An Urban Stormwater Contaminant Signature: Defining Priority Contaminants for Urban Stormwater Research

ABSTRACT

Stormwater ponds (SWPs) are a common feature of urban landscapes, designed to manage runoff and reduce flooding. Increasingly, they are also recognized as seminatural habitats supporting aquatic biodiversity. However, SWPs receive complex mixtures of contaminants from surrounding urban areas, and the extent of contamination within the ponds themselves remains underexplored. Most studies have focused on outflows or a narrow set of targeted analytes, limiting our understanding of the exposure risks for organisms residing within these systems. To address this gap, we assessed contaminant profiles in 21 SWPs across a highly urbanized city in Ontario, Canada, using three complementary sampling approaches: time‐integrated water samples, biofilm on artificial substrates, and organic diffusive gradients in thin films (o‐DGTs). Across all sites, we detected 200 organic compounds, including pesticides, pharmaceuticals, industrial chemicals, and compounds, linked to vehicles and infrastructure. Additionally, we documented widespread chloride and fecal contamination and elevated levels of traffic‐related metals in biofilms. From these data, we identified a set of frequently detected and environmentally relevant contaminants, which we term the urban stormwater contaminant signature (USCS). This proposed list may support the development of targeted monitoring strategies and help focus future research on mixture toxicity and risk to aquatic biota. Given the apparent ecological role of SWPs and the range of stressors they contain, assessing cumulative exposures is critical for understanding the potential impacts of urban runoff on resident organisms.

Summary

• Stormwater ponds accumulate diverse contaminants, including pesticides, pharmaceuticals, traffic metals, and fecal bacteria, across water and biofilm matrices.

• Targeted monitoring revealed recurring contaminants across sites, supporting the creation of an urban stormwater contaminant signature (USCS).

• The USCS can streamline monitoring efforts by focusing on frequently detected, environmentally relevant urban contaminants.

• Ecotoxicological assessment of urban contamination should consider cumulative exposure risks to resident aquatic organisms

We identified a core suite of frequently detected contaminants in urban stormwater ponds using three complementary sampling methods. This urban stormwater contaminant signature (USCS) highlights cumulative exposures in pond ecosystems and supports more targeted monitoring of urban runoff.

1. Introduction

Chemical pollution is a pervasive and escalating threat to global biodiversity, contributing significantly to ecological degradation across a wide range of environments (Groh et al. 2022; Sigmund et al. 2023; Osukoya et al. 2024). Globally, the number of registered chemicals and commercial mixtures is well over 350,000 (Wang et al. 2020). Stormwater management ponds (SWPs) are receiving bodies for chemical pollution from wet‐weather discharge in urban areas. These highly engineered hydraulic features integrate many contaminants from the surrounding catchment prior to their release into natural habitats, making them an excellent focal point for environmental monitoring in urban areas (Marques et al. 2024). SWPs can act as a barometer of contaminant exposure in urban landscapes because they accumulate contaminants from their sewersheds over spatial and temporal variability in concentration and occurrence (Gallagher et al. 2011; Tixier et al. 2011; Flanagan et al. 2021).

For the purpose of our study, we refer to the term stormwater as water from rain, melting snow, or outdoor residential discharge (e.g., car washing, lawn watering) that runs off impervious surfaces into the municipal storm sewer system. Stemming from an assortment of sources, diverse contaminants found in urban stormwater runoff can include organic contaminants, such as pharmaceuticals, pesticides, and biocides (Masoner et al. 2019); nutrients, such as phosphorus and nitrogen (Pamuru et al. 2022); metals and metalloids (Birch 2024); salt (Marsalek 2003); and bacterial contamination (Tiefenthaler et al. 2011). This assortment of contaminants is distinct from expected contaminant profiles in agricultural lotic and lentic surface waters (Gómez et al. 2012; Khatri and Tyagi 2015), and the overall toxicological risk that this mixture presents to biota living in urban environments is a complex, multifaceted area requiring more research (Tang et al. 2013; Young et al. 2018; Popick et al. 2022). In this manuscript, we present an approach to characterizing that risk through an integrated assessment of contaminants in stormwater ponds located in a highly urbanized landscape. We seek to identify the common chemicals that co‐occur and propose the term urban stormwater contaminant signature (USCS). The definition of a USCS benefits water quality monitoring and management in urban centers, including guiding source tracking and identifying priority contaminants for ecotoxicological investigations.

A contaminant signature is a distinct combination of chemical contaminants associated within a specific context. Contaminant signatures can be used as “fingerprints” to help identify sources of contamination and their contributions (Plumb 2004; Du et al. 2020; Fennell et al. 2021; Kaown et al. 2021) and to provide a basis for generalizations. Contaminant signatures have been developed for pesticides in urban streams across the United States (Nowell et al. 2021), metals in an urban river in Ukraine (Vystavna et al. 2013), and fecal pollution in Lake Michigan's urbanized coast (Newton et al. 2013). Predictable contaminant profiles have been found in other stormwater features (e.g., storm drains; Du et al. 2020; Gasperi et al. 2022). We define a UCSC as a characteristic mixture of contaminants found in stormwater runoff originating from urban areas. The USCS reflects the unique combination of contaminants introduced by human activities, infrastructure, and land use patterns within cities. Given the diversity of contaminants in urban areas, we determined that it would be useful to define a USCS to help focus future monitoring efforts of urban contamination in stormwater‐impacted aquatic ecosystems on the most ubiquitous contaminants, inform an approach to predicting the toxic effect of this commonly occurring mixture, and allow the comparison of contaminant signatures among regions to see how different urban areas might differ in their urban contaminant exposure profile.

Most assessments of contaminants in stormwater are focused on the outflows of SWPs and their potential impacts on receiving lakes and rivers (e.g., Koziel et al. 2019) or on other stormwater infrastructure (e.g., culverts and channels; Masoner et al. 2019). Consequently, research and monitoring on the level of contaminants in stormwater ponds proper are still limited. Five consecutive annual reviews of urban stormwater research found that studies tended to focus on a single contaminant type (e.g., metals, PCBs, PAHs, or pesticides), rather than surveying a broad suite of contaminant types (Vogel and Moore 2016; Moore et al. 2017, 2018; Rodak et al. 2019, 2020). A study of The International Stormwater Best Management Practices database and the National Stormwater Quality Database found a rich collection of observations of traditional water quality metrics, such as chloride and nutrient concentrations; however, reports of metal and organic contaminants were limited (Pamuru et al. 2022). Comprehensive approaches to water quality monitoring in SWPs are lacking. Yet understanding the concentration and occurrence of a broad range of possible contaminants in stormwater is necessary to evaluate the ecological risk posed to aquatic organisms that live in or are exposed to SWPs, given the diversity of potential toxicants released into urban environments (e.g., Masoner et al. 2019; Pamuru et al. 2022). With natural ponds and wetlands being replaced by SWPs at an alarming rate (Birch et al. 2022), SWPs now play an increasingly crucial role in supporting aquatic (Ferzoco and McCauley 2024) and terrestrial (Holtmann et al. 2017; Bishop et al. 2000) life as an unintentional urban habitat (McKercher et al. 2024). Conserving urban biodiversity depends on monitoring all habitat, conventional or not, where life resides.

