Evaluation of modifications to a physicochemical method for determination of readily biodegradable COD

 

 

A Escalas-Cañellas^I,II,*, MA Ortiz-Balderas^II; MG Barajas-López^I,II

^ICentro de Investigación y Estudios de Posgrado, Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Av. Dr. Manuel Nava 8, Edificio P, Zona Universitaria, C.P. 78290, San Luis Potosí, SLP, Mexico
^IIPrograma Multidisciplinario de Posgrado en Ciencias Ambientales, Universidad Autónoma de San Luis Potosí, Av. Dr. Manuel Nava 201, 2do piso, Zona Universitaria, C.P. 78210, San Luis Potosí, SLP, Mexico

 

 

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ABSTRACT

In the Mamais-Jenkins-Pitt method for determination of readily biodegradable COD (S_S), 2 alternatives were proposed for the intermediate determination of soluble inert COD (S_I). When a full-scale treatment plant exists, influent S_I = effluent truly soluble COD. When there is no full-scale plant, then the truly soluble COD of the effluent of a 24 h fill-and-draw batch reactor treating the wastewater is taken as influent S_I.

In this study, both S_I methods were statistically compared on 24 wastewater samples from 2 municipal wastewater treatment plants (WWTPs). While average S_I obtained for the 2 methods was the same, individual samples usually had very different S_I values. In fact, virtually no correlation was found between the 2 methods. Also, the S_S values obtained using both S_I alternatives were statistically compared. A good correlation was observed, in spite of the poor S_I correlation – low, dispersed S_I values did not seriously affect the correlation between both S_S determinations. A method was proposed for determination of the limit of detection and the limit of quantification (LOQ) for both S_S methods. The LOQ resulted in 28.6 mg/l and 32.6 mg/l, respectively, for the full-scale and the laboratory-scale alternatives.

Some assumptions of the original laboratory-scale (LS) method could potentially be sources of error in S_I determination. Two modifications to the laboratory-scale method were implemented in order to avoid these potential problems: Washing biomass with tap water, and correcting S_I in the fill-and-draw reactor by the S_I of the original biomass suspension.

These method modifications were tested on wastewater samples from the mentioned WWTPs. The fundamentals and results of both modifications are discussed in this paper, as well as the imprecision associated with estimating influent S_I from effluent CODsol in all studied methods, and its impact on S_S determination.

Keywords: readily biodegradable COD, physicochemical, wastewater

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Nomenclature

ASCE	American Society of Civil Engineers
ASM1	Activated Sludge Model No. 1
ASM2d	Activated Sludge Model No. 2d
ASM3	Activated Sludge Model No. 3
BNR	biological nutrient removal
BOD	biochemical oxygen demand
BTE	batch-test effluent
COD	chemical oxygen demand
CODsol	soluble COD (Mamais-Jenkins-Pitt method)
DO	dissolved oxygen
DOC	dissolved organic carbon
EFF	full-scale plant effluent
EPA US	Environmental Protection Agency
F/M	food to micro-organisms ratio
FS	full scale
HRT	hydraulic retention time
IWA	International Water Association
LOD	limit of detection
LOQ	limit of quantification
LS	laboratory scale
LSC	laboratory-scale method correcting effluent SI by biomass SI
LSC	laboratory-scale method correcting effluent SI by biomass SI
LSTW	laboratory-scale method using tap water for biomass washing
LSTW	laboratory-scale method using tap water for biomass washing
MCRT	mean cell retention time
PPT '	Parque Tangamanga 1-B' WWTP
R2	determination coefficient
RSD	relative standard deviation
SA	fermentation products
SBR	sequencing batch reactor
SF	fermentable matter
SI	soluble inert organic matter
SLP	State of San Luis Potosí, Mexico
SS	readily biodegradable organic substrate
TSS	total suspended solids
TTV	'Tanque Tenorio-Villa de Reyes' WWTP
VML	volume of initial mixed liquor in a batch test
VSS	volatile suspended solids
VWW	volume of wastewater in a batch test
WEF	Water Environment Federation (USA)
WW	wastewater
WWTP	wastewater treatment plant
XI	particulate inert organic matter
XS	slowly biodegradable substrate

 

Introduction

Increased usage of BNR has promoted COD fractionation as a tool for wastewater evaluation and process design and control (Spérandio and Paul, 2000). IWA models for BNR use several COD fractions as state variables, so COD fractions must be evaluated for model initialisation, calibration and validation. Both ASM1 and ASM3 models consider 4 wastewater COD components, namely: readily biodegradable organic substrate (S_S), soluble inert organic matter (S_I), slowly biodegradable substrate (X_S) and particulate inert organic matter (X_I) (Henze et al., 2000). Other wastewater COD fractions are biomass-related: biomass components, biomass decay particles and internal storage products. Though biomass can account for 10 to 20% of total organic matter in wastewater, not considering it in raw wastewater would not affect the modelling considerably (Henze, 1992). In this case, biomass would be included in the slowly hydrolysable organic fraction, according to the same author. In ASM2d, S_S is split into fermentable matter (S_F) and fermentation products (S_A). However, S_S is useful for ASM2d, since S_F can be obtained as the difference between S_S and S_A. So, readily biodegradable COD is a basic and useful COD fraction, and several respirometric and physicochemical methods have been developed for S_S determination.

