Predicting leachate impact on groundwater using electrical conductivity and oxidation–reduction potential measurements: An empirical and theoretical approach.

This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation–reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models—empirical and theoretical—were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate–groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management. [Display omitted] • EC and ORP measurements enable early identification of leachate impacts. • Supervised machine learning is enhanced for precise leachate pollution prediction. • The geochemical mixing model enables low-cost groundwater pollution monitoring. • This approach supports proactive groundwater management strategies. [ABSTRACT FROM AUTHOR]