We conducted an integrated assessment of stormwater pond water quality with three complementary sampling approaches: composite water grab sampling, biofilm sampling from artificial substrates, and passive sampling using organic diffusive gradients in thin films (o‐DGT) samplers. By sampling from three different substrate types in two different analytical labs, we were able to assess a holistic exposure profile by targeting a range of potential exposure pathways, as the presence of contaminants in the biofilm exposes aquatic organisms through their diets while the presence of contaminants in the water exposes biota through contact/gills. We measured 542 legacy and current‐use pesticides in composite water and biofilm samples and 491 urban‐use contaminants in o‐DGT samplers. We analyzed 20 metal and metalloids in biofilm samples. We also measured standard water quality parameters, including nutrient concentrations, salinity, bacterial coliforms, dissolved oxygen (DO), and total suspended solids (TSS). We used the Canadian Council of Ministers of the Environment (CCME) Canadian Water Quality Guidelines (CWQG) for the Protection of Aquatic Life to contextualize the quality of the stormwater. The research took place at 21 SWPs in a highly urbanized city representing the effects of present‐day rapid suburban expansion on stormwater runoff.

2. Methods

2.1. Study Area

We surveyed 21 stormwater ponds in Brampton, Ontario, Canada (Figure S1), one of the most rapidly expanding cities in Canada (Statistics Canada 2021). Brampton is a representative mix of urban, suburban, light industry, and highway land uses for a growing North American municipality. The ponds range in size from 1051 to 3686 m² (mean = 2272, stdev = 851 m²; McIsaac 2022). The impervious cover within 300 m of each pond ranges from 10.9% to 55.2%, representing a gradient of urbanization (McIsaac 2022; please refer to the Supporting Information for location and descriptions of each pond). These ponds are wet ponds, meaning they are designed to hold water throughout the year. We selected ponds that are at least 10 years old and had not been dredged in the past decade to prevent age or recent maintenance serving as confounding variables.

2.2. Sampling Methods

We sampled three media types (water, biofilms, and o‐DGTs) for various characteristics of contamination, as summarized in Table 1 and described in the following sections. The survey period took place over the course of 9 weeks, from May 25 to July 25, 2022.

TABLE 1.

Overview of sampling methods used to survey contamination of urban stormwater ponds (SWPs) in Brampton, Ontario.

Sampling media	Method	Analyses
Water	1.35‐L composite of 9 × 150 mL of surface water grab samples taken weekly over a 9‐week period	542 current‐use and legacy pesticides Nutrients E. coli and coliform bacteria Total suspended solids, dissolved oxygen, and chloride
Biofilm	300–500 g (wet weight) of composite samples from biofilm cultured on artificial substrates for 9 weeks	542 current‐use and legacy pesticides 20 metals and metalloids (in 15 ponds)
o‐DGT samplers	2 × 27‐day deployments of 2 duplicates	491 urban contaminants (including pesticides, pharmaceuticals, and other anthropogenic chemicals)

2.2.1. Water Sampling

Detailed sampling methods have been published in previous work (Izma et al. 2024a, 2024b, 2025). Over the 9‐week survey period, we collected weekly 150 mL of surface water grab samples to compose a time‐integrated composite sample of 1.35 L from each pond. Each composite sample was stored frozen at −20°C until submission to the Agriculture and Food Laboratory (AFL) (ISO/IEC 17025 accredited) in Guelph, Ontario.

We took weekly measurements of temperature (°C) and conductivity (mS cm⁻¹) using a Hach HQ1140 Portable Conductivity/TDS Meter (Hach Sales and Services LP, London, Canada) and DO (mg L⁻¹) using a YSI ProSolo ODO Optical Dissolved Oxygen Meter (Xylem Inc., Miami, FL) at three points in the open water at ~20 cm deep. Each week, we took 50 mL of surface water grab samples from a depth of ∼20 cm and placed them within a high‐density polyethylene Nalgene bottle to measure TSS (mg L⁻¹) gravimetrically and chloride ion concentration (Cl−; mg L⁻¹) colorimetrically, using a Chloride Checker HC (Hannah Instruments, Woonsocket, RI).

We submitted a separate, one‐time water grab sample from each site to ALS Laboratories in Waterloo, Ontario, for fluorometric determination of ammonia (NH₃), ion chromatographic determination of nitrate (NO₃) and nitrite (NO₂), and calorimetric analysis of orthophosphate (PO₄). These samples were taken during the week of June 21–27, 2022. No precipitation was recorded at the nearest weather station from June 21 to 26, and total rainfall for June 27 was 0.4 mm (Figure S2). To describe bacterial contamination, we collected water grab samples from each site on June 8, 2022, following a precipitation event on June 7 (total rainfall was measured at 12.6 mm at the nearest weather station; Figure S2), and submitted them to AFL for plate‐count analysis of total coliform bacteria and Escherichia coli .

These water chemistry parameters are summarized in Table S1. Results of all measured water chemistry parameters can be found in the Figshare repository linked in the Supporting Information and are summarized in Table S2.

2.2.2. Biofilm Sampling

At each SWP site, we cultured biofilm in situ, as described by Izma et al. (2025). Briefly, we used 10 acrylic plates (20.2 × 44.4 cm) as standard substrates on which to grow biofilm in the ponds. We suspended these on floating racks ~10 cm below the surface of the water for 54 days to allow for maximum biofilm growth (Figure S3). Upon retrieval, we scraped each side of each plate into a composite sample for each SWP. We then freeze‐dried 300–500 g (wet weight) of each composite sample using a FreeZone I Labconco benchtop freeze dryer (Labconco Corporation, Kansas City, MO) prior to submission to AFL for pesticide analysis on all 21 biofilm samples. An aliquot of freeze‐dried biofilms was taken from 15 samples with sufficient biomass, as well as a sample from a reference site, and sent to Biotron at the University of Western Ontario in London, Ontario, for analysis of 20 metals and metalloids. The reference site was the Batteaux River (44.48831889, −80.16709086), near Collingwood, Ontario. This river flows through undeveloped land and represents a relatively pristine environment (e.g., no pesticides were detected in these biofilm samples; Ijzerman et al. 2024). We sampled biofilms at this reference site under the same field sampling protocol as for the SWP sites, using similar acrylic plates as artificial substrates deployed over an 8‐week deployment.

2.2.3. o‐DGT Sampling

We conducted two consecutive 27‐day deployments of two duplicate o‐DGT (DGT Research Inc., UK; LSND‐AT with PTFE membranes; exposed area = 3.14 cm²) samplers in each pond, aligned with the water and biofilm sampling regimes. Each sampler pair was mounted on custom‐built holders (Figure S4) and attached to the biofilm sampler floating rack to suspend them at consistent depths (~20 cm) in the water column during deployment. A travel blank was used for each day of deployment and collection. After collection, samplers were wrapped in clean aluminum foil, transported on ice, and stored frozen at −20°C until processing.

