Search

Abstract Ambient fine particulate matter (PM2.5) is the world’s leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Abstract Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO _2 ) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO _2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO _2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO _2 levels were 37 (range: 1–189), 28 (range: 1–170) and 50 (range: 1–195) µ g m ^−3 , respectively. Unlike NO _2 , NO concentrations were highest in the non-Harmattan season (41 [range: 31–521] µ g m ^−3 ). Road traffic was the dominant factor for both pollutants, but NO _2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO _2 levels exceeding the World Health Organization (WHO) guideline of 10 µ g m ^−3 . Significant disparities in NO _2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µ g m ^−3 higher compared with the wealthiest ( p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city’s poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Abstract The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.

Introduction: The World Health Organization (WHO) recently revised its health guidelines for Nitrogen dioxide (NO2) air pollution, reducing the annual mean NO2 level to 10 μg/m3 (5.3 ppb) and the 24-h mean to 25 μg/m3 (13.3 ppb). NO2 is a pollutant of global concern, but it is also a criteria air pollutant that varies spatiotemporally at fine resolutions due to its relatively short lifetime (~hours). Current models have limited ability to capture both temporal and spatial NO2 variation and none are available with global coverage. Land use regression (LUR) models that incorporate timevarying predictors (e.g., meteorology and satellite NO2 measures) and land use characteristics (e.g., road density, emission sources) have significant potential to address this need.Methods: We created a daily Land use regression model with 50 × 50 m2 spatial resolution using 5.7 million daily air monitor averages collected from 8,250 monitor locations.Results: In cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual global NO2 variation. Daily, monthly, and annual root mean square error were 6.8, 5.0, and 4.4 ppb and absolute bias were 46%, 30%, and 21%, respectively. The final model has 11 variables, including road density and built environments with fine (30 m or less) spatial resolution and meteorological and satellite data with daily temporal resolution. Major roads and satellite-based estimates of NO2 were consistently the strongest predictors of NO2 measurements in all regions.Discussion: Daily model estimates from 2005–2019 are available and can be used for global risk assessments and health studies, particularly in countries without NO2 monitoring.

Antecedentes: Se estima que los factores de riesgo ambientales (FRA) fueron responsables en 2019 de nueve millones de muertes en el mundo. Objetivo: A partir de datos del estudio Global Burden of Disease, se analizaron indicadores de pérdida de salud asociada a la exposición a FRA en México. Material y métodos: Se analizaron números absolutos y porcentajes poblacionales de muertes y años de vida saludable (AVISA) perdidos según sexo atribuidos a FRA seleccionados en los ámbitos nacional y estatal, así como las tendencias estandarizadas por edad de 1990 a 2021. Resultados: En 2021, la contaminación por material particulado en ambiente exterior mostró la mayor mortalidad y AVISA perdidos por los FRA seleccionados (42.2 y 38.1 %, respectivamente), seguida de la exposición a plomo (20.6 y 13.4 %) y temperatura baja (19.8 y 12.3 %). Ambos indicadores han disminuido en todos los FRA seleccionados, en magnitudes diferentes entre 1991 y 2021, excepto la temperatura alta. Conclusiones: A pesar de las disminuciones en los últimos 32 años, el material particulado en ambiente exterior mostró la mayor mortalidad y AVISA perdidos, seguido de la exposición a plomo. Es fundamental fortalecer las políticas de calidad del aire y exposición a plomo en México.

Summary: Background: Influenza virus infection is associated with incident ischemic heart disease (IHD) events. Here, we estimate the global, regional, and national IHD mortality burden attributable to influenza. Methods: We used vital registration data from deaths in adults ≥50 years (13.2 million IHD deaths as underlying cause) to assess the relationship between influenza activity and IHD mortality in a non-linear meta-regression framework from 2010 to 2019. This derived relationship was then used to estimate the global influenza attributable IHD mortality. We estimated the population attributable fraction (PAF) of influenza for IHD deaths based on the relative risk associated with a given level of weekly influenza test positivity rate and multiplied PAFs by IHD mortality from the Global Burden of Disease study. Findings: Influenza activity was associated with increased risk of IHD mortality across all countries analyzed. The mean PAF of influenza for IHD mortality was 3.9% (95% uncertainty interval [UI] 2.5–5.3%), ranging from

Summary: Background: More than 3 billion people do not have access to clean energy and primarily use solid fuels to cook. Use of solid fuels generates household air pollution, which was associated with more than 2 million deaths in 2019. Although local patterns in cooking vary systematically, subnational trends in use of solid fuels have yet to be comprehensively analysed. We estimated the prevalence of solid-fuel use with high spatial resolution to explore subnational inequalities, assess local progress, and assess the effects on health in low-income and middle-income countries (LMICs) without universal access to clean fuels. Methods: We did a geospatial modelling study to map the prevalence of solid-fuel use for cooking at a 5 km × 5 km resolution in 98 LMICs based on 2·1 million household observations of the primary cooking fuel used from 663 population-based household surveys over the years 2000 to 2018. We use observed temporal patterns to forecast household air pollution in 2030 and to assess the probability of attaining the Sustainable Development Goal (SDG) target indicator for clean cooking. We aligned our estimates of household air pollution to geospatial estimates of ambient air pollution to establish the risk transition occurring in LMICs. Finally, we quantified the effect of residual primary solid-fuel use for cooking on child health by doing a counterfactual risk assessment to estimate the proportion of deaths from lower respiratory tract infections in children younger than 5 years that could be associated with household air pollution. Findings: Although primary reliance on solid-fuel use for cooking has declined globally, it remains widespread. 593 million people live in districts where the prevalence of solid-fuel use for cooking exceeds 95%. 66% of people in LMICs live in districts that are not on track to meet the SDG target for universal access to clean energy by 2030. Household air pollution continues to be a major contributor to particulate exposure in LMICs, and rising ambient air pollution is undermining potential gains from reductions in the prevalence of solid-fuel use for cooking in many countries. We estimated that, in 2018, 205 000 (95% uncertainty interval 147 000–257 000) children younger than 5 years died from lower respiratory tract infections that could be attributed to household air pollution. Interpretation: Efforts to accelerate the adoption of clean cooking fuels need to be substantially increased and recalibrated to account for subnational inequalities, because there are substantial opportunities to improve air quality and avert child mortality associated with household air pollution. Funding: Bill & Melinda Gates Foundation.

