Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

Population exposure to multiple air pollutants and its compound episodes in Europe

Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM2.5, PM10, NO2 and O3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO2 (2.45%) and PM2.5 (1.72%). In contrast, O3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM2.5-NO2 and PM2.5-O3. We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures.

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

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

Cross-sectional association between social and demographic factors and disease activity in rheumatoid arthritis

BACKGROUND

This study aimed to assess the association between social factors, demographic parameters, and disease activity among rheumatoid arthritis (RA) patients.

METHODS

The University of Pittsburgh Rheumatoid Arthritis Comparative Effectiveness Research (RACER) registry was used for this study and included patients meeting 1987 ACR criteria for RA enrolled between 2010-2015. The registry collected clinical and laboratory data at each visit, permitting the calculation of disease activity measures that included Disease Activity 28-C Reactive Protein (DAS28-CRP). The current study was conducted as a cross-sectional study in which baseline data were used to construct multiple logistic regression models assessing the relationship between disease activity measures (DAS28-CRP), functional capacity (health assessment questionnaire (HAQ)), selected demographic and social factors (occupation, education, income, marital status, race, gender, age, and BMI), and clinical/laboratory variables.

RESULTS

The analyses included 729 patients with baseline DAS28-CRP and social/demographic data. The mean age at enrollment was 59.5 (Standard Deviation (SD) = 12.7) years, 78% were female, and the median RA disease duration was 9.8 (Interquartile Range (IQR): 3.7, 19.1) years. We dichotomized the DAS28-CRP score and defined scores above or below 3.1 as high versus low RA disease activity. Most patients with high RA disease activity (N = 326, 45%) had less than a college degree (70%), were not working/retired/disabled (71%), and had an annual income under $50 K (55%). We found that higher body mass index (BMI) (Odds Ratio (OR) = 1.04, 95% CI: 1.01-1.08), longer disease duration (> 2 and < 10 years versus ≤ 2 years of disease) (OR = 0.45, 95% CI: 0.25-0.78), and being retired (OR = 1.74, 95% CI: 1.02-2.98) were associated with RA disease activity.

CONCLUSION

Increased RA activity may be associated with various social factors, potentially leading to more severe and debilitating disease outcomes. These findings provide evidence to support efforts to monitor disparities and achieve health equity in RA.

Effect of air pollution on disease burden, mortality, and life expectancy in North Africa and the Middle East: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Air pollution is the sixth highest risk factor for attributable disability-adjusted life-years (DALYs) in North Africa and the Middle East, but the relative importance of different subtypes of air pollution and any potential differences in their health effects by population demographics or country-level socioeconomic factors have not been fully explored. The objective of this study was to investigate the effect of high ambient particulate matter less than 2·5 μm in size (PM) and ambient ozone air pollution on disease burden, mortality, and life expectancy in 21 countries in the North Africa and the Middle East super-region from 1990 to 2019 using the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study estimates.

METHODS

The study data were derived from GBD 2019, examining data from 1999 to 2019 in North Africa and the Middle East. In this study, the types of air pollution investigated included PM pollution and ambient ozone pollution. PM pollution itself was categorised as household air pollution from solid fuels and ambient PM pollution. The burden attributable to each risk factor, directly or indirectly, was incorporated in the population attributable fraction to estimate the total attributable deaths and DALYs. The summary exposure value (SEV) as the relative risk-weighted prevalence of exposure was extracted to compare the distribution of excess risk times the exposure level in a population where everyone is at maximum risk and ranges from zero (no excess risk exists in a population) to 100 (highest risk). The effect of air pollution on life expectancy was estimated via a cause-deleted life table analysis.

FINDINGS

The age-standardised DALYs rate attributable to air pollution declined by 44·5%, from 4884·2 (95% uncertainty interval 4381·5-5555·4) to 2710·4 (2317·3-3125·6) per 100 000 from 1990 to 2019. Afghanistan (6992·3, 5627·7-8482·7), Yemen (4212·4, 3241·3-5418·1), and Egypt (4034·8, 3027·7-5138·6) had the highest age-standardised DALYs rates attributable to air pollution in 2019 per 100 000, whereas Türkiye (1329·2, 1033·7-1654·7), Jordan (1447·3, 1154·2-1758·5), and Iran (1603·0, 1404·7-1813·8) had the lowest rates. During the study period, the age-standardised SEV of air pollution (PM and ambient ozone in total) decreased by 10·9% (5·8-17·7%) in the super-region, whereas the SEV of ambient ozone pollution alone increased by 7·7% (0·7-14·3%). Among the components of PM pollution, the SEV of ambient PM pollution increased by 40·1% (25·2-63·7%); however, the SEV of household air pollution from solid fuels decreased by 70·6% (64·1-77·0%). Among the investigated types of air pollution, 98·9% of the DALYs from air pollution in the super-region were attributable to PM pollution. If air pollution had been lowered to the theoretical minimum risk exposure levels for 2019, then the average life expectancy would have been 1·6 years higher.

INTERPRETATION

The burden attributable to air pollution substantially decreased in the study period across the super-region as a whole. Most of the burden from air pollution is attributed to PM pollution, the exposure to which has substantially increased in the past three decades. Interventions and policies that reduce population exposure to PM pollution could potentially increase the average life expectancy in the super-region. This finding calls for concerted efforts from governments and public health authorities in the super-region to tackle air pollution as an important threat to population health.

FUNDING

Bill & Melinda Gates Foundation.

Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modelling study

BACKGROUND

Short-term exposure to ambient PM_2·5 is a leading contributor to the global burden of diseases and mortality. However, few studies have provided the global spatiotemporal variations of daily PM_2·5 concentrations over recent decades.

METHODS

In this modelling study, we implemented deep ensemble machine learning (DEML) to estimate global daily ambient PM_2·5 concentrations at 0·1° × 0·1° spatial resolution between Jan 1, 2000, and Dec 31, 2019. In the DEML framework, ground-based PM_2·5 measurements from 5446 monitoring stations in 65 countries worldwide were combined with GEOS-Chem chemical transport model simulations of PM_2·5 concentration, meteorological data, and geographical features. At the global and regional levels, we investigated annual population-weighted PM_2·5 concentrations and annual population-weighted exposed days to PM_2·5 concentrations higher than 15 μg/m³ (2021 WHO daily limit) to assess spatiotemporal exposure in 2000, 2010, and 2019. Land area and population exposures to PM_2·5 above 5 μg/m³ (2021 WHO annual limit) were also assessed for the year 2019. PM_2·5 concentrations for each calendar month were averaged across the 20-year period to investigate global seasonal patterns.

FINDINGS

Our DEML model showed good performance in capturing the global variability in ground-measured daily PM_2·5, with a cross-validation R² of 0·91 and root mean square error of 7·86 μg/m³. Globally, across 175 countries, the mean annual population-weighted PM_2·5 concentration for the period 2000-19 was estimated at 32·8 μg/m³ (SD 0·6). During the two decades, population-weighted PM_2·5 concentration and annual population-weighted exposed days (PM_2·5 >15 μg/m³) decreased in Europe and northern America, whereas exposures increased in southern Asia, Australia and New Zealand, and Latin America and the Caribbean. In 2019, only 0·18% of the global land area and 0·001% of the global population had an annual exposure to PM_2·5 at concentrations lower than 5 μg/m³, with more than 70% of days having daily PM_2·5 concentrations higher than 15 μg/m³. Distinct seasonal patterns were indicated in many regions of the world.