2.3. Analysis

2.3.1. Organic Chemical Analysis

2.3.1.1. Water and Biofilm Analysis

All analyses of water and biofilm samples were performed at the Agriculture and Food Lab of the Laboratory Service Division at the University of Guelph (ISO/IEC 17025 accredited). Methods of these analyses have been described in previous work (Izma et al. 2024a, 2024b, 2025). Samples were analyzed for 542 current‐use and legacy pesticides using multi‐residue liquid chromatography/electrospray ionization–tandem mass spectrometry (LC/ESI–MS/MS) and gas chromatography–tandem mass spectrometry (GC–MS/MS). A list of all chemical analytes from all pesticide screens, along with their detection and quantification limits, can be found the Figshare repository, linked in the Supporting Information.

2.3.1.2. o‐DGT Analysis

o‐DGT samplers were extracted and analyzed via previously described methodologies (Izma et al. 2025). Briefly, the samplers were thawed and disassembled, and the resin gels were sequentially extracted with methanol in 15‐mL polypropylene centrifuge tubes (Corning reference 430052). Analyses for 491 contaminants (including insecticides, herbicides, fungicides, pharmaceuticals, and other anthropogenic chemicals) were conducted by Agriculture and Agri‐Food Canada (AAFC; London, ON, Canada) using liquid chromatography–tandem mass spectrometry (LC–MS/MS) with a Vanquish Duo high‐performance liquid chromatograph (HPLC) coupled to a Thermo Fisher Scientific Altis triple‐quadrupole mass spectrometer. Details of the target analytes, including their limits of detection (LODs), limits of quantification (LOQs), multiple reaction monitoring (MRM) transitions, and retention times, are available in the Figshare repository linked in the Supporting Information. LODs were calculated based on guidelines from the International Council for Harmonisation (ICH), accessed in 2019. Quantitative analysis was performed using Thermo Fisher Scientific TraceFinder 5.0 software.

2.3.2. Metal Analysis

Freeze‐dried biofilm samples from 15 SWP sites (only 15 of the 21 SWP sites contained sufficient mass for analysis) and one reference site were analyzed by Biotron Laboratory, an ISO/IEC 17025–accredited laboratory. Samples were first microwave acid‐digested following EPA Method 200.8 (USEPA 1994). Analysis was completed by inductively coupled plasma mass spectrometry (ICP‐MS) using Agilent 7700x. Na, Mg, Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, As, Sr, Mo, Cd, Sn, Ba, Pb, and Zn were analyzed using the He Gas method, and Se was analyzed using the No Gas method. The coefficient of determination (R ²) was > 0.9980. The recovery of quality control samples was within 15% of the expected value. The recovery of matrix spike and matrix spike duplicates was within 30% of the known value, and the relative percentage difference in these spikes was within 20% of the control limit. The relative percentage difference in sample duplicates was within 20% of the control limit. The method detection limits (MDL) and method recovery limits (MRL) are available in the Figshare repository, linked in the Supporting Information. The MDL and MRL are calculated once every year to comply with EPA MDL revisions (USEPA 2016).

2.4. Detection Frequencies

We define a detection as a quantified amount of a compound above the level of detection in at least one of the four samplers deployed in each stormwater pond. A detection represents an occurrence of a compound at any time point during the deployment period; thus, measured quantities above the LOD in more than one sampler per pond is still defined overall as a singular detection of that compound for that particular site. Detection frequencies were calculated by dividing the number of detections of a compound by the number of sampling sites (n = 21) and multiplying by 100%. Our criteria for inclusion within the proposed USCS was a detection frequency of 80% or higher, representing detection in at least 17 of the 21 SWPs, to encapsulate contaminants with widespread occurrences. This arbitrary threshold was chosen to align with similar studies characterizing contaminant signatures (e.g., Nowell et al. 2021; Peter et al. 2018), although alternative approaches to formally identify common toxicological mixtures exist (e.g., Scott et al. 2013; Loken et al. 2023).

3. Results and Discussion

3.1. Breadth of Contaminants in Urban Stormwater Ponds

Our results reveal a complex mixture of urban contaminants across the three matrices and evidence that water quality is impaired and may present a risk to aquatic biota (Izma et al. 2024b). SWPs are engineered primarily to manage runoff quantity, accumulate and settle excess sediment, and improve downstream water quality; however, they are increasingly recognized as de facto urban aquatic habitats (Hassall and Anderson 2015; Moore and Hunt 2012). This dual role creates tension between their intended function as treatment infrastructure and their unintended role as ecological space—one that often lacks the physical or chemical conditions needed to sustain aquatic life, such as periodic draining and dredging to maintain storage volume. Despite not being designed for habitat provision, SWPs support diverse biotic communities (Ferzoco and McCauley 2024), making it important to assess the contaminants present and consider their implications for aquatic health.

Below, we discuss the results and implications of each contaminant group, starting with commonly measured water quality parameters like chloride concentration, TSS, and nutrients, before discussing the metals and metalloids and organic contaminant categories more unique to urban environments, specifically stormwater pond water quality.

3.1.1. Chloride

Composite water samples had mean chloride concentrations ranging from 61 mg L⁻¹ to above the upper method quantification limit (MQL; 200 mg L⁻¹; Table S2), and mean concentrations in 17 of the 21 SWPs surpassed the CWQG long‐term exposure guidelines for the protection of aquatic life (120 mg L⁻¹; CCME 2011). Existing monitoring in Ontario via the Provincial Stream Water Quality Monitoring Network has also observed chloride levels in urban waters exceeding the chronic CWQG guideline since the year 2000 (Sorichetti et al. 2022). Not only do elevated chloride concentrations (i.e., above the CCME guidelines) pose direct toxicological risks (Marsalek 2003; Hassell et al. 2006; McIsaac 2022), but also excessive amounts of chloride in ponds can create density gradients that inhibit mixing and limit aeration (Szklarek et al. 2022), leading to a lack of oxygen and high chloride levels in the bottom layers of the pond. These conditions can in turn alter chemical dynamics in the benthic region, for example, causing the leaching of metals (Marsalek 2003). This is an excellent example of the value of considering chemical mixtures rather than just single contaminants or contaminant classes in isolation: The presence of chloride may modify the bioavailability or toxicity of other contaminants (Hall and Anderson 1995).

3.1.2. TSS

Stormwater runoff is a major source of TSS into other aquatic systems; thus, one of the core objectives of stormwater management in Ontario is the 80% removal efficiency of TSS within SWPs prior to release (OME 2003). Suspended solids accumulate from construction sites, road dust, soil erosion, sediment erosion from fast flows, deposition, and litter (USEPA 2025). The turbidity caused by increased TSS levels can inhibit algal and submerged vegetation growth, and excess deposition of foreign sediments can smother important habitat for benthic invertebrates and bottom‐dwelling fish (Horner et al. 1994). These fine particulates may also serve as a sink for other contaminants, such as metals (Herngren et al. 2005) and fecal coliforms (Murray et al. 2001). Mean TSS concentrations at our ponds ranged from 7.3 to 135.4 mg L⁻¹, averaging 38.6 mg L⁻¹ across all sites (Table S2). This range fits into the lower end of the observed distributions reported in the international and national stormwater quality databases summarized by Pamuru et al. (2022) and in the Ontario Stormwater Management Planning and Design Manual (OME 2003). Guidelines for TSS in Canada are designed for stream systems and are based on background levels from reference sites (CCME 2002), which may not be appropriate for SWPs; therefore, we are unable to place our measured TSS concentrations into an ecotoxicological context.