Background: Autism spectrum disorder (ASD) incidence has increased in past decades. ASD etiology remains inconclusive, but research suggests genetic, epigenetic, and environmental contributing factors and likely prenatal origins. Few studies have examined modifiable environmental risk factors for ASD, and far fewer have examined protective exposures. Greenspace has been associated with positive child development, but very limited greenspace research has examined ASD risk or prenatal exposures. Only one ecological study in 2017 has evaluated the association between greenspace and ASD, observing protective benefits. Greenspace may have direct effects on ASD risk and indirect effects by reducing air pollution exposure, a growing suspected ASD risk factor. Objectives: To measure the association between prenatal greenspace exposure and ASD risk and examine if reduced air pollution levels in areas of higher greenspace mediate this association. Methods: We linked a population-based birth cohort of all deliveries in Metro Vancouver, Canada, from 2004 to 2009, with follow-up to 2014. Diagnoses were based on Autism Diagnostic Observation Schedule and Autism Diagnostic Interview-Revised instruments. Greenspace was quantified as the average of the annual mean Normalized Difference Vegetation Index (NDVI) within a 250 m buffer of a residential postal code. Air pollutant exposures—particulate matter with a diameter less than 2.5 µm (PM2.5), nitric oxide (NO), and nitrogen dioxide (NO2)—were derived from previously developed and temporally adjusted land use regression models. We estimated air pollutant exposures as the mean concentration per month during pregnancy. We calculated odds ratios (ORs) using logistic regression per NDVI interquartile range (IQR) increase, adjusting for child sex, birth month and year, maternal age and birthplace, and neighborhood-level urbanicity and income. To estimate the health impact of greenspace on ASD at the population level, we used the logistic regression model and marginal standardization to derive risk differences (RDs). Lastly, to quantify the mediating effect of greenspace on ASD risk through air pollution reduction, we used marginal structural models and a potential outcomes framework to calculate marginal risk differences (RDs) to decompose the total effect of greenspace on ASD into natural direct and indirect effects. Results: Of 129,222 births, 1,921 (1.5 %) children were diagnosed with ASD. The adjusted OR for ASD per NDVI IQR (0.12) increase was 0.96 (95 % CI: 0.90, 1.02) in 250 m buffer zones and 0.94 (95 % CI: 0.89, 1.00) in 100 m buffer zones. On the additive scale, the adjusted RDs were null. Natural direct, natural indirect, and total effect RDs were null for PM2.5, NO, and NO2 mediation models. Conclusion: Prenatal greenspace exposure was associated with reduced odds of ASD, but in the additive scale, this effect was null at the population level. No mediating effect was observed through reduced air pollution, suggesting that air pollution may act as a confounder rather than as a mediator.

Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5–10 min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO2 and PM2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m3 of NO2, 19.3 µg/m3 of PM2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m3 of NO2, 15.1 µg/m3 of PM2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.

Ambient fine particulate matter (PM2.5) is one of the most important environmental health risk factors in many regions. Here, the authors present an assessment of PM2.5 emission sources and the related health impacts across global to sub-national scales and find that over 1 million deaths were avoidable in 2017 by eliminating PM2.5 mass associated with fossil fuel combustion emissions.

Abstract Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.

Background: Growing evidence suggests that exposure to green space is associated with improved childhood health and development, but the influence of different green space types remains relatively unexplored. In the present study, we investigated the association between early-life residential exposure to vegetation and early childhood development and evaluated whether associations differed according to land cover types, including paved land. Methods: Early childhood development was assessed via kindergarten teacher-ratings on the Early Development Instrument (EDI) in a large population-based birth cohort (n = 27,539) in Metro Vancouver, Canada. The residential surrounding environment was characterized using a high spatial resolution land cover map that was linked to children by six-digit residential postal codes. Early-life residential exposure (from birth to time of EDI assessment, mean age = 5.6 years) was calculated as the mean of annual percentage values of different land cover classes (i.e., total vegetation, tree cover, grass cover, paved surfaces) within a 250 m buffer zone of postal code centroids. Multilevel models were used to analyze associations between respective land cover classes and early childhood development. Results: In adjusted models, one interquartile range increase in total vegetation percentage was associated with a 0.33 increase in total EDI score (95% CI: 0.21, 0.45). Similar positive associations were observed for tree cover (β-coefficient: 0.26, 95% CI: 0.15, 0.37) and grass cover (β-coefficient: 0.12, 95% CI: 0.02, 0.22), while negative associations were observed for paved surfaces (β-coefficient: −0.35, 95% CI: −0.47, −0.23). Conclusions: Our findings indicate that increased early-life residential exposure to vegetation is positively associated with early childhood developmental outcomes, and that associations may be stronger for residential exposure to tree cover relative to grass cover. Our results further indicate that childhood development may be negatively associated with residential exposure to paved surfaces. These findings can inform urban planning to support early childhood developmental health.

Background: Few studies have evaluated long-term cardiovascular effects of fine particulate matter (PM2.5) and its constituents in countries with high air pollution levels. We aimed to investigate the associations of long-term exposure to PM2.5 and constituents with cardiovascular mortality in China. Methods: We conducted a prospective cohort study of 90,672 adults ≥ 18 years from 2010 to 2017 in 161 districts/counties across China. The residential annual-average exposure to PM2.5 and 6 main components from 2011 to 2017 were estimated by satellite-based and chemical transport models. Associations of PM2.5 and constituents with cardiovascular mortality were analyzed by competing-risk Cox proportional hazards regression. Results: The average PM2.5 exposure throughout the whole period was 46 ± 22 μg/m3. The hazard ratios of mortality (95% confidence intervals) per 10 μg/m3 increase in PM2.5 concentrations were 1.02 (1.00, 1.05) for overall cardiovascular disease, 1.05 (1.01, 1.09) for ischemic heart disease, 1.03 (1.00, 1.06) for overall stroke, 0.99 (0.94, 1.04) for hemorrhagic stroke, and 1.11 (1.04, 1.19) for ischemic stroke. PM2.5 constituents from fossil fuel combustion (i.e., black carbon, organic matter, nitrate, ammonium, and sulfate) showed larger hazard ratios than PM2.5 total mass, while soil dust showed no risks. Conclusions: This nationwide cohort study demonstrated associations of long-term exposure to PM2.5 and its constituents with increased risks of cardiovascular mortality in the general population of China. Our study highlighted the importance of PM2.5 constituents from fossil fuel combustion in the long-term cardiovascular effects of PM2.5 in China.

