An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

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.

Residential air pollution, greenspace, and adverse mental health outcomes in the U.S. Gulf Long-term Follow-up Study

Air pollution and greenness are environmental determinants of mental health, though existing evidence typically considers each exposure in isolation. We evaluated relationships between co-occurring air pollution and greenspace levels and depression and anxiety. We estimated cross-sectional associations among 9015 Gulf Long-term Follow-up Study participants living in the southeastern U.S. who completed the Patient Health Questionnaire-9 (depression: score ≥ 10) and Generalized Anxiety Disorder Questionnaire-7 (anxiety: score ≥ 10). Participant residential addresses were linked to annual average concentrations of particulate matter (1 km PM2.5) and nitrogen dioxide (1 km NO2), as well as satellite-based greenness (2 km Enhanced Vegetation Index (EVI)). We used adjusted log-binomial regression to estimate prevalence ratios (PR) and 95 % confidence intervals (CI) for associations between exposures (quartiles) and depression and anxiety. In mutually adjusted models (simultaneously modeling PM2.5, NO2, and EVI), the highest quartile of PM2.5 was associated with increased prevalence of depression (PR = 1.17, 95 % CI: 1.06-1.29), whereas the highest quartile of greenness was inversely associated with depression (PR = 0.89, 95 % CI: 0.80-0.99). Joint exposure to greenness mitigated the impact of PM2.5 on depression (PRPM only = 1.20, 95 % CI: 1.06-1.36; PRPM+green = 0.98, 95 % CI: 0.83-1.16) and anxiety (PRPM only = 1.10, 95 % CI: 1.00-1.22; PRPM+green = 0.95, 95 % CI: 0.83-1.09) overall and in subgroup analyses. Observed associations were stronger in urbanized areas and among nonwhite participants, and varied by neighborhood deprivation. NO2 exposure was not independently associated with depression or anxiety in this population. Relationships between PM2.5, greenness, and depression were strongest in the presence of characteristics that are highly correlated with lower socioeconomic status, underscoring the need to consider mental health as an environmental justice issue.

Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using PM2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016)

BACKGROUND

Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.

OBJECTIVES

We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between PM 2.5 during pregnancy and the risk of preterm birth (PTB).

METHODS

We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM 2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM 2.5 was taken from a 1 -km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.

RESULTS

There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM 2.5 concentration was relatively stable across pregnancy at ∼ 7 μ g / m 3 . We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by - 0.009 (95% confidence interval: - 0.034 , 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.

DISCUSSION

We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Estimated Impacts of Prescribed Fires on Air Quality and Premature Deaths in Georgia and Surrounding Areas in the US, 2015-2020

Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 μg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 μg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

Air pollution and serious bleeding events in high-risk older adults

IMPORTANCE

Despite biological plausibility, very few epidemiologic studies have investigated the risks of clinically significant bleeding events due to particulate air pollution.

OBJECTIVE

To measure the independent and synergistic effects of PM2.5 exposure and anticoagulant use on serious bleeding events.

DESIGN

Retrospective cohort study (2008-2016).

SETTING

Nationwide Medicare population.

PARTICIPANTS

A 50% random sample of Medicare Part D-eligible Fee-for-Service beneficiaries at high risk for cardiovascular and thromboembolic events.

EXPOSURES

Fine particulate matter (PM2.5) and anticoagulant drugs (apixaban, dabigatran, edoxaban, rivaroxaban, or warfarin).

MAIN OUTCOMES AND MEASURES

The outcomes were acute hospitalizations for gastrointestinal bleeding, intracranial bleeding, or epistaxis. Hazard ratios and 95% CIs for PM2.5 exposure were estimated by fitting inverse probability weighted marginal structural Cox proportional hazards models. The relative excess risk due to interaction was used to assess additive-scale interaction between PM2.5 exposure and anticoagulant use.

RESULTS

The study cohort included 1.86 million high-risk older adults (mean age 77, 60% male, 87% White, 8% Black, 30% anticoagulant users, mean PM2.5 exposure 8.81 μg/m3). A 10 μg/m3 increase in PM2.5 was associated with a 48% (95% CI: 45%-52%), 58% (95% CI: 49%-68%) and 55% (95% CI: 37%-76%) increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis, respectively. Significant additive interaction between PM2.5 exposure and anticoagulant use was observed for gastrointestinal and intracranial bleeding.

CONCLUSIONS

Among older adults at high risk for cardiovascular and thromboembolic events, increasing PM2.5 exposure was significantly associated with increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis. In addition, PM2.5 exposure and anticoagulant use may act together to increase risks of severe gastrointestinal and intracranial bleeding. Thus, clinicians may recommend that high-risk individuals limit their outdoor air pollution exposure during periods of increased PM2.5 concentrations. Our findings may inform environmental policies to protect the health of vulnerable populations.

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.

Mortality attributable to PM2.5 from wildland fires in California from 2008 to 2018

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM2.5), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM2.5 estimates. We used a concentration-response function for PM2.5, applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Access to Prostate-Specific Antigen Testing and Mortality Among Men With Prostate Cancer

Importance

Prostate-specific antigen (PSA) screening for prostate cancer is controversial but may be associated with benefit for certain high-risk groups.

