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. 2026 Sep 25;16(45):57730–57741. doi: 10.1039/d6ra06792a

Stormwater impacts on the elemental and organic contaminant profiles of wastewater effluent

Jingyi Zhou a, Raees Ahmad a, Peiying Wang a, Nubia Zuverza-Mena a, Sara L Nason a,✉
PMCID: PMC13614482  PMID: 42799474

Abstract

Stormwater input can significantly alter the composition of wastewater effluents. However, its effect on the broad spectrum of emerging contaminants remains insufficiently understood. This study investigated stormwater impacts on treated wastewater effluent in a mid-sized city in the northeastern U.S. that has partially combined sewers and bypasses secondary treatment when flow levels exceed a certain threshold. Inorganic analysis demonstrated weather-dependent increases in copper, iron, and phosphorus during wet conditions. Targeted organic analysis identified 19 compounds, including pharmaceuticals, pesticides, and others, of which 10 showed significantly elevated concentrations during rain events. Individual and cumulative risk assessments based on predicted no effect values for Daphnia magna immobilization indicated that targeted analytes in both wet and dry weather wastewater posed chronic ecological risks to aquatic organisms. Carbendazim demonstrated the highest risk, with measured concentrations more than 100-fold greater than the no effect values. Additionally, non-targeted analysis revealed noteworthy differences in composition between wet and dry weather samples, with 1191 detected features increasing during rain events and few decreasing. This emphasizes the highly complex and variable nature of stormwater influenced effluent and has implications for ecological impacts of effluent release, as well as wastewater reuse. Overall, the current study demonstrates the importance of accounting for stormwater-driven variability in wastewater monitoring and assessment.

Stormwater impacts the elemental, organic, and toxicity profiles of wastewater effluent.graphic file with name d6ra06792a-ga.webp

1. Introduction

Wastewater treatment plants (WWTPs) play a vital role in protecting surface water quality by removing contaminants and pathogens from municipal and industrial wastewater (WW) prior to discharge.1 Despite substantial advances in WW treatment technologies, treated effluents still contain a wide range of chemical contaminants originating from domestic, commercial, and industrial activities.1–3 However, these unregulated pollutants that may pose environmental or health risks are not fully removed during conventional treatment processes and are released into surface water. The presence of these contaminants in treated wastewater (TWW) has gained a lot of attention regarding their persistence and accumulation in the environment, as well as their potential negative impacts on ecosystem and human health.1,3–5

Among the diverse contaminants detected in WW effluents, concerns about emerging contaminants (ECs) have grown, given their widespread occurrence and potential biological activity.5,6 ECs include a broad group of natural and synthetic chemicals such as pharmaceuticals, personal care products, and endocrine-disrupting compounds that are not fully removed during conventional WW treatment processes.2,4 Inorganic elements, including metals and nutrients, represent an additional class of contaminants that contribute to the chemical complexity of WW effluent. Previous studies have demonstrated the presence of ECs in WW effluents and downstream surface water worldwide and have documented that municipal WWTPs are one of the major basins of down-the-drain ECs.3,7–12 Such combined existence of organic and inorganic contaminants contributes to the overall chemical complexity of WW effluent and highlights the need for integrated analytical approaches to characterize contaminant mixtures in TWW.

High variability is found in TWW composition, commonly due to changes in industry and household activities, WWTP functions and capacities, and weather. Previous study by Comber et al. carried out in United Kingdom demonstrated that process technology, dilution, geography, and season are significant factors contributing to the large variations of contaminant concentrations in WW effluents.13 Specifically, higher concentrations of tested contaminants were observed in colder months compared to summer. Another study conducted in southern California, USA also showed seasonal variations in concentrations of pharmaceuticals and personal care products in reclaimed WW.14 Among the 26 tested contaminants, 14 were found in dry season WW samples, ranging from 0.65 µg L−1 to 22.3 µg L−1. In contrast, 11 of 26 were found in wet season WW samples. The ones that were found in both dry and wet season WW samples showed higher concentration in dry season WW compared to wet season WW.14 However, in study where combined sewer systems or rainwater infiltration resulted in untreated WW bypassing conventional treatment to be discharged with stormwater, wet weather WW samples exhibited higher concentrations of ECs than dry weather WW samples.15

In many regions of the United States and worldwide, stormwater inputs enter sewer systems through combined sewer infrastructure and through unintentional infiltration and inflow into sanitary sewer networks.16–18 Although a large flow of stormwater entering the WW collection system can dilute the contaminants in municipal WW, it also brings in additional chemicals of concern, such as contaminants derived from urban surfaces, roadways, atmospheric deposition, fertilizers, and other non-point sources, in a relatively short period of time. Additionally, large storms can surpass WWTP capacity, reducing efficacy of treatment. Therefore, both influent composition and effluent quality may be significantly altered during wet weather.15 Storm events can simultaneously influence nutrients and metals through runoff and resuspension, while also altering the occurrence and abundance of organic contaminants through runoff inputs, sewer-system mobilization, dilution, and changes in treatment performance.

