A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation

Excess deaths and loss of life expectancy attributed to long-term NO2 exposure in the Chinese elderly

BACKGROUND

Evidence linking nitrogen dioxide (NO2) air pollution to life span of high-vulnerability older adults is extensively scarce in low- and middle-income countries. This study seeks to quantify mortality risk, excess deaths, and loss of life expectancy (LLE) associated with long-term exposure to NO2 among elderly individuals in China.

METHODS

A nationwide dynamic cohort of 20352 respondents ≥65 years old were enrolled from the China Longitudinal Health and Longevity Survey during 2005-2018. Residential exposures to NO2 and co-pollutants were assessed by well-validated spatiotemporal prediction models. A Cox regression model with time-dependent covariates was utilized to quantify the association of all-cause mortality with NO2 exposure, controlling for confounders such as demographics, lifestyle, health status, and ambient temperature. NO2-attributable deaths and LLE were evaluated for the years 2010 and 2020 based on the pooled NO2-mortality relation derived from multi-national cohort investigations. Decomposition analyses were conducted to dissociate net shift in NO2-related deaths between 2010 and 2020 into four primary contributing factors.

RESULTS

A total of 14313 deaths were recorded during follow-up of approximately 100 hundred person-years (median 3.6 years). We observed an approximately linear relationship (nonlinear P = 0.882) of NO2 exposure with all-cause death across a broad range from 6.6 to 95.7 μg/m3. Every 10-μg/m3 rise in yearly average NO2 concentration was linked to a hazard ratio (HR) of 1.045 (95% confidence interval [CI]: 1.031-1.059). In the updated meta-analysis of this study and 9 existing cohorts, we estimated a pooled HR of 1.043 (95% CI: 1.023-1.063) for each 10-μg/m3 growth in NO2. Reaching a 10-μg/m3 counterfactual target of NO2 concentration in China could avoid 0.33 (95% empirical CI: 0.19-0.49) million premature deaths and an LLE of 0.40 (95% empirical CI: 0.23-0.59) years in 2010, which greatly dropped to 0.24 (95% empirical CI: 0.14-0.36) million deaths and 0.21 (95% empirical CI: 0.12-0.31) years of LLE in 2020. The net fall in NO2-attributable deaths (-26.8%) between 2010 and 2020 was primarily driven by the declines in both NO2 concentration (-41.6%) and mortality rate (-27.1%) under population growth (+41.0%) and age structure transition (+0.9%).

CONCLUSIONS

Our findings provide national evidence for increased risk of premature death and loss of life expectancy attributed to later-life NO2 exposure among the elderly in China. In an accelerated aging society, strengthened clean air actions should be formulated to minimize the health burden and regional inequality in NO2-attributable mortality.

Long-term ambient ozone exposure and incident cardiovascular diseases: National cohort evidence in China

BACKGROUND

Long-term ozone (O3) exposure has been associated with cardiovascular disease (CVD) mortality in mounting cohort evidence, yet its relationship with incident CVD was poorly understood, especially in low- and middle-income countries (LMICs) experiencing high ambient air pollution.

METHODS

We carried out a nationwide perspective cohort study from 2010 through 2018 by dynamically enrolling 36948 participants across Chinese mainland. Warm-season (April-September) O3 concentrations were estimated using satellite-based machine-learning models with national coverage. Cox proportional hazards model with time-varying exposures was employed to evaluate the association of long-term O3 exposure with incident CVD (overall CVD, hypertension, stroke, and coronary heart disease [CHD]). Assuming causality, a counterfactual framework was employed to estimate O3-attributable CVD burden based on the exposure-response (E-R) relationship obtained from this study. Decomposition analysis was utilized to quantify the contributions of four key direct driving factors (O3 exposure, population size, age structure, and incidence rate) to the net change of O3-related CVD cases between 2010 and 2018.

