Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly

Assessing the particulate matter emission reduction characteristics of small turbofan engine fueled with 100 % HEFA sustainable aviation fuel

With the continuous increase in global air transportation, the impact of ultrafine particulate matter (PM) emissions from aviation on human health and environmental pollution is becoming increasingly severe. In addition to carbon reduction throughout the lifecycle, Sustainable Aviation Fuels (SAF) also represent a significant pathway for reducing PM emissions. However, due to issues such as airworthiness safety and adaptability, existing research has mostly focused on the emission performance of SAF when blended with traditional fuels at <50 %, leaving the emission characteristics of higher blending ratios to be explored. In this study, using measurement methods recommended by the International Civil Aviation Organization (ICAO), the PM emission reduction characteristics of small turbofan engines fueled with 100 % Hydroprocessed Esters and Fatty Acids (HEFA)-SAF were experimentally evaluated and compared with traditional fuels RP-3 and Diesel, while avoiding the interference of lubricant blending combustion. The results showed that the peak number concentration of particle size distribution (PSD), PM total number, as well as the number and mass concentration of non-volatile particulate matter (nvPM) decreased initially and then increased with rising thrust conditions. HEFA-SAF exhibits PSD with smaller diameters, and the Geometric Mean Diameter (GMD) ranges from 7.7 nm to 20.3 nm under all conditions. Both volatile particulates (vPM) and nvPM from HEFA-SAF are significantly reduced, with nvPM number emission index (EIn) being 92 % and 71 % lower than Diesel and RP-3, respectively. The nvPM mass emission index (EIm) also shows reductions of 96 % and 89 % compared to Diesel and RP-3. Microscopic characterization also indicated that using HEFA-SAF emitted fewer and smaller PMs. This study establishes a foundation for evaluating the effectiveness of 100 % SAF in reducing PM emissions within the aviation sector, and contributes to the airworthiness regulations development related to the use of SAF in a variety of application environments, alongside enhancing environmental protection measures.

Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning

Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well-correlated with ground-level ozone variation and available with spatiotemporal high-resolution data. This study introduces a high-resolution ozone modeling method utilizing the XGBoost algorithm with satellite-derived land surface temperature (LST) as the primary predictor. Focusing on China in 2019, our model achieved a cross-validation R2 of 0.91 and a root-mean-square error (RMSE) of 13.51 μg/m3. We provide detailed maps highlighting ground-level ozone concentrations in urban areas, uncovering spatial variations previously unresolved, along with time series aligning with established understandings of ozone dynamics. Our local interpretation of the machine learning model underscores the significant contribution of LST to spatiotemporal ozone variations, surpassing other meteorological, pollutant, and geographical predictors in its influence. Validation results indicate that model performance decreases as spatial resolution becomes coarser, with R2 decreasing from 0.91 for the 1 km model to 0.85 for the 25 km model. The methodology and data sets generated by this study offer new insights into ground-level ozone variability and mapping and can significantly aid in exposure assessment and epidemiological research related to this critical environmental challenge.

Clean air policy reduces the atherogenic lipid profile levels: Results from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) Study

Evidence of the associations between long-term exposure to PM2.5 and O3 and human blood lipid concentrations is abundant yet inconclusive. Whether clean air policies could improve lipid profiles remains unclear. In total, 2979312 participants from a Chinese nationwide prospective study were included. For cross-sectional analyses, linear mixed-effects models were utilized to assess the associations of pollutants with lipid profiles (TC, LDL-C, TG, HDL-C). For longitudinal analyses, a quasi-experimental design and difference-in-differences models were employed to investigate the impact of China's Clean Air Act. In the cross-sectional analyses, each IQR increase in PM2.5 was associated with 2.49 % (95 % CI: 2.36 %, 2.62 %), 2.51 % (95 % CI: 2.26 %, 2.75 %), 3.94 % (95 % CI: 3.65 %, 4.23 %), and 1.54 % (95 % CI: 1.38 %, 1.70 %) increases in TC, LDL-C, TG, and HDL-C, respectively. For each IQR increase in O3, TC, LDL-C, TG, and HDL-C changed by 1.06 % (95 % CI: 0.95 %, 1.17 %), 1.21 % (95 % CI: 1.01 %, 1.42 %), 1.78 % (95 % CI: 1.54 %, 2.02 %), and -0.63 % (95 % CI: -0.76 %, -0.49 %), respectively. Longitudinal analyses showed that the intervention group experienced greater TC, LDL-C, and HDL-C reductions (1.77 %, 4.26 %, and 7.70 %, respectively). Our findings suggest that clean air policies could improve lipid metabolism and should be implemented in countries with heavy air pollution burdens.

Elevated Exposure to Air Pollutants Accelerates Primary Glomerular Disease Progression

Introduction

Environmental contributors to kidney disease progression remain elusive. We explored how residential air pollution affects disease progression in patients with primary glomerulopathies.

Methods

Nephrotic Syndrome Study Network (NEPTUNE) and CureGlomerulonephropathy (CureGN) participants with residential census tract data and ≥2 years of follow-up were included. Using Cox proportional hazards models, the associations per doubling in annual average baseline concentrations of total particulate matter with diameter ≤2.5 μm (PM2.5) and its components, black carbon (BC), and sulfate, with time to ≥40% decline in estimated glomerular filtration rate (eGFR) or kidney failure were estimated. Serum tumour necrosis factor levels and kidney tissue transcriptomic inflammatory pathway activation scores were used as molecular markers of disease progression.

Results

PM2.5, BC, and sulfate exposures were comparable in NEPTUNE (n = 228) and CureGN (n = 697). In both cohorts, participants from areas with higher levels of pollutants had lower eGFR, were older and more likely self-reported racial and ethnic minorities. In a fully adjusted model combining both cohorts, kidney disease progression was associated with PM2.5 (adjusted hazard ratio 1.55 [95% confidence interval: 1.00-2.38], P = 0.0489) and BC (adjusted hazard ratio 1.43 [95% confidence interval: 0.98-2.07], P = 0.0608) exposure. Sulfate and PM2.5 exposure were positively correlated with serum tumour necrosis factor (TNF) (P = 0.003) and interleukin-1β levels (P = 0.03), respectively. Sulfate exposure was also directly associated with transcriptional activation of the TNF and JAK-STAT signaling pathways in kidneys (r = 0.55-0.67, P-value <0.01).

Conclusion

Elevated exposure to select air pollutants is associated with increased risk of disease progression and systemic inflammation in patients with primary.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next?

Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China

BACKGROUND

Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods.

AIMS

We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods.

METHODS

We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality.

RESULTS

With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results.

CONCLUSION

This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis

Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.

Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors

There are large differences in premature mortality in the USA by race/ethnicity, education, rurality and social vulnerability index groups. Using existing concentration-response functions, published particulate matter (PM2.5) air pollution estimates, population estimates at the census tract level and county-level mortality data from the US National Vital Statistics System, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in PM2.5-attributable mortality were consistently more pronounced by race/ethnicity than by education, rurality or social vulnerability index, with the Black American population having the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Our model estimates that over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic white population was attributable to PM2.5 in the years 2000 to 2011. This difference decreased only marginally between 2000 and 2015, from 53.4% (95% confidence interval 51.2-55.9%) to 49.9% (95% confidence interval 47.8-52.2%), respectively. Our findings underscore the need for targeted air quality interventions to address environmental health disparities.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Exploring heterogeneity and dynamics of meteorological influences on US PM2.5: A distributed learning approach with spatiotemporal varying coefficient models

Particulate matter (PM) has emerged as a primary air quality concern due to its substantial impact on human health. Many recent research works suggest that PM2.5 concentrations depend on meteorological conditions. Enhancing current pollution control strategies necessitates a more holistic comprehension of PM2.5 dynamics and the precise quantification of spatiotemporal heterogeneity in the relationship between meteorological factors and PM2.5 levels. The spatiotemporal varying coefficient model stands as a prominent spatial regression technique adept at addressing this heterogeneity. Amidst the challenges posed by the substantial scale of modern spatiotemporal datasets, we propose a pioneering distributed estimation method (DEM) founded on multivariate spline smoothing across a domain's triangulation. This DEM algorithm ensures an easily implementable, highly scalable, and communication-efficient strategy, demonstrating almost linear speedup potential. We validate the effectiveness of our proposed DEM through extensive simulation studies, demonstrating that it achieves coefficient estimations akin to those of global estimators derived from complete datasets. Applying the proposed model and method to the US daily PM2.5 and meteorological data, we investigate the influence of meteorological variables on PM2.5 concentrations, revealing both spatial and seasonal variations in this relationship.

An AI assistant to help review and improve causal reasoning in epidemiological documents

Drawing sound causal inferences from observational data is often challenging for both authors and reviewers. This paper discusses the design and application of an Artificial Intelligence Causal Research Assistant (AIA) that seeks to help authors improve causal inferences and conclusions drawn from epidemiological data in health risk assessments. The AIA-assisted review process provides structured reviews and recommendations for improving the causal reasoning, analyses and interpretations made in scientific papers based on epidemiological data. Causal analysis methodologies range from earlier Bradford-Hill considerations to current causal directed acyclic graph (DAG) and related models. AIA seeks to make these methods more accessible and useful to researchers. AIA uses an external script (a "Causal AI Booster" (CAB) program based on classical AI concepts of slot-filling in frames organized into task hierarchies to complete goals) to guide Large Language Models (LLMs), such as OpenAI's ChatGPT or Google's LaMDA (Bard), to systematically review manuscripts and create both (a) recommendations for what to do to improve analyses and reporting; and (b) explanations and support for the recommendations. Review tables and summaries are completed systematically by the LLM in order. For example, recommendations for how to state and caveat causal conclusions in the Abstract and Discussion sections reflect previous analyses of the Study Design and Data Analysis sections. This work illustrates how current AI can contribute to reviewing and providing constructive feedback on research documents. We believe that such AI-assisted review shows promise for enhancing the quality of causal reasoning and exposition in epidemiological studies. It suggests the potential for effective human-AI collaboration in scientific authoring and review processes.

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Associations of fine particulate matter with incident cardiovascular disease; comparing models using ZIP code-level and individual-level fine particulate matter and confounders

BACKGROUND

PM2.5 has been positively associated with cardiovascular disease (CVD) incidence. Most evidence has come from cohorts and administrative databases. Cohorts typically have extensive information on potential confounders and residential-level exposures. Administrative databases are usually more representative but typically lack information on potential confounders and often only have exposures at coarser geographies (e.g., ZIP code). The weaknesses in both types of studies have been criticized for potentially jeopardizing the validity of their findings for regulatory purposes.

METHODS

We followed 101,870 participants from the US-based Nurses' Health Study (2000-2016) and linked residential-level PM2.5 and individual-level confounders, and ZIP code-level PM2.5 and confounders. We used time-varying Cox proportional hazards models to examine associations with CVD incidence. We specified basic models (adjusted for individual-level age, race and calendar year), individual-level confounder models, and ZIP code-level confounder models.

RESULTS

Residential- and ZIP code-level PM2.5 were strongly correlated (Pearson r = 0.88). For residential-level PM2.5, the hazard ratio (HR, 95 % confidence interval) per 5 μg/m3 increase was 1.06 (1.01, 1.11) in the basic and 1.04 (0.99, 1.10) in the individual-level confounder model. For ZIP code-level PM2.5, the HR per 5 μg/m3 was 1.04 (0.99, 1.08) in the basic and 1.02 (0.97, 1.08) in the ZIP code-level confounder model.

CONCLUSION

We observed suggestive positive, but not statistically significant, associations between long-term PM2.5 and CVD incidence, regardless of the exposure or confounding model. Although differences were small, associations from models with individual-level confounders and residential-level PM2.5 were slightly stronger than associations from models with ZIP code-level confounders and PM2.5.

Causal association between long-term exposure to air pollution and incident Parkinson's disease

Epidemiological evidence for long-term air pollution exposure and Parkinson's disease (PD) is controversial, and analysis of causality is limited. We identified 293,888 participants who were free of PD at baseline in the UK Biobank (2006-2010). Time-varying air pollution [fine particulate (PM2.5) and ozone (O3)] exposures were estimated using spatio-temporal models. Incident cases of PD were identified using validated algorithms. Four methods were used to investigate the associations between air pollution and PD, including (1) standard time-varying Cox proportional-hazard model; (2) Cox models weighted by generalized propensity score (GPS) and inverse-probability weights (IPW); (3) instrumental variable (IV) analysis; and (4) negative control outcome analysis. During a median of 11.6 years of follow-up, 1822 incident PD cases were identified. Based on standard Cox regression, the hazard ratios (95% confidence interval) for a 1 µg/m3 or ppb increase in PM2.5 and O3 were 1.23 (1.17, 1.30) and 1.02 (0.98, 1.05), respectively. Consistent results were found in models weighted by GPS and IPW, and in IV analysis. There were no significant associations between air pollution and negative control outcomes. This study provides evidence to support a causal association between PM2.5 exposure and PD. Mitigation of air pollution could be a protective measure against PD.

Sociodemographic and geographic variation in mortality attributable to air pollution in the United States

There are large differences in premature mortality in the USA by racial/ethnic, education, rurality, and social vulnerability index groups. Using existing concentration-response functions, particulate matter (PM2.5) air pollution, population estimates at the tract level, and county-level mortality data, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in mortality attributable to PM2.5 were consistently more pronounced between racial/ethnic groups than by education, rurality, or social vulnerability index, with the Black American population having by far the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic White population was attributable to PM2.5 in the years 2000 to 2011.

F. Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Potential causal links of long-term air pollution with lung cancer incidence: From the perspectives of mortality and hospital admission in a large cohort study in southern China

Evidence on the potential causal links of long-term air pollution exposure with lung cancer incidence (reflected by mortality and hospital admission) was limited, especially based on large cohorts. We examined the relationship between lung cancer and long-term exposure to particulate matter (PM, including PM2.5 , PM10 and PM10-2.5 ) and nitrogen dioxide (NO2 ) among a large cohort of general Chinese adults using causal inference approaches. The study included 575 592 participants who were followed up for an average of 8.2 years. The yearly exposure of PM and NO2 was estimated through satellite-based random forest approaches and the ordinary kriging method, respectively. Marginal structural Cox models were used to examine hazard ratios (HRs) of mortality and hospital admission due to lung cancer following air pollution exposure, adjusting for potential confounders. The HRs of mortality due to lung cancer were 1.042 (95% confidence interval [CI]: 1.033-1.052), 1.032 (95% CI:1.024-1.041) and 1.052 (95% CI:1.041-1.063) for each 1 μg/m3 increase in PM2.5 , PM10 and NO2 , respectively. In addition, we observed statistically significant effects of PMs on hospital admission due to lung cancer. The HRs (95%CI) were 1.110 (1.027-1.201), 1.067 (1.020-1.115) and 1.079 (1.010-1.153) for every 1 μg/m3 increase in PM2.5 , PM10 , PM10-2.5 , respectively. Furthermore, we found larger effect estimates among the elderly and those who exercised more frequently. We provided the most comprehensive evidence of the potential causal links between two outcomes of lung cancer and long-term air pollution exposure. Relevant policies should be developed, with special attention to protecting the vulnerable groups of the population.

PM2.5-Attributable Mortality Burden Variability in the Continental U.S

Epidemiologic studies have consistently observed associations between fine particulate matter (PM2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM2.5-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM2.5-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ~3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM2.5-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

BACKGROUND

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA.

METHODS

In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health.

FINDINGS

Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke.

INTERPRETATION

Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades.

FUNDING

National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Mortality risk from United States coal electricity generation

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

The effects of long-term exposure to air pollution on incident mental disorders among patients with prediabetes and diabetes: Findings from a large prospective cohort

BACKGROUND

The association between air pollution and mental disorders has been widely documented in the general population. However, the evidence among susceptible populations, such as individuals with prediabetes or diabetes, is still insufficient.

METHODS

We analyzed data from 48,515 participants with prediabetes and 24,393 participants with diabetes from the UK Biobank. Annual pollution data were collected for fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), and nitrogen dioxides (NOx) during 2006-2021. The exposure to air pollution and temperature for each participant were estimated by the bilinear interpolation approach and time-weighted method based on their geocoded home addresses and time spent at each address. We employed the generalized propensity score model based on the generalized estimating equation and the time-varying covariates Cox model to assess the effects of air pollution.

RESULTS

We observed causal links between air pollutants and mental disorders among both prediabetic and diabetic participants, with stronger effects among those with diabetes than prediabetes. The hazard ratios were 1.18 (1.12, 1.24), 1.15 (1.10, 1.20), 1.18 (1.13, 1.23), and 1.15 (1.11, 1.19) in patients with prediabetes, and 1.21 (1.13, 1.29), 1.17 (1.11, 1.24), 1.19 (1.13, 1.25), and 1.17 (1.12, 1.23) in patients with diabetes per interquartile range elevation in PM2.5, PM10, NO2, and NOx. Furthermore, the effects were more pronounced among people who were older, alcohol drinkers, and living in urban areas.

CONCLUSIONS

Our study indicates the potential causal links between long-term exposure to air pollution and incident mental disorders among those with prediabetes and diabetes. Reducing air pollution levels would significantly benefit this vulnerable population by reducing the incidence of mental disorders.

Low-level ambient sulfur dioxide exposure and genetic susceptibility associated with incidence of idiopathic pulmonary fibrosis: A national prospective cohort study

BACKGROUND

The association between long-term air pollution exposure and the development of idiopathic pulmonary fibrosis (IPF) has been established, but the evidence regarding the effect of low levels of air pollution, especially ambient sulfur dioxide (SO2), is limited. Besides, the combined effect and interaction between genetic susceptibility and ambient SO2 on IPF remain uncertain.

METHODS

This study retrieved data from 402,042 participants who were free of IPF at baseline in the UK Biobank. The annual mean concentration of ambient SO2 was estimated for each participant based on their residential addresses using a bilinear interpolation method. Cox proportional hazard models were used to examine the relationship between ambient SO2 and incident IPF. We further generated a polygenic risk score (PRS) for IPF and estimated the combined effects of genetic susceptibility and ambient SO2 on incident IPF.

RESULTS

After a median follow-up of 11.78 years, 2562 cases of IPF were identified. The results indicated that each 1 μg/m3 increase in ambient SO2 was associated with a hazard ratio (HR) (95% confidence interval [CI]) of 1.67 (1.58, 1.76) for incident IPF. The study found statistically significant synergistic additive interaction between genetic susceptibility and ambient SO2. Individuals with high genetic risk and high ambient SO2 exposure had a higher risk of developing IPF (HR = 7.48, 95% CI:5.66, 9.90).

CONCLUSION

The study suggests that long-term exposure to ambient SO2, even at concentrations lower than current air quality guidelines set by the Word Health Organization and European Union, may be an important risk factor for IPF. This risk is more pronounced among people with a high genetic risk. Therefore, these findings emphasize the need to consider the potential health effects of SO2 exposure and the necessity for stricter air quality standards.

Impact of Legislated and Best Available Emission Control Measures on UK Particulate Matter Pollution, Premature Mortality, and Nitrogen-Sensitive Habitats

Past emission controls in the UK have substantially reduced precursor emissions of health-hazardous fine particles (PM2.5) and nitrogen pollution detrimental to ecosystems. Still, 79% of the UK exceeds the World Health Organization (WHO) guideline for annual mean PM2.5 of 5 μg m-3 and there is no enforcement of controls on agricultural sources of ammonia (NH3). NH3 is a phytotoxin and an increasingly large contributor to PM2.5 and nitrogen deposited to sensitive habitats. Here we use emissions projections, the GEOS-Chem model, high-resolution data sets, and contemporary exposure-risk relationships to assess potential human and ecosystem health co-benefits in 2030 relative to the present day of adopting legislated or best available emission control measures. We estimate that present-day annual adult premature mortality attributable to exposure to PM2.5 is 48,625 (95% confidence interval: 45,188-52,595), that harmful amounts of reactive nitrogen deposit to almost all (95%) sensitive habitat areas, and that 75% of ambient NH3 exceeds levels safe for bryophytes and lichens. Legal measures decrease the extent of the UK above the WHO guideline to 58% and avoid 6,800 premature deaths by 2030. This improves with best available measures to 36% of the UK and 13,300 avoided deaths. Both legal and best available measures are insufficient at reducing the extent of damage of nitrogen pollution to sensitive habitats. Far more ambitious reductions in nitrogen emissions (>80%) than is achievable with best available measures (34%) are required to halve the amount of excess nitrogen deposited to sensitive habitats.

