Integrated meta-analyses of genome-wide effects of PM2.5 in human cells identifies widespread dysregulation of genes and pathways associated with cancer progression and patient survival.

Epidemiological studies have consistently shown a positive association between exposure to ambient PM2.5, a major component of air pollution, and various types of cancer. Previous biological research has primarily focused on the association between PM2.5 and lung cancer, with limited investigation into other cancer types. In this study, we conducted a meta-analysis on multiple PM2.5-treated normal human cell lines to identify potential molecular targets and pathways of PM2.5. Our analysis revealed 310 common differentially expressed genes (DEGs) that exhibited significant dysregulation upon exposure to PM2.5. These dysregulated genes covered a diverse range of functional categories, including oncogenes, tumor suppressor genes, and immune-related genes, which collectively contribute to PM2.5-induced carcinogenesis. Pathway enrichment analysis revealed the up-regulation of pathways associated with HIF-1, VEGF, and MAPK signalling, all of which have been implicated in various cancers. Induction in the levels of HIF pathway genes (HIF1⍺, HIF2⍺, VEGFA, BNIP3, EPO and PGK1) upon PM2.5 treatment was also confirmed by qRT-PCR. Furthermore, the construction of a protein-protein interaction (PPI) network unveiled hub genes, such as NQO1 and PDGFRB, that are known to be dysregulated and significantly correlated with overall survival in lung and breast cancer patients, suggesting their potential clinical significance. This study provides a deep insight into how PM2.5-mediated dysregulation of oncogenes or tumor suppressor genes across various human tissues may play an important role in PM2.5-induced carcinogenesis. Further exploration of these dysregulated molecular targets may enhance our understanding of the biological effects of PM2.5 and facilitate the development of preventive strategies and targeted therapies for PM2.5-associated cancers.

Protecting Child Health From Air Pollution in India.

Recent research has underscored the diverse ways in which air pollution detrimentally affects child health in India. Notably, India shoulders one of the highest burdens of mortality of children under five years of age globally due to exposure to air pollution. Distinct mitigation strategies are vital to reduce air pollution exposure and its resultant health burdens among children in India when compared to strategies applicable in the global West. This necessity arises due to the substantial influence of residential combustion of solid fuels, and considerable disparities prevalent among India's population. Addressing these unique challenges requires widespread awareness, community engagement, and sustainable policies. As India embarked on a mission to reduce air pollution, showcasing health benefits linked to interventions is crucial. Augmenting access to health data is equally essential to bolster evidence-based policymaking aimed at reducing the child health burden stemming from air pollution in India.

Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications.

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 µg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 µg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Excess Mortality Risk Due to Heat Stress in Different Climatic Zones of India.

India is at a high risk of heat stress-induced health impacts and economic losses owing to its tropical climate, high population density, and inadequate adaptive planning. The health impacts of heat stress across climate zones in India have not been adequately explored. Here, we examine and report the vulnerability to heat stress in India using 42 years (1979-2020) of meteorological data from ERA-5 and developed climate-zone-specific percentile-based human comfort class thresholds. We found that the heat stress is usually 1-4 °C higher on heatwave (HW) days than on nonheatwave (NHW) days. However, the stress on NHW days remains considerable and cannot be neglected. We then showed the association of a newly formulated India heat index (IHI) with daily all-cause mortality in three cities - Delhi (semiarid), Varanasi (humid subtropical), and Chennai (tropical wet and dry), using a semiparametric quasi-Poisson regression model, adjusted for nonlinear confounding effects of time and PM2.5. The all-cause mortality risk was enhanced by 8.1% (95% confidence interval, CI: 6.0-10.3), 5.9% (4.6-7.2), and 8.0% (1.7-14.2) during "sweltering" days in Varanasi, Delhi, and Chennai, respectively, relative to "comfortable" days. Across four age groups, the impact was more severe in Varanasi (ranging from a 3.2 to 7.5% increase in mortality risk for a unit rise in IHI) than in Delhi (2.6-4.2% higher risk) and Chennai (0.9-5.7% higher risk). We observed a 3-6 days lag effect of heat stress on mortality in these cities. Our results reveal heterogeneity in heat stress impact across diverse climate zones in India and call for developing an early warning system keeping in mind these regional variations.

