Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

Satellite data to support air quality assessment and management

Satellite data have long been recognized as valuable for air quality applications. These applications are in a stage of rapid growth: new geostationary satellites provide hourly or sub-hourly data; improvements in algorithms convert measured wavelengths into retrievals of atmospheric constituents; advances in machine learning support improved estimates of near-surface pollution; and growing interest among air quality managers has led to a range of new satellite data applications. Considering mainly activities in the United States under the Clean Air Act, we discuss proven applications relevant to air quality management, including: informing epidemiological studies and health risk assessments for setting regulatory standards; evaluating regulatory models; constraining emissions inventories; supporting Exceptional Event Demonstrations through tracking wildfire plumes and other sources; characterizing emission patterns and ozone-forming chemistry for State Implementation Plans; improving air quality forecasting; and tracking long-term trends to evaluate regulatory impact. Air quality professionals are increasingly using satellite data for these and related analyses, but barriers remain. This review provides a summary of satellite products used in applications for air quality and related health assessments; progress in using satellite observations for deriving surface-level air quality information across scales; and their use in air quality management.Implications: The review covers advancements in satellite data for air quality applications over the last 15 years. Success with satellite applications, especially for PM2.5 and NO2, include use in health risk assessment, constraining emissions inventories, and supporting tracking short- and long-term trends with regulatory relevance. Solutions co-developed between researchers and practitioners show promise for continued improvements in the use and value of satellite data for air quality applications.

Association between exposure to air pollutants and cardiovascular mortality in Iran: a case-crossover study

This space-time-stratified case-crossover study examined the association between short-term exposure to satellite-derived air pollutants and cardiovascular disease (CVD) mortality in eight Iranian cities from 2018 to 2022. Using quasi-Poisson regression and distributed lag non-linear models (DLNM), we estimated the effects of air pollutant exposure on cumulative lags (0-6, 0-14, 0-21, and 0-28 days) before mortality. The simultaneous effects of multiple air pollutants on CVD were also examined. This association was adjusted for potential confounders, including meteorological factors. Finally, we conducted a stratified analysis based on gender, age, and season to evaluate possible effect modification in the study. During the study period, 115,193 CVD deaths were reported across eight large cities in Iran. In single-pollutant models, CO, PM2.5, and O3 showed the strongest significant associations with CVD mortality during the cumulative lag of 0-28 days, while no significant association was observed for O3. In the two-pollutant models, the association between CVD mortality and NO2 was weakened when PM2.5 was added, whereas the associations with CO and O3 slightly strengthened. Adding CO to the model containing NO2 led to a significant reduction in the association with CO, while the association with NO2 remained unchanged. Similar patterns to the single-pollutant models were observed for the combination of NO2 and O3, as well as CO and O3. The association with PM2.5 remained unchanged in all two-pollutant models, preserving its lag structure and statistical significance. The findings indicate that estimates varied based on gender, age groups, and season. Men, individuals aged 40 or older, and winter seasons showed higher sensitivity to pollutants. Our findings highlight the importance of investigating the health-related multidimensional impact of air pollutants, particularly in more polluted developing countries like Iran. The results should warn national policy makers to set guided resource allocations for environmental health monitoring, and support the implementation of targeted interventions to reduce its impact on public health. Meanwhile, future research should explore the effectiveness of pollution mitigation strategies and investigate the long-term health impacts of sustained air pollutants.

The Dark Triad of Particulate Matter, Oxidative Stress and Coronary Artery Disease: What About the Antioxidant Therapeutic Potential

Particulate matter (PM) is a complex mixture of particles with different adverse effects on health, especially on the cardiovascular (CV) risk and disease (e.g., increased risk of total and CV mortality, ischemic heart disease, heart failure, stroke, hypertension, dyslipidemia and type 2 diabetes). Since oxidative stress (OS) and inflammation are the main key mechanisms by which PM exerted its biological effects on health, several oxidative and inflammatory-related biomarkers have been measured and associated with PM; abnormalities in these parameters in relation to PM highlight the key role of this relationship in terms of adverse health effects, including CV conditions. Antioxidant strategies might prevent/reverse, almost partly, CV effects related to PM exposure, by addressing OS and inflammation, although the clinical gain of these interventional tools is not yet clearly demonstrated. This review aims to summarize PM source and composition, discussing OS and inflammatory events associated with environmental PM exposure as key mechanistic determinants of CV risk and acute event precipitation. Moreover, the modifying potential of antioxidants, especially in subjects more susceptible to the adverse effects of air pollution and/or more highly exposed, will be discussed as a promising research area beyond conventional strategies actually available to prevent the harmful effects of PM (e.g., reduction of pollution sources and population exposure, assessment of air quality standards) in order to better face this dark triad composed of PM, OS and CV disease.

The effect of air pollution on morbidity and mortality among children aged under five in sub-Saharan Africa: Systematic review and meta-analysis

BACKGROUND

Air pollution from indoor and outdoor sources constitutes a substantial health risk to young children in sub-Saharan Africa (SSA). Although some systematic reviews have assessed air pollution and children's respiratory health in SSA, none have considered both ambient and indoor exposures.

METHODS

This systematic review and meta-analysis assessed the effect of air pollution (ambient and indoor) on respiratory hospitalization and mortality among children under five years in SSA. We retrieved relevant articles from PubMed, Embase, Scopus, African Journals Online (AJOL), Web of Science, and medRxiv. The protocol was registered with Prospero (CRD42023470010). We used guidelines from the preferred reporting items for systematic review and meta-analysis (PRISMA-2020) to guide the systematic review process. Risk of bias was assessed using the Office of Health Assessment and Translation (OHAT) quality appraisal tool. For exposures where there were sufficient studies/data we conducted meta-analyses using random effects models and used Stata version 17 software for analysis.

RESULTS

For the systematic review we screened 5619 titles and abstracts, reviewed 315 full texts, and included 31 articles involving 2,178,487 participants. Eleven studies examined exposure to solid fuel use in households and its association with all-cause mortality, while four studies explored the impact of passive smoking on mortality among children under five. Only two studies assessed ambient air pollution's effects on all-cause and respiratory-related mortality. Additionally, 13 studies reported varying associations between respiratory hospitalization and household tobacco smoke exposure. Meta-analyses on studies of solid fuel use and mortality and passive smoking and hospitalizations showed that children exposed to indoor solid fuels combustion had higher odds of mortality compared to non-exposed children (OR = 1.31; 95% CI: 1.16-1.47). The meta-analysis of exposure to second-hand smoke found an increased risk of respiratory hospitalization due to pneumonia, although the results were not significant (OR = 1.29; 95% CI: 0.45-3.68), and our certainty of evidence assessment indicated insufficient support to conclusively establish this association.

CONCLUSION AND RECOMMENDATION

Our review reveals that solid fuel use and ambient PM2.5 exposure were associated with increased mortality risk in children under five years in SSA. The meta-analysis showed evidence of an increased risk of under-five years mortality associated with solid fuel use in households. Associations between secondhand smoke and pneumonia hospitalization were less clear. We conclude that significant research gaps remain in understanding the impact of discrete sources of air pollution on the causation of respiratory illness in young children living in SSA. Prioritizing interventions targeting indoor sources is essential, along with further studies which use standardized and objective exposure and outcome measures to study these associations.

How growing up without siblings affects the adult brain and behaviour in the CHIMGEN cohort

With the worldwide increase in only-child families, it is crucial to understand the effects of growing up without siblings (GWS) on the adult brain, behaviour and the underlying pathways. Using the CHIMGEN cohort, we investigated the associations of GWS with adult brain structure, function, connectivity, cognition, personality and mental health, as well as the pathway from GWS to GWS-related growth environments to brain and to behaviour development, in 2,397 pairs of individuals with and without siblings well matched in covariates. We found associations linking GWS to higher language fibre integrity, lower motor fibre integrity, larger cerebellar volume, smaller cerebral volume and lower frontotemporal spontaneous brain activity. Contrary to the stereotypical impression of associations between GWS and problem behaviours, we found positive correlations of GWS with neurocognition and mental health. Despite direct effects, GWS affects most brain and behavioural outcomes through modifiable environments, such as socioeconomic status, maternal care and family support, suggesting targets for interventions to enhance children's healthy growth.