Is COPD mortality in South China causally linked to the long-term PM1 exposure? Evidence from a large community-based cohort

BACKGROUND

Long-term ambient particulate matter (PM) exposure has been found associated with chronic obstructive pulmonary disease (COPD) mortality in an increasing body of research. However, limited evidence was available on the potential causal links between PM1 and COPD mortality, especially in highly exposed areas.

OBJECTIVES

To examine the COPD mortality risk following long-term ambient PM1 exposure in south China.

METHODS

The cohort included 580,757 participants recruited during 2009-2015. Satellite-based annual concentrations of PM1 were estimated at a spatial resolution of 1 km × 1 km and assigned to each participant based on their residential addresses. We analyzed the potential causal links between time-varying PM1 exposure and COPD mortality using marginal structural cox models within causal frameworks. Stratified analyses were also performed to identify the potential susceptible groups.

RESULTS

The annual average PM1 concentration continuously decreased over time. After adjusting for confounders, each 1 μg/m3 increase in PM1 concentration corresponded to an 8.1 % (95% confidence interval: 6.4-9.9 %) increment in the risk of COPD mortality. The impact of PM1 was more pronounced among the elderly and those with low exercise frequency, with a 1.9-6.9 % higher risk than their counterparts. We further observed a 0.1-9.7 % greater risk among those who lived in lower greenness settings. Additionally, we observed higher effect estimates in participants with long-term low PM1 exposure compared to the general population.

CONCLUSIONS

COPD mortality risk significantly increased following long term ambient PM1 exposure, particularly among groups with certain demographics or long-term low exposure.

Air Pollution and Cardiovascular and Thromboembolic Events in Older Adults With High-Risk Conditions

Little epidemiologic research has focused on pollution-related risks in medically vulnerable or marginalized groups. Using a nationwide 50% random sample of 2008-2016 Medicare Part D-eligible fee-for-service participants in the United States, we identified a cohort with high-risk conditions for cardiovascular and thromboembolic events (CTEs) and linked individuals with seasonal average zip-code-level concentrations of fine particulate matter (particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5)). We assessed the relationship between seasonal PM2.5 exposure and hospitalization for each of 7 CTE-related causes using history-adjusted marginal structural models with adjustment for individual demographic and neighborhood socioeconomic variables, as well as baseline comorbidity, health behaviors, and health-service measures. We examined effect modification across geographically and demographically defined subgroups. The cohort included 1,934,453 individuals with high-risk conditions (mean age = 77 years; 60% female, 87% White). A 1-μg/m3 increase in PM2.5 exposure was significantly associated with increased risk of 6 out of 7 types of CTE hospitalization. Strong increases were observed for transient ischemic attack (hazard ratio (HR) = 1.039, 95% confidence interval (CI): 1.034, 1.044), venous thromboembolism (HR = 1.031, 95% CI: 1.027, 1.035), and heart failure (HR = 1.019, 95% CI: 1.017, 1.020). Asian Americans were found to be particularly susceptible to thromboembolic effects of PM2.5 (venous thromboembolism: HR = 1.063, 95% CI: 1.021, 1.106), while Native Americans were most vulnerable to cerebrovascular effects (transient ischemic attack: HR = 1.093, 95% CI: 1.030, 1.161).

Multidimensional intergenerational mobility

In this paper, we present novel evidence of the extent to which intergenerational mobility is generalized or specific across domains of human and health capital. That is, do children who experience greater mobility in one domain (e.g., income) also experience mobility in other domains (education, health status, health behaviors, crime). Using rich data in Add Health, we find evidence against generalized mobility-families that are more mobile in one domain are not more mobile in others. We then ask a place-based version of this question, motivated by Chetty et al. (2014)'s work showing high levels of geographically-based income mobility in the US. The school-based sampling combined with parent-child links across many outcome domains of the Add Health allows us to use a common dataset between the two analyses. Like our individual-based results, we find limited evidence of generalized mobility by place-indeed, most estimates suggest close-to-zero correlations between many of the ten domains we explore.

Potential causal links between long-term ambient particulate matter exposure and cerebrovascular mortality: Insights from a large cohort in southern China

Cohort studies conducted in North America and Europe have linked cerebrovascular mortality to long-term exposure to particulate matter (PM). However, limited evidence from large cohorts in high-exposure areas and the traditional approach of association assessment may cause residual confounding issues. In this study, we aimed to investigate the causal links between cerebrovascular mortality and long-term exposure to PM_2.5, PM₁₀, and PM_2.5-10 in an ongoing cohort study with 580,757 participants in southern China. Using satellite-based estimates of PM concentration at a 1-km² spatial resolution, we assigned exposure levels to each participant and used the marginal structural Cox model to assess the association between PM exposure and cerebrovascular mortality while accounting for time-varying covariates. We also explored the potential modification effects of sociodemographic and behavioral factors on the PM-health associations. Adjusted hazard ratios (HR) for overall cerebrovascular mortality were 1.041 (95% confidence interval (CI): 1.034-1.049) and 1.032 (95% CI: 1.026-1.038) for each 1 μg/m³ increase in PM_2.5, and PM₁₀, respectively. Similar trends were observed in the mortality risk from stroke and ischemic stroke, with HRs ranging from 1.040 to 1.069 and 1.025 to 1.052, respectively, across 2 p.m. exposures. The impact of PM exposure was generally more apparent among women, participants with primary school diplomas and below, and the subgroup under low-exposure. Multiple sensitivity analyses confirmed the robustness of the results. In conclusion, this sizable prospective cohort study hypothesizes causal links between long-term PM exposure and cerebrovascular mortality, particularly among vulnerable participants, supporting the rationale for reducing PM concentration in China to reduce cerebrovascular mortality.

Structural racism, air pollution and the association with adverse birth outcomes in the United States: the value of examining intergenerational associations

Structurally racist policies and practices of the past are likely to be a driving factor in current day differences in exposure to air pollution and may contribute to observed racial and ethnic disparities in adverse birth outcomes in the United States (U.S.). Non-Hispanic Black women in the U.S. experience poorer health outcomes during pregnancy and throughout the life course compared to non-Hispanic White women. This disparity holds even among non-Hispanic Black women with higher socioeconomic status. Reasons for this finding remain unclear, but long-term environmental exposure, either historical exposure or both historical and ongoing exposure, may contribute. Structural racism likely contributes to differences in social and environmental exposures by race in the U.S. context, and these differences can affect health and wellbeing across multiple generations. In this paper, we briefly review current knowledge and recommendations on the study of race and structural racism in environmental epidemiology, specifically focused on air pollution. We describe a conceptual framework and opportunities to use existing historical data from multiple sources to evaluate multi-generational influences of air pollution and structurally racist policies on birth and other relevant health outcomes. Increased analysis of this kind of data is critical for our understanding of structural racism's impact on multiple factors, including environmental exposures and adverse health outcomes, and identifying how past policies can have enduring legacies in shaping health and well-being in the present day. The intended purpose of this manuscript is to provide an overview of the widespread reach of structural racism, its potential association with health disparities and a comprehensive approach in environmental health research that may be required to study and address these problems in the U.S. The collaborative and methodological approaches we highlight have the potential to identify modifiable factors that can lead to effective interventions for health equity.

