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BACKGROUND: Estimates of the effectiveness of water, sanitation, and hygiene (WASH) interventions that provide high levels of service on childhood diarrhoea are scarce. We aimed to provide up-to-date estimates on the burden of disease attributable to WASH and on the effects of different types of WASH interventions on childhood diarrhoea in low-income and middle-income countries (LMICs). METHODS: In this systematic review and meta-analysis, we updated previous reviews following their search strategy by searching MEDLINE, Embase, Scopus, Cochrane Library, and BIOSIS Citation Index for studies of basic WASH interventions and of WASH interventions providing a high level of service, published between Jan 1, 2016, and May 25, 2021. We included randomised and non-randomised controlled trials conducted at household or community level that matched exposure categories of the so-called service ladder approach of the Sustainable Development Goal (SDG) for WASH. Two reviewers independently extracted study-level data and assessed risk of bias using a modified Newcastle-Ottawa Scale and certainty of evidence using a modified Grading of Recommendations, Assessment, Development, and Evaluation approach. We analysed extracted relative risks (RRs) and 95% CIs using random-effects meta-analyses and meta-regression models. This study is registered with PROSPERO, CRD42016043164. FINDINGS: 19 837 records were identified from the search, of which 124 studies were included, providing 83 water (62 616 children), 20 sanitation (40 799 children), and 41 hygiene (98 416 children) comparisons. Compared with untreated water from an unimproved source, risk of diarrhoea was reduced by up to 50% with water treated at point of use (POU): filtration (n=23 studies; RR 0·50 [95% CI 0·41-0·60]), solar treatment (n=13; 0·63 [0·50-0·80]), and chlorination (n=25; 0·66 [0·56-0·77]). Compared with an unimproved source, provision of an improved drinking water supply on premises with higher water quality reduced diarrhoea risk by 52% (n=2; 0·48 [0·26-0·87]). Overall, sanitation interventions reduced diarrhoea risk by 24% (0·76 [0·61-0·94]). Compared with unimproved sanitation, providing sewer connection reduced diarrhoea risk by 47% (n=5; 0·53 [0·30-0·93]). Promotion of handwashing with soap reduced diarrhoea risk by 30% (0·70 [0·64-0·76]). INTERPRETATION: WASH interventions reduced risk of diarrhoea in children in LMICs. Interventions supplying either water filtered at POU, higher water quality from an improved source on premises, or basic sanitation services with sewer connection were associated with increased reductions. Our results support higher service levels called for under SDG 6. Notably, no studies evaluated interventions that delivered access to safely managed WASH services, the level of service to which universal coverage by 2030 is committed under the SDG. FUNDING: WHO, Foreign, Commonwealth & Development Office, and National Institute of Environmental Health Sciences.

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.

Summary Background Prevention, control, and treatment of respiratory tract cancers are important steps towards achieving target 3.4 of the UN Sustainable Development Goals (SDGs)—a one-third reduction in premature mortality due to non-communicable diseases by 2030. We aimed to provide global, regional, and national estimates of the burden of tracheal, bronchus, and lung cancer and larynx cancer and their attributable risks from 1990 to 2019. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 methodology, we evaluated the incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs) of respiratory tract cancers (ie, tracheal, bronchus, and lung cancer and larynx cancer). Deaths from tracheal, bronchus, and lung cancer and larynx cancer attributable to each risk factor were estimated on the basis of risk exposure, relative risks, and the theoretical minimum risk exposure level input from 204 countries and territories, stratified by sex and Socio-demographic Index (SDI). Trends were estimated from 1990 to 2019, with an emphasis on the 2010–19 period. Findings Globally, there were 2·26 million (95% uncertainty interval 2·07 to 2·45) new cases of tracheal, bronchus, and lung cancer, and 2·04 million (1·88 to 2·19) deaths and 45·9 million (42·3 to 49·3) DALYs due to tracheal, bronchus, and lung cancer in 2019. There were 209 000 (194 000 to 225 000) new cases of larynx cancer, and 123 000 (115 000 to 133 000) deaths and 3·26 million (3·03 to 3·51) DALYs due to larynx cancer globally in 2019. From 2010 to 2019, the number of new tracheal, bronchus, and lung cancer cases increased by 23·3% (12·9 to 33·6) globally and the number of larynx cancer cases increased by 24·7% (16·0 to 34·1) globally. Global age-standardised incidence rates of tracheal, bronchus, and lung cancer decreased by 7·4% (−16·8 to 1·6) and age-standardised incidence rates of larynx cancer decreased by 3·0% (−10·5 to 5·0) in males over the past decade; however, during the same period, age-standardised incidence rates in females increased by 0·9% (−8·2 to 10·2) for tracheal, bronchus, and lung cancer and decreased by 0·5% (−8·4 to 8·1) for larynx cancer. Furthermore, although age-standardised incidence and death rates declined in both sexes combined from 2010 to 2019 at the global level for tracheal, bronchus, lung and larynx cancers, some locations had rising rates, particularly those on the lower end of the SDI range. Smoking contributed to an estimated 64·2% (61·9–66·4) of all deaths from tracheal, bronchus, and lung cancer and 63·4% (56·3–69·3) of all deaths from larynx cancer in 2019. For males and for both sexes combined, smoking was the leading specific risk factor for age-standardised deaths from tracheal, bronchus, and lung cancer per 100 000 in all SDI quintiles and GBD regions in 2019. However, among females, household air pollution from solid fuels was the leading specific risk factor in the low SDI quintile and in three GBD regions (central, eastern, and western sub-Saharan Africa) in 2019. Interpretation The numbers of incident cases and deaths from tracheal, bronchus, and lung cancer and larynx cancer increased globally during the past decade. Even more concerning, age-standardised incidence and death rates due to tracheal, bronchus, lung cancer and larynx cancer increased in some populations—namely, in the lower SDI quintiles and among females. Preventive measures such as smoking control interventions, air quality management programmes focused on major air pollution sources, and widespread access to clean energy should be prioritised in these settings. Funding Bill & Melinda Gates Foundation.