Abstract Background Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community’s exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard’s impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. Methods A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. Results Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16–49%) across all hazards. Distinct spatial patterns were observed – for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region. Conclusions The creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

Abstract Globally consistent measurements of airborne metal concentrations in fine particulate matter (PM2.5) are important for understanding potential health impacts, prioritizing air pollution mitigation strategies, and enabling global chemical transport model development. PM2.5 filter samples (N ~ 800 from 19 locations) collected from a globally distributed surface particulate matter sampling network (SPARTAN) between January 2013 and April 2019 were analyzed for particulate mass and trace metals content. Metal concentrations exhibited pronounced spatial variation, primarily driven by anthropogenic activities. PM2.5 levels of lead, arsenic, chromium, and zinc were significantly enriched at some locations by factors of 100–3000 compared to crustal concentrations. Levels of metals in PM2.5 and PM10 exceeded health guidelines at multiple sites. For example, Dhaka and Kanpur sites exceeded the US National Ambient Air 3-month Quality Standard for lead (150 ng m−3). Kanpur, Hanoi, Beijing and Dhaka sites had annual mean arsenic concentrations that approached or exceeded the World Health Organization’s risk level for arsenic (6.6 ng m−3). The high concentrations of several potentially harmful metals in densely populated cites worldwide motivates expanded measurements and analyses.

Background: Emerging studies have associated low greenspace and high air pollution exposure with risk of child attention deficit/hyperactivity disorder (ADHD). Population-based studies are limited, however, and joint effects are rarely evaluated. We investigated associations of ADHD incidence with greenspace, air pollution, and noise in a population-based birth cohort. Methods: We assembled a cohort from administrative data of births from 2000 to 2001 (N ∼ 37,000) in Metro Vancouver, Canada. ADHD was identified by hospital records, physician visits, and prescriptions. Cox proportional hazards models were applied to assess associations between environmental exposures and ADHD incidence adjusting for available covariates. Greenspace was estimated using vegetation percentage derived from linear spectral unmixing of Landsat imagery. Fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were estimated using land use regression models; noise was estimated using a deterministic model. Exposure period was from birth until the age of three. Joint effects of greenspace and PM2.5 were analysed in two-exposure models and by categorizing values into quintiles. Results: During seven-year follow-up, 1217 ADHD cases were diagnosed. Greenspace was associated with lower incidence of ADHD (hazard ratio, HR: 0.90 [0.81–0.99] per interquartile range increment), while PM2.5 was associated with increased incidence (HR: 1.11 [1.06–1.17] per interquartile range increment). NO2 (HR: 1.01 [0.96, 1.07]) and noise (HR: 1.00 [0.95, 1.05]) were not associated with ADHD. There was a 50% decrease in the HR for ADHD in locations with the lowest PM2.5 and highest greenspace exposure, compared to a 62% increase in HR in locations with the highest PM2.5 and lowest greenspace exposure. Effects of PM2.5 were attenuated by greenspace in two-exposure models. Conclusions: We found evidence suggesting environmental inequalities where children living in greener neighborhoods with low air pollution had substantially lower risk of ADHD compared to those with higher air pollution and lower greenspace exposure.

Summary: 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.

Introduction: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM2.5 exposure models. Methods: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM2.5 kitchen concentrations (n = 2,365) and male and/or female PM2.5 exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM2.5 exposures. Results: The final models explained half (R2 = 54%) of the variation in kitchen PM2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R2 = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM2.5 kitchen concentrations. Average national PM2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m3 (Chile); 55 μg/m3 (China)) and 12-fold among households primarily cooking with wood (36 μg/m3 (Chile)); 427 μg/m3 (Pakistan)). Average PM2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile). Conclusion: Using survey data to estimate PM2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Abstract Background As low- and middle-income countries urbanize and industrialize, they must also cope with pollution emitted from diverse sources. Main text Strong and consistent evidence associates exposure to air pollution and lead with increased risk of cardiovascular disease occurrence and death. Further, increasing evidence, mostly from high-income countries, indicates that exposure to noise and to both high and low temperatures may also increase cardiovascular risk. There is considerably less research on the cardiovascular impacts of environmental conditions in low- and middle-income countries (LMICs), where the levels of pollution are often higher and the types and sources of pollution markedly different from those in higher-income settings. However, as such evidence gathers, actions to reduce exposures to pollution in low- and middle-income countries are warranted, not least because such exposures are very high. Cities, where pollution, populations, and other cardiovascular risk factors are most concentrated, may be best suited to reduce the cardiovascular burden in LMICs by applying environmental standards and policies to mitigate pollution and by implementing interventions that target the most vulnerable. The physical environment of cities can be improved though municipal processes, including infrastructure development, energy and transportation planning, and public health actions. Local regulations can incentivize or inhibit the polluting behaviors of industries and individuals. Environmental monitoring can be combined with public health warning systems and publicly available exposure maps to inform residents of environmental hazards and encourage the adoption of pollution-avoiding behaviors. Targeted individual or neighborhood interventions that identify and treat high-risk populations (e.g., lead mitigation, portable air cleaners, and preventative medications) can also be leveraged in the very near term. Research will play a key role in evaluating whether these approaches achieve their intended benefits, and whether these benefits reach the most vulnerable. Conclusion Cities in LMICs can play a defining role in global health and cardiovascular disease prevention in the next several decades, as they are well poised to develop innovative, multisectoral approaches to pollution mitigation, while also protecting the most vulnerable.

Abstract Background Emerging evidence links road proximity and air pollution with cognitive impairment. Joint effects of noise and greenness have not been evaluated. We investigated associations between road proximity and exposures to air pollution, and joint effects of noise and greenness, on non-Alzheimer’s dementia, Parkinson’s and Alzheimer’s disease and multiple sclerosis within a population-based cohort. Methods We assembled administrative health database cohorts of 45–84 year old residents (N ~ 678,000) of Metro Vancouver, Canada. Cox proportional hazards models were built to assess associations between exposures and non-Alzheimer’s dementia and Parkinson’s disease. Given reduced case numbers, associations with Alzheimer’s disease and multiple sclerosis were evaluated in nested case-control analyses by conditional logistic regression. Results Road proximity was associated with all outcomes (e.g. non-Alzheimer’s dementia hazard ratio: 1.14, [95% confidence interval: 1.07–1.20], for living

