Fell Leslie G, Deeth Lorna E, Berke Olaf, Trotz-Williams Lise A
Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd. E., Guelph, Ontario N1G 2W1, Canada.
Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd. E., Guelph, Ontario N1G 2W1, Canada E-mail:
J Water Health. 2025 Jun;23(6):826-837. doi: 10.2166/wh.2025.046. Epub 2025 May 9.
This study aims to identify important well characteristics associated with increased odds of bacterial contamination in the Wellington-Dufferin-Guelph public health unit of Southern Ontario. Identifying risk factors associated with bacterial contamination can aid in the mandate of public health units to promote the safety, and facilitate the testing, of drinking water systems to help minimize the risk of illness. Logistic regression models for adverse bacterial test results based on physical well characteristics were created. Models with the lowest Akaike Information Criterion values were examined for consistently identified characteristics. The odds of bacterial contamination in the Wellington-Dufferin-Guelph region are most associated with the age of the well, the season of testing, having a treatment system on the well, and the presence of potential point contamination sources within 50 feet (15.24 m) of the well. While this information can support the design of targeted public health education campaigns, the current model leaves room for improvement, as the predictive abilities of the models based solely on well characteristic data are limited.
本研究旨在确定与安大略省南部惠灵顿-达弗林-圭尔夫公共卫生部门细菌污染几率增加相关的重要水井特征。识别与细菌污染相关的风险因素有助于公共卫生部门履行其促进饮用水系统安全并推动检测的职责，以帮助将疾病风险降至最低。基于水井物理特征建立了不良细菌检测结果的逻辑回归模型。对具有最低赤池信息准则值的模型进行了检查，以确定一致识别出的特征。惠灵顿-达弗林-圭尔夫地区细菌污染的几率与水井的使用年限、检测季节、水井上是否有处理系统以及水井50英尺（15.24米）范围内是否存在潜在的点污染源最为相关。虽然这些信息可为有针对性的公共卫生教育活动的设计提供支持，但当前模型仍有改进空间，因为仅基于水井特征数据的模型预测能力有限。
