Abstract
BackgroundWater contamination from pesticides is a significant environmental issue, affecting ecosystems and human health. Despite actions aimed at limiting pesticide levels in surface water, pollution persists. Pesticide contamination can stem from both non-point sources, such as agricultural runoff, and point sources, such as wastewater treatment plants. Accurate monitoring of these sources is challenging but crucial. Passive samplers have shown promising results in detecting pesticide levels over time but are not widely used for national monitoring. Effective monitoring of these sources is essential but remains challenging due to the limitations of conventional sampling techniques. This study shows how passive samplers can be used for identifying pesticide contamination from point sources of pollution linked to agricultural practices.ResultsPassive Samplers (POCIS) were placed in the studied water channel upstream and downstream of two potential point sources of pollution (a wastewater treatment plant and a water filling station) with an exposure time of 21 days. Furthermore, grab water samples were collected at the beginning and at the end of the POCIS exposure. The monitoring was performed continuously for several months, and the results obtained from both techniques were compared, using HPLC-MS/MS and GC-MS to measure pesticide concentrations. Passive samplers showed higher detection frequencies of pesticides compared to grab water samples in all sampling points, indicating its superior sensitivity and ability to provide valuable information. Significant differences in pesticide concentrations were observed downstream of the wastewater treatment plant, suggesting it as a relevant contamination source. Conversely, the water filling station had a minimal impact on pesticide concentration.ConclusionsThese findings highlight the potential of passive samplers as a powerful tool for high-resolution monitoring of pesticide pollution, offering a more effective approach for environmental assessments. Their ability to detect contamination trends over time makes them a valuable addition to monitoring programs, supporting more targeted mitigation strategies to improve water quality.