Water Quality Assessment and Spatial-temporal Variation Analysis in Yellow River Basin

被引：0

作者：

Liu Y.-L. ^{[1
]}

Zheng Y.-A. ^{[1
]}

机构：

[1] Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou

来源：

Huanjing Kexue/Environmental Science | 2022年 / 43卷 / 03期

关键词：

Comprehensive water quality identification index; Hierarchical clustering analysis; Principal component analysis; Spatio-temporal variation; Water quality assessment; Yellow River basin;

D O I：

10.13227/j.hjkx.202106111

中图分类号：

学科分类号：

摘要：

During the implementation of ecological protection in the Yellow River basin, understanding the water pollution status and spatio-temporal variation of water quality has become the most important thing for water safety in the basin. To analyze the water quality in recent years, the water quality data in the Yellow River basin from 2004 to 2018 were firstly collected from eight typical monitoring stations. Using a combination of multivariate data analysis methods including the Mann-Kendall (M-K) trend test, hierarchical clustering analysis (HCA), principal component analysis (PCA), and modified comprehensive water quality identification index (WQI), the spatio-temporal variation characteristics of the water quality were then explored in the Yellow River basin. The results indicated that in terms of time variation, the HCA from the water quality time series showed that the water quality of the Yellow River basin could be divided into the wet season, normal season, and dry season, being basically consistent with the hydrological period. Combined with the M-K trend test and WQI-based water quality assessment, the water quality of the Yellow River basin was improving gradually, with 2010 as the critical year. The water quality in the wet season was superior to that in the dry season. The pollution indicator NH4+-N and permanganate index were dominant in both the wet season and dry season. According to the spatial variation analysis, the water quality for all the studied stations improved significantly. Spatial clustering showed that the S6 (Shanxi Yuncheng Hejin Bridge) was obviously different from others, and further comparative study demonstrated that S6 was constantly seriously polluted. The S7 (Henan Jiyuan Xiaolangdi) exhibited different characteristics in the wet and dry season. In all stations, NH4+-N was considered to be the most common pollution indicator, whereas the permanganate index and DO were also relatively serious for S6. In different hydrological seasons, NH4+-N and the permanganate index showed different characteristics, and their variety was related to the fact that the former mainly came from domestic and industrial sources, whereas the latter was mainly derived from agricultural sources. The modified WQI showed obvious advantages over single-factor water quality assessment, and the findings from this study can provide scientific evidence for water pollution control and comprehensive water quality management in the Yellow River basin. © 2022, Science Press. All right reserved.

引用

页码：1332 / 1345

页数：13

共 55 条

[1] Jing Z X, Xia J, Zhang X, Et al., Spatial and temporal distribution and variation of water quality in the middle and downstream of Hanjiang River, Research of Environmental Sciences, 32, 1, pp. 104-115, (2019)
[2] Zhang T, Liu J L, Wang X M., Causal analysis of the spatial-temporal variation of water quality in Baiyangdian Lake, Acta Scientiae Circumstantiae, 30, 2, pp. 261-267, (2010)
[3] Horton R K., An index-number system for rating water quality, Journal of Water Pollution Control Federation, 37, 3, pp. 300-306, (1965)
[4] Bora M, Goswami D C., Water quality assessment in terms of water quality index (WQI): case study of the Kolong River, Assam, India, Applied Water Science, 7, 6, pp. 3125-3135, (2017)
[5] Fadel A, Kanj M, Slim K., Water quality index variations in a mediterranean reservoir: a multivariate statistical analysis relating it to different variables over 8 years, Environmental Earth Sciences, 80, 2, (2021)
[6] Li R R, Zou Z H, An Y., Water quality assessment in Qu River based on fuzzy water pollution index method, Journal of Environmental Sciences, 50, pp. 87-92, (2016)
[7] Han X G, Huang T L, Chen X Z., Improved fuzzy synthetic evaluation method and its application in raw water quality evaluation of water supply plant, Acta Scientiae Circumstantiae, 33, 5, pp. 1513-1518, (2013)
[8] Xiang N., Exploration for water quality assessment and prediction based on neural networks and artificial bee colony algorithm, (2012)
[9] Kong G, Wang Q J, Huang Q., Evaluation of groundwater quality in Changping piedmont plain of Beijing based on BP neural network, Transactions of the Chinese Society of Agricultural Engineering, 33, pp. 150-156, (2017)
[10] Xu Z X., Comprehensive water quality identification index for environmental quality assessment of surface water, Journal of Tongji University (Natural Science), 33, 4, pp. 482-488, (2005)

← 1 2 3 4 5 6 →