High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019

Associations of incident female breast cancer with long-term exposure to PM2.5 and its constituents: Findings from a prospective cohort study in Beijing, China

This study aimed to investigate the association between long-term exposure to fine particulate matter (PM2.5) and its constituents (black carbon (BC), ammonium (NH4+), nitrate (NO3-), organic matter (OM), inorganic sulfate (SO42-)) and incident female breast cancer in Beijing, China. Data from a prospective cohort comprising 85,504 women enrolled in the National Urban Cancer Screening Program in Beijing (2013-2019) and the Tracking Air Pollution in China dataset are used. Monthly exposures were aggregated to calculate 5-year average concentrations to indicate long-term exposure. Cox models and mixture exposure models (weighted quantile sum, quantile-based g-computation, and explanatory machine learning model) were employed to analyze the associations. Findings indicated increased levels of PM2.5 and its constituents were associated with higher breast cancer risk, with hazard ratios per 1-μg/m3 increase of 1.02 (95% confidence interval (CI): 1.01, 1.03), 1.39 (95% CI: 1.16, 1.65), 1.28 (95% CI: 1.12, 1.46), 1.15 (95% CI: 1.05, 1.24), 1.05 (95% CI: 1.02, 1.08), and 1.15 (95% CI: 1.07, 1.23) for PM2.5, BC, NH4+, NO3-, OM, and SO42-, respectively. Exposure-response curves demonstrated a monotonic risk increase without an evident threshold. Mixture exposure models highlighted BC and SO42- as key factors, underscoring the importance of reducing emissions of these pollutants.

Quantifying uncertainty: Air quality forecasting based on dynamic spatial-temporal denoising diffusion probabilistic model

Air pollution constitutes a substantial peril to human health, thereby catalyzing the evolution of an array of air quality prediction models. These models span from mechanistic and statistical strategies to machine learning methodologies. The burgeoning field of deep learning has given rise to a plethora of advanced models, which have demonstrated commendable performance. However, previous investigations have overlooked the salience of quantifying prediction uncertainties and potential future interconnections among air monitoring stations. Moreover, prior research typically utilized static predetermined spatial relationships, neglecting dynamic dependencies. To address these limitations, we propose a model named Dynamic Spatial-Temporal Denoising Diffusion Probabilistic Model (DST-DDPM) for air quality prediction. Our model is underpinned by the renowned denoising diffusion model, aiding us in discerning indeterminacy. In order to encapsulate dynamic patterns, we design a dynamic context encoder to generate dynamic adjacency matrices, whilst maintaining static spatial information. Furthermore, we incorporate a spatial-temporal denoising model to concurrently learn both spatial and temporal dependencies. Authenticating our model's performance using a real-world dataset collected in Beijing, the outcomes indicate that our model eclipses other baseline models in terms of both short-term and long-term predictions by 1.36% and 11.62% respectively. Finally, we conduct a case study to exhibit our model's capacity to quantify uncertainties.

Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries

Importance

The association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space.

Objective

To estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach.

Design, Setting, and Participants

This cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023.

Exposure

Day-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations.

Main Outcomes and Measures

Day-to-day changes in county- or municipality-level all-cause mortality rates.

Results

Among the 8 963 352 deaths in the 4 study regions, a 10-μg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2).

Conclusions and Relevance

The findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

High-resolution mapping of regional VOCs using the enhanced space-time extreme gradient boosting machine (XGBoost) in Shanghai

The accurate estimation of highly spatiotemporal volatile organic compounds (VOCs) is of great significance to establish advanced early warning systems and regulate air pollution control. However, the estimation of high spatiotemporal VOCs remains incomplete. Here, the space-time extreme gradient boost model (STXGB) was enhanced by integrating spatiotemporal information to obtain the spatial resolution and overall accuracy of VOCs. To this end, meteorological, topographical and pollutant emissions, was input to the STXGB model, and regional hourly 300 m VOCs maps for 2020 in Shanghai were produced. Our results show that the STXGB model achieve good hourly VOCs estimations performance (R2 = 0.73). A further analysis of SHapley Additive exPlanation (SHAP) regression indicate that local interpretations of the STXGB models demonstrate the strong contribution of emissions on mapping VOCs estimations, while acknowledging the important contribution of space and time term. The proposed approach outperforms many traditional machine learning models with a lower computational burden in terms of speed and memory.

Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.

Hourly forecasting on PM2.5 concentrations using a deep neural network with meteorology inputs

The PM2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM2.5 concentration prediction. In this study, we proposed a PM2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter's spatiotemporal correlation by concatenating the dataset with time series. The predicted PM2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

Indoor exposure to polycyclic aromatic hydrocarbons associated with solid fuel use in rural China

Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants associated with various health risks including lung cancer. Indoor exposure to PAHs, particularly from the indoor burning of fuels, is significant; however, long-term large-scale assessments of indoor PAHs are hampered by high costs and time-consuming in field sampling and laboratory experiments. A simple fuel-based approach and statistical regression models were developed as a trial to predict indoor BaP, as a typical PAH, in China, and consequently spatiotemporal variations in indoor BaP and indoor exposure contributions were discussed. The results show that the national population-weighted indoor BaP concentration has decreased substantially from 46.1 ng/m3 in 1992 to 6.60 ng/m3 in 2017, primarily due to the increased use of clean energies for cooking and heating. Indoor BaP exposure contributed to > 70% of the total inhalation exposure in most cities, particularly in regions where solid fuels are widely utilized. With limited experimental observation data in building statistical models, quantitative results of the study are associated with high uncertainties; however, the study undoubtedly supports effective countermeasures on indoor PAHs from solid fuel use and the importance of promoting clean household energy usage to improve household air quality.

Health burden evaluation of industrial parks caused by PM2.5 pollution at city scale

Industrial park is an important emission sector of PM2.5 pollution. Previous studies have provided valuable information on the impact of PM2.5 from industrial parks on human health, but relevant studies at city scale are limited. In this study, the health burden of industrial parks was evaluated based on PM2.5-related premature deaths and economic contributions. The premature deaths were calculated in terms of a novel research model by integrating the Bayesian maximum entropy (BME) model, weighted concentration-weighted trajectory (WCWT), and integrated exposure-response function (IER). Take Tianjin City for example, it was found that since the main diffusion direction of PM2.5 in Tianjin is from south to north, the industrial parks in the south of Tianjin and close to the central city with high population density have high health burden. These industrial parks need to be focused on or even relocated in the future. The research model can provide scientific basis for the health burden evaluation of industrial parks at city scale, so as to help local governments optimize the layout of industrial parks and formulate environmental responsibility management policies for industrial parks.

Assessing the timing and the duration of exposure to air pollution on cardiometabolic biomarkers in patients suspected of coronary artery disease

Air pollution can affect cardiometabolic biomarkers in susceptible populations, but the most important exposure window (lag days) and exposure duration (length of averaging period) are not well understood. We investigated air pollution exposure across different time intervals on ten cardiometabolic biomarkers in 1550 patients suspected of coronary artery disease. Daily residential PM2.5 and NO2 were estimated using satellite-based spatiotemporal models and assigned to participants for up to one year before the blood collection. Distributed lag models and generalized linear models were used to examine the single-day-effects by variable lags and cumulative effects of exposures averaged over different periods before the blood draw. In single-day-effect models, PM2.5 was associated with lower apolipoprotein A (ApoA) in the first 22 lag days with the effect peaking on the first lag day; PM2.5 was also associated with elevated high-sensitivity C-reactive protein (hs-CRP) with significant exposure windows observed after the first 5 lag days. For the cumulative effects, short- and medium-term exposure was associated with lower ApoA (up to 30wk-average) and higher hs-CRP (up to 8wk-average), triglycerides and glucose (up to 6 d-average), but the associations were attenuated to null over the long term. The impacts of air pollution on inflammation, lipid, and glucose metabolism differ by the exposure timing and durations, which can inform our understanding of the cascade of underlying mechanisms among susceptible patients.