However, relatively few studies have systematically evaluated how stormwater influences the contaminant profiles of TWW effluent. Previous research has often focused on a limited number of compounds or specific contaminant classes.19–22 As a result, assessments that consider both organic micropollutants and inorganic elements remain limited. Furthermore, many existing studies rely primarily on targeted analytical approaches, which focus on predefined lists of contaminants and may therefore neglect unknown or unexpected compounds present in WW effluent.

The current study aims to investigate the changes in TWW effluent composition associated with wet-weather events at a WWTP in a medium-sized northeastern US city. Effluent samples collected during both wet and dry weather events were analyzed to evaluate weather-dependent variations in chemical composition. Targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis was used for targeted analysis of 70 selected organic contaminants, including pharmaceuticals, pesticides, antibiotics, tire wear chemicals, and more. In addition, LC-HRMS-based non-targeted analysis was used to characterize additional chemical features and identify rain-responsive and rain-stable compounds. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) was used to characterize inorganic elements. By integrating these complementary analytical approaches with paired wet- and dry-weather sampling, this study evaluates how wet-weather conditions and associated secondary-treatment bypass coincide with changes across multiple contaminant classes in TWW effluent.

2. Materials and methods

2.1. Wastewater sampling

Wastewater (WW) samples were collected from a moderately sized, secondary treatment plant that services a population of approximately 200 000 and has capacity of 40 MGD (million gallons per day). Influent is predominantly municipal sewage, with minor industrial discharges a large regional hospital, and multiple universities also having influence. Treatment consists of preliminary screening and grit removal, primary sedimentation, conventional activated sludge (CAS) treatment with biological nitrogen removal (BNR), secondary clarification, and chlorine disinfection prior to discharge (Fig. 1), with a typical residence time of approximately 16 hours. While parts of the city have separate storm and sanitary sewers, there is still significant area where they are combined, leading to significantly increased inflow during rain events. At flow rates above approximately 60 MGD, the treatment plant begins to bypass secondary treatment for a portion of effluent, allowing it to flow directly from the grit chamber to the chlorination system prior to discharge into surface water.

Fig. 1. Wastewater treatment flow diagram.

Fig. 1

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Effluent samples were collected for 24 hour periods during rain (wet) and dry weather by an automatic sampler (Teledyne ISCO 6712, Lincoln, NE) between April 2022 and January 2023. The sample collection bottles were pre-cleaned with methanol twice and rinsed with ultrapure water three times prior to each sampling event. During each rain event, three composite effluent samples were collected, aiming to sample TWW before rain (low flow, composite A), first flush (3 hours surrounding the increase of flow at the start of the storm, composite B), and during rain (collected during the remainder of increased flow, composite C). Additionally, field blank samples were collected before and after each sampling event by running an ultrapure water sample through the automatic sampler. Initial and final field blanks were composited for each trip. Paired dry weather samples were collected at equivalent time intervals to each wet weather sample within approximately 1 week to provide a direct comparison with wet weather samples. Samples were collected in five pairs, i.e., 5 rain events and 5 dry days. An overall experimental setup diagram is shown in Fig. 2. Detailed WW sampling information is listed in Table S2. We note that two wet water sampling trips (3 and 5) had flows that exceeded the 60 MGD bypass threshold (97.2 and 90.3 MGD, respectively), suggesting a portion of the effluent bypassed secondary treatment. All the samples (including field blanks) were transported to the laboratory and filtered through quantitative filter paper (Whatman® Grade 40) into pre-cleaned brown glass bottles and stored at −20 °C before analysis. Bottle precleaning consisted of baking at 400 °C then rinsing with methanol twice and ultrapure water three times. Note that throughout the manuscript, wet WW refers to TWW effluent collected during rainfall events with increased flow, whereas dry WW refers to TWW effluent collected during the periods without rainfall.

Fig. 2. Experimental setup schematic diagram of the study.

Fig. 2

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2.2. Elemental analysis

The WW samples were acidified using 70% nitric acid (HNO3, Fisher Scientific) to achieve a final acid concentration of 2% (v/v). Analysis was conducted using ICP-OES (iCAP 6500, Thermo Fisher Scientific, Waltham, MA), which was calibrated with a method blank of 2% HNO3 and 0.1 ppm, 1 ppm, and 10 ppm standard solutions (quality control standard 7 and 21, SPEX CertiPrep, Metuchen, NJ), with 10 ppm yttrium as internal standard. Three replicates of each sample were analyzed, and a continuing calibration verification was analyzed after every 15 samples. Only samples from pairs 2–5 received elemental analysis. Method validation information is provided in SI section 1.3.

