline conc. = Baseline concentration (mg/L)

The Kruskal-Wallis H-test, non-parametric ANOVA, was used for the evaluation of Renosterveld phytoremediation vs. unvegetated soil; thereafter a Student's t-test was used for the evaluation of multiple indigenous wetland plant species vs. multiple IAP species and Palmiet. Statistical analyses were executed in Python by means of the data analytical library.

Phytoremediation versus unvegetated soil

Confirming the remediation capabilities of vegetation, the concentration of pollutants removed by individual plant species was compared to the pollutants removed by the unvegetated soil silos. The percentage pollutant removal is depicted on the vertical axis as a function of time, indicating days of sampling. The initial baseline nutrient and herbicide concentrations were taken into consideration to allow for comparison between influent and effluent. Figure 5 compares the average percentage removal of all nutrients (NH3-N, NO3-N and PO43-P/SRP) for vegetation and the unvegetated soil control for the duration of the experiment.



The plant species all reduced the effluent concentration of the nutrients. The percentage removal averaged 85.75%, 86.62% and 87.78% for NH3-N, NO3-N and PO43-P (SRP) respectively. The average percentage nutrient removal in Fig. 5 indicates that the vegetation, on average, was more effective in the removal of nutrient pollutants than was soil, attributed to the phytoremediatory capabilites of plants. There was no obvious difference between NH3, NO3 and PO43 remediation within vegetation, whereas considerable percentage nutrient removal variation existed between vegetated and unvegetated media.

Similar to the fertilizer nutrients, the vegetation removed a greater percentage of both 0.7 mg/L glyphosate and 225 mg/L glyphosate, compared to soil only. Although, from Fig. 6, percentage removal of the unvegetated soil was comparatively high, it is evident that vegetation more effectively remediated pollutants at both glyphosate concentrations. The percentage removal of the soil control dropped with time, indicating herbicide accumulation in the absence of vegetation, resulting in increased leaching and transportation of glyphosate, whereas vegetative performance remained constant. Environmentally, glyphosate leachate results in increased agricultural pollution of adjacent freshwater aquatic systems.



Indigenous versus alien plants plus Palmiet

In comparing the removal efficiencies of indigenous species and alien species currently implemented locally and internationally, the feasibility of replacing alien plants with local species was tested. Plants of similar physiology were selected for community comparison. The indigenous wetland species selected for this test included: Phragmites australis, Cyperus textilis, Typha capensis and Cynodon dactylon, which can be found in Renosterveld vegetation regions among others. The alien species were: Canna indica, Arundo donax and Pennisetum clandestinum. Prionium serratum, a South African indigenous plant species, is included with the alien assemblage due to its aggressive growth properties and absence in the agricultural area of interest in this study.

Figure 7 represents the mean percentage pollutants removed between the indigenous samples, the alien samples with Palmiet and the unvegetated soil control. It was inferred that there was no evidence that suggested one sample group to be more effective in removing pollutants than the other. It was however evident that both the Renosterveld and alien assemblages were more effective in the removal of pollutants than the unvegetated soil control. These comparative removal abilities of the two plant groups show the indigenous plant group to be as effective as internationally used species. This therefore supports the recommendation of rather implementing indigenous plants than their more invasive counterparts for remediation in sensitive contaminated sites.




The designed system allowed for the evaluation of the phytoremediatory capabilities of selected plant species. This design and the use of the multiple plant silo allows for the comparison of indigenous species efficacy with that of alien plants commonly used for phytoremediation. The findings produced by the experimental system are comparable with literature from previous local and international studies, indicating the system accurately measures phytoremediatory capabilities of plant species. The system has been specifically designed to evaluate individual plant species of varying physiology and may thus be used to analyse species occupying different habitat types, i.e., wetland or dryland. Plant species can be successfully evaluated in terms of bioremediation capabilities, with the opportunity to incorporate different soil types (growth media) and pollutants. However, silo width plays a significant role, where pollutants are intercepted by a dense root system more effectively. The contaminants bind to the root structure and cell walls and hemicellulose within the cell wall and bind hydrophobic organic chemicals. The system is not limited to seasonal variability and conditions, granting the researcher the freedom to analyse pollutant remediation throughout the year. Further, proven effective plant species need to be investigated in a field setting, and with a cost analysis included.