Objectives

To evaluate associations of county-level PSA screening prevalence with prostate cancer outcomes, as well as variation by sociodemographic and clinical factors.

Design, Setting, and Participants

This cohort study used data from cancer registries based in 8 US states on Hispanic, non-Hispanic Black, and non-Hispanic White men aged 40 to 99 years who received a diagnosis of prostate cancer between January 1, 2000, and December 31, 2015. Participants were followed up until death or censored after 10 years or December 31, 2018, whichever end point came first. Data were analyzed between September 2023 and January 2024.

Exposure

County-level PSA screening prevalence was estimated using the Behavior Risk Factor Surveillance System survey data from 2004, 2006, 2008, 2010, and 2012 and weighted by population characteristics.

Main Outcomes and Measures

Multivariable logistic, Cox proportional hazards regression, and competing risks models were fit to estimate adjusted odds ratios (AOR) and adjusted hazard ratios (AHR) for associations of county-level PSA screening prevalence at diagnosis with advanced stage (regional or distant), as well as all-cause and prostate cancer-specific survival.

Results

Of 814 987 men with prostate cancer, the mean (SD) age was 67.3 (9.8) years, 7.8% were Hispanic, 12.2% were non-Hispanic Black, and 80.0% were non-Hispanic White; 17.0% had advanced disease. There were 247 570 deaths over 5 716 703 person-years of follow-up. Men in the highest compared with lowest quintile of county-level PSA screening prevalence at diagnosis had lower odds of advanced vs localized stage (AOR, 0.86; 95% CI, 0.85-0.88), lower all-cause mortality (AHR, 0.86; 95% CI, 0.85-0.87), and lower prostate cancer-specific mortality (AHR, 0.83; 95% CI, 0.81-0.85). Inverse associations between PSA screening prevalence and advanced cancer were strongest among men of Hispanic ethnicity vs other ethnicities (AOR, 0.82; 95% CI, 0.78-0.87), older vs younger men (aged ≥70 years: AOR, 0.77; 95% CI, 0.75-0.79), and those in the Northeast vs other US Census regions (AOR, 0.81; 95% CI, 0.79-0.84). Inverse associations with all-cause mortality were strongest among men of Hispanic ethnicity vs other ethnicities (AHR, 0.82; 95% CI, 0.78-0.85), younger vs older men (AHR, 0.81; 95% CI, 0.77-0.85), those with advanced vs localized disease (AHR, 0.80; 95% CI, 0.78-0.82), and those in the West vs other US Census regions (AHR, 0.89; 95% CI, 0.87-0.90).

Conclusions and Relevance

This population-based cohort study of men with prostate cancer suggests that higher county-level prevalence of PSA screening was associated with lower odds of advanced disease, all-cause mortality, and prostate cancer-specific mortality. Associations varied by age, race and ethnicity, and US Census region.

A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations

Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective process control leads to highly unstable plasmid yields. In this study, a multi-parameter correlation analysis was first performed to discover a dynamic metabolic balance among the oxygen uptake rate, temperature, and plasmid yield, whilst revealing the heating rate and timing as the most important optimization factor for balanced cell growth and plasmid production. Then, based on the acquired on-line parameters as well as outputs of kinetic models constructed for describing process dynamics of biomass concentration, plasmid yield, and substrate concentration, a machine learning (ML) model with Random Forest (RF) as the best machine learning algorithm was established to predict the optimal heating strategy. Finally, the highest plasmid yield and specific productivity of 1167.74 mg L-1 and 8.87 mg L-1/OD600 were achieved with the optimal heating strategy predicted by the RF model in the 50 L bioreactor, respectively, which was 71% and 21% higher than those obtained in the control cultures where a traditional one-step temperature upshift strategy was applied. In addition, this study transformed empirical fermentation process optimization into a more efficient and rational self-optimization method. The methodology employed in this study is equally applicable to predict the regulation of process dynamics for other products, thereby facilitating the potential for furthering the intelligent automation of fermentation processes.

Associations of street-view greenspace with Parkinson's disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

INTRODUCTION

Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization).

METHODS

We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization.

RESULTS

There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density.

CONCLUSION

Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Future fire-smoke PM2.5 health burden under climate change in Paraguay

Recent years have seen a rise in wildfire and extreme weather activity across the globe, which is projected to keep increasing with climate-induced conditions. Air pollution, especially fine particulate matter (PM2.5) concentration, is heavily affected by PM2.5 emissions from wildfire activity. Paraguay has been historically suffering from fires, with an average of 2.3 million hectares burnt per year during the 2003-2021 period. Annual PM2.5 concentration in Paraguay is 13.2 μg/m3, more than double the recommended by the WHO. We estimate that, historically, almost 40 % of fine air particulates can be attributed to fires. Using a random forest algorithm, we estimate future fire activity and fire related PM2.5 under different climate change scenarios. With global warming, we calculate that fire activity could increase by up to 120 % by 2100. Annual fire smoke PM2.5 from fires is expected to increase by 7.7 μg/m3 by 2100. Under these conditions, Paraguay is expected to suffer an increase in 3500 deaths per year attributable to fire smoke PM2.5 by 2100. We estimate the economic cost of fire smoke-related mortality by 2100 at US $ 5600 million, equivalent to 2.6 % of Paraguay's GDP, excluding other health- and productivity-related impacts on society.