INTERPRETATION

The high-resolution estimates of daily PM_2·5 provide the first global view of the unequal spatiotemporal distribution of PM_2·5 exposure for a recent 20-year period, which is of value for assessing short-term and long-term health effects of PM_2·5, especially for areas where monitoring station data are not available.

FUNDING

Australian Research Council, Australian Medical Research Future Fund, and the Australian National Health and Medical Research Council.

A global review of the state of the evidence of household air pollution's contribution to ambient fine particulate matter and their related health impacts

Direct exposure to household fine particulate air pollution (HAP) associated with inefficient combustion of fuels (wood, charcoal, coal, crop residues, kerosene, etc.) for cooking, space-heating, and lighting is estimated to result in 2.3 (1.6-3.1) million premature yearly deaths globally. HAP emitted indoors escapes outdoors and is a leading source of outdoor ambient fine particulate air pollution (AAP) in low- and middle-income countries, often being a larger contributor than well-recognized sources including road transport, industry, coal-fired power plants, brick kilns, and construction dust. We review published scientific studies that model the contribution of HAP to AAP at global and major sub-regional scales. We describe strengths and limitations of the current state of knowledge on HAP's contribution to AAP and the related impact on public health and provide recommendations to improve these estimates. We find that HAP is a dominant source of ambient fine particulate matter (PM2.5) globally - regardless of variations in model types, configurations, and emission inventories used - that contributes approximately 20 % of total global PM2.5 exposure. There are large regional variations: in South Asia, HAP contributes ∼ 30 % of ambient PM2.5, while in high-income North America the fraction is ∼ 7 %. The median estimate indicates that the household contribution to ambient air pollution results in a substantial premature mortality burden globally of about 0.77(0.54-1) million excess deaths, in addition to the 2.3 (1.6-3.1) million deaths from direct HAP exposure. Coordinated global action is required to avert this burden.

Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates

Since the publication of the first epidemiological study to establish the connection between long-term exposure to atmospheric pollution and effects on human health, major efforts have been dedicated to estimate the attributable mortality burden, especially in the context of the Global Burden of Disease (GBD). In this work, we review the estimates of excess mortality attributable to outdoor air pollution at the global scale, by comparing studies available in the literature. We find large differences between the estimates, which are related to the exposure response functions as well as the number of health outcomes included in the calculations, aspects where further improvements are necessary. Furthermore, we show that despite the considerable advancements in our understanding of health impacts of air pollution and the consequent improvement in the accuracy of the global estimates, their precision has not increased in the last decades. We offer recommendations for future measurements and research directions, which will help to improve our understanding and quantification of air pollution-health relationships.

Update of the WHO global air quality guidelines: Systematic reviews - An introduction

This paper aims to serve as an introduction to the Special Issue in Environment International entitled "Update of the WHO Global Air Quality Guidelines: Systematic Reviews". The article has two main objectives. One is to provide the context to this Special Issue, related to (a) policy context, overall exposure to air pollution, and burden of disease attributable to air pollution, and the other is to describe (b) the WHO guideline development process, with special emphasis on the systematic reviews. In particular, this paper presents the systematic reviews and other supporting evidence that was used and discussed during the process and summarizes important methodological information about the approaches taken to conduct the systematic reviews. These approaches include the definition of population, exposure, comparator, outcomes and study design (PECOS) questions, the assessment of the risk of bias in individual studies and the assessment of the overall certainty of the evidence. In summary, the new WHO global air quality guidelines are informed by the best available scientific evidence covering a vast number of research papers published until September 2018, and appraised by experts and stakeholders in the field of air quality. However, research gaps remain and, therefore, further research is warranted.

Compositional Constraints are Vital for Atmospheric PM2.5 Source Attribution over India

India experiences some of the highest levels of ambient PM_2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM_2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMB_Modified: 0.19; NMB_Base: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMB_Modified: 0.08; NMB_Base: 1.64) and ammonium simulation (NMB_Modified: 0.49; NMB_Base: 0.90). We use this validated simulation to estimate a population-weighted annual PM_2.5 exposure of 61.4 μg m^-3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM_2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM_2.5 aerosol source attribution for health assessments and air quality management in India.

Global burden of chronic obstructive pulmonary disease attributable to ambient particulate matter pollution and household air pollution from solid fuels from 1990 to 2019

We aimed to estimate the spatiotemporal trends in the global burden of chronic obstructive pulmonary disease (COPD) attributable to both household air pollution from solid fuels (HAP) and ambient particulate matter (APM) from 1990 to 2019 and compared the possible differences between the burdens attributable to APM and HAP. The number of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLLs) of COPD attributable to HAP from solid fuels and APM during 1990-2019 were extracted from the Global Burden of Diseases Study 2019. The proportion of YLLs in DALYs and average YLLs per COPD death were also calculated. Subgroup analyses by sex, age, and socio-demographic index (SDI) were conducted. The estimated annual percentage change (EAPC) was used to assess the temporal trend of age-standardized rate of mortality (ASMR) and DALYs (ASDR). Over the past 30 years, we observed a clear downward trend in COPD deaths attributable to HAP and an upward trend by 97.61% in COPD deaths attributable to APM. The global COPD burden attributable to APM in 2019 was higher than those due to HAP, except in low-SDI regions. For both HAP and APM, YLLs continued to predominate in DALYs of COPD, with an average YLLs per death of more than 10 years in different regions. The ASMR was higher in males and lower in high-SDI regions. The ASMR and ASDR attributable to HAP decreased globally in all age groups during 1990-2019, while those attributable to APM increased among people older than 80 years and in regions with lower SDI. Our study reveals an increasing trend in APM-attributable COPD burden over the past three decades. Comparatively, the global burden due to HAP decreased markedly, but it was still pronounced in low-SDI regions. Continued efforts on PM mitigation are needed for COPD prevention.

Mapping Agricultural Lands: From Conventional to Regenerative

In an era in which conventional agriculture has come under question for its environmental and social costs, regenerative agriculture suggests that land management practices can be organized around farming and grazing practices that regenerate interdependent ecological and community processes for generations to come. However, little is known about the geographies of ‘regenerative’ and ‘conventional’ agricultural lands—what defines them, where they are, and the extent to which actual agricultural lands interweave both or are characterizable by neither. In the context of the Midwest of the United States, we develop and map an index quantifying the degrees to which the agricultural lands of counties could be said to be regenerative, conventional, or both. We complement these results by using a clustering method to partition the land into distinct agricultural regions. Both approaches rely on a set of variables characterizing land we developed through an iterative dialogue across difference among our authors, who have a range of relevant backgrounds. We map, analyze, and synthesize our results by considering local contexts beyond our variables, comparing and contrasting the resulting perspectives on the geographies of midwestern agricultural lands. Our results portray agricultural lands of considerable diversity within and between states, as well as ecological and physiographic regions. Understanding the general patterns and detailed empirical geographies that emerge suggests spatial relationships that can inform peer-to-peer exchanges among farmers, agricultural extension, civil society, and policy formation.

