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Assessing the factors influencing water quality using environment water quality index and partial least squares structural equation model in the Ebinur Lake Watershed, Xinjiang, China

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Abstract

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 (R2 = 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.

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Acknowledgements

We appreciate the anonymous reviewers and editors for appraising our manuscript and for offering instructive comments. This research was carried out with financial support provided by the National Natural Science Foundation of China (Xinjiang Local Outstanding Young Talent Cultivation (Grant No. U1503302,U1603241), the Tianshan Talent Project of the Xinjiang Uygur Autonomous region (Grant No.400070010209), and Group supporting project for study abroad sent by the People’s Government of the Autonomous Region (Grant No. L06).

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Authors and Affiliations

  1. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830046, China

    Changjiang Liu & Fei Zhang

  2. Xinjiang Institute of Technology, Aksu, 843000, China

    Changjiang Liu

  3. Key Laboratory of Wisdom City and Environment Modeling of Higher Education Institute, College of Resources and Environmental Science, Xinjiang University, Urumqi, 830046, China

    Changjiang Liu & Fei Zhang

  4. Engineering Research Center of Central Asia Geoinformation Development and Utilization, Mapping and Geoinformation, National Administration of Surveying, Urumqi, 830002, China

    Fei Zhang

  5. Commonwealth Scientific and Industrial Research Organization Land and Water, Canberra, ACT, 2601, Australia

    Fei Zhang

  6. International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China

    Xiaoping Wang

  7. GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia

    Ngai Weng Chan & Mou Leong Tan

  8. Institute for Social Science Studies, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Haliza Abdul Rahman

  9. College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China

    Shengtian Yang

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  1. Changjiang Liu
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Contributions

Changjiang Liu: Conceptualization, Methodology, Software, Data curation, Writing-original draft. Fei Zhang: Supervision, Funding acquisition. Ngai weng Chan, Haliza Abdul Rahman, Shengtian Yang: Writing-review and editing. Xiaoping Wang: Investigation. Mou Leong Tan: Language embellishment.

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Liu, C., Zhang, F., Wang, X. et al. Assessing the factors influencing water quality using environment water quality index and partial least squares structural equation model in the Ebinur Lake Watershed, Xinjiang, China. Environ Sci Pollut Res 29, 29033–29048 (2022). https://doi.org/10.1007/s11356-021-17886-5

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