In IWA models, S_S and X_S fractions are set in terms of their aerobic biodegradation rates, which can be determined by respirometry. Thus, several respirometric methods have been proposed or modified for estimation of readily biodegradable COD (Ekama et al., 1986; Spanjers and Vanrolleghem, 1995; Wentzel et al., 1999; Spérandio and Paul, 2000). At the same time S_S is considered soluble, while X_S is colloidal or particulate, which allows for physicochemical determination of S_S. When compared to respirometric methods, physicochemical methods require simpler and cheaper apparatus, since they are usually based on filtration techniques. However, equivalence between both methods has been a subject of discussion. Filtration through 0.45 µm filters does not ensure the complete removal of colloidal matter, which is slowly biodegradable (Melcer, 2004). The 0.45 µm filtrate can include about 50% of X_S, while the rest is S_S and S_I (Henze, 1992). The colloidal fraction has been successfully removed by either ultrafiltration (Dold et al, 1986) or flocculation and filtration (Mamais et al., 1993; Hu et al., 2002).

The method by Mamais et al. (1993) provides a simple physicochemical technique for determination of truly soluble COD (herein referred to as CODsol) and readily biodegradable COD. In this method wastewater CODsol is determined as the COD of the filtrate obtained after sample flocculation, and filtration through a 0.45 µm filter. Then, wastewater S_I is determined as the CODsol of the effluent of an aerobic biological plant treating the studied wastewater. More precisely, 2 alternatives were given by Mamais et al. (1993) for S_I:

• To determine S_I as the effluent CODsol of a full-scale plant treating the wastewater (if a full-scale plant exists)
• In the absence of a full-scale plant, to determine CODsol as the effluent CODsol of a laboratory-scale fill-and-draw plant treating the wastewater of interest, and having a mean cell retention time (MCRT) of greater than 3 d and an HRT of 24 h.

Some assumptions of the Mamais et al. (1993) method could be potential sources of error in S_I determination and, consequently, on S_S, as discussed next.

In the full-scale (FS) alternative, it is assumed that effluent S_S is null. According to a plant survey cited in WEF-ASCE (1998), 42 activated sludge treatment plants had an annual mean effluent BOD of 15.5 mg/l (EPA, 1981) with a wide range of plant averages, namely 11 mg/l to 39 mg/l. TSS averaged 18.5 mg/l, with a wide range too. Assuming effluent BOD and TSS being roughly equal, and assuming effluent SS having a BOD of approximately 50% of their weight (WEF-ASCE, 1998), soluble BOD would reasonably average 7.5 mg/l with a range of 5.5 to 19.5 mg/l in secondary effluents of activated sludge plants. In ASM1 and ASM3, soluble BOD, being soluble and biodegradable, can only be included in the S_S fraction of COD. Though the 5.5 mg/l to 19.5 mg/l range is rather low, it can account for a considerable fraction of effluent soluble COD in municipal wastewaters, thus affecting S_I determination by the FS alternative and, consequently, S_S determination in the low S_S range. The LS test conditions (24 h HRT, longer than FS activated sludge plants) could perhaps achieve lower effluent S_S.

In this study, CODsol, S_I and S_S were determined by both the FS and LS alternatives of the Mamais et al. (1993) method, on wastewater samples of 2 municipal, activated sludge WWTPs, and the results of both alternatives were compared for S_I and S_S, in order to determine whether these methods were equivalent and, particularly, whether S_I was lower, and S_S higher, by the LS alternative.

Since influent S_I can be assumed variable, effluent S_I must be variable too, even considering equalisation effects throughout the plant. In a previous research, Escalas et al. (2003) studied dissolved organic carbon (DOC) evolution throughout a municipal, continuous-flow activated sludge plant. Though primary clarifiers provoked a clear peak reduction, and secondary treatment yielded a smooth effluent DOC curve, the effluent DOC range was still 13 mg/l to 34 mg/l over a period of one week. This is a considerable variability for soluble organic matter, which includes the inert soluble fraction. The S_I subtracted from the CODsol in the method has an intrinsic variability that has not yet been evaluated and this issue is addressed in this paper.

In the LS alternative, a wastewater with a given S_I is mixed with a biomass (mixed liquor) that has a different S_I, since mixed liquor S_I depends on the variable S_I fed to the reactor in the previous batches. This could affect S_I and S_S determination.

In the present study, the issue of S_I variability was addressed by analysing effluent CODsol variability in the effluent of the 2 WWTPs studied. In order to suppress or minimise the effect of mixed-liquor S_I on LS effluent S_I, two LS method modifications were essayed:

• To wash biomass with tap water before the LS test, in order to remove CODsol from biomass, thus suppressing the biomass interference on S_I determination (LS_TW alternative)
• To determine biomass S_I, and correct effluent S_I for this value (LS_C alternative).

 

Experimental

General experimental design

Twenty-four wastewater samples and their corresponding full-scale secondary effluents were taken at 2 municipal wastewater treatment plants in the city of San Luis Potosí (SLP), central–north highlands of Mexico: Parque Tangamanga 1-B plant (PPT) and Tanque Tenorio-Villa de Reyes plant (TTV). In a first campaign, 16 wastewater samples were taken, corresponding to low and high influent concentration at the plants. A 2² factorial design (Montgomery and Runger, 2003) with 4 replicates was applied (4x2²), with plant (TTV/PPT) and influent concentration (low/high) as design variables. Eight additional wastewater samples were taken at the PPT plant, 4 corresponding to high concentration and 4 to low concentration time bands, for a total of 24 samples between the 2 plants. A previous 24 h sampling (12 samples per plant) was performed in order to determine the time bands of low- and high-influent COD concentration. An overview of the general methodology is shown in Fig. 1.