This research was funded by The Rufford Foundation (UK), with project identity: [23296-1].



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Received 1 October 2018
Accepted in revised form 26 September 2019



* Corresponding author, email:




Transport of pore-water oxygen with/without aeration in subsurface wastewater infiltration system



Siqi Wang; Yinghua Li*; Haibo Li; Lei Yang

School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China




In this study, three subsurface wastewater infiltration systems (SWISs) at different aeration were set up to study the transport of pore-water oxygen and quantify the amount of trapped gas. Bromide and dissolved oxygen were introduced into SWISs as partitioning tracer and non-partitioning tracer, respectively. A model named CXTFIT based on the convection diffusion equation was used to describe the shape of breakthrough curves for bromide and dissolved air in column experiments. In CXTFIT code, the parameter β obtained from the bromide test ranging from 0.2940 to 0.7600 indicates that the physical non-equilibrium model was relatively suitable for dissolved air transport. Retardation factors obtained by CXTFIT code indicate 2-20% porosity filled with gas. Tracing the transport of air and determining the percentage of porosity filled with trapped gas has lain a foundation for further study on gas clogging in SWISs.

Keywords: gas-partitioning tracer, convection diffusion equation, subsurface wastewater infiltration system, dissolved air transport, gas clogging




Subsurface wastewater infiltration systems (SWISs) are effective wastewater treatment methods due to the integrated mechanism of chemical, physical and biological reactions, as wastewater passes through the unsaturated soil in SWISs (Jiang, 2017). SWISs are widely used due to their low operation cost, easy maintenance, and low energy consumption (Li, 2017b).

Unfortunately, SWISs have the disadvantage of poor performance if clogging occurs. Clogging can be divided into four types: physical clogging (caused by suspended solids) (Yang, 2018; Alem, 2015); bioclogging (caused by accumulated biofilms) (Newcomer, 2016; Hua, 2014); chemical clogging (caused by chemical materials such as carbonate and iron oxide ) (Larroque, 2011; Weidner, 2012); and gas clogging (caused by gas bubbles) (Heilweil, 2013). Currently, researchers are devoted to the study of physical clogging, bioclogging and chemical clogging, but little is known about gas clogging. Gas bubbles can block pore throats, increasing the resistance to flow and reducing permeability (Martin, 2013). To study gas clogging, the sources and characteristics of gas in SWISs need to be understood.

The presence of gas bubbles in SWISs has been attributed to the biogenic gas and entrapped air. Previous studies have investigated biogenic gas. Nitrogen and nitrous oxide are produced by nitrification and denitrification. The impacts of influent loadings, drying-wetting cycles and carbon-nitrogen ratio on nitrous oxide emission and spatial distribution of nitrous oxide have been studied by Ying-Hua Li (Li, 2017a; Li, 2018). N2O conversion rate decreased with an increase in hydraulic loading (HL) but increased with increasing pollutant loading (PL) (Li, 2017a). A moderate carbon-nitrogen ratio leads to an increase in N2O emission rate and the 0-75 cm depth layer was the main contributor to N2O emissions (Li, 2018).

