Pollution Source Detection With Low-Cost Low-Accuracy Sensors Through Coupling Forward Data Assimilation and Inverse Optimization

Data uncertainty affects the accuracy of pollution source detection (PSD), particularly in the background of low-cost water quality sensing and low-accuracy data challenge. This study aims to develop a novel PSD method to use low-accuracy sensor data, namely, the method of coupled forward data Assimilation and inverse Optimization in PSD (A&O-PSD). This approach primarily employs filtering strategies to handle observation errors and extract hidden trend information during forward water quality data assimilation, and then optimal estimation of pollution source information through inverse optimization with enhanced trend information matching, avoiding the non-Gaussian distribution challenge of pollution source information. Both real-world pollution events and semi-synthetic cases were used to evaluate the methodology and compare its performance with the traditional optimization approach (T-PSD). The results indicated that T-PSD is significantly affected by observational and parameter noise, engendering noticeable biases in PSD under the low-accuracy sensor conditions. In contrast, the A&O-PSD could accomplish the estimation task of PSD in real-world pollution events, with improved robustness against noise interference. Furthermore, A&O-PSD achieved an accuracy improvement of over 10% compared to T-PSD in estimating pollution source locations within the typical noise distribution range of most low-accuracy sensors currently available, making it possible to use low-accuracy data that would otherwise be unusable in T-PSD. Overall, the A&O-PSD method, combined with low-cost low-accuracy water quality sensing, offers an effective solution for watershed environmental management.