Summary Background The health impacts of ambient air pollution impose large costs on society. Although all people are exposed to air pollution, the older population (ie, those aged ≥60 years) tends to be disproportionally affected. As a result, there is growing concern about the health impacts of air pollution as many countries undergo rapid population ageing. We investigated the spatial and temporal variation in the economic cost of deaths attributable to ambient air pollution and its interaction with population ageing from 2000 to 2016 at global and regional levels. Methods In this global analysis, we developed an age-adjusted measure of the value of a statistical life-year (VSLY) to estimate the economic cost of deaths attributable to ambient PM2·5 pollution using Global Burden of Diseases, Injuries, and Risk Factors Study 2017 data and country-level socioeconomic information. First, we estimated the global age-specific and cause-specific mortality and years of life lost (YLLs) attributable to PM2·5 pollution using the global exposure mortality model and global estimates of exposure at 0·1° × 0·1° (about 11 km × 11 km at the equator) resolution. Second, for each year between 2000 and 2016, we translated the YLLs within each age group into a health-related cost using a country-specific, age-adjusted measure of VSLY. Third, we decomposed the major driving factors that contributed to the temporal change in health costs related to PM2·5. Finally, we did a sensitivity test to analyse the variability of the estimated health costs to four alternative valuation measures. We identified the uncertainty intervals (UIs) from 1000 draws of the parameters and concentration–response functions by age, cause, country, and year. All economic values are reported in 2011 purchasing power parity-adjusted US dollars. All simulations were done with R, version 3.6.0. Findings Globally, in 2016, PM2·5 was estimated to have caused 8·42 million (95% UI 6·50–10·52) attributable deaths, which was associated with 163·68 million (116·03–219·44) YLLs. In 2016, the global economic cost of deaths attributable to ambient PM2·5 pollution for the older population was US$2·40 trillion (1·89–2·93) accounting for 59% (59–60) of the cost for the total population ($4·09 trillion [3·19–5·05]). The economic cost per capita for the older population was $2739 (2160–3345) in 2016, which was 10 times that of the younger population (ie, those aged Interpretation The economic cost of ambient PM2·5 borne by the older population almost doubled between 2000 and 2016, driven primarily by GDP growth, population ageing, and population growth. Compared with younger people, air pollution leads to disproportionately higher health costs among older people, even after accounting for their relatively shorter life expectancy and increased disability. As the world's population is ageing, the disproportionate health cost attributable to ambient PM2·5 pollution potentially widens the health inequities for older people. Countries with severe air pollution and rapid ageing rates need to take immediate actions to improve air quality. In addition, strategies aimed at enhancing health-care services, especially targeting the older population, could be beneficial for reducing the health costs of ambient air pollution. Funding National Natural Science Foundation of China, China Postdoctoral Science Foundation, and Qiushi Foundation.

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.

Fine particulate air pollution

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 minutes 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 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 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.

Summary Background Air pollution is an important public health concern in China, with high levels of exposure to both ambient and household air pollution. To inform action at provincial levels in China, we estimated the exposure to air pollution and its effect on deaths, disease burden, and loss of life expectancy across all provinces in China from 1990 to 2017. Methods In all 33 provinces, autonomous regions, municipalities, and special administrative regions in China, we estimated exposure to air pollution, including ambient particulate matter pollution (defined as the annual gridded concentration of PM2·5), household air pollution (defined as the percentage of households using solid cooking fuels and the corresponding exposure to PM2·5), and ozone pollution (defined as average gridded ozone concentrations). We used the methods of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 to estimate deaths and disability-adjusted life-years (DALYs) attributable to air pollution, and what the life expectancy would have been if air pollution levels had been less than the minimum level causing health loss. Findings The average annual population-weighted PM2·5 exposure in China was 52·7 μg/m3 (95% uncertainty interval [UI] 41·0–62·8) in 2017, which is 9% lower than in 1990 (57·8 μg/m3, 45·0–67·0). We estimated that 1·24 million (95% UI 1·08–1·40) deaths in China were attributable to air pollution in 2017, including 851 660 (712 002–990 271) from ambient PM2·5 pollution, 271 089 (209 882–346 561) from household air pollution from solid fuels, and 178 187 (67 650–286 229) from ambient ozone pollution. The age-standardised DALY rate attributable to air pollution was 1513·1 per 100 000 in China in 2017, and was higher in males (1839·8 per 100 000) than in females (1198·3 per 100 000). The age-standardised death rate attributable to air pollution decreased by 60·6% (55·7–63·7) for China overall between 1990 and 2017, driven by an 85·4% (83·2–87·3) decline in household air pollution and a 12·0% (1·4–22·1) decline in ambient PM2·5 pollution. 40·0% of DALYs for COPD were attributable to air pollution, as were 35·6% of DALYs for lower respiratory infections, 26·1% for diabetes, 25·8% for lung cancer, 19·5% for ischaemic heart disease, and 12·8% for stroke. We estimated that if the air pollution level in China was below the minimum causing health loss, the average life expectancy would have been 1·25 years greater. The DALY rate per 100 000 attributable to air pollution varied across provinces, ranging from 482·3 (371·1–604·1) in Hong Kong to 1725·6 (720·4–2653·1) in Xinjiang for ambient pollution, and from 18·7 (9·1–34·0) in Shanghai to 1804·5 (1339·5–2270·1) in Tibet for household pollution. Although the overall mortality attributable to air pollution decreased in China between 1990 and 2017, 12 provinces showed an increasing trend during the past 27 years. Interpretation Pollution from ambient PM2·5 and household burning of solid fuels decreased markedly in recent years in China, after extensive efforts to control emissions. However, PM2·5 concentrations still exceed the WHO Air Quality Guideline for the entire population of China, with 81% living in regions exceeding the WHO Interim Target 1, and air pollution remains an important risk factor. Sustainable development policies should be implemented and enforced to reduce the impact of air pollution on long-term economic development and population health. Funding Bill & Melinda Gates Foundation; and China National Key Research and Development Program.

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

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. This work was supported by the Canadian Institutes of Health Research (grant number 156152) and by the European Union's Horizon 2020 research and innovation programme under a Marie Skłodowska-Curie grant agreement No. 891538. We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S) and support from the Generalitat de Catalunya through the CERCA Program. NDVI metrics, indexed to DMTI Spatial Inc. postal codes, were provided by the Canadian Urban Environmental Health Research Consortium.

