Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors

Indigenized Characterization Factors for Health Damage Due to Ambient PM2.5 in Life Cycle Impact Assessment in China

Life cycle assessment (LCA) is a broadly used method for quantifying environmental impacts, and life cycle impact assessment (LCIA) is an important step as well as a major source of uncertainties in LCA. Characterization factors (CFs) are pivotal elements in LCIA models. In China, the health loss due to ambient PM2.5 is an important aspect of LCIA results, which, however, is generally assessed by adopting CFs developed by global models and there remains a need to integrate localized considerations and the latest information for more precise applications in China. In this study, we developed indigenized CFs for LCIA of health damage due to ambient PM2.5 in China by coupling the atmospheric chemical transport model GEOS-Chem, exposure-response model GEMM containing Chinese cohort studies, and the latest local data. Results show that CFs of four major PM2.5 precursors all exhibit significant interregional variation and monthly differences in China. Our results were generally an order of magnitude higher and show disparate spatial distribution compared to CFs currently in use, suggesting that the health damage due to ambient PM2.5 was underestimated in LCIA in China, and indigenized CFs need to be adopted for more accurate results in LCIA and LCA studies.

A review of geospatial exposure models and approaches for health data integration

BACKGROUND

Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

OBJECTIVE

Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

METHODS

We conduct a literature review and synthesis.

RESULTS

First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

Geographic Variations in Urban-Rural Particulate Matter (PM2.5) Concentrations in the United States, 2010-2019

Fine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban-rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts had significantly higher PM2.5 concentrations than rural tracts during this period. Levels of PM2.5 were lower in rural tracts compared to urban and fell more rapidly in rural than urban. Rural tract annual means for 2010 and 2019 were 8.51 [2.24] μg/m3 and 6.41 [1.29] μg/m3, respectively. Urban tract annual means for 2010 and 2019 were 9.56 [2.04] μg/m3 and 7.51 [1.40] μg/m3, respectively. Rural and urban majority Black communities had significantly higher PM2.5 pollution levels (10.19 [1.64] μg/m3 and 9.79 [1.10] μg/m3 respectively), in 2010. In 2019, they were: 7.75 [1.1] μg/m3 and 7.09 [0.78] μg/m3, respectively. Majority Hispanic communities had higher PM2.5 levels and were the highest urban concentration among all races/ethnicities (8.01 [1.73] μg/m3), however they were not the highest rural concentration among all races/ethnicities (6.22 [1.60] μg/m3) in 2019. Associations with higher levels of PM2.5 were found with communities in the poorest quartile and with higher proportions of residents age<15 years old. These findings suggest greater protections for those disproportionately exposed to PM2.5 are needed, such as, increasing the availability of low-cost air quality monitors.

Assessment of ecological civilization construction from the perspective of environment and health in China

This study innovatively evaluated ecological civilization in China from the perspective of environment and health. A Composite Environmental Health Index (CEHI) was constructed based on the Driving force-Pressure-State-Impact-Response (DPSIR) and Coupling Coordination Degree (CCD) models. Results showed that significant and sustained improvements were observed in the ecological environment after ecological civilization, while economic development continued to progress at a steady pace. However, the advancement in population health (impact subsystem), exhibited comparatively modest progress, potentially linked to issues such as demographic aging and the enduring consequences of past exposure to environmental pollutants. At the provincial level, the regional development was uneven. The CEHI performance was highest in the eastern regions, followed by the central regions, with the western regions showing the least progress. Beijing, Guangdong, Jiangsu, Shanghai, and Zhejiang emerged as top performers with higher CEHI scores, which can be attributed to their favorable geographical positioning and the response subsystem. Conversely, northeastern regions (Heilongjiang, Jilin, and Liaoning) and northwestern regions (Shanxi, Gansu, Ningxia, and Qinghai) experienced limited advancements in post-ecological civilization implementation. For these underperforming regions, there is a pressing need to intensify efforts aimed at enhancing their response subsystems. In summary, China's pursuit of ecological civilization has yielded significant successes, potentially offering valuable insights for other nations striving for sustainable development. The ecological civilization model's integration of ecological environmental protection into economic, political, cultural, and social constructs may serve as a meaningful reference for the sustainable development of other countries.

A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes

BACKGROUND

Reported associations between particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity.

OBJECTIVES

To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes.

METHODS

We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors.

RESULTS

Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors.

DISCUSSION

PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BACKGROUND

National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.

OBJECTIVE

Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.

METHODS

We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed "localized difference." We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.

RESULTS

We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.

IMPACT

Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Differing associations of PM2.5 exposure with systolic and diastolic blood pressures across exposure durations in a predominantly non-Hispanic Black cohort

Environmental health research has suggested that fine particulate matter (PM2.5) exposure can lead to high blood pressures, but it is unclear whether the impacts remain the same for systolic and diastolic blood pressures (SBP and DBP). This study aimed to examine whether the effects of PM2.5 exposure on SBP and DBP differ using data from a predominantly non-Hispanic Black cohort collected between 2013 and 2019 in the US. PM2.5 exposure was assessed based on a satellite-derived model across exposure durations from 1 to 36 months. The average PM2.5 exposure level was between 9.5 and 9.8 μg/m3 from 1 through 36 months. Mixed effects models were used to estimate the association of PM2.5 with SBP, DBP, and related hypertension types, adjusted for potential confounders. A total of 6381 participants were included. PM2.5 exposure was positively associated with both SBP and DBP. The association magnitudes depended on exposure durations. The association with SBP was null at the 1-month duration (β = 0.05, 95% CI: - 0.23, 0.33), strengthened as duration increased, and plateaued at the 24-month duration (β = 1.14, 95% CI: 0.54, 1.73). The association with DBP started with β = 0.29 (95% CI: 0.11, 0.47) at the 1-month duration, and plateaued at the 12-month duration (β = 1.61, 95% CI: 1.23, 1.99). PM2.5 was associated with isolated diastolic hypertension (12-month duration: odds ratio = 1.20, 95% CI: 1.07, 1.34) and systolic-diastolic hypertension (12-month duration: odds ratio = 1.18, 95% CI: 1.10, 1.26), but not with isolated systolic hypertension. The findings suggest DBP is more sensitive to PM2.5 exposure and support differing effects of PM2.5 exposure on SBP and DBP. As elevation of SBP and DBP differentially predict CVD outcomes, this finding is relevant for prevention and treatment.

