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.
Although infrequent, pipeline spills have the potential to contaminate source water supplies and disrupt drinking water production for extended periods. Detailed multiphase contaminant fate and transport models linked to hydrodynamic models are ideal for determining the potential impact of oil spills on drinking water sources. However, sufficient data are often unavailable to simulate spills scenarios. Thus, a simple semiquantitative modeling approach is proposed that is based on documented pipeline spills recorded in scientific literature. A risk matrix was used to combine the consequences of a spill with the probability that it would contaminate drinking water sources. The new Pipeline Spill Risk Assessment Framework was applied to 26 drinking water intakes located in the greater Montreal area (Quebec). The proposed framework allows for transparency and facilitation of public discussions with regard to oil spill risks and decision‐making for source water protection and water safety plans.
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