In China, atmospheric fine particulate matter (PM2.5) pollution is a challenging
environmental problem. Systematic PM2.5 measurements have started only in 2013, resulting in a
lack of historical data which is a key obstacle for the analysis of long-term PM2.5 trends and
forecasting the evolution over this hot region. Satellite data can provide a new approach to derive
historical PM2.5 information provided that the column-integrated aerosol properties can adequately
be converted to PM2.5. In this study, a recently developed formulation for the calculation of surface
PM2.5 concentrations using satellite data is introduced and applied to reconstruct a PM2.5 time series
over China from 2000 to 2015. The formulated model is also used to explore the PM2.5 driving factors
related to anthropogenic or meteorological parameters in this historical period. The results show
that the annually averaged PM2.5 over China’s polluted regions increased rapidly between 2004 and
2007 (with an average rate of 3.07 μg m−3 yr−1) to reach values of up to 61.1 μg m−3 in 2007, and
decreased from 2011 to 2015 with an average rate of −2.61 μg m−3 yr−1, to reach a value of 46.9 μg m−3
in 2015. The analysis shows that the increase in PM2.5 before 2008 was mainly associated with
increasing anthropogenic factors, further augmented by the effect of meteorological influences.
However, the decrease in PM2.5 after 2011 is mainly attributed to the effect of pollution control
measures on anthropogenic factors, whereas the effects of meteorological factors have continued to
increase since 2000. The results also suggest that further reduction in anthropogenic emissions is
needed to accelerate the decrease in PM2.5 concentrations to reach the target of 35 μg m−3 over major
polluted areas in China before 2025.
Y Zhang, Z Li, W Chang, Y Zhang, G de Leeuw, JJ Schauer. Satellite Observations of PM2.5 Changes and Driving Factors based Forecasting over China 2000-2025
Status: published, Journal: Remote Sensing, Volume: 12, Year: 2020, First page: 1, Last page: 20, doi: doi:10.3390/rs12162518