Air quality and cancer risk in the All of Us Research Program

INTRODUCTION

The NIH All of Us Research Program has enrolled over 544,000 participants across the US with unprecedented racial/ethnic diversity, offering opportunities to investigate myriad exposures and diseases. This paper aims to investigate the association between PM2.5 exposure and cancer risks.

MATERIALS AND METHODS

This work was performed on data from 409,876 All of Us Research Program participants using the All of Us Researcher Workbench. Cancer case ascertainment was performed using data from electronic health records and the self-reported Personal Medical History questionnaire. PM2.5 exposure was retrieved from NASA's Earth Observing System Data and Information Center and assigned using participants' 3-digit zip code prefixes. Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Generalized additive models (GAMs) were used to investigate non-linear relationships.

RESULTS

A total of 33,387 participants and 46,176 prevalent cancer cases were ascertained from participant EHR data, while 20,297 cases were ascertained from self-reported survey data from 18,133 participants; 9,502 cancer cases were captured in both the EHR and survey data. Average PM2.5 level from 2007 to 2016 was 8.90 μg/m3 (min 2.56, max 15.05). In analysis of cancer cases from EHR, an increased odds for breast cancer (OR 1.17, 95% CI 1.09-1.25), endometrial cancer (OR 1.33, 95% CI 1.09-1.62) and ovarian cancer (OR 1.20, 95% CI 1.01-1.42) in the 4th quartile of exposure compared to the 1st. In GAM, higher PM2.5 concentration was associated with increased odds for blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer.

CONCLUSIONS

We found evidence of an association of PM2.5 with breast, ovarian, and endometrial cancers. There is little to no prior evidence in the literature on the impact of PM2.5 on risk of these cancers, warranting further investigation.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Acute exposure to pollen and airway inflammation in adolescents

INTRODUCTION

Pollen exposure is known to exacerbate allergic asthma and allergic rhinitis symptoms, yet few studies have investigated if exposure to pollen affects lung function or airway inflammation in healthy children.

METHODS

We evaluated the extent to which higher pollen exposure was associated with differences in airway inflammation and lung function among 490 early adolescent participants (mean age of 12.9 years) in Project Viva, a prebirth cohort based in Massachusetts. We obtained regional daily total pollen counts, including tree, grass, and weed pollen, from a Rotorod pollen counter. We evaluated associations of 3- and 7-day moving averages of pollen with fractional exhaled nitric oxide (FeNO) and lung function using linear regression models and evaluated the linearity of associations with penalized splines. We tested if associations of pollen with FeNO and lung function were modified by current asthma diagnosis, history of allergic rhinitis, aeroallergen sensitivity, temperature, precipitation, and air pollution.

RESULTS

Three- and 7-day median pollen concentrations were 19.0 grains/m3 (IQR: 73.4) and 20.9 grains/m3 (IQR: 89.7). In main models, higher concentrations of total pollen over the preceding 3 and 7 days were associated with a 4.6% (95% CI: 0.1,9.2) and 7.4% (95% CI: 0.9,14.3) higher FeNO per IQR of pollen, respectively. We did not find associations of pollen with lung function in main models. Asthma, allergic rhinitis, precipitation, and air pollution (nitrogen dioxide and ozone) modified associations of pollen with lung function (Pinteraction < 0.1), while temperature, sex, and aeroallergen sensitization did not.

CONCLUSION

Short-term exposure to pollen was associated with higher FeNO in early adolescents, even in the absence of allergic sensitization and asthma.

A unified framework for assessing interaction effects among environmental exposures in epidemiologic studies: A case study on temperature, air pollution, and kidney-related conditions in New York state

BACKGROUND

There are various methods to assess interaction effects. However, current methods have limitations, and quantification of interaction effects is rarely performed. This study aimed to develop a unified quantitative framework for assessing interaction effects.

METHODS

We proposed a novel framework using log-linear models with a product term(s) across the exposures that generates parametric bi-variate association and interaction effect surfaces and allows flexible functional forms for exposures in the interaction term(s). In a case study, we assessed the interaction effects between temperature and air pollution (i.e., PM2.5, NO2, and O3) on risk for kidney-related conditions in New York State (2007-2016) using a case-crossover design with conditional logistic models. Our measures of exposure were the moving averages at lag 0-5 days for air pollution (linear) and daytime mean outdoor wet-bulb globe temperature (WBGT; using a natural cubic spline).

RESULTS

We derived closed-form expressions for the magnitude of multiplicative interaction effects (the joint relative risk divided by the product of the two conditional relative risks) and their uncertainties. In the case study, we found a Bonferroni-corrected significant multiplicative interaction effect (IE) between outdoor WBGT at the 99th percentile (median as the reference) and (1) PM2.5 (per 5 μg/m3 increase, IE = 1.052; 95 % confidence interval [CI]: 1.019, 1.087) for acute kidney failure and (2) O3 (per 5 ppb increase; IE = 1.022; 95 % CI: 1.008, 1.036) for urolithiasis (the latter being inconclusive based on the sensitivity analysis).

