Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region

Abstract

The economy has developed rapidly in China during recent decades, especially in the Beijing-Tianjin-Hebei (BTH) region. Environmental problems have thus become increasingly prominent, particularly the presence of fine particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5). High-quality and high-resolution PM2.5 data is urgently needed. Therefore, based on the newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth products, a high-quality PM2.5 data set with a high spatial resolution of 1 km is first reconstructed covering 2000 to 2018 in the BTH region using the linear mixed effect (LME) model. This model shows an excellent performance with a high cross-validation coefficient of determination (R2) of 0.85, a small root mean square error of 21.49 μg/m3, and a small mean absolute error of 15.26 μg/m3 from 2013 to 2018. It also has strong predictive power in estimating historical PM2.5 concentrations, with a monthly R2 equal to 0.72. There was a significant decreasing trend (i.e., −1.53 μg/m3, p < 0.01) in PM2.5 concentrations during the last two decades, and the largest downward trend (i.e., −6.83 μg/m3, p < 0.01) occurred from 2013 to 2018. In addition, the response of PM2.5 to the industrial structure is also examined using the vector autoregression model. In general, both the secondary industry and tertiary industry show significant influences and can contribute approximately 3.8% and 9.8% to the PM2.5 pollution in the BTH region, respectively. This suggests that further industrial structural adjustment, e.g., clean energy production, or low-carbon technology development, is required for the future prevention and control of air pollution and the sustainable development of the economy.