Spatiotemporal covariability between air pollution and meteorological variables over Khyber Pakhtunkhwa, Pakistan

This study analyzed spatiotemporal covariability of O3, SO2, NO2, CO, and PM2.5 with meteorological variables (rain precipitation rate, specific humidity, pressure, temperature, wind speed, latent heat flux, and solar radiation) using satellite data in Khyber Pakhtunkhwa province, Pakistan. Inverse Distance Weighted interpolation, ordinary least square regression, Pearson correlation, Generalized Linear, and Generalized Additive models were applied. Results revealed highest annual average pollutants as; NO₂ (3.87 ± 0.73) × 1015 molecules/cm2, PM2.5 (37.91 ± 17.75) µg/m3, SO2 (6.81 ± 8.27) × 1014, CO (1.34 ± 0.52) × 1018 molecules/cm2, and O3 (7.73 ± 0.10) × 1018 molecules/cm2. Seasonally NO2 peaked in summer and spring, SO₂ in autumn, CO in spring, PM2.5 in winter while O₃ in spring with minor seasonal variations. Annual spatial distribution of SO2, PM2.5, and CO were highest in central and southern areas while O3 in the central and NO2 in the central and southeastern. Wind speed was negatively correlated with NO2 annually and in winter, summer, and autumn. Temperature positively influenced NO2 and PM2.5 annually and seasonally, while O3 positively correlated with rain and specific humidity but negatively with solar radiation and temperature in spring. In autumn, O3 exhibited a positive association with rain and negative with solar radiation. SO2 indicated positive correlations with solar radiation annually and temperature in spring, while CO showed weak associations except for a positive correlation with specific humidity in summer. GAM models slightly better captured pollutant dynamics by explaining both linear and nonlinear relationships. These findings provide crucial insights for targeted air quality management strategies and pollutant mitigation.

Temporal and spatial heterogeneity of tropospheric O3 and NO2 and health impact analysis in Shaanxi, Gansu, and Ningxia regions of China

O3 and precursor pollution in the Shaanxi-Gansu-Ningxia region of China is becoming increasingly severe, and the regional pollution characteristics are also more prominent. To investigate the causes of O3 and NO2 pollution and the health impacts of O3, the spatial and temporal heterogeneity of O3 and NO2 pollution and the health and economic losses in the Shaanxi-Gansu-Ningxia region were analyzed by using a detector with optimal parameters and the BenMAP (Environmental Benefits Mapping and Analysis Program-community Edition) model. The results show that O3 in the troposphere of Shaanxi, Gansu, and Ningxia showed a "bimodal" distribution from 2005 to 2022, reaching a maximum value of 32.6 DU in 2010, and the change of NO2 increased first and then decreased in the troposphere, reaching a peak value of 5.2 × 1015 molec·cm-2 in 2011. The seasonal variations of O3 and NO2 were the highest in winter, the second highest in spring and fall, and the lowest in summer. The high-value area of O3 was mainly located in the northwest of Gansu. The concentration gradually decreased from northwest to southeast. In contrast, the high-value area of NO2 was concentrated in the east of Guanzhong Plain and the north of Yulin City, and the overall distribution was high in the east and low in the west. Among the interactions of the nine factors, the interactions of temperature and wind speed, precipitation and wind speed had the highest explanatory power for O3 changes, with 0.951 and 0.96, respectively, and the interactions of temperature and wind speed, and precipitation and sunshine hours had the highest explanatory power for NO2 changes, with 0.834 and 0.844, respectively; the interactions among pollutants were weaker than the interactions among meteorological factors. Cardiovascular disease is the leading cause of premature death in the population. The number of premature deaths in the three provinces gradually decreases from 2018 to 2020, and the proportion of health economic loss to GDP also gradually decreases, from 1.55%, 0.82%, and 3.99% to 0.2%, 0.34%, and 2.86%, respectively. This study can provide theoretical references for the control and health impacts of O3 and NO2 in Shaanxi, Gansu, and Ningxia regions of China.

Effects of PM2.5 exposure and air temperature on risk of cardiovascular disease: evidence from a prospective cohort study

Background and aims

Evidence from extensive cohort studies about the individual and combined associations of air pollution and air temperature with cardiovascular disease (CVD) morbidity is limited. This study aimed to examine the long-term effects of PM2.5 exposure and air temperature on CVD based on a cohort study of middle-aged and older populations in China.

Methods

A total of 9,316 non-CVD adults (≥40 years old) who joined the China Health and Retirement Longitudinal Study between 2011 and 2018 were included in our analysis. The two-year average PM2.5 concentration and air temperature of the city where participants lived were calculated. The Cox proportional hazards model was conducted to analyze the associations of PM2.5 exposure and air temperature with CVD morbidity.

Results

In the multivariable-adjusted model, each 10 μg/m3 rise in 2-y PM2.5 concentration was associated with an increased risk of developing CVD (hazard ratio [HR]: 1.30; 95% confidence interval [CI]: 1.27-1.32). Compared with individuals in the bearable temperature group, those with low temperatures had a higher risk of CVD (HR: 1.77; 95% CI: 1.53-2.04). Stratified analyses found that cardiovascular metabolic risk factors could not change these associations. Compared with individuals in the low-level PM2.5 exposure and bearable temperature group, those in the high-level PM2.5 exposure and low-temperature group had a 7.08 times higher risk of CVD (95% CI: 5.55-9.03).

Conclusion

Long-term PM2.5 exposure and low air temperature are associated with a higher risk of CVD. Consequently, efforts to reduce air pollution and enhance protection against cold temperatures are vital for mitigating CVD risk.

Evolving trends in lung cancer risk factors in the ten most populous countries: an analysis of data from the 2019 Global Burden of Disease Study

Background

Amid shifting tobacco policies and escalating air pollution levels, Lung Cancer (LC) risk factors have changed notably. Continuous assessment of these risk factors is necessary. This study compares trends in tobacco, air pollution, and asbestos-associated Age-Standardized Mortality Rates (ASMR) from Trachea, Bronchus, and Lung (TBL) Cancer across the top ten most populated countries (2023 censuses) and globally.

Methods

We extracted overall and risk-factor-associated TBL cancer ASMR of the ten most populated countries for 1990-2019 from the Global Burden of Disease (GBD) database using the dedicated results tool (http://ghdx.healthdata.org/gbd-results-tool). GBD mapped the mortality data related to ICD codes (C33-C34, D02.1-D02.2, D38.1, 162-162.9, 231.1, 231.2, 231.8, 235.7 from ICD10 and B101 from ICD9). Tobacco, occupational exposure to asbestos and air pollution (ambient particulate matter and household air pollution) associated TBL cancer mortality data were extracted to evaluate the trends based on risk factors. We calculated relative changes in ASMRs between 1990 and 2019 for each sex in each country for each risk factor. Joinpoint regression analysis was performed to calculate the Estimated Annual Percentage Change (EAPC) and its corresponding 95% confidence interval (CI) for each line segment, allowing for trend assessment.

Findings

Globally, TBL Cancer mortality has decreased by 8%, with a decrease for males but an increase for females. Globally, both tobacco and air pollution-associated TBL cancer ASMR have decreased. While tobacco-associated ASMR has increased in China and Indonesia, air pollution-associated ASMR has also increased in China, India, Pakistan, and Nigeria. On stratification, while PM-associated mortality increased by 25% globally, household-associated TBL cancer ASMR decreased by 62%. China had the highest PM-associated TBL Cancer in 2019 (8.8/100,000), twice higher than the global average. Despite a decline in asbestos-associated TBL cancer ASMR from 8.91/100,000 to 6.0/100,000, the rate in the United States remained twice higher than the global average for the entire study period.