Revealing the impact of built environment, air pollution and housing price on health inequality: an empirical analysis of Nanjing, China

Introduction

Residential segregation have become a common phenomenon in China recently. Socioeconomically disadvantaged residents were more likely to live in communities with higher PM2.5 concentrations and poorer built environment, which may ultimately lead to a higher health risk, further exacerbating health inequalities. However, the reasons for health inequalities under residential segregation remain unclear.

Methods

This study quantified the built environment, air pollution, mortality rate and housing price at 1 km × 1 km grid scale. Moderating effect model, mediating effect model, moderated mediating effect model were used to progressively clarify the relationship between the four.

Results

Results show that, in terms of spatial distribution, the central area has high housing price with good built environment, low PM2.5 concentration and low mortality rate. While the suburban area has low housing price, poor built environment, high PM2.5 concentration and high mortality rate. Additionally, built environment can not only reduce health risks through moderating effect, but also affect health through the mediating effect of PM2.5. There is heterogeneity in moderating effect of built environment in different locations. Housing prices can moderate the effect of built environment on health. This study would offer important reference for urban planning to mitigate the effect of built environment inequalities on health inequalities in China.

Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism

Ground-level fine particulate matter (PM_2.5) and ozone (O₃) are air pollutants that can pose severe health risks. Surface PM_2.5 and O₃ concentrations can be monitored from satellites, but most retrieval methods retrieve PM_2.5 or O₃ separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014-2021, we found a strong relationship between PM_2.5 and O₃ with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM_2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM_2.5 and O₃ simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM_2.5 and O₃ based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019-2021 to construct the network, we found that simultaneous retrievals of PM_2.5 and O₃ improved the performance compared with retrieving them independently: the temporal R² increased from 0.66 to 0.72 for PM_2.5, and from 0.79 to 0.82 for O₃. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Estimating particulate matter concentrations and meteorological contributions in China during 2000-2020

Estimating the effects of airborne particulate matter (PM) on climate and human health is highly dependent on the accurate prediction of its concentration and size distribution. High-complexity machine learning models have been widely used for PM concentration prediction, but such models are often considered as "black boxes", lacking interpretability. Here, a simple structure lightGBM model is built for ground PM estimation, and the SHAP approach is used to separate the meteorological contributions due to its strong influence on PM concentration. The models show good performance with correlation coefficient (R²) of 0.84-0.88, 0.80-0.85, and 0.71-0.79, for PM_2.5, PM₁₀, and PM_2.5-10 (2.5-10 μm), respectively. The lightGBM model trains 45 times faster than the XGBoost model while showing similar accuracy. More importantly, the models have small performance gaps between training and predicting (delta R²: 0.07-0.12), effectively reducing overfitting risk. The PM datasets (10 km daily) of three size ranges are then generated over China from 2000 to 2020. The SHAP method shows good agreement with the meteorological normalization approach in separating the meteorological contributions (R² > 0.5). In the Beijing-Tianjin-Hebei region (BTH), meteorology has greater influence on PM_2.5-10 (-5.66%-9.99%) than PM_2.5 and PM₁₀. In the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), albedo has a large contribution to PM_2.5 concentration under the influence of solar radiation. Notably, relative humidity (RH) has different seasonal effects on PM of three size ranges. In the BTH region, RH has negative effects on PM_2.5 (-0.52 μg/m³) and positive effects on PM₁₀ (1.01 μg/m³) and PM_2.5-10 (3.39 μg/m³) in spring, but has opposite effects in summer. The results of SHAP approach are consistent with existing conclusions and imply its feasibility in explaining haze formation. The generated PM datasets are useful in health assessment, environmental management, and climate change studies.