Abstract Background Approximately 2.9 million deaths are attributed to ambient fine particle air pollution around the world each year (PM2.5). In general, cohort studies of mortality and outdoor PM2.5 concentrations have limited information on individuals exposed to low levels of PM2.5 as well as covariates such as smoking behaviours, alcohol consumption, and diet which may confound relationships with mortality. This study provides an updated and extended analysis of the Canadian Community Health Survey-Mortality cohort: a population-based cohort with detailed PM2.5 exposure data and information on a number of important individual-level behavioural risk factors. We also used this rich dataset to provide insight into the shape of the concentration-response curve for mortality at low levels of PM2.5. Methods Respondents to the Canadian Community Health Survey from 2000 to 2012 were linked by postal code history from 1981 to 2016 to high resolution PM2.5 exposure estimates, and mortality incidence to 2016. Cox proportional hazard models were used to estimate the relationship between non-accidental mortality and ambient PM2.5 concentrations (measured as a three-year average with a one-year lag) adjusted for socio-economic, behavioural, and time-varying contextual covariates. Results In total, 50,700 deaths from non-accidental causes occurred in the cohort over the follow-up period. Annual average ambient PM2.5 concentrations were low (i.e. 5.9 μg/m3, s.d. 2.0) and each 10 μg/m3 increase in exposure was associated with an increase in non-accidental mortality (HR = 1.11; 95% CI 1.04–1.18). Adjustment for behavioural covariates did not materially change this relationship. We estimated a supra-linear concentration-response curve extending to concentrations below 2 μg/m3 using a shape constrained health impact function. Mortality risks associated with exposure to PM2.5 were increased for males, those under age 65, and non-immigrants. Hazard ratios for PM2.5 and mortality were attenuated when gaseous pollutants were included in models. Conclusions Outdoor PM2.5 concentrations were associated with non-accidental mortality and adjusting for individual-level behavioural covariates did not materially change this relationship. The concentration-response curve was supra-linear with increased mortality risks extending to low outdoor PM2.5 concentrations.

Summary: Background: Exposure to greenspace is associated with improved childhood development, but the pathways behind this relationship are insufficiently understood. Therefore, we aimed to investigate the association between lifetime residential exposure to greenspace and early childhood development and evaluate the extent to which this association is mediated by reductions in traffic-related air pollution and noise. Methods: This population-based birth cohort study comprised singleton births in Metro Vancouver, BC, Canada, between April 1, 2000, and Dec 31, 2005. Children and mothers had to be registered with the mandatory provincial health insurance programme, Medical Services Plan, and have lived within the study area from the child's birth to the time of outcome assessment. Early childhood development was assessed via teacher ratings on the Early Development Instrument (EDI), and we used the total EDI score as the primary outcome variable. We estimated greenspace using percentage vegetation derived from spectral unmixing of annual Landsat satellite image composites. Lifetime residential exposure to greenspace was estimated as the mean of annual percentage vegetation values within 250 m of participants’ residential postal codes. Multilevel modelling, adjusted for eight covariates, was used to investigate associations between greenspace exposure and EDI scores. We estimated the mediation effects of nitrogen dioxide (NO2), fine particulate matter (PM2·5), and noise levels using causal mediation analyses. Findings: Of the 37 745 children born in Metro Vancouver between April 1, 2000, and Dec 31, 2005, 27 372 were included in our final study sample. In the adjusted model, 1 IQR increase in percentage vegetation was associated with a 0·16 (95% CI 0·04–0·28; p=0·0073) increase in total EDI score, indicating small improvements in early childhood development. We estimated that 97·1% (95% CI 43·0–396·0), 29·5% (12·0–117·0), and 35·2% (17·9–139·0) of the association was mediated through reductions in NO2, PM2·5, and noise, respectively. Interpretation: Increased exposure to residential greenspace might improve childhood development by reducing the adverse developmental effects of traffic-related exposures, especially NO2 air pollution. Our study supports the implementation of healthy urban planning and green infrastructure interventions. Funding: Canadian Institutes of Health Research.

More than twenty percent of all cardiovascular disease (CVD) deaths are caused by air pollution — more than three million deaths every year — and these numbers will continue to rise unless the global community takes action. Nine out of ten people worldwide breathe polluted air, which disproportionately affects those living in low-resource settings. The World Heart Federation (WHF) is committed to reducing the impact of air pollution on people’s health and has made this a priority area of its global advocacy efforts. In pursuit of this goal, WHF has formed an Air Pollution Expert Group to inform action on air pollution for CVD health and recommend changes to public health policy. This policy paper lays out the health impacts of air pollution, examines its position on the global policy agenda, demonstrates its relevance to the cardiovascular community, and proposes actionable policy measures to mitigate this deadly risk factor to health. The paper considers the important roles to be played by the Members of WHF, including scientific societies and the physicians that constitute them, heart health foundations, and patient advocacy groups. The paper concludes with a detailed table of recommendations for the various sub-target groups at the global, national, local, and patient level.

BackgroundParticulate matter Methods and findingsWe 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.ConclusionsAmbient 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.

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to the high input data needs of existing estimation approaches. We introduced a computer vision method to estimate annual means for air pollution levels from street-level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250 k images for each city). Our experimental setup is designed to quantify intra- and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Similar to LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities (London, New York, and Vancouver), which have similar pollution source profiles, was moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on cities with very different source profiles, such as Accra in Ghana and Hong Kong, were lower (R2 between zero and 0.21). This suggests a need for local calibration, using additional measurement data from cities that share similar source profiles.

Abstract Exposure to ambient PM2.5 pollution has been linked to multiple adverse health effects. Additional effects have been identified in the literature and there is a need to understand its potential role in high prevalence diseases. In response to recent indications of PM2.5 as a risk factor for dementia, we examine the evidence by systematically reviewing the epidemiologic literature, in relation to exposure from ambient air pollution, household air pollution, secondhand smoke, and active smoking. We develop preliminary exposure‐response functions, estimate the uncertainty, and discuss sensitivities and model selection. We estimate the likely impact to be 2.1 M (1.4 M, 2.5 M; 5%–95% confidence) global incident dementia cases and 0.6 M (0.4 M, 0.8 M) deaths attributable to ambient PM2.5 pollution in 2015. This implies a combined toll from morbidity and mortality of dementia of 7.3 M (5.0 M, 9.1 M) lost disability‐adjusted life years. China, Japan, India, and the United States had the highest estimated total burden, and the per capita burden was highest in developed countries with large elderly populations. Compared to 2000, most countries in Europe, the Americas, and Southern Africa reduced the burden in 2015, while other regions had a net increase. Based on a recent systematic review of cost of illness studies for dementia, our estimates imply economic costs of US$ 26 billion worldwide in 2015. Based on this estimation, ambient PM2.5 pollution may be responsible for 15% of premature deaths and 7% of DALYs associated with dementia. Our estimates also indicate substantial uncertainty in this relationship, and future epidemiological studies at high exposure levels are especially needed.