Air pollution, health status and public awareness of environmental problems in China

Understanding public awareness of environmental problems is vital for effectively formulating sustainable policies. This paper aims to investigate the impacts of two perspectives-external air pollution and individual health status-on public awareness by leveraging panel data from two waves of the China Family Panel Studies (CFPS) conducted between 2018 and 2020. The model integrates provincial-level PM2.5 concentration indicators and SO2, PMs, and NOx emissions. The results reveal a significantly positive correlation between air pollution and public awareness of environmental problems in China. Additionally, this study examines the impact of self-assessed health shock by categorizing it into worse and better health. The influence of better health is insignificant. Conversely, when individuals experience worse health, they may perceive it as a psychological loss, leading to a significant increase in public awareness of environmental problems. This study provides valuable insights for mitigating air pollution and reinforcing public health in developing countries.

Ambient Air Pollution Exposure and Adverse Outcomes Among Medicare Beneficiaries With Heart Failure

BACKGROUND

Exposure to fine particulate matter (<2.5 um, particulate matter with an aerodynamic diameter <2.5 microns [PM2.5]) has been implicated in atherogenesis. Limited data in animal studies suggest that PM2.5 exposure leads to myocardial fibrosis and increased incidence of heart failure (HF). Whether PM2.5 is associated with adverse outcomes in patients with preexisting HF has not been widely studied.

METHODS AND RESULTS

In this retrospective cohort study, Medicare patients hospitalized with first HF between 2013 and 2020 were identified from the Medicare Provider Analysis and Review Part A 100% files. Patients were linked with integrated estimates of ambient PM2.5 obtained at 1×1 km using the zip code of participants' residence. The study outcomes were all-cause death, HF, and all-cause readmissions burden. A total of 2 599 525 patients were included in this study, with 6 321 731 person-years of follow-up. Mean PM2.5 was 7.3±1.7 μg/m3. Each interquartile range of PM2.5 was associated with 0.9% increased hazard of all-cause death, 4.5% increased hazard of first HF readmission, 3.1% increased risk of HF hospitalization burden, and 5.2% increase in all-cause readmission burden, after adjusting for 11 sociodemographic and medical factors. Subgroup analyses showed that the effects were more pronounced at levels <7 μg/m3 and in patients aged <75 years, Asians, and those residing in rural areas.

CONCLUSIONS

Ambient air pollution is associated with higher risk of adverse events in Medicare beneficiaries with established HF. These associations persist below the National Air Quality Standards (12 μg/m3), supporting that no threshold effect exists for health effects of air pollution exposure.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

Special economic zone and infant mortality: Evidence from China

By exploiting the development of special economic zones (SEZs) in China as a quasi-natural experiment, this paper evaluates how such zones affect infant mortality. Difference-in-differences analysis reveals that SEZs significantly decrease the local infant mortality rate, and the impact is larger for male infants and infants with less-educated mothers. Further studies show that the SEZs, which acts as an economic growth shock, improve infant survival by increasing the local income. Furthermore, there is no supportive evidence that the SEZs significantly alter either women's fertility-associated behaviors or environmental pollution. These results highlight the previously ignored human capital-related consequences of place-based policies in China.

Epidemiological studies likely need to consider PM2.5 composition even if total outdoor PM2.5 mass concentration is the exposure of interest

Background

Outdoor fine particulate air pollution, <2.5 µm (PM2.5) mass concentrations can be constructed through many different combinations of chemical components that have varying levels of toxicity. This poses a challenge for studies interested in estimating the health effects of total outdoor PM2.5 (i.e., how much PM2.5 mass is present in the air regardless of composition) because we must consider possible confounders of the version of treatment-outcome relationships.

Methods

We evaluated the extent of possible bias in mortality hazard ratios for total outdoor PM2.5 by examining models with and without adjustment for sulfate and nitrate in PM2.5 as examples of potential confounders of version of treatment-outcome relationships. Our study included approximately 3 million Canadians and Cox proportional hazard models were used to estimate hazard ratios for total outdoor PM2.5 adjusting for sulfate and/or nitrate and other relevant covariates.

Results

Hazard ratios for total outdoor PM2.5 and nonaccidental, cardiovascular, and respiratory mortality were overestimated due to the confounding version of treatment-outcome relationships, and associations for lung cancer mortality were underestimated. Sulfate was most strongly associated with nonaccidental, cardiovascular, and respiratory mortality suggesting that regulations targeting this specific component of outdoor PM2.5 may have greater health benefits than interventions targeting total PM2.5.

Conclusions

Studies interested in estimating the health impacts of total outdoor PM2.5 (i.e., how much PM2.5 mass is present in the air) need to consider potential confounders of the version of treatment-outcome relationships. Otherwise, health risk estimates for total PM2.5 will reflect some unknown combination of how much PM2.5 mass is present in the air and the kind of PM2.5 mass that is present.

Contextual Deprivation, Race and Ethnicity, and Income in Air Pollution and Cardiovascular Disease

Importance

Socioeconomically disadvantaged subpopulations are more vulnerable to fine particulate matter (PM2.5) exposure. However, as prior studies focused on individual-level socioeconomic characteristics, how contextual deprivation modifies the association of PM2.5 exposure with cardiovascular health remains unclear.

Objective

To assess disparities in PM2.5 exposure association with cardiovascular disease among subpopulations defined by different socioeconomic characteristics.

Design, Setting, and Participants

This cohort study used longitudinal data on participants with electronic health records (EHRs) from the All of Us Research Program between calendar years 2016 and 2022. Statistical analysis was performed from September 25, 2023, through February 23, 2024.

Exposure

Satellite-derived 5-year mean PM2.5 exposure at the 3-digit zip code level according to participants' residential address.

