Spatiotemporal evolution of PM2.5 concentrations in urban agglomerations of China

As the main form of new urbanization in China, urban agglomerations are an important platform to support national economic growth, promote coordinated regional development, and participate in international competition and cooperation. However, they have become core areas for air pollution. This study used PM2.5 data from NASA atmospheric remote sensing image inversion from 2000 to 2015 and spatial analysis including a spatial Durbin model to reveal the spatio-temporal evolution characteristics and main factors controlling PM2.5 in China’s urban agglomerations. The main conclusions are as follows: (1) From 2000 to 2015, the PM2.5 concentrations of China’s urban agglomerations showed a growing trend with some volatility. In 2007, there was an inflection point. The number of low-concentration cities decreased, while the number of high-concentration cities increased. (2) The concentrations of PM2.5 in urban agglomerations were high in the west and low in the east, with the “Hu Line” as the boundary. The spatial differences were significant and increasing. The concentration of PM2.5 grew faster in urban agglomerations in the eastern and northeastern regions. (3) The urban agglomeration of PM2.5 had significant spatial concentrations. The hot spots were concentrated to the east of the Hu Line, and the number of hot-spot cities continued to rise. The cold spots were concentrated to the west of the Hu Line, and the number of cold-spot cities continued to decline. (4) There was a significant spatial spillover effect of PM2.5 pollution among cities within urban agglomerations. The main factors controlling PM2.5 pollution in different urban agglomerations had significant differences. Industrialization and energy consumption had a significant positive impact on PM2.5 pollution. Foreign direct investment had a significant negative impact on PM2.5 pollution in the southeast coastal and border urban agglomerations. Population density had a significant positive impact on PM2.5 pollution in a particular region, but this had the opposite effect in neighboring areas. Urbanization rate had a negative impact on PM2.5 pollution in national-level urban agglomerations, but this had the opposite effect in regional and local urban agglomerations. A high degree of industrial structure had a significant negative impact on PM2.5 pollution in a region, but this had an opposite effect in neighboring regions. Technical support level had a significant impact on PM2.5 pollution, but there were lag effects and rebound effects.