Background: Epidemiological studies have consistently demonstrated that exposure to fine particulate matter (PM2.5) is associated with increased risks of mortality. To a lesser extent, a series of studies suggest that living in greener areas is associated with reduced risks of mortality. Only a handful of studies have examined the interplay between PM2.5, greenness, and mortality. Methods: We investigated the role of residential greenness in modifying associations between long-term exposures to PM2.5 and non-accidental and cardiovascular mortality in a national cohort of non-immigrant Canadian adults (i.e., the 2001 Canadian Census Health and Environment Cohort). Specifically, we examined associations between satellite-derived estimates of PM2.5 exposure and mortality across quintiles of greenness measured within 500 m of individual's place of residence during 11 years of follow-up. We adjusted our survival models for many personal and contextual measures of socioeconomic position, and residential mobility data allowed us to characterize annual changes in exposures. Results: Our cohort included approximately 2.4 million individuals at baseline, 194,270 of whom died from non-accidental causes during follow-up. Adjustment for greenness attenuated the association between PM2.5 and mortality (e.g., hazard ratios (HRs) and 95% confidence intervals (CIs) per interquartile range increase in PM2.5 in models for non-accidental mortality decreased from 1.065 (95% CI: 1.056–1.075) to 1.041 (95% CI: 1.031–1.050)). The strength of observed associations between PM2.5 and mortality decreased as greenness increased. This pattern persisted in models restricted to urban residents, in models that considered the combined oxidant capacity of ozone and nitrogen dioxide, and within neighbourhoods characterised by high or low deprivation. We found no increased risk of mortality associated with PM2.5 among those living in the greenest areas. For example, the HR for cardiovascular mortality among individuals in the least green areas was 1.17 (95% CI: 1.12–1.23) compared to 1.01 (95% CI: 0.97–1.06) among those in the greenest areas. Conclusions: Studies that do not account for greenness may overstate the air pollution impacts on mortality. Residents in deprived neighbourhoods with high greenness benefitted by having more attenuated associations between PM2.5 and mortality than those living in deprived areas with less greenness. The findings from this study extend our understanding of how living in greener areas may lead to improved health outcomes. Keywords: Greenness, Mortality, PM2.5, Cohort study, Canada, Effect modification

Background: Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering. Objectives: Here we discuss the promise, challenges, and probable data sources needed to apply AI in the fields of exposure science and environmental health. In particular, we focus on the use of deep convolutional neural networks to estimate environmental exposures using images and other complementary data sources such as cell phone mobility and social media information. Discussion: Characterizing the health impacts of multiple spatially-correlated exposures remains a challenge in environmental epidemiology. A shift toward integrated measures that simultaneously capture multiple aspects of the urban built environment could improve efficiency and provide important insights into how our collective environments influence population health. The widespread adoption of AI in exposure science is on the frontier. This will likely result in new ways of understanding environmental impacts on health and may allow for analyses to be efficiently scaled for broad coverage. Image-based convolutional neural networks may also offer a cost-effective means of estimating local environmental exposures in low and middle-income countries where monitoring and surveillance infrastructure is limited. However, suitable databases must first be assembled to train and evaluate these models and these novel approaches should be complemented with traditional exposure metrics. Conclusions: The promise of deep learning in environmental health is great and will complement existing measurements for data-rich settings and could enhance the resolution and accuracy of estimates in data poor scenarios. Interdisciplinary partnerships will be needed to fully realize this potential.

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited.

Public bicycle share programs have been implemented in cities around the world to encourage bicycling. However, there are limited evaluations of the impact of these programs on bicycling at the population level. This study examined the impact of a public bicycle share program on bicycling amongst residents of Vancouver, BC. Using an online panel, we surveyed a population-based sample of Vancouver residents three times: prior to the implementation of the public bicycle share program (T0, October 2015, n = 1111); in the early phase of implementation (T1, October 2016, n = 995); and one-year post implementation (T2, October 2017, n = 966). We used difference in differences estimation to assess whether there was an increase in bicycling amongst those living and/or working in close proximity (≤500 m) to Vancouver's Mobi by Shaw Go public bicycle share program, compared to those living and working outside this area. Results suggest that only living or only working inside the bicycle share service area was not associated with increases in bicycling at T1 or T2 relative to those outside the service area. Both living and working inside the bicycle share service area was associated with increases in bicycling at T1 (OR: 2.26, 95% CI: 1.07, 4.80), however not at T2 (OR: 1.37, 95% CI: 0.67, 2.83). These findings indicate that the implementation of a public bicycle share program may have a greater effect on bicycling for residents who both live and work within the service area, although this effect may not be sustained over time. Keywords: Bicycling, Bicycle share, Built environment, Active travel, Evaluation

The Global Household Air Pollution (HAP) Measurements database, commissioned by the World Health Organization, provides an organized summary of data reported in the literature describing HAP microenvironments, methods and measurements. As of June 2018, the database contains measurements from 43 countries obtained from 196 studies published through 2016. The database includes information useful for understanding the range of household and personal air pollution measurements that have been collected in a country, as well as characteristics of the cooking environment, including primary cooking fuel type, stove type, heating fuel type and kitchen location. Quantitative particulate matter (PM) of various size fractions and/or carbon monoxide (CO) exposure measurements included in the database can be aggregated and analyzed to generate summary statistics (e.g. average sub-national, national, regional and global HAP exposures) to assess temporal and spatial relationships. The quantitative PM exposure measurements in the database have been used in global predictive modeling of HAP-PM2.5 exposures (“Global Estimation of Exposure to Fine Particulate Matter (PM2.5) from Household Air Pollution” (Shupler et al., 2018) [1])

Abstract Background Cycling and other forms of active transportation provide health benefits via increased physical activity. However, direct evidence of the extent to which these benefits may be offset by exposure and intake of traffic-related air pollution is limited. The purpose of this study is to measure changes in endothelial function, measures of oxidative stress and inflammation, and lung function in healthy participants before and after cycling along a high- and low- traffic route. Methods Participants (n = 38) bicycled for 1 h along a Downtown and a Residential designated bicycle route in a randomized crossover trial. Heart rate, power output, particulate matter air pollution (PM10, PM2.5, and PM1) and particle number concentration (PNC) were measured. Lung function, endothelial function (reactive hyperemia index, RHI), C-reactive protein, interleukin-6, and 8-hydroxy-2′-deoxyguanosine were assessed within one hour pre- and post-trial. Results Geometric mean PNC exposures and intakes were higher along the Downtown (exposure = 16,226 particles/cm3; intake = 4.54 × 1010 particles) compared to the Residential route (exposure = 9367 particles/cm3; intake = 3.13 × 1010 particles). RHI decreased following cycling along the Downtown route and increased on the Residential route; in mixed linear regression models, the (post-pre) change in RHI was 21% lower following cycling on the Downtown versus the Residential route (−0.43, 95% CI: -0.79, −0.079) but RHI decreases were not associated with measured exposure or intake of air pollutants. The differences in RHI by route were larger amongst females and older participants. No consistent associations were observed for any of the other outcome measures. Conclusions Although PNC exposures and intakes were higher along the Downtown route, the lack of association between air pollutant exposure or intake with RHI and other measures suggests other exposures related to cycling on the Downtown route may have been influential in the observed differences between routes in RHI. Trial registration ClinicalTrials.gov, NCT01708356. Registered 16 October 2012.