Interpretation

Tobacco-associated TBL cancer mortality is declining but still predominant. Ambient particulate matter-associated TBL cancer mortality is rising, requiring policy and awareness efforts. Expanding access to preventive services and addressing underlying risk factors are essential steps required toward reducing lung cancer mortality at the global level.

Funding

This study did not receive any funding support.

Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast

Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m3 vs. -1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of -7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.

Exploring the pathways linking visual green space to depression in older adults in Shanghai, China: using street view data

OBJECTIVES

To examine (1) how visual green space quantity and quality affect depression among older adults; (2) whether and how the links may be mediated by perceived stress, physical activity, neighbourhood social cohesion, and air pollution (PM2.5); and (3) whether there are differences in the mediation across visual green space quantity and quality.

METHOD

We used older adults samples (aged over 65) from the WHO Study on Global Ageing and Adult Health in Shanghai, China. Depression was quantified by two self-reported questions related to the diagnosis of depression and medications or other treatments for depression. Visual green space quantity and quality were calculated using street view images and machine learning methods (street view green space = SVG). Mediators included perceived stress, social cohesion, physical activity, and PM2.5. Multilevel logistic and linear regression models were applied to understand the mediating roles of the above mediators in the link between visual green space quantity and quality and depression in older adults.

RESULTS

SVG quantity and quality were negatively related to depression. Significant partial mediators for SVG quality were social cohesion and perceived stress. For SVG quantity, there was no evidence that any of the above mediators mediated the association.

CONCLUSION

Our results indicated that visual green space quantity and quality may be related to depression in older adults through different mechanisms.

Location choice of Air quality monitors in China

Since 2013, China has added more than four thousand air quality monitoring stations that provide the public with real-time information on six airborne pollutants, i.e., particulate matter (PM) 2.5, PM10, sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide. Authorities manage these monitors at four levels of the government: state, provincial, municipal, and county. Typically, pollution monitors are located where they could be deemed useful, for example, within more air-polluted areas or near schools, hospitals, road traffic, and industries. While the real-time information has helped individuals, firms, and governments make decisions; it is unclear how a government body makes siting decisions. This paper aims to answer three questions: Where are the pollution monitors located? What drives the decision to add a new monitor in a particular location? Is there a difference in location choice behavior between central and local governments in China? We find that, in 2021, central monitors were located in cleaner areas than local monitors, and both monitors were located around public buildings like hospitals. However, when it comes to new placements, both monitors are generally positioned in more polluted areas, with local monitors more likely to be placed in the "dirtiest" locations. We also find that central and local monitors are both clustered around each other.

Association between residential environment quality with mild cognitive impairment among middle and elderly adults in China

BACKGROUND

Most studies have focused on the effects of individual environmental risk factors on cognitive function; however, none have evaluated the association between residential environmental quality and cognitive impairment.

METHODS

Data from the China Health and Retirement Longitudinal Study (CHARLS) were used to include 12,801 participants in a cross-sectional study and 8781 participants in a cohort study. Residential environmental quality was assessed using indicators such as particulate matter, types of household fuel, water sources, indoor temperature, and building types. Based on the residential environment quality score, participants were classified into three groups: comfortable (0-1 points), moderate (2-3 points), and poor (4-6 points). To evaluate the association between residential environmental quality and cognitive scores in the cross-sectional study, as well as the development of mild cognitive impairment (MCI) in the cohort study, ordinary least squares (OLS) regression and logistic regression models were applied.

RESULTS

In the cross-sectional study, cognitive scores and performance across four dimensions-orientation, computation, memory, and drawing-showed a significant decline from the comfortable to the poor residential environment groups. In the fully adjusted OLS regression model, scores across these dimensions were significantly reduced in the moderate and poor groups compared to the comfortable group (P for trend <0.001). The incidence of MCI from 2011 to 2018 was 10.1 %, 16.8 %, and 18.8 % for participants living in comfortable, moderate, and poor environments, respectively, with statistically significant differences among groups (all P < 0.07). Logistic regression analysis revealed an odds ratio of 1.25 (95 % CI: 1.02-1.53) for the moderate group and 1.31 (95 % CI: 1.04-1.65) for the poor group, compared to the comfortable group (P for trend<0.05).

CONCLUSIONS

An inferior residential environment is associated with lower cognitive scores and a higher rik of developing MCI in middle-aged and older Chinese adults.

Estimation of neighborhood scale PM2.5 impacts in rural towns in the Purepecha region of Mexico

The impact of cooking with solid fuels on neighborhood-scale PM2.5 concentrations in rural towns and communities is poorly quantified due to the lack of credible ground-level monitoring sites and spatial heterogeneity at a scale that is below the resolution of remote sensing GEOS-Chem hybrid models. Emissions of PM2.5 from use of open fires for cooking in rural Mexico are known to cause poor indoor air quality. The effectiveness of different intervention strategies to reduce such pollution exposures also varies because of different local building densities and source intensities. In this study, the effectiveness of stove intervention strategies on the neighborhood-scale PM2.5 concentrations were evaluated in a village Cucuchucho, located in the Purepecha highlands of Mexico. The Quick Urban & Industrial Complex (QUIC) is deployed in the assessment. The model's performance in simulating interactions between pollutants and flow around building structures is validated through comparison with a water channel experiment, which shows good quantitative agreement. The case study simulation results demonstrate that upstream households contributed ∼30% of concentrations, and current trends will not meet WHO air quality guidelines or interim targets. The magnitude of neighborhood-scale PM2.5 concentrations depends on the intervention and community structure. Based on these simulations, a statistical model is presented to estimate ambient neighborhood PM2.5 pollution concentrations for more communities at a regional level. The statistical model allows neighborhood PM2.5 pollution to be included in estimates of health burdens from household pollution in Mexico using readily accessible community parameters.

Air pollution and defensive behavior: Evidence from transaction data in China

This study presents empirical research about the defensive behavior of air pollution, that is, health insurance purchases. Using transaction-level data from a large insurance company, covering more than half a million insurance contracts from nineteen cities in China from 2014 to 2018, we empirically imply that an increase of 10% in AQI leads to a 0.37% uptick in the number of daily sales of health insurance contracts by the company within the city. The effect is non-linear and is more pronounced when the AQI exceeds 200. Besides, the defensive cost for a one-unit increase in AQI accounts for around 1.70% of individual income annually. We demonstrate that the positive impact of air pollution on health insurance purchases is primarily driven by health management awareness and social interaction.

Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model

Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %-20.00 %, reduced root mean square error (RMSE) by 13.22 %-17.11 %, improved coefficient of determination (R2) by 10 %-35.38 %, and improved accuracy (ACC) by 3.48 %-7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.

Estimation of PM2.5 using high-resolution satellite data and its mortality risk in an area of Iran

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM2.5 in the model. We statistically found different distributions of the predicted PM2.5 over the region. Compared to the reference value (5 µg/m3), 90th and 95th percentiles had significant adverse effect on total mortality (RR 90th percentile:1.45; CI 95%: 1.08-1.95 and RR 95th percentile:1.53; CI 95%: 1.11-2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies.