Main Outcome and Measures

Incident myocardial infarction (MI) and stroke were obtained from the EHRs. Stratified Cox proportional hazards regression models were used to estimate the hazard ratio (HR) between PM2.5 exposure and incident MI or stroke. We evaluated subpopulations defined by 3 socioeconomic characteristics: contextual deprivation (less deprived, more deprived), annual household income (≥$50 000, <$50 000), and race and ethnicity (non-Hispanic Black, non-Hispanic White). We calculated the ratio of HRs (RHR) to quantify disparities between these subpopulations.

Results

A total of 210 554 participants were analyzed (40% age >60 years; 59.4% female; 16.7% Hispanic, 19.4% Non-Hispanic Black, 56.1% Non-Hispanic White, 7.9% other [American Indian, Asian, more than 1 race and ethnicity]), among whom 954 MI and 1407 stroke cases were identified. Higher PM2.5 levels were associated with higher MI and stroke risks. However, disadvantaged groups (more deprived, income <$50 000 per year, Black race) were more vulnerable to high PM2.5 levels. The disparities were most pronounced between groups defined by contextual deprivation. For instance, increasing PM2.5 from 6 to 10 μg/m3, the HR for stroke was 1.13 (95% CI, 0.85-1.51) in the less-deprived vs 2.57 (95% CI, 2.06-3.21) in the more-deprived cohort; 1.46 (95% CI, 1.07-2.01) in the $50 000 or more per year vs 2.27 (95% CI, 1.73-2.97) in the under $50 000 per year cohort; and 1.70 (95% CI, 1.35-2.16) in White individuals vs 2.76 (95% CI, 1.89-4.02) in Black individuals. The RHR was highest for contextual deprivation (2.27; 95% CI, 1.59-3.24), compared with income (1.55; 95% CI, 1.05-2.29) and race and ethnicity (1.62; 95% CI, 1.02-2.58).

Conclusions and Relevance

In this cohort study, while individual race and ethnicity and income remained crucial in the adverse association of PM2.5 with cardiovascular risks, contextual deprivation was a more robust socioeconomic characteristic modifying the association of PM2.5 exposure.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

Prevalence and population attributable fractions of potentially modifiable risk factors for dementia in Canada: A cross-sectional analysis of the Canadian Longitudinal Study on Aging

OBJECTIVES

We investigated the prevalence and population attributable fraction (PAF) of 12 potentially modifiable risk factors for dementia in middle-aged and older Canadians.

METHODS

We conducted a cross-sectional study of 30,097 adults aged 45 to 85 with baseline data from the Canadian Longitudinal Study on Aging (2011‒2015). Risk factors and associated relative risks were taken from a highly cited systematic review. We calculated the prevalence of each risk factor using sampling weights. Individual PAFs were calculated both crudely and weighted for communality, and combined PAFs were calculated using both multiplicative and additive assumptions. Analyses were stratified by household income and repeated at CLSA's first follow-up (2015‒2018).

RESULTS

The most prevalent risk factors were physical inactivity (63.8%; 95% CI, 62.8-64.9), hypertension (32.8%; 31.7-33.8), and obesity (30.8%; 29.7-31.8). The highest crude PAFs were physical inactivity (19.9%), traumatic brain injury (16.7%), and hypertension (16.6%). The highest weighted PAFs were physical inactivity (11.6%), depression (7.7%), and hypertension (6.0%). We estimated that the 12 risk factors combined accounted for 43.4% (37.3‒49.0) of dementia cases assuming weighted multiplicative interactions and 60.9% (55.7‒65.5) assuming additive interactions. There was a clear gradient of increasing prevalence and PAF with decreasing income for 9 of the 12 risk factors.

CONCLUSION

The findings of this study can inform individual- and population-level dementia prevention strategies in Canada. Differences in the impact of individual risk factors between this study and other international and regional studies highlight the importance of tailoring national dementia strategies to the local distribution of risk factors.

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.

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

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

Fine Particulate Matter Components and Risk of Rheumatoid Arthritis: A Large General Canadian Open Cohort Study

OBJECTIVE

Exposure to fine particulate matter (PM2.5) has been linked to many diseases. However, it remains unclear which PM2.5 chemical components for these diseases, including rheumatoid arthritis (RA), are more harmful. This study aimed to assess potential associations between PM2.5 components and RA and quantify the individual effects of each chemical component on RA risk.

METHODS

An open cohort of 11,696,930 Canadian adults was assembled using Ontario administrative health data from January 2007 onward. Individuals were followed until RA onset, death, emigration from Ontario, or the end of the study (December 2019). Incident RA cases were defined by physician billing and hospitalization discharge diagnostic codes. The average levels of PM2.5 components (ammonium, black carbon, mineral dust, nitrate, organic matter, sea salt, and sulfate) for 5 years before cohort entry were assigned to participants based on residential postal codes. A quantile g-computation and Cox proportional hazard models for time to RA onset were developed for the mixture of PM2.5 components and environmental overall PM2.5, respectively.

RESULTS

We identified 67,676 new RA cases across 130,934,256 person-years. The adjusted hazard ratios for the time to RA onset were 1.027 and 1.023 (95% confidence intervals 1.021-1.033 and 1.017-1.029) per every decile increase in exposures to all seven components and per 1 μg/m3 increase in the overall PM2.5, respectively. Ammonium contributed the most to RA onset in the seven components.

CONCLUSION

Exposure to PM2.5 components was modestly associated with RA risk. Public health efforts focusing on specific components (eg, ammonium) may be a more efficient way to reduce RA burden.