Introduction/aims: Application of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover. Methods: NDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method. Results: For the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas. Conclusions: LSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in dense urban areas. Therefore, LSU potentially improves exposure assessment precision, implying reduced exposure misclassification.

Summary: Background: The association of air pollution with multiple adverse health outcomes is becoming well established, but its negative economic impact is less well appreciated. It is important to elucidate this impact for the states of India. Methods: We estimated exposure to ambient particulate matter pollution, household air pollution, and ambient ozone pollution, and their attributable deaths and disability-adjusted life-years in every state of India as part of the Global Burden of Disease Study (GBD) 2019. We estimated the economic impact of air pollution as the cost of lost output due to premature deaths and morbidity attributable to air pollution for every state of India, using the cost-of-illness method. Findings: 1·67 million (95% uncertainty interval 1·42–1·92) deaths were attributable to air pollution in India in 2019, accounting for 17·8% (15·8–19·5) of the total deaths in the country. The majority of these deaths were from ambient particulate matter pollution (0·98 million [0·77–1·19]) and household air pollution (0·61 million [0·39–0·86]). The death rate due to household air pollution decreased by 64·2% (52·2–74·2) from 1990 to 2019, while that due to ambient particulate matter pollution increased by 115·3% (28·3–344·4) and that due to ambient ozone pollution increased by 139·2% (96·5–195·8). Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28·8 billion (21·4–37·4) and $8·0 billion (5·9–10·3), respectively, in India in 2019. This total loss of $36·8 billion (27·4–47·7) was 1·36% of India's gross domestic product (GDP). The economic loss as a proportion of the state GDP varied 3·2 times between the states, ranging from 0·67% (0·47–0·91) to 2·15% (1·60–2·77), and was highest in the low per-capita GDP states of Uttar Pradesh, Bihar, Rajasthan, Madhya Pradesh, and Chhattisgarh. Delhi had the highest per-capita economic loss due to air pollution, followed by Haryana in 2019, with 5·4 times variation across all states. Interpretation: The high burden of death and disease due to air pollution and its associated substantial adverse economic impact from loss of output could impede India's aspiration to be a $5 trillion economy by 2024. Successful reduction of air pollution in India through state-specific strategies would lead to substantial benefits for both the health of the population and the economy. Funding: UN Environment Programme; Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Although the attention of the world and the global health community specifically is deservedly focused on the COVID-19 pandemic, other determinants of health continue to have large impacts and may also interact with COVID-19. Air pollution is one crucial example. Established evidence from other respiratory viruses and emerging evidence for COVID-19 specifically indicates that air pollution alters respiratory defense mechanisms leading to worsened infection severity. Air pollution also contributes to co-morbidities that are known to worsen outcomes amongst those infected with COVID-19, and air pollution may also enhance infection transmission due to its impact on more frequent coughing. Yet despite the massive disruption of the COVID-19 pandemic, there are reasons for optimism: broad societal lockdowns have shown us a glimpse of what a future with strong air pollution measures could yield. Thus, the urgency to combat air pollution is not diminished, but instead heightened in the context of the pandemic.

Background: Various aspects of the urban environment and neighbourhood socio-economic status interact with each other to affect health. Few studies to date have quantitatively assessed intersections of multiple urban environmental factors and their distribution across levels of deprivation. Objectives: To explore the spatial patterns of urban environmental exposures within three large Canadian cities, assess how exposures are distributed across socio-economic deprivation gradients, and identify clusters of favourable or unfavourable environmental characteristics. Methods: We indexed nationally standardized estimates of active living friendliness (i.e. “walkability”), NO2 air pollution, and greenness to 6-digit postal codes within the cities of Toronto, Montreal and Vancouver. We compared the distribution of within-city exposure tertiles across quintiles of material deprivation. Tertiles of each exposure were then overlaid with each other in order to identify potentially favorable (high walkability, low NO2, high greenness) and unfavorable (low walkability, high NO2, and low greenness) environments. Results: In all three cities, high walkability was more common in least deprived areas and less prevalent in highly deprived areas. We also generally saw a greater prevalence of postal codes with high vegetation indices and low NO2 in areas with low deprivation, and a lower greenness prevalence and higher NO2 concentrations in highly deprived areas, suggesting environmental inequity is occurring. Our study showed that relatively few postal codes were simultaneously characterized by desirable or undesirable walkability, NO2 and greenness tertiles. Discussion: Spatial analyses of multiple standardized urban environmental factors such as the ones presented in this manuscript can help refine municipal investments and policy priorities. This study illustrates a methodology to prioritize areas for interventions that increase active living and exposure to urban vegetation, as well as lower air pollution. Our results also highlight the importance of considering the intersections between the built environment and socio-economic status in city planning and urban public health decision-making.

Introduction Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution.Methods and analysis We designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities.Ethics and dissemination This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Abstract The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) “Indicators” Tiger Team collaboration between NASA‐supported scientists and civil society stakeholders aimed to develop satellite‐derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