3.1.3. DO

As receivers of thermal pollution (Sabouri et al. 2016), SWPs may be particularly susceptible to low‐oxygen (hypoxic) conditions because DO concentrations are inversely related to water temperature (Boyd and Boyd 2020). Across the 9‐week sampling period, mean daytime DO levels within the ponds (measured at depths of ~20 cm from the water surface between 10:00 and 16:00 h) ranged from 2.87 to 14.1 mg L⁻¹ (Table S2). Three ponds had means below 6 mg L⁻¹—the CWQG threshold for early‐life stage warmwater organisms (CCME 1999). Although SWPs are not designed to support oxygen‐sensitive taxa, many become colonized by aquatic biota. It is important to note that DO in shallow waters can fluctuate diurnally, especially in the presence of submersed aquatic vegetation (Boyd and Boyd 2020), of which there is commonly an abundance in SWPs. The concentration of DO could reach daily minima at late night when respiration proceeds but photosynthesis is absent, making our daytime observations a conservative assessment of ecological risk. There is evidence that some organisms, such as chironomids and snails, can acclimate or adapt to chronically low oxygen levels (CCME 1999); however, the presence of other stressors in hypoxic conditions can be confounded (Lowell and Culp 1999).

3.1.4. Nutrients

Ammonia (NH₃), nitrate (NO₃), nitrite (NO₂), and phosphate (PO₄) concentrations in the SWP water were generally found to be low, with most NO₃, NO₂, and PO₄ concentrations beign below detection limits (Table S2). Only one site exceeded the CWQG values for NH₃ for freshwaters (1.54 mg L⁻¹; CCME 2010) with a concentration of 2.50 mg L⁻¹. Governmental stormwater policies often aim to reduce nutrient loading to prevent eutrophication in sensitive downstream waters (e.g., OME 2003). Our findings suggest generally effective nutrient removal, but these one‐time samples may not capture seasonal variability or storm‐driven spikes. During rainfall events, nutrients can originate from pet waste, fertilizers, detergents, and decomposing vegetation washed into SWPs (USEPA 2023).

3.1.5. Bacterial Contaminants

Fecal indicator bacteria such as total coliforms and E. coli were detected in all ponds. Nineteen ponds exceeded 1000 cfu 100 mL⁻¹ for total coliforms and 13 exceeded 235 cfu 100 mL⁻¹ for E. coli —benchmarks used for recreational waters (Government of Ontario 1994; Government of Canada 2023). Samples from multiple ponds exceeded the upper analytical limits for these bacterial contaminants. While SWPs are not designated for swimming, they are often situated near parks and green spaces where incidental human exposure may occur. Bacterial contamination is primarily a public health concern rather than a direct toxic threat to aquatic organisms, yet may result in indirect ecological effects. For example, high bacterial loads can result in increased biological oxygen demand, reducing oxygen levels in the water (USEPA 2012). Feces from pets, birds, or other wildlife are likely sources of contamination (Staley et al. 2018; Beaudry 2019); however, contamination of stormwater with human sewage from leaking wastewater infrastructure is well documented in the literature and cannot be ruled out as a source of microbial contaminants in stormwater ponds (e.g., Sercu et al. 2011; Sauvé et al. 2012; Sidhu et al. 2013; Fairbairn et al. 2018; Masoner et al. 2019). Given the ambiguity in recreational use and ecological exposure, microbial contamination warrants further monitoring.

3.1.6. Metals and Metalloids

All 20 analyzed metal and metalloid were found in every SWP biofilm sample (n = 15). Traffic‐associated metals (Ba, Cr Co, Cu, Pb, Mn, Ni, Se, Sr. and Zn; as described by Hjortenkrans et al. 2006 and Wang et al. 2021) were higher in biofilm samples from at least half of ponds compared with the reference site. Traffic pollution, from both emission and non‐emission sources, is an important contributor of heavy metals to stormwater (Czemiel Berndtsson 2014), especially in car‐dominated cities like the study city Brampton. Nine of the 15 SWP sites were within 300 m of arterial roads, however whether they are included in the engineered sewershed drainage basin of the sites is unknown. Busy arterial roads (> 5000 cars per day) can contribute higher levels of heavy metals than highways, due to factors such as starting and stopping in traffic congestion and at streetlights, which results in increased shedding of tire and brake materials (Huber et al. 2016).

For seven of the 20 metal analytes (Na, Mn, Cu, Zn, As, Mo, Ba), concentrations in biofilm samples were higher at every single stormwater pond site than at a reference stream. Guidelines do not exist for dietary exposure to these metals through biological materials, thus inhibiting our ability to assess the potential effects of consumption of metal‐contaminated biofilms. The development of appropriate thresholds for this type of exposure is warranted. Metal profiles can reflect distinctive urban source signatures—vehicle wear, construction materials, and possibly building runoff (e.g., Zn from roofs; Wicke et al. 2022). Instead of focusing on threshold exceedances, which are limited in their applicability to biofilm or dietary exposures, these contaminants are better understood as indicators of urban influence and mechanisms for source tracking. Their presence is also an important consideration for exploring mixture effects, discussed further in Section 3.3.

3.1.7. Organic Contaminants

Across the three sampling matrices, we found a total of 200 organic contaminants, including 67 pharmaceuticals, 51 herbicides, 37 fungicides, 26 insecticides, and 20 other compounds associated with building materials, traffic, or other consumption and metabolic waste products (e.g., caffeine and its breakdown product paraxanthine). In a companion paper (Izma et al. 2025), we compared o‐DGTs, composite water samples, and biofilm samples, and concluded that combining all three matrices yielded valuable complementarity in contaminant detection. Importantly, o‐DGTs provided lower detection limits than the other two matrices; however, between‐duplicate discrepancies across sites revealed that analyte quantifications for o‐DGTs were not reliable (Izma et al. 2025). Consequently, herein, we limit our discussion of organic chemicals in all three matrices to presence/absence results only. The number of organic compounds detected at each site ranged from 45 to 138, indicating SWPs are exposed to a large assortment of contaminants. Chemicals known to have potential ecotoxicological consequences are present in these mixtures, such as imidacloprid which can be detrimental to mayfly nymphs (Raby et al. 2018; Macaulay et al. 2021), however reliable concentration data is required to define their toxicity. Some of these chemicals are also known to bioaccumulate in aquatic environments (Fernandes et al. 2020), potentially prolonging the duration of exposure. The omnipresent pesticides atrazine and azoxystrobin can induce toxic effects to non‐target organisms at low concentrations (Khoshnood 2024; Kunz et al. 2017). Further, the assortment of pesticides and coating preservatives sourced from building materials include both hazardous substances to aquatic biota, such as carbendazim (Jiang et al. 2015), and substances with little or no available ecotoxicological information, such as sebacic acid. Alarmingly, there is a critical lack of relevant exceedance guidelines for organic contaminants for the protection of aquatic life (e.g., of the 113 pesticides detected in our samples, only 11 [atrazine, bromacil, bromoxynil, carbaryl, dicamba, tebuthiuron, simazine, metolachlor, metribuzin, MCPA, and imidacloprid] have CWQG values). This data gap severely limits ecotoxicological interpretation. We discuss the sources and detection frequencies of pesticides, pharmaceuticals, and other human‐associated contaminants in the following two sections.

