IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9219-d873380.html
   My bibliography  Save this article

Water Environment Quality Evaluation and Pollutant Source Analysis in Tuojiang River Basin, China

Author

Listed:
  • Kai Zhang

    (School of Chemistry and Environment, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Shunjie Wang

    (School of Chemistry and Environment, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Shuyu Liu

    (School of Chemistry and Environment, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Kunlun Liu

    (Xinjiang Energy Co., Ltd. of State Energy Group, Wulumuqi 830000, China)

  • Jiayu Yan

    (School of Chemistry and Environment, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Xuejia Li

    (School of Chemistry and Environment, China University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

A water environment quality evaluation and pollution source analysis can quantitatively examine the relationship among water pollution, resources, and the economy, and investigate the main factors affecting water quality. This paper took COD, NH 3 -N, and TP of the Tuojiang River as the research objects. The water environment quality evaluation and pollution source analysis of the Tuojiang River Basin were conducted based on the grey water footprint, decoupling theoretical model, and correlation analysis method. The results showed that grey water footprint decreased, and the water environment quality improved. Among the pollution sources of the grey water footprint, TP accounted for the highest proportion. Moreover, the economic development level and the water environment were generally in a state of high-quality coordination. Farmland and stock breeding pollution accounted for the largest proportion of agricultural pollution and were thus the main source of the grey water footprint. The results of Pearson’s correlation analysis indicated that the source of the pollutants were the imported pollution from the tributaries and agricultural pollution (especially stock breeding and farmland irrigation). These results showed that the quality of the water environment was improving, and the main factors affecting the water environment were stock breeding and farmland pollution in agriculture. This study presents a decision-making basis for strengthening the ecological barrier in the Yangtze River.