Improved estimation of particulate matter in China based on multisource data fusion

Particulate matter (PM) is a global health concern and causes millions of premature deaths worldwide annually. High-resolution and full-coverage PM datasets are essential to support the accurate assessment of PM exposure. Here, a three-stage model framework is developed based on the Community Multiscale Air Quality (CMAQ) simulations (12 km) and multisource data fusion to estimate 1 km daily PM concentrations across China in 2015, including PM_2.5 (<2.5 μm) and PM₁₀ (<10 μm). The three-stage model performs well with cross-validation coefficient of determination (R²) of 0.91 and 0.87, and root mean square error (RMSE) of 17.3 μg/m³ and 27.2 μg/m³ for PM_2.5 and PM₁₀, respectively. After data fusion from multiple sources, the concentrations of PM_2.5 and PM₁₀ are in better agreement with ground observations compared to the CMAQ simulation with RMSE reduced by 72 % and 67 %. High PM_2.5 events mainly occur in the North China Plain, Yangtze River Delta, and Sichuan Basin, and PM₁₀ show similar spatial patterns to PM_2.5 in eastern China. These full-coverage PM datasets enable in-depth analysis of PM pollution over small areas and support future epidemiological studies and health assessments.

High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city

The Air Quality Index (AQI), which jointly accounts for levels of criteria air pollutants relative to their guidelines, is largely reported at the city level. Little is known about the spatial patterns of the AQI in terms of the magnitude, temporal variability, and predominant air pollutant contributions at the hyperlocal scale within a city. To fill this research gap, we developed spatiotemporal models for each criteria air pollutant based on an advanced geostatistical framework and estimated daily AQI levels at 100-meter resolution in a metropolitan city in 2019. The model prediction ability (cross-validation, CV, Coefficient of determination, R², and root mean square error, RMSE) ranged from 0.43 and 1.86 µg/m³ for sulfur dioxide (SO₂) to 0.92 and 6.25 µg/m³ for fine particulate matter (PM_2.5) across the six air pollutants, leading to good performance in the subsequent AQI estimations (CV R² = 0.86, RMSE = 10.05). The AQI varies substantially over space at a fine scale and differs from the distributions of individual air pollutants. The unhealthy air quality (AQI > 100 over 75 days) spatial pattern was dominated by excessive ground-level ozone exposure in a large area. Our research provides a useful tool for accurately estimating AQI spatiotemporal variations for population health studies.

Optimizing modeling windows to better capture the long-term variation of PM2.5 concentrations in China during 2005-2019

Including data of different time intervals during model development influences the predicting accuracy of PM2.5 but has not been widely discussed. Therefore, we included modeling data with multiple time windows to identify optimized modeling time windows for capturing the long-term variation of PM2.5 in China during 2005-2019. In general, we incorporated PM2.5 measurements, aerosol optical depth (AOD), meteorological parameters, land use data, and other predictors to train random forest models. The study period was separated into two phases (2013-2019 and 2005-2012) according to the availability of PM2.5 measurements. First, we trained models with two strategies of choosing time windows to compare model performance in predicting PM2.5 from 2013 to 2019, when measurements were available. Strategy 1a (ST1a) refers to training one model with all available data, and Strategy 1b (ST1b) refers to training multiple models each with one-year data. Second, we trained models with additional ten strategies (ST2a-ST2j) based on data from different time windows during 2013-2019 to compare the accuracy in predicting PM2.5 before 2013, when measurements were unavailable. The internal and external cross-validation (CV) indicated that the model performance of ST1b was better than ST1a. Predictions based on ST1a tended to underestimate PM2.5 levels in 2013 and 2014 when PM2.5 concentrations were high, and overestimate after 2017 when PM2.5 dropped dramatically. The external CV of predicting historical PM2.5 was the most robust in ST2i (averaged predictions from two models developed by 2013 and 2014 data, respectively). Models with data closer to historical years and PM2.5 levels performed better in predicting historical PM2.5 concentrations. Our results suggested that training models with data of current-years performed better during 2013-2019, and with data of 2013 and 2014 performed better in predicting historical PM2.5 before 2013 in China. The comparison provided evidence for choosing optimized time windows when predicting long-term PM2.5 concentrations in China.

Association of ambient fine particulate matter exposure with gestational diabetes mellitus and blood glucose levels during pregnancy

BACKGROUND

Previous studies have examined the associations between ambient fine particulate matter (PM_2.5) exposure and gestational diabetes mellitus (GDM). However, limited studies explored the relationships between PM_2.5 exposure and blood glucose levels during pregnancy, especially in highly polluted areas.