 

 

TSS, volatile suspended solids (VSS), COD and CODsol were determined on the samples, and the following alternatives were applied for S_I and S_S determination:

FS alternative: wastewater S_I was determined as the CODsol of the secondary treatment effluent of the full-scale plant, in accordance with one of the Mamais et al. (1993) alternatives (Eq. (1) and (2)).

where:

(S_I)_FS is the un-biodegradable soluble COD according to the FS alternative
(CODsol)_EFF is the truly soluble COD at the FS plant effluent, determined by the Mamais et al. (1993) method
(S_S)_FS is the readily biodegradable COD according to the FS alternative
(CODsol)_WW is the truly soluble COD of wastewater

LS alternative: wastewater S_I was determined as the effluent CODsol of a laboratory-scale 24 h fill-and-draw reactor, as in the other Mamais et al. (1993) alternative. Equations (3) and (4):

where:

(S_I)_LS is the wastewater un-biodegradable soluble COD according to the LS alternative (DQOsol) _BTE is the truly soluble COD of the batch test effluent

LS_TW alternative: As in the LS alternative, wastewater S_I was determined from the effluent CODsol of a laboratory-scale 24 h fill-and-draw reactor. However, the biomass was first washed with tap water in order to remove the CODsol present in the mixed liquor, before mixing with wastewater for the batch test. This process results in a dilution of wastewater S_I, so a correction must be applied to S_I (Eq. (5)) before S_S calculation (Eq. (6)).

where:

(S_I)_LSTW is the un-biodegradable soluble COD of wastewater according to this method alternative
V_ML and V_WW are respectively the volumes of initial mixed liquor and wastewater used for the batch test
S_S)_LSTW is the readily biodegradable COD according to this method alternative

LS_C alternative: As in the LS and LS_TW alternative, wastewater S_I was determined from the effluent CODsol of a laboratory-scale 24-h fill-and-draw reactor (Eq. (7)), but the S_I of the initial mixed liquor was determined before addition of wastewater to the reactor (Eq. (8)), and a correction was applied to wastewater S_I determination. An S_I balance applied to the batch test can be written (Eq. (9)), which allows calculating the corrected wastewater S_I (Eq. (10)). The usual calculation is applied for S_S (Eq. (11)):

where:

(S_I)_BTE is the un-biodegradable soluble COD of the batch test effluent
(S_I)_ML is determined as the CODsol of the batch reactor mixed liquor before addition of wastewater
(S_I)_LSC is the un-biodegradable soluble COD of wastewater according to this procedure

FS and LS alternatives of the Mamais et al. (1993) method were compared in order to check for equivalence between both methods of S_I determination. The resulting S_S was also compared between both methods. Also, the LS_TW and LS_C alternatives were compared to the FS alternative, which was taken as reference.

Sampling and storage

Wastewater samples were taken at the 2 municipal wastewater treatment plants mentioned above (PPT and TTV). PPT is an 8 640 m³/d SBR plant with 2 alternating reactors, and TTV is a 60 500 m³/d continuous-flow activated sludge plant with enhanced primary treatment. Both flow rates are operational. At the PPT plant, grab influent samples were taken at the SBR feed-pipe discharge. At the TTV plant, characterisation was applied to grab samples of the secondary treatment influent, in order to avoid interference from the physicochemical primary treatment on the method essayed. Effluent grab samples were taken at the SBR discharge (PTT) or at the secondary settling effluent (TTV). Sampling after disinfection (PPT) or after full tertiary treatment (TTV) was avoided, in order to prevent chemical oxidation of effluent CODsol to interfere with the measured S_I. Raw wastewater and mixed liquor samples were analysed and used in the batch tests upon arrival at the laboratory, while effluents and filtrates were stored at 4ºC for less than 12 h before analysis.

Standard analyses, CODsol, S_I and S_S

TSS, VSS, COD, pH and dissolved oxygen (DO) were determined according to Standard Methods (1998). COD was determined by the closed reflux, 5520 D spectrophotometric method (Standard Methods, 1998), using a DR/4000 spectrophotometer (Hach, Loveland, CO, USA). CODsol was determined by the Mamais et al. (1993) method of zinc sulphate flocculation, settling, and supernatant filtration through 0.45 µm filters (Whatman, cellulose nitrate membrane, Ø47 mm, 0.45 µm pore filters). S_I was determined as detailed above (Eq. (1), (3) and (6)), depending on the method alternative. S_S was always computed as the difference between CODsol and S_I. Duplicate analyses were run for all samples. In the case of CODsol and S_I, duplicate flocculation and filtration procedures were also run. Triplicate COD analyses were run on each sample replicate in the low-range COD method, duplicate analysis for the high-range method.

The high- and low-range variants of the Standard Methods (1998) spectrophotometric COD method were validated by analysing the potassium hydrogen phthalate calibration curves for linearity, limit of detection (LOD), limit of quantification (LOQ), repeatability and recovery. Linearity was evaluated through the determination coefficient (R²) (Miller and Miller, 2002). LOD was computed from the y-intercept absorbance in the calibration-line plus three times the standard deviation (3s) of the blank (from 10 blanks) (Miller and Miller, 2005). For the LOQ, a 5s criterion was applied (Eurachem, 1998). Repeatability was checked through the per cent relative standard deviation (RSD) of COD when computed from 3 calibration lines obtained on the same day (Eurachem, 1998).

