Pesticide occurrence in groundwater and the physical characteristics in association with these detections in Ireland |
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Authors: | Sarah-Louise McManus Karl G. Richards Jim Grant Anthony Mannix Catherine E. Coxon |
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Affiliation: | 1. Crops, Environment, and Land Use Research Centre, Teagasc, Johnstown Castle, Co, Wexford, Ireland 2. Department of Geology, School of Natural Sciences, Trinity College Dublin, Dublin 2, Ireland 3. Statistics and Applied Physics, Research Support Team, Teagasc, Ashtown, Dublin 15, Ireland 4. Hydrometric and Groundwater Section, Environmental Protection Agency, McCumiskey House, Richview, Dublin 14, Ireland
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Abstract: | This study explores the associations of pesticide occurrence in groundwater to geological characteristics of the monitoring points (MPs) contributing area. Pesticide analyses were undertaken during a 2-year groundwater monitoring campaign which generated 845 samples. MCPA and mecoprop were the most frequently detected pesticides in groundwater. Each MP (n?=?158) had a specifically delineated zone of contribution (ZOC) and the dominant physical characteristics present from nine national datasets were recorded for each ZOC. Associations between detections in groundwater and the dominant physical characteristic in each MPs ZOC tested were then statistically analyzed using Fisher’s exact test, logistic regression, and multiple logistic regression. The original physical characteristic datasets used that were associated with detections in groundwater were the type of MP, aquifer type, and Quaternary deposit type. Logistic regression revealed that springs, regionally important aquifer types, aquifers with a karstic flow regime, and alkaline Quaternary deposits in existence above karst aquifers in a MP’s ZOC were more likely to have a pesticide detection in groundwater. Multiple regression from this exploratory work showed some mutual dependency between soil association, aquifer type, and the Geological Survey of Ireland groundwater vulnerability map. The combination of national monitoring data and physical attribute datasets can be used to explore key areas where groundwater is more vulnerable to pesticide contamination. |
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