Peoples Water Data: Enabling Reliable Field Data Generation and Microbial Contamination Screening in Household Drinking Water

ArXi:2604.04240v1 Announce Type: new Unsafe drinking water remains a major public health concern globally, particularly in low-resource regions where routine microbiological surveillance is limited. Although Escherichia coli is the internationally recognized indicator of fecal contamination, laboratory-based testing is often inaccessible at scale. In this study, we developed and evaluated a two-stage machine-learning framework for predicting E. coli presence in decentralized household point-of-use drinking water in Chennai, India using low-cost physicochemical and contextual indicators.