Has the public habituated to the haze in China?

The concept of haze habituation was proposed based on haze perception and behavior in this paper. This study employed factor analysis and Potential Conflict Index (PCI) to analyze the dimensions, degrees, and internal differences of the public's haze habituation. Then, K-means clustering algorithm was applied to classify the public into four categories. The entropy method was used to quantitatively evaluate the public's haze habituation, and the natural breakpoint method was used to grade it into five levels. Finally, an ordered logistic regression model was chosen to analyze the influencing factors of the public's haze habituation. The results indicate that: (1) The public's haze habituation can be measured from five dimensions: protective behavior, haze reduction behavior, haze attention, life impact perception, and health impact perception. The public had the same views on protective behavior, haze reduction behavior, life impact perception, and health impact perception. However, there is a wide divergence among the public on the haze attention; (2) Based on the above five dimensions, the public can be divided into the protective sensitive group, attention sensitive group, health sensitive group, and environmental protection sensitive group; (3) Generally, the public has a low haze habituation where the protective behavior, haze reduction behavior, and health impact perception are the crucial elements; (4) Gender, self-health assessment, and travel mode have a significant positive impact on the public's haze habituation, respectively. Age, the family with elders or children, and annual family income have a significant negative impact on the public's haze habituation, respectively.

Modeling Air Pollution Health Risk for Environmental Management of an Internationally Important Site: The Salt Range (Kallar Kahar), Pakistan

This study aimed to assess the health effects of emissions released by cement industries and allied activities, such as mining and transportation, in the salt range area of district Chakwal, Pakistan. DISPER was used to estimate dispersion and contribution of source emission by cement industries and allied activities to surface accumulation of selected pollutants (PM2.5, PM10, NOx, and O3). To assess the long-term effects of pollutants on human health within the radius of 500 m to 3 km, Air Q+ software was used, which was designed by the World Health Organization (WHO). One-year average monitoring data of selected pollutants, coordinates, health data, and population data were used as input data for the model. Data was collected on lung cancer mortality among different age groups (25+ and 30+), infant post-neonatal mortality, mortality due to respiratory disease, and all-cause mortality due to PM2.5 and NO2. Results showed that PM2.5 with the year-long concentration of 27.3 µg/m3 contributes a 9.9% attributable proportion (AP) to lung cancer mortality in adults aged 25+, and 13.8% AP in adults age 30+. Baseline incidence is 44.25% per 100,000 population. PM10 with the year-long concentration of 57.4 µg/m3 contributes 16.96% AP to infant post-neonatal mortality and baseline incidence is 53.86% per 1000 live births in the country. NO2 with the year-long concentration of 14.33 µg/m3 contributes 1.73% AP to all-cause mortality. Results obtained by a simulated 10% reduction in pollutant concentration showed that proper mitigation measures for reduction of pollutants’ concentration should be applied to decrease the rate of mortalities and morbidities. Furthermore, the study showed that PM2.5 and PM10 are significantly impacting the human health in the nearby villages, even after mitigation measures were taken by the selected cement industries. The study provides a roadmap to policymakers and stakeholders for environmental and health risk management in the area.

Dietary shifts can reduce premature deaths related to particulate matter pollution in China

Shifting towards more meat-intensive diets may have indirect health consequences through environmental degradation. Here we examine how trends in dietary patterns in China over 1980-2010 have worsened fine particulate matter (PM_2.5) pollution, thereby inducing indirect health impacts. We show that changes in dietary composition alone, mainly by driving the rising demands for meat and animal feed, have enhanced ammonia (NH₃) emissions from Chinese agriculture by 63% and increased annual PM_2.5 by up to ~10 µg m^-3 (~20% of total PM_2.5 increase) over the period. Such effects are more than double that driven by increased food production solely due to population growth. Shifting the current diet towards a less meat-intensive recommended diet can decrease NH₃ emission by ~17% and PM_2.5 by 2-6 µg m^-3, and avoid ~75,000 Chinese annual premature deaths related to PM_2.5.

Substantial Reduction in Particulate Matter Air Pollution across Europe during 2006-2019: A Spatiotemporal Modeling Analysis

Air pollution poses the largest environmental health risk in Europe. Particulate matter (PM) concentrations are the most harmful pollutants representing the main air quality indicator in the Sustainable Development Goals (SDGs). The air quality surveillance in Europe is based on a monitoring network that is too coarse for a comprehensive evaluation of the air pollution burden. We link raw pollutant data with remotely sensed products using Bayesian geostatistical models and for the first time estimate pan-European near-surface concentrations of both fine (PM2.5) and coarse (PM10) particles at 1 km2 spatial resolution during 2006-2019. We evaluate the compliance with the air quality thresholds set by the World Health Organization (WHO) and the European Union (EU) and assess country-wise trends. The results show that during the last 14 years, PM10 and PM2.5 concentrations declined by 36.5% (95% credible interval: 30.3, 41.9%) and 39.1% (26.6, 50.5%), respectively. The number of people exposed to PM10 levels above the WHO thresholds decreased from 78.3% (52.6, 91.8%) in 2006 to 28.4% (16.2, 43.7%) in 2019; for PM2.5, the decrease was smaller: from 91.0% (61.3, 99.1%) exposed in 2006 to 53.6% (33.5, 76.3%) in 2019. Although there is a clear improvement in the overall picture, stricter measures are needed to ensure compliance with the WHO guidelines.

A guide to value of information methods for prioritising research in health impact modelling

Health impact simulation models are used to predict how a proposed policy or scenario will affect population health outcomes. These models represent the typically-complex systems that describe how the scenarios affect exposures to risk factors for disease or injury (e.g. air pollution or physical inactivity), and how these risk factors are related to measures of population health (e.g. expected survival). These models are informed by multiple sources of data, and are subject to multiple sources of uncertainty. We want to describe which sources of uncertainty contribute most to uncertainty about the estimate or decision arising from the model. Furthermore, we want to decide where further research should be focused to obtain further data to reduce this uncertainty, and what form that research might take. This article presents a tutorial in the use of Value of Information methods for uncertainty analysis and research prioritisation in health impact simulation models. These methods are based on Bayesian decision-theoretic principles, and quantify the expected benefits from further information of different kinds. The expected value of partial perfect information about a parameter measures sensitivity of a decision or estimate to uncertainty about that parameter. The expected value of sample information represents the expected benefit from a specific proposed study to get better information about the parameter. The methods are applicable both to situationswhere the model is used to make a decision between alternative policies, and situations where the model is simply used to estimate a quantity (such as expected gains in survival under a scenario). This paper explains how to calculate and interpret the expected value of information in the context of a simple model describing the health impacts of air pollution from motorised transport. We provide a general-purpose R package and full code to reproduce the example analyses.

Air Pollution as a Determinant of Undernutrition Prevalence among Under-Five Children in India: An Exploratory Study

AIM

The association of air pollution with prevalence of undernutrition indices (namely, anaemia, stunting, wasting and underweight) among under-five children in India was investigated.

