Spatial and Seasonal Variations of the Air Pollution Index and a Driving Factors Analysis in China

• Published in: Journal of environmental quality Vol. 43; no. 6; pp. 1853 - 1863
• Main Authors: Jiang, Hong‐yue, Li, Hai‐rong, Yang, Lin‐sheng, Li, Yong‐hua, Wang, Wu‐yi, Yan, Ya‐chen
• Format: Journal Article
• Language: English
• Published: United States The American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc 01.11.2014; American Society of Agronomy
• Subjects: Agglomeration; Air pollution; API; Autocorrelation; China; Cities; Clusters; Energy consumption; Environmental protection; GDP; Gross Domestic Product; Outdoor air quality; Pollutants; Pollution index; Population; Rainfall; Seasonal variations; Spatial analysis; Studies; Urbanization; Weather; Winter; China; China, People's Rep
• ISSN: 0047-2425; 1537-2537
• DOI: 10.2134/jeq2014.06.0254

In this study, the daily air pollution index (API) of 110 cities based on ground monitoring was conducted on the 2011 data set from the Ministry of Environmental Protection of China. The pollutant concentrations, seasonal variations, and spatial autocorrelations were evaluated. The results show that the major principal pollutants in China are inhalable particles. In addition, the total number of clean days (API ≤ 50) is apparently smaller in the northern cities than in the southern cities as a result of fuel utilization and large‐scale organized central heating. Seasonally, air pollution is most severe in winter and is caused by low‐frequency rainfall, strong northwest winds, dry climate, and high energy consumption; this is followed by spring, which is a season of frequent sandstorms. According to spatial autocorrelation analysis, clusters with high API value agglomeration (High–High clusters) are mainly concentrated in the middle and northern parts of China, whereas clusters with low API agglomeration (Low–Low clusters) are principally concentrated in the southern parts of China due to a favorable climate and abundant rainfall. Meteorological data, including wind speed and temperature, have great impacts on API. The air quality effects of industrial structure, energy use, urban greening, and traffic congestion were also analyzed. With the ecological function of purifying the air, industries that use natural resources and urban greening could help to reduce API, whereas secondary industry and gas use, which have a positive coefficient, increase the API value. The risk of exposure to poor air quality is largest in the winter, smallest in the summer, and remains relatively unchanged in the spring and autumn.