Cumulative effect of PM2.5 components is larger than the effect of PM2.5 mass on child health in India.

While studies on ambient fine particulate matter (PM2.5) exposure effect on child health are available, the differential effects, if any, of exposure to PM2.5 species are unexplored in lower and middle-income countries. Using multiple logistic regression, we showed that for every 10 µg m-3 increase in PM2.5 exposure, anaemia, acute respiratory infection, and low birth weight prevalence increase by 10% (95% uncertainty interval, UI: 9-11), 11% (8-13), and 5% (4-6), respectively, among children in India. NO3-, elemental carbon, and NH4+ were more associated with the three health outcomes than other PM2.5 species. We found that the total PM2.5 mass as a surrogate marker for air pollution exposure could substantially underestimate the true composite impact of different components of PM2.5. Our findings provide key indigenous evidence to prioritize control strategies for reducing exposure to more toxic species for greater child health benefits in India.

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India.

Ambient PM2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM2.5 in recent years (2017-2022) over India, which showed fivefold cross-validation R2 of 0.92 and root mean square error (RMSE) of 11.8 µg m-3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638-110,597) during these years. With the gap-filled data, we found that the rising ambient PM2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM2.5 for 2022 (991,058:798,220-1,183,896) comparable to the 2017 level (1,014,766:812,186-1,217,346). Therefore, a decline in exposure alone is not sufficient to significantly reduce the health burden attributable to ambient PM2.5 in India.

An environmental justice analysis of air pollution in India.

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 µg/m3 (95% CI 0.062 µg/m3, 0.192 µg/m3) and 0.199 µg/m3 (95% CI 0.116 µg/m3, 0.283 µg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

India's photovoltaic potential amidst air pollution and land constraints.

India aims for ambitious solar energy goal to fulfill its climate commitment but there are limited studies on solar resource assessment considering both environmental and land availability constraints. The present work attempts to address this issue using satellite-derived air pollution, radiation, and land use data over the Indian region. Surface insolation over India has been decreasing at a rate of -0.29 ± 0.19 Wm-2 y-1 between 2001 and 2018. Solar resources over nearly 98%, 40%, and 39% of the Indian landmass are significantly impacted by aerosols, clouds, and both aerosols and clouds respectively. Only 29.3% of the Indian landmass is presently suitable for effective solar photovoltaic harnessing, but this is further declining by -0.21% annually, causing a presumptive loss of 50 GW solar potential, translating 75 TWh power generation. Lowering two decades of aerosol burden can make 8% additional landmass apt for photovoltaic use. Alleviating aerosol-induced dimming can fast-track India's solar energy expansion.

A cross-sectional analysis of ambient fine particulate matter (PM2.5) exposure and haemoglobin levels in children aged under 5 years living in 36 countries.

Low haemoglobin (Hb) concentrations and anaemia in children have adverse effects on development and functioning, some of which may have consequences in later life. Exposure to ambient air pollution is reported to be associated with anaemia, but there is little evidence specific to low- and middle-income countries (LMICs), where childhood anaemia prevalence is greatest. We aimed to determine if long-term ambient fine particulate matter (≤2.5 µm in aerodynamic diameter [PM2.5]) exposure was associated with Hb levels and the prevalence of anaemia in children aged <5 years living in 36 LMICs. We used Demographic and Health Survey data, collected between 2010 and 2019, which included blood Hb measurements. Satellite-derived estimates of annual average PM2.5 was the main exposure variable, which was linked to children's area of residence. Anaemia was defined according to standard World Health Organization guidelines (Hb < 11 g/dL). The association of PM2.5 with Hb levels and anaemia prevalence was examined using multivariable linear and logistic regression models, respectively. We examined whether the effects of ambient PM2.5 were modified by a child's sex and age, household wealth index, and urban/rural place of residence. Models were adjusted for relevant covariates, including other outdoor pollutants and household cooking fuel. The study included 154,443 children, of which 89,904 (58.2%) were anaemic. The country-level prevalence of anaemia ranged from 15.8% to 87.9%. Mean PM2.5 exposure was 33.0 (±21.6) µg/m3. The adjusted model showed that a 10 µg/m3 increase in annual PM2.5 concentration was associated with greater odds of anaemia (OR = 1.098 95% CI: 1.087, 1.109). The same increase in PM2.5 was associated with a decrease in average Hb levels of 0.075 g/dL (95% CI: 0.081, 0.068). There was evidence of effect modification by household wealth index and place of residence, with greater adverse effects in children from lower wealth quintiles and children in rural areas. Exposure to annual PM2.5 was cross-sectionally associated with decreased blood Hb levels, and greater risk of anaemia, in children aged <5 years living in 36 LMICs.