METHODS

Estimates of population weighted annual average gridded PM2.5 concentrations and proportion of households using solid cooking fuel (HSCF usage percent) during 2017 in India, reflecting the magnitude of ambient and household air pollution respectively, were extracted in a state-wise manner from India State Level Disease Burden Initiative (ISLDBI) reports. Their relationships with the corresponding prevalence of anaemia, underweight, wasting and stunting in under-five children were analysed.

RESULTS

The state-level PM2.5 concentrations (mean: 65.5 µg/m3; median: 49.2 µg/m3; range: 17.3-209.0 µg/m3) correlated significantly (P < 0.01) with anaemia (r = 0.65), stunting (r = 0.58) and underweight (r = 0.50) prevalence; while HSCF usage (mean: 49.3%; median: 46.0%; range: 1.9-81.5%) correlated significantly (P < 0.01) with stunting (r = 0.69) and underweight (r = 0.58) prevalence. When examined across median cut-offs and after adjusting for socio-demographic index, the association of anaemia prevalence with PM2.5 concentrations persisted. This association was maintained even after controlling for the coverage of anaemia-specific interventions (namely, iron supplements and deworming medications). The mean difference in PM2.5 concentrations between the high and low PM2.5 states was 58.6 µg/m3, which accounted for 11.8% higher anaemia prevalence in the former as compared to the latter.

CONCLUSION

The burden of childhood undernutrition, particularly anaemia, in India may be linked to PM2.5 levels. To mitigate this burden, it may be necessary to complement the ongoing nutritional interventions with air pollution control measures.

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.

Ambient and household PM2.5 pollution and adverse perinatal outcomes: A meta-regression and analysis of attributable global burden for 204 countries and territories

BACKGROUND

Particulate matter <2.5 micrometer (PM2.5) is associated with adverse perinatal outcomes, but the impact on disease burden mediated by this pathway has not previously been included in the Global Burden of Disease (GBD), Mortality, Injuries, and Risk Factors studies. We estimated the global burden of low birth weight (LBW) and preterm birth (PTB) and impacts on reduced birth weight and gestational age (GA), attributable to ambient and household PM2.5 pollution in 2019.

METHODS AND FINDINGS

We searched PubMed, Embase, and Web of Science for peer-reviewed articles in English. Study quality was assessed using 2 tools: (1) Agency for Healthcare Research and Quality checklist; and (2) National Institute of Environmental Health Sciences (NIEHS) risk of bias questions. We conducted a meta-regression (MR) to quantify the risk of PM2.5 on birth weight and GA. The MR, based on a systematic review (SR) of articles published through April 4, 2021, and resulting uncertainty intervals (UIs) accounted for unexplained between-study heterogeneity. Separate nonlinear relationships relating exposure to risk were generated for each outcome and applied in the burden estimation. The MR included 44, 40, and 40 birth weight, LBW, and PTB studies, respectively. Majority of the studies were of retrospective cohort design and primarily from North America, Europe, and Australia. A few recent studies were from China, India, sub-Saharan Africa, and South America. Pooled estimates indicated 22 grams (95% UI: 12, 32) lower birth weight, 11% greater risk of LBW (1.11, 95% UI: 1.07, 1.16), and 12% greater risk of PTB (1.12, 95% UI: 1.06, 1.19), per 10 μg/m3 increment in ambient PM2.5. We estimated a global population-weighted mean lowering of 89 grams (95% UI: 88, 89) of birth weight and 3.4 weeks (95% UI: 3.4, 3.4) of GA in 2019, attributable to total PM2.5. Globally, an estimated 15.6% (95% UI: 15.6, 15.7) of all LBW and 35.7% (95% UI: 35.6, 35.9) of all PTB infants were attributable to total PM2.5, equivalent to 2,761,720 (95% UI: 2,746,713 to 2,776,722) and 5,870,103 (95% UI: 5,848,046 to 5,892,166) infants in 2019, respectively. About one-third of the total PM2.5 burden for LBW and PTB could be attributable to ambient exposure, with household air pollution (HAP) dominating in low-income countries. The findings should be viewed in light of some limitations such as heterogeneity between studies including size, exposure levels, exposure assessment method, and adjustment for confounding. Furthermore, studies did not separate the direct effect of PM2.5 on birth weight from that mediated through GA. As a consequence, the pooled risk estimates in the MR and likewise the global burden may have been underestimated.

CONCLUSIONS

Ambient and household PM2.5 were associated with reduced birth weight and GA, which are, in turn, associated with neonatal and infant mortality, particularly in low- and middle-income countries.

Contribution of fine particulate matter to present and future premature mortality over Europe: A non-linear response

The World Health Organization estimates that around 7 million people die every year from exposure to fine particles (PM2.5) inpolluted air. Here, the number of premature deaths in Europe from different diseases associated to the ambient exposure to PM2.5 have here been studied both for present (1991-2010) and future periods (2031-2050, RCP8.5 scenario). This contribution combines different state-of-the-art approaches (use of high-resolution climate/chemistry simulations over Europe for providing air quality data; use of different baseline mortality data for specific European regions; inclusion of future population projections and dynamical changes for 2050 obtained from the United Nations (UN) Population Projections or use of non-linear exposure-response functions) to estimate the premature mortality due to PM2.5. The mortality endpoints included in this study are Lung Cancer (LC), Chronic Obstructive Pulmonary Disease (COPD), Cerebrovascular Disease (CEV), Ischemic Heart Disease (IHD), Lower Respiratory Infection (LRI) and other Non-Communicable Diseases (other NCDs). Different risk ratio and baseline mortalities for each disease end each age range have been estimated individually. The results indicate that the annual excess mortality rate from fine particulate matter in Europe is 904,000 [95% confidence interval (95% CI) 733,100-1,067,800], increasing by 73% in 2050s (1,560,000; 95% CI 1,260,000-1,840,000); meanwhile population decreases from 808 to 806 million according to the UN estimations. The results show that IHD is the main cause of premature mortality in Europe associated to PM2.5 (around 48%) both for the present and future periods. Despite several marked regional differences, premature deaths associated to all the endpoints included in this study will increase in the future period due to the climate penalty but especially because of changes in the population projected and its aging.

Satellite Monitoring for Air Quality and Health

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM_2.5) and nitrogen dioxide (NO₂). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales

Ambient fine particulate matter (PM_2.5) is the world's leading environmental health risk factor. Reducing the PM_2.5 disease burden requires specific strategies that target dominant sources across multiple spatial scales. We provide a contemporary and comprehensive evaluation of sector- and fuel-specific contributions to this disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM_2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95% Confidence Interval: 0.74-1.36) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion (27.3% of the total PM_2.5 burden), with coal contributing to over half. Other dominant global sources included residential (0.74 [0.52-0.95] million deaths; 19.2%), industrial (0.45 [0.32-0.58] million deaths; 11.7%), and energy (0.39 [0.28-0.51] million deaths; 10.2%) sectors. Our results show that regions with large anthropogenic contributions generally had the highest attributable deaths, suggesting substantial health benefits from replacing traditional energy sources.

Ambient particulate matter burden of disease in the Kingdom of Saudi Arabia

BACKGROUND

Air pollution is one of the top 10 global health risk factors and has been associated with premature mortality, cardiovascular, cerebrovascular, respiratory, and metabolic disease. Currently, there is a lack of health assessments on the public health impacts of air pollution in the Kingdom of Saudi Arabia.

