Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Gestational exposure to wildfire PM2.5 and its specific components and the risk of gestational hypertension and eclampsia in the southwestern United States

In the southwestern United States, the frequency of summer wildfires has elevated ambient PM2.5 concentrations and rates of adverse birth outcomes. Notably, hypertensive disorders in pregnancy (HDP) constitute a significant determinant associated with maternal mortality and adverse birth outcomes. Despite the accumulating body of evidence, scant research has delved into the correlation between chemical components of wildfire PM2.5 and the risk of HDP. Derived from data provided by the National Center for Health Statistics, singleton births from >2.68 million pregnant women were selected across 8 states (Arizona, AZ; California, CA, Idaho, ID, Montana, MT; Nevada, NV; Oregon, OR; Utah, UT, and Wyoming, WY) in the southwestern US from 2001 to 2004. A spatiotemporal model and a Goddard Earth Observing System chemical transport model were employed to forecast daily concentrations of total and wildfire PM2.5-derived exposure. Various modeling techniques including unadjusted analyses, covariate-adjusted models, propensity-score matching, and double robust typical logit models were applied to assess the relationship between wildfire PM2.5 exposure and gestational hypertension and eclampsia. Exposure to fire PM2.5, fire-sourced black carbon (BC) and organic carbon (OC) were associated with an augmented risk of gestational hypertension (ORPM2.5 = 1.125, 95 % CI: 1.109,1.141; ORBC = 1.247, 95 % CI: 1.214,1.281; OROC = 1.153, 95 % CI: 1.132, 1.174) and eclampsia (ORPM2.5 = 1.217, 95 % CI: 1.145,1.293; ORBC = 1.458, 95 % CI: 1.291,1.646; OROC = 1.309, 95 % CI: 1.208,1.418) during the pregnancy exposure window with the strongest effect. The associations were stronger that the observed effects of ambient PM2.5 in which the sources primarily came from urban emissions. Social vulnerability index (SVI), education years, pre-pregnancy diabetes, and hypertension acted as effect modifiers. Gestational exposure to wildfire PM2.5 and specific chemical components (BC and OC) increased gestational hypertension and eclampsia risk in the southwestern United States.

Contributions of neighborhood physical and social environments to racial and ethnic disparities in birth outcomes in California: A mediation analysis

BACKGROUND

Racially minoritized populations experience higher rates of adverse birth outcomes than White populations in the U.S. We estimated the mediating effect of neighborhood social and physical environments on disparities in adverse birth outcomes in California.

METHOD

We used birthing parent's residential address for California live birth records from 2019 to estimate census block group Area Deprivation Index and census tract level measures of ambient fine particulate matter (PM2.5), drinking water contamination, tree canopy coverage, as a measure of greenspace, potential heat vulnerability, and noise. We performed mediation analysis to assess whether neighborhood factors explain racial/ethnic disparities in preterm birth (PTB) and term-birth low birth weight (TLBW) comparing Black, Latinx, and Asian with White births after controlling for individual-level factors.

RESULTS

Black, Latinx, and Asian parents had PTB rates that were 67%, 36%, and 11% higher, and TLBW rates that were 150%, 38%, and 81% higher than Whites. Neighborhood deprivation contributed 7% (95% CI: 3%, 11%) to the Black-White and 9% (95% CI: 6%, 12%) to the Latinx-White disparity in PTB, and 8% (95% CI: 3%, 12%) of the Black-White and 9% (95% CI: 5%, 15%) of the Latinx-White disparity in TLBW. Drinking water contamination contributed 2% (95% CI: 1%, 4%) to the Latinx-White disparity in PTB. Lack of greenspace accounted for 7% (95% CI: 2%, 10%) of the Latinx-White PTB disparity and 7% (95% CI: 3%, 12%) of the Asian-White PTB disparity. PM2.5 contributed 11% (95% CI: 5%, 18%), drinking water contamination contributed 3% (95% CI: 1%, 7%), and potential heat vulnerability contributed 2% (95% CI: 1%, 3%) to the Latinx-White TLBW disparity. Lack of green space contributed 3% (95% CI: 1%, 6%) to the Asian-White TLBW disparity.

CONCLUSIONS

Our study suggests social environments explain portions of Black/Latinx-White disparities while physical environments explain Latinx/Asian-White disparities in PTB and TLBW.

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

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

Exploring the spatiotemporal patterns of county-scale PM2.5 drivers in Shandong Province from 2000 to 2020

In the rapid development of air pollution over the past two decades in Shandong Province, it has played a detrimental role, causing severe damage to regional ecological security and public health. There has been little research at the county scale to explore the spatiotemporal causes and heterogeneity of PM2.5 pollution. This study utilizes a Geographically and Temporally Weighted Regression Model (GTWR) to environmentally model meteorological elements and socioeconomic conditions in Shandong Province from 2000 to 2020, aiming to identify the key driving factors of PM2.5 concentration changes across 136 counties. The results show that PM2.5 pollution in Shandong Province peaked in 2013, followed by a rapid decline in pollution levels. Geographically, counties in the western plains of Shandong generally exhibit higher pollution levels, while most counties in the central hills of Shandong and the Jiaodong Peninsula are in low pollution areas. Strong winds positively influence air quality in the southeast of Shandong; high temperatures can ameliorate air pollution in areas outside the southeast, whereas air pressure exhibits the opposite effect. Precipitation shows a significant negative correlation in the Laizhou Bay and central Shandong regions, while relative humidity primarily exerts a negative effect in coastal areas. The impact of fractional vegetation cover is relatively mild, with positive effects observed in southern Shandong and negative effects in other regions. Population density shows a significant positive correlation in the western plains of Shandong. Economic factors exhibit predominantly positive relationships, particularly in the northwest and the Jiaodong Peninsula. Electricity consumption in southern Shandong correlates positively, while industrial factors show positive effects province-wide. PM2.5 pollution in Shandong Province demonstrates significant spatiotemporal heterogeneity, aligning with governmental expectations for the effectiveness of air pollution control measures. The conclusions of this study can be utilized to assess the efficiency of air pollution abatement at the county level and provide quantitative data support for the revision of regional emission reduction policies.

Sociodemographic factors impacting the spatial distribution of private dental clinics in major cities of Peoples Republic of China

OBJECTIVES

Investigate the geographical distribution of private dental practices in major Chinese cities and analyze the variables influencing this distribution.

