Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade

Quan Han; Runze Tong; Wenchao Sun; Yue Zhao; Jingshan Yu; Guoqiang Wang; Sangam Shrestha; Yongliang Jin

doi:10.1016/j.scitotenv.2019.134929

Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade

Sci Total Environ. 2020 Jan 20:701:134929. doi: 10.1016/j.scitotenv.2019.134929. Epub 2019 Oct 24.

Authors

Quan Han¹, Runze Tong¹, Wenchao Sun², Yue Zhao³, Jingshan Yu¹, Guoqiang Wang⁴, Sangam Shrestha⁵, Yongliang Jin¹

Affiliations

¹ Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China.
² Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China. Electronic address: sunny@bnu.edu.cn.
³ Water Environment Institute, Chinese Academy of Environmental Planning, 8 Dayanfang BeiYuan Road, Beijing 100012, China.
⁴ Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China. Electronic address: wanggq@bnu.edu.cn.
⁵ Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand.

PMID: 31704407
DOI: 10.1016/j.scitotenv.2019.134929

Abstract

Baiyangdian Lake, the largest shallow lake in the North China Plain, is essential for maintaining ecosystem functioning in this highly populated region. To explore the influences of human activities on the lake's water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were adopted to assess the temporal and spatial variations of the lake's water quality and explore the dominant factors of these variations. Datasets for 11 water quality parameters from six monitoring stations were used to evaluate the period spanning from 2006 to 2016. Assessment of the annual WQI showed that the water quality of the lake has generally improved over the past decade. Cluster analysis divided 12 months into the dry and wet periods and the six monitoring stations into those located in the western and eastern parts of the lake. Discriminant analysis demonstrated that with only two parameters (water temperature and fluoride) and six parameters (dissolved oxygen, ammonia nitrogen, total nitrogen, total phosphorus, anionic surfactant, and fecal coliform), 96.0% and 93.8% of the water quality data can be classified into the correct spatial and temporal clusters, respectively. For the principal component analysis and factor analysis, the varifactors detected for the two temporal clusters were similar, and varifactors related to pollution explained more variance in the water quality variation than the ones representing natural factors. For the two spatial clusters, the varifactors were different, indicating they are influenced by different types of anthropogenic activities. Correlation analysis between lake water level and water quality indicated that environmental water allocation to the lake generally improve water quality. These findings provide a more thorough understanding of driving mechanism of water quality and may be helpful for making environmental management decisions in Baiyangdian Lake and other large, shallow lakes in highly populated dryland regions.

Keywords: Anthropogenic activities; Baiyangdian Lake; Multivariate statistical techniques; Water quality; Water quality index.