OBJECTIVES

To examine the associations of prenatal ambient PM_2.5 exposure with GDM and blood glucose levels, and to identify the sensitive exposure windows in a highly air-polluted area.

METHODS

From July 2016 to October 2017, a birth cohort study was conducted in Beijing, China. Participants were interviewed in each trimester regarding demographics, lifestyle, living and working environment, and medical conditions. Participant's daily ambient PM_2.5 levels from 3 m before last menstrual period (LMP) to the third trimester was estimated by a hybrid spatiotemporal model. Indoor air quality index was calculated based on environmental tobacco smoke, ventilation, cooking, painting, pesticide, and herbicide use. Distributed lag non-linear model was applied to explore the sensitive weeks of PM_2.5 exposure.

RESULTS

Of 165 pregnant women, 23 (13.94%) developed GDM. After adjusting for potential confounders, PM_2.5 exposure during the 1^st trimester was associated with higher odds of GDM (10 μg/m³ increase: OR = 1.89, 95% CI: 1.04-3.49). Each 10 μg/m³ increase in PM_2.5 during the 2^nd trimester was associated with 17.70% (2.21-33.20), 15.99% (2.96-29.01), 18.82% (4.11-33.52), and 17.10% (3.28-30.92) increase in 1-h, 2-h, Δ1h-fasting (1-h minus fasting), and Δ2h-fasting (2-h minus fasting) blood glucose levels, respectively. PM_2.5 exposure at 24^th-27^th weeks after LMP was associated with increased GDM risk. We identified sensitive exposure windows of 21^st-24^th weeks for higher 1-h and 2-h blood glucose levels and of 20^th-22^nd weeks for increased Δ1h-fasting and Δ2h-fasting.

CONCLUSIONS

Ambient PM_2.5 exposure during the second trimester was associated with higher odds of GDM and higher blood glucose levels. Avoiding exposure to high air pollution levels during the sensitive windows might prevent women from developing GDM.

Air Pollution and Human Health: Investigating the Moderating Effect of the Built Environment

Air pollution seriously threatens human health and even causes mortality. It is necessary to explore effective prevention methods to mitigate the adverse effect of air pollution. Shaping a reasonable built environment has the potential to benefit human health. In this context, this study quantified the built environment, air pollution, and mortality at 1 km × 1 km grid cells. The moderating effect model was used to explore how built environment factors affect the impact of air pollution on cause-specific mortality and the heterogeneity in different areas classified by building density and height. Consequently, we found that greenness played an important role in mitigating the effect of ozone (O3) and nitrogen dioxide (NO2) on mortality. Water area and diversity of land cover can reduce the effect of fine particulate matter (PM2.5) and NO2 on mortality. Additionally, gas stations, edge density (ED), perimeter-area fractal dimension (PAFRAC), and patch density (PD) can reduce the effect of NO2 on mortality. There is heterogeneity in the moderating effect of the built environment for different cause-specific mortality and areas classified by building density and height. This study can provide support for urban planners to mitigate the adverse effect of air pollution from the perspective of the built environment.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM_2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM_2.5 were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM_2.5 were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Ultrahigh-resolution PM2.5 estimation from top-of-atmosphere reflectance with machine learning: Theories, methods, and applications