However, there is limited knowledge on the pathways of entrapped air transportation. Dissolved gases carried with the influent were a major source of entrapped air bubbles. Compared to a saturated system, dissolved gases are more inclined to separate from the influent and penetrate into small pores in an unsaturated system like a SWIS. Entrapped air bubbles are no longer connected to the atmosphere, occurring in the form of small, immobilized, disconnected bubbles (Heilweil, 2013). By reducing the effective (quasi-saturated) hydraulic conductivity, entrapped air bubbles impact water flow and solute and contaminant transport (Marinas, 2013). In order to research entrapped air, a gas-partitioning tracer test was introduced to distinguish between two fluid phases. The gas-partitioning tracer test was initially used to determine residual oil saturation by the petroleum industry (Tang, 1991a; Tang, 1991b). This has been applied more recently for the movement of air in the non-saturated zone and to measure air-filled porosity. Both a partitioning tracer and a non-partitioning tracer are injected simultaneously with influent and measured in effluent. A partitioning tracer, the low-solubility dissolved gas, will partition to the gas phase partially while a non-partitioning tracer stays in the liquid phase, which results in a partitioning tracer travelling at a lower velocity than a non-partitioning tracer. Common partitioning tracers are oxygen (Fry, 1996) and noble gas (e.g. helium, neon and argon) (Burnard, 2013; Reid, 2013). Common non-partitioning tracers are chloride and bromide. A series of transport experiments showed that the presence of small amounts of entrapped gas in the pore space could result in retardation factors for dissolved oxygen (Fry, 1995). The retardation factor is interpreted as the ratio of groundwater velocity to dissolved gas velocity and Fry reported retardation factors for dissolved oxygen varying from 1.0 to 8.0 (Fry, 1995). Higher retardation factors indicate more entrapped air. Heilweil used retardation factors obtained from a gas partitioning tracer to quantify entrapped gas in an infiltration experiment and indicated that 7% to more than 26% of the porosity was filled with gas (Heilweil, 2004). Vulave (2002) used dissolved Kr and SF6 gases to determine hydrogeological parameters. Through there were lower aqueous diffusion coefficients for SF6 than that for Kr, both Kr and SF6 can be used with confidence to delineate and validate aquifer characteristics. It is noted that there is a special gas-partitioning tracer test referred as the 'Push-Pull' test (PPT). In the 'Push-Pull' test, a partitioning tracer and a non-partitioning tracer are injected ('pushed') into a porous media and then extracted ('pulled') in situ. Reid exploited the 'Push-Pull' test to research dissolved gas dynamics in wetland soils (Reid, 2015).

In this study, laboratory experiments were conducted, combined with numerical simulations, with the aim of achieving better understanding of the transport of dissolved air under various aeration conditions and quantifying the amount of trapped gas in SWISs. Tracing the transport of air and determining the percentage of porosity filled with trapped gas has lain the foundation for further studies on gas clogging in SWISs.



System description

The soil column made of plexiglass was constructed (height 180 cm and internal diameter 29 cm) and operated indoors. The 145 cm high substrate was built on top of 5 cm of gravel (0.6-0.9 cm, diameter). The substrate was evenly mixed with sand, slag and farmland soil at a ratio of 1:2.5:6.5. The porosity of the substrate was 55.0%. The gravel at the bottom was used to support the infiltration system, evenly distribute the treated water and prevent outlet blockage. Influent from the water tank to the column was pumped with a peristaltic pump and distributed via a 2-cm-diameter perforated water distribution pipe placed at 65 cm depth under the soil. The water was purified by the substrate and finally collected at the outlet. Rhizon soil solution samplers were placed at 40, 70, 100 and 130 cm depths (Fig. 1).




Artificial domestic wastewater was prepared with tap water including glucose, ammonium chloride, potassium nitrate, sodium nitrite and monopotassium phosphate. The parameters of the wastewater were as follows: COD 330.21 ± 7 mg/L, NO3-N 3.12 ± 0.1 mg/L, NO2-N 0.40 ± 0.004 mg/L, NH4+-N 49.08 ± 0.6 mg/L, TP 4.0 ± 0.3 mg/L. The domestic wastewater was treated in different ways to form the following 4 kinds of water:

Degassed water (DW): The domestic wastewater was filtered through a degasification membrane. DO concentration was 2.0 mg/L.

Non-aerated water (NW): The domestic wastewater without any treatment. DO concentration was 6.0 mg/L, the same as that of tap water.

Micro-aerated water (MW): The domestic wastewater was continuously aerated by a 45 L/min aeration pump. DO concentration was 9.1 mg/L. Gas bubble content was 3.6%. Gas content was measured following the method of Du (2016).

Aerated water (AW): The domestic wastewater was aerated continuously by a 70 L/min aeration pump. DO concentration was 9.2 mg/L. Gas bubble content was 6.3%.

