Spatiotemporal trends of PM_2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived...

Long-term PM_2.5 levels with high precision at fine spatiotemporal resolution are essential for quantitatively understanding the health risk of exposure to ambient fine particulate matter (PM_2.5) and making effective air pollution control policies. The emerging statistically derived PM_2.5 estimations from satellite remote sensing observations of aerosol optical depth (AOD) data are an effective alternative to reconstruct global, long-term, high spatiotemporal resolution PM_2.5 information. However, studies on PM_2.5 estimation and its application to exposure and health-related studies are limited in China due to the lack of historical in-situ measurements before 2013. In this study, we explored the long-term trends of PM_2.5 exposure in central China, a hotspot that has recently been experiencing severe particulate pollution, at the local scale. We first developed a spatiotemporal model incorporating periodical characteristics within the data to estimate daily concentrations of historical PM_2.5 at a fine scale of 1 km for 2003-2018. The linear effects of predictors including AOD, meteorological and land-use parameters and the non-linear interaction between AOD and meteorological parameters were considered in the modeling process. The most recently released high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction (MAIAC) was used to help to represent the fine-scale particle gradients. Our daily estimates correlated well with in-situ observations (cross-validation R² = 0.59), achieving precision comparable to previous statistical models. Through linking with gridded demographic data, the population-weighted PM_2.5 average during 2003 to 2018 was found to be high (62.23 μg/m³ for the whole domain) with obvious spatial variations and seasonality. An inverse U pattern was seen in the time series, with two inflection points around 2008 and 2015. Our model provides reliable particulate information with high spatial resolution and long-term temporal coverage, which can inform local-scale PM_2.5-related epidemiological studies and health-risk assessments for central China.