Spatial Patterns of Urban Wastewater Discharge and Treatment Plants Efficiency in China

Int J Environ Res Public Health. 2018 Aug 31;15(9):1892. doi: 10.3390/ijerph15091892.

Abstract

With the rapid economic development, water pollution has become a major concern in China. Understanding the spatial variation of urban wastewater discharge and measuring the efficiency of wastewater treatment plants are prerequisites for rationally designing schemes and infrastructures to control water pollution. Based on the input and output urban wastewater treatment data of the 31 provinces of mainland China for the period 2011⁻2015, the spatial variation of urban water pollution and the efficiency of wastewater treatment plants were measured and mapped. The exploratory spatial data analysis (ESDA) model and super-efficiency data envelopment analysis (DEA) combined Malmquist index were used to achieve this goal. The following insight was obtained from the results. (1) The intensity of urban wastewater discharge increased, and the urban wastewater discharge showed a spatial agglomeration trend for the period 2011 to 2015. (2) The average inefficiency of wastewater treatment plants (WWTPs) for the study period was 39.2%. The plants' efficiencies worsened from the eastern to western parts of the country. (3) The main reasons for the low efficiency were the lack of technological upgrade and scale-up. The technological upgrade rate was -4.8%, while the scale efficiency increases as a result of scaling up was -0.2%. Therefore, to improve the wastewater treatment efficiency of the country, the provinces should work together to increase capital investment and technological advancement.

Keywords: data envelopment analysis; exploratory spatial data analysis; spatial pattern; treatment efficiency; urban wastewater treatment plants.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Cities
  • Economic Development
  • Efficiency
  • Waste Disposal, Fluid / standards*
  • Wastewater / statistics & numerical data*
  • Water Pollution / statistics & numerical data*

Substances

  • Waste Water