An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

Sources and components of fine air pollution exposure and brain morphology in preadolescents

Air pollution is an emerging novel neurotoxicant during childhood and adolescence. However, little is known regarding how fine particulate matter (PM2.5) components and its sources impact brain morphology. We investigated air pollution exposure-related differences in brain morphology using cross-sectional magnetic resonance imaging data from 10,095 children ages 9-11 years-old enrolled in the United States' Adolescent Brain Cognitive Development Study [2016-2018]. Air pollution estimates included fifteen PM2.5 constituent chemicals and metals, and six major sources of PM2.5 (e.g., crustal materials, biomass burning, traffic) identified from prior source apportionment, as well as nitrogen dioxide (NO2) and ozone (O3). After adjusting for demographic, socioeconomic, and neuroimaging covariates, we used partial least squares analyses to identify associations between simultaneous co-exposures and morphological differences in cortical thickness, surface area, and subcortical volumes. We found that greater exposure to PM2.5 and NO2 was associated with decreases in frontal and increases in inferior temporal surface area. PM2.5 component and source analyses linked cortical surface area and thickness to biomass burning (e.g., organic carbon, potassium), crustal material (e.g., calcium, silicon), and traffic (e.g., copper, iron) exposures, while smaller subcortical volumes were linked to greater potassium exposure. This is the first study to show differential effects of several air pollution sources on development of children's brains. Significant associations were found in brain structures involved in several cognitive and social processes, including lower- and higher-order sensory processing, socioemotional behaviors, and executive functioning. These findings highlight differential effects of several air pollution sources on brain structure in preadolescents across the U.S.

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.

Short-term exposure to relative humidity and lung health in early adolescents

Background

Extremes in humidity can induce bronchoconstriction and trigger breathing symptoms in people with asthma. Less is known about how humidity influences measurements of lung health in children and adolescents. Our objective was to assess the extent to which short-term exposures to high and low relative humidity (RH) are associated with lung function and fractional exhaled nitric oxide (FeNO) in adolescents.

Methods

We included adolescents (mean age 13.2 y, SD: 0.9) from a northeast US prospective prebirth cohort (n = 1019). We assigned daily RH levels to geocoded participant addresses. We defined low or high RH as ≤10th or ≥90th internal percentiles, respectively, of the cohort-specific RH distribution and the reference RH as the median. We evaluated the linearity of associations of RH in the 1-7 days before assessment with forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FeNO using generalized additive models with penalized splines (df = 3). We log-transformed FeNO due to non-normality. For nonlinear relationships, we used distributed lag nonlinear models to explore the cumulative effects of lag 1-7 day RH on FEV1, FVC, and FeNO.

Results

Median RH was 65.6% (interquartile range [IQR] = 19.8%), 10th percentile 47.2%, 90th percentile 86.6%. Mean FeNO (SD) was 25.9ppb (26.9ppb). High (vs. median) RH was associated with 38.0% higher FeNO (95% CI = 10.3, 72.7). Exposure to low (vs. median) RH was associated with 186.2 ml lower FEV1 (95% CI = -299.2, -73.3) and -130.2 ml lower FVC (95% CI = -251.9, -8.5).

Conclusion

Short-term exposures to extremes of RH were associated with lower lung function and higher FeNO, a measure of airway inflammation, in adolescents.

Extreme Heat and Hospitalization Among Older Persons With Alzheimer Disease and Related Dementias

Importance

As US society ages and the climate changes, extreme outdoor heat may exacerbate the health burden of Alzheimer disease and related dementias (ADRD), but where, when, and among whom extreme heat may increase hospitalizations with ADRD remains understudied.

Objective

To investigate the association between extreme heat and the risk of hospitalization with ADRD, and to explore how associations differ across climates and population subgroups.

Design, Setting, and Participants

Population-based cohort study, using a time-stratified case-crossover design, of Medicare fee-for-service (Part A) claims from 2000 to 2018 among beneficiaries aged 65 years or older in the contiguous US; time-stratified case-crossover design implemented with distributed lag nonlinear models using conditional logistic regression. Data were analyzed from October to November 2024.

Exposures

Daily maximum heat index converted to percentiles of climate-specific warm season heat index distributions.

Main Outcomes and Measures

The main outcome was each beneficiary's first hospitalization with an ADRD diagnosis code, and other measures were county-level climates (arid, continental, temperate, or tropical).

Results

The sample included 3 329 977 beneficiaries (2 126 290 [63.9%] female, 33 887 [1.0%] Asian, 354 771 [10.7%] Black, 61 515 [1.8%] Hispanic, 2 831 391 [85.0%] White, and 891 815 [26.8%] dual eligible for Medicaid). The odds ratio (OR) of hospitalization with ADRD comparing days in the 99th vs 50th percentile of the heat index distribution was 1.02 (95% CI, 1.01-1.02), corresponding to 0.8 (95% CI, 0.5-1.1) additional hospitalizations with ADRD per 1000 beneficiaries. Results suggest extreme heat associations persist for 3 days beyond the initial day. The cumulative OR of hospitalization with ADRD after 4 days of continuous exposure to heat indexes at the 99th vs 50th percentile was 1.04 (95% CI, 1.03-1.04), or 1.7 (95% CI, 1.3-2.0) additional hospitalizations with ADRD per 1000 beneficiaries. Extrapolating these estimates to the 6.7 million adults currently living with ADRD suggests that each day of extreme heat could contribute to at least 5360 added hospitalizations with ADRD nationwide. Effects estimates were similar in temperate and continental climates. Arid and tropical climate estimates were somewhat similar but more uncertain. OR point estimates for hospitalization from 4 days of continuous extreme heat exposure for beneficiaries identifying as Asian (OR, 1.09; 95% CI, 1.02-1.17), Black (OR, 1.07; 95% CI, 1.05-1.10), and Hispanic (OR, 1.08; 95% CI, 1.03-1.13), were 2.6 to 3.2 times larger than for White beneficiaries (OR, 1.03; 95% CI, 1.02-1.04).

Conclusions and Relevance

This study found that extreme heat may pose a growing threat to older adults living with ADRD. This threat may be larger among Asian, Black, and Hispanic racial and ethnic groups. Clinicians should consider counseling patients living with ADRD on extreme heat risks, and policymakers should devise risk mitigation programs.

Air pollution and cortical myelin T1w/T2w ratio estimates in school-age children from the ABCD and NeuroSmog studies

Air pollution affects human health and may disrupt brain maturation, including axon myelination, critical for efficient neural signaling. Here, we assess the impact of prenatal and current long-term particulate matter (PM) and nitrogen dioxide (NO2) exposure on cortical T1w/T2w ratios - a proxy for myelin content - in school-age children from the Adolescent Brain Cognitive Development (ABCD) Study (United States; N = 2021) and NeuroSmog study (Poland; N = 577), using Siemens scanners. Across both samples, we found that NO2 and PM were not significantly associated with cortical T1w/T2w except for one association of PM10 with lower T1w/T2w in the precuneus in NeuroSmog. Superficially, ABCD Study analyses including data from all scanner types (Siemens, GE, Philips; N = 3089) revealed a negative association between NO₂ exposure and T1w/T2w ratios. However, this finding could be an artifact of between-site sociodemographic differences and large scanner-type-related measurement differences. While significant associations between air pollution and cortical myelin were largely absent, these findings do not rule out the possibility that air pollution affects cortical myelin during other exposure periods/stages of neurodevelopment. Future research should examine these relationships across diverse populations and developmental periods using unified analysis methods to better understand the potential neurotoxic effects of air pollution.