AIM

This study aims to assess the ambient particulate matter burden of disease in the Kingdom of Saudi Arabia.

METHODS

A comparative risk assessment (CRA) using the 2017 Global Burden of Disease was performed to estimate ambient particulate matter exposure, mortality, and lost years of a healthy life. Saudi Arabia population-weighted mean concentrations of particle mass with an aerodynamic diameter less than 2·5 μm (PM2.5), at an approximate 11 km × 11 km resolution was estimated using satellite-based estimates, chemical transport models, and ground-level measurements. The CRA for PM2.5 was based on relative risks originated from epidemiological studies using integrated exposure-response functions for ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, lower respiratory infections, and type 2 diabetes. Mortality, years of life lost (YLL), years lived with disability (YLD) and disability-adjusted life years (DALYs) attributable to PM2.5 were estimated at the national level for all ages and both sexes from 1990 to 2017.

RESULTS

In 2017, the annual exposure to ambient particulate matter in the population-weighted mean PM2.5 in Saudi Arabia was 87.9 μg/m³ (95% UI 29.6-197.9). The PM2.5 population-weighted mean has increased by 24% since 1990. Annual deaths attributable to PM2.5 were estimated at 8536 (95% UI 6046-11,080), representing 9% of the total annual deaths in Saudi Arabia. In 2017, 315,200 (95% UI 231,608-401,926) DALYs were attributable to PM2.5. Males contributed to 67% (209,822 (95% UI 151,322-277,503)) of DALYs, and females contributed to 33% (105,378 (95% UI 76,014-135,269) of DALYs. Ischemic heart disease represented 44% of the PM2.5 attributable DALYs, followed by type 2 diabetes (20%), lower respiratory infections (13%), stroke (11%), COPD (10%), and tracheal, bronchus, and lung cancer (3%). In 2017, 240,966 (95% UI 168,833-319,178) years of life lost (YLL) and 74,234 (95% UI 50,229-100,410) years lived with disability (YLD) were attributed to PM2.5.

CONCLUSION

Ambient particulate matter is the fifth health risk factor in Saudi Arabia, contributing 9% of total mortality. Over the past 27 years, estimated exposure levels of PM2.5 in Saudi Arabia have been above WHO's air quality guidelines. Although since 2011 mortality and DALY rates attributable to PM2.5 have decreased, air pollution concentrations continue to increase. National and local authorities in Saudi Arabia should consider policies to reduce industrial and traffic-related air pollution in combination with the strengthening of current investments and improvements in health care and prevention services.

Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990-2017

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M³Fusion and Bayesian Maximum Entropy (BME) methods. With M³Fusion, we create a multimodel composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8834 sites globally) in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multimodel composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R² = 0.81 at the test point, 0.63 at 0.1°, and 0.62 at 0.5°) than the multimodel mean (R² = 0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem

The burning of fossil fuels - especially coal, petrol, and diesel - is a major source of airborne fine particulate matter (PM_2.5), and a key contributor to the global burden of mortality and disease. Previous risk assessments have examined the health response to total PM_2.5, not just PM_2.5 from fossil fuel combustion, and have used a concentration-response function with limited support from the literature and data at both high and low concentrations. This assessment examines mortality associated with PM_2.5 from only fossil fuel combustion, making use of a recent meta-analysis of newer studies with a wider range of exposure. We also estimated mortality due to lower respiratory infections (LRI) among children under the age of five in the Americas and Europe, regions for which we have reliable data on the relative risk of this health outcome from PM_2.5 exposure. We used the chemical transport model GEOS-Chem to estimate global exposure levels to fossil-fuel related PM_2.5 in 2012. Relative risks of mortality were modeled using functions that link long-term exposure to PM_2.5 and mortality, incorporating nonlinearity in the concentration response. We estimate a global total of 10.2 (95% CI: -47.1 to 17.0) million premature deaths annually attributable to the fossil-fuel component of PM_2.5. The greatest mortality impact is estimated over regions with substantial fossil fuel related PM_2.5, notably China (3.9 million), India (2.5 million) and parts of eastern US, Europe and Southeast Asia. The estimate for China predates substantial decline in fossil fuel emissions and decreases to 2.4 million premature deaths due to 43.7% reduction in fossil fuel PM_2.5 from 2012 to 2018 bringing the global total to 8.7 (95% CI: -1.8 to 14.0) million premature deaths. We also estimated excess annual deaths due to LRI in children (0-4 years old) of 876 in North America, 747 in South America, and 605 in Europe. This study demonstrates that the fossil fuel component of PM_2.5 contributes a large mortality burden. The steeper concentration-response function slope at lower concentrations leads to larger estimates than previously found in Europe and North America, and the slower drop-off in slope at higher concentrations results in larger estimates in Asia. Fossil fuel combustion can be more readily controlled than other sources and precursors of PM_2.5 such as dust or wildfire smoke, so this is a clear message to policymakers and stakeholders to further incentivize a shift to clean sources of energy.

Personal PM2.5 exposure and lung function: Potential mediating role of systematic inflammation and oxidative damage in urban adults from the general population

BACKGROUND

Short-term effects of fine particulate matter (PM_2.5) exposure on lung function have been reported. However, few studies have assessed PM_2.5 exposure on the personal level, and the mechanism underlying the effects of PM_2.5 exposure on lung function remains less clear.

OBJECTIVES

To evaluate the association between personal PM_2.5 exposure and lung function alteration in general population and to explore the roles of systematic inflammation and oxidative damage in this association.

METHODS

A total of 7685 lung function tests were completed among 4697 urban adults in Wuhan, China. Plasma C-reactive protein (CRP), urinary 8-iso-prostaglandin-F_2α (8-iso-PGF_2α) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) levels were measured. Personal PM_2.5 exposure levels were estimated using an estimation model from the actual measurements of individual PM_2.5 levels in 191 participants. Mixed linear models were used to evaluate the association between personal PM_2.5 exposure and lung function. Mediation analyses were conducted to investigate the roles of CRP, 8-iso-PGF_2α and 8-OHdG in above associations.

RESULTS

After adjusting for confounders, each 10 μg/m³ increase in the previous-day personal PM_2.5 exposure was associated with 2.94 mL, 2.02 mL and 16.14 mL/s decreases in forced vital capacity (FVC), forced expiration volume in 1 s (FEV₁) and peak expiratory flow, respectively. The associations were more obvious among never smokers compared with current smokers. Cumulative 7-day exposure to PM_2.5 led to the strongest adverse effects on lung function. Among never smokers with high PM_2.5 exposure levels, a positive relationship was observed between personal PM_2.5 level and urinary 8-iso-PGF_2α, and 8-iso-PGF_2α meditated 4.69% and 12.30% of the association between the 7-day moving PM_2.5 concentration and FVC and FEV₁, respectively. We did not observe a significant positive association between PM_2.5 exposure and plasma CRP or urinary 8-OHdG.

CONCLUSION

Short-term personal exposure to PM_2.5 is associated with reduced pulmonary ventilation function. Urinary 8-iso-PGF_2α partly mediates these associations.