Gas-partitioning tracer test operation

Bromide tracer test

Phenol was added to the four types of water (DW, NW, MW and AW) as a biocide to prevent biological consumption of oxygen. Phenol concentration was 1%. When the operation of the system was steady, DW was dosed in the SWIS for the first time for several days to maintain the same initial conditions. Table 1 shows measured DO concentrations after DW was injected for several days. Potassium bromide (KBr) was added to the influent and Br concentration was 10 g/L. The influent with KBr was injected for 4 h via a peristaltic pump. Then DW was introduced to the SWIS for the second time for about 4 days to discharge all KBr. The hydraulic loading was 0.14 m/d. Water samples were collected at 40, 70, 100, 130 and 150 cm depths. KBr was measured to get breakthrough curves at different depths. KBr, a non-partitioning tracer without retardation, was used to select an adaptive model and parameters for the transport of DO.



Oxygen tracer test

Instead of the influent with KBr, NW, MW and AW were injected for 4 h after dosing DW for the first time. All other experimental procedures were the same as for bromide tracer test. With the model and parameters acquired from bromide tracer test, DO concentrations were measured to get breakthrough curves at different depths and various aerations. Aeration with air was used to increase air content, i.e., increase oxygen content; the transport of DO represents the transport of air.

Numerical simulations

CXTFIT code was developed by the U.S. Salinity Laboratory. Based on the convection-dispersion equation (CDE), CXTFIT code is a model developed to estimate parameters in equilibrium and non-equilibrium transport from laboratory or field tracer experiments (Toride, 1995). CXTFIT allows for analyses of concentration distributions versus time as well as depth, and permits the use of the equilibrium and non-equilibrium transport formulations (Van Genuchten, 2012).

In CXTFIT code, the dimensionless form of equilibrium transport according to the convection-dispersion equation (CDE) is written as:

where R is retardation factor, Cr is the dimensionless solute concentration, T is the dimensionless time, P is the Peclet number, Z is the dimensionless distance. The following equations define the dimensionless parameters:

where c is the measured concentration (g/L or mg/L), c0 is the initial concentration (g/L or mg/L), x is distance (m), L is the characteristic length (m) representing the column length, D is the dispersion coefficient (m2/d), t is time (d), v is the average pore-water velocity (m/d).

In CXTFIT code, the non-equilibrium transport includes chemical and physical non-equilibrium processes. The chemical non-equilibrium can be described by a two-site non-equilibrium model. The two-site non-equilibrium model divides adsorption sites into instantaneous equilibrium adsorption sites and non-equilibrium adsorption sites governed by first-order kinetics. The two-site non-equilibrium model reduced to the dimensionless form can be written as:

where β is a partitioning coefficient, ω is a dimensionless mass transfer coefficient, and the subscripts 1 and 2 represent the (mobile) liquid phase and the (immobile) trapped gas phase, respectively.

The physical non-equilibrium can be described by a two-region non-equilibrium model. The two-region non-equilibrium model supposes that the liquid phase can be partitioned into mobile and immobile regions. Mass transfer between the two regions is simulated as a first-order process. The two-region non-equilibrium model reduced to the dimensionless form can be written as:

In this study, the equilibrium and physical non-equilibrium model in CXTFIT code was used to simulate breakthrough and elution curves of KBr and DO in SWISs. CXTFIT code uses a non-linear least-square optimization approach based on the Levenberg-Marquardt method to estimate unknown parameters (Toride, 1995). R is fixed as 1 due to no retardation for non-partitioning tracers such as bromide. For equilibrium transport, CXTFIT code is used to fit D and v. For non-equilibrium transport, v is the set value, CXTFIT code is used to fit D. Partitioning tracers have same D and v with non-partitioning tracers. For partitioning tracers such as oxygen, D and v are fixed to be the same as for non-partitioning tracers. CXTFIT code is used to fit R in equilibrium transport and R, β as well as ω in non-equilibrium transport.

Sampling and analytical methods modification

Water sampling was done at 40, 70, 100 and 130 cm depths via Rhizon soil solution samplers and effluent was collected at 150 cm depth.

Bromide was measured via the phenol red spectrophotometric method (Tomiyasu, 1996). The phenol red spectrophotometric method is suitable for low concentrations nd water samples should be diluted. DO concentrations were measured using a dissolved oxygen meter.