The association between neighborhood environment, prenatal exposure to alcohol and tobacco, and structural brain development

Prenatal alcohol and tobacco exposure affects child brain development. Less is known about how neighborhood environment (built, institutional, and social) may be associated with structural brain development and whether prenatal exposure to alcohol or tobacco may modify this relationship. The current study aimed to examine whether neighborhood environment is associated with brain volume at age 9-11, and whether prenatal exposure to alcohol or tobacco modifies this relationship. Baseline data from Adolescent Brain and Cognitive Development (ABCD) study was analyzed (N = 7,887). Neighborhood environment was characterized by 10 variables from the linked external dataset. Prenatal alcohol and tobacco exposures were dichotomized based on the developmental history questionnaire. Bilateral volumes of three regions of interests (hippocampal, parahippocampal, and entorhinal) were examined as outcomes. High residential area deprivation was associated with smaller right hippocampal volume. Prenatal alcohol exposure was associated with larger volume in left parahippocampal and hippocampal regions, while prenatal tobacco exposure was associated with smaller volumes in bilateral parahippocampal, right entorhinal, and right hippocampal regions. In children without prenatal tobacco exposure, high residential area deprivation was associated with smaller right hippocampal volumes. In contrast, neighborhood environment was not significantly associated with brain volumes in children with prenatal tobacco exposure. In summary, neighborhood environment plays a role in child brain development. This relationship may differ by prenatal tobacco exposure. Future studies on prenatal tobacco exposure may need to consider how postnatal neighborhood environment interacts with the teratogenic effect.

Interaction between Neighborhood Exposome and Genetic Risk in Child Psychotic-like Experiences

Persistent distressing psychotic-like experiences (PLE) among children may be driven by genetics and neighborhood environmental exposures. However, the gene-environment interaction to persistent distressing PLE is unknown. The study included 6,449 participants from the Adolescent Brain and Cognitive Development Study. Genetic risk was measured by a multi-ancestry schizophrenia polygenic risk score (SCZ-PRS). Multi-dimensional neighborhood-level exposures were used to form a neighborhood exposome (NE) score. SCZ-PRS was not statistically significantly associated with odds of persistent distressing PLE (OR = 1.04, 95% CI: 0.97, 1.13, P = 0.280), whereas NE score was (OR = 1.15, 95% CI: 1.05, 1.26, P = 0.003). The association between NE score and persistent distressing PLE was statistically significantly attenuated as SCZ-PRS increased (OR for interaction = 0.92, 95% CI: 0.86, 1.00, P = 0.039). The findings indicate that persistent distressing PLE may be driven by detrimental neighborhood exposures, particularly among children with low genetic risks.

Exposure profiles of social-environmental neighborhood factors and persistent distressing psychotic-like experiences across four years among young adolescents in the US

BACKGROUND

Recent research has demonstrated that domains of social determinants of health (SDOH) (e.g. air pollution and social context) are associated with psychosis. However, SDOHs have often been studied in isolation. This study investigated distinct exposure profiles, estimated their associations with persistent distressing psychotic-like experiences (PLE), and evaluated whether involvement in physical activity partially explains this association.

METHODS

Analyses included 8,145 young adolescents from the Adolescent Brain and Cognitive Development Study. Data from the baseline and three follow-ups were included. Area-level geocoded variables spanning various domains of SDOH, including socioeconomic status, education, crime, built environment, social context, and crime, were clustered using a self-organizing map method to identify exposure profiles. Generalized linear mixed modeling tested the association between exposure profiles and persistent distressing PLE and physical activities (i.e. team and individual sports), adjusting for individual-level covariates including age, sex, race/ethnicity, highest level of parent education, family-relatedness, and study sites.

RESULTS

Five exposure profiles were identified. Compared to the reference Profile 1 (suburban affluent areas), Profile 3 (rural areas with low walkability and high ozone), and Profile 4 (urban areas with high SES deprivation, high crime, and high pollution) were associated with greater persistent distressing PLE. Team sports mediated 6.14% of the association for Profile 3.

CONCLUSIONS

This study found that neighborhoods characterized by rural areas with low walkability and urban areas with high socioeconomic deprivation, pollution concentrations, and crime were associated with persistent distressing PLE. Findings suggest that various social-environmental factors may differentially impact the development of psychosis.

Sleep duration and efficiency moderate the effects of prenatal and childhood ambient pollutant exposure on global white matter microstructural integrity in adolescence

Background

Air pollution is a ubiquitous neurotoxicant associated with alterations in structural connectivity. Good habitual sleep may be an important protective lifestyle factor due to its involvement in the brain waste clearance and its bidirectional relationship with immune function. Wearable multisensory devices may provide more objective measures of sleep quantity and quality. We investigated whether sleep duration and efficiency moderated the relationship between prenatal and childhood pollutant exposure and whole-brain white matter microstructural integrity at ages 10-13 years.

Methods

We used multi-shell diffusion-weighted imaging data collected on 3T MRI scanners and objective sleep data collected with Fitbit Charge 2 from the 2-year follow-up visit for 2178 subjects in the Adolescent Brain Cognitive Development Study®. White matter tracts were identified using a probabilistic atlas. Restriction spectrum imaging was performed to extract restricted normalized isotropic (RNI) and directional (RND) signal fraction parameters for all white matter tracts, then averaged to calculate global measures. Sleep duration was calculated by summing the time spent in each sleep stage; sleep efficiency was calculated by dividing sleep duration by time spent in bed. Using an ensemble-based modeling approach, air pollution concentrations of PM2.5, NO2, and O3 were assigned to each child's residential addresses during the prenatal period (9-month average before birthdate) as well as at ages 9-10 years. Multi-pollutant linear mixed effects models assessed the associations between global RNI and RND and sleep-by-pollutant interactions, adjusting for appropriate covariates.

Results

Sleep duration interacted with childhood NO2 exposure and sleep efficiency interacted with prenatal O3 exposure to affect RND at ages 10-13 years. Longer sleep duration and higher sleep efficiency in the context of higher pollutant exposure was associated with lower RND compared to those with similar pollutant exposure but shorter sleep duration and lower sleep efficiency.

Conclusions

Low-level air pollution poses a risk to brain health in youth, and healthy sleep duration and efficiency may increase resilience to its harmful effects on white matter microstructural integrity. Future studies should evaluate the generalizability of these results in more diverse cohorts as well as utilize longitudinal data to understand how sleep may impact brain health trajectories in the context of pollution over time.

Long-Term Exposure to Nitrogen Dioxide and Ozone and Respiratory Health in Children

Rationale: Further evaluation of the impact of long-term exposure to the gaseous air pollutants nitrogen dioxide (NO2) and ozone (O3) on child lung function and of NO2 or O3 on eosinophilic airway inflammation is needed. Objectives: To determine whether NO2 and O3 are associated with lung function and fractional exhaled nitric oxide (FeNO) in children. Methods: We measured lung function (forced expiratory volume in 1 second [FEV1] and forced vital capacity [FVC]) at midchildhood (mean age, 7.9 yr; n = 703), early teens (13.2 yr; n = 976), and midteens (17.6 yr; n = 624) study visits, and FeNO at the early and midteens study visits in Project Viva, a cohort of mother-child pairs in the Boston area. Long-term exposure to NO2 and O3 was estimated at the home address using geospatial models. We examined associations of home address NO2 and O3 exposure and proximity to roadway with lung function and FeNO using linear regression models, adjusting for age, sex, height, weight, season, relative humidity, temperature, parental smoking, and measures of socioeconomic status. We examined for effect modification of the midteen associations by blood eosinophil concentration, physical activity, aeroallergen sensitization, and parental atopy. Results: Median exposure to NO2 was 33.1 ppb (interquartile range [IQR], 10.4 ppb) and to O3 was 35.3 ppb (IQR, 3.4) in the first year of life. Exposure to NO2 was associated with lower FEV1 and FVC across all age groups and exposure time intervals: For example, an IQR increment of NO2 exposure from birth through the early teen visit was associated with 189.9 ml lower FEV1 (95% confidence interval, -273.3, -106.5) at the midteen visit. Lifetime NO2 exposure at was associated with higher FeNO at the early teen visit: for example, 16.2% higher FeNO (95% confidence interval, 7.1-26.4%) per IQR of lifetime NO2 through the early teen visit. O3 exposure was not associated with lung function or FeNO. Aeroallergen sensitization (measured in a subset of participants) modified associations of NO2 and O3 with FeNO. Conclusions: Exposure to NO2 was associated with lower lung function and higher FeNO among generally healthy children and teenagers. Because NO2 exposure levels were within the annual U.S. Environmental Protection Agency standard, these findings suggest a need to reduce exposure to this pollutant to optimize child respiratory health.