3.1.7.1. Pesticides

We detected 114 pesticides (including herbicides, fungicides, and insecticides) in our SWPs, many of which could be attributed to uses in landscaping. In Ontario, golf courses, sports turf, and transportation corridors are exempt from the Cosmetic Pesticide Ban, implemented in 2009, allowing intensive use of herbicides, insecticides, and fungicides in those areas (Government of Ontario 2009). Turfgrass management may contribute a disproportionate amount of pesticide releases to urban waters, as application rates are estimated to be significantly higher than on agricultural land (Schueler 2000). Levels of carbendazim in urban surface waters were previously found to be associated with the number of golf courses in two urban catchments in Ontario (Metcalfe et al. 2016). From visual assessment of aerial imagery of our SWPs, we can identify golf courses and sports fields in proximity to our study sites; however, as we did not have full access to the sewershed boundaries for the SWPs, we were unable to verify whether or not our SWP sewersheds actually contained golf courses or sports turf.

In addition to their use on lawns and turfgrass, herbicides are also used to control unwanted vegetation in private and public grounds. Some, such as 2,4‐D and mecoprop, are sold at hardware stores for use in residential landscaping (despite their ban for cosmetic use), while others, such as prometon, are often applied directly to municipal grounds, such as rights‐of‐way and roadside ditches (AERU 2024), facilitating their entry into the stormwater system (Table 2).

TABLE 2.

The most frequently detected organic compounds found in the 21 SWP sites (detection frequency = 81%–100%), listed in order of decreasing detection frequency. All information was sourced from NCBI's PubChem unless noted otherwise in parentheses.

Compound name	CAS number	Use	Possible urban source	Mode of action	Detection frequency (%)
2,4‐D	94‐75‐7	Herbicide	Lawn/turf care	Synthetic auxin d	100
4‐Methyl‐1H‐benzotriazole	29878‐‐31‐7	Industrial (UV stabilizer) a *	Building or other outdoor materials	Unknown but likely enzyme related	100
6PPD‐quinone	2754428‐18‐5	Traffic‐related (Component of vehicle tires)	Tire‐wear	Species‐dependent, often altering enzyme activity  b	100
Atrazine	1912‐24‐9	Herbicide	Lawn/turf care	Photosystem II inhibitor. d	100
Azoxystrobin	131860‐33‐8	Fungicide	Lawn/turf care	Respiration inhibitor d	100
Caffeine	58‐08‐2	Household (psychoactive stimulant, diuretic, respiratory and cardiac stimulant)	Unknown	Unknown	100
Carbendazim	10605‐21‐7	Fungicide	Lawn/turf care; building materials (paints, textiles, caulks, concrete, etc.)	Mitosis and cell division inhibitor d	100
Chlorantraniliprole	500008‐45‐7	Insecticide	Lawn/turf care	Ryanodine receptor modulator d	100
Clomazone	81777‐89‐1	Herbicide	Unknown	Chlorophyll and carotene synthesis disruptor d	100
DEET	134‐62‐3	Household (insect repellant)	Direct release	Neurotoxin	100
Desethylatrazine	6190‐65‐4	Herbicide (metabolite)	Lawn/turf care	Photosystem II inhibitor	100
Desisopropylatrazine	1007‐28‐9	Herbicide (metabolite)	Lawn/turf care	Photosystem II inhibitor	100
Fluopyram	658066‐35‐4	Fungicide	Lawn/turf care c	Succinate dehydrogenase inhibitor d	100
Hydroxyatrazine	2163‐68‐0	Herbicide (metabolite)	Lawn/turf care	Photosystem II inhibitor	100
MCPA	94‐74‐6	Herbicide	Lawn/turf care	Synthetic auxin d	100
Mecoprop	93‐65‐2	Herbicide	Lawn/turf care	Synthetic auxin d	100
Metalaxyl	57837‐19‐1	Fungicide; wood preservative	Lawn/turf care; construction and building materials	Fungal nucleic acid synthesis disruptor d	100
Metolachlor	51218‐45‐2	Herbicide	Lawn/turf care	Cell division inhibitor d	100
Mirtazapine	85650‐52‐8	Pharmaceutical (antidepressant)	Unknown	Unknown	100
Penflufen	494793‐67‐8	Fungicide	Unknown (agricultural)	Succinate dehydrogenase inhibitor d	100
Propazine	139‐40‐2	Herbicide	Greenhouses d	Photosystem II inhibitor	100
Propiconazole	60207‐90‐1	Fungicide; wood preservative	Lawn/turf care; construction and building materials.	Demethylation inhibitor	100
Pydiflumetofen	1228284‐64‐7	Fungicide	Lawn/turf care; greenhouses	Succinate dehydrogenase inhibitor d	100
Simazine	122‐34‐9	Herbicide; algicide	Lawn/turf care; direct release	Photosystem II inhibitor d	100
Tris(2‐chloroethyl) phosphate (TCEP)	5961‐85‐3	Industrial (flame retardant)	Unknown	Unknown	100
Tebuconazole	107534‐96‐3	Fungicide; wood preservative	Lawn/turf care; construction and building materials	Sterol biosynthesis inhibitor d	100
Tebufenozide	112410‐23‐8	Insecticide	Urban forestry	Ecdysone receptor agonist d	100
Thiabendazole	148‐79‐8	Fungicide; Pharmaceutical (anthelminthic agent); household (antimicrobial in cosmetics)	Urban forestry	Compromises cytoskeleton via selective interaction with ß‐tubulin d	100
Bentazon	25057‐89‐0	Herbicide	Lawn/turf care	Photosystem II inhibitor d	95
Carbamazepine‐10,11‐epoxide	36507‐30‐9	Pharmaceutical (antiepileptic)	Unknown	Unknown	95
Diuron	330‐54‐1	Herbicide (algicide)	Building/construction materials (paints/plasters/coatings)	Photosynthesis inhibitor d	95
Fluxapyroxad	907204‐31‐3	Fungicide	Lawn/turf	Succinate dehydrogenase inhibitor d	95
Imazethapyr	81335‐77‐5	Herbicide	Unknown (agricultural)	Plant amino acid synthesis inhibitor d	95
Lidocaine	137‐58‐6	Pharmaceutical (anesthetic)	Unknown	Unknown	95
Prometon	1610‐18‐0	Herbicide	Lawn/turf care; maintenance of railways and rights‐of‐way	Photosynthesis inhibitor d	95
Sebacic‐acid	111‐20‐6	Industrial (corrosion inhibitor, adhesive, paint additive, binder, etc.)	Building and construction materials; traffic related (lubricant additive)	Not found	95
Triclopyr	55335‐06‐3	Herbicide	Lawn/turf care; urban forestry.	Synthetic auxin d	95
Clothianidin	210880‐92‐5	Insecticide	Urban forestry e ; domestic pet care; domestic pest control	Nicotinic acetylcholine receptor competitive modulator d	90
Flupyradifurone	951659‐40‐8	Insecticide	Unknown (agricultural)	Nicotinic acetylcholine receptor competitive modulator d	90
Mefentrifluconazole	1417782‐03‐6	Fungicide	Unknown (agricultural)	Sterol biosynthesis inhibitor d	90
Paclobutrazol	76738‐62‐0	Herbicide (plant growth regulator)	Lawn/turf care f	Disruption of gibberellic acid production and abscisic acid destruction f	90
Paroxetine	61869‐08‐7	Pharmaceutical (antidepressant)	Wastewater contamination	Unknown	90
Piperonyl‐butoxide	51‐03‐6	Insecticide (synergist)	Domestic pest control	P450‐dependent monooxygenase inhibitor d	90
Imidacloprid	138261‐41‐3	Insecticide	Urban forestry e ; domestic pet care; domestic pest control	Nicotinic acetylcholine receptor competitive modulator d	86
Dimethenamid	87674‐68‐8	Herbicide	Unknown (agricultural)	Mitosis and cell division inhibitor d	81
Pyraclofos	89,784–60‐1	Insecticide	Unknown	Acetylcholinesterase inhibitor d	81