METHODS

This study used Python to extract various types of Point of Interest (POI) data spanning from 2016 to 2022 from the AutoNavi map. A 1km*1km grid was constructed to establish the study sample. Additional spatial pattern data, including nighttime lighting, population, and air quality data, were integrated into this grid. Global Moran's I index was used to analyze the spatial autocorrelation. The spatial lag model was used to explore the influencing factors of private dental practice distribution.

RESULTS

This study reveals a specific clustering pattern for private dental practices in major Chinese cities. The primary influencing factors include nighttime lights, population density, and housing prices, suggesting that dental practices are typically concentrated in highly developed regions with dense populations and high housing costs. Additionally, we discovered that patterns vary across different metropolises, with the most pronounced clustering patterns and substantial inequalities found in the most developed areas.

CONCLUSIONS

This study establishes that factors such as regional development and population density positively correlate with private dental practice. Additionally, it reveals a strong mutual correlation in the clustering of dental practices, which does not show a substantial correlation with public resources. Finally, it suggests that the spatial heterogeneity pattern implies a rising necessity to tackle inequality issues within urban areas as economic development progresses.

Ambient temperature and the risk of childhood epilepsy hospitalizations: Potentially neglected risk of temperature extremes and modifying effects of air pollution

PURPOSE

Extreme temperatures and air pollution are increasingly important risk factors for human health in the background of climate change, with limited evidence available for neurological disorders. This study intended to investigate the short-term effects of extreme temperatures on childhood epilepsy and explore the potential modifying effect of air pollution.

METHODS

Daily childhood epilepsy hospitalization, meteorological and air pollution data were collected from 10 cities in Anhui Province of China during 2016-2018. We firstly employed a space-time-stratified case-crossover design and conditional logistic regression model to fit the short-term relationship between temperature and epilepsy. Then, we conducted stratified analyses by the level of air pollution and individual characteristics.

RESULTS

Both extreme heat and extreme cold increased the risk of hospitalization for childhood epilepsy. The effect of extreme heat [97.5th vs. minimum hospitalization temperature (MHT)] on hospitalization was acute and emerged at lag0 [OR: 1.229 (95 %CI: 1.035 to 1.459)], while the effect of extreme cold (2.5th vs. MHT) was delayed and appeared at lag5 [OR: 1.098 (95 %CI: 1.043 to 1.156)]. We also found children aged 6-18 years were more susceptible to extreme cold than children aged 0-5 years. Besides, extreme heat and cold effects differed by the level of air pollutants.

CONCLUSION

This study suggests that extreme temperatures might be the novel but currently neglected risk factor for childhood epilepsy, and air pollution could further amplify the adverse effect of temperature.

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

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

PM2.5 exposure disparities persist despite strict vehicle emissions controls in California

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

Understanding the spatial patterns of atmospheric ammonia trends in South Asia

Ammonia (NH3) is the most abundant alkaline gas in the atmosphere, mainly emitted by agricultural activities. NH3 readily reacts with other atmospheric acidic pollutants, such as the oxidation products of sulfur dioxide (SO2) and nitrogen oxides (NOₓ), to create fine particulate matter, which has far-reaching effects on human health and ecosystems. Here, we investigated long-term atmospheric NH3 trends in South Asia (SA) using satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI). We analyzed 15 years (2008-2022) of IASI-NH3 retrievals against climate, biophysical, and chemical variables using an ensemble of multivariate statistical methods to identify the major factors driving the observed patterns in the region. Trend analysis of IASI-NH3 data reveals a significant rise in atmospheric NH3 over 51 % of SA plains, but a downward trend over 31 % of the region. Spatial correlation analysis reveals that biophysical factors, representing cropland expansion and agriculture intensification, have the highest positive correlation over 56 % of SA plains experiencing positive NH3 trends. However, our results reveal that the chemical conversion of NH3 to ammonium compounds, driven by the positive trends in NOₓ and SO2 pollution, is driving the apparently declining trend of NH3 in the other regions. Our results provide important insights into the NH3 trends detected by satellite data and can better inform the policy design aimed at reducing NH3 emissions and improving air quality for developing regions of the world.

Burden of disease in Germany attributed to ambient particulate matter pollution : Findings from the Global Burden of Disease Study 2019

INTRODUCTION

Ambient fine particulate matter pollution with a diameter less than 2.5 micrometers (PM2.5) is a significant risk factor for chronic noncommunicable diseases (NCDs), leading to a substantial disease burden, decreased quality of life, and deaths globally. This study aimed to investigate the disease and mortality burdens attributed to PM2.5 in Germany in 2019.

METHODS

Data from the Global Burden of Disease (GBD) Study 2019 were used to investigate disability-adjusted life-years (DALYs), years of life lost (YLLs), years lived with disability (YLDs), and deaths attributed to ambient PM2.5 pollution in Germany.

RESULTS

In 2019, ambient PM2.5 pollution in Germany was associated with significant health impacts, contributing to 27,040 deaths (2.82% of total deaths), 568,784 DALYs (2.09% of total DALYs), 135,725 YLDs (1.09% of total YLDs), and 433,058 YLLs (2.92% of total YLLs). The analysis further revealed that cardiometabolic and respiratory conditions, such as ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus, were the leading causes of mortality and disease burden associated with ambient PM2.5 pollution in Germany from 1990-2019. Comparative assessments between 1990 and 2019 underscored ambient PM2.5 as a consistent prominent risk factor, ranking closely with traditional factors like smoking, arterial hypertension, and alcohol use contributing to deaths, DALYs, YLDs, and YLLs.

CONCLUSION

Ambient PM2.5 pollution is one of the major health risk factors contributing significantly to the burden of disease and mortality in Germany, emphasizing the urgent need for targeted interventions to address its substantial contribution to chronic NCDs.

Linking weather and health outcomes: Examining the potential influences of weather factors and particulate matter pollution on adverse pregnancy outcomes in the Kavre district, Nepal

INTRODUCTION

Adverse pregnancy outcomes (APOs) include stillbirth, preterm birth, and low birthweight (LBW). Studies exploring the impact of weather factors and air pollution on APOs are scarce in Nepal. We examined the impacts of prenatal exposure to temperature, precipitation, and air pollution (PM2.5) on APOs among women living in Kavre, Nepal.

METHODS

We conducted a hospital and rural health centers-based historical cohort study that included health facility birth records (n = 1716) from the Nepali fiscal year 2017/18 through 2019/20. We linked health records to temperature, precipitation, and PM2.5 data for Kavre for the six months preceding each birth. A random intercept model was used to analyze birthweight, while a composite APO variable, was analyzed using multivariable logistic regression in relation to environmental exposures.

