Peer-Reviewed Journal Details
Mandatory Fields
O'Dwyer, J;Hynds, PD;Byrne, KA;Ryan, MP;Adley, CC
2018
June
Environmental Pollution
Development of a hierarchical model for predicting microbiological contamination of private groundwater supplies in a geologically heterogeneous region
Published
15 ()
Optional Fields
REPUBLIC-OF-IRELAND ESCHERICHIA-COLI VTEC DRINKING-WATER DOMESTIC WELLS RISK-FACTORS ENTERIC DISEASE QUALITY VULNERABILITY ASSOCIATION COMMUNITIES
237
329
338
Private groundwater sources in the Republic of Ireland provide drinking water to an estimated 750,000 people or 16% of the national population. Consumers of untreated groundwater are at increased risk of infection from pathogenic microorganisms. However, given the volume of private wells in operation, remediation or even quantification of public risk is both costly and time consuming. In this study, a hierarchical logistic regression model was developed to 'predict' contamination with E. coli based on the results of groundwater quality analyses of private wells (n = 132) during the period of September 2011 to November 2012. Assessment of potential microbial contamination risk factors were categorised into three groups: Intrinsic (environmental factors), Specific (local features) and Infrastructural (groundwater source characteristics) which included a total of 15 variables. Overall, 51.4% of wells tested positive for E. coil during the study period with univariate analysis indicating that 11 of the 15 assessed risk factors, including local bedrock type, local subsoil type, septic tank reliance, 5 day antecedent precipitation and temperature, along with well type and depth, were all significantly associated with E. coli presence (p < 0.05). Hierarchical logistic regression was used to develop a private well susceptibility model with the final model containing 8 of the 11 associated variables. The model was shown to be highly efficient; correctly classifying the presence of E. coli in 94.2% of cases, and the absence of E. coli in 84.7% of cases. Model validation was performed using an external data set (n = 32) and it was shown that the model has promising accuracy with 90% of positive E. coli cases correctly predicted. The developed model represents a risk assessment and management tool that may be used to develop effective water-quality management strategies to minimize public health risks both in Ireland and abroad. (C) 2018 Elsevier Ltd. All rights reserved.
OXFORD
0269-7491
10.1016/j.envpol.2018.02.052
Grant Details