Published Paper Details:

Access to safe drinking water remains a critical public health challenge, particularly in developing regions with limited laboratory infrastructure. This paper presents Purifix H2O, an intelligent Internet of Things (IoT) system for real- time water quality monitoring that integrates low-cost sensors (pH, TDS, turbidity, and temperature) with an Arduino UNO microcontroller and cloud-enabled mobile/web interfaces. The system employs sensor calibration, multi-parameter processing, rule-based safety classification, and an optional machine learning module for AI-driven waterborne disease risk prediction. Experimental evaluations on diverse water sources including tap, borewell, packaged drinking, pond, and contaminated samples demonstrate that the system reliably discriminates water quality categories and predicts health risks based on sensor patterns. Results show that the proposed approach is cost-effective, feasible for large-scale deployment, and significantly improves accessibility to continuous water quality assessment compared to traditional laboratory-based methods.