RESULTS

A total of 4428 CVD, 2600 hypertension, 1174 stroke, and 337 CHD events were reported during 9-year follow-up. Each 10-μg/m³ increase in warm-season O3 was associated with an incident risk of 1.078 (95% confidence interval [CI]: 1.050-1.106) for overall CVD, 1.098 (95% CI: 1.062-1.135) for hypertension, 1.073 (95% CI: 1.019-1.131) for stroke, and 1.150 (95% CI: 1.038-1.274) for CHD, respectively. We observed no departure from linear E-R relationships of O3 exposure with overall CVD (Pnonlinear= 0.22), hypertension (Pnonlinear= 0.19), stroke (Pnonlinear= 0.70), and CHD (Pnonlinear= 0.44) at a broad concentration range of 60-160 µg/m3. Compared with rural dwellers, those residing in urban areas were at significantly greater O3-associated incident risks of overall CVD, hypertension, and stroke. We estimated 1.22 million (10.6% of overall CVD in 2018) incident CVD cases could be attributable to ambient O3 pollution in 2018, representing an overall 40.9% growth (0.36 million) compared to 2010 (0.87 million, 9.7% of overall CVD in 2010). This remarkable rise in O3-attributable CVD cases was primary driven by population aging (+24.0%), followed by increase in O3 concentration (+10.5%) and population size (+6.7%).

CONCLUSIONS

Long-term O3 exposure was associated with an elevated risk and burden of incident CVD in Chinese adults, especially among urban dwellers. Our findings underscored policy priorities of implementing joint control measures for fine particulate matter and O3 in the context of accelerated urbanization and population aging in China.

A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Joint effects of green space and air pollutant exposure on preterm birth: evidence from a nationwide study in China

An association between green space exposure and preterm birth has been reported. However, evidence on the joint effects of air pollutant and green space exposure on preterm birth from nationwide research is limited in China. Based on a nationwide cohort, this study aims to explore the effect of green space exposure on preterm birth and analyze the joint effects of green space and air pollutant. Logistic regression models were developed to analyze the effects of green space exposure, and interaction effects were evaluated by adding interaction terms between green space and air pollutants. From 2013 to 2019, this study included 2,294,188 records of newborn births, of which 82,921 were preterm births. The results show that for buffer zones with 250 m, 500 m, 1000 m, and 1500 m, every 0.1 unit increase in NDVI exposure was associated with a decrease in the risk of preterm birth by 5.5% (95% CI: 4.6-6.4%), 5.8% (95% CI: 4.9-6.6%), 6.1% (95% CI: 5.3-7.0%), and 5.6% (95% CI: 4.7-6.5%), respectively. Under high-level exposure to air pollutants, high-level NDVI exposure was more strongly negatively correlated with preterm birth than low-level NDVI exposure. High-level green space exposure might mitigate the adverse effect of air pollutants on preterm birth by promoting physical activity, reducing stress, and adsorbing pollutants. Further investigation is needed to explore how green space and air pollution interact and affect preterm birth, in order to improve risk management and provide a reference for newborn health.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Long-term ozone exposure and all-cause mortality: Cohort evidence in China and global heterogeneity by region

BACKGROUND

Cohort evidence linking long-term ozone (O3) exposure to mortality remained largely mixed worldwide and was extensively deficient in densely-populated Asia. This study aimed to assess the long-term effects of O3 exposure on all-cause mortality among Chinese adults, as well as to examine potential regional heterogeneity across the globe.

METHODS

A national dynamic cohort of 42153 adults aged 16+ years were recruited from 25 provinces across Chinese mainland and followed up during 2010-2018. Annual warm-season (April-September) O3 and year-round co-pollutants (i.e., nitrogen dioxide [NO2] and fine particulate matter [PM2.5]) were simulated through validated spatial-temporal prediction models and were assigned to each enrollee in each calendar year. Cox proportional hazards models with time-varying exposures were employed to assess the O3-mortality association. Concentration-response (C-R) curves were fitted by natural cubic spline function to investigate the potential nonlinear association. Both single-pollutant model and co-pollutant models additionally adjusting for PM2.5 and/or NO2 were employed to examine the robustness of the estimated association. The random-effect meta-analysis was adopted to pool effect estimates from the current and prior population-based cohorts (n = 29), and pooled C-R curves were fitted through the meta-smoothing approach by regions.