2.3. Organic analysis

Water samples were extracted and analyzed in duplicate following a method based on Robles-Molina et al.23 Details of validation are available in SI Section S1.3. Water samples (50 mL) were spiked with internal standards (0.1 ng mL−1) and acidified to pH 4 with 1 M HNO3, before solid phase extraction (SPE). The SPE cartridges (Oasis HLB 3 cc, 60 mg, Waters, Milford, MA) were conditioned with 2 mL of methanol and 4 mL of Milli-Q water. After loading water samples, the cartridges were dried under vacuum for 2 h, and the target compounds were eluted from the cartridges using 2 × 1 mL of methanol. The extracts were evaporated under N2 flow in a 50 °C water bath and reconstituted in 500 µL of 50% methanol/water (v/v) and thoroughly mixed. Extracts were then filtered using 2 µm polyethylene filters into 2 mL autosampler vials for analysis.

Analysis was performed using LC-HRMS, consisting of an Ultimate™ 3000 liquid chromatograph and a Q-Exactive™ mass spectrometer (Thermo Fisher Scientific). Chromatographic separation of compounds was performed at 40 °C using Hypersil GOLD™ C-18 column (100 mm × 2.1 mm, 1.9 µm particles, Thermo Fisher Scientific). Mobile phases were 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). Injection volume was 2 µL and flow rate was 300 µL min−1. The column oven was kept at 40 °C and the autosampler at 10 °C. The solvent gradient is provided in Table S3, and ranged from 3% B to 100% B.

A calibration curve containing 70 analytes and 4 mass labeled internal standards was used (Table S1). The calibration range was 0.1 to 200 ng mL−1. All sample extracts and standards contained 10 ng mL−1 of mass labeled internal standard. Calibration standards and mass labeled internal standards were stored at −20 °C in brown glass bottles prior to analysis. Samples were run on the instrument in a single batch, and each sample was injected once. Sample order was randomized and a solvent blank and a standard solution were analyzed after every 10–15 sample to track instrument performance. Data-dependent acquisition (ddMS2) was performed on a Thermo Q-Exactive mass spectrometer using electrospray ionization (ESI) in positive ion mode. Detailed instrument method parameters are listed in Table S4. Preliminary testing using ESI negative ionization indicated few additional contaminant detections. Thus ESI negative ionization was not used in the present study.

The list of standards was determined based on previous findings for contaminants in WW effluent and availability of in-house standard mixtures.3,24 Mass-labeled standards were selected to represent the range of chemical properties present in targeted analytes. Note that PFAS were not able to be targeted in this study due to lack of access to appropriate sampling equipment.

2.3.1. Targeted data processing

Quantitative targeted analysis was performed in TraceFinder 4.1 (Thermo Fisher Scientific) using Full MS scans. Automated Genesis peak integration was used (8 smoothing points), and integrations were manually curated to ensure accuracy. Compounds that were inconsistently detected between duplicates or showed higher concentrations in extraction blanks than in test samples were excluded from the analysis.

2.3.2. Non-targeted analysis (NTA) data processing

NTA was performed using Compound Discoverer version 3.3 (CD3.3). The processing workflow is outlined in Fig. S2, and the specific parameters are provided in Table S10. The data processing workflow was designed to: (i) align the chromatographic retention times of the input data files, (ii) extract all chromatographic peaks that met predefined criteria, (iii) assign a molecular formula to each compound, and (iv) perform compound identification by screening MS2 data against built-in mzCloud library in Compound Discoverer 3.3.

The whole list of detected features (with blank subtraction) was used for principal component analysis (PCA) and volcano plot analyses. Subsequently, feature filtering was performed to generate quality lists of tentative identifications in different sample types, as shown in Fig. S3. These criteria included blank subtraction, full matching against the MS2 mzCloud library, selection of features with corresponding MS2 data, and the use of adjusted p-values and ratios across sample groups. Detections were manually reviewed using FreeStyle Software to ensure accuracy. The identified compounds are presented in two separate lists, each generated using specific data filters. Fig. S3A depicts the data filter applied to generate the list of rain-responsive compounds, while Fig. S3B shows the data filter used for the list of rain-stable compounds. Rain-responsive compounds had an adjusted p-value <0.05 when comparing peak areas in wet weather composite C to wet weather composite A or to dry weather composite C. Rain-stable compounds had p-values >0.05 for these comparisons. Features reported in this manuscript include level 1 (verified with a standard) and level 2a (verified with MS1 and MS2 spectral matching with the mzCloud library) identifications using the Schymanski scale.25

