Evidence-based framework to use in situ phycocyanin readings for cyanobacterial risk assessment within drinking water treatment plants - Ma, Liya; Dorner, Sarah; Prévost, Michèle (Environmental Science: Water Research & Technology, 2024)
A framework is proposed using multi-stream phycocyanin probe readings to manage cyanobacterial risks at the source water and across the drinking water treatment processes. , In situ phycocyanin (PC) probes have been deployed as a cost-effective and efficient way to monitor cyanobacterial (CB) abundance in drinking water sources and to identify periods of potential risk at drinking water treatment plants (DWTPs). Monitoring CB removal efficacies in near real-time by sequentially using a single probe for multiple streams across the treatment plant provides a more useful assessment of CB risk breakthrough in treated water. Removal efficacies were measured in three DWTPs using integrated mass fluxes estimated from PC readings and grab sample total CB biovolume estimations in raw, clarified, filtered, and treated water. Selective CB species removal during the treatment processes was also evaluated. In addition, relationships between physio-chemical parameters (turbidity, pH, dissolved oxygen, conductivity, chlorophyll-a and temperature) and PC across the treatment processes were investigated. Finally, a framework to use in situ multi-stream PC monitoring, gathering data across the treatment chain, is proposed to manage risks of CB cells breakthrough in treated water. Estimates of 2 hour moving average PC metrics are proposed to provide short term alert in raw water and establish periods of treatment vulnerability or dysfunction, while daily mean PC values can be used to estimate total and process specific log removals to justify treatment adjustments. Benefits and limitations of the tested in situ probes for the application of alert levels are discussed, and key knowledge gaps for future research and guidance are identified.
Keywords: Dorner_Sarah

Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality - Burnet, Jean-Baptiste; Habash, Marc; Hachad, Mounia; Khanafer, Zeinab; Prévost, Michèle; Servais, Pierre; Sylvestre, Emile; Dorner, Sarah (Water, 2021)
Waterborne pathogens are heterogeneously distributed across various spatiotemporal scales in water resources, and representative sampling is therefore crucial for accurate risk assessment. Since regulatory monitoring of microbiological water quality is usually conducted at fixed time intervals, it can miss short-term fecal contamination episodes and underestimate underlying microbial risks. In the present paper, we developed a new automated sampling methodology based on near real-time measurement of a biochemical indicator of fecal pollution. Online monitoring of β-D-glucuronidase (GLUC) activity was used to trigger an automated sampler during fecal contamination events in a drinking water supply and at an urban beach. Significant increases in protozoan parasites, microbial source tracking markers and E. coli were measured during short-term (<24 h) fecal pollution episodes, emphasizing the intermittent nature of their occurrence in water. Synchronous triggering of the automated sampler with online GLUC activity measurements further revealed a tight association between the biochemical indicator and culturable E. coli. The proposed event sampling methodology is versatile and in addition to the two triggering modes validated here, others can be designed based on specific needs and local settings. In support to regulatory monitoring schemes, it should ultimately help gathering crucial data on waterborne pathogens more efficiently during episodic fecal pollution events.
Keywords: Dorner_Sarah

Risk assessment of drinking water intake contamination from agricultural activities using a Bayesian network - Kammoun, Raja; McQuaid, Natasha; Lessard, Vincent; Goitom, Eyerusalem Adhanom; Prévost, Michèle; Bichai, Françoise; Dorner, Sarah (PLOS Water, 2023)
Agricultural activities can result in the contamination of surface runoff with pathogens, pesticides, and nutrients. These pollutants can enter surface water bodies in two ways: by direct discharge into surface waters or by infiltration and recharge into groundwater, followed by release to surface waters. Lack of financial resources makes risk assessment through analysis of drinking water pollutants challenging for drinking water suppliers. Inability to identify agricultural lands with a high-risk level and implement action measures might lead to public health issues. As a result, it is essential to identify hazards and conduct risk assessments even with limited data. This study proposes a risk assessment model for agricultural activities based on available data and integrating various types of knowledge, including expert and literature knowledge, to estimate the levels of hazard and risk that different agricultural activities could pose to the quality of withdrawal waters. To accomplish this, we built a Bayesian network with continuous and discrete inputs capturing raw water quality and land use upstream of drinking water intakes (DWIs). This probabilistic model integrates the DWI vulnerability, threat exposure, and threats from agricultural activities, including animal and crop production inventoried in drainage basins. The probabilistic dependencies between model nodes are established through a novel adaptation of a mixed aggregation method. The mixed aggregation method, a traditional approach used in ecological assessments following a deterministic framework, involves using fixed assumptions and parameters to estimate ecological outcomes in a specific case without considering inherent randomness and uncertainty within the system. After validation, this probabilistic model was used for four water intakes in a heavily urbanized watershed with agricultural activities in the south of Quebec, Canada. The findings imply that this methodology can assist stakeholders direct their efforts and investments on at-risk locations by identifying agricultural areas that can potentially pose a risk to DWIs.
Keywords: Dorner_Sarah

Full-scale characterization of the effects of a bioretention system on water quality and quantity following the replacement of a mixed stormwater and combined sewer system - Le Cauchois, Pia; Doucet, Samuel; Bouattour, Ons; McQuaid, Natasha; Beral, Henry; Kõiv-Vainik, Margit; Bichai, Françoise; McCarthy, David; St-Laurent, Julien; Dagenais, Danielle; Bennekrela, Nesrine; Guerra, Juan; et al. (Blue-Green Systems, 2025)
ABSTRACT Urbanization is leading to more frequent flooding as cities have more impervious surfaces and runoff exceeds the capacity of combined sewer systems. In heavy rainfall, contaminated excess water is discharged into the natural environment, damaging ecosystems and threatening drinking water sources. To address these challenges aggravated by climate change, urban blue-green water management systems, such as bioretention cells, are increasingly being adopted. Bioretention cells use substrate and plants adapted to the climate to manage rainwater. They form shallow depressions, allowing infiltration, storage, and gradual evacuation of runoff. In 2018, the City of Trois-Rivières (Québec, Canada) installed 54 bioretention cells along a residential street, several of which were equipped with access points to monitor performance. Groundwater quality was monitored through the installation of piezometers to detect potential contamination. This large-scale project aimed to improve stormwater quality and reduce sewer flows. The studied bioretention cells reduced the flow and generally improved water quality entering the sewer system, as well as the quality of stormwater, with some exceptions. Higher outflow concentrations were observed for contaminants such as manganese and nitrate. The results of this initiative provide useful recommendations for similar projects for urban climate change adaptation.
Keywords: Infolettre_Ete_2025