Search

Ambient PM 2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM 2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM 2.5 , this study quantified the spatiotemporal dynamics of premature mortality associated with PM 2.5 pollution in central China for each year during 2003-2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM 2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM 2.5 -attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM 2.5 -attributable premature mortality (∼8-12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM 2.5 -related all-cause mortality numbers (<1 K mortalities). Throughout the study period, the PM 2.5 -caused premature mortality decreased by 54 K, in which changes in PM 2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM 2.5 pollution in the country., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Exposure to ambient PM 2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM 2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to provide such PM 2.5 data for environmental monitoring and PM 2.5 -related epidemiologic study. However, current countrywide datasets in China have generally lower accuracies with lower spatiotemporal resolutions because surface PM 2.5 level was rarely recorded in historical years (i.e., preceding 2013). This study aimed to reconstruct daily ambient PM 2.5 concentrations from 2000 to 2018 over China at a fine scale of 1 km using advanced satellite datasets and ground measurements. Taking advantage of the newly released Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1-km AOD dataset, we developed a novel statistical strategy by establishing an advanced spatiotemporal model relying on adaptive model structures with linear and non-linear predictors. The estimates in historical years were validated against surface observations using a strict leave-one-year-out cross-validation (CV) technique. The overall daily leave-one-year-out CV R 2 and root-mean-square-deviation values were 0.59 and 27.18 μg/m 3 , respectively. The resultant monthly (R 2  = 0.74) and yearly (0.77) mean predictions were highly consistent with surface measurements. The national PM 2.5 levels experienced a rapid increase in 2001-2007 and significantly declined between 2013 and 2018. Most of the discernable decreasing trends occurred in eastern and southern areas, while air quality in western China changed slightly in the recent two decades. Our model can deliver reliable historical PM 2.5 estimates in China at a finer spatiotemporal resolution than previous approaches, which could advance epidemiologic studies on the health impacts of both short- and long-term exposure to PM 2.5 at both a large and a fine scale in China., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 R 2  = 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/m 3 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)