Rationale Sleep duration is critical to growth, learning, and immune function development in infancy. Strategies to ensure that national recommendations for sleep duration in infants are met require knowledge of perinatal factors that affect infant sleep. Objectives To investigate the mechanistic pathways linking maternal education and infant sleep. Methods An observational study was conducted on 619 infants whose mothers were enrolled at the Edmonton site of the CHILD birth cohort. Infant sleep duration at three months was assessed using the Brief Infant Sleep Questionnaire. Maternal education was collected via maternal report. Prenatal and postnatal depression scores were obtained from the 20-item Center for Epidemiologic Studies Depression Scale (CES-D). Birth records and maternal report were the source of covariate measures. Mediation analysis (PROCESS v3.0) was used to examine the indirect effects of maternal education on infant sleep duration mediated through prenatal depression and birth mode. Measurements and main results At three months of age, infants slept on average 14.1 h. Lower maternal education and prenatal depression were associated with significantly shorter infant sleep duration. Emergency cesarean section birth was associated with 1-hour shorter sleep duration at three months compared to vaginal birth [without intrapartum antibiotic prophylaxis] (β: −0.99 h; 95% CI: −1.51, −0.48). Thirty percent of the effect of lower maternal education on infant total sleep duration was mediated sequentially through prenatal depression and birth mode (Total Indirect Effects: −0.12, 95% CI: −0.22, −0.03, p Conclusions Prenatal depression and birth mode sequentially mediate the effect of maternal education on infant sleep duration.

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.

Studies on long-term effects of air pollutants in China are limited, in part due to a lack of appropriate spatial exposure models. Elsewhere, Land Use Regression (LUR) models have been used extensively to estimate individual-level air pollutant exposure variation within urban areas by combining air pollution measurements with geospatial predictors. Here, LUR models were developed to estimate spatial variation in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations during the heating and non-heating seasons in Beijing, China. Daily routine PM2.5 and NO2 measurements at 35 monitoring stations from October 2012 to December 2013 were collected as dependent variables, with 191 geospatial measures generated as potential independent variables. The PM2.5 LUR model included latitude and forest land (within a 5000m buffer) in the heating season; and latitude, residential land use (5000m) and main road length (100m) in the non-heating season. The NO2 LUR model included latitude, population density (5000m), water bodies (2000m) and road network factors in the heating season; and residential land use (5000m), water bodies (1000m) and road network factors in the non-heating season. Adjusted R2 and cross validation R2 values of all seasonal models were above 0.80, except for the heating season NO2 model (adjusted R2 = 0.87, cross validation R2 = 0.77). Different spatial patterns between NO2 and PM2.5 were evident in Beijing, with a more distinct local pattern for NO2 than PM2.5 and during the non-heating season, when regional pollution was less dominant. These 250m resolution LUR models can be used for health impact assessment or to estimate PM2.5 and NO2 concentrations at the residential addresses of participants in epidemiological cohorts of Beijing.

Residential wood burning is a major source of fine particulate matter (PMMobile monitoring was conducted with a nephelometer and multi-wavelength aethalometer, capable of identifying woodsmoke PMFixed site measurements of BFollowing the successful implementation of the mobile CANAW method, training materials were created and tested with lay volunteers along with an online mapping application. Volunteers were able to effectively operate the equipment, collect valuable data on woodsmoke concentrations, and map spatial patterns across their communities using the application. The CANAW method is a valuable option for advancing cost-effective data collection for residential woodsmoke in otherwise unmonitored communities, and to add spatial context to existing monitoring networks.

To investigate the impact of air pollutant control policies on future PM2.5 concentrations and their source contributions in China, we developed four future scenarios for 2030 based on a 2013 emission inventory, and conducted air quality simulations for each scenario using the chemical transport model GEOS-Chem (version 9.1.3). Two energy scenarios i.e., current legislation (CLE) and with additional measures (WAM), were developed to project future energy consumption, reflecting, respectively, existing legislation and implementation status as of the end of 2012, and new energy-saving policies that would be released and enforced more stringently. Two end-of-pipe control strategies, i.e., current control technologies (until 2017) and more stringent control technologies (until 2030), were also developed. The combinations of energy scenarios and end-of-pipe control strategies constitute four emission scenarios (2017-CLE, 2030-CLE, 2017-WAM, and 2030-WAM) evaluated in simulations. PM2.5 concentrations at national level were estimated to be 57 μg/m3 in the base year 2013, and 58 μg/m3, 42 μg/m3, 42 μg/m3, and 30 μg/m3 under the 2017-CLE, 2030-CLE, 2017-WAM, and 2030-WAM scenarios in 2030, respectively. Large PM2.5 reductions between 2013 and 2030 were estimated for heavily polluted regions (Sichuan Basin, Middle Yangtze River, North China). The energy-saving policies show similar effects to the end-of-pipe emission control measures, but the relative importance of these two groups of policies varies in different regions. Absolute contributions to PM2.5 concentrations from most major sources declined from 2017-CLE to 2030-WAM. With respect to fractional contributions, most coal-burning sectors (including power plant, industrial and residential coal burning) increased from 2017-CLE to 2030-WAM, due to larger reductions from non-coal sources, including transportation and biomass open burning. Residential combustion and open burning had much lower fractional contribution to ambient PM2.5 concentrations in the 2017-WAM/2030-WAM compared to the 2017-CLE/2030-CLE scenarios. Fractional contributions from transportation were reduced dramatically in 2030-CLE and 2030-WAM compared to 2017-CLE/2017-WAM, due to the enforcement of stringent end-of-pipe emission controls. Across all scenarios, coal combustion remained the single largest contributor to PM2.5 concentrations in 2030. Reducing PM2.5 emissions from coal combustion remains a strategic priority for air quality management in China.

Background: With much of the world’s population residing in urban areas, an understanding of city-level air pollution exposures can inform mitigation approaches. Prior studies of global urban air pollution have not considered trends in air pollutant concentrations nor corresponding attributable health burdens. Methods: We use high-resolution annual average PM2.5 concentrations, epidemiologically derived concentration response functions, and country-level baseline disease rates to estimate population-weighted PM2.5 concentrations and attributable cause-specific mortality in 13,160 urban centers between 2000 and 2019. Findings: While regional averages of urban PM2.5 concentrations decreased over this time period, we find considerable heterogeneity in trends of PM2.5 concentrations between urban areas. Approximately 86% of urban inhabitants (2·5 billion people) lived in urban areas exceeding the World Health Organization’s (WHO) guideline for annual average PM2.5 (10 µg/m3) resulting in an excess of 1·34-2·3 million deaths in 2019. Regional averages of PM2.5-attributable deaths increased in all regions except for Europe and the Americas, driven by population, age structure, and disease rate changes. In some cities, PM2.5-attributable health burdens increased despite decreases in PM2.5 concentrations, resulting from shifting age distributions and non-communicable disease rates. Interpretation: We find the majority of the world’s urban population lived in areas with unhealthy levels of PM2.5 between 2000 and 2019, leading to substantial contributions to non-communicable disease burdens. Results highlight that avoiding the large public health burden from urban PM2.5 will require strategies that reduce exposure through emissions mitigation as well as strategies that reduce vulnerability to PM2.5 by improving overall public health. Funding Information: NASA Grant No. 80NSSC19K0193 and Wellcome Trust Grant No. 216075-Z-19-Z. RVM acknowledges support from NASA Grant No. 80NSSC21K0508. Declaration of Interests: The authors declare they have no actual or potential competing financial interests.