The impacts of air pollution on mortality and hospital readmission among Medicare beneficiaries with Alzheimer's disease and Alzheimer's disease-related dementias: a national retrospective cohort study in the USA

BACKGROUND

Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD) are prevalent neurodegenerative disorders, posing a critical worldwide public health challenge. Ambient air pollution has been identified as a potential risk factor for AD progression based on toxicological and epidemiological studies. We aimed to evaluate the impacts of air pollution-including fine particulate matter (PM2·5), nitrogen dioxide (NO2), summer ozone (O3), and oxidant-on readmission or death among Medicare enrollees previously hospitalised with an AD/ADRD diagnosis code.

METHODS

We constructed a population-based nationwide retrospective cohort including all Medicare fee-for-service beneficiaries (aged ≥65 years) in the contiguous USA (2000-16) hospitalised with AD/ADRD, and followed them up from the year after their first hospitalisation until (1) year of death (mortality cohort) and (2) year of second hospitalisation for any cause (readmission cohort). We calculated annual average PM2·5, NO2, summer O3, and oxidant concentrations for each individual at their residential ZIP code in each year after their first hospitalisation with AD/ADRD. We applied Cox proportional hazard models for the mortality and readmission cohorts stratifying on individual risk factors and adjusting for socioeconomic status, seasonal temperatures, and relative humidity.

FINDINGS

Our cohort consisted of 5 544 118 individuals, of whom 4 543 759 (82·0%) died and 3 880 894 (70·0%) were readmitted to the hospital during the study period. The average follow-up times were 3·34 years (SD 2·60) for the mortality cohort and 1·98 years (SD 1·65) for the readmission cohort. In both the mortality and readmission cohorts we found significant associations with each pollutant. For an IQR increase in NO2, we found a hazard ratio (HR) for mortality of 1·012 (95% CI 1·009-1·015) and an HR for readmission of 1·110 (1·104-1·117). In the readmission cohort, we found an HR of 1·084 (1·079-1·089) for an IQR increase (3·87 μg/m3) in PM2·5. The results slightly decreased in multi-pollutant models. The results of effect modification for mortality and readmission varied by pollutant, but higher risks were found among Black males and among those eligible for Medicaid in general.

INTERPRETATION

We provide new evidence that among a susceptible population with previous AD/ADRD-related hospitalisations, annual air pollution exposure since first hospitalisation is associated with risk of readmission and death.

FUNDING

National Institute on Aging.

Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning

Ozone pollution was widely reported along with PM2.5 reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 pandemic. Here we first analyzed spatiotemporal variations of PM2.5 and ozone from 2015 to 2022 in eleven urban agglomerations of China. PM2.5 decreased in all regions, with the largest drop in Beijing-Tianjin-Hebei (BTH). In contrast, ozone declined initially but rose during the pandemic in most regions, especially in Cheng-Yu. Probability density curves showed pronounced increase (24.7%) and slight change in the proportion of PM2.5 and ozone meeting the pollution criterions during the pandemic, respectively. Leveraging Random Forest with SHAP analysis, we further established ozone models in typical urban agglomerations with good performance (CV-R2 = 0.80-0.90; CV-RMSE = 8.52-19.20 μg/m3) during the pandemic, and compared their relative importance of meteorological variables. Particularly, temperature and incoming shortwave flux at top of atmosphere were identified with high importance in high-ozone regions such as Middle Plain and BTH. Increasing importance of PM (e.g., PM10) was found in southern China, e.g., Yangtze River Delta and Pearl River Delta regions. The western China was characterized with more importance of meteorology, especially in Tibet. Surface albedo and sensible heat flux from turbulence were noted distinctively with high importance in Tibet, partly due to their impacts on ozone formation by generating heat source and sink. In addition, sea level pressure (SLP) was revealed with the highest importance (25.2%) in Cheng-Yu, consistent with the fact that synoptic patterns characterized by SLP field could affect ozone pollution in Sichuan Basin. Our results not only provide an understanding of meteorological factors in regional ozone formation in China, but also highlight the feasibility of explainable machine learning in ozone studies.

Regional-specific trends of PM2.5 and O3 temperature sensitivity in the United States

Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, the influence of rising temperature on air quality remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here, we examined the sensitivity of surface-level fine particulate matter (PM2.5) and ozone (O3) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning. Our findings demonstrate that in the eastern US, stringent emission control strategies have significantly reduced the positive responses of PM2.5 and O3 to summer temperature, thereby lowering the population exposure associated with warming-induced air quality deterioration. In contrast, PM2.5 in the western US became more sensitive to temperature, highlighting the urgent need to manage and mitigate the impact of worsening wildfires. Our results have important implications for air quality management and risk assessments of future climate change.

Comparing traditional and causal inference methodologies for evaluating impacts of long-term air pollution exposure on hospitalization with Alzheimer disease and related dementias

Alzheimer disease and related dementias (ADRDs) present a growing public health burden in the United States. One actionable risk factor for ADRDs is air pollution: multiple studies have found associations between air pollution and exacerbation of ADRDs. Our study builds on previous studies by applying modern statistical causal inference methodologies-generalized propensity score (GPS) weighting and matching-on a large, longitudinal data set. We follow 50 million Medicare enrollees to investigate impacts of 3 air pollutants-fine particular matter (PM2.5), nitrogen dioxide (NO2), and summer ozone (O3)-on elderly patients' rate of first hospitalization with an ADRD diagnosis. Similar to previous studies using traditional statistical models, our results found increased hospitalization risks due to increased PM2.5 and NO2 exposure, with less conclusive results for O3. In particular, our GPS weighting analysis finds IQR increases in PM2.5, NO2, or O3 exposure result in hazard ratios of 1.108 (95% CI, 1.097, 1.119), 1.058 (1.049-1.067), or 1.045 (1.036-1.054), respectively. GPS matching results are similar for PM2.5 and NO2 with attenuated effects for O3. Our results strengthen arguments that long-term PM2.5 and NO2 exposure increases risk of hospitalization with an ADRD diagnosis. Additionally, we highlight strengths and limitations of causal inference methodologies in observational studies with continuous treatments. This article is part of a Special Collection on Environmental Epidemiology.

