State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China; State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China.
State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China.
Environ Pollut. 2024 Nov 15;361:124813. doi: 10.1016/j.envpol.2024.124813. Epub 2024 Aug 27.
Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators-permanganate index (COD), NH-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)-and six air pollution indicators-O, CO, NO, SO, 2.5 μm particulate matter (PM), and inhalable particles (PM)-using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann-Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for COD and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO, SO, PM, and PM being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for COD, NH-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.
了解和量化空气污染排放对水质变化的影响和贡献,对于地表水水质保护和管理至关重要。为此,我们利用中国南水北调中线工程(MR-SNWD)沿线七个国家监测站的五年日数据,创建了一个包含六个水质指标(高锰酸盐指数(COD)、NH-N、pH 值、浊度、电导率和溶解有机物(DOM))和六个空气污染指标(O、CO、NO、SO、2.5μm 颗粒物(PM)和可吸入颗粒物(PM))的五年日数据矩阵。采用多元技术(Mann-Kendall、Spearman 相关、滞后相关和广义加性模型(GAMs))来检验空气污染对水质的非线性关系和滞后效应。MR-SNWD 沿线的空气污染和水质表现出明显的空间异质性,所有水质参数均符合国家 I 类或 II 类标准,而空气污染指标则超过了这些阈值。除 COD 和 DOM 外,其他水质和空气污染指标均表现出显著的季节性差异。空气污染对北部站点的水质具有显著的滞后效应,NO、SO、PM 和 PM 与 pH 值的变化高度相关,平均滞后时间为 17 天。基于 GAMs,滞后效应增强了空气污染和水质之间的显著非线性关系,使 COD、NH-N 和 pH 的平均偏差解释分别增加了 93%、24%和 41%。这些发现为保护长距离跨流域调水工程在人为空气污染下的水质提供了科学依据。