Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

Interfacial carbonyl groups of propylene carbonate facilitate the reversible binding of nitrogen dioxide

The interaction of NO2 with organic interfaces is critical in the development of NO2 sensing and trapping technologies, and equally so to the atmospheric processing of marine and continental aerosol. Recent studies point to the importance of surface oxygen groups in these systems, however the role of specific functional groups on the microscopic level has yet to be fully established. In the present study, we aim to provide fundamental information on the interaction and potential binding of NO2 at atmospherically relevant organic interfaces that may also help inform innovation in NO2 sensing and trapping development. We then present an investigation into the structural changes induced by NO2 at the surface of propylene carbonate (PC), an environmentally relevant carbonate ester. Surface-sensitive vibrational spectra of the PC liquid surface are acquired before, during, and after exposure to NO2 using infrared reflection-absorption spectroscopy (IRRAS). Analysis of vibrational changes at the liquid surface reveal that NO2 preferentially interacts with the carbonyl of PC at the interface, forming a distribution of binding symmetries. At low ppm levels, NO2 saturates the PC surface within 10 minutes and the perturbations to the surface are constant over time during the flow of NO2. Upon removal of NO2 flow, and under atmospheric pressures, these interactions are reversible, and the liquid surface structure of PC recovers completely within 30 min.

A state-of-the-science review of using mitochondrial DNA copy number as a biomarker for environmental exposure

Mitochondria are bioenergetic, biosynthetic, and signaling organelles in eukaryotes, and contain their own genomes, mitochondrial DNA (mtDNA), to supply energy to cells by generating ATP via oxidative phosphorylation. Therefore, the threat to mitochondria' integrity and health resulting from environmental exposure could induce adverse health effects in organisms. In this review, we summarized the association between mtDNA copy number (mtDNAcn), and environmental exposures as reported in the literature. We conducted a literature search in the Web of Science using [Mitochondrial DNA copy number] and [Exposure] as two keywords and employed three selection criteria for the final inclusion of 97 papers for review. The consensus of data was that mtDNAcn could be used as a plausible biomarker for cumulative exposures to environmental chemical and physical agents. In order to furtherly expand the application of mtDNAcn in ecological and environmental health research, we suggested a series of algorithms aiming to standardize the calculation of mtDNAcn based on the PCR results in this review. We also discussed the pitfalls of using whole blood/plasma samples for mtDNAcn measurements and regard buccal cells a plausible and practical alternative. Finally, we recognized the importance of better understanding the mechanistic analysis and regulatory mechanism of mtDNAcn, in particular the signals release and regulation pathways. We believe that the development of using mtDNAcn as an exposure biomarker will revolutionize the evaluation of chronic sub-lethal toxicity of chemicals to organisms in ecological and environmental health research that has not yet been implemented.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

BACKGROUND

Ambient nitrogen dioxide (NO 2 ) and fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants.

OBJECTIVES

Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO 2 - and PM 2.5 -attributable health burdens, considering NO 2 -attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants.

METHODS

Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO 2 and PM 2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study.

RESULTS

Despite overall decreases in the public health damages associated with NO 2 and PM 2.5 , racial and ethnic relative disparities in NO 2 -attributable pediatric asthma and PM 2.5 -attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM 2.5 -attributable premature mortality and NO 2 -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM 2.5 -attributable premature mortality have increased by 40% and NO 2 -attributable pediatric asthma by 10%.

DISCUSSION

Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

Development of a community severance index for urban areas in the United States: A case study in New York City

BACKGROUND AND AIMS

Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC).

METHODS

We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level.

RESULTS

Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC.

CONCLUSION

We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.

Real-World Evidence of Multiple Air Pollutants and Mortality: A Prospective Cohort Study in an Oldest-Old Population

We aimed to report real-world longitudinal ambient air pollutants levels compared to WHO Air Quality Guidelines (AQG) and analyze multiple air pollutants' joint effect on longevity, and the modification and confounding from the climate and urbanization with a focus on the oldest-old. This study included 13,207 old participants with 73.3% aged 80 and beyond, followed up from 2008 to 2018 in 23 Chinese provinces. We used the Cox-proportional hazards model and quantile-based g-computation model to measure separate and joint effects of the multiple pollutants. We adjusted for climate and area economic factors based on a directed acyclic graph. In 2018, no participants met the WHO AQG for PM2.5 and O3, and about one-third met the AQG for NO2. The hazard ratio (HR) for mortality was 1.07 (95% confidence interval-CI: 1.05, 1.09) per decile increase in all three pollutants, with PM2.5 being the dominant contributor according to the quantile-based g-computation model. In the three-pollutant model, the HRs (95% CI) for PM2.5 and NO2 were 1.27 (1.25, 1.3) and 1.08 (1.05, 1.12) per 10 μg/m3 increase, respectively. The oldest-old experienced a much lower mortality risk from air pollution compared to the young-old. The mortality risk of PM2.5 was higher in areas with higher annual average temperatures. The adjustment of road density considerably intensified the association between NO2 and mortality. The ambient PM2.5 and O3 levels in China exceeded the WHO AQG target substantially. Multiple pollutants coexposure, confounding, and modification of the district economic and climate factors should not be ignored in the association between air pollution and mortality.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

All-Cause NO2-Attributable Mortality Burden and Associated Racial and Ethnic Disparities in the United States