^a Khare et al. 2023. Please note that 4‐methyl‐1H‐benzotriazole is synonymous with 4‐methylbenzotrioazole.

^b Bohara et al. 2024.

^c Health Canada 2016.

^d AERU 2024.

^e Health Canada 2021.

^f Health Canada 2023.

Insecticides have limited permitted uses in outdoor urban spaces. Clothianidin, for example, is permitted for indoor use to control domestic pests (PMRA 2016) but may enter runoff through improper application or disposal practices. Clothianidin (90% detection frequency) and imidacloprid (86% detection frequency) are both permitted in Ontario for use in urban forestry, in horticulture, and in plant nurseries (OMECP 2014) and are evidently widely detected in urban stormwater sampled in our study (Table 2). Imidacloprid is also a common ingredient in ingestible and topical tick and flea medications for domestic pets (Anadón et al. 2025) and may have direct inputs into stormwater given pets are frequently walked in the green spaces adjacent to SWPs. Tebufenozide (100% detection frequency) is applied in urban forestry initiatives to treat areas affected by caterpillar pests (Natural Resources Canada 2024; e.g., spongy moths Lymantria dispar ; Linnaeus, 1758).

Fungicides, such as azoxystrobin and tebuconazole, often have dual uses in city spaces. In addition to their use on turfgrass and lawn care, these fungicides are added to paints and preservative coatings on urban structures, like roofs and building facades, and can leach into stormwater during rain events (Wittmer et al. 2010; Bollmann et al. 2014). The need for long‐term protection means these coatings can be applied throughout the year, extending the potential exposure window for receiving water bodies. Thiabendazole (100% detection frequency) is used in urban forestry, for example to combat Dutch elm disease (NCBI 2024).

Numerous information gaps exist on the registrations and permitted urban uses of our commonly detected pesticides in Canada. For example, we could not find pesticide registrations for pyraclofos or prometon by Health Canada. Flupyradifurone, imazethapyr, and dimethenamid are only registered for use in agricultural areas in Canada. Although several of our SWP sites are located in suburban neighborhoods adjacent to agricultural lands, the water delivered to the SWP sites is assumed to be delivered only from catch basins within each pond's sewersheds. The high occurrences of these agricultural pesticides in urban areas (Table 2) are unlikely to be driven by spray‐drift, as application guidelines enforced by Health Canada aim to limit mobilization into surrounding environments (e.g., imazethapyr cannot be sprayed by air and requires buffer zones to prevent drift to areas of human habitation; PMRA 2024). Sometimes, a substance can be subject to a ban under one use (e.g., as a pesticide), but be permitted for use under a different application (e.g., as a coating protector) (Drakvik et al. 2020). Pesticide monitoring in surface waters is often based on application trends, yet contaminants with unreported applications in urban catchments are evidently prominent. Defining contamination exposure necessitates a more unbiased approach that does not rely on accurate reporting of chemical sales and applications, given the extent of unknown or dual sources in an urban landscape.

3.1.7.2. Pharmaceuticals and Other Consumption and Metabolic Waste Products

With the exception of topical veterinary medications, the vast majority of pharmaceuticals detected have no obvious natural route into the stormwater systems. In theory, compounds associated with human waste should only be found in wastewater due to the separation of stormwater and wastewater systems and regulations protecting public and environmental health (e.g., combined sewer–stormwater systems were banned in Ontario in 1985; Government of Ontario 2016). However, urban stormwater is clearly widely contaminated with human wastewater products, a phenomenon also documented by Masoner et al. (2019) in their survey of stormwater contamination across the United States and by Kang et al. (2024) in an industrial and urbanized Korean watershed. Contamination could occur when aging infrastructure results in leaky sewage pipes, which are often near stormwater drainage pipes, or when infrastructure is improperly built, such as with illegal junctions between the wastewater and stormwater networks (Panasiuk et al. 2015; Yin et al. 2019). Caffeine (100% detection frequency) and its breakdown product paraxanthine (62% detection frequency) could be a source tracker for human waste contamination due to sewer leakages, as caffeine concentrations in the stormwater system increase with sewer system age (Ozaki et al. 2024). The prevalence of the antidepressants mirtazapine (100% detection frequency) and paroxetine (90% detection frequency) in our ponds could also indicate contamination with sewer lines, which has been reported in other urbanized areas (e.g., Sercu et al. 2011; Sauvé et al. 2012; Sidhu et al. 2013; Fairbairn et al. 2018; Olds et al. 2018; Masoner et al. 2019; Kang et al. 2024). This is an area requiring attention for the management of water quality in these systems.

Even fewer ecological guidelines are available for pharmaceuticals and other human‐associated contaminants than with pesticides, which are often tested prior to registration (e.g., carbamazepine, an antiepileptic drug, is the only pharmaceutical with environmental quality guidelines by the CCME). Thus, we were unable to systematically compare our findings to published guidelines. It is important to note, however, that even without exceedances, chronic exposures to low levels of a diverse mixture of contaminants is concerning given we know very little about the potential additive, synergistic, or antagonistic effects of exposures to such complex mixtures (Laetz et al. 2015; Martin et al. 2021).

3.2. An USCS: Identifying the Common Mixture

An USCS identifies the overlap in contaminant profiles in an urban setting, enabling a description of a common set of urban contaminants for targeted monitoring and more focused research. We found 28 organic contaminants in every single SWP site in our study (listed in Table 2, along with relevant source and toxicological information). We also found 18 additional organics present in over 80% of SWPs, which met our stringent criteria for inclusion in our proposed signature (Table 2). These co‐occurring contaminants may comprise a regional USCS which could aid in targeted monitoring of urban contamination of freshwater resources. Within the USCS, there are 19 herbicides or herbicide metabolites, 11 fungicides, six insecticides, four pharmaceuticals, three industrial products, a tire‐wear substance, an insect repellant, and caffeine—a stimulant. To this collection of organic contaminants, we also add chloride, TSS, bacterial contaminants, and traffic‐associated metals (Ba, Ch, Co, Cu, Pb, Mn, Ni, Se, Sr, and Zn) to compose a USCS reflective of our findings (Figure 1); we found these stressors of toxicological concern to be consistently elevated across sites and propose them as useful additions to the USCS.