Understanding Reductions of PM2.5 Concentration and Its Chemical Composition in the United States: Implications for Mitigation Strategies

Motivated by the recent tightening of the US annual standard of fine particulate matter (PM2.5) concentrations from 12 to 9 μg/m3, there is a need to understand the spatial variation and drivers of historical PM2.5 reductions. We evaluate and interpret the variability of PM2.5 reductions across the contiguous US using high-resolution estimates of PM2.5 and its chemical composition over 1998-2019, inferred from satellite observations, air quality modeling, and ground-based measurements. We separated the 3092 counties into four characteristic regions sorted by PM2.5 trends. Region 1 (primarily Central Atlantic states, 25.9% population) exhibits the strongest population-weighted annual PM2.5 reduction (-3.6 ± 0.4%/yr) versus Region 2 (primarily rest of the eastern US, -3.0 ± 0.3%/yr, 39.7% population), Region 3 (primarily western Midwest, -1.9 ± 0.3%/yr, 25.6% population), and Region 4 (primarily the Mountain West, -0.4 ± 0.5%/yr, 8.9% population). Decomposition of these changes by chemical composition elucidates that sulfate exhibits the fastest reductions among all components in 2720 counties (76% of population), mostly over Regions 1-3, with the 1998-2019 mean sulfate mass fraction in PM2.5 decreasing from Region 1 (29.5%) to Region 4 (11.8%). Complete elimination of the remaining sulfate may be insufficient to meet the new standard for many regions in exceedance. Additional measures are needed to reduce other PM2.5 sources and components for further progress.

Blue and red tides in the Chesapeake Bay watershed: Examining political and environmental framings of collective action during the 2016 and 2020 elections

Watersheds require collective care and management at local and regional levels to maintain their ecological health. The Chesapeake Bay's last several decades of stagnantly poor ecological health presents a distinctive case study for explicating the challenges of motivating collective action across a diverse regional natural resource. Our study uses county- and individual-level descriptive analysis to examine interrelated framings of environmental quality, environmental sentiment, and political action at two critical moments in time-the 2016 and 2020 presidential elections. We find that demographic, environmental, and political characteristics vary with distance to the Chesapeake Bay and that linked environmental and political characteristics appeared to become more polarized between 2016 and 2020. We found no evidence that local environmental quality influenced new political actions such as voting; however, people already likely to vote were influenced by their pro-environmental values such as priorities around climate change.

Effect modification by statin use status on the association between fine particulate matter (PM2.5) and cardiovascular mortality

BACKGROUND

Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke.

METHODS

In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI).

RESULTS

Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032-1.053) vs OR = 1.009 (95% CI, 0.996-1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019-1.047), RERI = 0.039 (95% CI, 0.025-0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users.

CONCLUSIONS

The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users.

Long-term exposure to ambient fine particulate matter (PM2.5) and attributable pulmonary tuberculosis notifications in Ningxia Hui Autonomous Region, China: a health impact assessment

INTRODUCTION

Long-term exposure to fine particulate matter (≤2.5 µm (PM2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM2.5- attributable TB notifications during 2007-2017 in Ningxia Hui Autonomous Region (NHAR), China.

METHODS

PM2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM2.5-associated TB-notifications were estimated for total and anthropogenic PM2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM2.5 (30 µg/m3) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m3) and 25th percentiles (38 µg/m3), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM2.5 concentrations, population growth and baseline TB-notification rates on PM2.5 attributable TB notifications.

RESULTS

Over 2007-2017, annual PM2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m3 as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM2.5. Relative to 2007, overall changes in PM2.5 attributable TB notifications were due to reduced TB-notification rates (-23.8%), followed by decreasing PM2.5 (-6.2%), and population growth (+4.9%).

CONCLUSION

We have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.

Fine Particulate Matter, Noise Pollution, and Greenspace and Prostate Cancer Risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Cohort

BACKGROUND

Greenspace is hypothesized as being protective against cancer, whereas noise pollution and fine particulate matter (<2.5 μm in diameter, PM2.5) are both potential risk factors. Findings from recent studies of greenspace and PM2.5 with prostate cancer are not conclusive and the association between noise exposure and cancer has not been evaluated in a U.S. study.

METHODS

We assessed PM2.5, noise, and greenspace exposure using spatiotemporal models and satellite-based estimates at enrollment addresses for N = 43,184 male participants of the prospective Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial cohort (enrolled 1994-2001). We used Cox regression models adjusted for age, race and ethnicity, study center, family history of prostate cancer, and Area Deprivation Index to estimate associations between ambient PM2.5 (μg/m3), greenspace (index range from -1 to 1), and noise pollution (loudest 10% of total existing sound, decibels) and incident prostate cancer risk through December 2017.

RESULTS

A total of 6,327 cases of prostate cancer were diagnosed among male participants during follow-up. PM2.5 and noise exposures were moderately positively correlated (Spearman ρ = 0.46), and PM2.5 and greenspace were not correlated (ρ = 0.10); greenspace and noise were inversely correlated (ρ = -0.32). In single-pollutant and multipollutant models mutually adjusted for coexposures, we found no associations with prostate cancer risk.

CONCLUSIONS

We did not find evidence that PM2.5, greenspace, and noise pollution were associated with prostate cancer risk in this large, geographically spread cohort.

IMPACT

This study contributes to a small body of existing literature investigating these biologically plausible associations.

Associations Between Early-Life Adversity, Ambient Air Pollution, and Telomere Length in Children

OBJECTIVE

Examine the independent associations and interaction between early-life adversity and residential ambient air pollution exposure on relative buccal telomere length (rBTL).

METHODS

Experiences of abuse, neglect, household challenges, and related life events were identified in a cross-sectional sample of children aged 1 to 11 years ( n = 197) using the 17-item Pediatric ACEs and Related Life Event Screener (PEARLS) tool. The PEARLS tool was analyzed both as a total score and across established domains (Maltreatment, Household Challenges, and Social Context). Ground-level fine particulate matter (PM 2.5 ) concentrations were matched to residential locations for the 1 and 12 months before biospecimen collection. We used multivariable linear regression models to examine for independent associations between continuous PM 2.5 exposure and PEARLS score/domains with rBTL. In addition, effect modification by PEARLS scores and domains on associations between PM 2.5 exposure and rBTL was examined.

RESULTS

Study participants were 47% girls, with mean (standard deviation) age of 5.9 (3.4) years, median reported PEARLS score of 2 (interquartile range [IQR], 4), median 12-month prior PM 2.5 concentrations of 11.8 μg/m 3 (IQR, 2.7 μg/m 3 ), median 1-month prior PM 2.5 concentrations of 10.9 μg/m 3 (IQR, 5.8 μg/m 3 ), and rBTL of 0.1 (IQR, 0.03). Mean 12-month prior PM 2.5 exposure was inversely associated with rBTL ( β = -0.02, 95% confidence interval = -0.04 to -0.01). Although reported PEARLS scores and domains were not independently associated with rBTL, we observed a greater decrement in rBTL with increment of average annual PM 2.5 as reported Social Context domain items increased ( p -interaction < .05).