Exposure to air pollution is associated with adipokines in midlife women: The Study of Women's Health Across the Nation

This study examined the associations between ambient air pollution exposure, including fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3), with serum levels of high molecular weight (HMW) adiponectin, leptin, and soluble leptin receptors (sOB-R) in midlife women. The analysis included 1551 participants from the Study of Women's Health Across the Nation (median age = 52.3 years) with adipokine data from 2002 to 2003. Annual air pollution exposures were assigned by linking residential addresses with high-resolution machine learning models at a 1-km2 resolution. Multivariable linear regression and Bayesian kernel machine regression (BKMR) were used to evaluate the associations for individual pollutants and pollutant mixtures. After adjusting for confounders in linear regression models, an interquartile range increase in PM2.5 (2.5 μg/m3) was associated with a 4.6 % lower HMW adiponectin level (95 % CI: -8.8 %, -0.3 %). Exposure to air pollutant mixtures showed negative associations with HMW adiponectin and positive associations with leptin levels in BKMR models. These findings suggest that exposures to PM2.5, NO2, and O3 are associated with adverse levels of adipokines, which may contribute to obesity-related outcomes. Further research is needed to confirm these findings and explore the underlying biological mechanisms.

Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk

Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m-3, a decrease of approximately 70% from its 2000 value of 0.44 ng m-3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m-3, which is the BaP concentration associated with an excess lifetime cancer risk of 10-5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

The effect of air pollution on hospitalizations with Parkinson's disease among medicare beneficiaries nationwide

We examined the effect of annual exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3), on the rate of first hospitalization with a PD-related diagnosis (hospitalization with PD) among Medicare Fee-for-Service beneficiaries (2001-2016). Machine learning-derived annual air pollution concentrations were linked to residential ZIP codes. For each exposure, we fitted four models: 1) traditional outcome stratification, 2) marginal structural, 3) doubly robust, and 4) generalized propensity score matching Poisson regression models, adjusted for sociodemographic and meteorological confounders and long-term trends. Among 49,121,026 beneficiaries, incidence rate ratios of 1.08 (95% CI: 1.07, 1.10), 1.07 (95% CI: 1.05, 1.08), and 1.03 (95% CI: 1.02, 1.05) for an interquartile range increase in PM2.5 (3.72 µg/m3), NO2 (13.84 ppb), and O3 (10.09 ppb), respectively, were estimated from doubly robust models. Results were similar across modeling approaches. In this nationwide study, higher air pollution exposure increased the rate of hospitalizations with PD.

Outdoor Air Pollution Relates to Amygdala Subregion Volume and Apportionment in Early Adolescents

Background

Outdoor air pollution is associated with an increased risk for psychopathology. Although the neural mechanisms remain unclear, air pollutants may impact mental health by altering limbic brain regions, such as the amygdala. Here, we examine the association between ambient air pollution exposure and amygdala subregion volumes in 9-10-year-olds.

Methods

Cross-sectional Adolescent Brain Cognitive DevelopmentSM (ABCD) Study® data from 4,473 participants (55.4% male) were leveraged. Air pollution was estimated for each participant's primary residential address. Using the probabilistic CIT168 atlas, we quantified total amygdala and 9 distinct subregion volumes from T1- and T2-weighted images. First, we examined how criteria pollutants (i.e., fine particulate matter [PM2.5], nitrogen dioxide, ground-level ozone) and 15 PM2.5 components related with total amygdala volumes using linear mixed-effect (LME) regression. Next, partial least squares correlation (PLSC) analyses were implemented to identify relationships between co-exposure to criteria pollutants as well as PM2.5 components and amygdala subregion volumes. We also conducted complementary analyses to assess subregion apportionment using amygdala relative volume fractions (RVFs).

Results

No significant associations were detected between pollutants and total amygdala volumes. Using PLSC, one latent dimension (LD) (52% variance explained) captured a positive association between calcium and several basolateral subregions. LDs were also identified for amygdala RVFs (ranging from 30% to 82% variance explained), with PM2.5 and component co-exposure associated with increases in lateral, but decreases in medial and central, RVFs.

Conclusions

Fine particulate and its components are linked with distinct amygdala differences, potentially playing a role in risk for adolescent mental health problems.

The association of short-term increases in ambient PM2.5 and temperature exposures with stillbirth: racial/ethnic disparities among Medicaid recipients

Racial/ethnic disparities in the association between short-term (eg, days, weeks), ambient fine particulate matter (PM2.5) and temperature exposures and stillbirth in the United States have been understudied. A time-stratified, case-crossover design using a distributed lag nonlinear model (0- to 6-day lag) was used to estimate stillbirth odds due to short-term increases in average daily PM2.5 and temperature exposures among 118 632 Medicaid recipients from 2000 to 2014. Disparities by maternal race/ethnicity (Black, White, Hispanic, Asian, American Indian) and zip code-level socioeconomic status (SES) were assessed. In the temperature-adjusted model, a 10 μg m-3 increase in PM2.5 concentration was marginally associated with increased stillbirth odds at lag 1 (0.68%; 95% CI, -0.04% to 1.40%) and lag 2 (0.52%; 95% CI, -0.03 to 1.06) but not lag 0-6 (2.80%; 95% CI, -0.81 to 6.45). An association between daily PM2.5 concentrations and stillbirth odds was found among Black individuals at the cumulative lag (0-6 days: 9.26% 95% CI, 3.12%-15.77%) but not among other races or ethnicities. A stronger association between PM2.5 concentrations and stillbirth odds existed among Black individuals living in zip codes with the lowest median household income (lag 0-6: 14.13%; 95% CI, 4.64%-25.79%). Short-term temperature increases were not associated with stillbirth risk among any race/ethnicity. Black Medicaid enrollees, and especially those living in lower SES areas, may be more vulnerable to stillbirth due to short-term increases in PM2.5 exposure. This article is part of a Special Collection on Environmental Epidemiology.

Associations between Fine Particulate Matter Components, Their Sources, and Cognitive Outcomes in Children Ages 9-10 Years Old from the United States

BACKGROUND

Emerging literature suggests that fine particulate matter [with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] air pollution and its components are linked to various neurodevelopmental outcomes. However, few studies have evaluated how PM 2.5 component mixtures from distinct sources relate to cognitive outcomes in children.

OBJECTIVES

This cross-sectional study investigated how ambient concentrations of PM 2.5 component mixtures relate to neurocognitive performance in 9- to 10-year-old children, as well as explored potential source-specific effects of these associations, across the US.

METHODS

Using spatiotemporal hybrid models, annual concentrations of 15 chemical components of PM 2.5 were estimated based on the residential address of child participants from the Adolescent Brain Cognitive Development (ABCD) Study. General cognitive ability, executive function, and learning/memory scores were derived from the NIH Toolbox. We applied positive matrix factorization to identify six major PM 2.5 sources based on the 15 components, which included crustal, ammonium sulfate, biomass burning, traffic, ammonium nitrate, and industrial/residual fuel burning. We then utilized weighted quantile sum (WQS) and linear regression models to investigate associations between PM 2.5 components' mixture, their potential sources, and children's cognitive scores.

RESULTS

Mixture modeling revealed associations between cumulative exposure and worse cognitive performance across all three outcome domains, including shared overlap in detrimental effects driven by ammonium nitrates, silicon, and calcium. Using the identified six sources of exposure, source-specific negative associations were identified between ammonium nitrates and learning & memory, traffic and executive function, and crustal and industrial mixtures and general cognitive ability. Unexpected positive associations were also seen between traffic and general ability as well as biomass burning and executive function.

DISCUSSION

This work suggests nuanced associations between outdoor PM 2.5 exposure and childhood cognitive performance, including important differences in cognition related both to individual chemicals as well as to specific sources of these exposures. https://doi.org/10.1289/EHP14418.