Association of Long-Term Exposure to PM2.5 Constituents and Green Space With Arthritis and Rheumatoid Arthritis

There is limited evidence regarding the effects of long-term exposure to PM2.5 constituents on the risk of arthritis and rheumatoid arthritis, and the interaction between PM2.5 and green space remains unclear. This study examined the relationship between long-term exposure to PM2.5 constituents and the risk of arthritis and rheumatoid arthritis, with the exposure period extending from recruitment until self-reported outcomes, death, loss to follow-up, or end of follow-up. Additionally, the study assessed whether there was an interactive effect between PM2.5 and green space on these risks. We gathered cohort data on 18,649 individuals aged ≥45 years. We applied generalized linear mixed-effects models to estimate the effects of PM2.5 constituents, NDVI, and their interaction on arthritis and rheumatoid arthritis. The quantile g-computation and weighted quantile sum regression model were applied to estimate the combined effect of PM2.5 constituents. Our results showed that exposure to single and mixed PM2.5 constituents adversely affected arthritis and rheumatoid arthritis, and was mainly attributed to the black carbon component. We observed "U" or "J" shaped exposure-response curves for the effects of PM2.5, OM, NO3 - and NH4 + exposure on the development of arthritis/rheumatoid arthritis. Additionally, the odds ratio of arthritis for per interquartile range (IQR) increase in PM2.5 was 1.209 (95% CI:1.198, 1.221), per 0.1-unit decrease in NDVI was 1.091 (95% CI:1.033, 1.151), and the interaction term was 1.005 (95% CI:1.002, 1.007). These findings flesh out the existing evidence for PM2.5 constituents, NDVI and arthritis, rheumatoid arthritis, but the underlying mechanisms still require further exploration.

Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways

Air pollution induced by fine particulate matter with diameter ≤ 2.5 μm (PM2.5) poses a significant challenge for global air quality management. Understanding how factors such as climate change, land use and land cover change (LULCC), and changing emissions interact to impact PM2.5 remains limited. To address this gap, we employed the Community Earth System Model and examined both the individual and combined effects of these factors on global surface PM2.5 in 2010 and projected scenarios for 2050 under different Shared Socioeconomic Pathways (SSPs). Our results reveal biomass-burning and anthropogenic emissions as the primary drivers of surface PM2.5 across all SSPs. Less polluted regions like the US and Europe are expected to experience substantial PM2.5 reduction in all future scenarios, reaching up to ~5 μg m-3 (70 %) in SSP1. However, heavily polluted regions like India and China may experience varied outcomes, with a potential decrease in SSP1 and increase under SSP3. Eastern China witness ~20 % rise in PM2.5 under SSP3, while northern India may experience ~70 % increase under same scenario. Depending on the region, climate change alone is expected to change PM2.5 up to ±5 μg m-3, while the influence of LULCC appears even weaker. The modest changes in PM2.5 attributable to LULCC and climate change are associated with aerosol chemistry and meteorological effects, including biogenic volatile organic compound emissions, SO2 oxidation, and NH4NO3 formation. Despite their comparatively minor role, LULCC and climate change can still significantly shape future air quality in specific regions, potentially counteracting the benefits of emission control initiatives. This study underscores the pivotal role of changes in anthropogenic emissions in shaping future PM2.5 across all SSP scenarios. Thus, addressing all contributing factors, with a primary focus on reducing anthropogenic emissions, is crucial for achieving sustainable reduction in surface PM2.5 levels and meeting sustainable pollution mitigation goals.

Health benefits of air pollution reduction: Evidence from economic slowdown in India

This paper evaluates health benefits associated with the impact of air pollution reduction on infant mortality in India. Leveraging plausibly exogenous geographic variation in air pollution due to the post-2010 economic slowdown-a period largely overlooked in the literature-I find that improvements in air quality resulted in a significant decline in infant mortality, particularly through respiratory diseases and biological pathways such as in utero and post-birth exposure. The associated health benefits correspond to 1338 saved infant lives, translating to monetary gains of $312.5 million. The paper advances our understanding of the link between air pollution and human health in settings with elevated air pollution and suboptimal regulatory frameworks.

Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth

Abstract. Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5–AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µg m−3, with regional values ranging from 59.8 µg m−3 in North America to more than 190 µg m−3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η, exhibiting a population-weighted mean difference in aerosol composition of 12.3 µg m−3, which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µg m−3, which reflects spatial variation in the column–surface relationship.

The impact of living environmental factors on cognitive function and mild cognitive impairment: evidence from the Chinese elderly population

OBJECTIVES

Mild cognitive impairment represents a pivotal stage in the cognitive decline of older adults, with a considerable risk of advancing to dementia. Recognizing how living environmental factors affect cognition is crucial for crafting effective prevention and intervention strategies. This study seeks to elucidate the relationship between various living environmental factors and cognitive function, with a specific focus on mild cognitive impairment, within a Chinese elderly population.

METHODS

This is a cross-section and longitudinal study. Utilizing data from CHARLS, our cross-sectional analysis included 4,401 participants, while the cohort study comprised 3,177 individuals. We assessed living environmental factors based on household fuel types, water sources, indoor temperatures, residential building types, and ambient PM2.5 levels. We employed multiple linear regression for cross-sectional analyses and Cox proportional hazards regression models for longitudinal assessments to determine the effects of living environments on cognitive function and MCI risk. Stratified analyses, interaction tests, and sensitivity analyses were conducted to further validate our findings.

RESULTS

The findings revealed that, compared to those in high-risk environments, participants in low-risk settings exhibited higher cognitive scores (β = 1.25, 95%CI: 0.85, 1.65), better mental status (β = 0.70, 95%CI: 0.48, 0.92), and improved episodic memory (β = 0.27, 95%CI: 0.13, 0.41). Over a 7-year follow-up, the use of low-risk living environments (HR = 0.67, 95%CI: 0.49, 0.91), including clean fuels (HR = 0.74, 95%CI: 0.57, 0.95) and tap water (HR = 0.84, 95%CI: 0.71, 1.00), demonstrated a protective effect against MCI development. This correlation remained significant regardless of age, gender, residence, education level, smoking, alcohol consumption, and depression.

CONCLUSION

This research provides substantial evidence that living environmental factors significantly affect cognitive function and MCI risk in Chinese older adults. Enhancing living conditions may be a key strategy for promoting cognitive health and preventing MCI in this demographic. Further research is necessary to explore the long-term impacts and potential intervention strategies to optimize living environments for better cognitive outcomes in aging populations.

Development of a Method to Determine the Environmental Burden of Diseases and an Application to Identify Factors Driving Changes in the Number of PM2.5-Related Deaths in China between 2000 and 2010

The attributable burden is codetermined by the exposure level and nontarget characteristics. However, the conventional method of health impact assessment based on preestablished exposure-response functions includes only a few well-known characteristics and thus is insufficient to capture the comprehensive variation. We aimed to develop a method to fuse health impact assessment with epidemiological analysis and to identify factors driving baseline risk. The method was applied to identify the factors underlying the change in the number of fine particulate matter (PM2.5) related deaths in China between 2000 and 2010. During the study period, the number of PM2.5-related deaths across mainland China increased by 0.62 (95% CI: 0.57, 0.69) million, with 0.65 (95% CI: 0.47, 0.91) million, 0.55 (95% CI: 0.39, 0.79) million, and 0.11 (95% CI: 0.06, 0.18) million deaths being associated with increased PM2.5 exposure, population aging, and growth in population size, respectively. However, economic growth, urbanization, improvement of welfare services, and improvement of hospital services resulted in 0.25 (95% CI: 0.15, 0.40) million, 0.16 (95% CI: 0.10, 0.27) million, 0.16 (95% CI: 0.09, 0.26) million, and 0.09 (95% CI: 0.05, 0.15) million fewer deaths, respectively. Results indicated that increased exposure was the major driver of the change in the number of PM2.5-related deaths, and economic growth was the main driver of increased resilience to air pollution.

Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter

Global geophysical satellite-derived ambient fine particulate matter (PM2.5) inference relies upon a geophysical relationship (η) from a chemical transport model to relate satellite retrievals of aerosol optical depth (AOD) to surface PM2.5. The resolution dependence of simulated η warrants further investigation. In this study, we calculate geophysical PM2.5 with simulated η from the GEOS-Chem model in its high-performance configuration (GCHP) at cubed-sphere resolutions of C360 (∼25 km) and C48 (∼200 km) and satellite AOD at 0.01° (∼1 km). Annual geophysical PM2.5 concentrations inferred from satellite AOD and GCHP simulations at ∼25 km and ∼200 km resolutions exhibit remarkable similarity (R 2 = 0.96, slope = 1.03). This similarity in part reflects opposite resolution responses across components with population-weighted normalized mean difference (PW-NMD) increasing by 5% to 11% for primary species while decreasing by -30% to -5% for secondary species at finer resolution. Despite global similarity, our results also identify larger resolution sensitivities of η over isolated pollution sources and mountainous regions, where spatial contrast of aerosol concentration and composition is better represented at fine resolution. Our results highlight the resolution dependence of representing near-surface concentrations and the vertical distribution of chemically different species with implications for inferring ground-level PM2.5 from columnar AOD.