CONCLUSIONS

Our results suggest that adverse Social Context factors may accelerate the association between chronic PM 2.5 exposure on telomere shortening during childhood.

Air pollution and the burden of long-term care: Evidence from China

We examine the causal effects of PM2.5 exposure on the burden of long-term care (LTC) by matching a satellite-based PM2.5 (particulate matter smaller than 2.5 micrometers (μm) in diameter) dataset with a nationally representative longitudinal study in China from 2011 to 2018. We find significant adverse effects of PM2.5 exposure-instrumented by thermal inversions-on the LTC burden. A 10 μg/m3 increase in annual PM2.5 exposure increases average monthly hours of LTC and the associated financial costs by 28 h and CNY 452, respectively. The effects are greater for those who had never smoked nor experienced severe PM2.5 pollution (annual average PM2.5 > 35 μg/m3) in the previous 5 years. We also find that as PM2.5 increases, chronic diseases, particularly cardiovascular diseases, could lead to a higher likelihood of LTC dependency but reduce the total hours and costs of LTC provision. Finally, we find that PM2.5 reduces the total years of LTC need, suggesting that PM2.5 increases LTC costs by increasing the severity of LTC dependency, rather than the duration of LTC need. Our findings can assist policymakers in planning for LTC provisions and clean air policies.

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

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

Association between ambient exposure to PM2.5 and upper aerodigestive tract cancer in Los Angeles

Fine particulate matter (PM2.5 ) contains carcinogens similar to those generated by tobacco smoking, which may increase the risks of developing smoking-related cancers, such as upper aerodigestive track (UADT) cancers, for both smokers and never-smokers. Therefore, it is imperative to understand the relation between ambient PM2.5 exposure and risk of UADT cancers. A population-based case-control study involving 565 incident UADT cancer cases and 983 controls was conducted in Los Angeles County from 1999 to 2004. The average residential PM2.5 concentration 1 year before the diagnosis date for cases and the reference date for controls was assessed using a chemical transport model. The association between ambient PM2.5 and the UADT cancers was estimated by unconditional logistic regression, adjusting for confounders at the individual and block-group level. Stratified analyses were conducted by sex, tobacco smoking status and UADT subsites. We also assessed the interaction between PM2.5 and tobacco smoking on UADT cancers. PM2.5 concentrations were associated with an elevated odds of UADT cancers (adjusted odds ratio = 1.21 per interquartile range [4.5 μg/m3 ] increase; 95% confidence interval: 1.02, 1.44). The association between PM2.5 and UADT cancers was similar across UADT subsites, sex and tobacco smoking status. The interaction between PM2.5 and tobacco smoking on UADT cancers was approximately additive on the odds scale. The effect estimate for PM2.5 and UADT cancers was similar among never smokers. Our findings support the hypothesis that exposure to PM2.5 increases the risk of UADT cancers. Improvements in air quality may reduce the risk of UADT cancers.

Impact of SARS-CoV-2 vaccines on Covid-19 incidence and mortality in the United States

BACKGROUND

Given the waning of vaccine effectiveness and the shifting of the most dominant strains in the U.S., it is imperative to understand the association between vaccination coverage and Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) disease and mortality at the community levels and whether that association might vary according to the dominant SARS-CoV-2 strains in the U.S.

METHODS

Generalized estimating equations were used to estimate associations between U.S. county-level cumulative vaccination rates and booster distribution and the daily change in county-wide Coronavirus 2019 disease (COVID-19) risks and mortality during Alpha, Delta and Omicron predominance. Models were adjusted for potential confounders at both county and state level. A 2-week lag and a 4-week lag were introduced to assess vaccination rate impact on incidence and mortality, respectively.

RESULTS

Among 3,073 counties in 48 states, the average county population complete vaccination rate of all age groups was 50.79% as of March 11th, 2022. Each percentage increase in vaccination rates was associated with reduction of 4% (relative risk (RR) 0.9607 (95% confidence interval (CI): 0.9553, 0.9661)) and 3% (RR 0.9694 (95% CI: 0.9653, 0.9736)) in county-wide COVID-19 cases and mortality, respectively, when Alpha was the dominant variant. The associations between county-level vaccine rates and COVID-19 incidence diminished during the Delta and Omicron predominance. However, each percent increase in people receiving a booster shot was associated with reduction of 6% (RR 0.9356 (95% CI: 0.9235, 0.9479)) and 4% (RR 0.9595 (95% CI: 0.9431, 0.9761)) in COVID-19 incidence and mortality in the community, respectively, during the Omicron predominance.

CONCLUSIONS

Associations between complete vaccination rates and COVID-19 incidence and mortality appeared to vary with shifts in the dominant variant, perhaps due to variations in vaccine efficacy by variant or to waning vaccine immunity over time. Vaccine boosters were associated with notable protection against Omicron disease and mortality.

Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China

The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.

How does air pollution affect household consumption? Evidence from China

Increasing air pollution not only affects the population's health but also changes its consumption structures and patterns. Using China Family Panel Studies, this study investigates the relationship between air pollution and household consumption. The findings reveal that household consumption is considerably affected by air pollution: One standard deviation rise of PM2.5 concentration will decrease the household consumption by 8.7%. Moreover, this effect is irreversible in the short term. What is more, air pollution has significantly changed consumption structure and patterns. Heterogeneous analysis indicates that the influence of air pollution on consumption generates the so-called Matthew Effect, wherein medium and low-income and rural households are exposed to a greater negative effect. Mechanism tests indicate that air pollution may reduce household consumption through three channels: increase negative emotions, decrease outdoor activities, and depress future expectations. The conclusions drawn in this paper enrich our understanding of the economic impact caused by air pollution and bring important significance to the government in promoting the coordination and sustainable development of the environment and economy.

