Literature DB >> 31883074

China's industrial gray water footprint assessment and implications for investment in industrial wastewater treatment.

Yuanyi Huang1, Beihai Zhou2, Ruru Han1, Xiaohui Lu3, Shuo Li3, Nan Li4,5.   

Abstract

Industrial wastewater is the largest contributor of toxic pollutants and third-largest contributor of nutrients to bodies of water in China, and understanding the characteristics of such pollution is important for water pollution control. In this study, the industrial gray water footprint (GWF) of each industry sector in China's 31 provinces in 2015 was calculated to identify the pollution characteristics of industrial wastewater discharge and determine how to efficiently allocate investment to pollution reduction. We show that the total industrial GWF of China was 300 billion m3 in 2015 and that the major pollutants were petroleum pollutant (PP), ammonia nitrogen (NH3-N), volatile phenol (VP), and chemical oxygen demand (COD). The water pollution level (WPL) was higher than 1 in Ningxia, Shanxi, Hebei, Tianjin, Shanghai, Henan, and Shandong, indicating that industrial pollution exceeded the carrying capacity of local water bodies in these seven regions. Given equivalent total investment, a scenario that takes the total reduction of the industrial GWF weighted by the WPL in each region as the investment target can better allocate funds to control industrial wastewater pollution in regions with high WPLs relative to a scenario in which investment targets the reduction of the unweighted total industrial GWF. For further industrial GWF reduction in regions with high WPLs, it is crucial to adjust the industrial structure and to upgrade relevant technologies.

Entities:  

Keywords:  China; Gray water footprint; Industrial wastewater; Investment scenario analysis; Pollution reduction; Water pollution level

Mesh:

Substances:

Year:  2019        PMID: 31883074     DOI: 10.1007/s11356-019-07405-y

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


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