Nitrogen dioxide (NO2) is a regulated pollutant that is associated with numerous health impacts. Recent advances in epidemiology indicate high confidence linking NO2 exposure with increased mortality, an association that recent studies suggest persists even at concentrations below regulatory thresholds. While large disparities in NO2 exposure among population subgroups have been reported, U.S. NO2-attributable mortality rates and their disparities remain unquantified. Here we provide the first estimate of NO2-attributable all-cause mortality across the contiguous U.S. (CONUS) at the census tract-level. We leverage fine-scale, satellite-informed, land use regression model NO2 concentrations and census tract-level baseline mortality data to characterize the associated disparities among different racial/ethnic subgroups. Across CONUS, we estimate that the NO2-attributable all-cause mortality is ∼170,850 (95% confidence interval: 43,970, 251,330) premature deaths yr-1 with large variability across census tracts and within individual cities. Additionally, we find that higher NO2 concentrations and underlying susceptibilities for predominately Black communities lead to NO2-attributable mortality rates that are ∼47% higher compared to CONUS-wide average rates. Our results highlight the substantial U.S. NO2 mortality burden, particularly in marginalized communities, and motivate adoption of more stringent standards to protect public health.

Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors

In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.

Ambient nitrogen dioxide in 47 187 neighbourhoods across 326 cities in eight Latin American countries: population exposures and associations with urban features

BACKGROUND

Health research on ambient nitrogen dioxide (NO2) is sparse in Latin America, despite the high prevalence of NO2-associated respiratory diseases in the region. This study describes within-city distributions of ambient NO2 concentrations at high spatial resolution and urban characteristics associated with neighbourhood ambient NO2 in 326 Latin American cities.

METHODS

We aggregated estimates of annual surface NO2 at 1 km2 spatial resolution for 2019, population counts, and urban characteristics compiled by the SALURBAL project to the neighbourhood level (ie, census tracts). We described the percentage of the urban population living with ambient NO2 concentrations exceeding WHO air quality guidelines. We used multilevel models to describe associations of neighbourhood ambient NO2 concentrations with population and urban characteristics at the neighbourhood and city levels.

FINDINGS

We examined 47 187 neighbourhoods in 326 cities from eight Latin American countries. Of the roughly 236 million urban residents observed, 85% lived in neighbourhoods with ambient annual NO2 above WHO guidelines. In adjusted models, higher neighbourhood-level educational attainment, closer proximity to the city centre, and lower neighbourhood-level greenness were associated with higher ambient NO2. At the city level, higher vehicle congestion, population size, and population density were associated with higher ambient NO2.

INTERPRETATION

Almost nine out of every ten residents of Latin American cities live with ambient NO2 concentrations above WHO guidelines. Increasing neighbourhood greenness and reducing reliance on fossil fuel-powered vehicles warrant further attention as potential actionable urban environmental interventions to reduce population exposure to ambient NO2.

FUNDING

Wellcome Trust, National Institutes of Health, Cotswold Foundation.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.

Lichens as spatially transferable bioindicators for monitoring nitrogen pollution

Excess nitrogen is a pollutant and global problem that harms ecosystems and can severely affect human health. Pollutant nitrogen is becoming more widespread and intensifying in the tropics. There is thus a requirement to develop nitrogen biomonitoring for spatial mapping and trend analysis of tropical biodiversity and ecosystems. In temperate and boreal zones, multiple bioindicators for nitrogen pollution have been developed, with lichen epiphytes among the most sensitive and widely applied. However, the state of our current knowledge on bioindicators is geographically biased, with extensive research effort focused on bioindicators in the temperate and boreal zones. The development of lichen bioindicators in the tropics is further weakened by incomplete taxonomic and ecological knowledge. In this study we performed a literature review and meta-analysis, attempting to identify characteristics of lichens that offer transferability of bioindication into tropical regions. This transferability must overcome the different species pools between source information - drawing on extensive research effort in the temperate and boreal zone - and tropical ecosystems. Focussing on ammonia concentration as the nitrogen pollutant, we identify a set of morphological traits and taxonomic relationships that cause lichen epiphytes to be more sensitive, or more resistant to this excess nitrogen. We perform an independent test of our bioindicator scheme and offer recommendations for its application and future research in the tropics.

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 [PM_2.5]) 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 PM_2.5 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 PM_2.5 with Hb levels and anaemia prevalence was examined using multivariable linear and logistic regression models, respectively. We examined whether the effects of ambient PM_2.5 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 PM_2.5 exposure was 33.0 (±21.6) μg/m³. The adjusted model showed that a 10 μg/m³ increase in annual PM_2.5 concentration was associated with greater odds of anaemia (OR = 1.098 95% CI: 1.087, 1.109). The same increase in PM_2.5 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 PM_2.5 was cross-sectionally associated with decreased blood Hb levels, and greater risk of anaemia, in children aged <5 years living in 36 LMICs.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

Air pollution and child health impacts of decarbonization in 16 global cities: Modelling study

