Spatial and temporal variations of PM_2.5 concentrations in Chinese cities during 2015-2019

Abstract

The study analyzed the current status and changing trends of PM 2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM 2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m 3 ). The spatial distribution of PM 2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM 2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Spatial-temporal heterogeneity and driving factors of PM2.5 in China: A natural and socioeconomic perspective

“…Many previous studies have concluded that the severely polluted areas in China by PM 2.5 were located in Beijing, Tianjin and Hebei and the surrounding areas ( 35 , 42 , 43 ). Some studies have also shown that Xinjiang has high concentrations of PM 2.5 in China ( 44 – 47 ).…”

“…The Chinese government has taken a series of strategies to control air pollution such as the Air Pollution Prevention and Control Action Plan (Action Plan) from 2013. Since the implementation of the Action Plan, PM 2.5 concentrations have been effectively controlled and have shown a downward trend ( 34 , 35 ). But there are few studies on the driving forces of the temporal and spatial variation of PM 2.5 in China since the implementation of the Action Plan using natural and socioeconomic factors.…”

The R Package HDSpatialScan for the Detection of Clusters of Multivariate and Functional Data using Spatial Scan Statistics

“…The detection of spatial clusters of iodine presence in drinking water allows an improvement of the monitoring of drinking water quality in these geographical areas. Finally in the context of pollution data, Wan et al (2020) and Shi et al (2021) respectively detected clusters of high concentrations of PM 2.5 in America and China. Such results may allow local authorities to specifically monitor these areas and make decisions to reduce pollution.…”

Improvement of Surface PM2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission

“…This region is characterized by high cloud cover (and consequently limited availability of valid satellite measurements of aerosol optical depth data) as well as a dearth of ground-based PM observations. In other PTAs in North America, Europe, the Middle East, East Asia, and Southeast Asia there already exists a wealth of datasets and research on particulate air pollution from the past two decades. − In Ethiopia, however, there was no observational network of surface PM 2.5 before 2016. As of 2022, to our knowledge, the only hourly observations of surface PM 2.5 concentrations from reference grade monitors using beta attenuation monitoring (BAM) technique, with a duration of more than one year and for which the data are publicly available, are from the U.S. Embassy Central and Jacros sites in Addis Ababa.…”