Method to quantify trapped gas

Fry put forward the following equation to describe the retardation factor (Fry, 1995):

where H is dimensionless Henry's law constant, Vg is volume of trapped gas per volume of pore space and Vw is volume of water per volume of pore space.

With R from CXTFIT code and dimensionless Henry's law constants for oxygen from Fry's research (Fry, 1995), the percentage of gas-filled porosity can be obtained by the following equation:

where θg is the per cent gas-filled porosity.



Transport of bromide

Figure 2 shows bromide transport at different depths in SWISs. The peak measured bromide concentration reached 7.83 g/L (C/C0 = 0.783) at 4 h (T = 0.028) and 0.7 m from the surface, due to the nearby water distribution pipe placed at 0.65 m depth under the soil. After bromide was injected for the first 4 h, the measured bromide concentration decreased rapidly to zero at 0.7 m from the surface. Under capillarity action, water travelled upward and the peak measured bromide concentration reached 1.07 g/L (C/C0= 0.107) at 6 h (T = 0.042) and 0.4 m from the surface. Subsequently, water moved down along the column. The time to reach peak measured bromide concentration was 12 h (T = 0.085), 32 h (T = 0.226) and 58 h (T = 0.410) at 1.0, 1.3, 1.5 m from the surface, respectively. Except for 0.4 m from the surface, the peak measured bromide concentrations decreased with increasing depth.



The bromide tracer test not only showed the transport of non-partitioning tracer, but was also used to choose a suitable model and parameters for the transport of DO. The constraint for the chemical non-equilibrium model is that 1/R β 0.9999. Because R is fixed as 1 in the simulation of non-partitioning tracers, the chemical non-equilibrium model is unfit for the transport of bromide. As shown in Fig.2, the equilibrium and physical non-equilibrium model were exploited to simulate bromide transport. Table 2 presents the fitted model parameters in the equilibrium and physical non-equilibrium model. Both the equilibrium and physical non-equilibrium model didn't fit bromide transport at 0.4 m. Compared with the equilibrium model, the physical non-equilibrium model was more suitable for bromide transport due to higher r2. In the physical non-equilibrium model, the simulation was strong and significant with r2 > 0.8 at 0.7, 1.0, 1.3, 1.5 m under the soil while it was very weak at 0.4 m under the soil (CXTFIT code didn't obtain a correlation coefficient). The highest dispersivity (0.01488 m) at 0.7 m under the soil was probably due to water distributed nearby, which meant much more complicated water flow conditions than other positions. The dispersivity increased from 2.712 × 103 to 7.280 × 103 m with depths from 1.0 to 1.5 m under the soil. The partitioning coefficient, β, determines the distribution of soil water between mobile and immobile regions. Through water in immobile regions doesn't move, mass exchange between mobile and immobile regions is carried out by molecular diffusion. β ranged from 0.2940 to 0.7600 (except for 0.4 m under the soil), which meant that mobile regions accounted for 0.2940 to 0.7600 of the soil water and the other was immobile regions, which also had a wide range of values. A wide range of immobile regions indicates that immobile regions played an important role in bromide transport and the physical non-equilibrium couldn't be neglected. The asymmetric breakthrough of bromide also proved the importance of immobile regions. Therefore the physical non-equilibrium model was shown to be suitable for bromide transport. With v and D obtained by bromide transport in Table 2, the physical non-equilibrium was used to simulate oxygen transport.



Transport of dissolved air in NW

Figure 3 shows the transport of oxygen in NW. DO concen-trations greater than 2.0 mg/L commonly refer to aerobic environments, while less than 0.2 mg/L represents anaerobic environments (Pan, 2016). DO concentration was 5.0 mg/L at 0.7 m under the soil, attributed to dissolved air carried by influent. DO climbed up first and then flowed down along the water's pathway. Without biological consumption of oxygen, DO could reach as deep as 1.0 m under the soil. All DO concentrations stabilized to be about 0.9 mg/L after 30 h, indicating that the system with NW was under an anoxic environment. DO concentrations remained at about 0.9 mg/L at 1.3 and 1.5 m under the soil for the whole run, revealing that DO couldn't run through the whole column due to dissolved air partitioning to gas phase. The time of the peak measured DO concentrations was at 7 h, 6 h, and 14 h at 0.4, 0.7, and 1.0 m under the soil. Comparing the breakthrough curves of DO at 0.4, 0.7 and 1.0 m under the soil with bromide, the time of peak measured DO concentrations was later than peak measured bromide at 0.4, 0.7 and 1.0 m under the soil, which is referred to as retardation.