FIGURE 1.

Detection frequencies (%) of organic compounds found in water, biofilm, and o‐DGT samples in 21 stormwater ponds in Brampton, Ontario. Only organic compounds with detection frequencies over 50% are shown. The vertical black line denotes the threshold of 80% detection frequency, above which contaminants meet the criteria for inclusion in the urban stormwater contaminant signature (USCS).

The complete breadth of our analyses was informative, with hundreds of possible contaminants screened for across three sampling matrices. However, this approach is very expensive and not one we could reasonably recommend for future monitoring programs, as all possible contaminants cannot be realistically screened for across temporal and spatial scales. Hence, the group of ubiquitous contaminants we identify here in our USCS is a useful tool to inform target analytes for monitoring in urban areas with similar catchment characteristics and reveals key areas for source control management. Additionally, future ecotoxicological research into chemical mixtures could better capture the cumulative effects of urban contamination by focusing on the contaminants in the USCS (Figure 2).

FIGURE 2.

An urban stormwater contaminant signature, as described by our characterization of stormwater ponds in Brampton, Canada. Contaminants are grouped by type: pesticides (orange), bacterial contaminants (blue), metals (gray), chloride (pink), sediment (brown), pharmaceuticals (purple), and other anthropogenic consumables (yellow).

A key limitation of our study lies in the use of a targeted approach to chemical analysis. Stormwater contamination is highly complex, with numerous and variable sources influenced by ongoing changes in product formulations, application practices, and land use (Gasperi et al. 2022; Flanagan et al. 2025). As a result, it is challenging to comprehensively characterize the full range of contaminants present. Targeted analytical methods, while useful for detecting known compounds, inherently exclude unknown or emerging substances and their transformation products, introducing a bias in the assessment of chemical exposure (Gasperi et al. 2022). In some cases, non‐target analyses can be coupled with targeted screenings to increase comprehensiveness (Gasperi et al. 2022; Kang et al. 2024) and address analyte selection bias.

The USCS of our study area indicates that most omnipresent organic contaminants have uses in turfgrass and lawn care, structure coatings, or may indicate possible human waste contamination (Table 2). Previous characterizations of organic pollutants in stormwater have been comparable to occurrences in wastewater treatment plant effluent (Launay et al. 2016; Masoner et al. 2019), and the presence of several human waste‐associated compounds (e.g., caffeine, mirtazapine) in all or nearly all SWP sites in our study (Table 2) cannot be solely attributed to wash off from accidental spills, and suggests widespread wastewater contamination of stormwater. One of the benefits of the USCS approach is its support in tracing the sources of widespread urban contaminants (Figure 3), whereby identifying groups of co‐occurring contaminants with similar uses (e.g., in turfgrass care) can inform managers of the contribution of relevant land uses within the sewershed. This insight can guide efforts to control emissions and support mitigation efforts, such as engineering solutions or behavioral changes that would prevent entry into the stormwater ponds (Figure 3).

FIGURE 3.

The urban stormwater contaminant signature (USCS) describes a common characterization of stormwater in urban areas. Here, the potential sources of common contaminant components of the USCS are shown.

3.2.1. Comparisons With Other Studies

Several contaminants with 100% detection frequencies were also reported as common contaminants of stormwater in other studies. For example, caffeine was widely detected in stormwater samples collected by Masoner et al. (2019; 96% detection frequency) in the United States and Tran et al. (2019; 100%) in Singapore. DEET, an ingredient in most insect repellents, also had near‐ubiquitous detections in both studies (Masoner et al., 98% detection frequency; Tran et al., 100% detection frequency), while carbendazim was also very common (94% detection frequency) in American stormwaters (Masoner et al. 2019). A recent survey of urban streams in the Greater Toronto Area in Ontario found 6PPD‐quinone, a tire‐related chemical of emerging ecological concern, in 80% of stream samples (Helm et al. 2024), two of which receive effluent from the SWP sites in our study. The UV stabilizer 4‐methyl‐1H‐benzotriazole has a number of industrial applications, ranging from antifreeze in cars and aircrafts, to firefighting substances (Davis et al. 1977), and had high presence in Masoner et al.'s samples (92% detection frequency).

Other compounds that were sufficiently common (detected in > 80% of ponds sampled) to be incorporated into our USCS were also comparable to other studies. For example, carbamazepine, an anticonvulsant, and lidocaine, a local anesthetic and antiarrhythmic drug, were detected in 95% of our samples, and carbamazepine was detected in all samples collected by Tran et al. (2019) and lidocaine detected in 69% of samples by Masoner et al. (2019). Clothianidin and imidacloprid had detection frequencies of 90% and 86%, respectively, and are also common in stormwater in other countries with permitted uses (e.g., 86% detection frequency of imidacloprid by Masoner et al. 2019), but have been banned in the European Union due to being highly toxic to non‐target invertebrates (EU Health and Food Safety, n.d.).

These remarkable similarities in contaminant profiles show that, although the exact blend of components in our USCS may not be universal, there is notable alignment with data from other stormwater surveys. The process of USCS development could be duplicated elsewhere to determine a USCS for different urban areas should there be more systematic differences. For example, locations in South America or Europe with differences in chemical permittance and applications would warrant an adaptation to the process of USCS development.

3.3. Considering Modes of Action (MOAs) in Mixtures

Evidently, SWPs contain multifarious mixtures of organic and inorganic contaminants spanning a wide range of chemical classes and MOAs. While some regulatory and assessment frameworks—such as whole effluent toxicity tests—account for mixture effects, most risk assessments still rely on a chemical‐by‐chemical approach, which may underestimate risk when compounds co‐occur at low concentrations yet interact additively or synergistically (Gomez‐Eyles et al. 2009; Martin et al. 2021).

Two models commonly used to predict mixture toxicity—concentration addition and independent action—are based on assumptions of similar or dissimilar MOAs, respectively. Both require extensive toxicological data, which are often unavailable for emerging contaminants or alternative exposure routes such as dietary intake via biofilms (Ijzerman et al. 2023; Izma et al. 2024a; Izma et al. 2024b). Additionally, promising tools have been developed such as effects‐based assays (e.g., Jager et al. 2010) and cell line assays (e.g., Hoover et al. 2019) for evaluating mixture toxicity.

Given the lack of effect data for 11 of our 46 most frequently detected organic compounds and the absence of biofilm‐based thresholds for metals, in addition to the concentration data obtained from o‐DGT analysis, we opted to qualitatively explore the toxicity potential of the proposed USCS based on the MOAs of the most frequently detected organics (Table 2). While detection does not equate to risk, certain groupings—such as synthetic auxin herbicides (e.g., 2,4‐D, MCPA, mecoprop, triclopyr) and succinate dehydrogenase inhibitor fungicides (e.g., fluopyram, fluxapyroxad, penflufen)—suggest potential for cumulative effects within shared MOA groups.

Overall, we identified over 13 distinct MOAs among the most frequently detected compounds (Table 2), alongside several substances with unknown or poorly characterized MOAs, especially among pharmaceuticals and building‐derived chemicals. This chemical diversity complicates risk predictions using additive models alone and underscores the need for approaches that can accommodate mixtures with overlapping, unique, and unknown effects.

