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.

中文翻译：

---

通过耦合前向数据同化和逆向优化，使用低成本、高精度传感器进行污染源检测


数据不确定性会影响污染源检测 （PSD） 的准确性，尤其是在低成本水质传感和低准确性数据挑战的背景下。本研究旨在开发一种使用低精度传感器数据的新型 PSD 方法，即 PSD 中的耦合正向数据同化和逆优化方法 （A&O-PSD）。该方法主要采用滤波策略处理水质数据正向同化过程中的观测误差并提取隐藏的趋势信息，然后通过逆优化和增强的趋势信息匹配对污染源信息进行最优估计，避免了污染源信息的非高斯分布挑战。使用真实世界的污染事件和半合成案例来评估该方法，并将其性能与传统优化方法 （T-PSD） 进行比较。结果表明，T-PSD 受观测和参数噪声的显着影响，在低精度传感器条件下导致 PSD 产生明显的偏差。相比之下，A&O-PSD可以完成真实世界污染事件中PSD的估计任务，并且对噪声干扰的鲁棒性更高。此外，与 T-PSD 相比，A&O-PSD 在目前可用的大多数低精度传感器的典型噪声分布范围内估计污染源位置的精度提高了 10% 以上，从而可以使用在 T-PSD 中无法使用的低精度数据。总体而言，A&O-PSD 方法与低成本、高精度的水质传感相结合，为流域环境管理提供了有效的解决方案。