Observational Constraints on the Aerosol Optical Depth-Surface PM2.5 Relationship during Alaskan Wildfire Seasons

Wildfire is one of the main sources of PM2.5 (particulate matter with aerodynamic diameter < 2.5 μm) in the Alaskan summer. The complexity in wildfire smokes, as well as limited coverage of ground measurements, poses a big challenge to estimate surface PM2.5 during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM2.5 over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD-surface PM2.5 conversion factor (η = PM2.5/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (ηGC) and from observations (ηobs). We show that ηGC is biased high compared to ηobs under smoky conditions, largely because GEOS-Chem assigns the majority of AOD (67%) within the planetary boundary layer (PBL) when AOD > 1, inconsistent with satellite retrievals from CALIOP. The overestimation in ηGC can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for ηobs across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM2.5 measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM2.5 over continental Alaska in the 2021 and 2022 summers. The derived satellite PM2.5 shows a good agreement with corrected PurpleAir PM2.5 in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM2.5 concentrations. This piecewise function, η'obs, shows the capability of providing an observation-based, quick and direct estimation of daily surface PM2.5 over the whole of Alaska during wildfires, without running a 3-D model in real time.

Addressing critical gaps in protected area coverage for bird habitats in China

Currently, protected areas cover approximately 14% of the Earth's land surface, yet 12.2% of the world's bird species remain unprotected by any designated areas and face significant threats. This study investigates the current status of bird conservation in China, aiming to evaluate the effectiveness of existing protected areas, analyze why certain bird species are not adequately protected, and propose strategies for optimizing protected area configurations. Utilizing citizen science data and the zonation optimization algorithm, we comprehensively assessed the conservation value of birds in China. We then employed anthropogenic stressor data to evaluate the conservation of threatened bird habitats through a binary conflict intensity model. Finally, we conducted a spatial overlap analysis to determine the coverage and effectiveness of Chinese nature reserves in regions with high conservation value and high conflict risk. Our findings indicate that only 10.0% of the highest conservation value bird habitats are covered by protected areas, and just 7.3% of these protected areas effectively safeguard these critical habitats. Additionally, only 5.9% of bird habitats impacted by human activity conflicts are within protected areas, and merely 22.0% of the total protected areas can effectively conserve high conflict risk habitats. Overall, China's current protected area system has substantial shortcomings in safeguarding bird habitats and requires further optimization and expansion to maximize conservation benefits.

The effects of fine particulate matter, solid fuel use and greenness on the risks of diabetes in middle-aged and older Chinese

BACKGROUND

Previous studies provided clues that environmental factors were closely related to diabetes incidence. However, the evidence from high-quality and large cohort studies about the effects of PM2.5, solid fuel use and greenness on the development of diabetes among middle-aged and older adults in China was scarce.

OBJECTIVE

To separately investigate the independent effects of PM2.5, solid fuel use and greenness on the development of diabetes among middle-aged and older adults.

METHODS

A total of 9242 participants were involved in this study extracted from the China Health and Retirement Longitudinal Study. Time-varying Cox regression was applied to detect the association of diabetes with PM2.5, solid fuel use and greenness, separately. The potential interactive effect of air pollution and greenness were explored using the relative excess risk due to interaction (RERI).

RESULTS

Per 10 μg/m3 increases in PM2.5 were associated with 6.0% (95% CI: 1.9, 10.2) increasing risks of diabetes incidence. Females seemed to be more susceptible to PM2.5. However, the effects of solid fuel use only existed in older and lower BMI populations, with hazard ratios (HRs) of 1.404 (1.116, 1.766) and 1.346 (1.057, 1.715), respectively. In addition, exposure to high-level greenness might reduce the risks of developing diabetes [HR = 0.801 (0.687, 0.934)]. Weak evidence of the interaction effect of PM2.5/solid fuel use and greenness on diabetes was found.

SIGNIFICANCE

Both PM2.5 and solid fuel use were associated with the increasing incidence of diabetes. In addition, high-level greenness might be a beneficial environmental factor for reducing the risks of developing diabetes. All in all, our findings might provide valuable references for public health apartments to formulate very fruitful policies to reduce the burden of diabetes.

IMPACT STATEMENT

Both PM2.5 and solid fuel use were associated with the increasing incidence of diabetes while high-level greenness was not, which might provide valuable references for public health apartments to make policies.

Spatiotemporal Patterns and Equity Analysis of Premature Mortality Due to Ischemic Heart Disease Attributable to PM2.5 Exposure in China: 2007-2022

Long-term exposure to PM2.5 pollution increases the risk of cardiovascular diseases, particularly ischemic heart disease (IHD). Current assessments of the health effects related to PM2.5 exposure are limited by sparse ground monitoring stations and applicable disease research cohorts, making accurate health effect evaluations challenging. Using satellite-observed aerosol optical depth (AOD) data and the XGBoost-PM25 model, we obtained 1 km scale PM2.5 exposure levels across China. We quantified the premature mortality caused by PM2.5-exposure-induced IHD using the Global Exposure Mortality Model (GEMM) and baseline mortality data. Furthermore, we employed the Gini coefficient, a measure from economics to quantify inequality, to evaluate the distribution differences in health impacts due to PM2.5 exposure under varying socioeconomic conditions. The results indicate that PM2.5 concentrations in China are higher in the central and eastern regions. From 2007 to 2022, the national overall level showed a decreasing trend, dropping from 47.41 μg/m3 to 25.16 μg/m3. The number of premature deaths attributable to PM2.5 exposure increased from 819 thousand in 2007 to 870 thousand in 2022, with fluctuations in certain regions. This increase is linked to population growth and aging because PM2.5 levels have decreased. The results also indicate disparities in premature mortality from IHD among different economic groups in China from 2007 to 2022, with middle-income groups having a higher cumulative proportion of IHD-related premature deaths compared with high- and low-income groups. Despite narrowing GDP gaps across regions from 2007 to 2022, IHD consistently "favored" the middle-income groups. The highest Gini coefficient was observed in the Northwest (0.035), and the lowest was in the South (0.019). Targeted policy interventions are essential to establish a more equitable atmospheric environment.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Source Attribution of Health Burdens From Ambient PM2.5, O3, and NO2 Exposure for Assessment of South Korean National Emission Control Scenarios by 2050

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO2, and ground-based observations of PM2.5, O3, and NO2. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM2.5, O3, and NO2, respectively, and 23,000 NO2-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM2.5-, O3-, and NO2-associated premature deaths and the NO2-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM2.5, O3, and NO2 exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO2. The Mitigation scenario doubles the NO2-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM2.5- and O3-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM2.5 and O3 pollution.

Moderating effect of a sodium-rich diet on the association between long-term exposure to fine particulate matter and blood lipids in children and adolescents

BACKGROUND

Several studies reported that exposure to higher levels of fine particulate matter (PM2.5) was associated with deteriorated lipid profiles in children and adolescents. However, whether a sodium-rich diet could modify the associations remains unknown. We aimed to examine the associations of long-term exposure to PM2.5 with blood lipids in children and adolescents, and further examine the effect modification by dietary and urinary sodium levels based on a multi-community population in China.

METHODS

The 3711 study participants were from a cross-sectional study, which interviewed children and adolescents aged 6 to 17 years across Sichuan Province, China between 2015 and 2017. Blood lipid outcomes including blood total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) were assessed. Information on daily dietary sodium consumption was estimated with a semi-quantitative food frequency questionnaire (FFQ), and urinary sodium was used as an internal exposure biomarker. A linear regression model was applied to estimate the associations of prior 2-years' average exposure to ambient PM2.5 with blood lipids. The effect modification by dietary and urinary sodium was examined by stratified analyses.