CONCLUSIONS

Our framework allows different functional forms of exposure variables in the interaction term, quantifies the magnitudes of entire-exposure-range (in addition to discrete exposure level) multiplicative interaction effects and their uncertainties in a categorical or continuous (linear or non-linear) manner, and harmonizes the two-way evaluation of effect modification. The case study underscores co-consideration of heat and air pollution when estimating health burden and designing heat/pollution alert systems.

Associations between long-term exposure to air pollution and kidney function utilizing electronic healthcare records: a cross-sectional study

BACKGROUND

Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures.

METHODS

Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables.

RESULTS

We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr < 60 mL/min/1.73m2. NO2 was inversely associated with a first-time diagnosis of CKD with aOR of 0.77 (95% CI: 0.66, 0.90).

CONCLUSIONS

One-year average PM2.5 was associated with reduced eGFRcr, while O3 and NO2 were inversely associated. Neither PM2.5 or O3 were associated with a first-time identification of CKD, NO2 was inversely associated. We recommend future research examining the relationship between air pollution and impaired renal function.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities

BACKGROUND

Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures.

METHODS

We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach.

RESULTS

We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively.

CONCLUSIONS

Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Size-resolved aerosol at a Coastal Great Lakes Site: Impacts of new particle formation and lake spray

The quantification of aerosol size distributions is crucial for understanding the climate and health impacts of aerosols, validating models, and identifying aerosol sources. This work provides one of the first continuous measurements of aerosol size distribution from 1.02 to 8671 nm near the shore of Lake Michigan. The data were collected during the Lake Michigan Ozone Study (LMOS 2017), a comprehensive air quality measurement campaign in May and June 2017. The time-resolved (2-min) size distribution are reported herein alongside meteorology, remotely sensed data, gravimetric filters, and gas-phase variables. Mean concentrations of key aerosol parameters include PM2.5 (6.4 μg m-3), number from 1 to 3 nm (1.80x104 cm-3) and number greater than 3 nm (8x103 cm-3). During the field campaign, approximately half of days showed daytime ultrafine burst events, characterized by particle growth from sub 10 nm to 25-100 nm. A specific investigation of ultrafine lake spray aerosol was conducted due to enhanced ultrafine particles in onshore flows coupled with sustained wave breaking conditions during the campaign. Upon closer examination, the relationships between the size distribution, wind direction, wind speed, and wave height did not qualitatively support ultrafine particle production from lake spray aerosol; statistical analysis of particle number and wind speed also failed to show a relationship. The alternative hypothesis of enhanced ultrafine particles in onshore flow originating mainly from new particle formation activity is supported by multiple lines of evidence.

Association between Fine Particulate Matter Exposure and Cerebrospinal Fluid Biomarkers of Alzheimer's Disease among a Cognitively Healthy Population-Based Cohort

BACKGROUND

Epidemiological evidence suggests air pollution adversely affects cognition and increases the risk of Alzheimer's disease (AD), but little is known about the biological effects of fine particulate matter (PM 2.5 , particulate matter with aerodynamic diameter ≤ 2.5 μ m ) on early predictors of future disease risk.

OBJECTIVES

We investigated the association between 1-, 3-, and 5-y exposure to ambient and traffic-related PM 2.5 and cerebrospinal fluid (CSF) biomarkers of AD.

METHODS

We conducted a cross-sectional analysis using data from 1,113 cognitively healthy adults (45-75 y of age) from the Emory Healthy Brain Study in Georgia in the United States. CSF biomarker concentrations of A β 42 , tTau, and pTau, were collected at enrollment (2016-2020) and analyzed with the Roche Elecsys system. Annual ambient and traffic-related residential PM 2.5 concentrations were estimated at a 1 -km and 250 -m resolution, respectively, and computed for each participant's geocoded address, using three exposure time periods based on specimen collection date. Associations between PM 2.5 and CSF biomarker concentrations, considering continuous and dichotomous (dichotomized at clinical cutoffs) outcomes, were estimated with multiple linear/logistic regression, respectively, controlling for potential confounders (age, gender, race, ethnicity, body mass index, and neighborhood socioeconomic status).

RESULTS

Interquartile range (IQR; IQR = 0.845 ) increases in 1-y [β : - 0.101 ; 95% confidence interval (CI): - 0.18 , - 0.02 ] and 3-y (β : - 0.078 ; 95% CI: - 0.15 , - 0.00 ) ambient PM 2.5 exposures were negatively associated with A β 42 CSF concentrations. Associations between ambient PM 2.5 and A β 42 were similar for 5-y estimates (β : - 0.076 ; 95% CI: - 0.160 , 0.005). Dichotomized CSF variables revealed similar associations between ambient PM 2.5 and A β 42 . Associations with traffic-related PM 2.5 were similar but not significant. Associations between PM 2.5 exposures and tTau, pTau tTau / A β 42 , or pTau / A β 42 levels were mainly null.

CONCLUSION

In our study, consistent trends were found between 1-y PM 2.5 exposure and decreased CSF A β 42 , which suggests an accumulation of amyloid plaques in the brain and an increased risk of developing AD. https://doi.org/10.1289/EHP13503.

