Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830046, China.
Xinjiang Institute of Technology, Aksu, 843000, China.
Environ Sci Pollut Res Int. 2022 Apr;29(19):29033-29048. doi: 10.1007/s11356-021-17886-5. Epub 2022 Jan 7.
Surface water quality deterioration is commonly associated with environmental changes and human activities. Although some research has been carried out to evaluate the relationship between various influencing factors and water quality, there is still very little scientific understanding on how to accurately define the key factors of water quality deterioration. This study aims to quantify the impact of environmental factors and land use land cover (LULC) changes on water quality in the Ebinur Lake Watershed, Xinjiang, China. A total of 20 water parameters were used to calculate the Environment Water Quality Index (CWQI). Meanwhile, the partial least squares-structural equation model (PLS-SEM) was used to quantify the impact of eleven factors influencing water quality in the watershed. About 33.3% of the monitoring points that located mostly in the downstream region with dominant anthropogenic activities were detected as poor quality. There were no obvious temporal changes in water quality from 2016 to 2019. The PLS-SEM simulation shows that the latent variable "land use/cover types" (path coefficient = - 0.600) and "Environmental factor" (path coefficient = - 0.313) are two major factors affected water quality in the Ebinur Lake Watershed, with a strong explanatory power to water quality change (R = 0.727). In the latent variable "Environmental factors", the "NDVI" and "night light brightness value" have a great influence on water quality, with the weights of 0.451 and 0.427, respectively. Correspondingly, the "farmland" and "forest land" within the latent variable of "Land use/cover type" have a considerable impact water quality, with the weights of 0.361 and - 0.340, respectively. In conclusion, the influence of anthropogenic activities on surface water quality of the Ebinur Lake Watershed is greater than that of environmental factors. Compared with the traditional multivariate statistical method, PLS-SEM provides a new insight for quantifying the complex relationship between different influencing factors and water quality.
地表水质恶化通常与环境变化和人类活动有关。尽管已经开展了一些研究来评估各种影响因素与水质之间的关系，但对于如何准确界定水质恶化的关键因素，仍知之甚少。本研究旨在量化环境因素和土地利用/土地覆被（LULC）变化对新疆艾比湖流域水质的影响。共使用 20 个水质参数来计算环境水质指数（CWQI）。同时，采用偏最小二乘-结构方程模型（PLS-SEM）来量化影响流域水质的 11 个因素的影响。约 33.3%的监测点位于以人为活动为主的下游地区，检测结果显示这些监测点水质较差。2016 年至 2019 年期间，水质没有明显的时间变化。PLS-SEM 模拟结果表明，潜在变量“土地利用/覆被类型”（路径系数=-0.600）和“环境因子”（路径系数=-0.313）是影响艾比湖流域水质的两个主要因素，对水质变化具有较强的解释力（R=0.727）。在潜在变量“环境因子”中，“归一化植被指数”和“夜光亮度值”对水质影响较大，权重分别为 0.451 和 0.427。相应地，潜在变量“土地利用/覆被类型”中的“耕地”和“林地”对水质有较大影响，权重分别为 0.361 和-0.340。总之，人为活动对艾比湖流域地表水水质的影响大于环境因素。与传统的多元统计方法相比，PLS-SEM 为量化不同影响因素与水质之间的复杂关系提供了新的视角。