Some components in the USCS are also known or suspected synergists. For instance, piperonyl butoxide (90% detection frequency) enhances insecticide potency (Cross et al. 2017), while azole fungicides such as tebuconazole (100% detection frequency) and flupyradifurone (90% detection frequency) can amplify toxicity of co‐occurring lipophilic insecticides (USEPA 2014; Cedergreen 2014). Such interactions suggest that even low concentrations of individual compounds may contribute to enhanced mixture toxicity.

Beyond chemical interactions, other factors such as salinity, microbial activity, and nutrient content, can modify toxicity. For instance, salinity may either intensify or mitigate toxicity depending on the mixture (Hall and Anderson 1995; Forget et al. 1999; Velasco et al. 2019), and bacteria in sediments can enhance the bioavailability of hydrophobic contaminants (Widenfalk 2005). Interactions between fecal bacteria and chemical mixtures remain poorly understood, but given that fecal indicators often signal wastewater intrusion (Sercu et al. 2011), further investigation is warranted.

Taken together, the chemical diversity, potential for interaction, and influence of environmental modifiers complicate risk predictions in urban stormwater. While our study did not quantify mixture toxicity, we emphasize that any framework aiming to assess urban stormwater impacts on both aquatic life within stormwater ponds and in receiving waters must acknowledge the limits of single‐compound assessments and consider cumulative and context‐dependent effects to better reflect real‐world exposures. The diversity and uncertainty of potential toxicities within the USCS further evidences the value of monitoring this group of contaminants.

3.3.1. Mixture Toxicity: Future Research Needs

Our findings emphasize the need to expand ecotoxicological testing to better address mixture effects, a call that has been repeated by researchers for years (Altenburger et al. 2013; Kortenkamp and Faust 2018; Kortenkamp et al. 2019; Raby et al. 2019; Drakvik et al. 2020; Salvito et al. 2020; Mueller et al. 2023). The diverse array of substances detected in the SWPs suggests that exposures in urban environments reflect complex mixtures rather than isolated compounds. Although most detected compounds in our SWP water samples fall below individual guideline thresholds (Izma et al. 2024a), their combined impacts remain largely unknown. Current mixture risk approaches often require toxicological data for individual substances, which are lacking for many emerging or understudied compounds (Fairbairn et al. 2018); for example, several key USCS components such as the pharmaceuticals mirtazapine and paroxetine, and urban‐use pollutants like 6PPD‐quinone, TCEP, and 4‐methyl‐1H‐benzotriazole, are currently lacking published ecological thresholds. Establishing predicted no‐effect concentrations (PNECs) for these compounds would enable more accurate risk quotient calculations. In the meantime, there are a number of approaches to mixture assessments incorporated by the research community to address the lack of thresholds and complexity of mixture exposures (Brack et al. 2016), such as cell‐line assays across MOAs (e.g., CALUX assays; Escher et al. 2013). We recommend future urban stormwater mixture studies prioritize compound groupings with shared MOAs (e.g., synthetic auxins, succinate dehydrogenase inhibitors) or known interactive effects (e.g., piperonyl butoxide with insecticides; azoles with pyrethroids or metals), as discussed above.

3.3.2. Monitoring and Risk Management Implications

Given the high number of contaminants in urban runoff, there is a critical need for streamlined monitoring frameworks. Although we were unable to monitor all relevant contaminants (other micropollutants such as polycyclic aromatic hydrocarbons and phthalates are also widespread in urban stormwaters; Gasperi et al. 2022), the USCS offers a practical starting point for focused surveillance, enabling programs to prioritize frequently detected and environmentally relevant compounds. This approach supports improved spatial and temporal resolution by reducing the number of analytes screened, while still capturing representative exposure profiles. This approach aligns with international efforts to identify priority mixtures and support mixture‐based risk policies (e.g., Eriksson et al. 2007; Knapp et al. 2021; Rorije et al. 2022; Mueller et al. 2023). In light of the complex nature of monitoring and assessing mixture effects, precautionary risk management strategies are warranted—particularly those that consider cumulative exposures and trigger mitigation when thresholds are exceeded.

4. Conclusion

Stormwater ponds (SWPs) provide a unique window into urban contaminant mixtures due to their role in concentrating runoff from diverse sources. Our multi‐matrix approach revealed frequent co‐occurrence of a wide array of contaminants, many with overlapping or interacting MOAs, underscoring the limitations of single‐compound risk assessments in these systems. To help streamline monitoring and guide future research, we propose the USCS: a targeted suite of frequently detected, environmentally relevant contaminants. The USCS can serve as a practical foundation for mixture‐focused risk assessment, helping to prioritize bioassays and guideline development for under‐regulated substances. As cities continue to densify, refining how we monitor and evaluate cumulative contaminant exposure will be essential for effective urban water management.

Author Contributions

Gab Izma: writing – original draft, methodology, investigation, formal analysis, conceptualization. Melanie Raby: writing – review and editing, project administration, resources, methodology, conceptualization. Moira Ijzerman: formal analysis, methodology, writing – review and editing. Ryan Prosser: writing – review and editing, conceptualization, resources, methodology, funding acquisition. Paul Helm: writing – review and editing, resources, methodology. Justin Renaud: writing – review and editing, formal analysis, methodology. Mark Sumarah: writing – review and editing, formal analysis, methodology. Daniel McIsaac: writing – review and editing, investigation, methodology. Rebecca Rooney: writing – review and editing, supervision, resources, methodology, project administration, funding acquisition, conceptualization.

Conflicts of Interest

The authors declare no conflicts of interest.

Disclaimer

The views expressed in this study are the those of the authors and do not necessarily reflect those of the Province of Ontario.

Supporting information

Figure S1 . Map of stormwater pond sites (represented by red pins) in Brampton, Ontario, Canada. Map adapted from Izma et al. (2024).

Figure S2. Schematic of climatic conditions during the sampling period from May 25 to July 25, 2022, showing daily maximum air temperatures and total rainfall amounts. Water quality parameters assessed on a weekly basis included temperatures, dissolved oxygen, chloride, conductivity, and total suspended solids. Samples for analysis of bacterial contamination were taken once on June 8 after a significant rainfall event from the previous day, and samples for analysis of nutrient contamination were taken throughout the week of June 21–17, as indicated on the chart.

Figure S3. The biofilm sampling rack shown from above and below the water surface.

Figure S4. Two o‐DGT samplers mounted on a custom‐built holder.

Table S1. Summary of water quality parameters measured at 21 stormwater pond sites in Brampton, Ontario.

Table S2. Summary of water quality measurements taken at 21 stormwater ponds in Brampton, Ontario. Half of the MDL value was used to calculate the means for parameters with values under the MDL. For parameters containing observations above the MQL, the mean was not calculated.

Izma, G. , Raby M., Ijzerman M., et al. 2025. “An Urban Stormwater Contaminant Signature: Defining Priority Contaminants for Urban Stormwater Research.” Water Environment Research 97, no. 7: e70150. 10.1002/wer.70150.

Data Availability Statement

The data that support the findings of this study are openly available in FigShare. DOIs for each dataset are provided in the Supplemental Information.