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.

Association between long-term exposure to ambient air pollution and lesion ischemia in patients with atherosclerosis.

BACKGROUND AND AIMS: Air pollution has been associated with coronary artery disease. The underlying mechanisms were understudied, especially in relation to coronary stenosis leading to myocardial ischemia. Advances in computed tomography (CT) allow for novel quantification of lesion ischemia. We aim to investigate associations between air pollution exposures and fractional flow reserve on CT (CT-FFR), a measure of coronary artery blood flow. METHODS: CT-FFR, which defines a ratio of maximal myocardial blood flow compared to its normal value (range: 0-100%), was characterized in 2017 patients with atherosclerosis between 2015 and 2017. Exposures to ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5) were estimated using high-resolution exposure models. Linear and logistic regression models were used to assess the association of each air pollutant with CT-FFR and with the prevalence of clinically relevant myocardial ischemia (CT-FFR <75%). RESULTS: Participants were on average 60.1 years old. Annual mean O3, NO2, PM2.5 were 61, 47 and 60 µg/m3, respectively. Mean CT-FFR value was 76.9%. In the main analysis, a higher level of O3 was associated with a lower CT-FFR value (-1.74%, 95% CI: -2.85, -0.63 per 8 µg/m3) and a higher prevalence of myocardial ischemia (odds ratio: 1.32, 95% CI: 1.05-1.65), adjusting for potential confounders such as risk factors and plaque phenotypes, independent of the effects of exposure to NO2 and PM2.5. No associations were observed for PM2.5 or NO2 with CT-FFR. CONCLUSIONS: Long-term exposure to O3 is associated with lower CT-FFR value in atherosclerotic patients, indicating higher risk of lesion ischemia.

Middle-term nitrogen dioxide exposure and electrocardiogram abnormalities: A nationwide longitudinal study.

BACKGROUND: Recently, professionals, such as those from the World Health Organization, have recommended a rigorous standard for nitrogen dioxide (NO2), a typical urban air pollutant affected by regular traffic emissions, based on its short-term and long-term cardiorespiratory effects. However, the association between middle-term NO2 exposure and cardiovascular disorders remains unknown. OBJECTIVES: This study was conducted to examine the relationship between NO2 exposure and its middle-term cardiovascular risks indicated by electrocardiogram (ECG) abnormalities. METHOD: We included 61,094 subjects (132,249 visits) with repeated ECG observations based on longitudinal data from the China National Stroke Screening Survey (CNSSS). The NO2 exposure concentration was derived from a predictive model, measured as the monthly average concentration in the 6 months of preceding the ECG measurement. We used the generalized estimation equation to assess the association between NO2 exposure and ECG abnormalities. RESULT: For each 10 µg/m3 increase in monthly average NO2 concentration, the odds ratio of ECG abnormalities was 1.10 (95% confidence interval [CI] 1.09-1.12) after multiple adjustments. Stratified regression analyses of urban and rural residents showed associations between middle-term NO2 exposure and ECG abnormalities in urban (OR 1.09 [95% CI 1.08-1.11]) and rural residents (OR 1.14 [95% CI 1.10-1.19]). The association was robust within different subpopulations. Associations generally remained statistically significant (OR 1.03 [95% CI 1.02-1.05]) after extra adjustment for PM2.5. Exposure-response relationship analysis revealed a nearly linear relationship between NO2 exposure and the risk for ECG abnormalities. CONCLUSION: Using the variation in ECG signals as a potentially reversible indicator for subclinical risk in cardiovascular systems, our study provides additional evidence on the increased risk posed by middle-term NO2 exposure. Our study showed that policies controlling for NO2 concentrations are beneficial to prevent cardiovascular diseases among Chinese adults.

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.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020.

Nitrogen dioxide (NO2) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO2 air quality guideline (AQG) (annual average <10 µg/m3). However, the burden of diseases attributable to long-term NO2 exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO2; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO2 exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO2 concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO2 exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO2-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO2 exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO2-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO2 exposure but also minimizing inequality.

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 (PM2.5) 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.

Global urbanicity is associated with brain and behaviour in young people.

Urbanicity is a growing environmental challenge for mental health. Here, we investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe (total n = 3,867). We developed a remote-sensing satellite measure (UrbanSat) to quantify population density at any point on Earth. UrbanSat estimates of urbanicity were correlated with brain volume, cortical surface area and brain network connectivity in the medial prefrontal cortex and cerebellum. UrbanSat was also associated with perspective-taking and depression symptoms, and this was mediated by neural variables. Urbanicity effects were greatest when urban exposure occurred in childhood for the cerebellum, and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it may enable assessments of correlations of urbanicity with mental illness and resilience globally.

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).