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

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

Long-term noise exposures and cardiovascular diseases mortality: A study in 5 U.S. states

BACKGROUND

Previous studies have linked noise exposure with adverse cardiovascular events. However, evidence remains inconsistent, and most previous studies only focused on traffic noise, excluding other anthropogenic sources like constructions, industrial process and commercial activities. Additionally, few studies have been conducted in the U.S. or evaluated the non-linear exposure-response relationships.

METHODS

We conducted a relative incidence analysis study using all cardiovascular diseases mortality as cases (n = 936,019) and external causes mortality (n = 232,491) as contrast outcomes. Mortality records geocoded at residential addresses were obtained from five U.S. states (Indiana, 2007; Kansas, 2007-2009, Missouri, 2010-2019, Ohio, 2007-2013, Texas, 2007-2016). Time-invariant long-term noise exposure was obtained from a validated model developed based on acoustical measurements across 2000-2014. Noises from both natural sources (natural activities, including animals, insects, winds, water flows, thunder, etc.) and anthropogenic sources (human activities, including transportation, industrial activities, community facilities & infrastructures, commercial activities, entertainments, etc.) were included. We used daytime and nighttime total anthropogenic noise & day-night average sound pressure level combining natural and anthropogenic sources as exposures. Logistic regression models were fit controlling for Census tract-level & individual-level characteristics. We examined potential modification by sex by interaction terms and potential non-linear associations by thin plate spline terms.

RESULTS

We observed positive associations for daytime anthropogenic L50 (sound level exceeded 50% of time) noise (10-dBA OR = 1.047, 95%CI 1.025-1.069), nighttime anthropogenic L50 noise (10-dBA OR = 1.061, 95%CI 1.033-1.091) in a two-exposure-term model, and overall Ldn (day-night average) sound pressure level (10-dBA OR = 1.064, 95%CI 1.040-1.089) in single-exposure-term model. Females were more susceptible to all three exposures. All exposures showed monotonic positive associations with cardiovascular mortality up to certain thresholds around 45-55 dBA, with a generally flattened or decreasing trend beyond those thresholds.

CONCLUSIONS

Both daytime anthropogenic and nighttime anthropogenic noises were associated with cardiovascular disease mortality, and associations were stronger in females.

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.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

Application of machine learning models and landsat 8 data for estimating seasonal pm 2.5 concentrations

Air pollution is a significant global challenge that affects many cities. In Europe, Bosnia and Herzegovina (BiH) are among the most highly polluted and are mainly affected by air pollution. In this study, we integrate open-source landsat 8 remote sensing products, topographical data, and the limited ground truth PM2.5 data to spatially predict the air quality level across different seasons in Tuzla Canton, BiH by adopting three pre-existing machine learning models, namely XGBoost, K-Nearest Neighbour (KNN) and Naive Bayes (NB). These classification models were implemented based on landsat 8 bands, environmental-derived indices, and topographical variables generated for the study area. Based on the predicted results, the XGBoost model exhibited the highest overall accuracy across all seasons. The predicted model results were used to generate spatial air quality maps. Based on the classification maps, the PM2.5 air quality level predicted for Tuzla Canton in the Winter Season is very unhealthy. The findings conclude that the PM2.5 air quality concentration in Tuzla Canton is relatively unsatisfactory and requires urgent intervention by the government to prevent further deterioration of air quality in Tuzla and other affected cantons in BiH.

Association of prenatal exposure to PM2.5 and NO2 with gestational diabetes in Western New York

BACKGROUND

Although many studies have examined the association between prenatal air pollution exposure and gestational diabetes (GDM), the relevant exposure windows remain inconclusive. We aim to examine the association between preconception and trimester-specific exposure to PM2.5 and NO2 and GDM risk and explore modifying effects of maternal age, pre-pregnancy body mass index (BMI), smoking, exercise during pregnancy, race and ethnicity, and neighborhood disadvantage.

METHODS

Analyses included 192,508 birth records of singletons born to women without pre-existing diabetes in Western New York, 2004-2016. Daily PM2.5 and NO2 at 1-km2 grids were estimated from ensemble-based models. We assigned each birth with exposures averaged in preconception and each trimester based on residential zip-codes. We used logistic regression to examine the associations and distributed lag models (DLMs) to explore the sensitive windows by month. Relative excess risk due to interaction (RERI) and multiplicative interaction terms were calculated.

RESULTS

GDM was associated with PM2.5 averaged in the first two trimesters (per 2.5 μg/m3: OR = 1.08, 95% CI: 1.01, 1.14) or from preconception to the second trimester (per 2.5 μg/m3: OR = 1.10, 95% CI: 1.03, 1.18). NO2 exposure during each averaging period was associated with GDM risk (per 10 ppb, preconception: OR = 1.10, 95% CI: 1.06, 1.14; first trimester: OR = 1.12, 95% CI: 1.08, 1.16; second trimester: OR = 1.10, 95% CI: 1.06, 1.14). In DLMs, sensitive windows were identified in the 5th and 6th gestational months for PM2.5 and one month before and three months after conception for NO2. Evidence of interaction was identified for pre-pregnancy BMI with PM2.5 (P-for-interaction = 0.023; RERI = 0.21, 95% CI: 0.10, 0.33) and with NO2 (P-for-interaction = 0.164; RERI = 0.16, 95% CI: 0.04, 0.27).