The international Perinatal Outcomes in the Pandemic (iPOP) study: protocol

Preterm birth is the leading cause of infant death worldwide, but the causes of preterm birth are largely unknown. During the early COVID-19 lockdowns, dramatic reductions in preterm birth were reported; however, these trends may be offset by increases in stillbirth rates. It is important to study these trends globally as the pandemic continues, and to understand the underlying cause(s). Lockdowns have dramatically impacted maternal workload, access to healthcare, hygiene practices, and air pollution - all of which could impact perinatal outcomes and might affect pregnant women differently in different regions of the world.

In the international Perinatal Outcomes in the Pandemic (iPOP) Study, we will seize the unique opportunity offered by the COVID-19 pandemic to answer urgent questions about perinatal health. In the first two study phases, we will use population-based aggregate data and standardized outcome definitions to: 1) Determine rates of preterm birth, low birth weight, and stillbirth and describe changes during lockdowns; and assess if these changes are consistent globally, or differ by region and income setting, 2) Determine if the magnitude of changes in adverse perinatal outcomes during lockdown are modified by regional differences in COVID-19 infection rates, lockdown stringency, adherence to lockdown measures, air quality, or other social and economic markers, obtained from publicly available datasets. We will undertake an interrupted time series analysis covering births from January 2015 through July 2020.

The iPOP Study will involve at least 121 researchers in 37 countries, including obstetricians, neonatologists, epidemiologists, public health researchers, environmental scientists, and policymakers. We will leverage the most disruptive and widespread “natural experiment” of our lifetime to make rapid discoveries about preterm birth. Whether the COVID-19 pandemic is worsening or unexpectedly improving perinatal outcomes, our research will provide critical new information to shape prenatal care strategies throughout (and well beyond) the pandemic.

A comparative study of the disease burden attributable to PM2.5 in China, Japan and South Korea from 1990 to 2017

BACKGROUND

Exposure to fine particulate matter (PM_2.5) is one of the leading contributors to disease burden. However, little is known about the spatial and temporal trends of the disease burden attributable to PM_2.5 in the three major economies in East Asia. We aimed to estimate the patterns and temporal variations of the disease burden attributable to PM_2.5 in China, Japan, and South Korea from 1990 to 2017.

METHODS

We obtained data on disease burden attributable to PM_2.5 from the Global Burden of Disease Study (GBD) 2017. We retrieved the numbers and age-standardized mortality rate (ASMR) and disability-adjusted life years (DALYs) rate (ASDR) of disease attributable to PM_2.5 by age, sex, socio-demographic index (SDI), and country. We used percentage change and estimated annual percentage change (EAPC) to assess the trends of ASMR and ASDR attributable to PM_2.5 between 1990 and 2017. We further calculated the contribution of population growth, population aging, and changes in mortality or DALYs rate to the net changes in total deaths and DALYs associated with PM_2.5.

RESULTS

We found considerable differences in the disease burden attributable to PM_2.5 in China, Japan, and South Korea. In 2017, the ASMR and ASDR of disease attributable to PM_2.5 in China were 49.37 (95% UI: 41.18, 57.5) per 100,000 population and 1065.9 (95% UI: 891.28, 1237.38) per 100,000 population, respectively, which was about four times higher than that of Japan and twice higher than that of South Korea. Regardless of country, the ASMR and ASDR were more pronounced among elders and males. From 1990 to 2017, the declines in ASMR and ASDR were more pronounced in Japan and South Korea than in China. The changes in PM_2.5 associated total deaths and DALYs between 1990 and 2017 were the combined effects of population aging, population growth, and changes in mortality or DALY rate, resulting in a net increase in total deaths and DALYs in China but little changes in Japan and South Korea.

CONCLUSIONS

PM_2.5 still contributed to significant disease burdens in 2017, although age-standardized disease burden has declined from 1990 to 2017. There has been an increasing trend in total deaths and DALYs in China, which was primarily driven by population aging.

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

Global Air Quality: An Inter-Disciplinary Approach to Exposure Assessment for Burden of Disease Analyses

Global assessments of air quality and health require comprehensive estimates of the exposures to air pollution that are experienced by populations in every country. However, there are many countries in which measurements from ground-based monitoring are sparse or non-existent, with quality-control and representativeness providing additional challenges. While ground-based monitoring provides a far from complete picture of global air quality, there are other sources of information that provide comprehensive coverage across the globe. The World Health Organization developed the Data Integration Model for Air Quality (DIMAQ) to combine information from ground measurements with that from other sources, such as atmospheric chemical transport models and estimates from remote sensing satellites in order to produce the information that is required for health burden assessment and the calculation of air pollution-related Sustainable Development Goals indicators. Here, we show an example of the use of DIMAQ with the Copernicus Atmosphere Monitoring Service Re-Analysis (CAMSRA) of atmospheric composition, which represents the best practices in meteorology and climate monitoring that were developed under the World Meteorological Organization’s Global Atmosphere Watch programme. Estimates of PM2.5 from CAMSRA are integrated within the DIMAQ framework in order to produce high-resolution estimates of air pollution exposure that can be aggregated in a coherent fashion to produce country-level assessments of exposures.

How Do Brazilian Fires Affect Air Pollution and Public Health?

Fires burning across the Amazon in the summer of 2019 attracted global attention for the widespread destruction of natural ecosystems and regional smoke production. Using a combination of satellite fire observations and atmospheric modeling, Nawaz and Henze (2020, https://doi.org.10.1029/2020GH000268) provide new evidence for the widespread regional public health consequences attributed to these fires. They find that approximately 10% of premature deaths in Brazil due to fine particulate matter (PM2.5) are attributable to smoke pollution and highlight how fire locations play a critical role in determining downwind health impacts.

Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010-2016: A multistage approach

The Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM_2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM_2.5. Therefore, this study developed a multistage approach 1) to first create dasymetric population data with moderate resolution (1 km) by using a random forest algorithm, brightness reflectance of nighttime light (NTL) images, a digital elevation model (DEM), and a MODIS-derived normalized difference vegetation index (NDVI), and 2) to apply the improved population dataset with a MODIS-derived PM_2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM_2.5 and lung cancer mortality attributable to PM_2.5 across YRD during 2010-2016 for microscale planning. The created dasymetric population data derived from a coarse census unit (administrative unit) were fairly matched with census data at a fine spatial scale (street block), with R² and RMSE of 0.64 and 27,874.5 persons, respectively. Furthermore, a significant urban-rural difference of population exposure was found. Additionally, population exposure in Shanghai was 2.9-8 times higher than the other major cities (7-year average: 192,000 μg·people/m³·km²). More importantly, the relative risks of lung cancer mortality in high-risk areas were 28%-33% higher than in low-risk areas. There were 12,574-14,504 total lung cancer deaths attributable to PM_2.5, and lung cancer deaths in each square kilometer of urban areas were 7-13 times higher than for rural areas. These results indicate that moderate-resolution information can help us understand the spatiotemporal variability of population exposure and related health risk in a high-density environment.