Most research on the air pollution-related health effects of decarbonization has focused on adults. We assess the potential health benefits that could be achieved in children and young people in a global sample of 16 cities through global decarbonization actions. We modelled annual average concentrations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) at 1x1 km resolution in the cities using a general circulation/atmospheric chemistry model assuming removal of all global combustion-related emissions from land transport, industries, domestic energy use and power generation. We modelled the impact on childhood asthma incidence and adverse birth outcomes (low birthweight, pre-term births) using published exposure-response relationships. Removal of combustion emissions was estimated to decrease annual average PM_2.5 by between 2.9 μg/m³ (8.4%) in Freetown and 45.4 μg/m³ (63.7%) in Dhaka. For NO₂, the range was from 0.3 ppb (7.9%) in Freetown to 18.8 ppb (92.3%) in Mexico City. Estimated reductions in asthma incidence ranged from close to zero in Freetown, Tamale and Harare to 149 cases per 100,000 population in Los Angeles. For pre-term birth, modelled impacts ranged from a reduction of 135 per 100,000 births in Dar es Salaam to 2,818 per 100,000 births in Bhubaneswar and, for low birthweight, from 75 per 100,000 births in Dar es Salaam to 2,951 per 100,000 births in Dhaka. The large variations chiefly reflect differences in the magnitudes of air pollution reductions and estimated underlying disease rates. Across the 16 cities, the reduction in childhood asthma incidence represents more than one-fifth of the current burden, and an almost 10% reduction in pre-term and low birthweight births. Decarbonization actions that remove combustion-related emissions contributing to ambient PM_2.5 and NO₂ would likely lead to substantial but geographically-varied reductions in childhood asthma and adverse birth outcomes, though there are uncertainties in causality and the precision of estimates.

Developing PM2.5 and PM10 prediction models on a national and regional scale using open-source remote sensing data

Clean air is the precursor to a healthy life. Air quality is an issue that has been getting under its well-deserved spotlight in the last few years. From a remote sensing point of view, the first Copernicus mission with the main purpose of monitoring the atmosphere and tracking air pollutants, the Sentinel-5P TROPOMI mission, has been widely used worldwide. Particulate matter of a diameter smaller than 2.5 and 10 μm (PM_2.5 and PM₁₀) significantly determines air quality. Still, there are no available satellite sensors that allow us to track them remotely with high accuracy, but only using ground stations. This research aims to estimate PM_2.5 and PM₁₀ using Sentinel-5P and other open-source remote sensing data available on the Google Earth Engine (GEE) platform for heating (December 2021, January, and February 2022) and non-heating seasons (June, July, and August 2021) on the territory of the Republic of Croatia. Ground stations of the National Network for Continuous Air Quality Monitoring were used as a starting point and as ground truth data. Raw hourly data were matched to remote sensing data, and seasonal models were trained at the national and regional scale using machine learning. The proposed approach uses a random forest algorithm with a percentage split of 70% and gives moderate to high accuracy regarding the temporal frame of the data. The mapping gives us visual insight between the ground and remote sensing data and shows the seasonal variations of PM_2.5 and PM₁₀. The results showed that the proposed approach and models could efficiently estimate air quality.

Ambient nitrogen dioxide in 47,187 neighborhoods across 326 cities in eight Latin American countries: population exposures and associations with urban features

Background

Health research on ambient nitrogen dioxide (NO2) is sparse in Latin America, despite the high prevalence of NO2-associated respiratory diseases in the region. This study describes within-city distributions of ambient NO2 concentrations at high spatial resolution and urban characteristics associated with neighborhood ambient NO2 in 326 Latin American cities.

Methods

We aggregated estimates of annual surface NO2 at 1 km2 spatial resolution for 2019, population counts, and urban characteristics compiled by the SALURBAL project to the neighborhood level (i.e., census tracts). We described the percent of the urban population living with ambient NO2 levels exceeding WHO Air Quality Guidelines. We used multilevel models to describe associations of neighborhood ambient NO2 concentrations with population and urban characteristics at the neighborhood and city levels.

Findings

We examined 47,187 neighborhoods in 326 cities from eight Latin American countries. Of the ≈236 million urban residents observed, 85% lived in neighborhoods with ambient annual NO2 above WHO guidelines. In adjusted models, higher neighborhood-level educational attainment, closer proximity to the city center, and lower neighborhood-level greenness were associated with higher ambient NO2. At the city level, higher vehicle congestion, population size, and population density were associated with higher ambient NO2.

Interpretation

Almost nine out of every 10 residents of Latin American cities live with ambient NO2 concentrations above WHO guidelines. Increasing neighborhood greenness and reducing reliance on fossil fuel-powered vehicles warrant further attention as potential actionable urban environmental interventions to reduce population exposure to ambient NO2.

Funding

Wellcome Trust, National Institutes of Health, Cotswold Foundation.

Ambient Air Pollution and All-Cause and Cause-Specific Mortality in an Analysis of Asian Cohorts

INTRODUCTION

Much of what is currently known about the adverse effects of ambient air pollution comes from studies conducted in high-income regions, with relatively low air pollution levels. The aim of the current project is to examine the relationship between exposure to ambient air pollution (as predicted from satellite-based models) and all-cause and cause-specific mortality in several Asian cohorts.

METHODS

Cohorts were recruited from the Asia Cohort Consortium (ACC). The geocoded residences of participants were assigned levels of ambient particulate material with aerodynamic diameter of 2.5 μm or less (PM2.5) and nitrogen dioxide (NO2) utilizing global satellite-derived models and assigned for the year of enrollment (or closest available year). The association between ambient exposure and mortality was established with Cox proportional hazard models, after adjustment for common confounders. Both single- and two-pollutant models were generated. Model robustness was evaluated, and hazard ratios were calculated for each cohort separately and combined via random effect meta-analysis for pooled risk estimates.