Previous COD regime characterisation

Sample diversity was achieved by sampling 2 different plants at different times of the day, so they could present different concentration and COD composition. It was necessary to know the COD evolution of wastewater at the sampling points (COD regime) beforehand. So, samples were taken every 2 h at the sampling points of both plants, in a daylong sampling operation starting at 20:00. Plant influent (PPT), secondary treatment influent (TTV) and secondary effluent samples (both plants) were analysed for COD and CODsol. S_I and S_S were calculated in accordance with the FS alternative of the physicochemical method (Eq. (1) and (2)).

Main sampling design

Table 1 displays the main sample design, a 2² factorial with four replicates (4x2²), based on the variables 'plant' (TTV/PPT) and 'COD concentration' (low/high). The replicates were set in four 2² blocks. Randomisation was applied inside each block, as a usual measure to ensure independent sampling. Due to higher COD and CODsol variability observed at that PPT, 8 additional samples were taken later at this plant for better characterisation, 4 samples corresponding to low concentration, and 4 to low concentration.

 

 

Laboratory-scale, 24-h batch tests

For the LS alternatives, mixed liquor from the biological reactors of each WWTP were mixed with wastewater and the mixture was kept aerated for 24 h. Mixed liquor samples were taken at the exit of the biological reactors (TTV) or at the end of the aerated 'react' phase of the SBR cycle (PPT). These biomasses were already acclimated to the wastewaters used in the batch tests, so they were directly used. Mixed liquor (0.5 ℓ) and appropriate wastewater volumes were mixed in order to maintain an F/M of 0.075 g COD/(g VSS.d). Duplicate batch tests were carried out for each sample and experimental condition, in 600 to 2000 ml beakers, aerated through ceramic diffusers. Dissolved oxygen was kept at above 2 mg/l throughout the tests.

Biomass washing

In the tests with previous tap water biomass washing, 0.5 l of mixed liquor from the full-scale plant were settled for 20 min in a 500 ml graduated cylinder. Thereafter the supernatant was decanted and tap water was added to complete the volume to 0.5 l. This process was repeated 2 more times to complete 3 settle-decant-refill operations, which allowed the removal of 99% of the original liquid phase.

Evaluation of method alternatives

The analytical precision of a given method alternative was evaluated through the standard deviation from a pair of sample replicates. First, sample standard deviations were calculated for each pair of replicates. Then, the squares of the maximum and the minimum sample standard deviations were compared in an F-test at 5% significance (Montgomery and Runger, 2003). If they resulted equal, all samples were assumed to have the same analytical variance which was then computed from individual sample standard deviations through the pooled estimator (s_P²) (Eq. (12)):

This is a generalisation of the 2-sample pooled estimator in Montgomery and Runger (2003), where s_i is the analytical standard deviation of sample i, n_i is the number of replicates for sample i, N is the number of samples (24 for all methods, except for LS_TW, 16), s_P is the pooled analytical standard deviation of the method.

If maximum and minimum individual variances were not equal in the above-mentioned F-test, it was concluded that there was not a common s for all samples, and the individual standard deviations lying outside the centred 95% percentile were discarded. Then a pooled s was calculated for the N_h samples sharing a common variance in the upper s range within the centred 95% percentile of standard deviations. These upper ranges included 75% to 100% of all samples. Finally, the analytical precision of a sample result (mean of two replicates) was calculated as S_p/.

This analytical precision was applied to all (CODsol)_WW and (CODsol)_EFF determinations. However, when directly estimating influent S_I from (CODsol)_EFF in grab samples, precision is not only affected by analytical issues, but by the fuzzy relationship between (CODsol)_EFF and influent S_I, as discussed next. The following assumptions were applied to all influent S_I values when directly estimated from (CODsol)_EFF, as in Eq. (1), (3) and (7):

• Population means of influent and effluent S_I are equal, as derived from ASM1 and Mamais et al. (1993) method assumptions
• However, it is not possible to precisely associate the CODsol of a given effluent grab sample to a particular influent sample, due to total or partial mixing and time-delays throughout the plant. Consequently, (CODsol)_EFF of a grab sample, i.e. influent S_I's estimate, should be considered a random variable having the same mean as influent S_I, and a variance which can be estimated as the square standard deviation of all (CODsol)_EFF values. Consequently, S_I precision for grab effluent samples from a WWTP was estimated from the standard deviation of all effluent samples (herein called 'overall standard deviation').

Method comparison was carried out through 3 different approaches:

• Means of all samples by 2 methods were compared through t-tests at 5% significance, with previous investigation of equal/different variances using F-tests for variance comparison at 5% significance
• Means of 2 methods were also compared through paired t-tests (Montgomery and Runger, 2003)
• Regressions of method modifications against a reference method were performed. Equality of methods would be ideally proved through zero y-intercept, unit slope and unit R² (Miller and Miller, 2005).

The FS alternative was taken as reference method for comparisons.