Substantial changes in PM2.5 pollution and corresponding premature deaths across China during 2015-2019: A model prospective

Long-term exposure to the ambient fine particulate matter (PM_2.5) is the major public health risk factor in China. Several past studies have assessed premature mortalities associated with PM_2.5 in China at varying levels of temporal and spatial scales using different methodological approaches. However, recently developed global exposure mortality model [GEMM NCD + LRI and GEMM 5-COD] provides a much more sophisticated methodology in capturing mortality due to PM_2.5-exposure than the commonly accepted integrated exposure-response (IER) model, which this study applied to China. This study provides a comparative assessment of the excess long-term PM_2.5-attributed nonaccidental deaths as well as cause-specific deaths for 349 cities in mainland China during five years (from 2015 to 2019) and compares the results with the spatial resolution scale of 0.1° × 0.1° across overall China. The results demonstrate that the national annual average PM_2.5 concentration declined from 51.9 ± 18.2 μg/m³ in 2015 to 39.0 ± 13.2 μg/m³ in 2019, and the overall annual negative trend was around -3.1 ± 2.2 μg/m³/year [-5.6 ± 3.4%/year] across China. Consequently, the number of PM_2.5-related deaths decreased by 383 thousand [95% CI: 331-429] to 1755 thousand [95% Confidence Interval: 1470-2025; GEMM NCD + LRI]; 315 thousand [95% CI: 227-370] to 1380 thousand [95% CI: 948-1740; GEMM 5-COD] and 125 thousand [95% CI: 64-140] to 876 thousand [95% CI: 394-1262; IER] in 2019, derived from the pre-established models (GEMM and IER). The estimate PM_2.5-attributed death with a spatial resolution of 0.1° × 0.1° was 2419 thousand [95% CI: 2041-2771; GEMM NCD + LRI], 1918 thousand [95% CI: 1333-2377; GEMM 5-COD] and 1162 thousand [95% CI: 534-1611; IER] in 2015, which is about 11-16% higher value than the city-level health risk assessment study. The estimated deaths by GEMM NCD + LRI and GEMM 5-COD were 104% and 61% higher than the estimated by IER, highlighting that total premature mortalities associated with PM_2.5 were substantially left behind based on the pre-existing model. The "other noncommunicable diseases" mortality, which IER method doesn't account for, was 375 thousand in 2019, 68 thousand less than in 2015. Such significant mortality was previously overlooked in estimation methods, which should now be considered for the air pollution-related policy development in China. The high number of premature deaths in central and northern parts of China, calls for the need for the Government to quickly impose even more stringent and effective pollution control measures.

Premature Deaths in Brazil Associated With Long-Term Exposure to PM2.5 From Amazon Fires Between 2016 and 2019

Amazonian deforestation from slash-and-burn practices is a significant contributor to biomass burning within Brazil. Fires emit carbonaceous aerosols that negatively impact human health by increasing fine particulate matter (PM2.5) exposure. These negative effects on health compound the already detrimental climatological and ecological impacts. Despite high biomass burning emissions in Brazil and the international attention drawn by the relaxation of Amazon protections in 2019, little is known about the health impacts from PM2.5 exposure attributable to these fires. We estimate PM2.5-related premature deaths in Brazil associated with biomass burning, focusing on temporal, interannual, and spatial trends. We find that during the fire season of 2019, 4,966 (2,427, 8,340) premature deaths were attributable to fire emissions making up 10% (5, 17) of all PM2.5-related premature deaths in Brazil. Between the 2019 and 2018 seasons, fire emissions increased by 1.37 Tg (1.00, 2.18) or 115% (60, 201), which was responsible for an increase in health impacts of 2,109 (965, 3,623) premature deaths or 74% (54, 98). Biomass burning emissions throughout Brazil contribute significantly to premature deaths, with the largest burning events occurring in northwestern Brazil. The impact of fires on PM2.5-related premature deaths is highest in heavily populated regions despite their fires being 1 to 2 orders of magnitude smaller than the largest burning events. Results from this study characterize the extent to which elevated PM2.5 exposure levels owing to fires affect public health in Brazil and present an additional, public health-focused, support for increased Amazon protections.

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)

Exposure to outdoor fine particulate matter (PM_2.5) is a leading risk factor for mortality. We develop global estimates of annual PM_2.5 concentrations and trends for 1998-2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM_2.5 concentrations using geophysical relationships between surface PM_2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM_2.5 estimates are highly consistent with globally distributed ground monitors (R² = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM_2.5 estimates to predict and account for the residual bias with PM_2.5 monitors, yielding even higher cross validated agreement (R² = 0.90-0.92; slope = 0.90-0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (-0.28 ± 0.03 μg/m³/yr), Europe (-0.15 ± 0.03 μg/m³/yr), India (1.13 ± 0.15 μg/m³/yr), and globally (0.04 ± 0.02 μg/m³/yr). The positive trend (2.44 ± 0.44 μg/m³/yr) for India over 2005-2013 and the negative trend (-3.37 ± 0.38 μg/m³/yr) for China over 2011-2018 are remarkable, with implications for the health of billions of people.

Health Impact Assessment of Volcanic Ash Inhalation: A Comparison With Outdoor Air Pollution Methods

This paper critically appraises the extrapolation of concentration-response functions (CRFs) for fine and coarse particulate matter, PM2.5 and PM10, respectively, used in outdoor air pollution health impact assessment (HIA) studies to assess the extent of health impacts in communities exposed to volcanic emissions. Treating volcanic ash as PM, we (1) consider existing models for HIA for general outdoor PM, (2) identify documented health effects from exposure to ash in volcanic eruptions, (3) discuss potential issues of applying CRFs based on the composition and concentration of ash-related PM, and (4) critically review available case studies of volcanic exposure scenarios utilizing HIA for outdoor air pollution. We identify a number of small-scale studies focusing on populations exposed to volcanic ash; exposure is rarely quantified, and there is limited evidence concerning the health effects of PM from volcanic eruptions. That limited evidence is, however, consistent with the CRFs typically used for outdoor air pollution HIA. Two health assessments of exposure to volcanic emissions have been published using population- and occupational-based CRFs, though each application entails distinct assumptions and limitations. We conclude that the best available strategy, at present, is to apply outdoor air pollution risk estimates to scenarios involving volcanic ash emissions for the purposes of HIA. However, due to the knowledge gaps on, for example, the health effects from exposure to volcanic ash and differences in ash composition, there is inherent uncertainty in this application. To conclude, we suggest actions to enable better prediction and assessment of health impacts of volcanic emissions.

How urban characteristics affect vulnerability to heat and cold: a multi-country analysis

BACKGROUND

The health burden associated with temperature is expected to increase due to a warming climate. Populations living in cities are likely to be particularly at risk, but the role of urban characteristics in modifying the direct effects of temperature on health is still unclear. In this contribution, we used a multi-country dataset to study effect modification of temperature-mortality relationships by a range of city-specific indicators.

METHODS

We collected ambient temperature and mortality daily time-series data for 340 cities in 22 countries, in periods between 1985 and 2014. Standardized measures of demographic, socio-economic, infrastructural and environmental indicators were derived from the Organisation for Economic Co-operation and Development (OECD) Regional and Metropolitan Database. We used distributed lag non-linear and multivariate meta-regression models to estimate fractions of mortality attributable to heat and cold (AF%) in each city, and to evaluate the effect modification of each indicator across cities.