CONCLUSION

PM2.5 and NO2 exposure may increase GDM risk, and sensitive windows may be the late second trimester for PM2.5 and periconception for NO2. Women with higher pre-pregnancy BMI may be more susceptible to exposure effects.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study

OBJECTIVE

To estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate matter (PM2.5) at concentrations below the new World Health Organization air quality guideline limit among adults with health insurance in the contiguous US.

DESIGN

Case time series study.

SETTING

US national administrative healthcare claims database.

PARTICIPANTS

50.1 million commercial and Medicare Advantage beneficiaries aged ≥18 years between 1 January 2010 and 31 December 2016.

MAIN OUTCOME MEASURES

Daily counts of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease based on the primary diagnosis code.

RESULTS

During the study period, 10.3 million hospital admissions and 24.1 million emergency department visits occurred for natural causes among 50.1 million adult enrollees across 2939 US counties. The daily PM2.5 levels were below the new WHO guideline limit of 15 μg/m3 for 92.6% of county days (7 360 725 out of 7 949 713). On days when daily PM2.5 levels were below the new WHO air quality guideline limit of 15 μg/m3, an increase of 10 μg/m3 in PM2.5 during the current and previous day was associated with higher risk of hospital admissions for natural causes, with an excess relative risk of 0.91% (95% confidence interval 0.55% to 1.26%), or 1.87 (95% confidence interval 1.14 to 2.59) excess hospital admissions per million enrollees per day. The increased risk of hospital admissions for natural causes was observed exclusively among adults aged ≥65 years and was not evident in younger adults. PM2.5 levels were also statistically significantly associated with relative risk of hospital admissions for cardiovascular and respiratory diseases. For emergency department visits, a 10 μg/m3 increase in PM2.5 during the current and previous day was associated with respiratory disease, with an excess relative risk of 1.34% (0.73% to 1.94%), or 0.93 (0.52 to 1.35) excess emergency department visits per million enrollees per day. This association was not found for natural causes or cardiovascular disease. The higher risk of emergency department visits for respiratory disease was strongest among middle aged and young adults.

CONCLUSIONS

Among US adults with health insurance, exposure to ambient PM2.5 at concentrations below the new WHO air quality guideline limit is statistically significantly associated with higher rates of hospital admissions for natural causes, cardiovascular disease, and respiratory disease, and with emergency department visits for respiratory diseases. These findings constitute an important contribution to the debate about the revision of air quality limits, guidelines, and standards.

Exposure-response associations between chronic exposure to fine particulate matter and risks of hospital admission for major cardiovascular diseases: population based cohort study

OBJECTIVE

To estimate exposure-response associations between chronic exposure to fine particulate matter (PM2.5) and risks of the first hospital admission for major cardiovascular disease (CVD) subtypes.

DESIGN

Population based cohort study.

SETTING

Contiguous US.

PARTICIPANTS

59 761 494 Medicare fee-for-service beneficiaries aged ≥65 years during 2000-16. Calibrated PM2.5 predictions were linked to each participant's residential zip code as proxy exposure measurements.

MAIN OUTCOME MEASURES

Risk of the first hospital admission during follow-up for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, valvular heart disease, thoracic and abdominal aortic aneurysms, or a composite of these CVD subtypes. A causal framework robust against confounding bias and bias arising from errors in exposure measurements was developed for exposure-response estimations.

RESULTS

Three year average PM2.5 exposure was associated with increased relative risks of first hospital admissions for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, and thoracic and abdominal aortic aneurysms. For composite CVD, the exposure-response curve showed monotonically increased risk associated with PM2.5: compared with exposures ≤5 µg/m3 (the World Health Organization air quality guideline), the relative risk at exposures between 9 and 10 µg/m3, which encompassed the US national average of 9.7 µg/m3 during the study period, was 1.29 (95% confidence interval 1.28 to 1.30). On an absolute scale, the risk of hospital admission for composite CVD increased from 2.59% with exposures ≤5 µg/m3 to 3.35% at exposures between 9 and 10 µg/m3. The effects persisted for at least three years after exposure to PM2.5. Age, education, accessibility to healthcare, and neighborhood deprivation level appeared to modify susceptibility to PM2.5.

CONCLUSIONS

The findings of this study suggest that no safe threshold exists for the chronic effect of PM2.5 on overall cardiovascular health. Substantial benefits could be attained through adherence to the WHO air quality guideline.

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

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

Long-term exposure to ambient PM2.5, particulate constituents and hospital admissions from non-respiratory infection

The association between PM2.5 and non-respiratory infections is unclear. Using data from Medicare beneficiaries and high-resolution datasets of PM2.5 and its constituents across 39,296 ZIP codes in the U.S between 2000 and 2016, we investigated the associations between annual PM2.5, PM2.5 constituents, source-specific PM2.5, and hospital admissions from non-respiratory infections. Each standard deviation (3.7-μg m-3) increase in PM2.5 was associated with a 10.8% (95%CI 10.8-11.2%) increase in rate of hospital admissions from non-respiratory infections. Sulfates (30.8%), Nickel (22.5%) and Copper (15.3%) contributed the largest weights in the observed associations. Each standard deviation increase in PM2.5 components sourced from oil combustion, coal burning, traffic, dirt, and regionally transported nitrates was associated with 14.5% (95%CI 7.6-21.8%), 18.2% (95%CI 7.2-30.2%), 20.6% (95%CI 5.6-37.9%), 8.9% (95%CI 0.3-18.4%) and 7.8% (95%CI 0.6-15.5%) increases in hospital admissions from non-respiratory infections. Our results suggested that non-respiratory infections are an under-appreciated health effect of PM2.5.