Background: Greenspace exposure is associated with improved childhood development, yet the pathways behind this relation are insufficiently understood. Here, we investigated the association between lifetime residential exposure to greenspace and early childhood development and evaluated the extent to which this association was mediated by reductions in traffic-related air pollution and noise. Methods: In a large population-based birth cohort in Metro Vancouver, Canada (n=27,593), early childhood development was assessed via teacher-ratings on the Early Development Instrument (EDI) (mean age = 5·6 years). Greenspace was estimated using vegetation percentage derived from spectral unmixing of annual Landsat satellite-image composites. Lifetime residential exposure to greenspace (from birth to time of EDI assessment) was estimated as the mean of annual vegetation percentage values within 250 m of participants’ residential postal code. Multilevel modeling was used to investigate associations between greenspace exposure and EDI scores. The mediation effects of NO2, PM2.5, and noise levels were estimated using causal mediation analyses. Findings: In adjusted models, one interquartile range increase in vegetation percentage was associated with a 0·16 increase in EDI score (95% CI: 0·03, 0·29), indicating small improvements in early childhood development. 86·6%, 20·6%, and 37·3% of the association was mediated through reductions of NO2, PM2.5, and noise exposure levels, respectively. Interpretation: Increased residential greenspace exposure may improve childhood development by reducing the adverse developmental effects of traffic-related exposures, especially NO2 air pollution. This study contributes to knowledge of pathways between greenspace exposure and childhood development and supports healthy urban planning. Funding Information: This work was supported by the Canadian Institute of Health Research (grant reference number: 156152). Declaration of Interests: We declare no competing interests. Ethics Approval Statement: Ethics approval for this study was granted by the University of British Columbia Behavioral Research Ethics Board (certificate H18-00908).

Background: A recent study estimated that 1·69 million (95% uncertainty interval [UI] 1·52–1·83) deaths were attributable to non-optimal temperature in 2019. Globally, most of this burden was attributed to low temperatures, but high heat-attributable mortality rates were observed in warmer regions such as south Asia, north Africa and the Middle East, and sub-Saharan Africa. The impact of increases in global temperatures and demographic trends on future temperature-related mortality is unclear. Methods: We linked cause-specific temperature-mortality risk curves to daily temperature forecasts from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The NEX-GDDP dataset comprises 21 individual General Circulation Models (GCM) that were spatially downscaled for two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5. For each pixel, day, and cause, we estimated the population attributable fraction (PAF) based on the risk of cause-specific mortality at a given temperature relative to the theoretical minimum risk exposure level (TMREL); we then estimated temperature-attributable deaths by multiplying these PAFs by location and cause-specific mortality estimates for the 1990 to 2020 reference period and by forecasted mortality estimates for the period from 2020 to 2100. Other upstream drivers in the mortality forecast model, including the Socio-demographic Index and air pollution levels, were held constant. We additionally estimated PAFs for a temperature scenario based on the 2015 Paris Agreement, assuming that countries meet their nationally determined contributions and continue to reduce emissions at the same rate after 2030. Findings: Globally, we estimate increases in the number and rate of deaths attributable to non-optimal temperature, with 2·26 million (1·66–2·94), 2·54 million (1·86–3·31) and 3·17 million (2·11–4·35) annual deaths by the end of the century, for the Paris scenario, and the RCP4.5 and RCP8.5, respectively. These increases are driven largely by heat-attributable burden, with smaller and more variable changes in cold-attributable deaths. Warm, low-latitude regions will be disproportionally affected, with the largest increases in temperature-attributable mortality expected in sub-Saharan Africa, south Asia, and Latin America and the Caribbean. Heat-attributable mortality was reduced by half for the Paris scenario relative to the RCP8.5 with 1·02 million (0·64–1·48) deaths versus 2·08 million (1·13–3·13) deaths in 2100. As our study accounts for adaptation and does not extrapolate relative risks beyond the exposures currently experienced, these estimates are likely conservative. Interpretation: Globally increasing temperatures are projected to substantially increase the mortality burden attributable to non-optimal temperature. Higher global temperatures only lead to minor decreases in cold-related mortality which do not offset increases in heat-related mortality. Funding: Bill & Melinda Gates Foundation Declaration of Interest: The authors declare that there are no conflicts of interest.

Few studies have evaluated long-term cardiovascular effects of fine particulate matter (PMWe 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 PMThe average PMThis nationwide cohort study demonstrated associations of long-term exposure to PM

Artículo con numerosos autores. Sólo se hace referencia al primero que coincide con el de la UAM y al colectivo, Background Previous attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017. Methods Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs. Findings In 2017, 544·9 million people (95% uncertainty interval [UI] 506·9–584·8) worldwide had a chronic respiratory disease, representing an increase of 39·8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex-specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7·0% [95% UI 6·8–7·2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578–4 044 819) in 2017, an increase of 18·0% since 1990, while total DALYs increased by 13·3%. However, when accounting for ageing and population growth, declines were observed in age-standardised prevalence (14·3% decrease), agestandardised death rates (42·6%), and age-standardised DALY rates (38·2%). In males and females, most chronic respiratory disease-attributable deaths and DALYs were due to COPD. In regional analyses, mortality rates from chronic respiratory diseases were greatest in south Asia and lowest in sub-Saharan Africa, also across both sexes. Notably, although absolute prevalence was lower in south Asia than in most other super-regions, YLLs due to chronic respiratory diseases across the subcontinent were the highest in the world. Death rates due to interstitial lung disease and pulmonary sarcoidosis were greater than those due to pneumoconiosis in all super-regions. Smoking was the leading risk factor for chronic respiratory disease-related disability across all regions for men. Among women, household air pollution from solid fuels was the predominant risk factor for chronic respiratory diseases in south Asia and sub-Saharan Africa, while ambient particulate matter represented the leading risk factor in southeast Asia, east Asia, and Oceania, and in the Middle East and north Africa super-region. Interpretation Our study shows that chronic respiratory diseases remain a leading cause of death and disability worldwide, with growth in absolute numbers but sharp declines in several age-standardised estimators since 1990. Premature mortality from chronic respiratory diseases seems to be highest in regions with less-resourced health systems on a per-capita basis, Bill & Melinda Gates Foundation.

