College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092, China.
Int J Environ Res Public Health. 2023 Jan 3;20(1):881. doi: 10.3390/ijerph20010881.
The comprehensive evaluation of water quality and identification of potential pollution sources has become a hot research topic. In this study, 14 water quality parameters at 4 water quality monitoring stations on the M River of a city in southeast China were measured monthly for 10 years (2011-2020). Multiple statistical methods, the water quality index (WQI) model, machine learning (ML), and positive matrix factorisation (PMF) models were used to assess the overall condition of the river, select crucial water quality parameters, and identify potential pollution sources. The average WQI values of the four sites ranged from 68.31 to 77.16, with a clear trend of deterioration from upstream to downstream. A random forest-based WQI model (WQI model) was developed, and the results showed that Mn, Fe, faecal coliform, dissolved oxygen, and total nitrogen were selected as the top five important water quality parameters. Based on the results of the WQI and PMF models, the contributions of potential pollution sources to the variation in the WQI values were quantitatively assessed and ranked. These findings prove the effectiveness of ML in evaluating water quality, and improve our understanding of surface water quality, thus providing support for the formulation of water quality management strategies.
水质的综合评价和潜在污染源的识别已成为一个热门的研究课题。本研究对中国东南部某城市的 M 河 4 个水质监测站的 14 项水质参数进行了长达 10 年（2011-2020 年）的逐月监测。采用多种统计方法、水质指数（WQI）模型、机器学习（ML）和正定矩阵因子分析（PMF）模型对河流的整体状况进行评估，选择关键水质参数，并识别潜在污染源。四个站点的平均 WQI 值范围为 68.31 至 77.16，从上游到下游呈明显恶化趋势。建立了基于随机森林的 WQI 模型（WQI 模型），结果表明 Mn、Fe、粪大肠菌群、溶解氧和总氮被选为前五个重要的水质参数。基于 WQI 和 PMF 模型的结果，定量评估和排名了潜在污染源对 WQI 值变化的贡献。这些发现证明了 ML 在水质评价中的有效性，提高了我们对地表水水质的认识，从而为制定水质管理策略提供了支持。