Intra-urban pollution monitoring requires fine particulate (PM_2.5) concentration mapping at ultrahigh-resolution (dozens to hundreds of meters). However, current PM_2.5 concentration estimation, which is mainly based on aerosol optical depth (AOD) and meteorological data, usually had a low spatial resolution (kilometers) and severe spatial missing problem, cannot be applied to intra-urban pollution monitoring. To solve these problems, top-of-atmosphere reflectance (TOAR), which contains both the information about land and atmosphere and has high resolution and large spatial coverage, may be efficiently used for PM_2.5 estimation. This study aims to systematically evaluate the feasibility of retrieving ultrahigh-resolution PM_2.5 concentration at a large scale (national level) from TOAR. Firstly, we make a detailed discussion about several important but unsolved theoretic problems on TOAR-based PM_2.5 retrieval, including the band selection, scale effect, cloud impact, and mapping quality evaluation. Secondly, four types and eight retrieval models are compared in terms of quantitative accuracy, mapping quality, model generalization, and model efficiency, with the pros and cons of each type summarized. Deep neural network (DNN) model shows the highest retrieval accuracy, and linear models were the best in efficiency and generalization. As a compromise, ensemble learning shows the best overall performance. Thirdly, using the highly accurate DNN model (cross-validated R² equals 0.93) and through combining Landsat 8 and Sentinel 2 images, a 90 m and ∼4-day resolution PM_2.5 product was generated. The retrieved maps were used for analyzing the fine-scale interannual pollution change inner the city and the pollution variations during novel coronavirus disease 2019 (COVID-19). Results of this study proves that ultrahigh resolution can bring new findings of intra-urban pollution change, which cannot be observed at previous coarse resolution. Lastly, some suggestions for future ultrahigh-resolution PM_2.5 mapping research were given.

Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021

China was seriously affected by air pollution in the past decade, especially for particulate matter (PM) and emerging ozone pollution recently. In this study, we systematically examined the spatiotemporal variations of six air pollutants and conducted ozone prediction using machine learning (ML) algorithms in the Beijing-Tianjin-Hebei (BTH) region. The annual-average concentrations of CO, PM₁₀, PM_2.5 and SO₂ decreased at a rate of 141, 11.0, 6.6 and 5.6 μg/m³/year, while a pattern of initial increase and later decrease was observed for NO₂ and O₃_8 h. The concentration of SO₂, CO and NO₂ was higher in Tangshan and Xingtai, while northern BTH region has lower levels of CO, NO₂ and PM. Spatial variations of ozone were relatively small in the BTH region. Monthly variations of PM₁₀ displayed an increase in March probably due to wind-blown dusts from Northwest China. A seasonal and diurnal pattern with summer and afternoon peaks was found for ozone, which was contrast with other pollutants. Further ML algorithms such as Random Forest (RF) model and Decision tree (DT) regression showed good ozone prediction performance (daily: R² = 0.83 and 0.73, RMSE = 30.0 and 37.3 μg/m³, respectively; monthly: R² = 0.93 and 0.88, RMSE = 12.1 and 15.8 μg/m³, respectively) based on 10-fold cross-validation. Both RF model and DT regression relied more on the spatial trend as higher temporal prediction performance was achieved. Solar radiation- and temperature-related variables presented high importance at daily level, whereas sea level pressure dominated at monthly level. The spatiotemporal heterogeneity in variable importance was further confirmed using case studies based on RF model. In addition, variable importance was possibly influenced by the emission reductions due to COVID-19 pandemic. Despite its possible weakness to capture ozone extremes, RF model was beneficial and suggested for predicting spatiotemporal variations of ozone in future studies.

Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach

Owing to a series of air pollution prevention and control policies, China’s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 μg/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86–0.87 and 12–12.33 μg/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.

Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020

Anthropogenic emissions and ambient fine particulate matter (PM2.5) concentrations have declined in recent years across China. However, PM2.5 exposure remains high, ozone (O3) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020. We show that PM2.5 exposure peaked in 2012 at 52.8 μg m-3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM2.5 exposure declined by 36% to 33.5 μg m-3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM2.5 exposure below the World Health Organization Interim Target 2 (25 μg m-3) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China.

Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree

Fine particulate matter (PM_2.5) with spatiotemporal continuity can provide important basis for the assessment of adverse effects on human health. In recent years, researchers have done a lot of work on the surface PM_2.5 simulation. However, due to the limitations of data and models, it is difficult to accurately evaluate the spatial and temporal PM_2.5 variations on a fine scale. In this study, we adopted the multi-angle implementation of atmospheric correction (MAIAC) aerosol products, and proposed a spatiotemporal model based on the gradient boosting decision tree (GBDT) algorithm to retrieve PM_2.5 concentration across China from 2015 to 2020 at 1-km resolution. Our model achieved excellent performance, with overall CV-R² of 0.92, and annual CV-R² of 0.90-0.93. In addition, the model can also be used for evaluation on different time scales. Compared with previous studies, the model developed in our study performed better and more stable, which showed the highest accuracies in PM_2.5 estimation works at 1-km resolution. During the study period, the overall national PM_2.5 pollution showed a downward trend, with the annual mean concentration dropping from 42.42 μg/m³ to 27.91 μg/m³. The largest decrease occurred in Beijing-Tianjin-Hebei (BTH), with a trend of -5.17 μg/m³/yr, while it remains the most polluted region. The area meeting the secondary national air quality standard (<35 μg/m³) increased from ∼34% to ∼79%. These results indicate that the atmospheric environment has improved significantly. Moreover, different regions have different time nodes for the start of the continuous standard-met day during the year, and the duration is different as well. Overall, this study can provide reliable large-scale PM_2.5 estimations.

