Sterols indicate water quality and wastewater treatment efficiency

Water Research 108 (2017) 401e411

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Sterols indicate water quality and wastewater treatment efficiency Elke S. Reichwaldt a, *, Wei Y. Ho a, Wenxu Zhou b, Anas Ghadouani a a

Aquatic Ecology and Ecosystem Studies, M015, School of Civil, Environmental and Mining Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia b M316, ARC Centre of Excellence for Plant Energy Biology, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 May 2016 Received in revised form 4 September 2016 Accepted 6 November 2016 Available online 8 November 2016

As the world's population continues to grow, water pollution is presenting one of the biggest challenges worldwide. More wastewater is being generated and the demand for clean water is increasing. To ensure the safety and health of humans and the environment, highly efficient wastewater treatment systems, and a reliable assessment of water quality and pollutants are required. The advance of holistic approaches to water quality management and the increasing use of ecological water treatment technologies, such as constructed wetlands and waste stabilisation ponds (WSPs), challenge the appropriateness of commonly used water quality indicators. Instead, additional indicators, which are direct measures of the processes involved in the stabilisation of human waste, have to be established to provide an in-depth understanding of system performance. In this study we identified the sterol composition of wastewater treated in WSPs and assessed the suitability of human sterol levels as a bioindicator of treatment efficiency of wastewater in WSPs. As treatment progressed in WSPs, the relative abundance of human faecal sterols, such as coprostanol, epicoprostanol, 24-ethylcoprostanol, and sitostanol decreased significantly and the sterol composition in wastewater changed significantly. Furthermore, sterol levels were found to be correlated with commonly used wastewater quality indicators, such as BOD, TSS and E. coli. Three of the seven sterol ratios that have previously been used to track sewage pollution in the environment, detected a faecal signal in the effluent of WSPs, however, the others were influenced by high prevalence of sterols originating from algal and fungal activities. This finding poses a concern for environmental assessment studies, because environmental pollution from waste stabilisation ponds can go unnoticed. In conclusion, faecal sterols and their ratios can be used as reliable indicators of treatment efficiency and water quality during wastewater treatment in WSPs. They can complement the use of commonly used indicators of water quality, to provide essential information on the overall performance of ponds and whether a pond is underperforming in terms of stabilising human waste. Such a holistic understanding is essential when the aim is to improve the performance of a treatment plant, build new plants or expand existing infrastructure. Future work should aim at further establishing the use of sterols as reliable water quality indicators on a broader scale across natural and engineered systems. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Coprostanol Sterols Water quality Waste stabilisation pond Indicator Wastewater

1. Introduction Declining water quality has become a major global issue (UNWater, 2011) and the supply of water represents a major chal€ro € smarty et al., 2010). Land-use lenge for future generations (Vo changes related to agriculture, husbandry and urbanisation contribute to the deterioration of water quality worldwide (Foley et al., 2005). Run-off from these areas contains high

* Corresponding author. E-mail address: [email protected] (E.S. Reichwaldt). http://dx.doi.org/10.1016/j.watres.2016.11.029 0043-1354/© 2016 Elsevier Ltd. All rights reserved.

concentrations of nutrients, pathogens, metals and other micropollutants, which can contaminate groundwater and surface water. In addition, an increasing volume of sewage is produced by the growing human population and this requires adequate treatment before being discharged into the environment or re-used. Constructed wetlands and waste stabilisation ponds (WSP) have been established as sustainable treatment technologies that harness the natural self-purification ability of water bodies (Hench et al., 2003; Mara, 2004; Mitchell et al., 1995; Nelson et al., 2004; Wu et al., 2016) and are increasingly used on a global scale. Although used extensively worldwide for decades, WSPs are known to suffer from

