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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.)

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM 2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM 2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM 2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM 2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM 2.5 concentration greater than 80 μg/m 3 in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM 2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM 2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM 2.5 - AOD samples (Cross-validation (CV) R 2  = 0.82) and showed better predictive power for the days without PM 2.5 - AOD pairs (the R 2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R 2  = 0.84) significantly outperformed the daily geographically weighted regression model (CV R 2  = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Ambient exposure to fine particulate matter (PM2.5) in eastern China, a densely populated region with very high-level PM2.5 pollution, has attracted great concern from the public, government, and scientific community. By taking advantage of advanced statistical methods and a high-resolution PM2.5 dataset, this study explicitly investigated the spatiotemporal changes in PM2.5 in eastern China from 2001 to 2018 at multiple spatial and temporal scales and examined its links with natural and socioeconomic factors to explore their effects on PM2.5 changes. This study found that the PM2.5 concentration in most of eastern China declined recently, while most of the discernable decreasing trends occurred in the southern and western areas of the study domain, and the statistically significant increasing trends were primarily in the North China Plain. The influencing factors analysis found that, among the selected four natural and five anthropogenic factors, temperature, and population density exerted more potent effects than the other influencing factors, and all the influencing factors were found to impose complex effects on the PM2.5 concentration over space and time. Our study draws a complete picture of the changes in PM2.5 and its possible influences, which could guide future actions to mitigate PM2.5 pollution in eastern China. [ABSTRACT FROM AUTHOR]

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM 2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM 2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM 2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM 2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM 2.5 concentration greater than 80 μg/m3 in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM 2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM 2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated. Image 1 • PM 2.5 exposure levels and health outcomes are dynamically assessed. • Satellite-station-based PM 2.5 concentrations with fine resolution are used. • LBS data from social media are used to track population distribution. • Five exposure models employing different datasets are compared. An improved PM 2.5 exposure assessment method is developed using satellite-ground-integrated PM 2.5 concentrations and LBS-based dynamic population maps. [ABSTRACT FROM AUTHOR]

The use of satellite-retrieved aerosol optical depth (AOD) data and statistical modeling provides a promising approach to estimating PM 2.5 concentrations for a large region. However, few studies have conducted high spatial resolution assessments of ground-level PM 2.5 for China at the national scale, due to the limitations of high-resolution AOD products. To generate high-resolution PM 2.5 for the entirety of mainland China, a combined 3 km AOD dataset was produced by blending the newly released 3 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD data with the 10 km-resolution MODIS Deep Blue AOD data. Using this dataset, surface PM 2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM 2.5 concentrations. Comparisons with the popular spatiotemporal models (daily geographically weighted regression and two-stage model) indicated that the proposed GTWR model, with an R 2 of 0.80 in cross-validation (CV), performs notably better than the two other models, which have an R 2 in CV of 0.71 and 0.72, respectively. The use of the combined 3-km high resolution AOD data was found not only to present detailed particle gradients, but also to enhance model performance (CV R 2 is only 0.32 for the non-combined AOD data). Furthermore, the GTWR's ability to predict PM 2.5 for days without PM 2.5 -AOD paired samples and to generate historical PM 2.5 estimates was demonstrated. As a result, fine-scale PM 2.5 maps over China were generated, and several PM 2.5 hotspots were identified. Therefore, it becomes possible to generate daily high-resolution PM 2.5 estimates over a large area using GTWR, due to its synergy of spatial and temporal dimensions in the data and its ability to extend the temporal coverage of PM 2.5 observations. [ABSTRACT FROM AUTHOR]

