Towler, Erin L听1听;听Rajagopalan, Balaji听2听;听Summers, Scott听3听;听Seidel, Chad听4
1听University of 蜜糖直播 at Boulder
2听University of 蜜糖直播 at Boulder
3听University of 蜜糖直播 at Boulder
4听Damon S. Williams
Drinking water utilities face complex decisions when balancing new and changing regulatory requirements with competing finished water quality objectives. Tools are needed to help utilities better understand treatment plant performance in light of natural influent water quality variability and regulation compliance. To this end, the natural variability of select water quality variables in the United States were characterized in terms of their spatial and temporal variability. Based on these relationships, a K-nearest neighbor (K-NN) bootstrap technique was developed to generate ensembles of influent water quality. Next, a statistical model was developed to simulate conventional water treatment using local polynomial (nonparametric) regression methods. Finally, input uncertainty was incorporated into the model to see output scenarios and the probability of exceeding a given regulation limit.