Predicting Daily PM2.5 Exposure with Spatially Invariant Accuracy Using Co-Existing Pollutant Concentrations as Predictors

The spatiotemporal variation of PM2.5 should be accurately estimated for epidemiological studies. However, the accuracy of prediction models may change over geographical space, which is not conducive for proper exposure assessment. In this study, we developed a prediction model to estimate daily PM2.5 concentrations from 2010 to 2017 in the Kansai region of Japan with co-existing pollutant concentrations as predictors. The overall objective was to obtain daily estimates over the study domain with spatially homogeneous accuracy. We used random forest algorithm to model the relationship between the daily PM2.5 concentrations and various predictors. The model performance was evaluated via spatial and temporal cross-validation and the daily PM2.5 surface was estimated from 2010 to 2017 at a 1 km × 1 km resolution. We achieved R2 values of 0.91 and 0.92 for spatial and temporal cross-validation, respectively. The prediction accuracy for each monitoring site was found to be consistently high, regardless of the distance to the nearest monitoring location, up to 10 km. Even for distances greater than 10 km, the mean R2 value was 0.88. Our approach yielded spatially homogeneous prediction accuracy, which is beneficial for epidemiological studies. The daily PM2.5 estimates will be used in a related birth cohort study to evaluate the potential impact on human health.

Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations

3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.

Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015

As the concentrations of primary components of fine particulate matter (PM_2.5) have substantially decreased, the contribution of secondary inorganic aerosols to PM_2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM_2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the concentrations of secondary inorganic aerosols (SO^2-₄, NO^-₃, and NH⁺₄) across the Greater Bay Area (GBA) in 2005 and 2015. The ensemble model provided a better estimation than the 3D numerical air quality model, with higher correlation coefficients (approximately 0.85) and lower root mean square errors. The model revealed that the concentrations of the SO^2-₄, NO^-₃, and NH⁺₄ decreased by 1.91, 0.20, and 0.49 μg/m³, respectively, from 2005 to 2015. To investigate the oxidation and acidy of sulfate, the sulfur oxidation ratio (SOR), degree of sulfate neutralization (DSN), and particle neutralization ratio (PNR) were calculated and analyzed for 2005 and 2015 across the GBA region. The SOR slightly increased in summer, but decreased in other seasons in 2015, indicating the overall weaker sulfate chemical formation due to sulfur emission control measures. The increasing DSN and PNR indicated that more sulfate was neutralized due to reduced sulfur emission and increased ammonia availability. Our study suggests that more effort is needed to control ammonia emission to further reduce the concentrations of SO^2-₄, NO^-₃, and NH⁺₄ across the GBA region in the future.

Development of high-resolution spatio-temporal models for ambient air pollution in a metropolitan area of China from 2013 to 2019