Comparison of traditional Cox regression and causal modeling to investigate the association between long-term air pollution exposure and natural-cause mortality within European cohorts

Most studies investigating the health effects of long-term exposure to air pollution used traditional regression models, although causal inference approaches have been proposed as alternative. However, few studies have applied causal models and comparisons with traditional methods are sparse. We therefore compared the associations between natural-cause mortality and exposure to fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) using traditional Cox and causal models in a large multicenter cohort setting. We analysed data from eight well-characterized cohorts (pooled cohort) and seven administrative cohorts from eleven European countries. Annual mean PM_2.5 and NO₂ from Europe-wide models were assigned to baseline residential addresses and dichotomized at selected cut-off values (PM_2.5: 10, 12, 15 μg/m³; NO₂: 20, 40 μg/m³). For each pollutant, we estimated the propensity score as the conditional likelihood of exposure given available covariates, and derived corresponding inverse-probability weights (IPW). We applied Cox proportional hazards models i) adjusting for all covariates ("traditional Cox") and ii) weighting by IPW ("causal model"). Of 325,367 and 28,063,809 participants in the pooled and administrative cohorts, 47,131 and 3,580,264 died from natural causes, respectively. For PM_2.5 above vs. below 12 μg/m³, the hazard ratios (HRs) of natural-cause mortality were 1.17 (95% CI 1.13-1.21) and 1.15 (1.11-1.19) for the traditional and causal models in the pooled cohort, and 1.03 (1.01-1.06) and 1.02 (0.97-1.09) in the administrative cohorts. For NO₂ above vs below 20 μg/m³, the HRs were 1.12 (1.09-1.14) and 1.07 (1.05-1.09) for the pooled and 1.06 (95% CI 1.03-1.08) and 1.05 (1.02-1.07) for the administrative cohorts. In conclusion, we observed mostly consistent associations between long-term air pollution exposure and natural-cause mortality with both approaches, though estimates partly differed in individual cohorts with no systematic pattern. The application of multiple modelling methods might help to improve causal inference. 299 of 300 words.

Associations between illness-related absences and ventilation and indoor PM2.5 in elementary schools of the Midwestern United States

This study monitored indoor environmental data in 144 classrooms in 31 schools in the Midwestern United States for two consecutive days every fall, winter, and spring during a two-year period; 3,105 pupils attended classrooms where the measurements were conducted. All classrooms were ventilated with mechanical systems that had recirculation; there were no operable exterior windows or doors. The daily absence rate at the student level and demographic data at the classroom level were collected. The overall mean ventilation rate, using outdoor air, was 5.5 L/s per person (the corresponding mean carbon dioxide concentrations were < 2,000 ppm), and the mean indoor PM_2.5 was 3.6 μg/m³. The annual illness-related absence rate at the classroom level was extracted from the student-level absence data and regressed on measured indoor environmental parameters. Significant associations were found. Every 1 L/s per person increase in ventilation rate was associated with a 5.59 decrease in days with absences per year. This corresponds to a 0.15% increase in the annual daily attendance rate. Every additional 1 μg/m³ of indoor PM_2.5 was associated with a 7.37 increase in days with absences per year. This corresponds to a 0.19% decrease in the annual daily attendance rate. No other relationships were significant. Present results agree with the previously demonstrated benefits of reduced absence rates when classroom ventilation is improved and provide additional evidence on the potential benefits of reducing indoor inhalable particles. Overall, reduced absence rates are expected to provide socioeconomic benefits and benefits for academic achievements, while higher ventilation rates and reduced particle levels will also contribute to reduced health risks, including those related to airborne respiratory pathogens.

Association of long-term exposure to ambient PM2.5 and its constituents with gut microbiota: Evidence from a China cohort

Accumulating animal experiments and epidemiological studies have found that exposure to fine particulate matter (PM_2.5) is associated with altered gut microbiota (GM). However, it is unclear what kind of role the PM_2.5 constituents play in the PM_2.5-GM association. Therefore, this study aimed to investigate the association of long-term exposure to PM_2.5 and its constituents (PM_cons) with GM. This study included 1583 participants from a cohort in Southwest China. Satellite remote sensing and chemical transport modelling were used to determine the yearly average concentrations of PM_cons. GM data were derived from 16 s sequencing based on stool samples. Generalized propensity score weighting regression and Bayesian Kernel Machine Regression (BKMR) were used to estimate the individual and joint association of exposure to PM_cons with the Shannon index. The weighted correlation analysis was used to estimate the association of PM_cons with the composition of GM. The result showed that an interquartile range increase of 3-year average black carbon (BC), ammonium, nitrate, organic matter (OM), sulfate, and soil particles (SOIL) were negatively associated with Shannon index with mean difference (95 % confidence interval) being -0.144 (-0.208, -0.080), -0.141 (-0.205, -0.078), -0.126 (-0.184, -0.068), -0.117 (-0.172, -0.062), -0.153 (-0.221, -0.085), and - 0.153 (-0.222, -0.085). BKMR indicated joint exposure to PM_cons was associated with decreased Shannon index, and BC had the largest posterior inclusion probability (0.578). Weighted correlation analyses indicated PM_cons were associated with decreased Bacteroidetes (r = -0.204, P < 0.001 for PM_2.5) and increased Proteobacteria (r = 0.273, P < 0.001 for PM_2.5). These results revealed that long-term exposure to PM_cons was associated with GM. BC was the most important constituent in the association, indicating that the source of BC should be controlled to mitigate the negative effects of PM_2.5 on GM.

Air Pollution and Mortality at the Intersection of Race and Social Class

BACKGROUND

Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 μm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM2.5 standards.

METHODS

We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible).

RESULTS

Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 from 12 μg per cubic meter to 8 μg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval [CI], 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation.

CONCLUSIONS

Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).

Potential causal links between long-term ambient particulate matter exposure and cardiovascular mortality: New evidence from a large community-based cohort in South China

BACKGROUND

Cardiovascular disease (CVD) mortality is associated with long-term particulate matter (PM) exposure. However, evidence from large, highly-exposed population cohort and observational-data-based causal inference approaches remains limited.