Air pollution below US regulatory standards and cardiovascular diseases using a double negative control approach

Growing evidence suggests that long-term air pollution exposure is a risk factor for cardiovascular mortality and morbidity. However, few studies have investigated air pollution below current regulatory limits, and causal evidence is limited. We use a double negative control approach to examine the association between long-term exposure to air pollution at low concentration and cardiovascular hospitalizations among US Medicare beneficiaries aged ≥65 years between 2000 and 2016. The expected values of the negative outcome control (preceding-year hospitalizations) regressed on exposure and negative exposure control (subsequent-year exposure) are treated as a surrogate for omitted confounders. With analyses separately restricted to low-pollution areas (PM2.5 < 9 μg/m³, NO2 < 75.2 µg/m3 [40 ppb], warm-season O3 < 88.2 μg/m3 [45 ppb]), we observed positive associations of the three pollutants with hospitalization rates of stroke, heart failure, and atrial fibrillation and flutter. The associations generally persisted in demographic subgroups. Stricter national air quality standards should be considered.

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

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

Exposure profiles of social-environmental neighborhood factors and psychotic-like experiences

Importance

Recent research has demonstrated that domains of social determinants of health (SDOH) (e.g., air pollution and social context) are associated with psychosis. However, SDOHs have often been studied in isolation.

Objective

To identify distinct exposure profiles, estimate their associations with persistent distressing psychotic-like experiences (PLE), and evaluate whether involvement with physical activities partially explains this association.

Design Setting and Participants

This population-based study used data from the Adolescent Brain and Cognitive Development (ABCD) Study. Participants were recruited from 22 US sites between September 2016 and January 2022. Data from baseline and three follow-ups were included.

Exposures

Area-level geocoded variables spanning various domains of SDOH, including socioeconomic status (SES), education, crime, built environment, social context, and crime, were clustered using a self-organizing map method to identify exposure profiles.

Main Outcomes and Measures

Persistent distressing PLE was derived from the Prodromal Questionnaire-Brief Child Version across four years. Generalized linear mixed modeling tested the association between exposure profiles and persistent distressing PLE as well as physical activities (i.e., team and individual sports), adjusting for individual-level covariates including age, sex, race/ethnicity, highest level of parent education, family-relatedness, and study sites.

Results

Among 8,145 participants (baseline mean [SD] age, 9.92 [0.63] years; 3,868 (47.5%) females; 5,566 (68.3%) White, 956 (11.7%) Black, 159 (2.0%) Asian, and 1,480 (18.4%) Hispanic participants), five exposure profiles were identified. Compared to the reference Profile 1 (suburban affluent areas, 2521 children, 30.9%), Profile 3 (rural areas with low walkability and high ozone; 1459 children, 17.9%; adjusted OR: 1.34, 95% CI: 1.09-1.64) and Profile 4 (urban areas with high SES deprivation, high crime, and high pollution; 715 children, 8.8%; adjusted OR: 1.40, 95% CI: 1.08-1.81), were associated with persistent distressing PLE. Team sports mediated 6.14% of the association for Profile 3.

Conclusion and Relevance

This study found that neighborhoods characterized by rural areas with low walkability and urban areas with high socioeconomic deprivation, air pollutants, and crime were associated with persistent distressing PLE. Further research is needed to explore the pathways through which different environmental factors may impact the development of psychosis.

What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas?

The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities.

Landscape analysis of environmental data sources for linkage with SEER cancer patients database

One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients' potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients' records collected by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence

Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD®) study. Annual ambient PM2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.

Prenatal and childhood air pollution exposure, cellular immune biomarkers, and brain connectivity in early adolescents

Introduction

Ambient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®.

Methods

Pollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately.

Results

Air pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11-13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11-13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox.

Conclusions

We identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all.

Long-term exposure to wildland fire smoke PM2.5 and mortality in the contiguous United States

Despite the substantial evidence on the health effects of short-term exposure to ambient fine particles (PM2.5), including increasing studies focusing on those from wildland fire smoke, the impacts of long-term wildland fire smoke PM2.5 exposure remain unclear. We investigated the association between long-term exposure to wildland fire smoke PM2.5 and non-accidental mortality and mortality from a wide range of specific causes in all 3,108 counties in the contiguous U.S., 2007-2020. Controlling for non-smoke PM2.5, air temperature, and unmeasured spatial and temporal confounders, we found a non-linear association between 12-month moving average concentration of smoke PM2.5 and monthly non-accidental mortality rate. Relative to a month with the long-term smoke PM2.5 exposure below 0.1 μg/m3, non-accidental mortality increased by 0.16-0.63 and 2.11 deaths per 100,000 people per month when the 12-month moving average of PM2.5 concentration was of 0.1-5 and 5+ μg/m3, respectively. Cardiovascular, ischemic heart disease, digestive, endocrine, diabetes, mental, and chronic kidney disease mortality were all found to be associated with long-term wildland fire smoke PM2.5 exposure. Smoke PM2.5 contributed to approximately 11,415 non-accidental deaths/year (95% CI: 6,754, 16,075) in the contiguous U.S. Higher smoke PM2.5-related increases in mortality rates were found for people aged 65 above. Positive interaction effects with extreme heat (monthly number of days with daily mean air temperature higher than the county's 90th percentile warm season air temperature) were also observed. Our study identified the detrimental effects of long-term exposure to wildland fire smoke PM2.5 on a wide range of mortality outcomes, underscoring the need for public health actions and communications that span the health risks of both short- and long-term exposure.

Cooperative and Bifunctional Adsorbent-Catalyst Materials for In-situ VOCs Capture-Conversion

Volatile organic compounds (VOCs) are gases that are emitted into the air from products or processes and are major components of air pollution that significantly deteriorate air quality and seriously affect human health. Different types of metals, metal oxides, mixed-metal oxides, polymers, activated carbons, zeolites, metal-organic frameworks (MOFs) and mixed-matrixed materials have been developed and used as adsorbent or catalyst for diversified VOCs detection, removal, and destruction. In this comprehensive review, we first discuss the general classification of VOCs removal materials and processes and outline the historical development of bifunctional and cooperative adsorbent-catalyst materials for the removal of VOCs from air. Subsequently, particular attention is devoted to design of strategies for cooperative adsorbent-catalyst materials, along with detailed discussions on the latest advances on these bifunctional materials, reaction mechanisms, long-term stability, and regeneration for VOCs removal processes. Finally, challenges and future opportunities for the environmental implementation of these bifunctional materials are identified and outlined with the intent of providing insightful guidance on the design and fabrication of more efficient materials and systems for VOCs removal in the future.

Grouped mixtures of air pollutants and seasonal temperature anomalies and cardiovascular hospitalizations among U.S. Residents

BACKGROUND

Air pollution is a recognized risk factor for cardiovascular disease (CVD). Temperature is also linked to CVD, with a primary focus on acute effects. Despite the close relationship between air pollution and temperature, their health effects are often examined separately, potentially overlooking their synergistic effects. Moreover, fewer studies have performed mixture analysis for multiple co-exposures, essential for adjusting confounding effects among them and assessing both cumulative and individual effects.

METHODS

We obtained hospitalization records for residents of 14 U.S. states, spanning 2000-2016, from the Health Cost and Utilization Project State Inpatient Databases. We used a grouped weighted quantile sum regression, a novel approach for mixture analysis, to simultaneously evaluate cumulative and individual associations of annual exposures to four grouped mixtures: air pollutants (elemental carbon, ammonium, nitrate, organic carbon, sulfate, nitrogen dioxide, ozone), differences between summer and winter temperature means and their long-term averages during the entire study period (i.e., summer and winter temperature mean anomalies), differences between summer and winter temperature standard deviations (SD) and their long-term averages during the entire study period (i.e., summer and winter temperature SD anomalies), and interaction terms between air pollutants and summer and winter temperature mean anomalies. The outcomes are hospitalization rates for four prevalent CVD subtypes: ischemic heart disease, cerebrovascular disease, heart failure, and arrhythmia.