The simulated DO breakthrough curves and model parameters are presented in Fig. 4 and Table 3, respectively. Retardation factors ranged from 1.5-7.8 indicating 1.6-18.3% pore space was filled with gas. 1.6-18.3% of the pore space was filled with gas, mainly occurring at 0.65-1.0 m under the soil. DO couldn't run through the whole column, so R at 1.3 m and 1.5 m couldn't be obtained, thus pore space filled with gas at 1.3 m and 1.5 m couldn't be determined. A possible explanation for the low percentage of gas-filled porosity at 0.7 m under the soil is that the influent was distributed nearby and drove away the gas. With water moving down along the column, the dissolved air in water would gradually partition to the gas phase, resulting in higher gas-filled porosity at a deeper position.





In oxygen tracer tests, C and C0 was obtained by measured values minus the background values in Table 1.

Transport of dissolved air in MW

Breakthrough curves of DO in MW at different depths are shown in Fig. 5. The peak measured DO concentrations in MW were higher than NW at all depths. The low DO concentrations were measured at 48-49 h and 1.3 m under the soil. That deeper DO reach position can be explained by higher initial DO concentrations due to aeration.



The simulated DO breakthrough curves and model parameters are presented in Fig. 6 and Table 4, respectively. Although DO concentrations were measured at 1.3 m under the soil, the measured values were too few to obtain a simulated curve. Retardation factors ranging from 1.4-8.1 indicate 1.3-23.4% pore space filled with gas.





Transport of dissolved air in AW

Breakthrough curves of DO in AW at different depths are shown in Fig. 7. Compared to the breakthrough curves of DO in NW, MW and AW, breakthrough curves showed a similar shape and tendency. Both peak measured DO concentrations in MW and AW were much higher than in NW, mirroring that aeration improved the aerobic and anaerobic conditions in the substrate. The peak measured DO concentration at 0.7 m under the soil in AW was almost the same as in MW. But the peak measured DO concentration at 1.0 m under the soil in AW was a little higher than in MW, which is probably due to the higher gas content in AW than in MW. DO concentrations in MW and AW were both measured at 1.3 m under the soil, reflecting that higher DO concentrations could cause deeper breakthrough positions.



The simulated DO breakthrough curves and model parameters are presented in Fig. 8 and Table 5, respectively. Simulations of NW, MW and AW produce better model fitting at 0.7 m under the soil and relatively worse fitting at 1.0 m under the soil. This is probably because water climbing to 0.4 m under the soil moved down to 1.0 m under the soil and affected the simulation effect. At 0.7 m under the soil, the measured DO concentrations were relatively high and water climbing to 0.4 m under the soil had little effect. Retardation factors indicate 1.9-17.1% of porosity filled with gas. Retardation factors remained similar at various aerations. Two possible causes can explain this phenomenon: (i) When gases in dissolved air (e.g. nitrogen, oxygen, carbon dioxide, noble gas) partition to the gas phase, gases in the gas phase (e.g. nitrogen, nitrous oxide, methane, nitrogen dioxide) partition to the liquid phase simultaneously. Gas exchange between the gas phase and the liquid phase reach equilibrium, keeping almost unchanged the percentage of porosity filled with gas. (ii) Gases in dissolved air partitioning to the gas phase move with the mobile water, resulting in a stable percentage of porosity filled with gas.






CXTFIT code was first used to study dissolved air transport in SWISs. The parameter β, ranging from 0.2940 to 0.7600, indicates that immobile regions crucially affect solute transport. So the physical non-equilibrium model was suitable for DO transport.

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