2.5. Statistical analysis

Results are reported as mean ± standard error (SE). Potential outliers were evaluated on a case-by-case basis, considering both statistical deviation and analytical context. Data points were excluded only when there was clear evidence of measurement error or analytical anomalies. To determine significant differences between groups, a One-Way Analysis of Variance (ANOVA) was performed, followed by Tukey's multiple comparison test. For each element and targeted organic compound, all composites and blanks for wet and dry weather were included in the statistical comparisons (A, B, C, and field blanks), though not all are shown in the results figures. All data were analyzed using SPSS 29.0 software (Chicago, IL) and graphs were plotted using GraphPad Prism software (San Diego, CA).

2.6. Ecotoxicological risk assessment

The potential ecological risks posed by the detected compounds were evaluated using a risk quotient (RQ)-based approach following the ecological risk assessment framework outlined in the European Commission Technical Guidance Document (TGD).26 The assessment included all 19 target compounds identified in effluent samples, consisting of nine rain-stable compounds and ten rain-responsive compounds.

The toxicity data available from the EPA ECOTOX database was utilized for chronic endpoint selection. No-observed-effect concentration (NOEC) for Daphnia magna was selected as the standard representative chronic toxicity endpoint, because among the aquatic organisms evaluated, Daphnia magna exhibited the highest sensitivity for the majority of investigated compounds. NOEC were estimated using the OECD QSAR Toolbox v4.8.2,27 particularly for compounds lacking adequate experimentally derived toxicity data. The QSAR Toolbox estimates ecotoxicological endpoints based on molecular structures, physicochemical properties, and toxicity information from structurally similar substances, providing a scientifically supported approach for preliminary hazard characterization.

The ecotoxicological risk for each contaminant was calculated considering its predicted no-effect concentrations (PNEC) and measured environmental concentration (MEC) values. The PNEC values were calculated as follows:

2.6. i

where AF in eqn (i) represents the assessment factor. An assessment factor of 100 was applied based on the ECHA guidance on information requirement and chemical safety assessment (Chapter R.10, Table R.10.4) to account for uncertainties associated with interspecies extrapolation, differences in species sensitivity, and the limited availability of chronic toxicity data for environmental risk characterization.

The ecological RQ for each compound was subsequently calculated by eqn (ii):

2.6. ii

The calculated RQs were interpreted according to previously established ecological risk classification criteria.28 Specifically, RQ values <0.1 were classified as representing no ecological risk, 0.1 < RQ < 1 as negligible risk, 1 < RQ < 10 as low risk, 10 < RQ < 100 as medium risk, and RQ > 100 as very high risk.

Furthermore, the cumulative ecological risk of the detected compounds was assessed using the concentration addition (CA) model.29 The CA model assumes that compounds have similar modes of action and contribute additively to the overall toxic response. The cumulative risk quotient (RQmixture) was calculated according to eqn (iii).

2.6. iii

3. Results and discussion

3.1. Elemental profiles of wastewater effluent

Twenty-six elements were analyzed in the collected WW effluent samples and 15 were detected. Weather dependent concentration changes were noticed for Cu, Fe, and P (Fig. 3). Specifically, the level of Cu was found to be around 4 µg L−1 before the rain started, which then experienced a significant increase to approximately 10.8 µg L−1 during the first 3 hour flush. The average level of Cu dropped to 7.4 µg L−1 in the remainder of increased flow (Fig. 3A). A similar trend was observed for Fe. Specifically, the concentration of Fe also significantly increased during the first 3 hour flush of increased flow and then decreased in the remainder flow (Fig. 3B). An increase in P level was also observed during the first flush, although not significant, while a substantial increase was noticed in the remainder flow, which reached approximately 2260 µg L−1 (Fig. 3C). When comparing across the dry weather composites (A wet, and A, B, and C dry), there were no significant differences in concentrations of Cu, Fe, and P. The increased levels of Cu and Fe during early rainfall may come from increased sediment levels. Although the increase of Cu and Fe in WW effluents was significant after rain, their observed levels are still below the upper limits for concentrations in drinking water published by U.S. EPA and WHO, and are not likely to be toxic in the environment.30,31 On the other hand, the observed increase of P is likely due to decreased treatment of high WW flows and increased amounts of fertilizer runoff entering sewers during rainfall. The P concentrations for both dry- and wet-weather samples are lower than typical levels in municipal WW.32

Fig. 3. Concentration of (A) copper (Cu), (B) iron (Fe), and (C) phosphorous (P) of wastewater effluent collected during wet and dry weather. Data are means of four replicates ± standard error (SE). (*p ≤ 0.05; **p ≤ 0.01; n = 4, one-way ANOVA with a Tukey post hoc test). On wet sampling days, A samples are before the rain, B is first flush, and C is during the remainder of the rain.

