Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China

Abstract

Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired. This study took five highly populated and severely polluted provinces along the Huaihe River, China, as the research area. The ground-level concentrations of four major air pollutants including nitrogen dioxide (NO2), sulfate dioxide (SO2), particulate matters with diameter equal or less than 10 (PM10) or 2.5 micron (PM2.5) were estimated based on relevant remote sensing data using the geographically weighted regression (GWR) model. The health impacts of these pollutants were then assessed with the aid of co-located gridded population data. The results show that the annual average concentrations of ground-level NO2, SO2, PM10, and PM2.5 in 2016 were 31 µg/m3, 26 µg/m3, 100 µg/m3, and 59 µg/m3, respectively. In terms of the health impacts attributable to NO2, SO2, PM10, and PM2.5, there were 546, 1788, 10,595, and 8364 respiratory deaths, and 1221, 9666, 46,954, and 39,524 cardiovascular deaths, respectively. Northern Henan, west-central Shandong, southern Jiangsu, and Wuhan City in Hubei are prone to large health risks. Meanwhile, air pollutants have an overall greater impact on cardiovascular disease than respiratory disease, which is primarily attributable to the inhalable particle matters. Our findings provide a good reference to local decision makers for the implementation of further emission control strategies and possible health impacts assessment.

Measuring the Environmental Efficiency and Technology Gap of PM2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model

“…China’s air pollution has seriously affected the country’s international image and social development [1]. Air pollution, especially PM 2.5 pollution (diameter of fine particulate matter less than 2.5 μm), also threatens the health of people in China.…”

Filling the gaps of in situ hourly PM<sub>2.5</sub> concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles

“…As a critical air quality indicator, PM 2.5 mass concentration data have been widely used in many previous hazerelated studies (Gao et al, 2018;Miao et al, 2018;Bai et al, 2019a, b;Zhang et al, 2019). Nevertheless, how data gaps were treated in the data exploration process (e.g., data integration and data transformation), especially for those using daily or monthly averaged PM 2.5 data sets (e.g., Guo et al, 2009;Miao et al, 2018;Ye et al, 2018;Zhang et al, 2018;Q. Yang et al, 2019), is oftentimes unclear.…”

A homogenized daily in situ PM<sub>2.5</sub> concentration dataset from the national air quality monitoring network in China

“…Although in situ PM 2.5 concentration data have played critical roles in improving our understanding of regional air quality variations and relevant influential factors (D. Q. Yang et al, 2019;Zheng et al, 2017), little concern was raised about the quality of such dataset itself (Bai et al, 2019a, c;He and Huang, 2018;Zhang et al, 2019Zhang et al, , 2018Zou et al, 2016). Meanwhile, few studies provided a detailed description of the accuracy or bias level (uncertainty) of the observed PM 2.5 data in recent years (Xin et al, 2015;You et al, 2016;Guo et al, 2017;Shen et al, 2018).…”