A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China.

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR2 of 0.68 at the daily level and 0.93 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was enforced in 2013, ranging from 10.4 to 11.5 µg m-3. But the sulfate level dramatically decreased to 7.7 µg m-3 in 2018, with a change rate of -28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.

A cross-sectional analysis of long-term exposure to ambient air pollution and cognitive development in children aged 3-4 years living in 12 low- and middle-income countries.

Exposure to ambient air pollution may affect cognitive functioning and development in children. Unfortunately, there is little evidence available for low- and middle-income countries (LMICs), where air pollution levels are highest. We analysed the association between exposure to ambient fine particulate matter (≤2.5 µm [PM2.5]) and cognitive development indicators in a cross-sectional analysis of children (aged 3-4 years) in 12 LMICs. We linked Demographic and Health Survey data, conducted between 2011 and 2018, with global estimates of PM2.5 mass concentrations to examine annual average exposure to PM2.5 and cognitive development (literacy-numeracy and learning domains) in children. Cognitive development was assessed using the United Nations Children's Fund's early child development indicators administered to each child's mother. We used multivariable logistic regression models, adjusted for individual- and area-level covariates, and multi-pollutant models (including nitrogen dioxide and surface-level ozone). We assessed if sex and urban/rural status modified the association of PM2.5 with the outcome. We included 57,647 children, of whom, 9613 (13.3%) had indicators of cognitive delay. In the adjusted model, a 5 µg/m3 increase in annual all composition PM2.5 was associated with greater odds of cognitive delay (OR = 1.17; 95% CI: 1.13, 1.22). A 5 µg/m3 increase in anthropogenic PM2.5 was also associated with greater odds of cognitive delay (OR = 1.05; 95% CI: 1.00, 1.10). These results were robust to several sensitivity analyses, including multi-pollutant models. Interaction terms showed that urban-dwelling children had greater odds of cognitive delay than rural-dwelling children, while there was no significant difference by sex. Our findings suggest that annual average exposure to PM2.5 in young children was associated with adverse effects on cognitive development, which may have long-term consequences for educational attainment and health.

Atmospheric elemental carbon pollution and its regional health disparities in China.

Previous studies have reported that atmospheric elemental carbon (EC) may pose potentially elevated toxicity when compared to total ambient fine particulate matter (PM2.5). However, most research on EC has been conducted in the US and Europe, whereas China experiences significantly higher EC pollution levels. Investigating the health impact of EC exposure in China presents considerable challenges due to the absence of a monitoring network to document long-term EC levels. Despite extensive studies on total PM2.5 in China over the past decade and a significant decrease in its concentration, changes in EC levels and the associated mortality burden remain largely unknown. In our study, we employed a combination of satellite remote sensing, available ground observations, machine learning techniques, and atmospheric big data to predict ground EC concentrations across China for the period 2005-2018, achieving a spatial resolution of 10 km. Our findings reveal that the national average annual mean EC concentration has remained relatively stable since 2005, even as total PM2.5 levels have substantially decreased. Furthermore, we calculated the all-cause non-accidental deaths attributed to long-term EC exposure in China using baseline mortality data and pooled mortality risk from a cohort study. This analysis unveiled significant regional disparities in the mortality burden resulting from long-term EC exposure in China. These variations can be attributed to varying levels of effectiveness in EC regulations across different regions. Specifically, our study highlights that these regulations have been effective in mitigating EC-related health risks in first-tier cities. However, in regions characterized by a high concentration of coal-power plants and industrial facilities, additional efforts are necessary to control emissions. This observation underscores the importance of tailoring environmental policies and interventions to address the specific challenges posed by varying emission sources and regional contexts.

