Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis.

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

National Wastewater Reconnaissance of Analgesic Consumption in Australia.

A wastewater-based epidemiology (WBE) method is presented to estimate analgesic consumption and assess the burden of treated pain in Australian communities. Wastewater influent samples from 60 communities, representing ∼52% of Australia's population, were analyzed to quantify the concentration of analgesics used to treat pain and converted to estimates of the amount of drug consumed per day per 1000 inhabitants using pharmacokinetics and WBE data. Consumption was standardized to the defined daily dose per day per 1000 people. The population burden of pain treatment was classified as mild to moderate pain (for non-opioid analgesics) and strong to severe pain (for opioid analgesics). The mean per capita weighted total DDD of non-opioid analgesics was 0.029 DDD/day/person, and that of opioid-based analgesics was 0.037 DDD/day/person across Australia. A greater burden of pain (mild to moderate or strong to severe pain index) was observed at regional and remote sites. The correlation analysis of pain indices with different socioeconomic descriptors revealed that pain affects populations from high to low socioeconomic groups. Australians spent an estimated US $3.5 (AU $5) per day on analgesics. Our findings suggest that WBE could be an effective surveillance tool for estimating the consumption of analgesics at a population scale and assessing the total treated pain burden in communities.

Novel Per- and Polyfluoroalkyl Substances Discovered in Cattle Exposed to AFFF-Impacted Groundwater.

The leaching of per- and polyfluoroalkyl substances (PFASs) from Australian firefighting training grounds has resulted in extensive contamination of groundwater and nearby farmlands. Humans, farm animals, and wildlife in these areas may have been exposed to complex mixtures of PFASs from aqueous film-forming foams (AFFFs). This study aimed to identify PFAS classes in pooled whole blood (n = 4) and serum (n = 4) from cattle exposed to AFFF-impacted groundwater and potentially discover new PFASs in blood. Thirty PFASs were identified at various levels of confidence (levels 1a-5a), including three novel compounds: (i) perfluorohexanesulfonamido 2-hydroxypropanoic acid (FHxSA-HOPrA), (ii) methyl((perfluorohexyl)sulfonyl)sulfuramidous acid, and (iii) methyl((perfluorooctyl)sulfonyl)sulfuramidous acid, belonging to two different classes. Biotransformation intermediate, perfluorohexanesulfonamido propanoic acid (FHxSA-PrA), hitherto unreported in biological samples, was detected in both whole blood and serum. Furthermore, perfluoroalkyl sulfonamides, including perfluoropropane sulfonamide (FPrSA), perfluorobutane sulfonamide (FBSA), and perfluorohexane sulfonamide (FHxSA) were predominantly detected in whole blood, suggesting that these accumulate in the cell fraction of blood. The suspect screening revealed several fluoroalkyl chain-substituted PFAS. The results suggest that targeting only the major PFASs in the plasma or serum of AFFF-exposed mammals likely underestimates the toxicological risks associated with exposure. Future studies of AFFF-exposed populations should include whole-blood analysis with high-resolution mass spectrometry to understand the true extent of PFAS exposure.

Improving Wastewater-Based Tobacco Use Estimates Using Anabasine.

In wastewater-based epidemiology (WBE), nicotine metabolites have been used as biomarkers for monitoring tobacco use. Recently, the minor tobacco alkaloids anabasine and anatabine have been suggested as more specific biomarkers for tobacco use since nicotine use can be from both tobacco and non-tobacco sources. This study aimed to provide an in-depth evaluation of the suitability of anabasine and anatabine as WBE biomarkers of tobacco and subsequently estimate their excretion factors for WBE applications. Pooled urine (n = 64) and wastewater samples (n = 277), collected between 2009 and 2019 in Queensland, Australia, were analyzed for nicotine and its metabolites (cotinine and hydroxycotinine), as well as anabasine and anatabine. Anabasine performed as the better biomarker, showing a similar per capita load in pooled urine (2.2 ± 0.3 µg/day/person) and wastewater samples (2.3 ± 0.3 µg/day/person), while the per capita load of anatabine in wastewater was 50% higher than its load in urine. It is estimated that 0.9 µg of anabasine was excreted per cigarette smoked. Triangulation of tobacco sales data and tobacco use estimated from either anabasine or cotinine showed that anabasine-based estimates were 5% higher than sales data, while cotinine-based estimates were between 2 and 28% higher. Our results provided concrete evidence to confirm the suitability of anabasine as a specific biomarker for monitoring tobacco use by WBE.