RESULTS

The proportion of LBW (<2500 gm), preterm birth (babies born alive before 37 weeks of gestation), and stillbirth was 13%, 4.3%, and 1.5%, respectively, in this study. Overall, around 16% of the study participants had one or more APOs. Total precipitation (β: 0.17, 95% CI 0.01 to 0.33, p = 0.03) had a positive effect on birthweight in the wetter season. Negative effects for mean maximum (β: 33.37, 95% CI -56.68 to -10.06, p = 0.005), mean (β: 32.35, 95% CI -54.44 to -10.27, p = 0.004), and mean minimum temperature (β: 29.28, 95% CI -49.58 to -8.98, p = 0.005) on birthweight was also observed in the wetter season.

CONCLUSION

A positive effect of temperature (mean maximum, mean, and mean minimum) and total precipitation on birthweight was found in the wetter season. This study emphasizes the need for future research using larger cohorts to elucidate these complex relationships in Nepal.

Synergy of Urban Heat, Pollution, and Social Vulnerability in One of America's Most Rapidly Growing Cities: Houston, We Have a Problem

During the first two decades of the twenty-first century, we analyze the expansion of urban land cover, urban heat island (UHI), and urban pollution island (UPI) in the Houston Metropolitan Area (HMA) using land cover classifications derived from Landsat and land/aerosol products from NASA's Moderate Resolution Imaging Spectroradiometer. Our approach involves both direct utilization and fusion with in situ observations for a comprehensive characterization. We also examined how social vulnerability within the HMA changed during the study period and whether the synergy of UHI, UPI, and social vulnerability enhances environmental inequalities. We found that urban land cover within the HMA increased by 1,345.09 km2 and is accompanied by a 171.92 (73.93) % expansion of the daytime (nighttime) UHI. While the UPI experienced an overall reduction in particulate pollution, the magnitude of change is smaller compared to the surroundings. Further, the UPI showed localized enhancement in particulate pollution caused by increases in vehicular traffic. Our analysis found that the social vulnerability of the HMA urban regions increased during the study period. Overall, we found that the urban growth during the first two decades of the twenty-first century resulted in a synergy of UHI, UPI, and social vulnerability, causing an increase in environmental inequalities within the HMA.

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration

The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities.

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

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

Association between antibiotic usage during infancy and asthma incidence among children: a population-level ecological study in British Columbia, Canada

Background

This study follows published associations in BC to 2014 (updated in 2019) to model the predicted incidence of asthma in BC children attributable to antibiotic use within the context of reduced antibiotic use and increased breastfeeding in BC infants from 2000 to 2019.

Methods

A population-based ecological study was conducted in BC from 2000 to 2019, using outpatient antibiotic prescription data from BC PharmaNet and asthma diagnoses from the Chronic Disease Registry. Breastfeeding estimates were calculated using the Canadian Community Health Survey (CCHS). Population attributable risk (PAR) was calculated using a blended relative risk (RR) of asthma in antibiotic-exposed children who were and were not breastfed. PAR was used to calculate predicted vs. actual asthma incidence in 2019. Negative binomial regression was used to estimate the association between the average antibiotic prescription rate in infants under 1 and asthma incidence in 1-4 year olds, stratified by periods between 2000-2014 and 2015-2019.

Results

In BC, antibiotic prescribing decreased by 77% in infants under 1 and asthma incidence decreased by 41% in children 1-4 years from 2000 to 2019. BC breastfeeding rates increased from 46% in the 2005 CCHS to 71% in the 2017/18 CCHS. After calculating the PAR using a blended RR, the predicted asthma incidence in 2019 was 18.8/1,000 population. This was comparable to the observed asthma incidence in children 1-4 years of 16.6/1,000 population in 2019. During 2000-2014, adjusted incidence risk ratio (aIRR) for children under Quintile 5 of average antibiotic prescribing was 1.75 (95% CI: 1.63-1.88, P < 0.0001) times higher than that for Quintile 1. However, between 2015 and 2019, this association weakened (as expected because of increasing prevalence of breastfeeding), with the expected asthma incidence for Quintile 5 only 11% (aIRR 1.11, 95% CI: 0.78-1.57) higher than for Quintile 1.

Conclusion

We identified that over the past 20 years, antibiotic exposure in infants under 1 and asthma incidence in children 1-4 years has decreased significantly. Decreasing antibiotic exposure and increasing breastfeeding (which further mitigates risk associated with antibiotics) are of sufficient scale to explain much of this population trend. Changes in environmental, social and other exposures remain relevant to this complicated etiological pathway.

Aspirational nitrogen interventions accelerate air pollution abatement and ecosystem protection

Although reactive nitrogen (Nr) emissions from food and energy production contribute to multi-dimensional environmental damages, integrated management of Nr is still lacking owing to unclear future mitigation potentials and benefits. Here, we find that by 2050, high-ambition compared to low-ambition N interventions reduce global ammonia and nitrogen oxide emissions by 21 and 22 TgN/a, respectively, equivalent to 40 and 52% of their 2015 levels. This would mitigate population-weighted PM2.5 by 6 g/m3 and avoid premature deaths by 817 k (16%), mitigate ozone by 4 ppbv, avoid premature deaths by 252k (34%) and crop yield losses by 122 million tons (4.3%), and decrease terrestrial ecosystem areas exceeding critical load by 420 Mha (69%). Without nitrogen interventions, most environmental damages examined will deteriorate between 2015 and 2050; Africa and Asia are the most vulnerable but also benefit the most from interventions. Nitrogen interventions support sustainable development goals related to air, health, and ecosystems.