Variance estimation for linear combinations of variables

When estimated from a simple set of data, variance was estimated as the square standard deviation. When estimating the variance of a linear combination of variables (Eq. (13)), it was computed from the square standard deviations of the individual variables and their sample covariance, through a general equation assuming correlation (Montgomery and Runger, 2003) (Eq. (14)). This was the case in Eqs. (2), (4), (5), (6), (10) and (11), where S_I and S_S are linear combinations.

where

x, y and z are variables (e.g., CODsol, S_I and S_S),
a and b are the coefficients of the linear combination,
s_x, s_y and s_z are the respective standard deviations, and
cov(xy) is the sample covariance of x and y.

Non-correlation was not assumed, so cov(xy) was always computed when using Eq. (14). When effluent S_I was involved in these calculations, the overall S_I standard deviation was used, while the analytical standard deviation was used for (CODsol)_WW.

Limit of detection and limit of quantification for S_S

The limit of detection is generally defined as the concentration which gives an instrument signal (y) significantly different from the blank signal. Typically, the sample signal should be greater than the mean blank signal plus three times the blank signal standard deviation, i.e., LOD = y_B+3s_B (Miller and Miller, 2002). Considering CODsol as the method 'signal', then effluent CODsol (i.e., S_I) can be considered a blank for the method, since it nominally corresponds to zero S_S, and its 'signal' is subtracted to the influent sample signal ((CODsol)_WW) (see Eq. (2) and (4)). So, for an S_S value to be detectable, wastewater CODsol should be significantly higher than effluent CODsol. Accordingly, the CODsol corresponding to the limit of detection was computed as the mean S_I plus 3 times the standard deviation of S_I (Eq. (15):

where:

(CODsol)_LOD is the 'signal' corresponding to the limit of detection for S_S
is the average S_I and s(S_I) is the overall standard deviation of S_I

The limit of detection for S_S was obtained from (CODsol)_LOD by substituting this value into the regression equations of CODsol vs. S_S (Fig. 6), and then isolating S_S.

 

 

 

 

 

 

 

 

 

 

The limit of quantification was considered under a 5s criterion. The 'signal' corresponding to the LOQ was computed (Eq. (16)), and then the LOQ for S_S was computed by substituting (CODsol)_LOQ into the regression equations in Fig. 6, and then isolating S_S.

 

Results and discussion

COD method validation

Table 2 displays the results of COD method validation. Linearity was excellent (0.9995 to 0.9996). LOD and LOQ for the low-range method were respectively 3.0 and 4.9 mg/l, below all COD or CODsol values obtained in this study, and well below most of them. LOD and LOQ for the high range method were nominally 4.6 and 7.7 mg/l, though the high range was 80-500 mg/l.

 

 

Previous COD regime characterisation

Figure 2 shows the evolution of COD (a) and CODsol (b) in influent samples from TTV and PPT. Both WWTPs presented typical COD daily oscillations, with a sharper COD profile at PTT, which does not have primary treatment and its peak-reduction effect mentioned above. The CODsol curve was also sharper for PPT, while the TTV curve was rather flat, making it difficult to define a high/low CODsol concentration regime. For this reason, time bands of high and low COD concentration were determined, and used to set up the experimental design for the main sampling.

The high concentration intervals were 16:00 to 04:00 (TTV) and 12:00 to 02:00 (PPT), while low-concentration intervals were, respectively, 06:00 to 14:00 and 04:00 to 10:00. Table 3 displays time bands and their COD and CODsol averages. The overall COD and CODsol ranges for all samples in both WWTPs were, respectively, 127 to 577 and 38 to 78 mg/l. The different wastewater origin and concentration ranges ensured considerable wastewater variability for the samples involved in the main sampling, as it was intended.

Results from the main sampling

Table 4 displays raw results from the main sampling at TTV and PPT plants, while Table 5 presents S_I and S_S obtained by the four method alternatives presented above (FS, LS, LS_TW and LS_C). These results are discussed below.

Influent CODsol

The influent CODsol had an overall average (both WWTPs) of 123 mg/l, with a range of 36 to 215 mg/l, much wider than in the main sampling. The analytical standard deviation of CODsol ranged 0 to 11.0 mg/l for the set of 24 samples (centred 95% percentile), with a pooled analytical standard deviation of 3.9 mg/l (3.2% RSD), 2.8 mg/l for the mean of 2 samples.

Precision of S_I determination by the FS and LS alternatives

The analytical standard deviation of the pairs of replicates of (S_I)_FS ranged 0.1 to 2.9 mg/l (centred 95% percentile), with a pooled standard deviation of 1.2 mg/l to 0.9 mg/l for the mean of 2 samples – which is excellent. However, the overall standard deviation of S_I by the FS method was computed for each WWTP, resulting in 6.7 mg/l at TTV plant (50% RSD), and 5.0 mg/l at PPT plant (29% RSD). These s values were found statistically equal in an F-test, and a pooled overall standard deviation of 5.7 mg/l was used for (S_I)_FS. These results indicate that a high relative imprecision (36% RSD) was found associated to S_I determination as CODsol of the FS plant effluent.

Similar results were obtained for the LS alternative. The analytical precision – standard deviation of (S_I)_LS in individual samples – ranged 0.3 to 4.0 mg/l, with a pooled standard deviation of 1.6 mg/l to 1.1 mg/l for the mean's standard deviation. The overall standard deviation of estimating S_I by the LS method was 6.7 mg/l (46% RSD) and 6.6 mg/l (38% RSD) for, respectively, TTV and PPT. Again, a high relative imprecision was associated to (S_I)_LS determination as the CODsol of the LS plant effluent. The pooled overall standard deviation for both plants was 6.6 mg/l (40% RSD).