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 (PM2.5) may partially contribute to the relationship between green space and atherosclerosis, and for walkability index, partially explained by increased PM2.5 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.

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 (PM2.5), 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 PM2.5 (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 PM2.5, NO2, 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.

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

Exposure to fine particulate matter (PM2.5) has become a major global health concern. Although modeling exposure to PM2.5 has been examined in China, accurate long-term assessment of PM2.5 exposure with high spatiotemporal resolution at the national scale is still challenging. We aimed to establish a hybrid spatiotemporal modeling framework for PM2.5 in China that incorporated extensive predictor variables (satellite, chemical transport model, geographic, and meteorological data) and advanced machine learning methods to support long-term and short-term health studies. The modeling framework included three stages: (1) filling satellite aerosol optical depth (AOD) missing values; (2) modeling 1 km × 1 km daily PM2.5 concentrations at a national scale using extensive covariates; and (3) downscaling daily PM2.5 predictions to 100-m resolution at a city scale. We achieved good model performances with spatial cross-validation (CV) R2 of 0.92 and temporal CV R2 of 0.85 at the air quality sites across the country. We then estimated daily PM2.5 concentrations in China from 2013 to 2019 at 1 km × 1 km grid cells. The downscaled predictions at 100 m resolution greatly improved the spatial variation of PM2.5 concentrations at the city scale. The framework and data set generated in this study could be useful to PM2.5 exposure assessment and epidemiological studies.

Impacts by expansion of human settlements on nature reserves in China.

Expansion of human settlements affects nature reserves in various ways. Planning ahead can help to divert or mitigate the impacts but a good understanding of these impacts is a prerequisite. In this study, we estimated the impacts caused by the expansion of human settlements on nature reserves in China by 2050 under different development and conservation scenarios. Our results show that 5016 km2 of nature reserves may be encroached by the expansion of human settlements under the scenario of high growth and weak protection, a ten-fold increase compared to 2010. In addition, new settlements may fragment landscapes in 243 nature reserves and increase the level of fragmentation in 109 nature reserves. Furthermore, expansion of human settlements in surrounding areas may expose 164 nature reserves to threats of human activities and increase the threat levels to 540 nature reserves. The impacts will be lower if protection is stronger or economic growth is slower. Among all nature reserves, those administered at the county level will be affected the most. Nature reserves that protect forests and inland wetlands will be affected more than nature reserves protecting other objects. Nature reserves in East and South China will be influenced more than reserves in other regions. Findings from China show that the expansion of human settlements poses serious challenges to nature reserves in the future, especially in places where economic growth is fast and nature reserves are weakly protected. Proactive conservation strategies have to be developed and implemented forcefully to manage these impacts. Our findings contribute to a better understanding of the potential conflict between human settlements and nature reserves.

The potential impact of invasive woody oil plants on protected areas in China under future climate conditions.

Biodiesel produced from woody oil plants is considered a green substitute for fossil fuels. However, a potential negative impact of growing woody oil plants on a large scale is the introduction of highly invasive species into susceptible regions. In this study, we examined the potential invasion risk of woody oil plants in China's protected areas under future climate conditions. We simulated the current and future potential distributions of three invasive woody oil plants, Jatropha curcas, Ricinus communis, and Aleurites moluccana, under two climate change scenarios (RCP2.6 and RCP8.5) up to 2050 using species distribution models. Protected areas in China that will become susceptible to these species were then identified using a spatial overlay analysis. Our results showed that by 2050, 26 and 41 protected areas would be threatened by these invasive woody oil plants under scenarios RCP2.6 and RCP8.5, respectively. A total of 10 unique forest ecosystems and 17 rare plant species could be potentially affected. We recommend that the invasive potential of woody oil plants be fully accounted for when developing forest-based biodiesel, especially around protected areas.

The contribution of socioeconomic factors to PM2.5 pollution in urban China.

PM2.5 pollution poses severe health risks to urban residents in low and middle-income countries. Existing studies have shown that the problem is affected by multiple socioeconomic factors. However, the relative contribution of these factors is not well understood, which sometimes leads to controversial controlling measures. In this study, we quantified the relative contribution of different socioeconomic factors, including the city size, industrial activities, and residents' activities, to PM2.5 pollution in urban China between 2014 and 2015 by using structural equation model (SEM). Our results showed that industrial activities contributed more to PM2.5 pollution than other factors. The city size and residents' activities also had significant impacts on PM2.5 pollution. The combined influence of all socioeconomic factors could explain between 44% and 48% of variation in PM2.5 pollution, which indicated the existence of influences from other factors such as weather conditions and outside sources of pollutants. Findings from our study can contribute to a more comprehensive understanding of the socioeconomic causes of PM2.5 pollution.