Suggested Citation

  • Kai Zhang & Shunjie Wang & Shuyu Liu & Kunlun Liu & Jiayu Yan & Xuejia Li, 2022. "Water Environment Quality Evaluation and Pollutant Source Analysis in Tuojiang River Basin, China," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9219-:d:873380
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9219/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/15/9219/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Sipan Li & Qunxi Gong & Shaolei Yang, 2019. "Analysis of the Agricultural Economy and Agricultural Pollution Using the Decoupling Index in Chengdu, China," IJERPH, MDPI, vol. 16(21), pages 1-11, October.
    3. Zheng Zeng & Wei-Ge Luo & Zhe Wang & Fa-Cheng Yi, 2021. "Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    4. Jing Li & Lipeng Hou & Lin Wang & Lina Tang, 2021. "Decoupling Analysis between Economic Growth and Air Pollution in Key Regions of Air Pollution Control in China," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    5. Edelmann, Dominic & Móri, Tamás F. & Székely, Gábor J., 2021. "On relationships between the Pearson and the distance correlation coefficients," Statistics & Probability Letters, Elsevier, vol. 169(C).
    6. Man Zhang & Xiaolong Chen & Shuihua Yang & Zhen Song & Yonggui Wang & Qing Yu, 2021. "Basin-Scale Pollution Loads Analyzed Based on Coupled Empirical Models and Numerical Models," IJERPH, MDPI, vol. 18(23), pages 1-19, November.
    7. Sean A. Cahill, 1997. "Calculating The Rate Of Decoupling For Crops Under Cap/Oilseeds Reform," Journal of Agricultural Economics, Wiley Blackwell, vol. 48(1‐3), pages 349-378, January.
    8. Pier Paolo Miglietta & Pierluigi Toma & Francesco Paolo Fanizzi & Antonella De Donno & Benedetta Coluccia & Danilo Migoni & Francesco Bagordo & Francesca Serio, 2017. "A Grey Water Footprint Assessment of Groundwater Chemical Pollution: Case Study in Salento (Southern Italy)," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    9. Yingzhuang Guo & Xiaoyan Wang & Lili Zhou & Charles Melching & Zeqi Li, 2020. "Identification of Critical Source Areas of Nitrogen Load in the Miyun Reservoir Watershed under Different Hydrological Conditions," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
    10. Xiaonan Ji & Jianghai Chen & Yali Guo, 2022. "A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    11. Sisi Que & Hanyu Luo & Liang Wang & Wenqiang Zhou & Shaochun Yuan, 2020. "Canonical Correlation Study on the Relationship between Shipping Development and Water Environment of the Yangtze River," Sustainability, MDPI, vol. 12(8), pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nishijima, Daisuke, 2017. "The role of technology, product lifetime, and energy efficiency in climate mitigation: A case study of air conditioners in Japan," Energy Policy, Elsevier, vol. 104(C), pages 340-347.
    2. Shigemi Kagawa & Yuriko Goto & Sangwon Suh & Keisuke Nansai & Yuki Kudoh, 2012. "Accounting for Changes in Automobile Gasoline Consumption in Japan: 2000–2007," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 1(1), pages 1-27, December.
    3. Sierra, Jaime Cevallos, 2016. "Estimating road transport fuel consumption in Ecuador," Energy Policy, Elsevier, vol. 92(C), pages 359-368.
    4. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    5. Mulder, Peter & de Groot, Henri L.F. & Pfeiffer, Birte, 2014. "Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005," Ecological Economics, Elsevier, vol. 100(C), pages 1-15.
    6. Löschel, Andreas & Pothen, Frank & Schymura, Michael, 2015. "Peeling the onion: Analyzing aggregate, national and sectoral energy intensity in the European Union," Energy Economics, Elsevier, vol. 52(S1), pages 63-75.
    7. Zhang, Shulin & Su, Xiaoling & Singh, Vijay P & Ayantobo, Olusola Olaitan & Xie, Juan, 2018. "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 208(C), pages 422-430.
    8. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2007. "Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and South Korea," Energy Policy, Elsevier, vol. 35(6), pages 3226-3235, June.
    9. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    10. Chen, Yufeng & Miao, Jiafeng, 2023. "What Determines China’s Agricultural Non-Point Source Pollution? An Improved LMDI Decomposition Analysis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 48(2), May.
    11. Wang, Wenwen & Li, Man & Zhang, Ming, 2017. "Study on the changes of the decoupling indicator between energy-related CO2 emission and GDP in China," Energy, Elsevier, vol. 128(C), pages 11-18.
    12. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    13. Ling Yang & Michael L. Lahr, 2019. "The Drivers of China’s Regional Carbon Emission Change—A Structural Decomposition Analysis from 1997 to 2007," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    14. Jeffrey C. Peters & Thomas W. Hertel, 2017. "Achieving the Clean Power Plan 2030 CO2 Target with the New Normal in Natural Gas Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    15. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    16. Román-Collado, Rocío & Colinet, María José, 2018. "Are labour productivity and residential living standards drivers of the energy consumption changes?," Energy Economics, Elsevier, vol. 74(C), pages 746-756.
    17. Yong Bian & Zhi Yu & Xuelan Zeng & Jingchun Feng & Chao He, 2018. "Achieving China’s Long-Term Carbon Emission Abatement Targets: A Perspective from Regional Disparity," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    18. Zhai, Yijie & Zhang, Tianzuo & Ma, Xiaotian & Shen, Xiaoxu & Ji, Changxing & Bai, Yueyang & Hong, Jinglan, 2021. "Life cycle water footprint analysis of crop production in China," Agricultural Water Management, Elsevier, vol. 256(C).
    19. Mohlin, Kristina & Camuzeaux, Jonathan R. & Muller, Adrian & Schneider, Marius & Wagner, Gernot, 2018. "Factoring in the forgotten role of renewables in CO2 emission trends using decomposition analysis," Energy Policy, Elsevier, vol. 116(C), pages 290-296.
    20. Zheng, Jiali & Mi, Zhifu & Coffman, D'Maris & Milcheva, Stanimira & Shan, Yuli & Guan, Dabo & Wang, Shouyang, 2019. "Regional development and carbon emissions in China," Energy Economics, Elsevier, vol. 81(C), pages 25-36.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9219-:d:873380. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.