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. 2020 Jan;87:213-227.
doi: 10.1016/j.jes.2019.05.024. Epub 2019 May 30.

Analysis of Air Quality Characteristics of Beijing-Tianjin-Hebei and Its Surrounding Air Pollution Transport Channel Cities in China

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Analysis of Air Quality Characteristics of Beijing-Tianjin-Hebei and Its Surrounding Air Pollution Transport Channel Cities in China

Cuicui Xiao et al. J Environ Sci (China). .

Abstract

Beijing-Tianjin-Hebei (BTH) and its surrounding areas are very important to air pollution control in China. To analyze the characteristics of BTH and its surrounding areas of China, we collected 5,641,440 air quality data from 161 air monitoring stations and 37,123,000 continuous monitoring data from air polluting enterprises in BTH and surrounding cities to establish an indicator system for urban air quality portraits. The results showed that particulate matter with aerodynamic diameters of <2.5 μm (PM2.5), particulate matter with aerodynamic diameters of <10 μm (PM10) and SO2 improved significantly in 31 cities from 2015 to 2018, but ozone deteriorated. Air quality in BTH and the surrounding areas showed obvious seasonal characteristics, among which PM2.5, PM10, SO2, and NO2 showed a "U" type distribution from January to December, while O3 had an "inverted U" distribution. The hourly changes in air quality revealed that peaks of PM2.5, PM10 and NO2 appeared from 8:00 to 10:00, while those for O3 appeared at 15:00-16:00. The exposure characteristics of the 31 cities showed that six districts in Beijing had the highest air quality population exposure, and that exposure levels in Zhengzhou, Puyang, Anyang, Jincheng were higher than the average of the 31 investigated cities. Additionally, multiple linear regression revealed a negative correlation between meteorological factors (especially wind and precipitation) and air quality, while a positive correlation existed between industrial pollution emissions and air quality in most of BTH and its surrounding cities.

Keywords: Air quality population exposure; Beijing–Tianjin–Hebei; Hourly change; Industrial pollution source emission; Meteorology factors; Seasonal index.

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