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Heavy Metal Pollution of Lakes along the Mid-Lower Reaches of the Yangtze River in China: Intensity, Sources and Spatial Patterns

Author

Listed:
  • Haiao Zeng

    (State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China)

  • Jinglu Wu

    (State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China)

Abstract

Lakes in the middle and lower reaches of the Yangtze River form a shallow lake group unique in the World that is becoming increasingly polluted by heavy metals. Previous studies have largely focused on individual lakes, with limited exploration of the regional pattern of heavy metal pollution of the lake group in this area. This paper explores the sources, intensity and spatial patterns of heavy metal pollution of lake sediments. A total of 45 sample lakes were selected and the concentrations of key metal elements in the sediments of each lake were measured. The cluster analysis (CA), principal component analysis (PCA) and Geo-accumulation index (I g ) analysis permitted analysis of the source and pollution intensity of the target lakes. Results suggested a notable spatial variation amongst the sample lakes. Lakes in the upper part of the lower reach of the Yangtze River surrounded by typical urban landscapes were strongly or extremely polluted, with high concentrations of Pb, Zn, Cu and Cd in their sediments. This was attributed to large amount of untreated industrial discharges and municipal sewage produced within the lake catchments. In contrast, the heavy-metal pollution of lakes in the Taihu Delta area was notably lower due to industrial restructuring and implementation of effective environmental protection measures. Lakes along the middle reach of Yangtze River surrounded by agricultural areas were unpolluted to moderately polluted by heavy metals overall. Our results suggested that lakes in the central part of China require immediate attention and efforts should be made to implement management plans to prevent further degradation of water quality in these lakes.

Suggested Citation

  • Haiao Zeng & Jinglu Wu, 2013. "Heavy Metal Pollution of Lakes along the Mid-Lower Reaches of the Yangtze River in China: Intensity, Sources and Spatial Patterns," IJERPH, MDPI, vol. 10(3), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:10:y:2013:i:3:p:793-807:d:23799
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    References listed on IDEAS

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    1. V. M. Maldonado & H. O. Rubio Arias & R. Quintana & R. A. Saucedo & M. Gutierrez & J. A. Ortega & G. V. Nevarez, 2008. "Heavy Metal Content in Soils under Different Wastewater Irrigation Patterns in Chihuahua, Mexico," IJERPH, MDPI, vol. 5(5), pages 1-9, December.
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    Cited by:

    1. Lei Zhang & Qianjiahua Liao & Shiguang Shao & Nan Zhang & Qiushi Shen & Cheng Liu, 2015. "Heavy Metal Pollution, Fractionation, and Potential Ecological Risks in Sediments from Lake Chaohu (Eastern China) and the Surrounding Rivers," IJERPH, MDPI, vol. 12(11), pages 1-17, November.
    2. Hong Yao & Weixin Li & Xin Qian, 2015. "Identification of Major Risk Sources for Surface Water Pollution by Risk Indexes (RI) in the Multi-Provincial Boundary Region of the Taihu Basin, China," IJERPH, MDPI, vol. 12(8), pages 1-21, August.
    3. Xuexia Huang & Dinggui Luo & Dongye Zhao & Ning Li & Tangfu Xiao & Jingyong Liu & Lezhang Wei & Yu Liu & Lirong Liu & Guowei Liu, 2019. "Distribution, Source and Risk Assessment of Heavy Metal(oid)s in Water, Sediments, and Corbicula Fluminea of Xijiang River, China," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    4. Yang Guan & Chaofeng Shao & Meiting Ju, 2014. "Heavy Metal Contamination Assessment and Partition for Industrial and Mining Gathering Areas," IJERPH, MDPI, vol. 11(7), pages 1-18, July.
    5. Ping Zhang & Bo Hong & Liang He & Fei Cheng & Peng Zhao & Cailiang Wei & Yunhui Liu, 2015. "Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural N," IJERPH, MDPI, vol. 12(10), pages 1-25, September.

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