Fig. 3

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No weather dependent effect on concentration was observed for other detected elements, including Al, B, Ca, K, Li, Mg, Mn, Na, Ni, S, Si, and Zn (Fig. S4). The concentration of Ag, As, Cd, Ce, Co, Cr, Mo, Pb, Sb, Se, and Ti in effluents were below detection. Ni and Al had similar levels in effluent samples and field blanks. The average levels of these elements are summarized in Table S11.

The concentration of dissolved elements in TWW effluents is an important and necessary factor in evaluating overall water quality. Elevated level of these elements could contribute to increased chemical loading in receiving surface waters, potentially leading to degraded water quality. For instance, the accumulation of nutrients such as P, may promote eutrophication, resulting in algal blooms, oxygen depletion, and associated ecological impacts on aquatic organisms.33 The P levels detected in this study are well above the ecological threshold associated with eutrophication risk (25 µg L−1),34–36 but we note that the effluent from this treatment plant is significantly diluted upon release to surface waters. The P levels in the dry-weather samples (B dry and C dry) and the A wet sample (collected prior to rainfall on wet days) were all below recommended discharge limits (1000–2000 µg L−1).37,38 In contrast, the wet-weather samples (B wet and C wet) exceeded these limits, indicating an elevated risk of eutrophication specifically associated with wet-weather discharge events.

Additionally, Na also represents a specific concern, with an average concentration of around 200 ppm (Table S11), which is relatively high and could increase water salinity. Elevated salinity can negatively affect freshwater ecosystems and may also reduce the suitability of water for drinking and pose potential health concerns. Although there is no health-based guideline of Na level in drinking water proposed by the WHO, there could be taste issues with Na levels above 200 ppm.39

Heavy metals could pose direct toxicity risks depending on their concentration and speciation. Heavy metals commonly reported in WW effluents, such as Pb, Cd, and Cr, were not detected in this study. This may reflect relatively low concentrations in the source or effective removal during treatment processes. In addition to the factors discussed above, the absence of detectable heavy metals may also be related to analytical limitations, though we note that our limits of detection were below levels designated in U.S. EPA drinking water guidelines.40 Further monitoring using more sensitive analytical methods may be required to fully assess level of trace metal. Overall, careful management of TWW effluent discharge is necessary to minimize impacts on surface water quality and protect downstream water resources.

3.2. Organic analytes in wastewater effluent

3.2.1. Targeted organic analytes

Overall, 19 targeted organic analytes were detected, including 11 pharmaceuticals, 6 pesticides, 1 industrial chemical, and 1 personal care product (Fig. 4 and S5). Regardless of the weather condition, there were no differences (p > 0.05) between first flush samples (B wet) and samples collected during the remainder of the rain (C wet), though high variability was noted in first flush samples (B wet). Therefore, subsequent analyses focus only on composites A (before rain) and C (during rain).

Fig. 4. Concentration of (A) carbamazepine, (B) carbaryl, (C) carbendazim, (D) carbofuran, (E) codeine, (F) diphenhydramine, (G) diuron, (H) levorphanol, (I) losartan, and (J) oxamyl in treated wastewater samples. Data are means of ten replicates ± standard error (SE). (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.005; ****p ≤ 0.001, n = 10, one-way ANOVA with a Tukey post hoc test). Each point represents the average of duplicates for each sampling trip. On wet sampling days, A samples are before the rain and C is during the rain, after first flush.

Fig. 4

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Ten compounds, including 5 pharmaceuticals (carbamazepine, codeine, diphenhydramine, levorphanol, and losartan) and 5 pesticides (carbaryl, carbendazim, carbofuran, oxamyl, and diuron) showed significantly increased concentrations in rain events. Nine compounds were elevated during rain (composite C) compared to before the rain (composite A) during rain sampling events and three showed a significant weather-dependent increase when comparing composite C in wet and paired dry weather samples (Fig. 4). During dry weather, there were no significant differences between composites A and C. Additionally, there were no differences between the concentrations seen on dry days (A dry and C dry) and those seen before the rain started on wet days (A wet).