Rethinking ecological niches and geographic distributions in face of pervasive human influence in the Anthropocene

Species are distributed in predictable ways in geographic spaces. The three principal factors that determine geographic distributions of species are biotic interactions (B), abiotic conditions (A), and dispersal ability or mobility (M). A species is expected to be present in areas that are accessible to it and that contain suitable sets of abiotic and biotic conditions for it to persist. A species' probability of presence can be quantified as a combination of responses to B, A, and M via ecological niche modeling (ENM; also frequently referred to as species distribution modeling or SDM). This analytical approach has been used broadly in ecology and biogeography, as well as in conservation planning and decision-making, but commonly in the context of 'natural' settings. However, it is increasingly recognized that human impacts, including changes in climate, land cover, and ecosystem function, greatly influence species' geographic ranges. In this light, historical distinctions between natural and anthropogenic factors have become blurred, and a coupled human-natural landscape is recognized as the new norm. Therefore, B, A, and M (BAM) factors need to be reconsidered to understand and quantify species' distributions in a world with a pervasive signature of human impacts. Here, we present a framework, termed human-influenced BAM (Hi-BAM, for distributional ecology that (i) conceptualizes human impacts in the form of six drivers, and (ii) synthesizes previous studies to show how each driver modifies the natural BAM and species' distributions. Given the importance and prevalence of human impacts on species distributions globally, we also discuss implications of this framework for ENM/SDM methods, and explore strategies by which to incorporate increasing human impacts in the methodology. Human impacts are redefining biogeographic patterns; as such, future studies should incorporate signals of human impacts integrally in modeling and forecasting species' distributions.

Ecological security assessment of Yunnan Province, China in the context of Production-Living-Ecological space division

With the rapid development of population, society and economy, human activities have caused serious adverse impacts on the environment, ecosystems and landscape patterns over the long term. In order to address the series of impacts of human activities on the environment, territorial space and resource use, the study of Production-Living-Ecological Space (PLES) and ecological security have all become academic frontiers in the field of sustainable development. In this study, we applied multi-source data and GIS technology to construct an ecological security evaluation model based on the results of PLES delineation and the Pressure-State-Response (PSR) framework, and carried out the three-period PLES ecological security evaluation for 2000, 2010 and 2020 at the county and grid scales in Yunnan Province. The PLES pattern in Yunnan Province is dominated by ecological space, which accounts for 75%, followed by 23% of production space, with ecological space shrinking from 2000 to 2020. Ecological security in ecological space and living space shows an improving trend from 2000 to 2020. The ecological security of production space improved in 2010 compared to 2000 but then showed a decreasing trend in 2020. Ecological security in ecological space shows that north-western and southern Yunnan is safer than central Yunnan, while ecological security in living space is safer in central Yunnan, and ecological security in production space is better in southern Yunnan than in northern Yunnan. Comparison with related research results shows that the ecological security evaluation results of PLES in Yunnan Province in this study are scientific and reasonable. The ecological security evaluation model of PLES constructed in this study solves the problem of complex and incomplete ecological security evaluation indexes in the past, and the results of the study are more refined and precise, which provides new ideas for the study of regional ecological security.

A health inequality analysis of childhood asthma prevalence in urban Australia

BACKGROUND

Long-standing health inequalities in Australian society that were exposed by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were described as "fault lines" in a recent call to action by a consortium of philanthropic organizations. With asthma a major contributor to childhood disease burden, studies of its spatial epidemiology can provide valuable insights into the emergence of health inequalities early in life.

OBJECTIVE

The aims of this study were to characterize the spatial variation of asthma prevalence among children living within Australia's 4 largest cities and quantify the relative contributions of climatic and environmental factors, outdoor air pollution, and socioeconomic status in determining this variation.

METHODS

A Bayesian model with spatial smoothing was developed to regress ecologic health status data from the 2021 Australian Census against groups of explanatory covariates intended to represent mechanistic pathways.

RESULTS

The prevalence of asthma in children aged 5 to 14 years averages 7.9%, 8.2%, 8.5%, and 7.6% in Sydney, Melbourne, Brisbane, and Perth, respectively. This small inter-city variation contrasts against marked intracity variation at the small-area level, which ranges from 6% to 12% between the least and most affected locations in each. Statistical variance decomposition on a subsample of Australian-born, nonindigenous children attributes 66% of the intracity spatial variation to the assembled covariates. Of these covariates, climatic and environmental factors contribute 30%, outdoor air pollution contributes 19%, and areal socioeconomic status contributes the remaining 51%.

CONCLUSION

Geographic health inequalities in the prevalence of childhood asthma within Australia's largest cities reflect a complex interplay of factors, among which socioeconomic status is a principal determinant.

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

Importance

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

Objective

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

Design, Setting, and Participants

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

Exposure

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

Main Outcome and Measures

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

Results

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

Conclusions and Relevance

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

Burden of Child Anemia Attributable to Fine Particulate Matters Brought by Sand Dusts in Low- and Middle-Income Countries

Addressing environmental factors has recently been recommended to curb the growing trend of anemia in low- and middle-income countries (LMICs). Fine particulate matter (PM2.5) generated by dust storms were concentrated in place with a high prevalence of anemia. In a multicounty, multicenter study, we analyzed the association between anemia and life-course averaged exposure to dust PM2.5 among children aged <5 years based on 0.65 million records from 47 LMICs. In the fully adjusted mixed effects model, each 10 μg/m3 increase in life-course averaged exposure to dust PM2.5 was associated with a 9.3% increase in the odds of anemia. The estimated exposure-response association was nonlinear, with a greater effect of dust PM2.5 exposure seen at low concentrations. Applying this association, we found that, in 2017, among all children aged <5 years in the 125 LMICs, dust PM2.5 contributed to 37.98 million cases of anemia. Results indicated that dust PM2.5 contributed a heavier burden than all of the well-identified risk factors did, except for iron deficiency. Our study revealed that long-term exposure to dust PM2.5 can be a novel risk factor, pronouncedly contributed to the burden of child anemia in LMICs, affected by land degradations or arid climate.

Governance effects of pollution reduction and carbon mitigation of carbon emission trading policy in China

China's carbon emission trading policy plays a crucial role in achieving both its "3060" dual carbon objectives and the United Nations Sustainable Development Goal 13 (SDG 13) on climate action. The policy's effectiveness in reducing pollution and mitigating carbon emissions holds significant importance. This paper investigated whether China's carbon emission trading policy affects pollution reduction (PM2.5 and SO2) and carbon mitigation (CO2) in pilot regions, using panel data from 30 provinces and municipalities in China from 2005 to 2019 and employing a multi-period difference-in-differences (DID) model. Furthermore, it analyzed the heterogeneity of carbon market mechanisms and regional variations. Finally, it examined the governance pathways for pollution reduction and carbon mitigation from a holistic perspective. The results indicate that: (1) China's carbon emission trading policy has reduced CO2 emissions by 18% and SO2 emissions by 36% in pilot areas, with an immediate impact on the "carbon mitigation" effect, while the "pollution reduction" effect exhibits a time lag. (2) Higher carbon trading prices lead to stronger "carbon mitigation" effect, and larger carbon market scales are associated with greater "pollution reduction" effects on PM2.5. Governance effects on pollution reduction and carbon mitigation vary among pilot regions: Carbon markets of Beijing, Chongqing, Shanghai, and Tianjin show significant governance effects in both "pollution reduction" and "carbon mitigation", whereas Guangdong's carbon market exhibits only a "pollution reduction" effect, and Hubei's carbon market demonstrates only a "carbon mitigation" effect. (3) Currently, China's carbon emission trading policy achieves pollution reduction and carbon mitigation through "process management" and "end-of-pipe treatment". This study could provide empirical insights and policy implications for pollution reduction and carbon mitigation, as well as for the development of China's carbon emission trading market.

