Streamlined Models for Predicting Microbiological Quality Dynamics in Distribution Systems

Drinking water post-treatment is far from sterile, and in situ microbial contamination occurs in the water distribution systems (WDS), leading to quality and health concerns. Computer-based tools, based on mechanistic models, that are adept at interpreting the microbial dynamics within distribution pipes, can be a practical approach to predict the microbiological quality fluctuations during WDS operation. Despite the availability of numerous such models, the high number of variables and parameters associated with them, and the complications in obtaining accurate high-resolution data, their calibration is quite difficult, and their real-world applications are challenging. This problem can be resolved by streamlining the mechanistic models via overseeing the processes within the WDS domain that are least sensitive to controlling the overall microbiological water quality. This would reduce the number of variables and the model parameters. Nevertheless, from our past analysis, we realized that such approaches might not guarantee many advantages in translating the existing mechanistic models to be user-friendly in terms of calibration. Therefore, in this direction, we propose an alternate approach involving applying data-driven modeling to develop surrogate models with minimal parameters to approximate the temporal dynamics of physicochemical and biochemical reactions within the aquatic domain. These surrogate models, once developed, can be integrated within the mechanistic modeling framework to arrive at streamlined models. Such models, with a significantly lesser number of variables and parameters than the existing ones, will be easy to calibrate and will facilitate the prediction of the spatiotemporal dynamics of microbiological quality in real-world WDS.