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differing levels of efficiency, partially due to the accumulation of sludge at the bottom (Chua et al., 2013). A low efficiency will lead to low quality effluent and potentially the presence of environmental risks such as the development of toxic cyanobacterial blooms (Barrington et al., 2013, 2015) or greenhouse gas emissions (Glaz et al., 2016). Low water quality and the presence of toxic cyanobacteria pose a direct risk to humans (Barrington et al., 2014; Reichwaldt et al., 2016) and the environment through eutrophication (Ghadouani and Coggins, 2011) or cyanotoxins presence (Parker et al., 2016; Sinang et al., 2015; Song et al., 2015) emphasising the importance to accurately quantify treatment efficiency. However assessing the efficiency of the treatment process is currently difficult due to a lack of reliable, stable and meaningful water quality indicators. Contamination of water resources through sewage is an ongoing problem worldwide (Deblonde et al., 2011; Pal et al., 2010, 2014; Wang et al., 2015). Commonly used indicators for assessing the quality of wastewater after treatment and water contaminated by wastewater include nutrients, biological oxygen demand (BOD), total suspended solids (TSS) and Escherichia coli (e.g., ANZECC, 2000). However, there are several problems related to the use and quantification of some of these indicators, resulting in an inadequate assessment of wastewater quality and identification of sewage pollution of surface water. These problems are mainly related to difficulties with methods (e.g., BOD, E. coli; see Augustin and Carlier, 2006; Corry et al., 2007; Gray, 2004; Servais et al., 2009), the indicators’ drastic response towards changes of environmental conditions (e.g., E. coli, BOD, TSS: see Gray, 2004), a lack of comparability with other human contaminants (E. coli: see Maiga et al., 2009), or their general relevance (e.g., TSS: Maynard et al., 1999). Therefore, to reliably identify human pollution and quality of water impaired by humans, indicators specific to human contamination that are stable under varying environmental conditions must be established. Sterols and their ratios have long been used as indicators of sewage pollution in a variety of aquatic systems due to their resistance to environmental changes (de Castro Martins et al., 2007; Furtula et al., 2012a; Bujagic et al., 2016; McCalley et al., 1981; Walker et al., 1982). Sterols are naturally occurring lipid molecules produced by any organism. The high specificity of some sterols for their faecal source (Bull et al., 2002; Leeming et al., 1996; Sinton et al., 1998) makes them ideal indicators to trace human contamination in a variety of environmental systems (reviewed in Field and Samadpour, 2007) such as sediments of rivers (Ayebo et al., 2006; Writer et al., 1995) and estuaries (Carreira et al., 2004; Reeves and Patton, 2005), coastal marine water (Leeming et al., 1998), inner-shelf sediments (Leeming et al., 1997), and to estimate human populations (Daughton, 2012). Further, one study quantified changes in sterol compositions during secondary and tertiary wastewater treatment to propose new criteria for identification of human faecal contamination (Furtula et al., 2012a). However, so far there has been no attempt to use sterols as indicators of the treatment process. Secondary wastewater treatment in waste stabilisation ponds (WSPs) relies on a mutualistic relationship between bacteria and algae to remove contaminants. In WSPs it can be expected that the relative abundance of human sterols decreases, while sterols originating from natural aquatic organisms such as non-faecal bacteria, phytoplankton, fungi and zooplankton increase (Fig. 1). The main removal mechanism of sterols from human faeces during wastewater treatment is through a physical removal of particulate matter to which the sterols are adsorbed; however bacterial degradation rates of sterols have been found to be system specific (Walker et al., 1982) and have been determined to range from being substantial (Teshima and Kanazawa, 1978) to minor (Daughton, 2012).