As conclusion, the effluent CODsol of a single grab sample can be determined with a considerable precision (s = 1.2 mg/l). However, when attributing effluent CODsol to an (S_I)_EFF representing influent S_I, the overall variability of effluent CODsol applies, which is much greater, ranging 5.0-6.7 mg/l (29 to 50% RSD) for the two plants and methods herein analysed.

Comparing FS and LS alternatives for S_I determination

It would be reasonable that a 24 h HRT laboratory-scale reactor could yield lower effluent CODsol than the FS method, since the hypothesis of zero effluent S_S seems more feasible for a 24 h HRT reactor. However, the overall means for the 2 series of 24 results (15.8 and 16.4 mg/l) were compared in a simple t-test for equal variances (verified through an F-test), resulting in statistically equal means. Also, a paired t-test was applied to the two series of S_I values, resulting again in equality between the means. It can be conclude that the average (S_I)_FS and (S_I)_LS obtained from 24 samples were statistically equal for the set of samples obtained from the two WWTPs.

Finally, a correlation study between LS and FS alternatives was carried out for S_I by running a linear regression of (S_I)_LS vs. (S_I)_FS. Figure 3 displays the correlation results. The determination coefficient was R²=0.0598, pointing to very low correlation between both methods. The y-intercept was quite imprecise and different from zero (11.9 mg/l± 8.4 mg/l), while the slope had large imprecision and was statistically null (0.284 ± 0.498). These results do not meet the conditions for equivalence between two analytical methods (Miller and Miller, 2005). Figure 3 shows great dispersion between (S_I)_LS and (S_I)_FS. The difference between both S_I estimates distributes almost randomly over a wide range (-10.3 to +12.5 mg/l, 95% centred percentile) for a mean difference of 0.7 mg/l. In conclusion, while the S_I averages of a number of samples by both methods were very close and statistically equal, a very poor correlation between (S_I)_FS and (S_I)_LS confirms that these methods proved to be non equivalent for individual sample determination. Regarding the hypothesis of (S_I)_LS being lower than (S_I)_FS, it was rejected in the t-tests.

S_S by FS and LS alternatives

Table 5 displays the results from S_S determination by these 2 method alternatives. Similar means (107 and 106 mg/l), ranges (29.6 to 197 and 25.1 to 203 mg/l) and overall standard deviations (50.6 and 50.9 mg/l) were obtained for, respectively, the FS and LS alternatives. Equivalence between both methods is discussed further in this section. First, LOD, LOQ and variance issues of these methods are addressed.

The limits of detection were computed for (S_S)_FS and (S_S)_LS. The LOD signal values ((CODsol)_LOD) were respectively 33.0 and 36.4 mg/l. In order to calculate the corresponding LOD for S_S, plots and regressions of CODsol vs. S_S were performed (Fig. 6). The linearity was quite good (R² of 0.987 and 0.983), so LOD(S_S) a was computed from the regression equations in Fig. 6, resulting in 17.2 and 19.2 mg/l, respectively, for (S_S)_FS and (S_S)_LS. LOQ were analogously obtained and were 28.6 and 32.6 mg/l, respectively, for (S_S)_FS and (S_S)_LS. No sample fell below the limit of quantification for (S_S)_FS, while 2 samples (8.3%) did fall below that of (S_S)_LS. However, lower S_S can be common in WWTP influents values. In fact, 15% of samples in Mamais et al. (1993) fell below this LOQ for (S_S)_FS, as well as 8% in Orhon et al. (1997), 17% in Spérandio and Paul (2000) and 57% in Ginestet et al (2002).

Standard deviation of (S_S)_FS replicates – computed through Eq. (14) – ranged between 5.7 and 17.0 mg/l. A pooled s existed for all (S_S)_FS (7.5 mg/l, 5.3 mg/l for the mean of 2 replicates). Since S_S variance was computed through Eq. (14) the contributions of CODsol and S_I variances were analysed, using s² as variance estimates. (S_I)_FS accounted for 78% of (S_S)_FS variance, while CODsol variance represented 21% only. The rest was due to covariance. So, most of (S_S)_FS uncertainty was attributable to (S_I)_FS uncertainty. The latter derives from effluent CODsol variability and its fuzzy relationship with influent S_I, as pointed above. Similar results were obtained for the LS alternative. Standard deviation of (S_S)_LS replicates – computed through Eq. (14) – ranged 6.6-15.5 mg/l. A pooled s could be computed (8.1 mg/l, 5.7 mg/l for the mean of two replicates). Since the (S_S)_LS range was 25.1 to 203 mg/l, RSD ranged, respectively, 22.9-2.8%.

Equivalence between FS and LS alternatives for S_S is discussed next, based on the same tests applied to S_I methods. An overall t-test for S_S mean comparison (for variances found equal) was resulted in statistically equal averages at 5% significance; a paired t-test yielded the same result. So, the two means of 24 samples were statistically equal at 5% significance.