RESULTS

The participants from rural areas had higher levels of daily sodium consumptions. The results of multivariable regression analysis indicated that per 10 μg/m3 incremental change in PM2.5 was associated with a 1.56% (95% confidence interval 0.90%-2.23%) and a 2.26% (1.15%-3.38%) higher blood TC and LDL-C levels, respectively. Among the study participants with higher levels of dietary sodium or urinary sodium, exposure to higher levels of PM2.5 was significantly associated with deteriorated lipid profiles. For example, each 10 μg/m3 incremental change in exposure to PM2.5 was correlated with a 2.83 (-4.65 to -0.97) lower percentage decrease in blood HDL-C levels among the participants who were from the highest quartile of urinary sodium levels. While, these associations changed to be nonsignificant in the participants who were from the lowest quartile of dietary sodium levels.

CONCLUSION

Exposure to higher levels of PM2.5 was associated with deteriorated blood lipid levels in children and adolescents. It is noteworthy that these associations might be ameliorated through the adoption of a low-sodium dietary regimen.

Cross-sectional analysis of socioeconomic drivers of PM2.5 pollution in emerging SAARC economies

Within the intricate interplay of socio-economic, natural and anthropogenic factors, haze pollution stands as a stark emblem of environmental degradation, particularly in the South Asian Association for Regional Cooperation (SAARC) region. Despite significant efforts to mitigate greenhouse gas emissions, several SAARC nations consistently rank among the world's most polluted. Addressing this critical research gap, this study employs robust econometric methodologies to elucidate the dynamics of haze pollution across SAARC countries from 1998 to 2020. These methodologies include the Pooled Mean Group (PMG) and Augmented Mean Group (AMG) estimator, Panel two-stage least squares (TSLS), Feasible Generalized Least Squares (FGLS) and Dumitrescu-Hurlin (D-H) causality test. The analysis reveals a statistically significant cointegrating relationship between PM2.5 and economic indicators, with economic development and consumption expenditure exhibiting positive associations and rainfall demonstrating a mitigating effect. Furthermore, a bidirectional causality is established between temperature and economic growth, both influencing PM2.5 concentrations. These findings emphasize the crucial role of evidence-based policy strategies in curbing air pollution. Based on these insights, recommendations focus on prioritizing green economic paradigms, intensifying forest conservation efforts, fostering the adoption of eco-friendly energy technologies in manufacturing and proactively implementing climate-sensitive policies. By embracing these recommendations, SAARC nations can formulate comprehensive and sustainable approaches to combat air pollution, paving the way for a healthier atmospheric environment for their citizens.

Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 μg/m3, 5.33 μg/m3, 5.63 μg/m3, 3.89 μg/m3, 9.14 μg/m3, and 0.52 μg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

Bi-directional association between outdoor or social activities and cognitive function: do the PM2.5 exposure catalyze the detrimental inactivity-poor cognition cycle?

BACKGROUND

Previous research has shown that lack of leisure activities, either outdoor or social activities, impedes cognitive function. However, the interrelationship between poor cognition and deficient activities is understudied. In addition, whether exposure to air pollution, such as PM2.5, can accelerate the detrimental 'inactivity-poor cognition' cycle, is worthy of investigation.

METHODS

We used data from the 2008, 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We assessed the frequency of outdoor or social activities at each wave. The cognitive function was examined using a China-Modified Mini-mental State Examination. We estimated the residential exposure to fine particular matter (PM2.5) via a satellite-based model. We applied cross-lagged panel (CLP) model to examine the bi-directional relationship between outdoor or social activities and cognitive function. We then examined the effect of PM2.5 exposure with sequent cognitive function and activities using generalized estimation equation (GEE) model.

FINDINGS

Overall, we observed significant bi-directional associations between outdoor or social activities and cognitive function. Participants with better cognitive function in the last wave were more likely to engage in outdoor or social activities in the following wave (outdoor activities: β = 0.37, 95% CI [0.27,0.48], P < 0.01; social activities: β = 0.05, 95% CI [0.02,0.09] P < 0.01). Meanwhile, higher engagement in outdoor or social activities in the last wave was associated with more favorable cognitive function in the following wave (outdoor activities: β = 0.06, 95% CI [0.03,0.09], P < 0.01; social activities: β = 0.10, 95% CI [0.03,0.18], P < 0.01). Notably, an increase in PM2.5 exposure during the preceding year was significantly associated with a declining cognitive function (β = -0.05, 95% CI [-0.08,-0.03], P < 0.01), outdoor activities (β = -0.02, 95% CI [-0.04, -0.01], P < 0.01) and social activities (β = -0.02, 95% CI [-0.02, -0.01], P < 0.01) in the current year; the lagged effects of the PM2.5 exposure in the past year of the last wave on activities and cognitive function of the following wave were also observed.

INTERPRETATION

Our findings not only indicate the bi-directional links between the frequency of outdoor or social activities and cognitive function, but also report that PM2.5 exposure plays a role in catalyzing the detrimental inactivity-poor cognition cycle. Future research should investigate whether the policy-driven interventions, such as clean air policies, can break the unfavorable activity-cognition cycle, and thereby promoting health from the dual gains in leisure activities and cognition.

Long-term exposure to particulate matter and all-cause and cause-specific mortality in an analysis of multiple Asian cohorts

BACKGROUND

Exposure to ambient air pollution is associated with a significant number of deaths. Much of the evidence associating air pollution with adverse effects is from North American and Europe, partially due to incomplete data in other regions limiting location specific examinations. The aim of the current paper is to leverage satellite derived air quality data to examine the relationship between ambient particulate matter and all-cause and cause-specific mortality in Asia.

METHODS

Six cohorts from the Asia Cohort Consortium provided residential information for participants, recruited between 1991 and 2008, across six countries (Bangladesh, India, Iran, Japan, South Korea, and Taiwan). Ambient particulate material (PM2·5) levels for the year of enrolment (or 1998 if enrolled earlier) were assigned utilizing satellite and sensor-based maps. Cox proportional models were used to examine the association between ambient air pollution and all-cause and cause-specific mortality (all cancer, lung cancer, cardiovascular and lung disease). Models were additionally adjusted for urbanicity (representing urban and built characteristics) and stratified by smoking status in secondary analyses. Country-specific findings were pooled via random-effects meta-analysis.

FINDINGS

More than 300,000 participants across six cohorts were included, representing more than 4-million-person years. A positive relationship was observed between a 5 µg/m (Dockery et al., 1993) increase in PM2·5 and cardiovascular mortality (HR: 1·06, 95 % CI: 0.99, 1·13). The additional adjustment for urbanicity resulted in increased associations between PM2.5 and mortality outcomes, including all-cause mortality (1·04, 95 % CI: 0·97, 1·11). Results were generally similar regardless of whether one was a current, never, or ex-smoker.

INTERPRETATION

Using satellite and remote sensing technology we showed that associations between PM2.5 and all-cause and cause-specific Hazard Ratios estimated are similar to those reported for U.S. and European cohorts.

FUNDING

This project was supported by the Health Effects Institute. Grant number #4963-RFA/18-5. Specific funding support for individual cohorts is described in the Acknowledgements.

Urban health advantage and penalty in aging populations: a comparative study across major megacities in China

Background

Urban living is linked to better health outcomes due to a combination of enhanced access to healthcare, transportation, and human development opportunities. However, spatial inequalities lead to disparities, resulting in urban health advantages and penalties. Understanding the relationship between health and urban development is needed to generate empirical evidence in promoting healthy aging populations. This study provides a comparative analysis using epidemiological evidence across diverse major Chinese cities, examining how their unique urban development trajectories over time have impacted the health of their aging residents.