RESULTS

Chronic exposure to air pollutant mixtures was associated with increased hospitalization rates for all CVD subtypes, with heart failure being the most susceptible subtype. Sulfate, nitrate, nitrogen dioxide, and organic carbon posed the highest risks. Mixtures of the interaction terms between air pollutants and temperature mean anomalies were associated with increased hospitalization rates for all CVD subtypes.

CONCLUSIONS

Our findings identified critical pollutants for targeted emission controls and suggested that abnormal temperature changes chronically affected cardiovascular health by interacting with air pollution, not directly.

Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China

Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models☆

South America is underrepresented in research on air pollution exposure disparities by sociodemographic factors, although such disparities have been observed in other parts of the world. We investigated whether exposure to and information about air pollution differs by sociodemographic factors in the city of Rio de Janeiro, the second most populous city in Brazil with dense urban areas, for 2012-2017. We developed machine learning-based models to estimate daily levels of O3, PM10, and PM2.5 using high-dimensional datasets from satellite remote sensing, atmospheric and land variables, and land use information. Cross-validations demonstrated good agreement between the estimated levels and measurements from ground-based monitoring stations: overall R 2 of 76.8 %, 63.9 %, and 69.1 % for O3, PM2.5, and PM10, respectively. We conducted univariate regression analyses to investigate whether long-term exposure to O3, PM2.5, PM10 and distance to regulatory monitors differs by socioeconomic indicators, the percentages of residents who were children (0-17 years) or age 65+ years in 154 neighborhoods. We also examined the number of days exceeding the Brazilian National Air Quality Standard (BNAQS). Long-term exposures to O3 and PM2.5 were higher in more socially deprived neighborhoods. An interquartile range (IQR) increment of the social development index (SDI) was associated with a 3.6 μg/m3 (95 % confidence interval [CI]: 2.9, 4.4; p-value≤0.001) decrease in O3, and 0.3 μg/m3 (95 % CI: 0.2, 0.5; p-value = 0.010) decrease in PM2.5. An IQR increase in the percentage of residents who are children was associated with a 4.1 μg/m3 (95 % CI: 3.1, 5.0; p-value≤0.001) increase in O3, and 0.4 μg/m3 (95 % CI: 0.3, 0.6; p-value = 0.009) increase in PM2.5. An IQR increase in the percentage of residents age ≥65was associated with a 3.3 μg/m3 (95 % CI: 2.4, 4.3; p-value=<0.001) decrease in O3, and 0.3 μg/m3 (95 % CI: 0.1, 0.5; p-value = 0.058) decrease in PM2.5. There were no apparent associations for PM10. The association for daily O3 levels exceeding the BNAQS daily standard was 0.4 %p-0.8 %p different by the IQR of variables, indicating a 7-15 days difference in the six-year period. The association for daily PM2.5 levels exceeding the BNAQS daily standard showed a 0.7-1.5 %p difference by the IQR, meaning a 13-27 days difference in the period. We did not find statistically significant associations between the distance to monitors and neighborhood characteristics but some indication regarding SDI. We found that O3 levels were higher in neighborhoods situated farther from monitoring stations, suggesting that elevated levels of air pollution may not be routinely measured. Exposure disparity patterns may vary by pollutants, suggesting a complex interplay between environmental and socioeconomic factors in environmental justice.

Elucidating contributions of volatile organic compounds to ozone formation using random forest during COVID-19 pandemic: A case study in China

Ozone has been reported to increase despite nitrogen oxides reductions during the COVID-19 pandemic, and ozone formation needs to be revisited using volatile organic compounds (VOCs), which are rarely measured during the pandemic. Here, a total of 98 VOCs species were monitored in an economy-active city in China from January 2021 to August 2022 to assess contributions to ozone formation during the pandemic. Total VOCs concentrations were 35.55 ± 21.47 ppb during the entire period, among which alkanes account for the largest fraction (13.78 ppb, 38.0%), followed by aromatics (6.16 ppb, 16.8%) and oxygenated VOCs (OVOCs, 5.69 ppb, 15.7%). Most VOCs groups (e.g., alkenes, OVOCs) and individual species (e.g., isoprene, methyl vinyl ketone) display obvious seasonal and diurnal variations, which are related to their sources and reactivities. No weekend effects of VOCs suggest limited influences from traffic emissions during pandemic. Aromatics and alkenes are the major contributors (39% and 33%) to ozone formation potential, largely driven by o/m/p-xylene (21%), ethylene (15%), toluene (9%). Secondary organic aerosol formation potential is dominated by toluene (>50%) despite its low proportion (5%). Further inclusion of VOCs and meteorology in the Random Forest model shows good ozone prediction performance (R2 = 0.77-0.86, RMSE = 11.95-19.91 μg/m3, MAE = 8.89-14.58 μg/m3). VOCs and NO2 contribute >50% of total importance with the largest difference in importance ratio of VOCs/NO2 in the summer and winter, implying ozone formation regime may vary. No seasonal variations in importance of meteorology are observed, while importance of other variables (e.g., PM2.5) is highest in the summer. This work identifies critical VOCs groups and species for ozone formation during the pandemic, and demonstrates the feasibility of machine learning algorithms in elucidation of ozone formation mechanisms.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

Time-sensitive prediction of NO2 concentration in China using an ensemble machine learning model from multi-source data

Nitrogen dioxide (NO2) poses a critical potential risk to environmental quality and public health. A reliable machine learning (ML) forecasting framework will be useful to provide valuable information to support government decision-making. Based on the data from 1609 air quality monitors across China from 2014-2020, this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range. The ensemble ML model incorporates a residual connection to the gated recurrent unit (GRU) network and adopts the advantage of Transformer, extreme gradient boosting (XGBoost) and GRU with residual connection network, resulting in a 4.1%±1.0% lower root mean square error over XGBoost for the test results. The ensemble model shows great prediction performance, with coefficient of determination of 0.91, 0.86, and 0.77 for 1-hr, 3-hr, and 24-hr averages for the test results, respectively. In particular, this model has achieved excellent performance with low spatial uncertainty in Central, East, and North China, the major site-dense zones. Through the interpretability analysis based on the Shapley value for different temporal resolutions, we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions, while the impact of meteorological conditions would be ever-prominent for the latter. Compared with existing models for different spatiotemporal scales, the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO2, which will help developing effective control policies.

Warm season ambient ozone and children's health in the USA

BACKGROUND

Over 120 million people in the USA live in areas with unsafe ozone (O3) levels. Studies among adults have linked exposure to worse lung function and higher risk of asthma and chronic obstructive pulmonary disease (COPD). However, few studies have examined the effects of O3 in children, and existing studies are limited in terms of their geographic scope or outcomes considered.

METHODS

We leveraged a dataset of encounters at 42 US children's hospitals from 2004-2015. We used a one-stage case-crossover design to quantify the association between daily maximum 8-hour O3 in the county in which the hospital is located and risk of emergency department (ED) visits for any cause and for respiratory disorders, asthma, respiratory infections, allergies and ear disorders.

RESULTS

Approximately 28 million visits were available during this period. Per 10 ppb increase, warm-season (May through September) O3 levels over the past three days were associated with higher risk of ED visits for all causes (risk ratio [RR]: 0.3% [95% confidence interval (CI): 0.2%, 0.4%]), allergies (4.1% [2.5%, 5.7%]), ear disorders (0.8% [0.3%, 1.3%]) and asthma (1.3% [0.8%, 1.9%]). When restricting to levels below the current regulatory standard (70 ppb), O3 was still associated with risk of ED visits for all-cause, allergies, ear disorders and asthma. Stratified analyses suggest that the risk of O3-related all-cause ED visits may be higher in older children.