Increased Nicotine Consumption in Australia During the First Months of the COVID-19 Pandemic.

INTRODUCTION: Mixed findings have been reported about the impact of the COVID-19 pandemic on smoking behavior in different populations. AIMS AND METHODS: In this study, we aimed to quantify changes in smoking prevalence through the proxy of nicotine consumption in the Australian population from 2017 to 2020 inclusive. Estimates of nicotine consumption between 2017 and 2020 were retrieved from a national wastewater monitoring program that covers up to 50% of the Australian population. National sales data for nicotine replacement therapy (NRT) products from 2017 to 2020 were also acquired. Linear regression and pairwise comparison were conducted to identify data trends and to test differences between time periods. RESULTS: The average consumption of nicotine in Australia decreased between 2017 and 2019 but increased in 2020. Estimated consumption in the first half of 2020 was significantly higher (~30%) than the previous period. Sales of NRT products increased gradually from 2017 to 2020 although sales in the first half of the year were consistently lower than in the second half. CONCLUSION: Total nicotine consumption increased in Australia during the early stage of the pandemic in 2020. Increased nicotine consumption may be due to people managing higher stress levels, such as from loneliness due to control measures, and also greater opportunities to smoke/vape while working from home and during lockdowns in the early stage of the pandemic. IMPLICATIONS: Tobacco and nicotine consumption have been decreasing in Australia but the COVID-19 pandemic may have temporarily disrupted this trend. In 2020, the higher impacts of lockdowns and working from home arrangements may have led to a temporary reversal of the previous downward trend in smoking during the early stage of the pandemic.

From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization.

The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.

In-Sewer Stability Assessment of Anabolic Steroids and Selective Androgen Receptor Modulators.

Wastewater-based epidemiology is a potential complementary technique for monitoring the use of performance- and image-enhancing drugs (PIEDs), such as anabolic steroids and selective androgen receptor modulators (SARMs), within the general population. Assessing in-sewer transformation and degradation is critical for understanding uncertainties associated with wastewater analysis. An electrospray ionization liquid chromatography mass spectrometry method for the quantification of 59 anabolic agents in wastewater influent was developed. Limits of detection and limits of quantification ranged from 0.004 to 1.56 µg/L and 0.01 to 4.75 µg/L, respectively. Method performance was acceptable for linearity (R2 > 0.995, few exceptions), accuracy (68-119%), and precision (1-21%RSD), and applicability was successfully demonstrated. To assess the stability of the selected biomarkers in wastewater, we used laboratory-scale sewer reactors to subject the anabolic agents to simulated realistic sewer environments for 12 h. Anabolic agents, including parent compounds and metabolites, were spiked into freshly collected wastewater that was then fed into three sewer reactor types: control sewer (no biofilm), gravity sewer (aerobic conditions), and rising main sewer (anaerobic conditions). Our results revealed that while most glucuronide conjugates were completely transformed following 12 h in the sewer reactors, 50% of the investigated biomarkers had half-lives longer than 4 h (mean residence time) under gravity sewer conditions. Most anabolic agents were likely subject to biofilm sorption and desorption. These novel results lay the groundwork for any future wastewater-based epidemiology research involving anabolic steroids and SARMs.

Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology.

Wastewater is a potential treasure trove of chemicals that reflects population behavior and health status. Wastewater-based epidemiology has been employed to determine population-scale consumption of chemicals, particularly illicit drugs, across different communities and over time. However, the sociodemographic or socioeconomic correlates of chemical consumption and exposure are unclear. This study explores the relationships between catchment specific sociodemographic parameters and biomarkers in wastewater generated by the respective catchments. Domestic wastewater influent samples taken during the 2016 Australian census week were analyzed for a range of diet, drug, pharmaceutical, and lifestyle biomarkers. We present both linear and rank-order (i.e., Pearson and Spearman) correlations between loads of 42 biomarkers and census-derived metrics, index of relative socioeconomic advantage and disadvantage (IRSAD), median age, and 40 socioeconomic index for area (SEIFA) descriptors. Biomarkers of caffeine, citrus, and dietary fiber consumption had strong positive correlations with IRSAD, while tramadol, atenolol, and pregabalin had strong negative correlation with IRSAD. As expected, atenolol and hydrochlorothiazide correlated positively with median age. We also found specific SEIFA descriptors such as occupation and educational attainment correlating with each biomarker. Our study demonstrates that wastewater-based epidemiology can be used to study sociodemographic influences and disparities in chemical consumption.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

Systematic Evaluation of the In-Sample Stability of Selected Pharmaceuticals, Illicit Drugs, and Their Metabolites in Wastewater.