Summary: Background: Most studies of long-term exposure to outdoor fine particulate matter (PM2·5) and cardiovascular disease are from high-income countries with relatively low PM2·5 concentrations. It is unclear whether risks are similar in low-income and middle-income countries (LMICs) and how outdoor PM2·5 contributes to the global burden of cardiovascular disease. In our analysis of the Prospective Urban and Rural Epidemiology (PURE) study, we aimed to investigate the association between long-term exposure to PM2·5 concentrations and cardiovascular disease in a large cohort of adults from 21 high-income, middle-income, and low-income countries. Methods: In this multinational, prospective cohort study, we studied 157 436 adults aged 35–70 years who were enrolled in the PURE study in countries with ambient PM2·5 estimates, for whom follow-up data were available. Cox proportional hazard frailty models were used to estimate the associations between long-term mean community outdoor PM2·5 concentrations and cardiovascular disease events (fatal and non-fatal), cardiovascular disease mortality, and other non-accidental mortality. Findings: Between Jan 1, 2003, and July 14, 2018, 157 436 adults from 747 communities in 21 high-income, middle-income, and low-income countries were enrolled and followed up, of whom 140 020 participants resided in LMICs. During a median follow-up period of 9·3 years (IQR 7·8–10·8; corresponding to 1·4 million person-years), we documented 9996 non-accidental deaths, of which 3219 were attributed to cardiovascular disease. 9152 (5·8%) of 157 436 participants had cardiovascular disease events (fatal and non-fatal incident cardiovascular disease), including 4083 myocardial infarctions and 4139 strokes. Mean 3-year PM2·5 at cohort baseline was 47·5 μg/m3 (range 6–140). In models adjusted for individual, household, and geographical factors, a 10 μg/m3 increase in PM2·5 was associated with increased risk for cardiovascular disease events (hazard ratio 1·05 [95% CI 1·03–1·07]), myocardial infarction (1·03 [1·00–1·05]), stroke (1·07 [1·04–1·10]), and cardiovascular disease mortality (1·03 [1·00–1·05]). Results were similar for LMICs and communities with high PM2·5 concentrations (>35 μg/m3). The population attributable fraction for PM2·5 in the PURE cohort was 13·9% (95% CI 8·8–18·6) for cardiovascular disease events, 8·4% (0·0–15·4) for myocardial infarction, 19·6% (13·0–25·8) for stroke, and 8·3% (0·0–15·2) for cardiovascular disease mortality. We identified no consistent associations between PM2·5 and risk for non-cardiovascular disease deaths. Interpretation: Long-term outdoor PM2·5 concentrations were associated with increased risks of cardiovascular disease in adults aged 35–70 years. Air pollution is an important global risk factor for cardiovascular disease and a need exists to reduce air pollution concentrations, especially in LMICs, where air pollution levels are highest. Funding: Full funding sources are listed at the end of the paper (see Acknowledgments).

New rapid transit investments have been motivated by environmental, economic, and health benefits. Given transit's potential to increase active travel, recent research leverages transit changes for natural experiment studies to examine physical activity outcomes. We aimed to quantify the association size, critically examine existing literature, and make recommendations for future studies to advance research and policies on active travel, transportation, and physical activity. Studies of physical activity impacts following transit interventions were systematically reviewed using seven health and transport databases (May–July 2017). Two investigators extracted data on sample size, intervention, pre- and post-intervention physical activity, and relevant measurement information. Inconsistency of results and estimated overall mean physical activity change post-intervention were assessed. Forest plots were created from physical activity change in each study using a general variance-based random effects model. Of 18 peer-reviewed articles examining health behaviors, 15 addressed physical activity and five were natural experiment studies with pre- and post- intervention measurements. Studies varied by intervention, duration, outcome measurement, sampling location, and spatial method. Q (201) and I2 (98%) indicated high study heterogeneity. Among these five studies, after transit interventions, total physical activity decreased (combined mean - 80.4 min/week, 95% CI - 157.9, −2.9), but transport-related physical activity increased (mean 6.7 min/week, 95% CI - 10.1, 23.5). Following new transit infrastructure, total physical activity may decline but transport-related physical activity may increase. Positive transit benefits were location, sociodemographic, or activity-specific. Future studies should address context, ensure adequate follow-up, utilize controls, and consider non-residential environments or participants. Keywords: Transportation, Physical activity, Meta-analysis, Longitudinal studies, Walking, Active travel

Exposure to ambient fine particulate matter (PM2.5) is the leading global environmental risk factor for mortality and disease burden, with associated annual global welfare costs of trillions of dollars. Examined within is the ability of current data to answer a basic question about PM2.5, namely the location of the city with the highest PM2.5 concentration. The ability to answer this basic question serves as an indicator of scientific progress to assess global human exposure to air pollution and as an important component of efforts to reduce its impacts. Despite the importance of PM2.5, we find that insufficient monitoring data exist to answer this basic question about the spatial pattern of PM2.5 at the global scale. Only 24 of 234 countries have more than 3 monitors per million inhabitants, while density is an order of magnitude lower in the vast majority of the world's countries, with 141 having no regular PM2.5 monitoring at all. The global mean population distance to nearest PM2.5 monitor is 220 km, too large for exposure assessment. Efforts to fill in monitoring gaps with estimates from satellite remote sensing, chemical transport modeling, and statistical models have biases at individual monitor locations that can exceed 50 μg m−3. Progress in advancing knowledge about the global distribution of PM2.5 will require a harmonized network that integrates different types of monitoring equipment (regulatory networks, low-cost monitors, satellite remote sensing, and research-grade instrumentation) with atmospheric and statistical models. Realization of such an integrated framework will facilitate accurate identification of the location of the city with the highest PM2.5 concentration and play a key role in tracking the progress of efforts to reduce the global impacts of air pollution. Keywords: Fine particulate matter, PM2.5, Air quality

Summary: Background: Paediatric asthma incidence is associated with exposure to traffic-related air pollution (TRAP), but the TRAP-attributable burden remains poorly quantified. Nitrogen dioxide (NO2) is a major component and common proxy of TRAP. In this study, we estimated the annual global number of new paediatric asthma cases attributable to NO2 exposure at a resolution sufficient to resolve intra-urban exposure gradients. Methods: We obtained 2015 country-specific and age-group-specific asthma incidence rates from the Institute for Health Metrics and Evaluation for 194 countries and 2015 population counts at a spatial resolution of 250 × 250 m from the Global Human Settlement population grid. We used 2010–12 annual average surface NO2 concentrations derived from land-use regression at a resolution of 100 × 100 m, and we derived concentration-response functions from relative risk estimates reported in a multinational meta-analysis. We then estimated the NO2-attributable burden of asthma incidence in children aged 1–18 years in 194 countries and 125 major cities at a resolution of 250 × 250 m. Findings: Globally, we estimated that 4·0 million (95% uncertainty interval [UI] 1·8–5·2) new paediatric asthma cases could be attributable to NO2 pollution annually; 64% of these occur in urban centres. This burden accounts for 13% (6–16) of global incidence. Regionally, the greatest burdens of new asthma cases associated with NO2 exposure per 100 000 children were estimated for Andean Latin America (340 cases per year, 95% UI 150–440), high-income North America (310, 140–400), and high-income Asia Pacific (300, 140–370). Within cities, the greatest burdens of new asthma cases associated with NO2 exposure per 100 000 children were estimated for Lima, Peru (690 cases per year, 95% UI 330–870); Shanghai, China (650, 340–770); and Bogota, Colombia (580, 270–730). Among 125 major cities, the percentage of new asthma cases attributable to NO2 pollution ranged from 5·6% (95% UI 2·4–7·4) in Orlu, Nigeria, to 48% (25–57) in Shanghai, China. This contribution exceeded 20% of new asthma cases in 92 cities. We estimated that about 92% of paediatric asthma incidence attributable to NO2 exposure occurred in areas with annual average NO2 concentrations lower than the WHO guideline of 21 parts per billion. Interpretation: Efforts to reduce NO2 exposure could help prevent a substantial portion of new paediatric asthma cases in both developed and developing countries, and especially in urban areas. Traffic emissions should be a target for exposure-mitigation strategies. The adequacy of the WHO guideline for ambient NO2 concentrations might need to be revisited. Funding: George Washington University.