EAACI guidelines on environmental science for allergy and asthma: The impact of short-term exposure to outdoor air pollutants on asthma-related outcomes and recommendations for mitigation measures

The EAACI Guidelines on the impact of short-term exposure to outdoor pollutants on asthma-related outcomes provide recommendations for prevention, patient care and mitigation in a framework supporting rational decisions for healthcare professionals and patients to individualize and improve asthma management and for policymakers and regulators as an evidence-informed reference to help setting legally binding standards and goals for outdoor air quality at international, national and local levels. The Guideline was developed using the GRADE approach and evaluated outdoor pollutants referenced in the current Air Quality Guideline of the World Health Organization as single or mixed pollutants and outdoor pesticides. Short-term exposure to all pollutants evaluated increases the risk of asthma-related adverse outcomes, especially hospital admissions and emergency department visits (moderate certainty of evidence at specific lag days). There is limited evidence for the impact of traffic-related air pollution and outdoor pesticides exposure as well as for the interventions to reduce emissions. Due to the quality of evidence, conditional recommendations were formulated for all pollutants and for the interventions reducing outdoor air pollution. Asthma management counselled by the current EAACI guidelines can improve asthma-related outcomes but global measures for clean air are needed to achieve significant impact.

Ambient PM2.5 exposure and rapid population aging: A double threat to public health in the Republic of Korea

Particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5) can infiltrate deep into the respiratory system, posing significant health risks. Notably, the health burden of PM2.5 is more pronounced among the older adult population. With an aging population, the public health burden attributable to PM2.5 could escalate even if the current PM2.5 level remains stable. This study evaluated the number of deaths attributable to long-term PM2.5 exposure in the Republic of Korea between 2020 and 2050 and identified the PM2.5 concentration required at least to maintain the current PM2.5 health burden. To calculate mortality for 2020-2050, we performed a health impact assessment using 3-year (2019-2021) average population-weighted PM2.5 concentrations, age-specific population and mortality rates. In 2020, 33,578 [95% confidence interval (CI) = 31,708-35,448] deaths were attributable to PM2.5 exposure. Projecting forward, if the 2019-2021 average PM2.5 level remains constant, mortality is projected to be 112,953 (95% CI = 109,963-115,943) in 2050, more than three times higher than in 2020. To maintain the same level of health burden in 2050 as in 2020, the PM2.5 concentration needs to be immediately reduced to 5.8 μg/m3. In an age-specific analysis, the proportion of older adults (ages 65+) to total mortality would increase from 83% (2020) to 96% (2050), indicating that the rising mortality is predominantly driven by the aging population. By region, the reduction of PM2.5 concentrations, which is required immediately in 2020 to have the health burden in 2050 equal to that in 2020, varied from 3.6 μg/m3 in Goheung-gun (25% reduction) to 20.8 μg/m3 in Heungdeok-gu (82% reduction). Our study emphasizes the critical need for air quality management to consider aging populations when establishing PM2.5 air quality standards, as well as their associated policies and regulations.

Reduced human fecundity attributable to ambient fine particles in low- and middle-income countries

BACKGROUND

Exposure to ambient fine particulate matter (PM2.5) has been associated with reduced human fecundity. However, the attributable burden has not been estimated for low- and middle-income countries (LMICs), where the exposure-response function between PM2.5 and the infertility rate has been insufficiently studied.

OBJECTIVE

This study examined the associations between long-term exposure to PM2.5 and human fecundity indicators, namely the expected time to pregnancy (TTP) and 12-month infertility rate (IR), and then estimated PM2.5-attributable burden of infertility in LMICs.

METHODS

We analyzed 164,593 eligible women from 100 Demographic and Health Surveys conducted in 49 LMICs between 1999 and 2021. We assessed PM2.5 exposures during the 12 months before a pregnancy attempt using the global satellite-derived PM2.5 estimates produced by Atmospheric Composition Analysis Group (ACAG). First, we created a series of pseudo-populations with balanced covariates, given different levels of PM2.5 exposure, using a matching approach based on the generalized propensity score. For each pseudo-population, we used 2-stage generalized Gamma models to derive TTP or IR from the probability distribution of the questionnaire-based duration time for the pregnancy attempt before the interview. Second, we used spline regressions to generate nonlinear PM2.5 exposure-response functions for each of the two fecundity indicators. Finally, we applied the exposure-response functions to estimate number of infertile couples attributable to PM2.5 exposure in 118 LMICs.

RESULTS

Based on the Gamma models, each 10 µg/m3 increment in PM2.5 exposure was associated with a TTP increase by 1.7 % (95 % confidence interval [CI]: -2.3 %-6.0 %) and an IR increase by 2.3 % (95 %CI: 0.6 %-3.9 %). The nonlinear exposure-response function suggested a robust effect of an increased IR for high-concentration PM2.5 exposure (>75 µg/m3). Based on the PM2.5-IR function, across the 118 LMICs, the number of infertile couples attributable to PM2.5 exposure exceeding 35 µg/m3 (the first-stage interim target recommended by the World Health Organization global air quality guidelines) was 0.66 million (95 %CI: 0.061-1.43), accounting for 2.25 % (95 %CI: 0.20 %-4.84 %) of all couples affected by infertility. Among the 0.66 million, 66.5 % were within the top 10 % high-exposure infertile couples, mainly from South Asia, East Asia, and West Africa.

CONCLUSION

PM2.5 contributes significantly to human infertility in places with high levels of air pollution. PM2.5-pollution control is imperative to protect human fecundity in LMICs.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

PM2.5, vegetation density, and childhood cancer: a case-control registry-based study from Texas 1995-2011

BACKGROUND

Air pollution is positively associated with some childhood cancers, whereas greenness is inversely associated with some adult cancers. The interplay between air pollution and greenness in childhood cancer etiology is unclear. We estimated the association between early-life air pollution and greenness exposure and childhood cancer in Texas (1995 to 2011).