CONCLUSIONS

Results from this national study extend prior research on the impacts of daily O3 on children's health and reinforce the presence of important adverse health impacts even at levels below the current regulatory standard in the USA.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Building towards an adolescent neural urbanome: Expanding environmental measures using linked external data (LED) in the ABCD study

Many recent studies have demonstrated that environmental contexts, both social and physical, have an important impact on child and adolescent neural and behavioral development. The adoption of geospatial methods, such as in the Adolescent Brain Cognitive Development (ABCD) Study, has facilitated the exploration of many environmental contexts surrounding participants' residential locations without creating additional burdens for research participants (i.e., youth and families) in neuroscience studies. However, as the number of linked databases increases, developing a framework that considers the various domains related to child and adolescent environments external to their home becomes crucial. Such a framework needs to identify structural contextual factors that may yield inequalities in children's built and natural environments; these differences may, in turn, result in downstream negative effects on children from historically minoritized groups. In this paper, we develop such a framework - which we describe as the "adolescent neural urbanome" - and use it to categorize newly geocoded information incorporated into the ABCD Study by the Linked External Data (LED) Environment & Policy Working Group. We also highlight important relationships between the linked measures and describe possible applications of the Adolescent Neural Urbanome. Finally, we provide a number of recommendations and considerations regarding the responsible use and communication of these data, highlighting the potential harm to historically minoritized groups through their misuse.

Short-Term Increases in NO2 and O3 Concentrations during Pregnancy and Stillbirth Risk in the U.S.: A Time-Stratified Case-Crossover Study

Associations between gaseous pollutant exposure and stillbirth have focused on exposures averaged over trimesters or gestation. We investigated the association between short-term increases in nitrogen dioxide (NO2) and ozone (O3) concentrations and stillbirth risk among a national sample of 116 788 Medicaid enrollees from 2000 to 2014. A time-stratified case-crossover design was used to estimate distributed (lag 0-lag 6) and cumulative lag effects, which were adjusted for PM2.5 concentration and temperature. Effect modification by race/ethnicity and proximity to hydraulic fracturing (fracking) wells was assessed. Short-term increases in the NO2 and O3 concentrations were not associated with stillbirth in the overall sample. Among American Indian individuals (n = 1694), a 10 ppb increase in NO2 concentrations was associated with increased stillbirth odds at lag 0 (5.66%, 95%CI: [0.57%, 11.01%], p = 0.03) and lag 1 (4.08%, 95%CI: [0.22%, 8.09%], p = 0.04) but not lag 0-6 (7.12%, 95%CI: [-9.83%, 27.27%], p = 0.43). Among participants living in zip codes within 15 km of active fracking wells (n = 9486), a 10 ppb increase in NO2 concentration was associated with increased stillbirth odds in single-day lags (2.42%, 95%CI: [0.37%, 4.52%], p = 0.02 for lag 0 and 1.83%, 95%CI: [0.25%, 3.43%], p = 0.03 for lag 1) but not the cumulative lag (lag 0-6) (4.62%, 95%CI: [-2.75%, 12.55%], p = 0.22). Odds ratios were close to the null in zip codes distant from fracking wells. Future studies should investigate the role of air pollutants emitted from fracking and potential racial disparities in the relationship between short-term increases in NO2 concentrations and stillbirth.

Associations between exposure to air pollution and sex hormones during the menopausal transition

Menopause is a significant milestone in a woman's life, characterized by decreasing estradiol (E2) and increasing follicle-stimulating hormone (FSH) levels. Growing evidence suggests that air pollution may affect reproductive health and disrupt hormone profiles, yet the associations in women undergoing menopausal transition (MT) remains underexplored. We examined the associations between annual air pollutant exposures and repeated measures of E2 and FSH in 1365 women with known final menstrual period (FMP) date from the Study of Women's Health Across the Nation. Air pollution was calculated as the annual averages of 24-h average PM2.5 levels, daily one-hour maximum NO2 levels, and daily 8-h maximum O3 levels. Linear mixed models with piece-wise linear splines were used to model non-linear trajectories of E2 and FSH in three distinct time periods: up to 2 years before the FMP (early MT), within 2 years before and 2 years after FMP (transmenopause), and >2 years post-FMP (postmenopause). In the transmenopausal period, an interquartile (5 μg/m3) increase in PM2.5 was associated with a significant decrease in E2 levels (-15.7 %, 95 % CI: -23.7, -6.8), and a 10 ppb increase in NO2 was associated with a significant decrease in E2 levels (-9.2 %, 95 % CI: -16.2, -1.7). A higher PM2.5 was also associated with an accelerated rate of decline in E2. Regarding FSH, a 10 ppb increase in NO2 was associated with decline in FSH levels (-11.7 %, 95 % CI: -21.8, -0.1) in the early MT and accelerated rates of decline in the postmenopause (-1.1 % per year, 95 % CI: -2.1, -0.1). Additionally, inverse associations between O3 and FSH were observed in the transmenopause and postmenopause. Our study suggests that increases in PM2.5, NO2, and O3 exposures are linked to significant declines in E2 and FSH levels across menopausal stages, suggesting the detrimental impact of air pollutants on women's reproductive hormones.

Satellite data for environmental justice: a scoping review of the literature in the United States

In support of the environmental justice (EJ) movement, researchers, activists, and policymakers often use environmental data to document evidence of the unequal distribution of environmental burdens and benefits along lines of race, class, and other socioeconomic characteristics. Numerous limitations, such as spatial or temporal discontinuities, exist with commonly used data measurement techniques, which include ground monitoring and federal screening tools. Satellite data is well poised to address these gaps in EJ measurement and monitoring; however, little is known about how satellite data has advanced findings in EJ or can help to promote EJ through interventions. Thus, this scoping review aims to (1) explore trends in study design, topics, geographic scope, and satellite datasets used to research EJ, (2) synthesize findings from studies that use satellite data to characterize disparities and inequities across socio-demographic groups for various environmental categories, and (3) capture how satellite data are relevant to policy and real-world impact. Following PRISMA extension guidelines for scoping reviews, we retrieved 81 articles that applied satellite data for EJ research in the United States from 2000 to 2022. The majority of the studies leveraged the technical advantages of satellite data to identify socio-demographic disparities in exposure to environmental risk factors, such as air pollution, and access to environmental benefits, such as green space, at wider coverage and with greater precision than previously possible. These disparities in exposure and access are associated with health outcomes such as increased cardiovascular and respiratory diseases, mental illness, and mortality. Research using satellite data to illuminate EJ concerns can contribute to efforts to mitigate environmental inequalities and reduce health disparities. Satellite data for EJ research can therefore support targeted interventions or influence planning and policy changes, but significant work remains to facilitate the application of satellite data for policy and community impact.

Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS)

Biomass fuel burning is a significant contributor of household fine particulate matter (PM2.5) in the low to middle income countries (LMIC) and assessing PM2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM2.5 concentrations than those using electricity/liquefied petroleum gas (626 μg/m3 vs. 213 μg/m3). Exposure model performances showed 10-fold cross validated R2 ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM2.5 from the third wave of monitoring and ambient PM2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM2.5 included ambient PM2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM2.5 with moderate to high confidence using ambient PM2.5 and household characteristics. Our results present a framework for estimating household PM2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

Racial/ethnic disparities in PM2.5-attributable cardiovascular mortality burden in the United States

Average ambient fine particulate matter (PM2.5) concentrations have decreased in the US in recent years, but the health benefits of this improvement among different racial/ethnic groups are unknown. We estimate the associations between long-term exposure to ambient PM2.5 and cause-specific cardiovascular disease (CVD) mortality rate and assess the PM2.5-attributable CVD deaths by race/ethnicity across 3,103 US counties during 2001-2016 (n = 595,776 county-months). A 1 µg m-3 increase in PM2.5 concentration was associated with increases of 7.16 (95% confidence interval (CI): 3.81, 10.51) CVD deaths per 1,000,000 Black people per month, significantly higher than the estimates for non-Hispanic white people (1.76 (95% CI: 1.37, 2.15); difference in coefficients: 5.40 (95% CI: 2.03, 8.77), P = 0.001). No significant difference in this association was observed between Hispanic (2.66 (95% CI: -0.03, 5.35)) and non-Hispanic white people (difference in coefficients: 0.90 (95% CI: -1.81, 3.61), P = 0.523). From 2001 to 2016, the absolute disparity in PM2.5-attributable CVD mortality burden was reduced by 44.04% between non-Hispanic Black and white people and by 2.61% between Hispanic and non-Hispanic white people. However, in 2016, the burden remained 3.47 times higher for non-Hispanic Black people and 0.45 times higher for Hispanic people than for non-Hispanic white people. We call for policies that aim to reduce both exposure and vulnerability to PM2.5 for racial/ethnic minorities.

Long-Term Exposure to Air Pollution Below Regulatory Standards and Cardiovascular Diseases Among US Medicare Beneficiaries: A Double Negative Control Approach

Growing evidence suggests that long-term air pollution exposure is a risk factor for cardiovascular mortality and morbidity. However, few studies have investigated air pollution below current regulatory limits, and causal evidence is limited. We used a double negative control approach to examine the association between long-term exposure to air pollution at low concentrations and three major cardiovascular events among Medicare beneficiaries aged ≥ 65 years across the contiguous United States between 2000 and 2016. We derived ZIP code-level estimates of ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and warm-season ozone (O3) from high-resolution spatiotemporal models. The outcomes of interest were hospitalizations for stroke, heart failure (HF), and atrial fibrillation and flutter (AF). The analyses were restricted to areas with consistently low pollutant levels on an annual basis (PM2.5 <10 μg/m3, NO2 < 45 or 40 ppb, warm-season O3 < 45 or 40 ppb). For each 1 μg/m3 increase in PM2.5, the hospitalization rates increased by 2.25% (95% confidence interval (CI): 1.96%, 2.54%) for stroke and 3.14% (95% CI: 2.80%, 3.94%) for HF. Each ppb increase in NO2 increased hospitalization rates for stroke, HF, and AF by 0.28% (95% CI: 0.25%, 0.31%), 0.56% (95% CI: 0.52%, 0.60%), and 0.45% (95% CI: 0.41%, 0.49%), respectively. For each ppb increase in warm-season O3, there was a 0.32% (95% CI: 0.21%, 0.44%) increase in hospitalization rate for stroke. The associations for NO2 and warm-season O3 became stronger under a more restrictive upper threshold. Using an approach robust to omitted confounders, we concluded that long-term exposure to low-level PM2.5, NO2, and warm-season O3 was associated with increased risks of cardiovascular diseases in the US elderly. Stricter national air quality standards should be considered.

Estimating daily air temperature and pollution in Catalonia: A comprehensive spatiotemporal modelling of multiple exposures

Environmental epidemiology studies require models of multiple exposures to adjust for co-exposure and explore interactions. We estimated spatiotemporal exposure to surface air temperature and pollution (PM2.5, PM10, NO2, O3) at high spatiotemporal resolution (daily, 250 m) for 2018-2020 in Catalonia. Innovations include the use of TROPOMI products, a data split for remote sensing gap-filling evaluation, estimation of prediction uncertainty, and use of explainable machine learning. We compiled meteorological and air quality station measurements, climate and atmospheric composition reanalyses, remote sensing products, and other spatiotemporal data. We performed gap-filling of remotely-sensed products using Random Forest (RF) models and validated them using Out-Of-Bag (OOB) samples and a structured data split. The exposure modelling workflow consisted of: 1) PM2.5 station imputation with PM10 data; 2) quantile RF (QRF) model fitting; and 3) geostatistical residual spatial interpolation. Prediction uncertainty was estimated using QRF. SHAP values were used to examine variable importance and the fitted relationships. Model performance was assessed via nested CV at the station level. Evaluation of the gap-filling models using the structured split showed error underestimation when using OOB. Temperature models had the best performance (R2 =0.98) followed by the gaseous air pollutants (R2 =0.81 for NO2 and 0.86 for O3), while the performance of the PM2.5 and PM10 models was lower (R2 =0.57 and 0.63 respectively). Predicted exposure patterns captured urban heat island effects, dust advection events, and NO2 hotspots. SHAP values estimated a high importance of TROPOMI tropospheric NO2 columns in PM and NO2 models, and confirmed that the fitted associations conformed to prior knowledge. Our work highlights the importance of correctly validating gap-filling models and the potential of TROPOMI measurements. Moderate performance in PM models can be partly explained by the poor station coverage. Our exposure estimates can be used in epidemiological studies potentially accounting for exposure uncertainty.

Long-term association of air pollution and incidence of lung cancer among older Americans: A national study in the Medicare cohort

BACKGROUND

Despite strong evidence of the association of fine particulate matter (PM2.5) exposure with an increased risk of lung cancer mortality, few studies had investigated associations of multiple pollutants simultaneously, or with incidence, or using causal methods. Disparities were also understudied.

OBJECTIVES

We investigated long-term effects of PM2.5, nitrogen dioxide (NO2), warm-season ozone, and particle radioactivity (PR) exposures on lung cancer incidence in a nationwide cohort.

METHODS

We conducted a cohort study with Medicare beneficiaries (aged ≥ 65 years) continuously enrolled in the fee-for-service program in the contiguous US from 2001 to 2016. Air pollution exposure was averaged across three years and assigned based on ZIP code of residence. We fitted Cox proportional hazards models to estimate the hazard ratio (HR) for lung cancer incidence, adjusted for individual- and neighborhood-level confounders. As a sensitivity analysis, we evaluated the causal relationships using inverse probability weights. We further assessed effect modifications by individual- and neighborhood-level covariates.

RESULTS

We identified 166,860 lung cancer cases of 12,429,951 studied beneficiaries. In the multi-pollutant model, PM2.5 and NO2 exposures were statistically significantly associated with increased lung cancer incidence, while PR was marginally significantly associated. Specifically, the HR was 1.008 (95% confidence interval [CI]: 1.005, 1.011) per 1-μg/m3 increase in PM2.5, 1.013 (95% CI: 1.012, 1.013) per 1-ppb increase in NO2, and 1.005 (0.999, 1.012) per 1-mBq/m3 increase in PR. At low exposure levels, all pollutants were associated with increased lung cancer incidence. Men, older individuals, Blacks, and residents of low-income neighborhoods experienced larger effects of PM2.5 and PR.

DISCUSSION

Long-term PM2.5, NO2, and PR exposures were independently associated with increased lung cancer incidence among the national elderly population. Low-exposure analysis indicated that current national standards for PM2.5 and NO2 were not restrictive enough to protect public health, underscoring the need for more stringent air quality regulations.

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses

BACKGROUND

Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration.

METHOD

In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data.

RESULTS

We found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants.

CONCLUSIONS

The results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.