Literature DB >> 28555400

Groundwater quality assessment and pollution source apportionment in an intensely exploited region of northern China.

Qianqian Zhang1, Huiwei Wang2, Yanchao Wang3, Mingnan Yang2, Liang Zhu2.   

Abstract

Deterioration in groundwater quality has attracted wide social interest in China. In this study, groundwater quality was monitored during December 2014 at 115 sites in the Hutuo River alluvial-pluvial fan region of northern China. Results showed that 21.7% of NO3- and 51.3% of total hardness samples exceeded grade III of the national quality standards for Chinese groundwater. In addition, results of gray relationship analysis (GRA) show that 64.3, 10.4, 21.7, and 3.6% of samples were within the I, II, IV, and V grades of groundwater in the Hutuo River region, respectively. The poor water quality in the study region is due to intense anthropogenic activities as well as aquifer vulnerability to contamination. Results of principal component analysis (PCA) revealed three major factors: (1) domestic wastewater and agricultural runoff pollution (anthropogenic activities), (2) water-rock interactions (natural processes), and (3) industrial wastewater pollution (anthropogenic activities). Using PCA and absolute principal component scores-multivariate linear regression (APCS-MLR), results show that domestic wastewater and agricultural runoff are the main sources of groundwater pollution in the Hutuo River alluvial-pluvial fan area. Thus, the most appropriate methods to prevent groundwater quality degradation are to improve capacities for wastewater treatment and to optimize fertilization strategies.

Entities:  

Keywords:  Anthropogenic activities; Groundwater quality; Pollution source; Source apportionment

Mesh:

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Year:  2017        PMID: 28555400     DOI: 10.1007/s11356-017-9114-2

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  17 in total

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6.  Spatio-temporal Variation of Groundwater Quality and Source Apportionment using Multivariate Statistical Techniques for the Hutuo River Alluvial-Pluvial Fan, China.

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