Pesticide presence in stream water, suspended sediment and biofilm is strongly linked to upstream catchment land use and crop type.
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abstract
Pesticide pollution can present high ecological risks to aquatic ecosystems. Small streams are particularly susceptible. There is a need for reproducible and readily available methods to identify aquatic regions at risk of pesticide contamination. There is currently a limited understanding of the relationship between upstream catchment land use and the presence of pesticides in multiple aquatic matrices. The aim of this study was to develop empirical relationships between different land uses and the levels of pesticides detected in multiple aquatic matrices. The inclusion of biofilm and suspended sediment as monitoring matrices has recently been proven effective for the characterization of pesticide exposure in stream ecosystems. Ten streams in Ontario, Canada with a variety of upstream catchment land uses were sampled in 2021 and 2022. Water, suspended sediment and biofilm were collected and analyzed from each site for the presence of approximately 500 different pesticides. Each of the three matrices exhibited distinctive pesticide exposure profiles. We found a significant relationship between the percentage of agriculture and urban land use and the detection of multiple pesticides in water, sediment and biofilm (logistic regressions, P<0.05). Statistically significant probabilistic models capable of predicting pesticide detections based on upstream catchment land use were developed. High-resolution cover crop maps identified soybeans, corn and other agriculture (e.g., vegetables, berries, canola) as the key variables associated with individual pesticide detection frequencies in each of the three matrices (linear regressions, P<0.05). Soybean land use was also the strongest predictor of site-wide pesticide pollution. This modelling approach using upstream catchment land use variables has the potential to be a powerful tool to identify streams at risk of pesticide pollution.