Application of machine learning in atmospheric pollution research: A state-of-art review

Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Association between gestational exposure to solar activity and pregnancy loss using live births from a Massachusetts-based medical center

BACKGROUND

Solar activity has been linked to biological mechanisms important to pregnancy, including folate and melatonin levels and inflammatory markers. Thus, we aimed to investigate the association between gestational solar activity and pregnancy loss.

METHODS

Our study included 71,963 singleton births conceived in 2002-2016 and delivered at an academic medical center in Eastern Massachusetts. We studied several solar activity metrics, including sunspot number, Kp index, and ultraviolet radiation, with data from the NASA Goddard Space Flight Center and European Centre for Medium-Range Weather Forecasts. We used a novel time series analytic approach to investigate associations between each metric from conception through 24 weeks of gestation and the number of live birth-identified conceptions (LBICs) -the total number of conceptions in each week that result in a live birth. This approach fits distributed lag models to data on LBICs, adjusted for time trends, and allows us to infer associations between pregnancy exposure and pregnancy loss.

RESULTS

Overall, the association between solar activity during pregnancy and pregnancy loss varied by exposure metric. For sunspot number, we found that an interquartile range increase in sunspot number (78·7 sunspots) in all of the first 24 weeks of pregnancy was associated with 14·0 (95% CI: 6·5, 21·3) more pregnancy losses out of the average 92 LBICs in a week, and exposure in weeks ten through thirteen was identified as a critical window. Although not statistically significant, higher exposure to Kp index and to UV radiation across all 24 weeks of pregnancy was associated with more and less pregnancy losses, respectively.

CONCLUSION

While exposure to certain metrics of solar activity (i.e., sunspot number) throughout the first 24 weeks of pregnancy may be associated with pregnancy losses, exposure to other metrics were not. Solar activity is a complex phenomenon, and more studies are needed to clarify underlying pathways.

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.

Air pollution and age-dependent changes in emotional behavior across early adolescence in the U.S

Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence - a vulnerable time for developing mental health difficulties. This study investigates if annual average PM2.5 and NO2 exposure at ages 9-10 years moderates age-related changes in internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study®. Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at the baseline, 1-year follow-up, and 2-year follow-up visits, for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Against our hypothesis, there was no evidence that greater air pollution exposure was related to more behavioral problems with age over time.

Public Health Benefits From Improved Identification of Severe Air Pollution Events With Geostationary Satellite Data

Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national-level alert-day identification relies predominately on surface monitor networks and forecasters. Satellite-based estimates of surface air quality have rapidly advanced and have the capability to inform exposure-reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite-based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 μg m-3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person-alerts (alert days × population) in 2020 over polar-orbiting satellite data. We apply pre-existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person-alerts may lead to 1,200 (800-1,500) or 54% more averted PM2.5-attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8-17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.

Weekly prenatal PM2.5 and NO2 exposures in preterm, early term, and full term infants: Decrements in birth weight and critical windows of susceptibility

BACKGROUND

Previous studies have observed associations between birth weight and prenatal air pollution exposure, but there is not consensus on timing of critical windows of susceptibility.

OBJECTIVE

We estimated the difference in birth weight among preterm, early term and full term births associated with weekly exposure to PM2.5 and NO2 throughout gestation.

METHODS

We included all singleton live births in the Lower Peninsula of Michigan (United States) between 2007 and 2012 occurring at or after 32 weeks gestational age (n = 497,897). Weekly ambient PM2.5 and NO2 concentrations were estimated at maternal residences using 1-km gridded data from ensemble-based models. We utilized a distributed lag nonlinear model to estimate the difference in birth weight associated with weekly exposures from the last menstrual period (week 0) through 31 weeks gestation for preterm births; through 36 weeks gestation for early term births; and through 38 weeks gestation for full term births.

RESULTS

In single-pollutant models, a 5 μg/m3 increase in PM2.5 exposure was associated with a reduction in birth weight among preterm births (-37.1 g [95% confidence interval [CI]: 60.8 g, -13.5 g]); early term births (-13.5 g [95% CI: 26.2 g, -0.67 g]); and full term births (-8.23 g [95% CI: 15.8 g, -0.68 g])]. In single-pollutant models, a 10 ppb increase in NO2 exposure was associated with a -11.7 g (95% CI: 14.46 g, -8.92 g) decrement in birth weight among full term births only. In models co-adjusted for PM2.5 and NO2, PM2.5 exposure was associated with reduced birth weight among preterm births (-36.9 g [95% CI: 61.9 g, -11.8 g]) and NO2 exposure was associated with reduced birth weight among full term births (-11.8 g [95% CI: 14.7 g, -8.94 g]). The largest decrements in birth weight were associated with PM2.5 exposure between approximately 10 and 26 weeks of pregnancy; for NO2 exposure, the largest decrements in birth weight in full term births were associated with exposure between weeks 6-18.