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China.

Ambient PM2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R2 of 0.84 and an RMSE of 4.9 µg/m3, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.

Assessment of long-term particulate nitrate air pollution and its health risk in China.

Air pollution is a major environmental and public health challenge in China and the Chinese government has implemented a series of strict air quality policies. However, particulate nitrate (NO3 -) concentration remains high or even increases at monitoring sites despite the total PM2.5 concentration has decreased. Unfortunately, it has been difficult to estimate NO3 - concentration across China due to the lack of a PM2.5 speciation monitoring network. Here, we use a machine learning model incorporating ground measurements and satellite data to characterize the spatiotemporal patterns of NO3 -, thereby understanding the disease burden associated with long-term NO3 - exposure in China. Our results show that existing air pollution control policies are effective, but increased NO3 - of traffic emissions offset reduced NO3 - of industrial emissions. In 2018, the national mean mortality burden attributable to NO3 - was as high as 0.68 million, indicating that targeted regulations are needed to control NO3 - pollution in China.

Impact of Air Pollution on Child Health in India and the Way Forward.

Recent research in epidemiological modelling reveals that air pollution affects child health in various ways resulting in low birthweight, stillbirth, preterm birth, developmental delay, growth failure, poor respiratory and cardiovascular health, and a higher risk of anemia. India has embarked on the national clean air program, but a much stronger coordinated multi-sectoral approach is required to minimize the child health burden caused by air pollution. Air pollution should be treated as a public health crisis that can only be managed with policy backed by science, gradual transition to clean energy use, emission reduction supported by clean air technologies, long-term commitment from the Government, and cooperation of the citizens.

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India.

The association between daily all-cause mortality and short-term fine particulate matter (PM2.5) exposure is well established in the literature. However, association between acute exposure to PM2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO3-, NH4NO3, Cr, NH4+, EC, and OC showed a higher mortality impact than the total PM2.5 mass. Males were at higher risk from NO3-, SO42-, and their NH4+ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

Robust relationship between ambient air pollution and infant mortality in India.

BACKGROUND: Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. We estimate the associations between in-utero and perinatal exposure to PM2.5 and infant, neonatal and postneonatal mortality in India. We evaluate the sensitivity of this association to two widely-used exposure assessments. METHOD: We linked nationally representative anthropometric data from India's 2015-2016 Demographic and Health Survey (n = 259,627 children under five across 640 districts of India) with satellite-based PM2.5 concentrations during the month of birth of each child. We then estimated the associations between PM2.5 from each dataset and child mortality, after controlling for child, mother and household factors including trends in time and seasonality. We examined if factors: urban/rural, sex, wealth quintile and state modified the associations derived from the two datasets using Wald tests. RESULTS: We found evidence that PM2.5 impacts infant mortality primarily through neonatal mortality. The estimated association between neonatal mortality and PM2.5 in trimester 3 was OR: 1.016 (95% CI: 1.003, 1.030) for every 10 µg/m3 increase in exposure. This association was robust to the exposure assessment used. Child sex was a significant effect modifier, with PM2.5 impacting mortality in infant girls more than boys. CONCLUSIONS: Our results revealed a robust association between ambient exposure to PM2.5 in the latter period of pregnancy and early life with infant and neonatal mortality in India. Urgent air pollution management plans are needed to improve infant mortality in India.

Child Survival and Early Lifetime Exposures to Ambient Fine Particulate Matter in India: A Retrospective Cohort Study.