A correlation study between FS and LS alternatives for S_S was carried out by running a linear regression of (S_S)_LS vs. (S_S)_FS (Fig. 4). A quite good linearity was obtained (R²=0.977), with statistically unit slope (0.995 ± 0.067) and statistically zero y-intercept (-0.01 ± 7.9 mg/l). These results indicate that FS and LS alternatives are equivalent. However, the standard error of estimate was 7.8 mg/l, showing some dispersion between both methods, particularly affecting the lower S_S results. This can be mostly attributed to uncertainties in S_I determination by both methods, as pointed above.

In conclusion, the main source of uncertainty for S_S in FS and LS alternatives was the lack of precision in S_I determination, which seriously affected S_S determination in the low range. Also, S_I imprecision greatly determined LOD and LOQ. In a regression analysis, FS and LS methods for S_S proved to be equivalent though some dispersion between both methods was found, attributable to S_I imprecision. Regarding the hypothesis of (S_I)_LS being lower than (S_I)_FS, it was rejected, since they were found statistically equal.

S_I and S_S by the LS_TW alternative

The results of this section are shown in Tables 4 and 5. (DQOsol)_BTE in LS_TW alternative was somewhat lower than in the LS alternative. However, when the dilution correction was applied (Eq. (5)), higher (S_I)_LSTW values were obtained. The (S_I)_LSTW mean was 32.4 mg/l, vs. 15.8 mg/l the mean of the FS alternative. This difference was statistically significant (t-test at 5% significance). It has been shown that a common batch test (LS) estimated the same mean S_I as the FS alternative. Obtaining systematically higher S_I values in this modified LS test (LS_TW) should be caused by the particular conditions of this method.

It was hypothesised that submitting biomass to tap water, with usually lower salinity and organic matter concentrations than mixed liquor or wastewater, could provoke some COD solubilisation either by desorption, osmotic processes or, even, biomass lysis. An experiment was used to confirm and eventually quantify solubilisation in the LS_TW batch tests. A fixed amount of tap water washed biomass from PPT was mixed with three different volumes of the same influent sample from PPT. The volumes were calculated to keep three food to microorganism (F/M) ratios of 0.025, 0.05 and 0.1 g COD/(g VSS.d), corresponding to 25%, 50% and 100% of the full-scale plant F/M. The mixtures were aerated for 24 h in new LSTW tests, and replicates were run for each F/M ratio. The whole design was repeated on another day. Under no-solubilisation conditions, the final amount of CODsol (mg) in the batch test reactor should be proportional to the volume of wastewater added to the mixture, since CODsol had been removed from biomass by tap water washing. So, a plot of mg CODsol vs. wastewater volume should yield a line with positive slope, good linearity and zero y-intercept.

Figure 5 shows the plots of mg CODsol vs. added wastewater volume at the end of the LS_TW batch tests. The lines presented quite good linearity (R² of 0.930 and 0.981) and positive slope (0.037 and 0.068 mg CODsol/ml). The y-intercepts of the lines in Fig. 5 were statistically non-zero, as verified in t-tests at 5% significance (95% confidence intervals: 12.1 ± 3.2 and 3.8 ± 2.1 mg CODsol). These y-intercepts represent the extrapolation of residual CODsol if no wastewater volume had been added to the reactor. It means this CODsol was not supplied by wastewater, and could only be supplied by the biomass. Though these amounts can seem low or moderate, they are assigned to wastewater volumes between 60 and 350 ml, introducing an average concentration perturbation of +62 mg CODsol/l (ranging from +16 to +138 mg/l). The lower the wastewater volume, the higher the perturbation in CODsol concentration. The amounts of CODsol released per unit biomass were, respectively, 7.5 and 3.2 mg CODsol/g VSS. As a result of COD solubilisation, (S_S)_LSTW average and range in the main sampling were significantly lower than those of (S_S)_LS, in accordance with the higher S_I obtained by the LSTW method.

In conclusion, the LS_TW alternative led to COD solubilisation during the batch LS test, resulting in abnormally high S_I values and lower S_S estimates. Consequently, this alternative was discarded for S_I and S_S determination. Washing with a solution having a more controlled salinity and osmotic pressure could be explored as an alternative.

S_I and S_S by the LS_C alternative

Table 5 displays the results for these variables, while Table 4 shows other variables required for calculations, namely means and standard deviations of CODsol, (CODsol)_BTE and (CODsol)_ML. The mean (S_I)_LSC was 12.9 mg/l, vs. 15.8 mg/l for the FS alternative. This difference was not significant in a t-test for different variances at 5% significance, neither in an analogous paired t test. However, (S_I)_LSC vs. (S_I)_FS presented very poor correlation (R²=0.0027) and a large standard error of estimate (38.8 mg/l), indicating that while the S_I means of two sets of 24 samples were equal, individual samples presented large differences between the 2 method alternatives, which should not be considered equivalent.

In addition, 7 samples (29%) presented negative (S_I)_LSC values, which makes no sense. This can be attributed to a sharp variance amplification through (S_I)_LSC calculation, see Eq. (10), a linear combination of (S_I)_BTE and (S_I)_ML. For the samples studied, the a² and b² coefficients in Eq. (14) averaged 16.7 and 9.9, thus introducing a strong variance amplification in (S_I)_LSC calculation. While the independent variables in Eq. (10) ((S_I)_BTE and (S_I)_ML) had sample variances of 44.1 and 72.2 mg²/l², the sample variance estimated through Eq. (14) for (S_I)_LSC was 1.465 mg²/l², resulting in a standard deviation of 38.3 mg/l. This value is in accordance with that obtained from the 24 (S_I)_LSC values (38.0 mg/l). Since the mean difference between 1^st and 2^nd terms in the right side of Eq. (10) was just 12.9 mg/l, the probability of (S_I)_LSC being negative was 37%, assuming a normal distribution with µ=12.9 mg/l and σ=38.3 mg/l. Actually, 29% of samples presented negative (S_I)_LSC, as pointed out above.