Methods

We tracked changes in air pollution (NO2, PM2.5, O3), green space (measured by NDVI), road infrastructure (ring road areas), and nighttime lighting over 20 years in six major cities in China. We followed a longitudinal cohort of 4992 elderly participants (average age 87.8 years) over 16,824 person-years. We employed Cox proportional hazard regression to assess longevity, assessing 14 variables, including age, sex, ethnicity, marital status, residence, household income, occupation, education, smoking, alcohol consumption, exercise, and points of interest (POI) count of medicine-related facilities, sports, and leisure service-related places, and scenic spots within a 5 km-radius buffer.

Findings

Geographic proximity to points of interest significantly improves survival. Elderly living in proximity of the POI-rich areas had a 34.6%-35.6% lower mortality risk compared to those in POI-poor areas, for the highest compared to the lowest quartile. However, POI-rich areas had higher air pollution levels, including PM2.5 and NO2, which was associated with a 21% and 10% increase in mortality risk for increase of 10 μg/m3, respectively. The benefits of urban living had higher effect estimates in monocentric cities, with clearly defined central areas, compared to polycentric layouts, with multiple satellite city centers.

Interpretation

Spatial inequalities create urban health advantages for some and penalties for others. Proximity to public facilities and economic activities is associated with health benefits, and may counterbalance the negative health impacts of lower green space and higher air pollution. Our empirical evidence show optimal health gains for age-friendly urban environments come from a balance of infrastructure, points of interest, green spaces, and low air pollution.

Funding

Natural Science Foundation of Beijing (IS23105), National Natural Science Foundation of China (82250610230, 72061137004), World Health Organization (2024/1463606-0), Research Fund Vanke School of Public Health Tsinghua University (2024JC002), Beijing TaiKang YiCai Public Welfare Foundation, National Key R&D Program of China (2018YFC2000400).

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

Emission characteristics and health effects of PM2.5 from vehicles in typical areas

Introduction

Vehicle emissions have become an important source of urban air pollution, and the assessment of air pollution emission characteristics and health effects caused by specific pollution sources can provide scientific basis for air quality management.

Methods

In this paper, vehicle PM2.5 pollution in typical urban agglomerations of China (the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), the triangle of the Central China urban agglomeration (TCCUA) and the Chengdu-Chongqing urban agglomeration (CCUA)) were used as research samples to evaluate the emission characteristics, health effects and economic losses of vehicle PM2.5 pollution based on the emission inventory, air quality model and exposure-response function from 2010 to 2020.

Results

The results indicated that PM2.5 emissions from vehicles in the three urban agglomerations during 2010-2020 first showed an upward yearly trend and then showed a slow decrease in recent years. Heavy-duty trucks and buses are the main contribution vehicles of PM2.5, and the contribution rates of light-duty vehicles to PM2.5 is increasing year by year. The contribution rate of PM2.5 in Beijing decreased significantly. In addition to capital cities and municipalities directly under the central Government, the emission of pollutants in other cities cannot be ignored. The evaluation results of the impact of PM2.5 pollution from vehicles on population health show that: the number of each health endpoint caused by PM2.5 pollution from vehicles in the BTHUA and CCUA showed an overall upward trend, while the TCCUA showed a downward trend in recent years. Among them, PM2.5 pollution from vehicles in the three major urban agglomerations cause about 78,200 (95% CI: 20,500-138,800) premature deaths, 122,800 (95% CI: 25,600-220,500) inpatients, and 628,400 (95% CI: 307,400-930,400) outpatients and 1,332,400 (95% CI: 482,700-2,075,600) illness in 2020. The total health economic losses caused by PM2.5 pollution from vehicles in the three major urban agglomerations in 2010, 2015 and 2020 were 68.25 billion yuan (95% CI: 21.65-109.16), 206.33 billion yuan (95% CI: 66.20-326.20) and 300.73 billion yuan (95% CI: 96.79-473.16), accounting for 0.67% (95% CI: 0.21-1.07%), 1.19% (95% CI: 0.38%-1.88%) and 1.21% (95% CI: 0.39%-1.90%) of the total GDP of these cities.

Discussion

Due to the differences in vehicle population, PM2.5 concentration, population number and economic value of health terminal units, there are differences in health effects and economic losses among different cities in different regions. Among them, the problems of health risks and economic losses were relatively prominent in Beijing, Chengdu, Chongqing, Tianjin and Wuhan.

Does technological innovation in National Sustainable Development Agenda Innovation Demonstration Zones promote green development?-the case from Chengde City, China

The National Sustainable Development Agenda Innovation Demonstration Zones (NSDAIDZs) aim to spearhead green development through scientific and technological innovation, showcasing sustainable development to other regions in China and offering valuable insights for countries worldwide. Taking Chengde City, which is one of the cities in the second batch of NSDAIDZs, as a case study, we examine the quantitative impact of technological innovation on green development. Additionally, it investigates the threshold effect of Research and development investments (R&D investments) on the relationship between technological innovation and green development. The results indicate that: (1) technological innovation has a positive promoting effect on green development, with a 1.01% increase in green development for every one unit increase in technological innovation; (2) The positive effect of technological innovation on green development becomes fully realized only when R&D investments and the upgrading of industrial structure surpass a specific threshold value. We contribute to the existing research on the connection between technological innovation and green development in innovation demonstration zones. It also provides empirical insights to foster a mutually beneficial relationship between R&D investments, industrial structure upgrading, and technological innovation, ultimately maximizing the promoting role of technological innovation in green development.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Early life ambient air pollution, household fuel use, and under-5 mortality in Ghana

INTRODUCTION

Environmental exposures, such as ambient air pollution and household fuel use affect health and under-5 mortality (U5M) but there is a paucity of data in the Global South. This study examined early-life exposure to ambient particulate matter with a diameter of 2.5 µm or less (PM2.5), alongside household characteristics (including self-reported household fuel use), and their relationship with U5M in the Navrongo Health and Demographic Surveillance Site (HDSS) in northern Ghana.

METHODS

We employed Satellite-based spatiotemporal models to estimate the annual average PM2.5 concentrations with the Navrongo HDSS area (1998 to 2016). Early-life exposure levels were determined by pollution estimates at birth year. Socio-demographic and household data, including cooking fuel, were gathered during routine surveillance. Cox proportional hazards models were applied to assess the link between early-life PM2.5 exposure and U5M, accounting for child, maternal, and household factors.

FINDINGS

We retrospectively studied 48,352 children born between 2007 and 2017, with 1872 recorded deaths, primarily due to malaria, sepsis, and acute respiratory infection. Mean early-life PM2.5 was 39.3 µg/m3, and no significant association with U5M was observed. However, Children from households using "unclean" cooking fuels (wood, charcoal, dung, and agricultural waste) faced a 73 % higher risk of death compared to those using clean fuels (adjusted HR = 1.73; 95 % CI: 1.29, 2.33). Being born female or to mothers aged 20-34 years were linked to increased survival probabilities.

INTERPRETATION

The use of "unclean" cooking fuel in the Navrongo HDSS was associated with under-5 mortality, highlighting the need to improve indoor air quality by introducing cleaner fuels.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data

BACKGROUND

Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability.

METHODS

This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities.

RESULTS

The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road".