Background: The Global Burden of Disease Study 2019 (GBD 2019) estimated that approximately 2 million (95% uncertainty interval [UI] 1·7–2·3) deaths and 37·8 million (32·2–47·6) DALYs were attributable to non-optimal temperature in 2019. Globally, most of this burden was attributed to low temperatures, but high heat-attributable mortality rates were observed in warmer regions such as south Asia, north Africa and the Middle East, and sub-Saharan Africa. The impact of increases in global temperatures and demographic trends on future temperature-related mortality is unclear. Methods: We linked cause-specific temperature-mortality risk curves to daily temperature forecasts from the NEX-GDDP dataset. The NEX-GDDP data set comprises 21 individual General Circulation Models (GCM) that were spatially downscaled for two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5. For each pixel, day, and cause, we estimated the population attributable fraction (PAF) based on the risk of cause-specific mortality at a given temperature relative to the theoretical minimum risk exposure level (TMREL); we then estimated temperature-attributable deaths by multiplying these PAFs with location-specific mortality estimates for the reference period from 1990 to 2020 and for the forecasted period from 2020 to 2100. Other upstream drivers in the model, including the Socio-demographic Index and air pollution levels, were held constant. Findings: Globally, we estimate increases in the number and rate of deaths attributable to non-optimal temperature, with 3.27 (2.11 – 4.70) million and 4.08 (2.67 – 5.86) million annual deaths by the end of the century, for RCP4.5 and RCP8.5, respectively. These increases are driven largely by heat-attributable burden, with smaller and more variable changes in cold-attributable deaths. Warm, low-latitude regions will be disproportionally affected, with the largest increases in temperature-attributable mortality expected in sub-Saharan Africa, south Asia, and Latin America and the Caribbean. As our study accounts for adaptation and does not extrapolate relative risks beyond the exposure currently experienced, these estimates are likely conservative. Interpretation: Globally increasing temperatures are projected to substantially increase the mortality burden attributable to non-optimal temperature. Higher global temperatures only lead to minor decreases in cold-related mortality which do not offset increases in heat-related mortality. Funding Statement: Bill & Melinda Gates Foundation Declaration of Interests: None to declare.

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NOx levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NOx (n = 428) and NO2 (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO2 concentrations. NO and NO2 concentrations were highest in commercial/business/industrial (66 and 76 μg/m3, respectively) and high-density residential areas (47 and 59 μg/m3, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 μg/m3 for NO and NO2, and a clear seasonal variation, with the mean NO2 of 63 μg/m3 (non-Harmattan) increased by 25–56% to 87 μg/m3 (Harmattan) across different site types. The NO2/NOx ratio was also elevated by 19–28%. Both NO and NO2 levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO2 and NO2/NOx and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25–180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

Background Air pollution from cooking with solid fuels is a potentially modifiable risk factor for increased blood pressure and may lead to eye irritation. Objectives To evaluate whether a climate motivated cookstove intervention reduced blood pressure and eye irritation symptoms in Indian women. Methods Households using traditional stoves were randomized to receive a rocket stove or continue using traditional stoves. Systolic (SBP) and diastolic blood pressure (DBP), and self-reported eye symptoms were measured twice, pre-intervention and at least 124 days post-intervention in women > 25 years old in control (N = 111) and intervention (N = 111) groups in rural Karnataka, India. Daily (24-h) fine particle (PM2.5) mass and absorbance (Abs) were measured in cooking areas at each visit. Mixed-effect models were used to estimate before-and-after differences in SBP, DBP and eye symptoms. Results We observed a lower SBP (−2.0 (−4.5, 0.5) mmHg) and DBP (−1.1 (−2.9, 0.6) mmHg) among exclusive users of intervention stove, although confidence intervals included zero. Stacking or mixed use of intervention and traditional stoves contributed to a small increase in SBP 2.6 (−0.4, 5.7) mmHg) and DBP (1.2 (−0.9, 3.3) mmHg). Exclusive and mixed stove users experienced higher post-intervention reductions, on average, in self-reported eye irritation symptoms for burning sensation in eyes, and eyes look red often compared to control. Median air pollutant concentrations increased post-intervention in all stove groups, with the lowest median PM2.5 increase in the exclusive intervention stove group. Conclusions Health benefits were limited due to stacking and lower-than-predicted efficiency of the intervention stove in the field. Stove adoption and use behavior, in addition to stove technology, affects achievement of health co-benefits. Carbon-financing schemes need to align with international guidelines that have been set based on health outcomes to maximize health co-benefits from cookstove interventions.

Background: Several studies have reported associations between long term exposure to air pollutants and cause-specific mortality. However, since the concentrations of air pollutants in Asia are much higher compared to those reported in North American and European cohort studies, cohort studies on long term effects of air pollutants in Asia are needed for disease burden assessment and to inform policy. Objectives: To assess the effects of long-term exposure to particulate matter with aerodynamic diameter

Forest fire smoke is a growing public health concern as more intense and frequent fires are expected under climate change. Remote sensing is a promising tool for exposure assessment, but its utility for health studies is limited because most products measure pollutants in the total column of the atmosphere, and not the surface concentrations most relevant to population health. Information about the vertical distribution of smoke is vital for addressing this limitation. The CALIPSO satellite can provide such information but it cannot cover all smoke events due to its narrow ground track. In this study, we developed a random forests model to predict the minimum height of the smoke layer observed by CALIPSO at high temporal and spatial resolution, using information about fire activity in the vicinity, geographic location, and meteorological conditions. These pieces of information are typically available in near-real-time, ensuring that the resulting model can be easily operationalized. A total of 15,617 CALIPSO data blocks were identified as impacted by smoke within the province of British Columbia, Canada from 2006 to 2015, and 52.1% had smoke within the boundary layer, where the population might be exposed. The final model explained 82.1% of the variance in the observations with a root mean squared error of 560 m. The most important variables in the model were wind patterns, the month of smoke observation, and fire intensity within 500 km. Predictions from this model can be 1) directly applied to smoke detection from the existing remote sensing products to provide another dimension of information; 2) incorporated into statistical smoke models with inputs from remote sensing products; or 3) used to inform estimates of vertical dispersion in deterministic smoke models. These potential applications are expected to improve the assessment of ground-level population exposure to forest fire smoke.