RESULTS

The study population comprised of 42153 participants who contributed 258921.5 person-years at risk (median 6.4 years), of whom 2382 death events occurred during study period. Participants were exposed to an annual average of 51.4 ppb (range: 22.7-74.4 ppb) of warm-season O3 concentration. In the single-pollutant model, a significantly increased hazard ratio (HR) of 1.098 (95% confidence interval [CI]: 1.023-1.179) was associated with a 10-ppb rise in O3 exposure. Associations remained robust to additional adjustments of co-pollutants, with HRs of 1.099 (95% CI: 1.023-1.180) in bi-pollutant model (+PM2.5) and 1.093 (95% CI: 1.018-1.174) in tri-pollutant model (+PM2.5+NO2), respectively. A J-shaped C-R relationship was identified among Chinese general population, suggesting significant excess mortality risk at high ozone exposure only. The combined C-R curves from Asia (n = 4) and North America (n = 17) demonstrated an overall increased risk of all-cause mortality with O3 exposure, with pooled HRs of 1.124 (95% CI: 0.966-1.307) and 1.023 (95% CI: 1.007-1.039) per 10-ppb rise, respectively. Conversely, an opposite association was observed in Europe (n = 8, HR: 0.914 [95% CI: 0.860-0.972]), suggesting significant heterogeneity across regions (P < 0.01).

CONCLUSIONS

This study provided national evidence that high O3 exposure may curtail long-term survival of Chinese general population. Great between-region heterogeneity of pooled O3-mortality was identified across North America, Europe, and Asia.

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.

Incident risk and burden of cardiovascular diseases attributable to long-term NO2 exposure in Chinese adults

BACKGROUND

A number of studies suggested a nexus between long-term exposure to nitrogen dioxide (NO2) and the incidence of cardiovascular disease (CVD), while population-based cohort evidence in low- and middle-income countries was extensively sparse.

METHODS

We carried out an 8-year longitudinal study (2010-2018) in a nationwide dynamic cohort of 36,948 Chinese adult participants, who were free of CVD at baseline. Annual average estimates of NO2 exposure were predicted using a well-validated spatiotemporal model and assigned to study participants based on their residential counties. Considering death as a competing risk event, Fine-Gray competing risk models with time-varying exposures at an annual scale were used to quantify incident risks of overall CVD, hypertension, and stroke associated with a 10-μg/m3 rise in NO2 exposure. Using the meta-analysis approach, we performed a pooled analysis of hazard ratio (HR) drawn from this and prior multinational cohort studies for the assessment of attributable burden. NO2-attributable overall CVD incidents in China were evaluated by city and province for years 2010 and 2018, referring to a counterfactual exposure level of 10 μg/m3 (2021 World Health Organization [WHO] air quality guidelines). A decomposition method was used to decompose net change in NO2-attributable CVD incidents during 2010 and 2018 into 3 primary contributions of driving factors (i.e., changes in NO2 exposure, population size, and incidence rate).

