The Likelihood of Coliform Bacteria in NJ Domestic Wells Based on Precipitation and Other Factors |
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| Authors: | Nicholas A. Procopio Thomas B. Atherholt Sandra M. Goodrow Lori A. Lester |
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| Affiliation: | Division of Science, Research and Environmental Health, New Jersey Department of Environmental Protection, 428 East State St., Trenton, NJ |
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| Abstract: | The influence of precipitation on coliform bacteria detection rates in domestic wells was investigated using data collected through the New Jersey Private Well Testing Act. Measured precipitation data from the National Weather Service (NWS) monitoring stations was compared to estimated data from the Multisensor Precipitation Estimate (MPE) in order to determine which source of data to include in the analyses. A strong concordance existed between these two precipitations datasets; therefore, MPE data was utilized as it is geographically more specific to individual wells. Statewide, 10 days of cumulative precipitation prior to testing was found to be an optimal period influencing the likelihood of coliform detections in wells. A logistic regression model was developed to predict the likelihood of coliform occurrence in wells from 10 days of cumulative precipitation data and other predictive variables including geology, season, coliform bacteria analysis method, pH, and nitrate concentration. Total coliform (TC) and fecal coliform or Escherichia coli (FC/EC) were detected more frequently when the preceding 10 days of cumulative precipitation exceeded 34.5 and 54 mm, respectively. Furthermore, the likelihood of coliform detection was highest in wells located in the bedrock region, during summer and autumn, analyzed with the enzyme substrate method, with pH between 5 and 6.99, and (for FC/EC but not TC) nitrate greater than 10 mg/L. Thus, the likelihood of coliform presence in domestic wells can be predicted from readily available environmental factors including timing and magnitude of precipitation, offering outreach opportunities and potential changes to coliform testing recommendations. |
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