Background Approximately three billion people are exposed to household air pollution (HAP) from solid fuel cookstoves. Studies from single settings have linked HAP with elevated blood pressure (BP), but no evidence exists from multi-country analyses. Objectives Using nationally representative and internationally comparable data, we examined the association between solid fuel use and BP in 77,605 largely premenopausal women (aged 15–49) from ten resource-poor countries. Methods We obtained data on systolic and diastolic BP, self-reported primary cooking fuel, health and socio-demographic characteristics from 12 Demographic and Health Surveys conducted in Albania, Armenia, Azerbaijan, Bangladesh, Benin, Ghana, Kyrgyzstan, Lesotho, Namibia, and Peru. We estimated associations between history of fuel use [solid fuel (coal or biomass) versus clean fuel (electricity or gas)] with systolic and diastolic BP and hypertension using a meta-analytical approach. Results Overall, the country-level mean systolic and diastolic BP were 117 (range: 111–127) and 74 (71–83) mmHg, respectively. The country-level mean age of the women was 30.8 years (range: 28.4–32.9). The prevalence of solid fuel use was 46.0% (range: 4.1–95.8). In adjusted, pooled analyses, primary use of solid fuel was associated with 0.58 mmHg higher systolic BP (95% CI: 0.23, 0.93) as compared to primary use of clean fuel. The pooled estimates for diastolic BP and pulse pressure were also positive, but the confidence intervals contained zero. The pooled odds of hypertension was [OR = 1.07 (95% CI: 0.99, 1.16)], an effect that was driven by rural participants for whom solid fuel use was associated with a 16% greater odds of hypertension [OR = 1.16 (95% CI: 1.01, 1.35)]. Conclusions Cooking with solid fuels was associated with small increases in BP and odds of hypertension. Use of cleaner fuels like gas or electricity may reduce cardiovascular risk in developing countries, particularly among rural residents.

Background Large cohort studies have been used to characterise the association between long-term exposure to fine particulate matter (PM2.5) air pollution with non-accidental, and cause-specific mortality. However, there has been no consensus as to the shape of the association between concentration and response. Methods To examine the shape of this association, we developed a new cohort based on respondents to the 2001 Canadian census long-form. We applied new annual PM2.5 concentration estimates based on remote sensing and ground measurements for Canada at a 1 km spatial scale from 1998 to 2011. We followed 2.4 million respondents who were non-immigrants aged 25–90 years and did not reside in an institution over a 10 year period for mortality. Exposures were assigned as a 3-year mean prior to the follow-up year. Income tax files were used to account for residential mobility among respondents using postal codes, with probabilistic imputation used for missing postal codes in the tax data. We used Cox survival models to determine hazard ratios (HRs) for cause-specific mortality. We also estimated Shape Constrained Health Impact Functions (a concentration-response function) for selected causes of death. Results In models stratified by age, sex, airshed, and population centre size, and adjusted for individual and neighbourhood socioeconomic variables, HR estimates for non-accidental mortality were HR = 1.18 (95% CI: 1.15–1.21) per 10 μg/m3 increase in concentration. We observed higher HRs for cardiovascular disease (HR=1.25; 95% CI: 1.19–1.31), cardio-metabolic disease (HR = 1.27; 95% CI: 1.21–1.33), ischemic heart disease (HR = 1.36; 95% CI: 1.28–1.44) and chronic obstructive pulmonary disease (COPD) mortality (HR = 1.24; 95% CI: 1.11–1.39) compared to HR for all non-accidental causes of death. For non-accidental, cardio-metabolic, ischemic heart disease, respiratory and COPD mortality, the shape of the concentration-response curve was supra-linear, with larger differences in relative risk for lower concentrations. For both pneumonia and lung cancer, there was some suggestion that the curves were sub-linear. Conclusions Associations between ambient concentrations of fine particulate matter and several causes of death were non-linear for each cause of death examined.

Background Natural spaces can provide psychological benefits to individuals, but population-level epidemiologic studies have produced conflicting results. Refining current exposure-assessment methods is necessary to advance our understanding of population health and to guide the design of health-promoting urban forms. Objectives The aim of this study was to develop a comprehensive Natural Space Index that robustly models potential exposure based on the presence, form, accessibility, and quality of multiple forms of greenspace (e.g., parks and street trees) and bluespace (e.g., oceans and lakes). Material and methods The index was developed for greater Vancouver, Canada. Greenness presence was derived from remote sensing (NDVI/EVI); forms were extracted from municipal and private databases; and accessibility was based on restrictions such as private ownership. Quality appraisals were conducted for 200 randomly sampled parks using the Public Open Space Desktop Appraisal Tool (POSDAT). Integrating these measures in GIS, exposure was assessed for 60,242 postal codes using 100- to 1,600-m buffers based on hypothesized pathways to mental health. A single index was then derived using principal component analysis (PCA). Results Comparing NDVI with alternate approaches for assessing natural space resulted in widely divergent results, with quintile rankings shifting for 22–88% of postal codes, depending on the measure. Overall park quality was fairly low (mean of 15 on a scale of 0–45), with no significant difference seen by neighborhood-level household income. The final PCA identified three main sets of variables, with the first two components explaining 68% of the total variance. The first component was dominated by the percentages of public and private greenspace and bluespace and public greenspace within 250 m, while the second component was driven by lack of access to bluespace within 1 km. Conclusions Many current approaches to modeling natural space may misclassify exposures and have limited specificity. The Natural Space Index represents a novel approach at a regional scale with application to urban planning and policy-making.