The main sterols that can be found in human faeces are coprostanol, 24-ethylcoprostanol, cholesterol, sitosterol and sitostanol (Bull et al., 2002; Leeming et al., 1996; Sinton et al., 1998; Walker et al., 1982). Cholesterol and sitosterol are both unsuitable to serve as strict indicators of faecal contamination, due to their ubiquitous nature and their origin from ingested plant material, respectively (Furtula et al., 2012a; Murtaugh and Bunch, 1967). Coprostanol on the other hand, as the major sterol in human faeces (Leeming et al., 1996) has been used as an indicator of faecal contamination in the environment (Mudge and Seguel, 1999; Peng et al., 2005; Reeves and Patton, 2005) even under conditions when pathogens (e.g., E. coli) have been destroyed by chlorination or heat (Devane et al., 2006; Goodfellow et al., 1977; Walker et al., 1982). However, because coprostanol is in small amounts ubiquitous as it can be produced in-situ under anoxic conditions (Grimalt et al., 1990; Nishimura, 1982; Nishimura and Koyama, 1977), and because correlations between organic carbon and coprostanol have been found (Chan et al., 1998), ratios between sterols have been suggested as indicators of sewage pollution in environmental systems (Carreira et al., 2004; Devane et al., 2015; Froehner and Sanez, 2013; Furtula et al., 2012a, 2012b; Reeves and Patton, 2005) (Table 1). Sterol ratios that are commonly used include 5b/(5aþ5b) [Coprostanol/(Coprostanol þ Cholestanol)], which identifies if the source of the sterols is biogenic or from sewage (Grimalt et al., 1990), Coprostanol/Epicoprostanol which discriminates between human and non-human pollution (Reeves and Patton, 2005), Coprostanol/Cholestanol, which discriminates between human and algal sterols (Devane et al., 2006; Leeming et al., 1997, 1998), and Coprostanol/Cholesterol, which distinguishes between biogenic sources e.g., human versus phytoplankton and zooplankton (Fattore et al., 1996). Further, under natural conditions, coprostanol is biosynthesised to epicoprostanol by microbes, making it possible to use the ratio Epicoprostanol/Coprostanol to discriminate between untreated and treated sewage (de Castro Martins et al., 2007; Mudge and Seguel, 1999) and to determine the age of faecal matter (Adnan et al., 2012; Mudge and Duce, 2005; Mudge and Seguel, 1999). The threshold for each ratio to identify contamination differs between the published literature, which might be due to system specific differences in biophysicochemical processes, including the temperature dependent microbial activity (Carreira et al., 2004). The development of these thresholds has largely been based on studies investigating sewage contamination from advanced treatment plants (Furtula et al., 2012a; Glassmeyer et al., 2005; Venkatesan and Kaplan, 1990; Walker et al., 1982). However, these differ in their treatment processes from waste stabilisation ponds, which rely more on a healthy bacteria-algae mutualism to stabilise the waste (Mara, 2004). To establish the use of sterol ratios as reliable, universal indicators of sewage pollution in the environment, it is therefore essential to identify sterol fingerprints during the treatment of wastewater in a range of sewage treatment technologies. As such, this study focusses on changes in the sterol composition during the treatment of wastewater through waste stabilisation ponds (WSP) as a first step towards an integrative approach that uses sterols as indicators of pollution. The overall aim of this study was to evaluate the suitability of human sterol levels as an indicator for treatment efficiency of wastewater in waste stabilisation ponds and to assess the use of sterols as a biomarker of the relative importance of human waste within waste stabilisation ponds. More specifically, the objectives of this study are i) to quantify the change in the relative contribution of faecal sterols to the total sterols during the wastewater treatment process, ii) to investigate the dynamics of sterol fingerprints as a function of treatment, iii) to assess the suitability of sterol ratios to detect faecal signals in a continuum of treated wastewater, and iv) to identify the relationship between sterols and common water


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Fig. 1. Conceptual framework showing the sterol composition of wastewater, the sources, and possible pathways of sterols during treatment in a waste stabilisation pond.

Table 1 Sterol ratios and their criteria values for the identification of human faecal contamination. Criteria used are suggested by Furtula et al. (2012a), except the criteria for Coprostanol/(Coprostanol þ Cholestanol) [5b/(5aþ5b)] for which the more conservative criteria of 0.7 (Grimalt et al., 1990; Reeves and Patton, 2001) was used, and the % coprostanol criteria which was taken from Devane et al. (2006). Sterol ratio

Indicator for

Criteria

Coprostanol/(Coprostanol þ Cholestanol) Coprostanol/Epicoprostanol Coprostanol/Cholestanol Coprostanol/Cholesterol Epicoprostanol/Coprostanol Coprostanol/(Cholestanol þ Cholesterol) % Coprostanol of total sterols

sewage vs. biogenic source human vs. non-human human vs. algae sterols human vs. phyto-, zooplankton untreated vs. treated sewage sewage vs. non-sewage Pollution by human sewage

>0.7 >1.5 >0.5 0.5 0.2 >5.5

quality indicators such as biological oxygen demand (BOD), total suspended solids (TSS) and E. coli abundance. 2. Materials and methods 2.1. Study sites and experimental design Five waste stabilisation pond systems (P1 e P5) in Western Australia that predominantly receive domestic sewage were used in this study. All waste stabilisation ponds comprised of one facultative pond for removal of BOD and TSS, and one (P2, 4) or two (P1, 3, 5) maturation ponds in series for nutrient and pathogen removal. P1, 3, 5 had two facultative ponds in parallel, while P1 also had two parallel first maturation ponds (Table 2). Water samples were collected at four sites inside the ponds, representing a wastewater quality and treatment time gradient: wastewater of high strength and shortest treatment time was collected directly at the inlet pipe into facultative ponds (site 1); wastewater having been treated by

Table 2 Number and types of ponds at each treatment plant. * indicates that these ponds are in parallel. Treatment plant