RESULTS

A total of 4428 overall CVD events (hypertension 2448, stroke 1044) occurred during a median follow-up period of 6.1 years. Annual mean NO2 concentration from 2010 to 2018 was 20.0 μg/m3 (range: 6.9-57.4 μg/m3). An increase of 10-µg/m3 in NO2 was associated with an HR of 1.558 (95% confidence interval [CI]: 1.477, 1.642) for overall CVD, 1.521 (95% CI: 1.419, 1.631) for hypertension, and 1.664 (95% CI: 1.485, 1.865) for stroke. Longitudinal associations of NO2 exposure with incident CVD were nearly linear over the exposure range, suggesting no discernible thresholds. Subgroup analyses indicated significantly higher NO2-associated risks of incident CVD among urban residents and overweight/obese individuals. According to pooled HR of NO2-CVD association (1.108, 95% CI: [1.007, 1.219]) from 10 multinational cohort studies, we estimated totally 1.44 million incident CVD cases attributable to NO2 exposure in 2018, representing a substantial decrease of 0.41 million compared to the estimate in 2010 (1.85 million) in mainland of China. Nationally, from 2010 to 2018, the attributable incident cases greatly dropped by 22.4%, which was dominantly driven by declined NO2 concentration (-47.1%) that had offset far from the rise of CVD incidence rate (+19.6%) and population growth (+5.1%).

CONCLUSIONS

This study provided nationwide cohort evidence for elevated risks of CVD incidence associated with long-term ambient NO2 exposure among Chinese adults, particularly in urban areas and among overweight/obese individuals. Our findings highlighted that reducing NO2 exposure below 2021 WHO guideline could help prevent a substantial portion of incident CVD cases in China.

Excess mortality associated with high ozone exposure: A national cohort study in China

Emerging epidemiological studies suggest that long-term ozone (O₃) exposure may increase the risk of mortality, while pre-existing evidence is mixed and has been generated predominantly in North America and Europe. In this study, we investigated the impact of long-term O₃ exposure on all-cause mortality in a national cohort in China. A dynamic cohort of 20882 participants aged ≥40 years was recruited between 2011 and 2018 from four waves of the China Health and Retirement Longitudinal Study. A Cox proportional hazard regression model with time-varying exposures on an annual scale was used to estimate the mortality risk associated with warm-season (April-September) O₃ exposure. The annual average level of participant exposure to warm-season O₃ concentrations was 100 μg m^-3 (range: 61-142 μg m^-3). An increase of 10 μg m^-3 in O₃ was associated with a hazard ratio (HR) of 1.18 (95% confidence interval [CI]: 1.13-1.23) for all-cause mortality. Compared with the first exposure quartile of O₃, HRs of mortality associated with the second, third, and highest exposure quartiles were 1.09 (95% CI: 0.95-1.25), 1.02 (95% CI: 0.88-1.19), and 1.56 (95% CI: 1.34-1.82), respectively. A J-shaped concentration-response association was observed, revealing a non-significant increase in risk below a concentration of approximately 110 μg m^-3. Low-temperature-exposure residents had a higher risk of mortality associated with long-term O₃ exposure. This study expands current epidemiological evidence from China and reveals that high-concentration O₃ exposure curtails the long-term survival of middle-aged and older adults.

Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO2 Concentrations and Attributable Health Burdens in China From 2005 to 2020

Despite the recent development of using satellite remote sensing to predict surface NO2 levels in China, methods for estimating reliable historical NO2 exposure, especially before the establishment of NO2 monitoring network in 2013, are still rare. A gap-filling model was first adopted to impute the missing NO2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO2 associated mortality burdens in China. The coverage of satellite NO2 column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R 2 were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO2 concentrations, with both by-year CV R 2 and external separate year validation R 2 achieving 0.80. The estimated national NO2 levels showed a increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The estimated annual mortality burden attributable to long-term NO2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.

Exposure to air pollution and gains in body weight and waist circumference among middle-aged and older adults

INTRODUCTION

Emerging research suggested a nexus between air pollution exposure and risks of overweight and obesity, while existing longitudinal evidence was extensively sparse, particularly in densely populated regions. This study aimed to quantify concentration-response associations of changes in weight and waist circumference (WC) related to air pollution in Chinese adults.