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO2), nitric oxide (NO), fine particulate matter (PM2.5), and black carbon (BC) concentrations were measured during two sampling campaigns (April-May and November-January) in Hong Kong (a prototypical high-density high-rise city). Along with 365 potential geospatial predictor variables, these concentrations were used to build two-dimensional land use regression (LUR) models for the territory. Summary statistics for combined measurements over both campaigns were: a) NO2 (Mean=106μg/m3, SD=38.5, N=95), b) NO (M=147μg/m3, SD=88.9, N=40), c) PM2.5 (M=35μg/m3, SD=6.3, N=64), and BC (M=10.6μg/m3, SD=5.3, N=76). Final LUR models had the following statistics: a) NO2 (R2=0.46, RMSE=28μg/m3) b) NO (R2=0.50, RMSE=62μg/m3), c) PM2.5 (R2=0.59; RMSE=4μg/m3), and d) BC (R2=0.50, RMSE=4μg/m3). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO2 prediction surface values were highest in Kowloon and the northern region of Hong Kong Island (downtown Hong Kong). NO showed a similar pattern in the built-up region. Both PM2.5 and BC predictions exhibited a northwest-southeast gradient, with higher concentrations in the north (close to mainland China). For BC, the port was also an area of elevated predicted concentrations. The results matched with existing literature on spatial variation in concentrations of air pollutants and in relation to important emission sources in Hong Kong. The success of these models suggests LUR is appropriate in high-density, high-rise cities.

Background Mobile air temperature monitoring is a promising method to better understand temperature distributions at fine spatial resolutions across urban areas. The study objectives were to collect microscale measurements for evaluate different data sources used to assess heat exposure in greater Vancouver, Canada. Methods Mobile air temperature monitoring was conducted on foot at least twice for each of 20 routes. First, the mobile data were compared with 1-minute measurements from the nearest fixed site. Second, the mobile data from runs corresponding with Landsat overpass days were compared with satellite-derived land surface temperature (LST). Third, the mobile data were compared with estimates from a previously developed heat map for the region. Results Mobile measurements were typically higher and more variable than simultaneous fixed site measurements. Correlations between mobile measurements and LST were weak and highly variable (r 2 = 0.04–0.38). The z-score differentials between mobile measurements and the heat map suggested that spatial variability in temperatures is captured by the heat map. Conclusion Microscale measurements confirm that fixed sites do not characterize the variability in thermal conditions within nearby streetscapes. Microscale monitoring of air temperatures is a valuable tool for temporally and spatially evaluating other high resolution temperature data within small areas.

Resolving spatial variability in ambient air pollutant and quantifying contributing factors are critical to human exposure assessment and effective pollution control. Data-driven techniques have been employed in air quality modeling due to their capability to capture the complex relationships in data as well as for the benefit of fast and easy implementation. In this study, we addressed two issues on model evaluation and interpretability by applying two common data-driven approaches, linear regression (LR) and random forest (RF) with potentially predictive land-use predictor variables to predict spatial variations of air pollution in an urban setting. The data came from the measurement of ambient nitrogen dioxide (NO 2 ) concentrations in the Greater Vancouver Regional District in Canada. First, we showed that the model performance was sensitive to the division of training and test sets. Applying a limited number of hold-out validations or cross-validations and reporting the mean model metrics cannot capture the variability and fairly evaluate the model performance. We proposed repeated cross-validations (RCVs) as a reliable evaluation method that accounts for both mean and variance. Second, there is not a consistent approach to measure the importance of predictor variables and quantify their contributions among different types of data-driven models. Traditional approaches only reflect the relative importance among predictor variables in terms of predictive power without a quantification of contribution to the model output. We proposed to apply SHapley Additive exPlanations (SHAP), a Shapley-value-based explanation method based on the coalitional game theory, as a unifying framework to interpret and compare different types of data-driven methods. We showed that SHAP is capable of 1) calculating predictor variable’s contribution to each data point; 2) ranking the importance of predictor variables in terms of their contributions to the model output. The results indicated that different models may favor different predictor variables and result in different interpretability.

Previous studies of environmental justice in Canadian cities have linked lower socioeconomic status to greater air pollution exposures at coarse geographic scales, (i.e., Census Tracts). However, studies that examine these associations at finer scales are less common, as are comparisons among cities. To assess differences in exposure to air pollution among socioeconomic groups, we assigned estimates of exposure to ambient nitrogen dioxide (NO2), a marker for traffic-related pollution, from city-wide land use regression models to respondents of the 2006 Canadian census long-form questionnaire in Toronto, Montreal, and Vancouver. Data were aggregated at a finer scale than in most previous studies (i.e., by Dissemination Area (DA), which includes approximately 400-700 persons). We developed simultaneous autoregressive (SAR) models, which account for spatial autocorrelation, to identify associations between NO2 exposure and indicators of social and material deprivation. In Canada's three largest cities, DAs with greater proportions of tenants and residents who do not speak either English or French were characterised by greater exposures to ambient NO2. We also observed positive associations between NO2 concentrations and indicators of social deprivation, including the proportion of persons living alone (in Toronto), and the proportion of persons who were unmarried/not in a common-law relationship (in Vancouver). Other common measures of deprivation (e.g., lone-parent families, unemployment) were not associated with NO2 exposures. DAs characterised by selected indicators of deprivation were associated with higher concentrations of ambient NO2 air pollution in the three largest cities in Canada.

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.

Background Extreme heat events have been associated with excess morbidity and mortality worldwide. Previous research mainly evaluated extreme heat exposures at the municipal and local scales, but individuals are exposed in much smaller areas. The goal of this study was to assess whether land use regression (LUR) models could be developed for air temperature using measurements collected by a pedestrian. Methods Microscale air temperature ( Results The most predictive LUR variables were Distance to Large Water Body, Distance to Major Road, Normalized Difference Water Index (NDWI), and Sky-View Factor (SVF). On average, the best individual route models explained 39% of the variation in microscale air temperatures for the 20 routes. The overall model explained only 10% of the variation in the 20 combined routes. Conclusion Mobile air temperatures were associated with geographic and built environment features at the microscale. The collected data were used to build moderately predictive LUR models for some locations, but could not be used to successfully model the entire study area.

Socioeconomic factors often affect the distribution of exposure to air pollution. The relationships between health, air pollution, and poverty potentially have important public health and policy implications, especially in areas of Asia where air pollution levels are high and income disparity is large. The objective of the study was to characterize the levels, determinants of exposure, and relationships between children personal exposures and ambient concentrations of multiple air pollutants amongst different socioeconomic segments of the population of Ho Chi Minh City, Vietnam. Using repeated (N = 9) measures personal exposure monitoring and determinants of exposure modeling, we compared daily average PM2.5, PM10, PM2.5 absorbance and NO2 concentrations measured at ambient monitoring sites to measures of personal exposures for (N = 64) caregivers of young children from high and low socioeconomic groups in two districts (urban and peri-urban), across two seasons. Personal exposures for both PM sizes were significantly higher among the poor compared to non-poor participants in each district. Absolute levels of personal exposures were under-represented by ambient monitors with median individual longitudinal correlations between personal exposures and ambient concentrations of 0.4 for NO2, 0.6 for PM2.5 and PM10 and 0.7 for absorbance. Exposures of the non-poor were more highly correlated with ambient concentrations for both PM size fractions and absorbance while those for NO2 were not significantly affected by socioeconomic position. Determinants of exposure modeling indicated the importance of ventilation quality, time spent in the kitchen, air conditioner use and season as important determinant of exposure that are not fully captured by the differences in socioeconomic position. Our results underscore the need to evaluate how socioeconomic position affects exposure to air pollution. Here, differential exposure to major sources of pollution, further influenced by characteristics of Ho Chi Minh City's rapidly urbanizing landscape, resulted in systematically higher PM exposures among the poor.