All detected compounds were subjected to aquatic risk characterization for chronic toxicity using the RQ approach and data for Daphnia magna toxicity (Tables S13 and S14). Of the 19 analytes, two were detected at concentrations of high risk (RQ > 10). Notably, levorphanol was in the medium risk category (1< RQ < 10) during dry weather (RQ = 2.16), and high risk during wet weather (RQ = 14.53). Carbendazim had the overall highest RQ at 151 in dry weather and 272 in wet weather. Benzotriazole, a rain-stable analyte, had an RQ of 1.25, in the low risk category. The remaining analytes were designated as negligible to no risk. Using a summative approach to assess mixture toxicity, the rain-responsive mixture is dominated by the high risk compounds (dry RQ = 154, wet RQ = 290). The rain-stable analytes have a total RQ of 2.61. Although this approach may overestimate mixture toxicity when compounds differ in their mechanisms of action, it represents a valuable worst-case scenario assessment for identifying potential ecological concerns associated with multi-contaminant exposures in aquatic environments.

3.2.2. NTA of organic chemicals

The NTA workflow implemented in CD 3.3 for the WW samples initially identified 2691 features after blank subtraction. PCA of all detected features reveals a clear separation among the sample groups (Fig. 5A). PC1 and PC2 together explained approximately 77% of the total variance (PC1 = 66.4%, PC2 = 10.8%). In general, there is good agreement between duplicate samples. Dry weather samples (including A wet) cluster together strongly (green oval). Samples during rain events (C wet) also cluster and are well distinguished from other composites. However, there are variations across all C wet samples over time, which may be attributed to differences in the seasons during which the effluent samples were collected. First flush samples (B wet) displayed a broader distribution, suggesting more heterogeneous characteristics, in line with previous work.41–43 As first flush refers to the initial surface runoff, its chemical composition varies greatly depending on several factors, such as timing of the storm event, rainfall intensity, and the length of antecedent dry weather periods, etc.44 Volcano plot analysis (Fig. 5B) of all the features with a p-value ≥0.05 and log 2 fold change ≥2 when comparing C wet and dry samples, reveals that a substantial percentage of features have higher peak area in the wet weather samples than in the dry weather. When comparing C wet vs. C dry a total of 1191 compounds exhibited significantly higher abundances in wet samples, while only 86 compounds were significantly higher during dry weather.

Fig. 5. (A) PCA plot of composite A, B, and C collected during wet and dry events from different trips. (B) Volcano plot of wet and dry samples data showing −log 10 (p-values) against log 2 fold changes. The highlighted regions indicate significant different compounds, defined as a p ≥ 0.05 and log 2 fold change ≥2.

Fig. 5

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Of the detected features, 162 were confidently identified using the mzCloud library and classified as rain-responsive (84 compounds) or rain-stable (78 compounds) using the data filters shown in Fig. S3. Among the rain-responsive compounds, endogenous metabolites and pharmaceuticals (including pharmaceutical metabolites) together accounted for more than 85% of the detected chemicals (Fig. 6A). In contrast, among the compounds that did not show a weather-dependent effect, more than 62% were pharmaceuticals (Fig. 6B). This pattern suggests that wet-weather conditions did not affect compounds within the same chemical category uniformly and that compounds with different environmental persistence, treatment removal, and transport behavior may respond differently. The complete lists of rain-responsive and rain-stable compounds are included in Tables S15 and S16. The NTA results confirm the detection of 14 of the targeted analytes.

Fig. 6. Classification of compounds detected via NTA: (A) compounds exhibiting higher abundance after rain and (B) compounds with no or minimal abundance variation after rain.

Fig. 6

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3.2.3. Organics discussion

Overall, there was a significant increase in a substantial subset of detected organic contaminants during wet events, consistent across both targeted and non-targeted analyses. Measured levels of targeted analytes showed high risk for toxicity during dry weather that was further increased during wet weather. Carbendazim, a commonly used fungicide had especially toxic levels, with the RQ exceeding 150 in dry weather and reaching 272 during rain events. The measured concentrations in our study are much higher compared to levels reported in TWW from studies conducted in Germany,45 Switzerland,46 and Chile.47 Concentrations of benzotriazole, the highest risk rain stable analyte (classified as low risk based on the RQ approach), were also higher than has been reported elsewhere.48,49 Other detected analytes including carbamazepine, carbaryl, losartan, and diuron are close to levels reported in the literature.50–53 Overall, the high total RQ from the targeted analytes combined with unknown levels of toxicity from non-targeted compounds, indicates that this WW effluent is a significant risk for aquatic organisms. While risk is higher during increased flow due to stormwater, dry weather WW is still a concern.