BACKGROUND: Ambient fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] is a major health risk for children, particularly in South Asia, which currently experiences the highest PM2.5 levels globally. Nevertheless, there is comparatively little epidemiological evidence from this region to quantify the effects of PM2.5 on child survival. OBJECTIVES: We estimated the association between PM2.5 exposure and child survival in India. METHODS: We constructed a large, retrospective, and nationally representative cohort of children <5 years of age, born between 2009-2016, from the publicly available, cross-sectional 2015-2016 Demographic Health Surveys in India. In utero and post-delivery lifetime average ambient PM2.5 exposures were estimated with data from satellite remote sensing, meteorology, and land use information (model R2= 0.82). We used Cox proportional hazards regression to estimate the association between both average in utero and post-delivery lifetime PM2.5 and all-cause child mortality, controlling for individual- and household-level covariates, seasonality, location, and meteorology. RESULTS: Over 7,447,724 child-months of follow-up, there were 11,559 deaths at <5 years of age reported by the children's mothers. The mean concentrations of 9-month in utero and post-delivery lifetime average ambient PM2.5 exposure were 71.1 µg/m3 (range: 20.9-153.5 µg/m3) and 73.7 µg/m3 (range: 14.0-247.3 µg/m3), respectively. Estimated child mortality adjusted hazard ratios were 1.023 [95% confidence interval (CI): 1.008, 1.038] and 1.013 (95% CI: 1.001, 1.026) per 10-µg/m3 increase of in utero and post-delivery lifetime PM2.5, with both exposures in the model. DISCUSSION: This study adds to the growing body of evidence about the adverse health effects of PM2.5 by demonstrating the association between exposure, both in utero and post-delivery, on child survival at the national level in India. Strategies to reduce ambient air pollution levels, including steps to minimize in utero and early life exposures, are urgently needed in India and other countries where exposures are above recommended guideline values. https://doi.org/10.1289/EHP8910.

Ambient air pollution and acute respiratory infection in children aged under 5 years living in 35 developing countries.

BACKGROUND: Evidence from developed countries suggests that fine particulate matter (≤2.5 µm [PM2.5]) contributes to childhood respiratory morbidity and mortality. However, few analyses have focused on resource-limited settings, where much of this burden occurs. We aimed to investigate the cross-sectional associations between annual average exposure to ambient PM2.5 and acute respiratory infection (ARI) in children aged <5 years living in low- and middle-income countries (LMICs). METHODS: We combined Demographic and Health Survey (DHS) data from 35 countries with gridded global estimates of annual PM2.5 mass concentrations. We analysed the association between PM2.5 and maternal-reported ARI in the two weeks preceding the survey among children aged <5 years living in 35 LMICs. We used multivariable logistic regression models that adjusted for child, maternal, household and cluster-level factors. We also fitted multi-pollutant models (adjusted for nitrogen dioxide [NO2] and surface-level ozone [O3]), among other sensitivity analyses. We assessed whether the associations between PM2.5 and ARI were modified by sex, age and place of residence. RESULTS: The analysis comprised 573,950 children, among whom the prevalence of ARI was 22,506 (3.92%). The mean (±SD) estimated annual concentration of PM2.5 to which children were exposed was 48.2 (±31.0) µg/m3. The 5th and 95th percentiles of PM2.5 were 9.8 µg/m3 and 110.9 µg/m3, respectively. A 10 µg/m3 increase in PM2.5 was associated with greater odds of having an ARI (OR: 1.06; 95% CI: 1.05-1.07). The association between PM2.5 and ARI was robust to adjustment for NO2 and O3. We observed evidence of effect modification by sex, age and place of residence, suggesting greater effects of PM2.5 on ARI in boys, in younger children, and in children living in rural areas. CONCLUSIONS: Annual average ambient PM2.5, as an indicator for long-term exposure, was associated with greater odds of maternal-reported ARI in children aged <5 years living in 35 LMICs. Longitudinal studies in LMICs are required to corroborate our cross-sectional findings, to further elucidate the extent to which lowering PM2.5 may have a role in the global challenge of reducing ARI-related morbidity and mortality in children.