RESULTS

Six cohort studies from the ACC participated: the Community-based Cancer Screening Program (CBCSCP, Taiwan), the Golestan Cohort Study (Iran), the Health Effects for Arsenic Longitudinal Study (HEALS, Bangladesh), the Japan Public Health Center-based Prospective Study (JPHC), the Korean Multi-center Cancer Cohort Study (KMCC), and the Mumbai Cohort Study (MCS, India). The cohorts represented over 340,000 participants.

Mean exposures to PM2.5 ranged from 8 to 58 μg/m3. Mean exposures to NO2 ranged from 7 to 23 ppb. For PM2.5, a positive, borderline nonsignificant relationship was observed between PM2.5 and cardiovascular mortality. Other relationships with PM2.5 tended toward the null in meta-analysis. For NO2, an overall positive relationship was observed between exposure to NO2 and all cancers and lung cancer. A borderline association between NO2 and nonmalignant lung disease was also observed. The findings within individual cohorts remained consistent across a variety of subgroups and alternative analyses, including two-pollutant models.

CONCLUSIONS

In a pooled examination of cohort studies across Asia, ambient PM2.5 exposure appears to be associated with an increased risk of cardiovascular mortality and ambient NO2 exposure is associated with an increased cancer (and lung cancer) mortality. This project has shown that satellite-derived models of pollution can be used in examinations of mortality risk in areas with either incomplete or missing air pollution monitoring.

The importance of data splitting in combined NOx concentration modelling

The polluted air breathed every day by those living in large conurbations poses a significant risk to their health. Through effective modelling (prediction) of concentrations of pollutants and identification of the factors influencing them, it should be possible to obtain advance information on dangers and to plan and implement measures to reduce them. This work describes two different modelling approaches: based on the NO_x concentration of the previous hour (C&RT models); and based on meteorological factors, traffic flow, and past (up to two previous hours) NO_x and NO₂ concentrations (CA models). For each approach, three alternative machine learning methods were applied: artificial neutral network (ANN), random forest (RF), and support vector regression (SVR). The best fits were obtained for the models using ANN and RF (MAPE values in the range 18.3-18.5 %). Poorer fits were found for the SVR models (MAPE equal to 23.4 % for the C&RT approach and 29.3 % for CA). No significant preferences were identified between the C&RT and CA approaches (based on various goodness-of-fit measures). The choice should be determined by the purposes for which the forecast is to be used.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020

Nitrogen dioxide (NO₂) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO₂ air quality guideline (AQG) (annual average <10 μg/m³). However, the burden of diseases attributable to long-term NO₂ exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO₂; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO₂ exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO₂ concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO₂ exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO₂-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO₂ exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO₂-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO₂ exposure but also minimizing inequality.

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 [PM_2.5]) 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 PM_2.5 mass concentrations to examine annual average exposure to PM_2.5 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 PM_2.5 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 PM_2.5 was associated with greater odds of cognitive delay (OR = 1.17; 95% CI: 1.13, 1.22). A 5 μg/m³ increase in anthropogenic PM_2.5 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 PM_2.5 in young children was associated with adverse effects on cognitive development, which may have long-term consequences for educational attainment and health.

Trends in urban air pollution over the last two decades: A global perspective

Ground-level ozone (O₃), fine particles (PM_2.5), and nitrogen dioxide (NO₂) are the most harmful urban air pollutants regarding human health effects. Here, we aimed at assessing trends in concurrent exposure of global urban population to O₃, PM_2.5, and NO₂ between 2000 and 2019. PM_2.5, NO₂, and O₃ mean concentrations and summertime mean of the daily maximum 8-h values (O₃ MDA8) were analyzed (Mann-Kendall test) using data from a global reanalysis, covering 13,160 urban areas, and a ground-based monitoring network (Tropospheric Ozone Assessment Report), collating surface O₃ observations at nearly 10,000 stations worldwide. At global scale, PM_2.5 exposures declined slightly from 2000 to 2019 (on average, - 0.2 % year^-1), with 65 % of cities showing rising levels. Improvements were observed in the Eastern US, Europe, Southeast China, and Japan, while the Middle East, sub-Saharan Africa, and South Asia experienced increases. The annual NO₂ mean concentrations increased globally at 71 % of cities (on average, +0.4 % year^-1), with improvements in North America and Europe, and increases in exposures in sub-Saharan Africa, Middle East, and South Asia regions, in line with socioeconomic development. Global exposure of urban population to O₃ increased (on average, +0.8 % year^-1 at 89 % of stations), due to lower O₃ titration by NO. The summertime O₃ MDA8 rose at 74 % of cities worldwide (on average, +0.6 % year^-1), while a decline was observed in North America, Northern Europe, and Southeast China, due to the reduction in precursor emissions. The highest O₃ MDA8 increases (>3 % year^-1) occurred in Equatorial Africa, South Korea, and India. To reach air quality standards and mitigate outdoor air pollution effects, actions are urgently needed at all governance levels. More air quality monitors should be installed in cities, particularly in Africa, for improving risk and exposure assessments, concurrently with implementation of effective emission control policies that will consider regional socioeconomic imbalances.