Long-term exposure to air pollution is associated with a higher risk of cognitive impairment; however, the understanding of this association is incomplete. We aimed to explore the relationship between fine particulate matter (PM 2.5) exposure and cognitive function using a prospective cohort of ageing adults, including 19,389 respondents in four waves of the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018) linked with the historical PM 2.5 concentrations (2000–2018) in China. By extending the measurement of PM 2.5 exposure from exposure intensity (averaged PM 2.5 concentrations) to exposure duration (the number of months with higher PM 2.5 concentrations), we employed two linear models, the fixed-effect and mixed-effect linear models, to estimate the associations between PM 2.5 exposure and cognitive impairment, with adjustments for individual and regional covariates. Our findings show that the higher PM 2.5 intensity was associated with worse cognitive function, but the associations were only statistically significant in a longer exposure period (more than one year), especially in the 10-year exposure (Coefficient: −0.13; 95% Confidence Interval: −0.22, −0.04). Similar patterns were seen for fully adjusted models of PM 2.5 duration: a longer duration in PM 2.5 exposure was associated with lower cognitive scores, and the duration with higher cut-off points had stronger effects on cognitive function except for the duration at 75 μ g /m3, suggesting a possible coincidence of increasing air pollution and economic development. The stronger exposure to PM 2.5 was associated with poorer cognitive function among Chinese adults, while more work is necessary to explore the causal effect of air pollution, independent of individual and contextual background characteristics. • The measure of exposure to air pollution extends beyond exposure intensity to duration. • Both intensity and duration of exposure to PM 2.5 are associated with cognitive function. • There is a possible coincidence of increasing air pollution and economic development. • Cumulative PM 2.5 exposure might not hasten cognitive impairment across ageing. [ABSTRACT FROM AUTHOR]

• Generated daily 1-km PM 2.5 estimates across China in historical years of 2000–2018. • Proposed a more advanced statistical method with adaptive model structures. • Improved coverage of daily MAIAC 1-km AOD by a customized fusion method. • Estimates gained higher precision by a strict validation and comparison method. • Conducted a multi-scale spatiotemporal trend and PM 2.5 exposure analysis. 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 R2 and root-mean-square-deviation values were 0.59 and 27.18 μg/m3, respectively. The resultant monthly (R2 = 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. [ABSTRACT FROM AUTHOR]

Satellite-based estimation of fine particulate matter of 2.5 μm or less (PM 2.5) at a high spatiotemporal resolution is important to understand the detailed dynamics of PM 2.5 pollution and exposure. Stricter clean air policies have been enacted in recent years to tackle China's serious problem with PM 2.5 pollution, including the implementation of the Air Pollution Prevention and Control Action Plan between 2013 and 2017. However, assessment of the change in national PM 2.5 exposure during this period is difficult due to the limitation of high-resolution PM 2.5 data. To address this issue, a satellite-based spatiotemporal model was developed to predict daily high-resolution surface PM 2.5 concentrations in China during the designated period, and quantitative analysis was then performed regarding the spatiotemporal characteristics of this critical pollutant. The corresponding changes in the population exposure to PM 2.5 were also explored at a fine scale. The overall concentrations of PM 2.5 declined from 2013 to 2017, with substantial decreases in eastern China but negligible decreases in western China. The national PM 2.5 concentration declined remarkably from 2013 to 2014 to 2015–2017. The Beijing–Tianjin–Hebei and Pearl River Delta regions and most cities reached the goals set by the Air Pollution Prevention and Control Action Plan. However, despite the overall reduction in the PM 2.5 concentration, by 2017 the vast majority of the Chinese population still lived in areas with sustained levels of high risk from fine particle pollution. The findings from this study have crucial environmental policy implications for the mitigation of PM 2.5 pollution and could benefit PM 2.5 –related health studies in China. Image 1 • Daily PM 2.5 in 2013–2017 with high spatial scale of 3-km was derived from MODIS. • Study the long-term exposure to PM 2.5 in the 5 years on regional and city scales. • PM 2.5 decreased significantly in eastern China during 2013–2017. • Severe health risk from PM 2.5 decreased significantly in eastern Chinese cities. • The BTH and PRD regions and most cities achieved the stricter clean air targets. [ABSTRACT FROM AUTHOR]