CONCLUSION

We observed the largest and most persistent adverse associations between PM2.5 exposure and birth weight in preterm infants, and between NO2 exposure and birth weight in full term infants. Exposure during the first half of pregnancy had a greater impact on birthweight.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

IMPACT

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

A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

Effects of air pollution on adverse birth outcomes and pregnancy complications in the U.S. state of Kansas (2000-2015)

Neonatal mortality and morbidity are often caused by preterm birth and lower birth weight. Gestational diabetes mellitus (GDM) and gestational hypertension (GH) are the most prevalent maternal medical complications during pregnancy. However, evidence on effects of air pollution on adverse birth outcomes and pregnancy complications is mixed. Singleton live births conceived between January 1st, 2000, and December 31st, 2015, and reached at least 27 weeks of pregnancy in Kansas were included in the study. Trimester-specific and total pregnancy exposures to nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), and ozone (O3) were estimated using spatiotemporal ensemble models and assigned to maternal residential census tracts. Logistic regression, discrete-time survival, and linear models were applied to assess the associations. After adjustment for demographics and socio-economic status (SES) factors, we found increases in the second and third trimesters and total pregnancy O3 exposures were significantly linked to preterm birth. Exposure to the second and third trimesters O3 was significantly associated with lower birth weight, and exposure to NO2 during the first trimester was linked to an increased risk of GDM. O3 exposures in the first trimester were connected to an elevated risk of GH. We didn't observe consistent associations between adverse pregnancy and birth outcomes with PM2.5 exposure. Our findings indicate there is a positive link between increased O3 exposure during pregnancy and a higher risk of preterm birth, GH, and decreased birth weight. Our work supports limiting population exposure to air pollution, which may lower the likelihood of adverse birth and pregnancy outcomes.

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.

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

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

Associations between weekly gestational exposure of fine particulate matter, ozone, and nitrogen dioxide and preterm birth in a North Carolina Birth Cohort, 2003-2015

Background

Preterm birth (PTB; <37 weeks completed gestation) is associated with exposure to air pollution, though variability in association magnitude and direction across exposure windows exists. We evaluated associations between weekly gestational exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) with PTB in a North Carolina Birth Cohort from 2003 to 2015 (N = 1,367,517).

Methods

Daily average PM2.5 and daily 8-hour maximum NO2 concentration estimates were obtained from a hybrid ensemble model with a spatial resolution of 1 km2. Daily 8-hour maximum census tract-level concentration estimates for O3 were obtained from the EPA's Fused Air Quality Surface Using Downscaling model. Air pollutant concentrations were linked by census tract to residential address at delivery and averaged across each week of pregnancy. Modified Poisson regression models with robust errors were used to estimate risk differences (RD [95% confidence intervals (CI)]) for an interquartile range increase in pollutants per 10,000 births, adjusted for potential confounders.

Results

Associations were similar in magnitude across weeks. We observed positive associations for PM2.5 and O3 exposures, but generally null associations with NO2. RDs ranged from 15 (95% CI = 11, 18) to 32 (27, 37) per 10,000 births for PM2.5; from -7 (-14, -1) to 0 (-5, 4) for NO2; and from 4 (1, 7) to 13 (10, 16) for O3.

Conclusion

Our results show that increased PM2.5 exposure is associated with an increased risk of PTB across gestational weeks, and these associations persist in multipollutant models with NO2 and/or O3.

Family- and neighborhood-level environmental associations with physical health conditions in 9- and 10-year-olds

OBJECTIVE

To determine how environmental factors are associated with physical health conditions in 9- to 10-year-old participants in the Adolescent Brain Cognitive Development (ABCD) Study, and how they are moderated by family-level socioeconomic status (SES).

METHOD

We performed cross-sectional analyses of 8,429 youth participants in the ABCD Study, in which nine physical health conditions (having underweight or overweight/obesity, not participating in sports activities, short sleep duration, high sleep disturbances, lack of vigorous and strengthening-related physical activity, miscellaneous medical problems, and traumatic brain injury) were regressed on three environmental factors [neighborhood disadvantage (area deprivation index [ADI]), risk of lead exposure, and concentrations of particulate matter 2.5 (PM2.5)] and their interaction with family-level SES (i.e., parent-reported annual household income). Environmental data were geocoded to participants' primary residential addresses at 9- to 10-year-olds.

RESULTS

Risk of lead exposure and ADI were positively associated with the odds of having overweight/obesity, not participating in sports activity, and short sleep durations. ADI was also positively associated with high sleep disturbances. PM2.5 was positively associated with the odds of having overweight/obesity and reduced vigorous physical activity. Family-level SES moderated relationships between ADI and both underweight and overweight/obesity, with high SES being associated with more pronounced changes given increased ADI.

CONCLUSIONS

Policymakers and public health officials must implement policies and remediation strategies to ensure children are free from exposure to neurotoxicant and environmental factors. Physical health conditions may be less of a product of an individual's choices and more related to environmental influences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).