Modeling high-resolution air pollution concentrations is essential to accurately assess exposure for population studies. The aim of this study is to establish an advanced exposure model to predict spatiotemporal changes in fine particulate matter (PM_2.5), nitrogen dioxides (NO₂), and ozone (O₃) concentrations in Shanghai, China. The model is constructed on a geo-statistical modeling framework that incorporates a dimension reduction regression approach and a spatial smoothing function to deal with fine-scale exposure variations. We used a dataset with comprehensive observational and predictor variables that included monitoring data from both national and local agencies from 2013 to 2019, a high-resolution geographical dataset of predictor variables, and a full-coverage weekly satellite data of the aerosol optical depth at a 1 × 1 km² resolution. Our model performed well in terms of the spatial and temporal prediction ability assessed by cross-validation (CV) for PM_2.5 (spatial R² = 0.89, temporal R² = 0.91), NO₂ (R² = 0.49, 0.78), and O₃ (R² = 0.67, 0.81) at the national monitors over seven years according to the leave-one-out CV. For the predictions at the local agency monitoring stations, the overall CV R² was between 0.77 and 0.89 across the air pollutants. We visualized the long-term and seasonal averaged predictions of the PM_2.5, NO_2, and O₃ exposure on maps with a spatial resolution of 100 × 100 m². Our study provides a useful tool to accurately estimate air pollution exposure with high spatial and temporal resolution at the urban scale. These model predictions will be useful to assess both short-term and long-term air pollution exposure for health studies.

Estimating 2013-2019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO2). Current studies in China at the national scale were less focused on NO2 exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO2 predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO2, TROPOspheric Monitoring Instrument NO2, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO2 concentrations from 2013 to 2019 across China at 1×1 km2 resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R2 = 0.72) and the spatial (R2 = 0.85) variations of the NO2 predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R2 > 0.68) or regions far away from monitors (CV R2 > 0.63). We identified a clear decreasing trend of NO2 exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%-14% in some megacities and captured substantial NO2 variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Associations of exposure to residential green space and neighborhood walkability with coronary atherosclerosis in Chinese adults

Residential green space and neighborhood walkability are important foundations of a healthy and sustainable city. Yet, their associations with atherosclerosis, the disease underlying clinical coronary heart disease (CHD), is unknown, especially in susceptible populations. We aim to explore the associations of exposure to residential green space and neighborhood walkability with coronary atherosclerosis. In this study of 2021 adults with suspected CHD, we evaluated the associations of exposure to green space (using Normalized Difference Vegetation Index [NDVI] and enhanced vegetation index [EVI] surrounding each participant's home) and neighborhood walkability (using walkability index and number of parks near home) with atherosclerosis (using coronary artery calcium score, CAC) using linear regression model adjusted for individual-level characteristics. Mediation analysis was further applied to explore potential mechanisms through the pathways of physical activity, air pollution, and psychological stress. In the primary model, an interquartile increase in annual mean NDVI and EVI within the 1-km area was associated with -15.8% (95%CI: 28.7%, -0.7%), and -18.6% (95%Cl: 31.3%, -3.6%) lower CAC score, respectively. However, an interquartile increase in the walkability index near home was associated with a 7.4% (95% CI: 0.1%, 15.2%) higher CAC score. The combined exposure to a green space area in a 1-km area and the walkability index were inversely associated with atherosclerosis, albeit with a smaller magnitude than a single-exposure model. The findings from a mediation analysis suggested that increased physical exercise and ameliorated particulate matter <2.5 μm (PM_2.5) may partially contribute to the relationship between green space and atherosclerosis, and for walkability index, partially explained by increased PM_2.5 exposure. Our study suggested a beneficial association between green space and atherosclerosis, but an adverse association between neighborhood walkability and atherosclerosis. Therefore, urban development that aims to improve neighborhood walkability should jointly account for enhancing green space properties from a public health perspective.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

Toward Clean Residential Energy: Challenges and Priorities in Research

Solid fuels used for cooking, heating, and lighting are major emission sources of many air pollutants, specifically PM_2.5 and black carbon, resulting in adverse environmental and health impacts. At the same time, the transition from using residential solid fuels toward using cleaner energy sources can result in significant health benefits. Here, we briefly review recent research progress on the emissions of air pollutants from the residential sector and the impacts of emissions on ambient and indoor air quality, population exposure, and health consequences. The major challenges and future research priorities are identified and discussed.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion

Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM_2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM_2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R² of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM_2.5 data allow us to track the day-to-day variations in PM_2.5 concentrations over China in a timely manner. The long-term records of PM_2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM_2.5 data are publicly available through our website for sharing with the research and policy communities.