The in-sample stability of selected pharmaceuticals, illicit drugs, and their metabolites in wastewater was assessed under six different conditions-untreated, addition of hydrochloric acid or sodium metabisulfite solution, combined with or without sterile filtration, and at four representative temperatures, at 35 °C for up to 28 days, 22 °C for 56 days, and 4 °C and -20 °C for 196 days, or freeze/thaw cycles for 24 weeks. Paracetamol, 6-monoacetylmorphine, morphine, and cocaine were poorly stable in untreated wastewater-e.g., with 50% transformation within 1.2-8.1 days at 22 °C, and acidification reduced their in-sample transformations. Acesulfame, carbamazepine, cotinine, methamphetamine, 3,4-methylenedioxy-methamphetamine (MDMA), ketamine, norfentanyl, 3,4-methylenedioxy-N-ethylamphetamine (MDEA), and norbuprenorphine were highly or moderately stable over the observed period, even in untreated wastewater. Fitting of pseudo-first-order kinetics and the Arrhenius equation was used to develop a multistage transformation estimation model combined with an interactive tool to evaluate possible transformation scenarios of selected biomarkers for the processes from sampling to preanalysis. However, as the wastewater composition can vary between sites and over time, the variability of in-sample stability requires further exploration.

Using Prescription and Wastewater Data to Estimate the Correction Factors of Atenolol, Carbamazepine, and Naproxen for Wastewater-Based Epidemiology Applications.

The correction factor (CF) is a critical parameter in wastewater-based epidemiology (WBE) that significantly influences the accuracy of the final consumption estimates. However, most CFs have been derived from a few old pharmacokinetic studies and should be re-evaluated and refined to improve the accuracy of the WBE approach. This study aimed to review and estimate the CFs for atenolol, carbamazepine, and naproxen for WBE using the daily mass loads of those pharmaceuticals in wastewater and their corresponding dispensed prescription data in Australia. Influent wastewater samples were collected from wastewater treatment plants serving approximately 24% of the Australian population and annual national dispensed prescription data. The estimated CFs for atenolol and carbamazepine are 1.37 (95% CI: 1.17-1.66) and 8.69 (95% CI: 7.66-10.03), respectively. Due to significant over-the-counter sales of naproxen, a reliable CF could not be estimated based on prescription statistics. Using an independent dataset of 186 and 149 wastewater samples collected in an urban catchment in 2011 and 2012, WBE results calculated using the new CFs matched well with the dispensed data for atenolol and carbamazepine in the catchment area.

Urinary Concentrations of Bisphenols in the Australian Population and Their Association with the Per Capita Mass Loads in Wastewater.

Health concerns and related regulation of bisphenol A (BPA) in some countries have led to an increase in the production and use of unregulated and poorly understood BPA analogues, including bisphenol S (BPS), bisphenol F (BPF), bisphenol B (BPB), and bisphenol AF (BPAF). To assess the temporal trends of human exposure to BPA analogues, urine and wastewater samples were collected from South East Queensland, Australia between 2012 and 2017 and analyzed for five bisphenols using validated isotope dilution liquid chromatography tandem mass spectrometry methods. BPA and BPS were the predominant bisphenols detected in both urine and wastewater samples, with median concentrations of 2.5 and 0.64 µg/L in urine and 0.94 and 1.1 µg/L in wastewater, respectively. BPB, BPF, and BPAF had low detection frequencies in both urine and wastewater samples. Concentrations of BPA in both urine and wastewater decreased over the sampling period, whereas concentrations of BPS increased, suggesting that BPS has become a BPA replacement. The contributions of urinary excretion to wastewater were calculated by the ratio of daily per capita urinary excretion to wastewater-based mass loads of bisphenols. Urinary BPA and BPS contributed to less than 1% of the load found in wastewater, indicating that much of the BPA and BPS originates from other sources.