RESULTS

Heat- and cold-related deaths amounted to 0.54% (95% confidence interval: 0.49 to 0.58%) and 6.05% (5.59 to 6.36%) of total deaths, respectively. Several city indicators modify the effect of heat, with a higher mortality impact associated with increases in population density, fine particles (PM2.5), gross domestic product (GDP) and Gini index (a measure of income inequality), whereas higher levels of green spaces were linked with a decreased effect of heat.

CONCLUSIONS

This represents the largest study to date assessing the effect modification of temperature-mortality relationships. Evidence from this study can inform public-health interventions and urban planning under various climate-change and urban-development scenarios.

Small-area methods for investigation of environment and health

Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.

The global and national burden of chronic kidney disease attributable to ambient fine particulate matter air pollution: a modelling study

Introduction

We aimed to integrate all available epidemiological evidence to characterise an exposure-response model of ambient fine particulate matter (PM2.5) and the risk of chronic kidney disease (CKD) across the spectrum of PM2.5 concentrations experienced by humans. We then estimated the global and national burden of CKD attributable to PM2.5.

Methods

We collected data from prior studies on the association of PM2.5 with CKD and used an integrative meta-regression approach to build non-linear exposure-response models of the risk of CKD associated with PM2.5 exposure. We then estimated the 2017 global and national incidence, prevalence, disability-adjusted life-years (DALYs) and deaths due to CKD attributable to PM2.5 in 194 countries and territories. Burden estimates were generated by linkage of risk estimates to Global Burden of Disease study datasets.

Results

The exposure-response function exhibited evidence of an increase in risk with increasing PM2.5 concentrations, where the rate of risk increase gradually attenuated at higher PM2.5 concentrations. Globally, in 2017, there were 3 284 358.2 (95% UI 2 800 710.5 to 3 747 046.1) incident and 122 409 460.2 (108 142 312.2 to 136 424 137.9) prevalent cases of CKD attributable to PM2.5, and 6 593 134.6 (5 705 180.4 to 7 479 818.4) DALYs and 211 019.2 (184 292.5 to 236 520.4) deaths due to CKD attributable to PM2.5. The burden was disproportionately borne by low income and lower middle income countries and exhibited substantial geographic variability, even among countries with similar levels of sociodemographic development. Globally, 72.8% of prevalent cases of CKD attributable to PM2.5 and 74.2% of DALYs due to CKD attributable to PM2.5 were due to concentrations above 10 µg/m3, the WHO air quality guidelines.

Conclusion

The global burden of CKD attributable to PM2.5 is substantial, varies by geography and is disproportionally borne by disadvantaged countries. Most of the burden is associated with PM2.5 levels above the WHO guidelines, suggesting that achieving those targets may yield reduction in CKD burden.

Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM 2.5 , and PM 2.5 speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM 2.5 (test R 2 = 0.68 ) and its chemical components (test R 2 = –0.71). In single-pollutant models, FEV 1 decreased by 131 mL (95% CI: − 232 , − 35 ) per 10.7-µg/m 3 increase in PM 2.5 , by 158 mL (95% CI: − 273 , − 43 ) per 1.2-µg/m 3 in sulfates (SO 4 2 − ), and by 177 mL (95% CI: − 306 , − 56 ) per 1.6-µg/m 3 increase in dust; FVC decreased by 175 mL (95% CI: − 310 , − 29 ) per 1.2-µg/m 3 increase in SO 4 2 − and by 212 mL (95% CI: − 391 , − 28 ) per 2.5-µg/m 3 increase in nitrates (NO 3 − ). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates.

Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.

Incorporation of Remote PM2.5 Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces

The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA's Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m-3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Human Health Benefits from Fish Consumption vs. Risks from Inhalation Exposures Associated with Contaminated Sediment Remediation: Dredging of the Hudson River

BACKGROUND

Billions of dollars are spent on environmental dredging (ED) to remediate contaminated sediments, with one goal being reduced human health risks. However, ED may increase health risks in unanticipated ways, thus potentially reducing net benefits.

OBJECTIVES

To assess the ways that ED may increase health risks in unanticipated ways, thus potentially reducing net benefits, we quantitatively assessed a subset of population health benefits and risks of ED, using the 2009-2015 remediation of the Hudson River Polychlorinated Biphenyls (PCBs) Superfund Site as a case study. Three remediation scenarios were evaluated: No Action (NA), Source Control (SC), and ED.

METHODS

We quantified health benefits for each scenario from reduced PCB levels in Hudson River fish, and health risks from ED operations due to increased inhalation exposures to PCBs and fine particulate matter (PM 2.5 ), using disability-adjusted life years (DALYs) as a common metric. Occupational health risks were also considered in a separate sensitivity analysis. Estimates of population-level benefits and risks included Monte Carlo simulation-based uncertainty analysis.

RESULTS

Under NA, fish consumption would result in an estimated health burden of 112 DALYs, and ED would lead to a reduction of 15 DALYs in excess of SC. ED operations were estimated to induce a total burden of 33 DALYs, dominated by PM 2.5 impacts from rail transport emissions (32 DALYs). Including uncertainty, the net health benefit of ED ranged from - 138 to + 1,326 avoided DALYs (90% confidence), with a median of - 11 avoided DALYs.

CONCLUSIONS

For the considered impacts, ED in the Hudson River might not have led to an overall net positive human health impact. The benefits and risks of ED, however, have different degrees of uncertainty and involve different populations. Reducing long-distance transport of dredged sediment is a priority. This comparative approach could be used prospectively to better determine trade-offs involved in different remediation scenarios and to improve remediation design to maximize benefits while minimizing risks. https://doi.org/10.1289/EHP5034.

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

Air pollution: the emergence of a major global health risk factor

Air pollution is now recognized by governments, international institutions and civil society as a major global public health risk factor. This is the result of the remarkable growth of scientific knowledge enabled by advances in epidemiology and exposure assessment. There is now a broad scientific consensus that exposure to air pollution increases mortality and morbidity from cardiovascular and respiratory disease and lung cancer and shortens life expectancy. Although air pollution has markedly declined in high-income countries, it was still responsible for some 4.9 million deaths in 2017, largely in low- and middle-income countries, where air pollution has increased over the past 25 y. As governments act to reduce air pollution there is a continuing need for research to strengthen the evidence on disease risk at very low and very high levels of air pollution, identify the air pollution sources most responsible for disease burden and assess the public health effectiveness of actions taken to improve air quality.

Opportunities and Challenges for Filling the Air Quality Data Gap in Low- and Middle-Income Countries

Given the millions of people suffering from air pollution, filling the air quality monitoring gap in low- and middle-income countries has been recognized as a global challenge. To meet this challenge and make it work will require private enterprise, multiple levels of government, international organizations, academia and civil society to work together toward the common goal of characterizing, understanding better, and then reducing, the air pollution that causes sickness and preventable death for millions of people each year in lowand middle-income countries around the world. This article offers concrete next steps on how to make progress toward increasing air quality monitoring using a combination of emerging technologies, adaptation to country-specific conditions, and building capacity towards the development of lasting institutions.