The β-PdBi2 monolayer for efficient electrocatalytic NO reduction to NH3: a computational study

β-PdBi2 was proposed as a novel NORR catalyst for NH3 synthesis with high efficiency and high selectivity, and its catalytic activity can be enhanced by a tensile strain.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

Assessment of NO2 population exposure from 2005 to 2020 in China

Nitrogen dioxide (NO₂) is a major air pollutant with serious environmental and human health impacts. A random forest model was developed to estimate ground-level NO₂ concentrations in China at a monthly time scale based on ground-level observed NO₂ concentrations, tropospheric NO₂ column concentration data from the Ozone Monitoring Instrument (OMI), and meteorological covariates (the MAE, RMSE, and R² of the model were 4.16 µg/m³, 5.79 µg/m³, and 0.79, respectively, and the MAE, RMSE, and R² of the cross-validation were 4.3 µg/m³, 5.82 µg/m³, and 0.77, respectively). On this basis, this article analyzed the spatial and temporal variation in NO₂ population exposure in China from 2005 to 2020, which effectively filled the gap in the long-term NO₂ population exposure assessment in China. NO₂ population exposure over China has significant spatial aggregation, with high values mainly distributed in large urban clusters in the north, east, south, and provincial capitals in the west. The NO₂ population exposure in China shows a continuous increasing trend before 2012 and a continuous decreasing trend after 2012. The change in NO₂ population exposure in western and southern cities is more influenced by population density compared to northern cities. NO₂ pollution in China has substantially improved from 2013 to 2020, but Urumqi, Lanzhou, and Chengdu still maintain high NO₂ population exposure. In these cities, the Environmental Protection Agency (EPA) could reduce NO₂ population exposure through more monitoring instruments and limiting factory emissions.

Association between air pollution and CSF sTREM2 in cognitively normal older adults: The CABLE study

Objectives

Ambient air pollution aggravates the process of Alzheimer's disease (AD) pathology. Currently, the exact inflammatory mechanisms underlying these links from clinical research remain largely unclear.

Methods

This study included 1,131 cognitively intact individuals from the Chinese Alzheimer's Biomarker and LifestylE database with data provided on cerebrospinal fluid (CSF) AD biomarkers (amyloid beta‐peptide 42 [Aβ42], total tau [t‐tau], and phosphorylated tau [p‐tau]), neuroinflammatory (CSF sTREM2), and systemic inflammatory markers (high sensitivity C‐reactive protein and peripheral immune cells). The 2‐year averaged levels of ambient fine particulate matter with diameter <2.5 μm (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) were estimated at each participant's residence. Multiple‐adjusted models were approached to detect associations of air pollution with inflammatory markers and AD‐related proteins.

Results

Ambient 2‐year averaged exposure of PM_2.5 was associated with changes of neuroinflammatory markers, that is, CSF sTREM2 (β = −0.116, p = 0.0002). Similar results were found for O₃ exposure among the elderly (β = −0.111, p = 0.0280) or urban population (β = −0.090, p = 0.0144). No significant evidence supported NO₂ related to CSF sTREM2. For potentially causal associations with accumulated AD pathologies, the total effects of PM_2.5 on CSF amyloid‐related protein (CSF Aβ42 and p‐tau/Aβ42) were partly mediated by CSF sTREM2, with proportions of 14.22% and 47.15%, respectively. Additional analyses found inverse associations between peripheral inflammatory markers with PM_2.5 and NO₂, but a positive correlation with O₃.

Interpretation

These findings demonstrated a strong link between PM_2.5 exposure and microglial dysfunction. Furthermore, CSF sTREM2 as a key mediator modulated the influences of PM_2.5 exposure on AD amyloid pathologies.

NO2 and PM2.5 air pollution co-exposure and temperature effect modification on pre-mature mortality in advanced age: a longitudinal cohort study in China

BACKGROUND

There is a discourse on whether air pollution mixture or air pollutant components are causally linked to increased mortality. In particular, there is uncertainty on whether the association of NO₂ with mortality is independent of fine particulate matter (PM_2.5). Furthermore, effect modification by temperature on air pollution-related mortality also needs more evidence.

METHODS

We used the Chinese Longitudinal Healthy Longevity Study (CLHLS), a prospective cohort with geographical and socio-economic diversity in China. The participants were enrolled in 2008 or 2009 and followed up in 2011-2012, 2014, and 2017-2018. We used remote sensing and ground monitors to measure nitrogen dioxide (NO₂), fine particulate matter (PM_2.5) , and temperature. We used the Cox-proportional hazards model to examine the association between component and composite air pollution and all-cause mortality, adjusted for demographic characteristics, lifestyle, geographical attributes, and temperature. We used the restricted cubic spline to visualize the concentration-response curve.

RESULTS

Our study included 11 835 individuals with an average age of 86.9 (SD: 11.4) at baseline. Over 55 606 person-years of follow-up, we observed 8 216 mortality events. The average NO₂ exposure was 19.1 μg/m³ (SD: 14.1); the average PM_2.5 exposure was 52.8 μg/m³ (SD: 15.9). In the single pollutant models, the mortality HRs (95% CI) for 10 μg/m³ increase in annual average NO₂ or PM_2.5 was 1.114 (1.085, 1.143) and 1.244 (1.221, 1.268), respectively. In the multi-pollutant model co-adjusting for NO₂ and PM_2.5, the HR for NO₂ turned insignificant: 0.978 (0.950, 1.008), but HR for PM_2.5 was not altered: 1.252 (1.227, 1.279). PM_2.5 and higher mortality association was robust, regardless of NO₂. When acccounting for particulate matter, NO₂ exposure appeared to be harmful in places of colder climates and higher seasonal temperature variation.

CONCLUSIONS

We see a robust relationship of PM_2.5 exposure and premature mortality in advance aged individuals, however, NO₂ exposure and mortality was only harmful in places of colder climate such as northeast China, indicating evidence of effect modification by temperature. Analysis of NO₂ without accounting for its collinearity with PM_2.5, may lead to overestimation.