Quantitative Analysis of Selected Plastics in High-Commercial-Value Australian Seafood by Pyrolysis Gas Chromatography Mass Spectrometry.

Microplastic contamination of the marine environment is widespread, but the extent to which the marine food web is contaminated is not yet known. The aims of this study were to go beyond visual identification techniques and develop and apply a simple seafood sample cleanup, extraction, and quantitative analysis method using pyrolysis gas chromatography mass spectrometry to improve the detection of plastic contamination. This method allows the identification and quantification of polystyrene, polyethylene, polyvinyl chloride, polypropylene, and poly(methyl methacrylate) in the edible portion of five different seafood organisms: oysters, prawns, squid, crabs, and sardines. Polyvinyl chloride was detected in all samples and polyethylene at the highest total concentration of between 0.04 and 2.4 mg g-1 of tissue. Sardines contained the highest total plastic mass concentration (0.3 mg g-1 tissue) and squid the lowest (0.04 mg g-1 tissue). Our findings show that the total concentration of plastics is highly variable among species and that microplastic concentration differs between organisms of the same species. The sources of microplastic exposure, such as packaging and handling with consequent transference and adherence to the tissues, are discussed. This method is a major development in the standardization of plastic quantification techniques used in seafood.

Release of Plastics to Australian Land from Biosolids End-Use.

Plastics are contaminants of emerging concern that can enter the environment from multiple sources, including via land application of treated sewage sludge (biosolids). Biosolids samples collected from 82 wastewater treatment plants (WWTPs) across Australia and covering 34% of the population during census week in 2016 were quantitatively analyzed to estimate the release of seven common plastics. Quantitative analysis was performed by pressurized liquid extraction followed by double-shot microfurnace pyrolysis coupled to gas chromatography mass spectrometry. Ninety nine percent of the samples contained plastics (Σ6plastics) at concentrations of between 0.4 and 23.5 mg/g dry weight (median; 10.4 mg/g dry weight), while polycarbonate was not detected in any sample. Per-capita mass loads of plastics (Σ6plastics) released were between 8 and 877 g/person/year across all investigated WWTPs. Polyethylene was the predominant plastic detected, contributing to 69% of Σ6plastics. Based on the concentrations measured, it was projected that around 4700 metric tons (Mt) of plastics are released into the Australian environment through biosolids end-use each year, equating to approximately 200 g/person/year, which represents 0.13% of total plastics use in Australia. Of this, 3700 Mt of plastics are released to agricultural lands and 140 Mt to landscape topsoil. Our results provide a first quantitative per-capita mass loads and emission estimate of plastic types through biosolids end-use.

Can wastewater analysis be used as a tool to assess the burden of pain treatment within a population?

Pain is a global health priority that is challenging to asses. Here we propose a new approach to estimating the burden of pain treatment in a population using wastewater-based epidemiology (WBE). WBE is able to quantify multiple pharmaceutical compounds in order to estimate consumption by a population. Wastewater samples collected from areas representing whole communities can be analysed to estimate the consumption of drugs used to treat pain, such as nonsteroidal anti-inflammatory drugs (NSAIDs) and opioids. The collection and analysis of wastewater can be conducted systematically to estimate the total consumption of NSAIDs and/or opioids in the population of a catchment area and to compare changes over time within the catchment or between different catchment populations. Consumption estimates can be combined by standardising the mass consumed to Defined Daily Doses (DDD) or morphine equivalents in order to assess, the population burden of pain treatment from mild to moderate (for NSAIDs) and for strong and severe pain (for opioids). We propose this method could be used to evaluate the total pain treatment burden between locations and over time. While this concept shows promise, future studies should evaluate the applicability as a tool to measure the burden of pain receiving treatment in a community.

Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data.