Summary: Background: Air pollution is a major planetary health risk, with India estimated to have some of the worst levels globally. To inform action at subnational levels in India, we estimated the exposure to air pollution and its impact on deaths, disease burden, and life expectancy in every state of India in 2017. Methods: We estimated exposure to air pollution, including ambient particulate matter pollution, defined as the annual average gridded concentration of PM2.5, and household air pollution, defined as percentage of households using solid cooking fuels and the corresponding exposure to PM2.5, across the states of India using accessible data from multiple sources as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. The states were categorised into three Socio-demographic Index (SDI) levels as calculated by GBD 2017 on the basis of lag-distributed per-capita income, mean education in people aged 15 years or older, and total fertility rate in people younger than 25 years. We estimated deaths and disability-adjusted life-years (DALYs) attributable to air pollution exposure, on the basis of exposure–response relationships from the published literature, as assessed in GBD 2017; the proportion of total global air pollution DALYs in India; and what the life expectancy would have been in each state of India if air pollution levels had been less than the minimum level causing health loss. Findings: The annual population-weighted mean exposure to ambient particulate matter PM2·5 in India was 89·9 μg/m3 (95% uncertainty interval [UI] 67·0–112·0) in 2017. Most states, and 76·8% of the population of India, were exposed to annual population-weighted mean PM2·5 greater than 40 μg/m3, which is the limit recommended by the National Ambient Air Quality Standards in India. Delhi had the highest annual population-weighted mean PM2·5 in 2017, followed by Uttar Pradesh, Bihar, and Haryana in north India, all with mean values greater than 125 μg/m3. The proportion of population using solid fuels in India was 55·5% (54·8–56·2) in 2017, which exceeded 75% in the low SDI states of Bihar, Jharkhand, and Odisha. 1·24 million (1·09–1·39) deaths in India in 2017, which were 12·5% of the total deaths, were attributable to air pollution, including 0·67 million (0·55–0·79) from ambient particulate matter pollution and 0·48 million (0·39–0·58) from household air pollution. Of these deaths attributable to air pollution, 51·4% were in people younger than 70 years. India contributed 18·1% of the global population but had 26·2% of the global air pollution DALYs in 2017. The ambient particulate matter pollution DALY rate was highest in the north Indian states of Uttar Pradesh, Haryana, Delhi, Punjab, and Rajasthan, spread across the three SDI state groups, and the household air pollution DALY rate was highest in the low SDI states of Chhattisgarh, Rajasthan, Madhya Pradesh, and Assam in north and northeast India. We estimated that if the air pollution level in India were less than the minimum causing health loss, the average life expectancy in 2017 would have been higher by 1·7 years (1·6–1·9), with this increase exceeding 2 years in the north Indian states of Rajasthan, Uttar Pradesh, and Haryana. Interpretation: India has disproportionately high mortality and disease burden due to air pollution. This burden is generally highest in the low SDI states of north India. Reducing the substantial avoidable deaths and disease burden from this major environmental risk is dependent on rapid deployment of effective multisectoral policies throughout India that are commensurate with the magnitude of air pollution in each state. Funding: Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in growing cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM _2.5 ) and black carbon (BC) pollution in Accra, Ghana. Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM _2.5 concentrations at 146 unique locations, comprising of 10 fixed (∼1 year) and 136 rotating (7 day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10 ^−5 m ^−1 ) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006–2007) were compared to assess changes in PM _2.5 concentrations. The mean annual PM _2.5 across the fixed sites ranged from 26 μ g m ^−3 at a peri-urban site to 43 μ g m ^−3 at a commercial, business, and industrial (CBI) site. CBI areas had the highest PM _2.5 levels (mean: 37 μ g m ^−3 ), followed by high-density residential neighborhoods (mean: 36 μ g m ^−3 ), while peri-urban areas recorded the lowest (mean: 26 μ g m ^−3 ). Both PM _2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM _2.5 : 89 μ g m ^−3 ) compared to non-Harmattan season (mean PM _2.5 : 23 μ g m ^−3 ). PM _2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a 50% reduction (71 vs 37 μ g m ^−3 ) in mean annual PM _2.5 concentrations when compared to measurements in 2006–2007 in Accra. Ambient PM _2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health.

Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the 'Tropospheric Ozone Assessment Report (TOAR)' is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.

We studied 2011 cycling mode share at the census-tract level in Montréal and Vancouver, Canadian cities with relatively high mode shares and diverse bike infrastructure. We examined whether mode share variability, for all commuters and male and female commuters separately, was related to proximity to any bikeway, proximity to four bikeway types, slopes on routes to bikeways, or commute times. Cycling mode shares at the census-tract level varied from 0 to 20.4%. About a third of cycle commuters were female, but this proportion approached parity with males in census tracts with mode shares of 7% and higher. A one-kilometer closer proximity to any bikeway was associated with four times higher cycling mode share. Proximity to cycle tracks was associated with higher cycling mode shares in both cities. Other bikeway types did not have similar associations in the two cities, and the pattern of results suggested that the networks formed may have been more important than specific bikeway characteristics. Uphill slopes to bikeways were associated with somewhat lower mode shares in bivariate analyses but not in adjusted models. Cycle commuting was most common in neighborhoods with intermediate average commute durations: 20 to 29 minutes. Our results suggest that cycle tracks and bikeways that form a connected network are associated with higher neighborhood cycling commute mode shares. These features appeared even more important to women, and their cycling (or not) was strongly related to overall cycling mode shares.

To identify exposure-related knowledge, attitudes and behaviors of individuals occupationally exposed to diesel exhaust (DE); to reveal strengths, knowledge gaps and misperceptions therein.A Mental Models approach was used to gather information about current scientific understanding of DE exposure hazards and the ways in which exposure can be reduced. Thirty individuals in British Columbia who were regularly exposed to occupational DE were interviewed. The audio was recorded and transcribed. Data was grouped together and examined to draw out themes around DE awareness, hazard assessment and risk reduction behaviors. These themes were then compared and contrasted with existing grey and research literature in order to reveal strengths, gaps and misperceptions regarding DE exposure.Study participants were aware and concerned about their exposure to DE but had incomplete and sometimes incorrect understanding of exposure pathways, health effects, and effective strategies to reduce their exposures. The perceived likelihood of exposure to DE was significantly greater compared to that of other work hazards (p