Ambient air pollution and rate of spontaneous abortion

Spontaneous abortion (SAB), defined as a pregnancy loss before 20 weeks of gestation, affects up to 30% of conceptions, yet few modifiable risk factors have been identified. We estimated the effect of ambient air pollution exposure on SAB incidence in Pregnancy Study Online (PRESTO), a preconception cohort study of North American couples who were trying to conceive. Participants completed questionnaires at baseline, every 8 weeks during preconception follow-up, and in early and late pregnancy. We analyzed data on 4643 United States (U.S.) participants and 851 Canadian participants who enrolled during 2013-2019 and conceived during 12 months of follow-up. We used country-specific national spatiotemporal models to estimate concentrations of particulate matter <2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) during the preconception and prenatal periods at each participant's residential address. On follow-up and pregnancy questionnaires, participants reported information on pregnancy status, including SAB incidence and timing. We fit Cox proportional hazards regression models with gestational weeks as the time scale to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of time-varying prenatal concentrations of PM2.5, NO2, and O3 with rate of SAB, adjusting for individual- and neighborhood-level factors. Nineteen percent of pregnancies ended in SAB. Greater PM2.5 concentrations were associated with a higher incidence of SAB in Canada, but not in the U.S. (HRs for a 5 μg/m3 increase = 1.29, 95% CI: 0.99, 1.68 and 0.94, 95% CI: 0.83, 1.08, respectively). NO2 and O3 concentrations were not appreciably associated with SAB incidence. Results did not vary substantially by gestational weeks or season at risk. In summary, we found little evidence for an effect of residential ambient PM2.5, NO2, and O3 concentrations on SAB incidence in the U.S., but a moderate positive association of PM2.5 with SAB incidence in Canada.

Association between particulate air pollution and hypertensive disorders in pregnancy: A retrospective cohort study

BACKGROUND

Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy.

METHODS AND FINDINGS

A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP.

CONCLUSIONS

Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.

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

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

Global economic structure transition boosts PM2.5-related human health impact in Belt and Road Initiative

The Belt and Road Initiative (BRI) is an open platform for international cooperation proposed by China to promote common global development and prosperity. The BRI can promote the optimal allocation of resources and promote in-depth cooperation in international trade. Meanwhile, it can establish a green supply chain cooperation network to help BRI countries achieve green transformation. BRI has made a notable contribution to the rapid growth of cross-border trade. However, it has also brought environmental impacts. Given that little attention has been paid to the trade-embodied particulate matter 2.5 related human health impacts (PM2.5-HHI) throughout the BRI, this study accounts for and traces the embodied PM2.5-HHI flows between the BRI countries and non-Belt and Road Initiative (non-BRI) countries. Moreover, this study also uncovers the critical socioeconomic drivers of PM2.5-HHI changes in BRI countries during 1990-2015, based on the multi-regional input-output based structural decomposition analysis (MRIO-SDA). Results show that, firstly, BRI countries had significantly increased their economic added value by exporting products to the non-BRI countries. They also have brought PM2.5-HHI to themselves. Secondly, the final demand of BRI countries was the largest potential driving force of PM2.5-HHI of BRI countries. Thirdly, the emission intensity change of BRI is the key socioeconomic factor for reducing PM2.5-HHI. While per capita final demand level change of BRI and production structure change of non-BRI are the key socioeconomic factors for increasing PM2.5-HHI. The study's findings on the one hand can help reduce the PM2.5-HHI and impacts of environmental pollution of BRI countries from a global perspective by providing scientific support. On the other hand, they can help provide relevant policy recommendations for the green transformation of BRI and the construction of green BRI.

Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining

Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO2 columns and modeled PM2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM2.5 surface concentrations of 6.5 μg m-3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 μg m-3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

BACKGROUND

Ambient nitrogen dioxide (NO 2 ) and fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants.

OBJECTIVES

Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO 2 - and PM 2.5 -attributable health burdens, considering NO 2 -attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants.

METHODS

Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO 2 and PM 2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study.

RESULTS

Despite overall decreases in the public health damages associated with NO 2 and PM 2.5 , racial and ethnic relative disparities in NO 2 -attributable pediatric asthma and PM 2.5 -attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM 2.5 -attributable premature mortality and NO 2 -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM 2.5 -attributable premature mortality have increased by 40% and NO 2 -attributable pediatric asthma by 10%.

DISCUSSION

Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Vulnerability assessment of heat waves within a risk framework using artificial intelligence

Current efforts to adapt to climate change are not sufficient to reduce projected impacts. Vulnerability assessments are essential to allocate resources where they are needed most. However, current assessments that use principal component analysis suffer from multiple shortcomings and are hard to translate into concrete actions. To address these issues, this article proposes a novel data-driven vulnerability assessment within a risk framework. The framework is based on the definitions from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, but some definitions, such as sensitivity and adaptive capacity, are clarified. Heat waves that occurred between 2001 and 2018 in Quebec (Canada) are used to validate the framework. The studied impact is the daily mortality rates per cooling degree-days (CDD) region. A vulnerability map is produced to identify the distributions of summer mortality rates in aggregate dissemination areas within each CDD region. Socioeconomic and environmental variables are used to calculate impact and vulnerability. We compared abilities of AutoGluon (an AutoML framework), Gaussian process, and deep Gaussian process to model the impact and vulnerability. We offer advice on how to avoid common pitfalls with artificial intelligence and machine-learning algorithms. Gaussian process is a promising approach for supporting the proposed framework. SHAP values provide an explanation for the model results and are consistent with current knowledge of vulnerability. Recommendations are made to implement the proposed framework quantitatively or qualitatively.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Exposure to ambient black carbon and particulate matter during pregnancy in associations with risk of pre-eclampsia: A meta-analysis based on population-based studies