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO₂ models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO₂. Our results suggest that street view imagery alone may provide sufficient information to explain NO₂ variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R² of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO₂ pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China's coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO₂ emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China's current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and the R ² increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO₂ emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO₂ emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available in the online version of this article at 10.1007/s11783-023-1608-1 and is accessible for authorized users.

High-Performance Room-Temperature Conductometric Gas Sensors: Materials and Strategies

Chemiresistive sensors have gained increasing interest in recent years due to the necessity of low-cost, effective, high-performance gas sensors to detect volatile organic compounds (VOC) and other harmful pollutants. While most of the gas sensing technologies rely on the use of high operation temperatures, which increase usage cost and decrease efficiency due to high power consumption, a particular subset of gas sensors can operate at room temperature (RT). Current approaches are aimed at the development of high-sensitivity and multiple-selectivity room-temperature sensors, where substantial research efforts have been conducted. However, fewer studies presents the specific mechanism of action on why those particular materials can work at room temperature and how to both enhance and optimize their RT performance. Herein, we present strategies to achieve RT gas sensing for various materials, such as metals and metal oxides (MOs), as well as some of the most promising candidates, such as polymers and hybrid composites. Finally, the future promising outlook on this technology is discussed.

An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.

Association of Long-term Exposure to Ambient Air Pollution With Cognitive Decline and Alzheimer's Disease-Related Amyloidosis

BACKGROUND

Air pollution induces neurotoxic reactions and may exert adverse effects on cognitive health. We aimed to investigate whether air pollutants accelerate cognitive decline and affect neurobiological signatures of Alzheimer's disease (AD).

METHODS

We used a population-based cohort from the Chinese Longitudinal Healthy Longevity Survey with 31,573 participants and a 10-year follow-up (5878 cognitively unimpaired individuals in Chinese Longitudinal Healthy Longevity Survey followed for 5.95 ± 2.87 years), and biomarker-based data from the Chinese Alzheimer's Biomarker and Lifestyle study including 1131 participants who underwent cerebrospinal fluid measurements of AD-related amyloid-β (Aβ) and tau proteins. Cognitive impairment was determined by education-corrected performance on the China-Modified Mini-Mental State Examination. Annual exposures to fine particulate matter (PM_2.5), ground-level ozone (O₃), and nitrogen dioxide (NO₂) were estimated at areas of residence. Exposures were aggregated as 2-year averages preceding enrollments using Cox proportional hazards or linear models.

RESULTS

Long-term exposure to PM_2.5 (per 20 μg/m³) increased the risk of cognitive impairment (hazard ratio, 1.100; 95% CI: 1.026-1.180), and similar associations were observed from separate cross-sectional analyses. Exposures to O₃ and NO₂ yielded elevated risk but with nonsignificant estimates. Individuals exposed to high PM_2.5 manifested increased amyloid burdens as reflected by cerebrospinal fluid-AD biomarkers. Moreover, PM_2.5 exposure-associated decline in global cognition was partly explained by amyloid pathology as measured by cerebrospinal fluid-Aβ₄₂/Aβ₄₀, P-tau/Aβ₄₂, and T-tau/Aβ₄₂, with mediation proportions ranging from 16.95% to 21.64%.

CONCLUSIONS

Long-term exposure to PM_2.5 contributed to the development of cognitive decline, which may be partly explained by brain amyloid accumulation indicative of increased AD risk.

Machine learning-based estimation of ground-level NO2 concentrations over China

Most current scientific research on NO₂ remote sensing focuses on tropospheric NO₂ column concentrations rather than ground-level NO₂ concentrations; however, ground-level NO₂ concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO₂ concentrations throughout China based on the tropospheric NO₂ column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO₂ and its influencing factors. The R² values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO₂ concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO₂ concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO₂ concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO₂ concentrations indicates that pollution decreased continuously from 2018 to 2020.

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 [PM_2.5]) 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 PM_2.5 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 PM_2.5 mass concentrations. We analysed the association between PM_2.5 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 PM_2.5 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 PM_2.5 to which children were exposed was 48.2 (±31.0) µg/m³. The 5th and 95th percentiles of PM_2.5 were 9.8 µg/m³ and 110.9 µg/m³, respectively. A 10 µg/m³ increase in PM_2.5 was associated with greater odds of having an ARI (OR: 1.06; 95% CI: 1.05-1.07). The association between PM_2.5 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 PM_2.5 on ARI in boys, in younger children, and in children living in rural areas.

CONCLUSIONS

Annual average ambient PM_2.5, 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 PM_2.5 may have a role in the global challenge of reducing ARI-related morbidity and mortality in children.

A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R² of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets

BACKGROUND

Combustion-related nitrogen dioxide (NO₂) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO₂ concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019.

METHODS

We scaled an existing annual average NO₂ concentration dataset for 2010-12 from a land use regression model (based on 5220 NO₂ monitors in 58 countries and land use variables) to other years using NO₂ column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO₂-attributable paediatric asthma incidence.

FINDINGS

We estimated that 1·85 million (95% uncertainty interval [UI] 0·93-2·80 million) new paediatric asthma cases were attributable to NO₂ globally in 2019, two thirds of which occurred in urban areas (1·22 million cases; 95% UI 0·60-1·8 million). The proportion of paediatric asthma incidence that is attributable to NO₂ in urban areas declined from 19·8% (1·22 million attributable cases of 6·14 million total cases) in 2000 to 16·0% (1·24 million attributable cases of 7·73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO₂ concentrations, paediatric population size, and asthma incidence rates to the change in NO₂-attributable paediatric asthma incidence differed regionally.