(S_S)_LSC estimation resulted in high imprecision, with a pooled standard deviation of 38.6 mg/l (27.1 mg/l for the mean of 2 values) which would be unacceptable for at least the lower half of the (S_S)_LSC range, assuming a maximum acceptable RSD of 20%. Most of (S_S)_LSC variance (97%) was contributed by (S_I)_LSC. In conclusion, the LS_C alternative did not allow a reliable S_I estimation, mostly due to a sharp increase in S_I variance, introduced via (S_I)_LSC calculation through Eq. (10). This resulted in excessively dispersed (S_S)_LSC values. Consequently, the proposed LS_C alternative was discarded.

Importance of S_I precision

It has been found that most of the methods' shortcomings derive from the lack of precision in S_I determination by either the FS or the LS methods. Improving precision for S_I would allow reliable physicochemical measurement of S_S below the limits found in this study. This would require an improved calculation of influent S_I from effluent CODsol, either by the FS or the LS alternative, taking into account influent and effluent CODsol regimes, as well as mixing conditions inside the WWTP.

 

Conclusions

A high relative imprecision is associated with determination of un-biodegradable soluble COD by the full-scale and the laboratory-scale variants of the Mamais et al. (1993) method. This is due to the variability of effluent CODsol both in full-scale and laboratory-scale plants, and to the fact that it is not possible to associate a grab effluent sample with a given influent sample, due to complete or partial mixing in the WWTP. FS and LS S_I had relatively high standard deviations, and ranges somewhat greater than the mean S_I values. When comparing the full-scale and the laboratory-scale variants, (S_I)_FS and (S_I)_LS averages were statistically equal at 5% significance. However, the differences between (S_I)_FS and (S_I)_LS in individual samples were very wide. In addition, a very poor correlation between (S_I)_LS and (S_I)_FS was found, indicating that these methods were not equivalent for the samples in this study.

The LOQ for (S_S)_FS and (S_S)_LS were respectively 28.6 mg/l and 32.6 mg/ℓ. Most samples (92%) were above these limits, because their S_S were rather high. However, significant fractions of samples fell below this LOQ in some literature studies. Determination of S_S by the FS and LS methods had standard deviations of, respectively 5.3 mg/l and 5.7 mg/l, mostly associated with S_I determination uncertainty. This affected the precision of S_S determination by both methods at low S_S values.

On the other hand, good correlation was found between FS and LS alternatives (R²=0.978, zero y-intercept and unit slope), which means equality of the methods. However, a standard error of estimate of 7.6 g/l indicates a moderate dispersion between methods, relatively more important at low S_S. The hypothesis of (S_I)_LS being lower than (S_I)_FS was rejected, since they were found statistically equal.

Washing biomass with tap water before the laboratory-scale test (LS_TW alternative) resulted in significant COD solubilisation from biomass, which tended to overestimate S_I and underestimate S_S, especially at low F/M ratios. The solubilisation was quantified as a function of F/M, but the mechanism was not determined. Consequently, this method was discarded as a modification for suppressing influent mixed liquor S_I interference. Washing with a solution having a more controlled salinity and osmotic pressure could be explored as an alternative.

The LS_C alternative did not result in a statistically different average for S_I when compared with the original LS method. However, a regression analysis could not conclude equality between both methods. In addition, Eq. (10) used to calculate (S_I)_LSC introduced a sharp increase in S_I variance, resulting in much larger dispersion of results, including some negative, nonsense S_I values. Under the conditions of this research, the probability of a sample to have a negative estimated (S_I)_LSC was 37%. This method was discarded as an alternative for improving S_I and S_S determination. However, increasing precision of S_I determination in mixed liquor and batch test effluent could allow a re-evaluation of this method alternative.

Most of the methods' shortcomings derive from the lack of and precision in S_I determination by either the FS or the LS methods. Improving precision for S_I could allow more reliable physicochemical measurement of S_I below the limit found in this study. It is possible that the assumption of S_I conservation, implicit in the ASM1 model and in the Mamais et al. (1993) method, could be a source of uncontrolled error. However, S_I generation in the biological reactors is difficult to quantify and would complicate this simple physicochemical method.

 

Acknowledgements

The authors thank the Comisión Estatal del Agua in San Luis Potosí for authorisations, Grupo Proaqua SA de CV and Degrémont de México SA de CV for plant information and sampling support. Ma. de los Angeles Ortiz Balderas received scholarships from CONACYT (No. 198456) and Santander-Universia. This research was funded through CONACYT Project No. C07-ACIPC-01-08.88, UASLP Projects C06-FAI-03-18.21, C07-FAI-11-33.69; Programa Integral de Fortalecimiento de la UASLP (PIFI 1.0 to 3.4) – Plan de Desarrollo del Cuerpo Académico Tecnología Ambiental 2006 – 2008 (P/CA-116 2006-24-36 and P/CA 116-2007-24-42).

 

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Received 10 February 2009;
accepted in revised form 14 July 2009.

 

 

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