Air pollution control protocols and policies formulated for PM2.5 and PM10 (particulate matter [PM] with an aerodynamic diameter of ≤2.5 and 10 μm), however, such protocols and policies have not been available for black carbon (BC). A growing number of studies have evaluated the association between long-term exposure to ambient air pollution with BC and PM and pre-eclampsia. We applied a meta-analysis to estimate pooled odds ratios (ORs) and 95 % confidence intervals (CIs) based on four exposure windows (first/second/third trimester and entire pregnancy). 24 studies meeting our selection criteria (8 for BC, 21 and 15 for PM2.5 and PM10) were finally included after screening studies published up to June 22, 2023. An increase of 1 μg/m3 BC during the second trimester and entire pregnancy were associated with a 16 % (OR: 1.16, 95 % CI: [1.02, 1.32]) and a 15 % (OR: 1.15, 95 % CI: [1.03, 1.29]) increased risk of pre-eclampsia, respectively. A 10 μg/m3 increase in second-trimester exposure to PM2.5 and PM10 was associated with a 1 % (OR: 1.01, 95 % CI: [1.00, 1.03]) and a 5 % (OR: 1.05, 95 % CI: [1.01, 1.10]) increased risk of pre-eclampsia. An 11 % (OR: 1.11, 95 % CI: [1.03, 1.21]) increased risk of pre-eclampsia was found to be associated with a 10 μg/m3 increase in PM10 exposure during the entire pregnancy. The results support the potential effect of exposure to ambient particulate pollutants on risk of pre-eclampsia and emphasize the necessity of strategies and protocols for controlling BC. Greater efforts in controlling ambient particulate pollution and especially BC are needed in order to prevent pregnant women from developing pre-eclampsia.

Synergistic monitoring of PM2.5 and CO2 based on active and passive remote sensing fusion during the 2022 Beijing Winter Olympics

Green and low-carbon are the keywords of the 2022 Beijing Winter Olympic Games (WOG) and the core of sustainable development. Beijing's P M 2.5 and C O 2 emissions attracted worldwide attention during WOG. However, the complex emission sources and frequently changing weather patterns make it impossible for a single monitoring approach to meet the high-resolution, full-coverage monitoring requirements. Therefore, we proposed an active-passive remote sensing fusion method to address this issue. The haze layer height (HLH) was first retrieved from vertical aerosol profiles measured by our high-spectral-resolution lidar located near Olympic venues, which provides new insights into the nonuniform boundary layer and the residual aerosol aloft above it. Second, we developed a bootstrap aggregating (bagging) method that assimilates the lidar-based HLH, satellite-based AOD, and meteorological data to estimate the hourly P M 2.5 with 1 km resolution. The P M 2.5 at Beijing region, Bird's Nest, and Yanqing venues during WOG was 23.00±18.33, 22.91±19.48, and 16.33±10.49µg/m 3, respectively. Third, we also derived the C O 2 enhancements, C O 2 spatial gradients resulting from human activities, and annual growth rate (AGR) to estimate the performance of carbon emission management in Beijing. Based on the top-down method, the results showed an average C O 2 enhancement of 1.62 ppm with an annual decline rate of 2.92 ppm. Finally, we compared the monitoring data with six other international cities. The results demonstrated that Beijing has the largest P M 2.5 annual decline rate of 7.43µg/m 3, while the C O 2 AGR is 1.46 ppm and keeps rising, indicating Beijing is still on its way to carbon peaking and needs to strive for carbon neutrality.

Long-term effect of extreme temperature on cognitive function of middle-aged and older adults in China

BACKGROUND

Several studies have explored the association between temperature and cognitive function. However, few studies have examined the effect of extreme temperature on cognitive function. In this study, we aimed to quantify the long-term effect of extreme temperature (e.g., heat waves, cold spells, and hot night excess (HNE)) on cognitive performance in middle-aged and older people in China.

METHOD

We investigated 7915 aged >45 years people from the China Health and Retirement Longitudinal Study (CHARLS), surveyed in 2011 and 2015. A structured questionnaire was utilized to assess cognitive function, including four dimensions: episodic memory, attention, orientation, and visuo-construction. Hourly ambient temperature from the ERA5-Land datasets were used to calculate variables indicating extreme temperature. We performed difference-in-difference (DID) models to assess the potential causal relationship between extreme temperature and cognitive function.

RESULTS

Non-linear analyses suggested that both sustained increases in temperature and excessive variability in temperature increased the risk of cognitive decline. Meanwhile, we observed the extra risk of global cognitive function decline was 2.3% (95% Confidence interval (95% CI): 0.2%, 4.4%) for heat waves (one unit increase) and 5.9% (95% CI: 0.6%, 11.6%) for HNE (one unit increase), while the association for cold spells was insignificant. Two cognitive dimensions, episodic memory and visuo-construction, were sensitive to these two heat-related factors.

CONCLUSION

Extreme temperature was inversely related to cognitive performance in middle-aged and older adults, which was substantial for heat waves and HNE particularly. The effect size varied by cognitive dimensions.

Air Pollution and Adverse Cardiovascular Events After Coronary Artery Bypass Grafting: A 10-Year Nationwide Study

Background

Increased particulate matter <2.5 μm (PM2.5) air pollution is associated with adverse cardiovascular outcomes. However, its impact on patients with prior coronary artery bypass grafting (CABG) is unknown.

Objectives

The purpose of this study was to evaluate the association between major adverse cardiovascular events (MACE) (defined as myocardial infarction, stroke, or cardiovascular death) and air pollution after CABG.

Methods

We linked 26,403 U.S. veterans who underwent CABG (2010-2019) nationally with average annual ambient PM2.5 estimates using residential address. Over a 5-year median follow-up period, we identified MACE and fit a multivariable Cox proportional hazard model to determine the risk of MACE as per PM2.5 exposure. We also estimated the absolute potential reduction in PM2.5 attributable MACE simulating a hypothetical PM2.5 lowered to the revised World Health Organization standard of 5 μg/m3.

Results

The observed median PM2.5 exposure was 7.9 μg/m3 (IQR: 7.0-8.9 μg/m3; 95% of patients were exposed to PM2.5 above 5 μg/m3). Increased PM2.5 exposure was associated with a higher 10-year MACE rate (first tertile 38% vs third tertile 45%; P < 0.001). Adjusting for demographic, racial, and clinical characteristics, a 10 μg/m3 increase in PM2.5 resulted in 27% relative risk for MACE (HR: 1.27, 95% CI: 1.10-1.46; P < 0.001). Currently, 10% of total MACE is attributable to PM2.5 exposure. Reducing maximum PM2.5 to 5 μg/m3 could result in a 7% absolute reduction in 10-year MACE rates.