AIMS

We examined the potential causal links between PM exposure and the CVD mortality in South China.

METHODS

580,757 participants were recruited during 2009-2015 and followed up through 2020. Satellite-based annual concentrations of PM_2.5, PM₁₀, and PM_coarse (i.e., PM₁₀ - PM_2.5) at 1 km² spatial resolution were estimated and assigned to each participant. Marginal structural Cox models with time-varying covariates, adjusted using inverse probability weighting, were developed to evaluate the association between prolonged PM exposure and CVD mortality.

RESULTS

For overall CVD mortality, the hazard ratios and 95% confidence interval for each 1 μg/m³ increase in the annual average concentration of PM_2.5, PM₁₀, and PM_coarse were 1.033 (1.028-1.037), 1.028 (1.024-1.032), and 1.022 (1.012-1.033), respectively. All three PMs were linked to a higher mortality risk for myocardial infarction and ischemic heart disease (IHD). The mortality risk of chronic IHD and hypertension was linked to PM_2.5 and PM₁₀. Significant association between PM_coarse and other heart disease mortality was also observed. The older, women, less-educated participants, or inactive participants exhibited particularly higher susceptibility. Participants who were generally exposed to PM₁₀ concentrations below 70 μg/m³ were more vulnerable to PM_2.5-, PM₁₀- and PM_coarse-CVD mortality risks.

CONCLUSION

This large cohort study provides evidence for the potential causal links between increased CVD mortality and ambient PM exposure, as well as socio-demographics linked to the highest vulnerability.

Comparing "causal" and "traditional" approaches in the association of long-term exposure to ambient air pollution on mortality: How sensitive are the results?

BACKGROUND

Few comparisons between causal inference and traditional approaches have been performed. We applied "causal" and "traditional" methods to investigate the association between long-term air pollution exposure (PM_2.5 and NO₂) and mortality.

METHODS

We analyzed pooled data from eight well-characterized cohorts and one administrative cohort. We defined the generalized propensity score (GPS) as the conditional likelihood of exposure given confounders, and derived corresponding inverse-probability weights (IPW). We applied Cox-proportional hazard models weighted by IPW, adjusted for GPS, and directly adjusting for all confounders.

RESULTS

In IPW models, PM_2.5 5 µg/m³ increases were associated with hazard ratios (HR) = 1.141 (95% confidence interval (CI): 1.107, 1.176) and 1.050 (1.014, 1.088) in the pooled and administrative cohorts. Corresponding estimates for traditional Cox models were 1.132 (1.107, 1.158) and 1.057 (1.025, 1.089). Almost identical results were found for all approaches and both pollutants, when unbalanced covariates were adjusted for in causal models.

CONCLUSIONS

Traditional and causal approaches provided consistent associations between long-term exposure to air pollution and mortality.

Long-term exposure to particulate matter and COPD mortality: Insights from causal inference methods based on a large population cohort in southern China

BACKGROUND

Evidence of the association between long-term exposure to particulate matter (PM) and chronic obstructive pulmonary disease (COPD) mortality from large population-based cohort study is limited and often suffers from residual confounding issues with traditional statistical methods. We hereby assessed the casual relationship between long-term PM (PM_2.5, PM₁₀ and PM_10-2.5) exposure and COPD mortality in a large cohort of Chinese adults using state-of-the-art causal inference approaches.

METHODS

A total of 580,757 participants in southern China were enrolled in a prospective cohort study from 2009 to 2015 and followed up until December 2020. Exposures to PM at each residential address were obtained from the Long-term Gap-free High-resolution Air Pollutant Concentration dataset. Marginal structural Cox models were used to investigate the association between COPD mortality and annual average exposure levels of PM exposure.

RESULTS

During an average follow-up of 8.0 years, 2250 COPD-related deaths occurred. Under a set of causal inference assumptions, the hazard ratio (HR) for COPD mortality was estimated to be 1.046 (95 % confidence interval: 1.034-1057), 1.037 (1.028-1.047), and 1.032 (1.006-1.058) for each 1-μg/m³ increase in annual average concentrations of PM_2.5, PM₁₀, and PM_10-2.5 respectively. Additionally, the detrimental effects appeared to be more pronounced among the elderly (age ≥ 65) and inactive participants. The effect estimates of PM_2.5, PM₁₀, and PM_10-2.5 tend to be greater among participants who were generally exposed to PM₁₀ concentrations below 70 μg/m³ than that among the general population.

CONCLUSION

Our results support causal links between long-term PM exposure and COPD mortality, highlighting the urgency for more effective strategies to reduce PM exposure, with particular attention on protecting potentially vulnerable groups.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Hospitalisations for cardiovascular and respiratory disease among older adults living near unconventional natural gas development: a difference-in-differences analysis

BACKGROUND

During 2008-15, the Marcellus shale region of the US state of Pennsylvania experienced a boom in unconventional natural gas development (UNGD) or "fracking". However, despite much public debate, little is known about the effects of UNGD on population health in local communities. Among other mechanisms, air pollution from UNGD might affect individuals living nearby through cardiovascular or respiratory disease, and older adults could be particularly susceptible.

METHODS

To study the health impacts of Pennsylvania's fracking boom, we exploited the ban on UNGD in neighbouring New York state. Using 2002-15 Medicare claims, we conducted difference-in-differences analyses over multiple timepoints to estimate the risk of living near UNGD for hospitalisation with acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD) and bronchiectasis, heart failure, ischaemic heart disease, and stroke among older adults (aged ≥65 years).

FINDINGS

Pennsylvania ZIP codes that started UNGD in 2008-10 were associated with more hospitalisations for cardiovascular diseases in 2012-15 than would be expected in the absence of UNGD. Specifically, in 2015, we estimated an additional 11·8, 21·6, and 20·4 hospitalisations for AMI, heart failure, and ischaemic heart disease, respectively, per 1000 Medicare beneficiaries. Hospitalisations increased even as UNGD growth slowed. Results were robust in sensitivity analyses.

INTERPRETATION

Older adults living near UNGD could be at high risk of poor cardiovascular outcomes. Mitigation policies for existing UNGD might be needed to address current and future health risks. Future consideration of UNGD should prioritise local population health.

FUNDING

University of Chicago and Argonne National Laboratories.

Using Parametric g-Computation to Estimate the Effect of Long-Term Exposure to Air Pollution on Mortality Risk and Simulate the Benefits of Hypothetical Policies: The Canadian Community Health Survey Cohort (2005 to 2015)

BACKGROUND

Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges.

METHOD

We applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication.

RESULTS

All hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 μg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications.

DISCUSSION

We found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.