METHODS

We included 6101 cancer cases and 109 762 controls (aged 0 to 16 years). We linked residential birth address to census tract annual average fine particulate matter <2.5 µg/m³ (PM2.5) and Normalized Difference Vegetation Index (NDVI). We estimated odds ratios (ORs) and 95% confidence intervals (CIs) between PM2.5/NDVI interquartile range increases and cancer. We assessed statistical interaction between PM2.5 and NDVI (likelihood ratio tests).

RESULTS

Increasing residential early-life PM2.5 exposure was associated with all childhood cancers (OR = 1.10, 95% CI = 1.06 to 1.15), lymphoid leukemias (OR = 1.15, 95% CI = 1.07 to 1.23), Hodgkin lymphomas (OR = 1.27, 95% CI = 1.02 to 1.58), non-Hodgkin lymphomas (OR = 1.24, 95% CI = 1.02 to 1.51), ependymoma (OR = 1.27, 95% CI = 1.01 to 1.60), and others. Increasing NDVI exposure was inversely associated with ependymoma (0- to 4-year-old OR = 0.75, 95% CI = 0.58 to 0.97) and medulloblastoma (OR = 0.75, 95% CI = 0.62 to 0.91) but positively associated with malignant melanoma (OR = 1.75, 95% CI = 1.23 to 2.47) and Langerhans cell histiocytosis (OR = 1.56, 95% CI = 1.07 to 2.28). There was evidence of statistical interaction between NDVI and PM2.5 (P < .04) for all cancers.

CONCLUSION

Increasing early-life exposure to PM2.5 increased the risk of childhood cancers. NDVI decreased the risk of 2 cancers yet increased the risk of others. These findings highlight the complexity between PM2.5 and NDVI in cancer etiology.

How far can air pollution affect tourism in China? Evidence from panel unconditional quantile regressions

Previous studies provide empirical evidence for the connection between air pollution and tourism. However, many of them take the nexus as a linear one. It remains unexplored whether any thresholds are required for the nexus to materialize. This study systematically investigates whether PM2.5 concentrations-an essential indicator of air pollution-affect tourism in China at various tourism development levels. We analyze 284 Chinese cities from 2008 to 2018 using the Unconditional Quantile Regression method. Our statistical results reveal that air pollution positively influences tourism (regarding tourist visits and tourism revenue) in areas with low tourism development levels. However, a complex correlation between air pollution and tourism emerges when tourism development has reached a certain level. The correlation is initially negative, then positive, and finally disappears. But, the overall correlation remains negative. The effects of the interaction between air pollution and tourism resources on tourism are inverted U-shaped, implying that tourism resources can mitigate the negative effects of air pollution on tourism only when tourism development has reached a certain level. Based on the above findings, the associated policy implications are discussed.

Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

Particulate matter 2.5 exposure during pregnancy and birth outcomes: Evidence from Colombia

Particulate matter is a type of air pollution that consists of fine particles with a diameter <2.5 μm (PM2.5), which can easily penetrate the respiratory system and enter the bloodstream, increasing health risks for pregnant women and their unborn babies. Recent reports have suggested that there is a positive association between PM2.5 exposure and adverse pregnancy outcomes. However, most evidence of this relationship comes from Western countries. Thus, the objective of this study was to evaluate the association between PM2.5 exposure during pregnancy and birth outcomes among pregnant women in Colombia. This study included 542,800 singletons born in 2019 to Colombian women, aged 15+ years, residing in 981 municipalities. Data on parental, child and birth characteristics were extracted from anonymized live birth records. Satellite-based estimates of monthly PM2.5 concentrations at the surface level were extracted for each municipality from the Atmospheric Composition Analysis Group (ACAG). PM2.5 exposure during pregnancy was indicated by the monthly average of PM2.5 concentrations across the pregnancy duration for the municipality where the child was born. The associations of municipality-level PM2.5 concentration during pregnancy with pre-term birth (PTB) and low birth weight (LBW) were tested in separate two-level logistic regression models, with babies nested within municipalities. The prevalence of PTB and LBW were 8.6 % and 8.3 %, respectively. The mean PM2.5 concentration across the 981 municipalities was 18.26 ± 3.30 μg/m3, ranging from 9.11 to 31.44 μg/m3. Greater PM2.5 concentration at municipality level was associated with greater odds of PTB (1.05; 95%CI: 1.04-1.06) and LBW (1.04; 95%CI: 1.03-1.05), after adjustment for confounders. Our findings provide new evidence on the association between PM2.5 on adverse pregnancy outcomes from a middle-income country.

Quantifying air quality co-benefits to industrial decarbonization: the local Air Emissions Tracking Atlas

Introduction

Many decarbonization technologies have the added co-benefit of reducing short-lived climate pollutants, such as particulate matter (PM), nitrogen oxides (NOx), and sulfur dioxide (SO2), creating a unique opportunity for identifying strategies that promote both climate change solutions and opportunities for air quality improvement. However, stakeholders and decision-makers may struggle to quantify how these co-benefits will impact public health for the communities most affected by industrial air pollution.

Methods

To address this problem, the LOCal Air Emissions Tracking Atlas (LOCAETA) fills a data availability and analysis gap by providing estimated air quality benefits from industrial decarbonization options, such as carbon capture and storage (CCS). These co-benefits are calculated using an algorithm that connects disparate datasets that separately report greenhouse gas emissions and other pollutants at U.S. industrial facilities.

Results

Version 1.0 of LOCAETA displays the estimated primary PM2.5 emission reduction co-benefits from additional pretreatment equipment for CCS on industrial and power facilities across the state of Louisiana, as well as the potential for VOC and NH3 generation. The emission reductions are presented in the tool alongside facility pollutant emissions information and relevant air quality, environmental, demographic, and public health datasets, such as air toxics cancer risk, satellite and in situ pollutant measurements, and population vulnerability metrics.

Discussion

LOCAETA enables regulators, policymakers, environmental justice communities, and industrial and commercial users to compare and contrast quantifiable public health benefits due to air quality impacts from various climate change mitigation strategies using a free and publicly-available tool. Additional pollutant reductions can be calculated using the same methodology and will be available in future versions of the tool.