CONCLUSION

These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Short-term associations between fine particulate air pollution and cardiovascular and respiratory mortality in 337 cities in Latin America

Ambient air pollution is a health concern in Latin America given its large urban population exposed to levels above recommended guidelines. Yet no studies have examined the mortality impact of air pollutants in the region across a wide range of cities. We assessed whether short-term levels of fine particulate matter (PM2.5) from modeled estimates, are associated with cardiovascular and respiratory mortality among adults in 337 cities from 9 Latin American countries. We compiled mortality, PM2.5 and temperature data for the period 2009-2015. For each city, we evaluated the association between monthly changes in PM2.5 and cardiovascular and respiratory mortality for sex and age subgroups using Poisson models, adjusted for seasonality, long-term trend, and temperature. To accommodate possibly different associations of mortality with PM2.5 by age, we included interaction terms between changes in PM2.5 and age in the models. We combined the city-specific estimates using a random effects meta-regression to obtain mortality relative risks for each sex and age group. We analyzed 3,026,861 and 1,222,623 cardiovascular and respiratory deaths, respectively, from a study population that represents 41 % of the total population of Latin America. We observed that a 10 μg/m3 increase in monthly PM2.5 is associated with an increase of 1.3 % (95 % confidence interval [CI], 0.4 to 2.2) in cardiovascular mortality and a 0.9 % increase (95 % CI -0.6 to 2.4) in respiratory mortality. Increases in mortality risk ranged between -0.5 % to 3.0 % across 6 sex-age groups, were larger in men, and demonstrated stronger associations with cardiovascular mortality as age increased. Socioeconomic, environmental and health contexts in Latin America are different than those present in higher income cities from which most evidence on air pollution impacts is drawn. Locally generated evidence constitutes a powerful instrument to engage civil society and help drive actions to mitigate and control ambient air pollution.

Associations between heat waves and chronic kidney disease in China: The modifying role of land cover

The increasing frequency of heat waves under the global urbanization and climate change background poses elevating risks of chronic kidney disease (CKD). Nevertheless, there has been no evidence on associations between long-term exposures to heat waves and CKD as well as the modifying effects of land cover patterns. Based on a national representative population-based survey on CKD covering 47,086 adults and high spatial resolution datasets on temperature and land cover data, we found that annual days of exposure to heat waves were associated with increased odds of CKD prevalence. For one day/year increases in HW_975_4d (above 97.5 % of annual maximum temperature and lasting for at least 4 consecutive days), the odds ratio (OR) of CKD was 1.14 (95 %CI: 1.12, 1.15). Meanwhile, stronger associations were observed in regions with lower urbanicity [rural: 1.14 (95 %CI: 1.12, 1.16) vs urban: 1.07 (95 %CI: 1.03, 1.11), Pinteraction < 0.001], lower water body coverage [lower: 1.14 (95 %CI: 1.12, 1.16) vs higher: 1.02 (95 %CI: 0.98, 1.05), Pinteraction < 0.001], and lower impervious area coverage [lower: 1.16 (95 %CI: 1.14, 1.18) vs higher: 1.06 (95 %CI: 1.03, 1.10), Pinteraction = 0.008]. In addition, this study found disparities in modifying effects of water bodies and impervious areas in rural and urban settings. In rural regions, the associations between heat waves and CKD prevalence showed a consistent decreasing trend with increases in both proportions of water bodies and impervious areas (Pinteraction < 0.05). Nevertheless, in urban regions, we observed significant effect modification by water bodies, but not by impervious areas. Our study indicates the need for targeted land planning as part of adapting to the kidney impacts of heat waves, with a focus on urbanization in rural regions, as well as water body construction and utilization in both rural and urban regions.

Spatial-temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5)

OBJECTIVE

This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China.

METHODS

This study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression.

RESULTS

PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas.

CONCLUSION

PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

Climate stress and anxiety, environmental context, and civic engagement: A nationally representative study

There is increasing recognition that people are experiencing stress and anxiety around climate change, and that this climate stress/anxiety may be associated with more pro-environmental behavior. However, less is known about whether people's own environmental exposures affect climate stress/anxiety or the relationship between climate stress/anxiety and civic engagement. Using three waves of survey data (2020-2022) from the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Study of US adults (n = 1071), we assessed relationships among environmental exposures (county-level air pollution, greenness, number of toxic release inventory sites, and heatwaves), self-reported climate stress/anxiety, and civic engagement measures (canvasing behavior, collaborating to solve community problems, personal efficacy to solve community problems, group efficacy to solve community problems, voting behavior). Most participants reported experiencing climate stress/anxiety (61%). In general, the environmental exposures we assessed were not significantly associated with climate stress/anxiety or civic engagement metrics, but climate stress/anxiety was positively associated with most of the civic engagement outcomes (canvassing, personal efficacy, group efficacy, voter preference). Our results support the growing literature that climate stress/anxiety may spur constructive civic action, though do not suggest a consistent relationship between adverse environmental exposures and either climate stress/anxiety or civic engagement. Future research and action addressing the climate crisis should promote climate justice by ensuring mental health support for those who experience climate stress anxiety and by promoting pro-environmental civic engagement efforts.

Modeling the air pollution process using a novel multi-site and multi-scale method with adaptive utilization of spatio-temporal information

This study focuses on modeling air quality with an adaptive utilization of spatio-temporal information from multiple air quality monitoring stations under a multi-scale framework. To this end, it is necessary to consider different strategies to combine different methods to decompose the given series and to fuse multi-site information. Based on a systematic comparative analysis, we propose a novel multi-scale and multi-site modeling method named the multivariate empirical mode decomposition and spatial cosine-attention-based long short-term memory (MEMD-SCA-LSTM). The MEMD-SCA-LSTM first employs MEMD to decompose the multi-site air quality series into the scale-aligned components and then models the components at different scales. The high-frequency components are modeled by a novel SCA-LSTM, which employs LSTM with residual blocks to extract the temporal information and utilizes an attention mechanism based on the cosine similarity to adaptively extract interactions among different sites. Other relatively regular components are modeled by the LSTM. Empirical study on PM2.5 in Hong Kong has demonstrated the effectiveness of fusing multi-site information using the spatial attention (SA) mechanism under the multi-scale framework with MEMD. The proposed MEMD-SCA-LSTM can improve the one-day ahead modeling performance with the mean absolute error and the root mean square error reduced over 10%, compared to the baseline modeling methods. For the two-day and three-day ahead performance, the MEMD-SCA-LSTM is still the best one. Furthermore, by visualizing the attention weights, we illustrate that our proposed SCA-LSTM can overcome some limitations of the traditional attention mechanisms and that the attention weights exhibit more informative patterns which could be used to analysis the transport of air pollutant between sites. The proposed modeling method is a general method, which is feasible and applicable to other pollutants in other cities or regions.

Unpacking the factors contributing to changes in PM2.5-associated mortality in China from 2013 to 2019

From 2013 to 2019, a series of air pollution control actions significantly reduced PM2.5 pollution in China. Control actions included changes in activity levels, structural adjustment (SA) policy, energy and material saving (EMS) policy, and end-of-pipe (EOP) control in several sources, which have not been systematically studied in previous studies. Here, we integrate an emission inventory, a chemical transport model, a health impact assessment model, and a scenario analysis to quantify the contribution of each control action across a range of major emission sources to the changes in PM2.5 concentrations and associated mortality in China from 2013 to 2019. Assuming equal toxicity of PM2.5 from all the sources, we estimate that PM2.5-related mortality decreased from 2.52 (95 % confidence interval, 2.13-2.88) to 1.94 (1.62-2.24) million deaths. Anthropogenic emission reductions and declining baseline incidence rates significantly contributed to health benefits, but population aging partially offset their impact. Among the major sources, controls on power plants and industrial boilers were responsible for the highest reduction in PM2.5-related mortality (∼80 %), followed by industrial processes (∼40 %), residential combustion (∼40 %), and transportation (∼30 %). However, considering the potentially higher relative risks of power plant PM2.5, the adverse effects avoided by their control could be ∼2.4 times the current estimation. Our power plant sensitivity analyses indicate that future estimates of source-specific PM2.5 health effects should incorporate variations in individual source PM2.5 effect coefficients when available. As for the control actions, while activity levels increased for most sources, SA policy significantly reduced the emissions in residential combustion and industrial boilers, and EOP control dominated the contribution in health benefits in most sources except residential combustion. Considering the emission reduction potential by source and control actions in 2019, our results suggest that promoting clean energy in residential combustion and enforcing more stringent EOP control in the iron and steel industry should be prioritized in the future.

Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7-10.8 %, 3.7-7.5 %, and 1.6-14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

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.