4.7 Article

Quantifying public health benefits of PM2.5 reduction and spatial distribution analysis in China

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 719, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2020.137445

关键词

PM2.5; Health benefits; Spatial distribution; BenMAP; China

资金

  1. Natural Science Foundation of Fujian Province, China [2018J01634]
  2. Social Science Project of Fujian Educational Department, China [JAS170150]
  3. Science and Technology Innovation Project of Fujian Agriculture and Forestry University [CXZX2016038]

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In recent years, particulate matter (PM) air pollution has become a significant and growing public health problem in China. In this study, the daily PM2.5 exposure level at a spatial resolution of 100 km(2) was simulated based on the data of 1328 monitoring sites and the Voronoi Neighborhood Averaging (VNA) interpolation method. The results reveal that the daily mean PM2.5 concentration reduced from 47.82 mu g/m(3) (2016) to 40.87 mu g/m(3) (2018), a reduction of 14.53%. We first calculated the heath impacts and economic benefits of this reduction (Scenario 1) by using Environmental Benefits Mapping and Analysis Program ( BenMAP). The estimated avoided premature mortalities for all-cause, cardiovascular diseases, respiratory diseases, and lung cancer were in the range of 7214 to 81,681 cases (total of 154,176 cases). The estimated economic benefits based on willingness to pay (WTP) ranged from 3.96 to 44.85 billion RMB (total of 84.66 billion RMB). Moreover, the PM2.5 concentration in the control scenario was rolled back to the Grade I standards (35 mu g/m(3), Scenario 2). The avoided deaths are in the range of 58,820 to 590,464 cases (total of 1,217,671 cases). The estimated monetary value of the avoided cases of all health endpoints range from 36.63 to 367.66 billion RMB based on WTP (total of 758.21 billion RMB). In addition, the spatial autocorrelation analysis reveals that the distribution of both avoided premature mortality and economic benefits exhibit a certain spatial aggregation. (C) 2020 Published by Elsevier B.V.

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