METHODS

We conceived a nationally representative longitudinal study from 2011 to 2015, by collecting 34,854 observations from 13,757 middle-aged and older adults in 28 provincial regions of China. Participants' height, weight and WC were measured by interviewers using standardized devices. Concentrations of major air pollutants including fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and ozone (O₃) predicted by well-validated spatiotemporal models were assigned to participants according to their residential cities. Possible exposure biases were checked through 1000 random simulated exposure at individual level, using a Monte Carlo simulation approach. Linear mixed-effects models were applied to estimate the relationships of air pollution with weight and WC changes, and restricted cubic spline functions were adopted to smooth concentration-response (C-R) curves.

RESULTS

Each 10-μg/m³ rise in PM_2.5, NO₂ and O₃ was associated with an increase of 0.825 (95% confidence interval: 0.740, 0.910), 0.921 (0.811, 1.032) and 1.379 (1.141, 1.616) kg in weight, respectively, corresponding to WC gains of 0.688 (0.592, 0.784), 1.189 (1.040, 1.337) and 0.740 (0.478, 1.002) cm. Non-significant violation for linear C-R relationships was observed with exception of NO₂-weight and PM_2.5/NO₂-WC associations. Sex-stratified analyses revealed elevated vulnerability in women to gain of weight in exposure to PM_2.5 and NO₂. Sensitive analyses largely supported our primary findings via assessing exposure estimates from 1000 random simulations, and performing reanalysis based on non-imputed covariates and non-obese participants, as well as alternative indicators (i.e., body mass index and waist-to-height ratio).

CONCLUSIONS

We found positively robust associations of later-life exposure to air pollutants with gains in weight and WC based on a national sample of Chinese adult men and women. Our findings suggested that mitigation of air pollution may be an efficient intervention to relieve obesity burden.

Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region

Surface ozone (O3) is an important atmospheric trace gas, posing an enormous threat to ecological security and human health. Currently, the core objective of air pollution control in China is to realize the joint treatment of fine particulate matter (PM2.5) and O3. However, high-accuracy near-surface O3 maps remain lacking. Therefore, we established a new model to determine the full-coverage hourly O3 concentration with the WRF-Chem and random forest (RF) models combined with anthropogenic emission data and meteorological datasets. Based on this method, choosing the Beijing-Tianjin-Hebei (BTH) region in 2018 as an example, full-coverage hourly O3 maps were generated at a horizontal resolution of 9 km. The performance evaluation results indicated that the new model is reliable with a sample (station)-based 10-fold cross-validation (10-CV) R2 value of 0.94 (0.90) and root mean square error (RMSE) of 14.58 (19.18) µg m−3. In addition, the estimated O3 concentration is accurately determined at varying temporal scales with sample-based 10-CV R2 values of 0.96, 0.98 and 0.98 at the daily, monthly, and seasonal scales, respectively, which is highly superior to traditional derivation algorithms and other techniques in previous studies. An initial increase and subsequent decrease, which constitute the diurnal variation in the O3 concentration associated with temperature and solar radiation variations, were captured. The highest concentration reached approximately 112.73 ± 9.65 μg m−3 at 15:00 local time (1500 LT) in the BTH region. Summertime O3 posed a high pollution risk across the whole BTH region, especially in southern cities, and the pollution duration accounted for more than 50% of the summer season. Additionally, 43 and two days exhibited light and moderate O3 pollution, respectively, across the BTH region in 2018. Overall, the new method can be beneficial for near-surface O3 estimation with a high spatiotemporal resolution, which can be valuable for research in related fields.

A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning

Until recently, Northern China was one of the most SO₂ polluted regions in the world. The lack of long-term and spatially resolved surface SO₂ data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO₂ across Northern China during 2005-2019. As "concept drift" causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO₂. The results show that the population-weighted regional SO₂ ([SO₂]_pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO₂]_pw decreased by 80.4% from 74.2 ± 28.4 μg/m³ in 2007 to 14.6 ± 4.8 μg/m³ in 2019. The predicted spatial distributions for each year show that the SO₂ pollution was severe (more than 20 μg/m³) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO₂ column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO₂ data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.

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.

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).