Conclusions

In this large nationwide CABG cohort, ambient PM2.5 air pollution was strongly associated with adverse 10-year cardiovascular outcomes. Reducing levels to World Health Organization-recommended standards would result in a substantial risk reduction at the population level.

Short-term effects of exposure to ambient PM2.5 and its components on hospital admissions for threatened and spontaneous abortions: A multicity case-crossover study in China

BACKGROUND

The adverse effects of short-term exposure to PM2.5 and its components on hospital admissions for threatened and spontaneous abortions (TSAB) are still controversial.

METHODS

Data on daily hospitalizations for TSAB and PM2.5 and its components, including sulfate (SO42-), nitrate (NO3-), ammonium salt (NH4+), organic matter (OM), and black carbon (BC), were collected from January 2015 to December 2021 (total 2,557 days) in five cities in China. Case-crossover analyses were conducted to investigate the short-term associations between PM2.5 and its components and TSAB. Additionally, the modification effects by age (<35 and ≥35 years), season (cold and warm seasons), and the "Three-Year Action Plan to Win the Blue Sky Defense War" (before and after implementation) on the above associations were further conducted.

RESULTS

For each 10 μg/m3 (1 μg/m3 for BC) increase, the strongest relative risks (95% confidence intervals) of hospitalization for TSAB were 1.011 (1.001-1.021) for PM2.5 in lag02, 1.060 (1.003-1.120) for SO42- in lag02, 1.035 (1.000-1.070) for NO3- in lag02, 1.065 (1.009-1.124) for NH4+ in lag02, 1.047 (1.008-1.088) for OM in lag01 and 1.029 (1.005-1.054) for BC in lag02 (all P <0.05). Furthermore, significant modifying effects of age and the Action Plan were found. The effects of NO3- (lag2), NH4+ (lag2), and BC (lag2) were more pronounced in mothers aged ≥35 years and the effects of PM2.5 (lag4), NO3- (lag4), NH4+ (lag4), OM (lag4), and BC (lag4) was more pronounced in the period before the Action Plan was implemented (all P modification <0.05).

CONCLUSION

Short-term exposure to ambient PM2.5 and its components (SO42-, NO3-, NH4+, OM, and BC) was related to increased risks of hospitalization for TSAB. The effects were more pronounced in mothers aged ≥35 years and the period before the Action Plan.

Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: A spatiotemporal analysis in China

Long- and short-term exposure to constituents of fine particulate matter (PM2.5) substantially affects human health. However, assessments of the health and economic benefits of reducing PM2.5 constituents are scarce. This study estimates the number of premature deaths from all-cause, cardiovascular (CVD), and respiratory diseases avoided due to reductions in daily and annual average concentrations of PM2.5 constituents. The Environmental Benefits Mapping and Analysis Program was used for two scenarios: we used yearly concentrations of PM2.5 constituents from 2013 to 2020 as the baseline concentration surface (Scenario I), and 2021 as the baseline year (Scenario II). With reductions in daily and annual average concentrations of PM2.5 constituents, 309,099 (95 % confidence interval [CI]: 37,265-571,485) and 195,297 (95 % CI: 178,192-211,914) premature deaths were avoided in Scenario I, respectively; meanwhile, 347,296 (95 % CI: 79,258-604,758) and 201,567 (95 % CI: 185,038-217,530) premature deaths were avoided in Scenario II, respectively. Moreover, economic benefits associated with the prevention of premature deaths were estimated using the willingness to pay (WTP) and modified human capital (AHC) methods. The total estimated economic benefits amounted to 563.32 billion RMB (WTP) and 322.03 billion RMB (AHC) in Scenario I. In Scenario II, the associated economic benefits were 751.48 billion RMB (WTP) and 427.56 billion RMB (AHC), accounting for 0.657 and 0.374 % of China's gross domestic product in 2021, respectively. Additionally, we analyzed the sensitivity of CVD-related premature deaths to the concentrations of PM2.5 constituents, and found that CVD-related premature deaths were more sensitive to black carbon.

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

Particulate matter (PM) is a ubiquitous component of air pollution that is epidemiologically linked to human pulmonary diseases. PM chemical composition varies widely, and the development of high-throughput experimental techniques enables direct profiling of cellular effects using compositionally unique PM mixtures. Here, we show that in a human bronchial epithelial cell model, exposure to three chemically distinct PM mixtures drive unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability, DNA damage responses, and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Finally, we observed that PM mixtures with higher cadmium content induced increased DNA damage and drove redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of individual cellular morphologies provides a robust, high-throughput approach to gauge the effects of environmental stressors on biological systems and score cellular susceptibilities to pollution.

Adverse effects of exposure to fine particles and ultrafine particles in the environment on different organs of organisms

Particulate pollution is a global risk factor that seriously threatens human health. Fine particles (FPs) and ultrafine particles (UFPs) have small particle diameters and large specific surface areas, which can easily adsorb metals, microorganisms and other pollutants. FPs and UFPs can enter the human body in multiple ways and can be easily and quickly absorbed by the cells, tissues and organs. In the body, the particles can induce oxidative stress, inflammatory response and apoptosis, furthermore causing great adverse effects. Epidemiological studies mainly take the population as the research object to study the distribution of diseases and health conditions in a specific population and to focus on the identification of influencing factors. However, the mechanism by which a substance harms the health of organisms is mainly demonstrated through toxicological studies. Combining epidemiological studies with toxicological studies will provide a more systematic and comprehensive understanding of the impact of PM on the health of organisms. In this review, the sources, compositions, and morphologies of FPs and UFPs are briefly introduced in the first part. The effects and action mechanisms of exposure to FPs and UFPs on the heart, lungs, brain, liver, spleen, kidneys, pancreas, gastrointestinal tract, joints and reproductive system are systematically summarized. In addition, challenges are further pointed out at the end of the paper. This work provides useful theoretical guidance and a strong experimental foundation for investigating and preventing the adverse effects of FPs and UFPs on human health.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

BACKGROUND

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.

OBJECTIVES

Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.

METHODS

We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.

RESULTS

With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region.

CONCLUSIONS

Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.