School of the Environment, Geography and Geosciences, University of Portsmouth, Burnaby Road, Portsmouth PO1 3QL, UK.
School of Pharmacy and Biomedical Sciences, University of Portsmouth, White Swan Road, Portsmouth PO1 2DT, UK.
Sci Total Environ. 2021 Sep 15;787:147519. doi: 10.1016/j.scitotenv.2021.147519. Epub 2021 May 7.
Pollution of surface water by polar pesticides is a major environmental risk, particularly in river catchments where potable water supplies are abstracted. In these cases, there is a need to understand pesticide sources, occurrence and fate. Hence, we developed a novel strategy to improve water quality management at the catchment scale using passive sampling coupled to suspect screening and multivariate analysis. Chemcatcher® passive sampling devices were deployed (14 days) over a 12 month period at eight sites (including a water supply works abstraction site) in the Western Rother, a river catchment in South East England. Sample extracts (n = 197) were analysed using high-resolution liquid chromatography-quadrupole-time-of-flight mass spectrometry and compounds identified against a commercially available database. A total of 128 pesticides from different classes were found. Statistical analysis of the qualitative screening data was used to identify clusters of pesticides with similar spatiotemporal pollution patterns. This enabled pesticide sources and fate to be identified. At the water supply works abstraction site, spot sampling and passive sampling were found to be complementary, however, the passive sampling method in conjunction with suspect screening detected 50 pesticides missed by spot sampling combined with targeted analysis. Geospatial data describing pesticide application rates was found to be poorly correlated to their detection frequency using the Chemcatcher®. Our analysis prioritised 61 pesticides for inclusion in a future water quality risk assessment at the abstraction site. It was also possible to design a seasonal monitoring programme to effectively characterise the spatiotemporal pesticide profiles within the catchment. A work flow of how to incorporate passive sampling coupled to suspect screening into existing regulatory monitoring is proposed. Our novel approach will enable water quality managers to target the mitigation (non-engineered actions) of pesticide pollution within the catchment and hence, to better inform drinking water treatment processes and save on operational costs.
地表水受极性农药污染是一个主要的环境风险,特别是在饮用水供应被抽取的河流集水区。在这些情况下,需要了解农药的来源、存在和归宿。因此,我们开发了一种新的策略,通过被动采样与可疑筛选和多元分析相结合,在集水区尺度上改善水质管理。在英格兰东南部的 Western Rother 河流集水区的八个地点(包括一个供水工程取水点)部署了 Chemcatcher®被动采样器(14 天),时间跨度为 12 个月。使用高分辨率液相色谱-四极杆飞行时间质谱法对样本提取物(n = 197)进行分析,并根据商业上可用的数据库对化合物进行鉴定。共发现了 128 种来自不同类别的农药。对定性筛选数据的统计分析用于识别具有相似时空污染模式的农药簇,从而确定农药的来源和归宿。在供水工程取水点,定点采样和被动采样被发现是互补的,但与定点采样和靶向分析相比,被动采样方法结合可疑筛选检测到了 50 种定点采样漏检的农药。描述农药施用量的地理空间数据与 Chemcatcher®的检测频率相关性较差。我们的分析确定了 61 种农药,以便在未来的取水点水质风险评估中纳入。还可以设计一个季节性监测计划,有效地描述集水区内的时空农药分布。提出了一种将被动采样与可疑筛选相结合纳入现有监管监测的工作流程。我们的新方法将使水质管理者能够针对集水区内的农药污染进行缓解(非工程措施),从而更好地为饮用水处理过程提供信息,并节省运营成本。