Examining contaminant transport hotspots and their predictability across contrasted watersheds.

Hydrobiogeochemical processes governing water quantity and quality are highly variable in space and time. Focusing on thirty river locations in Québec, Canada, three water quality hotness indices were used to classify watersheds as contaminant transport hotspots. Concentration and load data for suspended solids (SS), total nitrogen (TN), and total phosphorous (TP) were used to identify transport hotspots, and results were compared across hotness indices with different data requirements. The role of hydroclimatic and physiographic characteristics on the occurrence and temporal persistence of transport hotspots was examined. Results show that the identification of transport hotspots was dependent on both the type of data and the hotness index used. Relationships between temporal and spatial predictors, however, were generally consistent. Annual transport hotspot occurrence was found to be related to temporal characteristics such as the number of dry days, potential evapotranspiration, and snow water equivalent, while hotspot temporal persistence was correlated to landcover characteristics. Stark differences in the identification of SS, TN, and TP transport hotspots were attributed to differences in mobilization processes and provided insights into dominant water and nutrient flowpaths in the studied watersheds. This study highlighted the importance of comparing contaminant dynamics across watersheds even when high-frequency water quality data or discharge data are not available. Characterizing hotspot occurrence and persistence, among hotness indices and water quality parameters, could be useful for watershed managers when identifying problematic watersheds, exploring legacy effects, and establishing a prioritization framework for areas that would benefit from enhanced routine monitoring or targeted mitigation strategies.

Isotope-based source assessment of water flowing from storm sewer systems to a receiving river during dry weather periods.

Urban stormwater management systems, particularly storm sewers, are critical for managing runoff in urban areas. These systems are designed to function during wet weather events; however, field-based observations of these systems suggest that they may also be active flow pathways in dry weather conditions, ultimately contributing to streamflow. Unlike dry weather flow in wastewater systems, storm sewer dry weather flow has not been thoroughly explored. This research used stable isotopes of oxygen and hydrogen in water to examine the sources of dry weather flow from storm sewers in a highly urban catchment. A stable isotope mixing model was applied at the outfalls of two stormwater catchments and the receiving Black Creek, located in Toronto, Canada. Findings suggest that during dry periods, storm sewers receive non-stormwater inputs from tap water, wastewater, and groundwater, along with some precipitation, and that these sources may constitute up to 19 % of Black Creek's flow at the watershed scale. Seasonal patterns in flow and water sources were observed for the Black Creek and outfalls. At one outfall, dry weather flow was predominantly from the water distribution system (i.e., tap water and/or wastewater) throughout spring, summer, and fall. In contrast, at the second outfall, groundwater dominated in spring and summer, and groundwater and water distribution were equally proportioned in fall. Black Creek baseflow comprises a dynamic mix of water sources that at times are similar to the sources observed at the stormwater outfalls. Considering these findings, future work should incorporate strategic sampling of additional outfalls, and multiple years of data collection to explore inter-annual variability in these processes and focus on replicating a similar study in other urban watersheds with different climates and/or water infrastructure design. The study findings highlight that our understanding of dry weather flow from storm sewers is relatively limited, emphasizing the need for further exploration of this phenomenon to inform urban hydrological modelling, water quality studies, and urban water management.

Identification of environmental and methodological factors driving variability of Pepper Mild Mottle Virus (PMMoV) across three wastewater treatment plants in the City of Toronto.

PMMoV has been widely used to normalize the concentration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, influenza, and respiratory syncytial virus (RSV) to account for variations in the fecal content of wastewater. PMMoV is also used as an internal RNA recovery control for wastewater-based epidemiology (WBE) tests. While potentially useful for the interpretation of WBE data, previous studies have suggested that PMMoV concentration can be affected by various physico-chemical characteristics of wastewater. There is also the possibility that laboratory methods, particularly the variability in centrifugation steps to remove supernatant from pellets can cause PMMoV variability. The goal of this study is to improve our understanding of the main drivers of PMMoV variability by assessing the relationship between PMMoV concentration, the physico-chemical characteristics of wastewater, and the methodological approach for concentrating wastewater samples. We analyzed 24-hour composite wastewater samples collected from the influent stream of three wastewater treatment plants (WWTPs) located in the City of Toronto, Ontario, Canada. Samples were collected 3 to 5 times per week starting from the beginning of March 2021 to mid-July 2023. The influent flow rate was used to partition the data into wet and dry weather conditions. Physico-chemical characteristics (e.g., total suspended solids (TSS), biological oxygen demand (BOD), alkalinity, electrical conductivity (EC), and ammonia (NH3)) of the raw wastewater were measured, and PMMoV was quantified. Spatial and temporal variability of PMMoV was observed throughout the study period. PMMoV concentration was significantly higher during dry weather conditions. Multiple linear regression analysis demonstrates that the number and type of physico-chemical parameters that drive PMMoV variability are site-specific, but overall BOD and alkalinity were the most important predictors. Differences in PMMoV concentration for a single WWTP between two different laboratory methods, along with a weak correlation between pellet mass and TSS using one method may indicate that differences in sample concentration and subjective subsampling bias could alter viral recovery and introduce variability to the data.

Analysis of sampling strategies for pulse loads of SARS-CoV-2: implications for wastewater-based epidemiology.

A faecal transport model was applied to a 11.3 km2 wastewater servicing area in Toronto, Ontario, Canada to explore the role that different wastewater sampling campaigns have on estimating the prevalence of SARS-CoV-2 in a population of 60,000. A stochastic wastewater and water quality model was used to evaluate the effectiveness of 11 sampling campaigns during periods of high and low COVID-19 infection among the population, tested using virtual sampling during dry-weather flow. The virtual sampling campaigns were based on the most common automatic sampler programming capabilities and widely used wastewater-based epidemiology (WBE) sampling campaigns reported in the literature. Sampling campaigns differ in weighting method (time, volume, or flow-weighted sampling), sample count, collection period, or sample time. Results suggest that grab samples should be avoided and/or that sampling campaigns with the greatest sample counts and durations are the most robust at capturing COVID-19 infection among the population. Most surprisingly, changes to the weighting method were negligible indicating that a greater number of samples, and larger sample volumes are preferred. This work suggests that investment in flow monitoring equipment for flow- or volume-weighted sampling will not improve WBE results, and that standard time based sampling is sufficient.