Machine learning-enabled river water quality monitoring using lithography-free 3D-printed sensors

Abstract

River water quality monitoring is important for aquatic life, livestock, and humans because clean water is critical to meeting food demand during the global food crisis. Excessive contaminants, including phosphate, deplete dissolved oxygen and trigger eutrophication, leading to serious health and ecological problems. Continuous sensors that track phosphate levels can therefore help prevent eutrophication. In this work we present a lithography-free phosphate sensor (P-sensor) that detects phosphate in river water at parts-per-billion levels. The device uses a solid-state indicator electrode formed by 3D-printed periodic polymer patterns (8 um feature size) coated with a thin phosphate ion-selective membrane. The P-sensor detects as little as 1 ppb phosphate across 0 - 475 ppm with a response time under 30 seconds. We validated the sensor on Rappahannock River water, Virginia (less than 0.8 ppm phosphate) at sites upstream and downstream of a sewage treatment plant and benchmarked the results against a commercial phosphate meter. A feed-forward neural network was trained to predict phosphate levels, achieving a mean-squared error below 1e-3, zero standard deviation, and a Pearson correlation coefficient of 0.997 for river samples. These results demonstrate a practical tool for continuous water-quality monitoring that can inform stakeholders and policymakers and ultimately improve public health.