Nontargeted feature detection in data from high resolution mass spectrometry is a challenging task, due to the complex and noisy nature of data sets. Numerous feature detection and preprocessing strategies have been developed in an attempt to tackle this challenge, but recent evidence has indicated limitations in the currently used methods. Recent studies have indicated the limitations of the currently used methods for feature detection of LC-HRMS data. To overcome these limitations, we propose a self-adjusting feature detection (SAFD) algorithm for the processing of profile data from LC-HRMS. SAFD fits a three-dimensional Gaussian into the profile data of a feature, without data preprocessing (i.e., centroiding and/or binning). We tested SAFD on 55 LC-HRMS chromatograms from which 44 were composite wastewater influent samples. Additionally, 51 of 55 samples were spiked with 19 labeled internal standards. We further validated SAFD by comparing its results with those produced via XCMS implemented through MZmine. In terms of ISs and the unknown features, SAFD produced lower rates of false detection (i.e., ≤ 5% and ≤10%, respectively) when compared to XCMS (≤11% and ≤28%, respectively). We also observed higher reproducibility in the feature area generated by SAFD algorithm versus XCMS.

Harnessing the Power of the Census: Characterizing Wastewater Treatment Plant Catchment Populations for Wastewater-Based Epidemiology.

Wastewater studies that provide per capita estimates of consumption (influent) or release (effluent) via wastewater systems rely heavily on accurate population data. This study evaluated the accuracy of Wastewater Treatment Plant (WWTP) reported populations, as well as hydrochemical parameters, against accurate populations from a population census. 104 catchment maps were received from WWTPs, geolocated in geospatial software and overlaid with the smallest area unit of the Australian census, equating to 14.9 million Australians or 64% of the national population. We characterized each catchment for population counts, as well as by age profile, income profile, and education level. For a subset of sites, population estimates using hydrochemical parameters BOD, COD, and dissolved ammonia were evaluated for accuracy against census populations. Population estimates provided by WWTP personnel were on average 18% higher than census-based populations. Furthermore, hydrochemical-based population estimates had high RSD (>44%) for BOD, COD, and ammonium between sites, suggesting that their applicability for use in population estimation may not be appropriate for every WWTP. Catchment age distributions were evaluated and 46% of catchments had skewed age distributions: 6% were skewed older, and 40% were skewed younger. Through this process WWTP catchment populations can be characterized in a way that will enhance the interpretations of per capita estimates.

Medium-Chain Chlorinated Paraffins (CPs) Dominate in Australian Sewage Sludge.

To simultaneously quantify and profile the complex mixture of short-, median-, and long-chain CPs (SCCPs, MCCPs, and LCCPs) in Australian sewage sludge, we applied and further validated a recently developed novel instrumental technique, using quadrupole time-of-flight high resolution mass spectrometry running in the negative atmospheric pressure chemical ionization mode (APCI-qTOF-HRMS). Without using an analytical column the cleaned extracts were directly injected into the qTOF-HRMS followed by quantification of the CPs by a mathematical algorithm. The recoveries of the four SCCP, MCCP and LCCP-spiked sewage sludge samples ranged from 86 to 123%. This APCI-qTOF-HRMS method is a fast and promising technique for routinely measuring SCCPs, MCCPs, and LCCPs in sewage sludge. Australian sewage sludge was dominated by MCCPs with concentrations ranging from 542 to 3645 ng/g dry weight (dw). Lower SCCPs concentrations (<57-1421 ng/g dw) were detected in the Australian sewage sludge, which were comparable with the LCCPs concentrations (116-960 ng/g dw). This is the first time that CPs were reported in Australian sewage sludge. The results of this study gives a first impression on the distribution of the SCCPs, MCCPs, and LCCPs in Australia wastewater treatment plants (WWTPs).

Impact of in-Sewer Degradation of Pharmaceutical and Personal Care Products (PPCPs) Population Markers on a Population Model.

A key uncertainty of wastewater-based epidemiology is the size of the population which contributed to a given wastewater sample. We previously developed and validated a Bayesian inference model to estimate population size based on 14 population markers which: (1) are easily measured and (2) have mass loads which correlate with population size. However, the potential uncertainty of the model prediction due to in-sewer degradation of these markers was not evaluated. In this study, we addressed this gap by testing their stability under sewer conditions and assessed whether degradation impacts the model estimates. Five markers, which formed the core of our model, were stable in the sewers while the others were not. Our evaluation showed that the presence of unstable population markers in the model did not decrease the precision of the population estimates providing that stable markers such as acesulfame remained in the model. However, to achieve the minimum uncertainty in population estimates, we propose that the core markers to be included in population models for other sites should meet two additional criteria: (3) negligible degradation in wastewater to ensure the stability of chemicals during collection; and (4) < 10% in-sewer degradation could occur during the mean residence time of the sewer network.