A new perspective to satellite-based retrieval of ground-level air pollution: Simultaneous estimation of multiple pollutants based on physics-informed multi-task learning

Remote sensing of air pollution is essential for air quality management and health risk assessment. Many machine-learning-based retrieval models have been established for estimating various kinds of air pollutants. These methods mainly aimed to estimate a single pollutant (single-pollutant approach). However, different air pollutants interact with each other and are highly correlated. Building a unified model and conducting a joint retrieval of multiple pollutant can make a better use of these connections and improve the model efficiency. This study proposed a physics-informed multi-task deep neural network (phyMTDNN) for the joint retrieval of six main air pollutants, i.e., PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃. The relationships among these pollutants were used to design the physics-informed network structure and loss function. Top-of-atmosphere reflectance which can generate retrieval results at ultrahigh resolution was used as model input. Experiments for mainland China in 2019 showed that the proposed model successfully achieved simultaneous estimation of six air pollutants, with the cross-validated R² for the six pollutants varying from 0.72 to 0.90. The contrast experiments proved that physics-informed network structure contributed to the most of the model performance improvement. Compared to the single-pollutant approach, phyMTDNN ameliorated the model accuracy on traces gases retrieval. Furthermore, the modeling efficiency was largely improved in that a lot of repetitive work was avoided and modeling method was optimized. The proposed new multiple-pollutant retrieval frame can be applied to various fields for multi-variate retrieval or estimation.

Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias

Alzheimer's disease and related dementias (ADRD) poses substantial health challenges among an aging population. One of the primary challenges in studying ADRD is that biological processes underlying these ailments begin decades prior to diagnosis. Previous studies indicate a relationship between ADRD and air pollution exposure to both fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) but are limited in their interpretation because they consider exposure measurements at a single time point. Our retrospective cohort study considered 27 + million Medicare enrollees in the United States followed up to 17 years and matched with highly accurate annual air pollution exposure measurements for PM_2.5, NO₂, and summer ozone. We applied distributed lag models and estimated the lagged associations between air pollution and odds of first hospitalization with ADRD. We found significantly increased odds due to overall PM_2.5 and NO₂ exposure and time-lagged exposure 10 and 8 years prior to admission, respectively. Furthermore, we found the connection between air pollution exposure and increased odds of first hospitalization with ADRD exists at air pollution levels below current National Ambient Air Quality Standards set by the US Environmental Protection Agency, with the steepest increase in odds occurring at low concentrations of PM_2.5. Our findings are the first to show that air pollution exposures from as many as 10 years prior to the admission are related to increased odds of hospitalizations with ADRD. As there are no clear treatments available for ADRD, identifying modifiable risk factors such as air pollution exposure may make significant contributions towards prevention or delayed disease progression.

Decreased birth weight after prenatal exposure to wildfires on the eastern coast of Korea in 2000

OBJECTIVES

In April 2000, a series of wildfires occurred simultaneously in five adjacent small cities located on the eastern coast of Korea. These wildfires burned approximately 23,794 hectares of forestland over several days. We investigated the effects of prenatal exposure to the by-products generated by wildfire disasters on birth weight.

METHODS

Birth weight data were obtained for 1999-2001 from the birth registration database of the Korean National Statistical Office and matched with the zip code and exposed/unexposed pregnancy week for days of the wildfires. Generalized linear models were then used to assess the associations between birth weight and exposure to wildfires after adjusting for fetal sex, gestational age, parity, maternal age, maternal education, paternal education, and average exposed atmospheric temperature.

RESULTS

Compared with unexposed pregnancies before and after the wildfires, mean birth weight decreased by 41.4 g (95% confidence interval [CI], -72.4 to -10.4) after wildfire exposure during the first trimester, 23.2 g (95% CI, -59.3 to 13.0) for exposure during the second trimester, and 27.0 g (95% CI, -63.8 to 9.8) during the third trimester. In the adjusted model for infants exposed in utero during any trimester, the mean birth weight decreased by 32.5 g (95% CI, -53.2 to -11.7).

CONCLUSIONS

We observed a 1% reduction in birth weight after wildfire exposure. Thus, exposure to by-products generated during a wildfire disaster during pregnancy may slow fetal growth and cause developmental delays.

The protective effects of taurine and fish oil supplementation on PM2.5-induced heart dysfunction among aged mice: A random double-blind study

As it is nearly impossible to reduce PM2.5 concentrations in most cities to safe limits in a short period of time, dietary supplementation presents a promising approach for mitigating the adverse effects of PM2.5 exposure. A cross-sectional study showed that the elderly population of Linfen (PM2.5: 102 μg/m3) exhibited significantly lower serum taurine levels, as well as higher oxidative stress levels and cardiovascular health risks, than the corresponding population in Guangzhou (PM2.5: 39 μg/m3). We conducted a random double-blind study on aged mice that employed a "real-world" PM2.5 exposure system to simulate the conditions of Linfen with the aim of investigating the protective effects of taurine and fish oil supplementation on PM2.5-induced heart dysfunction. When compared with the placebo group, supplementation with taurine and fish oil not only maintained normal taurine levels, but also suppressed oxidative stress and inflammation in aged mice subjected to high concentrations of PM2.5. Variations in heart rate, contractile function, cardiac oxidative stress, inflammation and fibrosis among different groups of aged mice were used to clarify the beneficial effects of taurine and fish oil supplementation. Our results not only revealed the protective effects of taurine and fish oil supplementation on heart dysfunction induced by PM2.5 exposure from the aged mice experiments and also provided new means for the elderly to resist PM2.5 pollution at the individual level.

Can urban rail transit reduce haze pollution? A spatial difference-in-differences approach

This paper explores the influence and mechanism of urban rail transit on haze pollution in mainland China. Based on the satellite remote sensing dataset released by the Earth Observing System of Data and Information System (EOSDIS) of the National Aeronautics and Space Administration (NASA) and urban rail transit network data of Robert Schwan, the prefecture-year urban rail transit and haze concentration-related dataset from 2001 to 2018 is collected. Considering the significant spatial autocorrelation of urban haze pollution, a spatial difference-in-differences (SDID) approach is applied to empirically investigate the influence of urban rail transit on haze pollution. The results show that the connection of urban rail transit significantly reduces the urban PM2.5 concentration, and the effect has significant regional heterogeneity. Furthermore, it is found that the substitution effect on motor vehicles is the mechanism in which urban rail transit impacts haze pollution. Based on our findings, accelerating urban rail transit network support, appropriately relaxing the subway application in some large cities, and taking comprehensive measures to attract more residents to choose subway travel is proposed.

