The Likelihood of Coliform Bacteria in NJ Domestic Wells Based on Precipitation and Other Factors

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.

Escherichia coli contamination of rural well water in Alberta, Canada is associated with soil properties, density of livestock and precipitation

“…A 2015 study of manure impact on E. coli concentrations used precipitation lags of 24, 48, 72, 96 and 120 hours prior to sampling, and found no direct relationship between rainfall and E. coli concentration on their particular study site, indicating that rainfall lag and groundwater bacterial concentration may be dependent on a number of other factors (Arnaud et al 2015). For the State of New Jersey, an optimal lag time of 10 days cumulative precipitation pre-sampling was determined and used in a similar logistic regression study (Procopio et al 2017). The timing of the influence of precipitation on ground water is complex, and dependent on a number of factors, some specific to individual wells, such as depth, construction, and proximity to surface water.…”

Sources and Risk Factors for Nitrate and Microbial Contamination of Private Household Wells in the Fractured Dolomite Aquifer of Northeastern Wisconsin

“…There is ample evidence showing precipitation favors microbial contamination of private wells. Precipitation quantity in the period preceding sampling was positively associated with the occurrence in private wells of indicator bacteria (Hynds et al 2012;O'Dwyer et al 2014;Procopio et al 2017;Invik 2019) human enteric viruses (Allen et al 2017) and the human-specific Bacteroides marker HF183 (Murphy et al 2020). The antecedent precipitation periods associated with contamination varied between 30 (Invik et al 2019) and 5 d (Hynds et al 2012), and even shorter periods of rainfall (24 h) may be associated with contamination of vulnerable aquifers (Morrissey et al 2015).…”

Long-Term Temporal and Spatial Distribution of Coliform Bacteria in Jiaozhou Bay Associated With Human Activities and Environmental Governance

“…Consequently, we infer that the abundance of CB may have been influenced by land-sourced pollutants carried into Jiaozhou Bay by rainfall. The hypothesis had some resemblance to Procopio et al (2017) and Tabanelli et al (2017). Station A5 (near the mouth of Licun River), A3, C4 (near the mouth of Haibo River), C1, and C3 were at similar latitudes, but the differences in CB abundance at these stations varied by a factor of 10.…”