Facultative

First maturation

Second maturation

P1 P2 P3 P4 P5

2* 1 2* 1 2*

2* 1 1 1 1

1 1 1

facultative processes was collected at the outlet pipe of the facultative pond (site 2); wastewater that had been processed in one maturation pond was collected at the outlet pipe of the first maturation pond (Site 3); lastly, wastewater that had been processed in a second maturation pond and had been treated longest was collected at the outlet pipe of the second maturation pond (Site 4). Therefore, wastewater from site 1 is of low quality (almost raw wastewater) as it has spent only a short time within the treatment

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plant, while wastewater from site 4 has been processed longest within the treatment plant and is of better quality. One sample per site was collected 10 cm below the surface with a manual sampler. Each sample was filtered through a 500 mm mesh to exclude large zooplankton and stored in an acid-washed glass bottle on ice in the dark until further processing in the laboratory within 24 h. Water samples for sterol analysis were collected three (P1, P2, P4, P5) or four times (from P3) between August 2012 and February 2013: in August (P3), September (all WSPs), November (P1, P5), December (P2, P3, P4) and February (all WSPs). Water samples for quantification of total phosphorous (TP), total nitrogen (TN), BOD, TSS, and E. coli concentrations were taken for raw wastewater and from sites 2e4 at the same day or one day before/after sampling for sterols. Average BOD loading to the WSPs during the study period was 17, 31, 68, 72, and 55% of the total treatment capacity for P1 e P5, respectively. 2.2. Sample preparation Depending on the amount of organic matter in the sample, 200e1250 ml of the sample were centrifuged at 3273g (Beckman and Coulter, Allegra X-12 Series) for 10 min at room temperature. After washing the resulting biomass once with 1.5 ml Milli-Q water, the biomass was transferred into 2 ml eppendorf centrifuge tubes and further centrifuged at 16,100g (13,200 rpm) (Eppendorf; 5415D) for 5 min. The supernatant was discarded and the sample was frozen at 20 C until further extraction. 2.3. Sterol sample analysis Sterols were extracted from suspended particles with 500e1000 ml of methanol extraction solution (MeOH:H2O:0.2 mg ml1 Ribitol; 20:2:1 (v/v)), depending on the amount of biomass at 60 C for 30 min in a thermomixer (Eppendorf Thermomixer Comfort). After cooling to room temperature, 0.5 ml of well mixed sample was transferred into a new eppendorf tube, 2 ml of 20% KOH/MeOH solution was added, and each sample was incubated at 80 C for 1.5 h for saponification. After cooling, 500 ml hexane was added, the sample was shaken and centrifuged at 16,100g (13,200 rpm) (Eppendorf; 5415D) for 5 min 250 ml of the hexane layer was transferred to a gas chromatography vial and air dried overnight. The sterols were then converted to their trimethylsilane esters using 15 ml N,O-Bis(Trimethylsilyl) trifluoroacetamide (BSTFA) with Trimethylchlorosilane (TMCS) (99:1), and 15 ml of pyridine as catalyst. 5 ml of derivatized samples were injected splitless into a GC/MS (Agilent technologies; GC 6890N, MS 5975B Inert Mass selective quadrupole detector) that was fitted with a 30 m Agilant FactorFour VF-5ms column (0.25 mm i.d., 0.25 mm film thickness) plus a 10 m integrated guard column. Inlet temperature was constant at 300 C, and the carrier gas (helium) flowed at a constant rate of 1 ml min1. The initial temperature of the GC oven was set at 100 C and subsequently increased at a rate of 37 C min1 until a temperature of 320 C was reached and then held for 2 min. The transfer line temperature was set at 280 C, MS source and quadrupole temperatures at 230 C and 150 C, respectively. Ionisation was by electron impact at 70 eV. The mass calibrant perfluorotributylamine was used to pretune the MS. Identification and relative abundance of sterols within a sample was performed through Agilent GC-MS Productivity ChemStation software and Automated Mass Spectral Deconvolution and Identification System (AMDIS), where the peaks of each sterol were identified by their retention time and mass fragmentation pattern. Only peaks with a purity >50% were considered for analysis. Percentage of each sterol (of total sterols) and sterol ratios were calculated by using the abundance of the integrated signal of each sterol peak.