Exploring the Burden of PM2.5-Related Deaths and Economic Health Losses in Beijing

Air pollution is one of the major global public health challenges. Using annual fine particulate matter (PM2.5) concentration data from 2016 to 2021, along with the global exposure mortality model (GEMM), we estimated the multi-year PM2.5-pollution-related deaths divided by different age groups and diseases. Then, using the VSL (value of statistical life) method, we assessed corresponding economic losses and values. The number of deaths attributed to PM2.5 in Beijing in 2021 fell by 33.74 percent from 2016, while health economic losses would increase by USD 4.4 billion as per capita disposable income increases year by year. In 2021, the average annual concentration of PM2.5 in half of Beijing's municipal administrative districts is less than China's secondary ambient air quality standard (35 μg/m3), but it can still cause 48,969 deaths and corresponding health and economic losses of USD 16.31 billion, equivalent to 7.9 percent of Beijing's GDP. Therefore, it is suggested that more stringent local air quality standards should be designated to protect public health in Beijing.

Continued Rise in Health Burden from Ambient PM2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations

Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock-Lung study. KRAS mutations were 3.8-fold more common in adenocarcinomas of never smokers from North America and Europe, while a 1.6-fold higher prevalence of EGFR and TP53 mutations was observed in adenocarcinomas from East Asia. Signature SBS40a, with unknown cause, was found in most samples and accounted for the largest proportion of single base substitutions in adenocarcinomas, being enriched in EGFR-mutated cases. Conversely, the aristolochic acid signature SBS22a was almost exclusively observed in patients from Taipei. Even though LCINS exposed to secondhand smoke had an 8.3% higher mutational burden and 5.4% shorter telomeres, passive smoking was not associated with driver mutations in cancer driver genes or the activities of individual mutational signatures. In contrast, patients from regions with high levels of air pollution were more likely to have TP53 mutations while exhibiting shorter telomeres and an increase in most types of somatic mutations, including a 3.9-fold elevation of signature SBS4 (q-value=3.1 × 10-5), previously linked mainly to tobacco smoking, and a 76% increase of clock-like signature SBS5 (q-value=5.0 × 10-5). A positive dose-response effect was observed with air pollution levels, which correlated with both a decrease in telomere length and an elevation in somatic mutations, notably attributed to signatures SBS4 and SBS5. Our results elucidate the diversity of mutational processes shaping the genomic landscape of lung cancer in never smokers.

Examining dynamic developmental trends: the interrelationship between age-friendly environments and healthy aging in the Chinese population-evidence from China Health and Retirement Longitudinal Study, 2011-2018

BACKGROUND

The objective of this research is to investigate the dynamic developmental trends between Age-Friendly Environments (AFE) and healthy aging in the Chinese population.

METHODS

This study focused on a sample of 11,770 participants from the CHARLS and utilized the ATHLOS Healthy Aging Index to assess the level of healthy aging among the Chinese population. Linear mixed model (LMM) was used to explore the relationship between AFE and healthy aging. Furthermore, a cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM) were used to examine the dynamic developmental trends of healthy aging, taking into account both Between-Person effects and Within-Person effects.

RESULTS

The results from LMM showed a positive correlation between AFE and healthy aging (β = 0.087, p < 0.001). There was a positive interaction between the geographic distribution and AFE (central region * AFE: β = 0.031, p = 0.038; eastern region * AFE: β = 0.048, p = 0.003). In CLPM and RI-CLPM, the positive effect of healthy aging on AFE is a type of Between-Person effects (β ranges from 0.147 to 0.159, p < 0.001), while the positive effect of AFE on healthy aging is Within-Person effects (β ranges from 0.021 to 0.024, p = 0.004).

CONCLUSION

Firstly, individuals with high levels of healthy aging are more inclined to actively participate in the development of appropriate AFE compared to those with low levels of healthy aging. Furthermore, by encouraging and guiding individuals to engage in activities that contribute to building appropriate AFE, can elevate their AFE levels beyond the previous average level, thereby improving their future healthy aging levels. Lastly, addressing vulnerable groups by reducing disparities and meeting their health needs effectively is crucial for fostering healthy aging in these populations.

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

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

Rapid hydrolysis of NO2 at High Ionic Strengths of Deliquesced Aerosol Particles

Nitrogen dioxide (NO2) hydrolysis in deliquesced aerosol particles forms nitrous acid and nitrate and thus impacts air quality, climate, and the nitrogen cycle. Traditionally, it is considered to proceed far too slowly in the atmosphere. However, the significance of this process is highly uncertain because kinetic studies have only been made in dilute aqueous solutions but not under high ionic strength conditions of the aerosol particles. Here, we use laboratory experiments, air quality models, and field measurements to examine the effect of the ionic strength on the reaction kinetics of NO2 hydrolysis. We find that high ionic strengths (I) enhance the reaction rate constants (kI) by more than an order of magnitude compared to that at infinite dilution (kI=0), yielding log10(kI/kI=0) = 0.04I or rate enhancement factor = 100.04I. A state-of-the-art air quality model shows that the enhanced NO2 hydrolysis reduces the negative bias in the simulated concentrations of nitrous acid by 28% on average when compared to field observations over the North China Plain. Rapid NO2 hydrolysis also enhances the levels of nitrous acid in other polluted regions such as North India and further promotes atmospheric oxidation capacity. This study highlights the need to evaluate various reaction kinetics of atmospheric aerosols with high ionic strengths.

Wildfire particulate exposure and risks of preterm birth and low birth weight in the Southwestern United States

OBJECTIVES

Wildfire air pollution is a growing concern on human health. The study aims to assess the associations between wildfire air pollution and pregnancy outcomes in the Southwestern United States.

STUDY DESIGN

This was a retrospective cohort study.

METHODS

Birth records of 627,404 singleton deliveries in 2018 were obtained in eight states of the Southwestern United States and were linked to wildfire-sourced fine particulate matter (PM2.5) and their constituents (black carbon [BC] and organic carbon [OC]) during the entire gestational period. A double-robust logistic regression model was used to assess the associations of wildfire-sourced PM2.5 exposures and preterm birth and term low birth weight, adjusting for non-fire-sourced PM2.5 exposure and individual- and area-level confounder variables.

RESULTS

Wildfire-sourced PM2.5 contributed on average 15% of the ambient total PM2.5 concentrations. For preterm birth, the strongest association was observed in the second trimester (odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.05-1.07 for PM2.5; 1.06, 95% CI: 1.05-1.07 for BC; 1.04, 95% CI: 1.03-1.05 for OC, per interquartile range increment of exposure), with higher risks identified among non-smokers or those with low socio-economic status. For term low birth weight, the associations with wildfire-sourced PM2.5 exposures were consistently elevated for all trimesters except for the exposure averaged over the entire gestational period. Overall, the associations between wildfire-sourced PM2.5 and pregnancy outcomes were stronger than those with total PM2.5.