INTERPRETATION

Despite improvements in some regions, combustion-related NO₂ pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children.

FUNDING

Health Effects Institute, NASA.

Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China

Existing national- or continental-scale models of nitrogen dioxide (NO₂) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO₂ sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO₂ for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO₂ measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO₂, optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO₂ in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R² values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO₂ in the relatively sparsely-populated areas in NHAR and neighbouring provinces.

Predicting Perceptions of the Built Environment using GIS, Satellite and Street View Image Approaches

BACKGROUND

High quality built environments are important for human health and wellbeing. Numerous studies have characterized built environment physical features and environmental exposures, but few have examined urban perceptions at geographic scales needed for population-based research. The degree to which urban perceptions are associated with different environmental features, and traditional environmental exposures such as air pollution or urban green space, is largely unknown.

OBJECTIVE

To determine built environment factors associated with safety, lively and beauty perceptions across 56 cities.

METHODS

We examined perceptions collected in the open source Place Pulse 2.0 dataset, which assigned safety, lively and beauty scores to street view images based on crowd-sourced labelling. We derived built environment measures for the locations of these images (110,000 locations across 56 global cities) using GIS and remote sensing datasets as well as street view imagery features (e.g. trees, cars) using deep learning image segmentation. Linear regression models were developed using Lasso penalized variable selection to predict perceptions based on visible (street level images) and GIS/remote sensing built environment variables.

RESULTS

Population density, impervious surface area, major roads, traffic air pollution, tree cover and Normalized Difference Vegetation Index (NDVI) showed statistically significant differences between high and low safety, lively, and beauty perception locations. Visible street level features explained approximately 18% of the variation in safety, lively, and beauty perceptions, compared to 3-10% explained by GIS/remote sensing. Large differences in prediction were seen when modelling between city (R2 67-81%) versus within city (R2 11-13%) perceptions. Important predictor variables included visible accessibility features (e.g. streetlights, benches) and roads for safety, visible plants and buildings for lively, and visible green space and NDVI for beauty.

CONCLUSION

Substantial within and between city differences in built environment perceptions exist, which visible street level features and GIS/remote sensing variables only partly explain. This offers a new research avenue to expand built environment measurement methods to include perceptions in addition to physical features.

Are greenspace quantity and quality associated with mental health through different mechanisms in Guangzhou, China: A comparison study using street view data

Residential greenspace quality may be more important for people's mental health than the quantity of greenspace. Existing literature mainly focuses on greenspace quantity and is limited to exposure metrics based on an over-head perspective (i.e., remote sensing data). Thus, whether greenspace quantity and quality influence mental health through different mechanisms remains unclear. To compare the mechanisms through which greenspace quantity and quality influence mental health, we used both remote sensing and street view data. Questionnaire data from 1003 participants in Guangzhou, China were analysed cross-sectionally. Mental health was assessed through the World Health Organization Well-Being Index (WHO-5). Greenspace quantity was measured by both remote sensing-based Normalized Difference Vegetation Index (NDVI) and Street View Greenness-quantity (SVG-quantity). Greenspace quality was measured by both Street View Greenness-quality (SVG-quality) and questionnaire-based self-reported greenspace quality. Structural equation models were used to assess mechanisms through which neighbourhood greenspace exposure has an influence on mental health. Stress, social cohesion, physical activity and life satisfaction were found to mediate both SVG-quality - WHO-5 scores and self-reported greenspace quality - WHO-5 scores associations. However, only NO₂ (nitrogen dioxide) mediated the association between NDVI and WHO-5 scores, while NO₂, perceived pollution and social cohesion mediated the association between SVG-quantity and WHO-5 scores. The mechanisms through which neighbourhood greenspace exposure influences mental health may vary across different exposure assessment strategies. Greenspace quantity influences mental health through reducing harm from pollution, while greenspace quality influences mental health through restoring and building capacities.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO₂, PM_2.5, O₃, CO, PM₁₀, SO₂) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R² increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R² = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale

Despite the success of passive sampler-based monitoring networks in capturing global atmospheric distributions of semivolatile organic compounds (SVOCs), their limited spatial resolution remains a challenge. Adequate spatial coverage is necessary to better characterize concentration gradients, identify point sources, estimate human exposure, and evaluate the effectiveness of chemical regulations such as the Stockholm Convention on Persistent Organic Pollutants. Land use regression (LUR) modelling can be used to integrate land use characteristics and other predictor variables (industrial emissions, traffic intensity, demographics, etc.) to describe or predict the distribution of air concentrations at unmeasured locations across a region or country. While LUR models are frequently applied to data-rich conventional air pollutants such as particulate matter, ozone, and nitrogen oxides, they are rarely applied to SVOCs. The MONET passive air sampling network (RECETOX, Masaryk University) continuously measures atmospheric SVOC levels across Czechia in monthly intervals. Using monitoring data from 29 MONET sites over a two-year period (2015-2017) and a variety of predictor variables, we developed LUR models to describe atmospheric levels and identify sources of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and DDT across the country. Strong and statistically significant (R² > 0.6; p < 0.05) models were derived for PAH and PCB levels on a national scale. The PAH model retained three predictor variables - heating emissions represented by domestic fuel consumption, industrial PAH point sources, and the hill:valley index, a measure of site topography. The PCB model retained two predictor variables - site elevation, and secondary sources of PCBs represented by soil concentrations. These models were then applied to Czechia as a whole, highlighting the spatial variability of atmospheric SVOC levels, and providing a tool that can be used for further optimization of sampling network design, as well as evaluating potential human and environmental chemical exposures.

Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe

The lockdown and related measures implemented by many European countries to stop the spread of the SARS-CoV-2 virus (COVID-19) pandemic have altered the economic activities and road transport in many cities. To rigorously evaluate how these measures have affected air quality in Europe, we developed Bayesian spatio-temporal (BST) models that assess changes in the surface nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) concentration across the continent. We fitted BST models to measurements of the two pollutants in 2020 using a lockdown indicator covariate, while accounting for the spatial and temporal correlation present in the data. Since other factors, such as weather conditions, local combustion sources and/or land surface characteristics may contribute to the variation of pollutant concentrations, we proposed two model formulations that allowed the differentiation between the variations in pollutant concentrations due to seasonality from the variations associated to the lockdown policies. The first model compares the changes in 2020, with the ones during the same period in the previous five years, by introducing an offset term, which controls for the long-term average concentrations of each pollutant during 2014–2019. The second approach models only the 2020 data, but adjusts for confounding factors. The results indicated that the latter can better capture the lockdown effect. The measures taken to tackle the virus in Europe reduced the average surface concentrations of NO₂ and PM_2.5 by 29.5% (95% Bayesian credible interval: 28.1%, 30.9%) and 25.9% (23.6%, 28.1%), respectively. To our knowledge, this research is the first to account for the spatio-temporal correlation present in the monitoring data during the pandemic and to assess how it affects estimation of the lockdown effect while accounting for confounding. The proposed methodology improves our understanding of the effect of COVID-19 lockdown policies on the air pollution burden across the continent.

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.

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.

Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations

Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO₂) concentrations using approximately 20,000 ground-level measurements in Flanders, Belgium combined with aerial images and deep neural networks. Our final model explained 79% of the spatial variability in NO₂ (root mean square error of 10-fold cross-validation = 3.58 μg/m³) using only images as model inputs. This novel approach offers an alternative means of estimating large-scale spatial variations in ambient air quality and may be particularly useful for regions of the world without detailed emissions data or land use information typically used to estimate outdoor air pollution concentrations.

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine-scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day-of-the-week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2-m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C-32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface-level concentrations yields relatively strong correlation (R 2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

BACKGROUND

Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.

OBJECTIVES

We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.

METHODS

We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.

RESULTS

Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)].

DISCUSSION

Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

Premature mortality due to air pollution in European cities: a health impact assessment

BACKGROUND

Ambient air pollution is a major environmental cause of morbidity and mortality worldwide. Cities are generally hotspots for air pollution and disease. However, the exact extent of the health effects of air pollution at the city level is still largely unknown. We aimed to estimate the proportion of annual preventable deaths due to air pollution in almost 1000 cities in Europe.

METHODS

We did a quantitative health impact assessment for the year 2015 to estimate the effect of air pollution exposure (PM_2·5 and NO₂) on natural-cause mortality for adult residents (aged ≥20 years) in 969 cities and 47 greater cities in Europe. We retrieved the cities and greater cities from the Urban Audit 2018 dataset and did the analysis at a 250 m grid cell level for 2015 data based on the global human settlement layer residential population. We estimated the annual premature mortality burden preventable if the WHO recommended values (ie, 10 μg/m³ for PM_2·5 and 40 μg/m³ for NO₂) were achieved and if air pollution concentrations were reduced to the lowest values measured in 2015 in European cities (ie, 3·7 μg/m³ for PM_2·5 and 3·5 μg/m³ for NO₂). We clustered and ranked the cities on the basis of population and age-standardised mortality burden associated with air pollution exposure. In addition, we did several uncertainty and sensitivity analyses to test the robustness of our estimates.

FINDINGS

Compliance with WHO air pollution guidelines could prevent 51 213 (95% CI 34 036-68 682) deaths per year for PM_2·5 exposure and 900 (0-2476) deaths per year for NO₂ exposure. The reduction of air pollution to the lowest measured concentrations could prevent 124 729 (83 332-166 535) deaths per year for PM_2·5 exposure and 79 435 (0-215 165) deaths per year for NO₂ exposure. A great variability in the preventable mortality burden was observed by city, ranging from 0 to 202 deaths per 100 000 population for PM_2·5 and from 0 to 73 deaths for NO₂ per 100 000 population when the lowest measured concentrations were considered. The highest PM_2·5 mortality burden was estimated for cities in the Po Valley (northern Italy), Poland, and Czech Republic. The highest NO₂ mortality burden was estimated for large cities and capital cities in western and southern Europe. Sensitivity analyses showed that the results were particularly sensitive to the choice of the exposure response function, but less so to the choice of baseline mortality values and exposure assessment method.

INTERPRETATION

A considerable proportion of premature deaths in European cities could be avoided annually by lowering air pollution concentrations, particularly below WHO guidelines. The mortality burden varied considerably between European cities, indicating where policy actions are more urgently needed to reduce air pollution and achieve sustainable, liveable, and healthy communities. Current guidelines should be revised and air pollution concentrations should be reduced further to achieve greater protection of health in cities.

FUNDING

Spanish Ministry of Science and Innovation, Internal ISGlobal fund.