Emission Characterization of Particulate Matters from the Combustion of Pulverized Coals in a Simulated Fluidized Bed Boiler

Coal-fired power generation is one of the main causes of air pollution, and the fluidized bed technology is currently a commercially used coal-fired technology. Therefore, it is of great significance to investigate the characteristics of particulate matter released from the fluidized bed boiler. In this study, lignite, bituminous coal and anthracite with particle sizes of <75 μm and 180-830 μm were selected and burned completely at 700, 800, and 900 °C for the purpose of simulating the process of pulverized coal combustion in a small sized simulated fluidized bed boiler and exploring the effects of coal rank, particle size, and burning temperature on the characteristics of the released particulate matter. The results show that, under the same mass, bituminous coal combustion releases the most PM1, PM2.5, and PM10, followed by lignite and anthracite. For all combusted coals the released PM1 accounts for half and one-third of the PM2.5 and PM10, respectively, and the released PM2.5 accounts for half of the PM10. A smaller particle size of pulverized coal and a higher burning temperature correspond to the release of more submicron to micron particulate matter. The mass concentration of released particulate matter for lignite and bituminous coal shows a bimodal distribution, with the two peak values in the ranges of 0.1-0.18 and 3.2-10 μm, respectively. As the burning temperature increases and the particle size of pulverized coal decreases, the first peak value falls and the second peak shifts to a small particle size range. This study can serve as reference for diminishing the emission of submicron to micron particulate matter by coal-fired power plants and preventing air pollution.

The effect of long-term exposure to air pollution and seasonal temperature on hospital admissions with cardiovascular and respiratory disease in the United States: A difference-in-differences analysis

BACKGROUND

Few studies have simultaneously examined the effect of long-term exposure to air pollution and ambient temperature on the rate of hospital admissions with cardiovascular and respiratory disease using causal inference methods.

METHODS

We used a variation of a difference-in-difference (DID) approach to assess the effects of long-term exposure to warm-season temperature, cold-season temperature, NO2, O3, and PM2.5 on the rate of hospital admissions for cardiovascular disease (CVD), myocardial infarction (MI), ischemic stroke, and respiratory diseases from 2001 to 2016 among Medicare beneficiaries who use fee-for-service programs. We computed the rate of admissions by zip code and year. Covariates included demographic and socioeconomic variables which were obtained from the decennial Census, the American Community Survey, the Behavioral Risk Factor Surveillance System, and the Dartmouth Health Atlas. As a secondary analysis, we restricted the analysis to zip code-years that had exposure to low concentrations of our pollutants.

RESULTS

PM2.5 was associated with a significant increase in the absolute rate of annual admissions with cardiovascular disease by 47.71 admissions (95 % CI: 41.25-56.05) per 100,000 person-years, myocardial infarction by 7.44 admissions (95 % CI: 5.53-9.63) per 100,000 person-years, and 18.58 respiratory admissions (95 % CI: 12.42-23.72) for each one μg/m3 increase in two-year average levels. O3 significantly increased the rates of all the studied outcomes. NO2 was associated with a decreased rate of admissions with MI by 0.83 admissions (95 % CI: 0.10-1.55) per 100,000 person-years but increased rate of admissions for respiratory disease by 3.16 admissions (95 % CI: 1.34-5.24) per 100,000 person-years. Warmer cold-season temperature was associated with a decreased admissions rate for all outcomes.

CONCLUSION

Air pollutants, particularly PM2.5 and O3, increased the rate of hospital admissions with cardiovascular and respiratory disease among the elderly, while higher cold-season temperatures decreased the rate of admissions with these conditions.

Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration

BACKGROUND

Numerous studies have documented PM_2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM_2.5 components. The lack of exposure measurements and high correlation among different PM_2.5 components are two limitations.

METHODS

We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM_2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO₄^2-, NO₃^-, NH₄⁺, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM_2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM_2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM_2.5 components. Total PM_2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models.

RESULTS

Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM_2.5 mass analysis, and single component associations.

CONCLUSION

We have found positive associations between the mixture of 15 PM_2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM_2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM_2.5, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM_2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO₂ concentration in 2021 in Beijing. Notably, the O₃ concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM_2.5, NO₂, SO₂, and CO in February of 2020, but PM_2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM_2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM_2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM_2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Associations between long-term exposure to ambient air pollution and renal function in Southwest China: The China Multi-Ethnic Cohort (CMEC) study

BACKGROUND

Limited studies have examined associations between air pollutants exposure and renal function, especially in China, with the most extensive chronic kidney disease (CKD) disease burden worldwide.

OBJECTIVES

This study examines associations between long-term exposure to ambient PM_2.5, NO₂, CO, O₃, SO₂ and renal function.

METHODS

We included 80,225 participants aged 30-79 years from the baseline data of the China Multi-Ethnic Cohort (CMEC) study. Three-year average concentrations of PM_2.5, NO₂, CO, O₃, and SO₂ were estimated using satellite-based spatiotemporal models. Renal function is determined by the estimated glomerular filtration rate (eGFR) using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. After adjusting for covariates, generalized propensity scores (GPS) weighting regression was used to estimate associations between ambient air pollutants and renal function.

RESULTS

An increase of 0.1 mg/m³ CO (OR [odds ratio] =1.20 95% CI [confidence interval], 1.05-1.37) was positively associated with CKD. An increase of 1 μg/m³ in SO₂ (1.07, 1.00-1.14) concentration was positively associated with CKD. An increase of 10 μg/m³ in PM_2.5 (1.17, 0.99-1.38), NO₂ (1.12, 0.83-1.51) and O₃ (1.10, 0.81-1.50) concentration was not associated with CKD. These effects are stronger in those younger than 65, smoking and with low BMI.

CONCLUSIONS

In this study, we found that long-term exposure to ambient CO and SO₂ were positively associated with CKD. Gaseous pollutants should also arouse the concern of relevant departments.

The role of outdoor and indoor air quality in the spread of SARS-CoV-2: Overview and recommendations by the research group on COVID-19 and particulate matter (RESCOP commission)

There are important questions surrounding the potential contribution of outdoor and indoor air quality in the transmission of SARS-CoV-2 and perpetuation of COVID-19 epidemic waves. Environmental health may be a critical component of COVID-19 prevention. The public health community and health agencies should consider the evolving evidence in their recommendations and statements, and work to issue occupational guidelines. Evidence coming from the current epidemiological and experimental research is expected to add knowledge about virus diffusion, COVID-19 severity in most polluted areas, inter-personal distance requirements and need for wearing face masks in indoor or outdoor environments. The COVID-19 pandemic has highlighted the need for maintaining particulate matter concentrations at low levels for multiple health-related reasons, which may also include the spread of SARS-CoV-2. Indoor environments represent even a more crucial challenge to cope with, as it is easier for the SARS-COV2 to spread, remain vital and infect other subjects in closed spaces in the presence of already infected asymptomatic or mildly symptomatic people. The potential merits of preventive measures, such as CO₂ monitoring associated with natural or controlled mechanical ventilation and air purification, for schools, indoor public places (restaurants, offices, hotels, museums, theatres/cinemas etc.) and transportations need to be carefully considered. Hospital settings and nursing/retirement homes as well as emergency rooms, infectious diseases divisions and ambulances represent higher risk indoor environments and may require additional monitoring and specific decontamination strategies based on mechanical ventilation or air purification.