CONCLUSIONS

Wildfire-sourced PM2.5 and its constituents are linked to higher risks of preterm birth and term low birth weight among a significant US population than the effects of ambient total PM2.5.

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.

Elucidating the changing particulate matter pollution and associated health effects in rural India during 2000-2019

Atmospheric pollution is a serious problem in many countries, including India, and it is generally considered as an urban issue. To fill the knowledge gap about particulate pollution and its adverse health effects in rural India for well-informed region-specific policy interventions, we present new insights on the rural pollution of India in terms of PM2.5. Here, we analyse PM2.5 pollution and its associated health burden in rural India using satellite and reanalyses data for the period 2000-2019. We observe a gradual and consistent rise of atmospheric pollution in rural areas of India. The highest PM2.5 levels are observed in Indo-Gangetic Plain (IGP) during winter and post-monsoon seasons (107.0 ± 17.0 and 91.0 ± 21.7 μg/m3, respectively). A dipole reversal in seasonal trends between winter and post-monsoon seasons is found for black carbon (BC) and organic carbon (OC) in the rural IGP. The rural North West India (NWI) experiences elevated PM2.5 concentrations due to dust storms, while the rural hilly region (HR) in the Himalaya remains the least polluted region in India. The highest PM2.5 associated cardiopulmonary mortality in 2019 is observed in the rural IGP districts (1000-5100), whereas the highest mortality due to lung cancer at district level accounts for 10-60 deaths. The highest mortality attributed to PM2.5 is observed in districts of Uttar Pradesh, Bihar, West Bengal, Punjab, Haryana and Rajasthan. The priority-wise segregation of states as per World Health Organisation (WHO) Interim targets (ITs), as assessed in this study, might be helpful in implementation and development of policies in phases. We, therefore, present the first detailed study on the PM2.5 pollution in rural India, and provide valuable insights on its distribution, variability, sources and associated mortality, and emphasize the need for addressing this issue to protect public health.

Global synergy of carbon and pollution emissions among countries with different income levels and development stages

The world was drift away on the sustainable development goals (SDGs), whatever global countries claimed fighting for. It's thus essential to illustrate the status of development and environmental quality simultaneously. Resource consumption and energy consumption as the basic needs in supporting human societal development, are commonly used, because they come from the same source and are most directly observed in the open air. We thus examined nexus of carbon and pollution emissions, which also directly indicate residents' livelihood and lifestyle. The possibility of the nexus shifts among income levels with population stack analysis was further investigated. Our findings indicate that the diverse nexus is strongly correlated with development levels, with urban areas being the primary contributor to high carbon and/or pollution emissions despite occupying only 0.5% of global territory. We conclude that expecting leapfrog stages of the nexus is unrealistic, as cross-income-level change requires approximately 80% of the population to significant change its livelihood and lifestyle. Therefore, we recommend setting science-based targets for decoupling carbon and pollution emissions from development are necessary, but should be adapted and tailored to each country's local practice.

The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.

The mortality burden attributable to wood heater smoke particulate matter (PM2.5) in Australia

Air pollution is the leading environmental risk factor for mortality worldwide. In Australia, residential wood heating is the single largest source of pollution in many regions of the country. Estimates around the world and in some limited locations across Australia have shown that the health burden attributable to wood heating PM2.5 is considerable, and that there is great potential to reduce this burden. Here, we aimed to calculate the mortality burden attributable to wood heating emissions (WHE)-related PM2.5 throughout Australia and estimate the potential health benefits of reducing WHE-related air pollution, by replacing wood heaters with cleaner heating technologies. In summary, we used a four-stage process to (1) compile a nationwide WHE inventory, (2) generate annual exposure estimates of WHE-PM2.5, (3) estimate the annual mortality burden attributable to wood heater use across Australia for the year 2015, and (4) assess the potential health benefits of replacing existing wood heaters with cleaner heating technologies. We estimated that population weighted WHE-PM2.5 exposure across Australia for 2015 ranged between 0.62 μg/m3 and 1.35 μg/m3, with differing exposures across State/Territories. We estimated a considerable mortality burden attributable to WHE-PM2.5 ranging between 558 (95 % CI, 364-738) and 1555 (95 % CI, 1180-1740) deaths annually, depending on the scenario assessed. We calculated that replacing 50 % of the current wood heater stock, with zero or lower emission technologies could produce relevant health benefits, of between $AUD 1.61 and $AUD 1.93 billion per year (303-364 attributable deaths). These findings provide a preliminary and likely conservative assessment of the health burden of wood heater smoke across Australia, and an estimation of the potential benefits from replacing the current wood heater stock with cleaner technologies. The results presented here underscore the magnitude of the health burden attributable to wood heating in Australia.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Quantifying Prescribed-Fire Smoke Exposure Using Low-Cost Sensors and Satellites: Springtime Burning in Eastern Kansas

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March-May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hr median PM2.5 to increase by 3.0-5.3 μg m-3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite-derived product aligned with our ground-based measurements. Satellite-derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Differential DNA methylation in the brain as potential mediator of the association between traffic-related PM2.5 and neuropathology markers of Alzheimer's disease

INTRODUCTION

Growing evidence indicates that fine particulate matter (PM2.5) is a risk factor for Alzheimer's disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as a potential mediator of this association.

METHODS

We assessed genome-wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic-related PM2.5 exposure 1, 3, and 5 years prior to death. We used a combination of the Meet-in-the-Middle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs.

RESULTS

PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-four CpG sites were identified as mediators of the association between PM2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation.

DISCUSSION

Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM2.5 and AD.

HIGHLIGHTS

First study to evaluate the potential mediation effect of DNA methylation for the association between PM2.5 exposure and neuropathological changes of Alzheimer's disease. Study was based on brain tissues rarely investigated in previous air pollution research. Cg10495669, assigned to RBCK1 gene playing a role in inflammation, was associated consistently with 1-year, 3-year, and 5-year traffic-related PM2.5 exposures prior to death. Meet-in-the-middle approach and high-dimensional mediation analysis were used simultaneously to increase the potential of identifying the differentially methylated CpGs. Differential DNAm related to neuroinflammation was found to mediate the association between traffic-related PM2.5 and Alzheimer's disease.