The increase in contaminant levels during rainfall is overall consistent with existing literature, which has identified various contaminants increase by rainfall runoff including biocides and insecticides.54,55 Additionally, desorption of organic contaminants from sediments that accumulated in the sewer system during preceding dry periods has been observed.15 Compounds, such as carbamazepine, diphenhydramine, and diuron, are known for being persistent in the environment and are poorly removed or broken down in WWTPs.56–58 Therefore, these persistent compounds could accumulate or be retained within the WW collection system during preceding dry periods and could become mobilized during increased flow. Sewer system overflows could be another potential reason for contaminant increases, as WW bypasses treatment and enter the effluent stream directly, consequently leading to an increase for contaminants. The impact of increased flow on the dilution of contaminant concentrations appears to be minimal.

Although both the targeted and NTA results showed a similar pattern with increased concentrations or abundances observed for a substantial subset of detected contaminants while many compounds still remained relatively stable, the profiles of rain-sensitive vs. rain-stable analytes were distinct in both datasets. In the targeted results, 5 out of 6 detected pesticides were increased during rain events. This may reflect contributions from stormwater runoff from outdoor areas treated with pesticides, although increased flow and secondary-treatment bypass may also have contributed to the observed increases. Atrazine, the only targeted pesticide that was not weather sensitive, was detected at lower levels and is also the most hydrophobic of the detected pesticides.

In the NTA, there were more than twice as many endogenous metabolites identified as rain responsive compared to those that were rain stable. This is a logical outcome, as secondary treatment was bypassed during rain events, and much of the organic matter in WW is broken down during activated sludge processing. Additionally, stormwater influx could be a potential source of additional organic contaminants.

In both targeted and non-targeted results, pharmaceuticals were common in both categories, but the majority were not rain responsive. In some cases, chemicals and their metabolites were in different categories. For example, carbamazepine was identified as rain responsive in the targeted analysis, but NTA identified 3 carbamazepine metabolites as rain stable. Likewise, sulfamethoxazole and benzotriazole were found to be rain stable in the targeted analysis, but each had a metabolite identified as rain responsive in NTA. This suggests that the effluent concentrations of organic contaminants are impacted by transformation processes that differ as the water flow and treatment process change with the weather.

Chemical properties may also impact their behavior in WW in different weather conditions. In the targeted results, the average log Kow value for the rain responsive compounds was lower, though the range was overlapping (−0.5 to 3.11 for rain responsive and 0.7 to 5.0 for rain stable, Table S1). Notably, this trend was not present in the non-targeted results, which had similar log Kow profiles for rain stable and rain responsive analytes (Tables S15 and S16).

While both targeted and non-targeted results are valuable, there are some potential shortcomings of the NTA results we have presented. Due to software limitations, we did not conduct internal standard-based normalizations for the NTA data. This resulted in several targeted compounds being placed in a different category during NTA. Six rain responsive targets were determined to be stable based on NTA, while one rain stable target (sulfamethoxazole) was determined to be rain responsive. The difference for sulfamethoxazole was likely due to differences in the statistical tests used, as the targeted data visually shows an increase in C wet samples over the other categories (Fig. S5). Overall, this indicates that NTA potentially underreported rain-sensitivity, though the majority of detected features were already found to be rain responsive (Fig. 5B). Additionally, hydrocodone was identified using NTA, while it was found inconsistently and had poor recovery in targeted testing. This finding is representative of the poorer quality control that is possible with the NTA techniques utilized in this project.

The analytical workflow also has inherent selectivity that may limit chemical coverage. In this study, positive-mode ESI was used to maximize the breadth of detectable features within a single LC-HRMS workflow. However, compounds that ionize preferentially in negative mode may have lower detection sensitivity or may not be detected under the current testing conditions. In addition, the SPE extraction and methanol elution conditions are not suitable for all analytes and favor moderately hydrophobic compounds. Therefore, non-detection in the NTA dataset should not be interpreted as evidence of the absence of a compound from the WW effluent. Complementary analyses using negative-mode ESI and/or additional extraction conditions could provide broader chemical coverage in future studies. Furthermore, since influent samples were not collected in the current study, whether the observed increases in effluent concentrations resulted primarily from increased contaminant loading associated with stormwater inputs, reduced removal during secondary treatment bypass, or a combination of these processes cannot be determined.