2.4. Total phytoplankton biomass, total nitrogen (TN), total phosphorus (TP), BOD, TSS, and E. coli Total phytoplankton biomass for sites 1e4 was quantified by using a bench-top version of the FluoroProbe (bbe Moldaenke, Germany) as mg chl-a L1 (Beutler et al., 2002). TN, TP, BOD and TSS were quantified according to standard methods at SGS Australia (methods: 5210, 2540D, 4500 in APHA, 2011), E. coli was quantified as the most probable number using enzyme hydrolysable substrates (chromogenic and fluorogenic) incubated at 37 C at PathWest Laboratory Medicine WA, Australia (Australian Standard, 2005). 2.5. Statistical analysis Differences between the relative abundance of each sterol at each site and between sterol ratios were calculated with one-way ANOVA (with Games-Howell post hoc test) or Kruskal-Wallis ANOVA on ranks (with Dunn's post hoc test), depending on whether or not the data was normally distributed. To identify differences in the sterol composition between sites and ponds, the relative abundances of the sterols at each site were transformed into two principle components that accounted for most of the variability in the data using principal component analysis (PCA) based on a co-variance matrix. The principle components were then subjected to a one-way ANOVA to test if differences were significant. To analyse differences for total phytoplankton biomass, TP, TN, TSS, E. coli, and BOD along the wastewater gradient (facultative, first maturation, second maturation pond), one-way ANOVA was calculated. Data were log-transformed to achieve normalized distribution where necessary. The relationships between sterol ratios or the percentage of sterol and common indicators of water quality (E. coli, BOD, TSS) were analysed by using the curve estimation function in SPSS (IBM© SPSS Statistics Version 21). The significance level was set to p < 0.05 and all statistical analyses were performed in SPSS (IBM© SPSS Statistics Version 21). 3. Results 3.1. Total phytoplankton biomass, nutrients, BOD, TSS, and E. coli The quality of wastewater was improved by the treatment processes. This was indicated by the significantly lower concentrations of E. coli, TSS, BOD, TP and TN at sites 3 and 4 compared to site 2 (TP: F(2,48) ¼ 5.02; TN: F(2,48) ¼ 10.22; TSS: F(2,48) ¼ 8.51; BOD: F(2,48) ¼ 6.41; E. coli: F(2,31) ¼ 8.87) (Table 3). Total phytoplankton biomass was higher in the facultative ponds compared to the maturation ponds (one-way ANOVA; F(2,51) ¼ 3.43) but was highly variable over time and between treatment plants leading to large standard errors (Table 3). 3.2. Sterol composition Twenty sterols could be identified in the wastewater (Fig. 2). In wastewater that received very little treatment (site 1) ergosterol, coprostanol, and cholesterol were the most abundant sterols (as % of total sterols), while ergosterol, cholesterol, and stigma 7,22dienol were most abundant in wastewater processed longest within the treatment plant (site 4) (Fig. 2). The relative abundance of all sterols that are indicators of human feces (coprostanol, epicoprostanol, 24-ethylcoprostanol, sitostanol) and one ubiquitous sterol (cholestanol) was significantly higher at the two sites with the lowest degree of treatment (sites 1, 2) than at the two sites with longer treatment (sites 3, 4) (ANOVA; coprostanol: H ¼ 50.49, d.f. ¼ 3; epicoprostanol: H ¼ 28.39, d.f. ¼ 3; cholestanol:


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Table 3 Mean (SE) values for water quality parameters for raw sewage and at sites 2 (facultative pond), 3 (first maturation), and 4 (second maturation). Identical superscript letters indicate no statistical difference between sites (one-way ANOVA). TSS ¼ total suspended solids, BOD ¼ biological oxygen demand, TP ¼ total phosphorous, TN ¼ total nitrogen, Phyto ¼ total phytoplankton biomass. e indicates no value.

Raw sewage Site 2 Site 3 Site 4

E. coli (cfu ml1)

TSS (mg L1)

BOD (mg L1)

TP (mg L1)

TN (mg L1)

Phyto (mg chl-a L1)

>24,000 7692a (1866) 1127b (464) 457b (270)

178.0 (23.7) 161.7a (28.0) 75.3b (12.0) 60.6b (9.9)

185.7 (21.9) 47.3a (6.1) 30.6ab (6.3) 15.0b (2.2)

10.5 (1.3) 9.9a (0.6) 7.9ab (0.6) 6.4b (1.1)

76.3 (8.4) 38.6a (3.3) 23.9b (2.6) 17.8b (3.7)

e 720a (147) 364b (103) 284b (75)

Fig. 2. Mean relative abundance of the main sterols, calculated as the percentage of total sterols, along a treatment gradient with site 1 representing high strength and almost untreated wastewater while wastewater sourced from site 4 has been processed longest within the treatment plant. Within each sterol, bars with different letters indicate significant differences (Dunn's pairwise multiple comparison). Error bars indicate standard errors. Note that non-human sterols with an abundance of the integrated signal