4. Broader environmental implications

The results of the current work suggest that stormwater-impacted WW effluent should be considered an event-driven source of complex contaminant mixtures instead of an average-driven condition. While stormwater may be expected to dilute WW-derived contaminants, both the targeted and non-targeted results in this study indicate that wet-weather conditions can instead increase the abundance of multiple organic contaminants, including pesticides, pharmaceuticals, pharmaceutical metabolites, and endogenous metabolites. This finding is environmentally relevant because contaminant exposure may be driven by short-duration wet-weather pulses that are not captured by routine dry-weather monitoring. Overall, our results highlight the significant influence of stormwater on WW effluent compositions. These changes likely reflect a combination of stormwater-associated contaminant inputs, dilution, mobilization within the wastewater collection system, reduced contaminant removal during secondary treatment bypass, and in-sewer transformation process which warrant further investigation. In addition, while first flush and subsequent flow were distinguished, the high variability observed in first flush samples likely reflects rapid and heterogeneous dynamics that were not fully resolved by the sampling approach, limiting their inclusion in comparative targeted organic analyses. Furthermore, as this study was conducted at a single treatment facility, the generalization of these findings to other systems may be limited and depend on local infrastructure and flow conditions.

Wet-weather changes in effluent are also relevant for receiving waters. The observed increases in Cu, Fe, P, and organic contaminants together suggest that stormwater-influenced effluent may contribute multiple stressors simultaneously. The co-occurrence of organic and inorganic compounds may also increase chemical mixture complexity and potential toxicity. The combined occurrence of nutrients, metals, pesticides, pharmaceuticals, and transformation products highlights the necessity to evaluate WW effluent as a dynamic mixture rather than as a set of isolated contaminants. It is essential to consider cumulative exposure and potential ecological impacts in receiving surface waters. Notably, the individual and cumulative risk assessments in this study showed high risk in both wet and dry weather wastewater, with significant increases in risk during wet weather, particularly from carbendazim. Overall, there is potential for chronic toxic impacts to sensitive aquatic organisms such as Daphnia magna.

Future studies should expand these assessments toward a multi-trophic approach. The QSAR predictions utilized here provide a practical basis for preliminary toxicity screening but cannot substitute for experimental toxicological evidence. Further in vitro or in vivo toxicity testing is therefore needed for future studies to validate these predictions and establish the ecological risks associated with the detected contaminants. Additionally, approaches for integrating NTA data into toxicity assessment are needed.

These findings also have implications for WW reuse and wet-weather management. Stormwater inputs may introduce contaminants from urban runoff, mobilize materials accumulated in sewer systems, and alter treatment performance during high-flow conditions. If TWW is used for irrigation or other reuse applications, changes in effluent composition during storm events may affect the suitability and risk profile of the water. Additionally, the results indicate that monitoring programs based primarily on dry-weather or routine sampling may underestimate contaminant variability and peak chemical loads. As extreme precipitation events become more frequent in many regions, it is increasingly important and indispensable to understand how stormwater inputs, inflow, and treatment bypass influence effluent quality in order to plan for WW infrastructure, receiving water protection, and contaminant management.

Author contributions

J. Z. methodology, data curation, formal analysis, investigation, validation, visualization, writing – original draft, writing – review and editing; R. A. data curation, validation, visualization, writing – review and editing; P. W. conceptualization, methodology, writing – review and editing; N. Z. conceptualization, methodology, resources, writing – review and editing, funding acquisition, project administration, supervision. S. L. N. conceptualization, methodology, data curation, resources, visualization, writing – original draft, writing – review and editing, funding acquisition, project administration, supervision.

Conflicts of interest

None to disclose

Supplementary Material

RA-016-D6RA06792A-s001
RA-016-D6RA06792A-s002

Acknowledgments

This work was supported by USDA NIFA Agreement 2021-67019-33679. We would like to thank the relevant wastewater treatment plant staff for their participation in this project. Additionally, we would like to thank Simon Duggan for help with water sampling.

Data availability

Data, as needed to understand and verify the research, are provided in the main text and supplementary information (SI). Raw instrumentation files are available from the authors upon request.

Supplementary information: sections on chemicals and materials, details of sample collection including times, dates, WWTP flow rates, and total rainfall amounts, instrumental analysis parameters, method validation data, data processing parameters for NTA, and detailed results for chemicals in wastewater that were not weather dependent. See DOI: https://doi.org/10.1039/d6ra06792a.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

RA-016-D6RA06792A-s001
RA-016-D6RA06792A-s001.pdf (939.9KB, pdf)
RA-016-D6RA06792A-s002
RA-016-D6RA06792A-s002.xlsx (37.2KB, xlsx)

Data Availability Statement

Data, as needed to understand and verify the research, are provided in the main text and supplementary information (SI). Raw instrumentation files are available from the authors upon request.

Supplementary information: sections on chemicals and materials, details of sample collection including times, dates, WWTP flow rates, and total rainfall amounts, instrumental analysis parameters, method validation data, data processing parameters for NTA, and detailed results for chemicals in wastewater that were not weather dependent. See DOI: https://doi.org/10.1039/d6ra06792a.

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