The human gut is host to a diverse and abundant community of bacteria that influence health and disease susceptibility. This community develops in infancy, and its composition is strongly influenced by environmental factors, notably perinatal anthropogenic exposures such as delivery mode (Cesarean vs. vaginal) and feeding method (breast vs. formula); however, the built environment as a possible source of exposure has not been considered. Here we report on a preliminary investigation of the associations between bacteria in house dust and the nascent fecal microbiota from 20 subjects from the Canadian Healthy Infant Longitudinal Development (CHILD) Study using high-throughput sequence analysis of portions of the 16S rRNA gene. Despite significant differences between the dust and fecal microbiota revealed by Nonmetric Multidimensional Scaling (NMDS) analysis, permutation analysis confirmed that 14 bacterial OTUs representing the classes Actinobacteria (3), Bacilli (3), Clostridia (6) and Gammaproteobacteria (2) co-occurred at a significantly higher frequency in matched dust-stool pairs than in randomly permuted pairs, indicating an association between these dust and stool communities. These associations could indicate a role for the indoor environment in shaping the nascent gut microbiota, but future studies will be needed to confirm that our findings do not solely reflect a reverse pathway. Although pet ownership was strongly associated with the presence of certain genera in the dust for dogs (Agrococcus, Carnobacterium, Exiguobacterium, Herbaspirillum, Leifsonia and Neisseria) and cats (Escherichia), no clear patterns were observed in the NMDS-resolved stool community profiles as a function of pet ownership.

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., Highlights • Five natural experiments reported sufficient activity metrics for meta-analysis. • Total physical activity decreased; transport-related physical activity increased. • Positive associations are specific to subgroup, proximity, or travel behavior. • Studies should consider context, measurement accuracy, follow-up, and controls. • Health may be a co-benefit of rapid transit investments aimed at other purposes.

India faces one of the highest disease burdens from air pollution in the world, with an estimated 100% of the population living in areas with PM2.5 concentrations above the World Health Organization Guideline (10 μg/m3 annual average). With development and population growth, increases in ambient air pollution are anticipated. Combined with an aging population and increasing burden of chronic diseases, ambient air pollution will remain a concern for India well into the current century. Air quality measurements make critical contributions to the identification and prioritization of sources and locations of greatest concern, benchmarking against standards and guidelines, and in the evaluation of effectiveness of actions to reduce emissions. We compare the density of India's monitoring network with that of comparator countries and find large differences. For example, given the ~200 PM2.5 monitoring sites in operation during the 2010–2016 period, we find that India's monitor density of ~0.14 monitors/million persons (1 monitor for every 6.8 million people) is well below that of other highly populated countries such as China (1.2 monitors/million persons), the USA (3.4 monitors/million persons), Japan (0.5 monitors/million persons), Brazil (1.8) and most European countries (2–3 monitors/million persons). To address these gaps between India and monitor densities of comparator countries will require 1600–4000 monitors (1.2–3 monitors/million persons) at an estimated capital (annual operating) cost of US $212–540 ($106–270) million. Even at these densities, only relatively basic information on common air pollutants at high temporal, but limited spatial, resolution would be available. Small-scale variability in air pollution levels within urban areas would not be well-characterized, nor would there be information on chemical constituents useful for evaluating and improving simulations and forecasts, or for characterizing source contributions. As a sufficiently dense traditional network is developed over time, the potential for an integrated monitoring framework to serve as a near-term complement to a traditional network is assessed. In this design, a smaller number of traditional monitoring sites would be linked to a single advanced surface monitoring station in each of ~11 airsheds identified as a minimal number for India. These sites would combine measurement of chemical speciation of particulate matter with measurements of aerosol scatter and aerosol optical depth to link measurements with global and regional satellite-based estimates. In turn, the advanced and traditional sites could serve as calibration nodes for low-cost sensor networks designed to complement periodic mobile monitoring campaigns and/or land use regression models, to provide high spatial and temporal resolution. Such a framework could be established at a substantially reduced cost relative to that of a traditional networks, subject to specific design and complexity considerations. The same general approach may also be applicable to the many other countries with limited or no air quality monitoring and where estimates suggest air quality is a concern.

Neighbourhood socioeconomic status (SES) has been associated with numerous chronic diseases, yet little information exists on its association with lung cancer incidence. This outcome presents two key empirical challenges: a long latency period that requires study participants' residential histories and long-term neighbourhood characteristics; and adequate data on many risk factors to test hypothesized mediating pathways between neighbourhood SES and lung cancer incidence. Analysing data on urban participants of a large Canadian population-based lung cancer case-control study, we investigate three issues pertaining to these challenges. First, we examine whether there is an association between long-term neighbourhood SES, derived from 20 years of residential histories and five national censuses, and lung cancer incidence. Second, we determine how this long-term neighbourhood SES association changes when using neighbourhood SES measures based on different latency periods or at time of study entry. Third, we estimate the extent to which long-term neighbourhood SES is mediated by a range of individual-level smoking behaviours, other health behaviours, and environmental and occupational exposures. Results of hierarchical logistic regression models indicate significantly higher odds of lung cancer cases residing in the most compared to the least deprived quintile of the long-term neighbourhood SES index (OR: 1.46; 95% CI: 1.13-1.89) after adjustment for individual SES. This association remained significant (OR: 1.38; 1.01-1.88) after adjusting for smoking behaviour and other known and suspected lung cancer risk factors. Important differences were observed between long-term and study entry neighbourhood SES measures, with the latter attenuating effect estimates by over 50 percent. Smoking behaviour was the strongest partial mediating pathway of the long-term neighbourhood SES effect. This research is the first to examine the effects of long-term neighbourhood SES on lung cancer risk and more research is needed to further identify specific, modifiable pathways by which neighbourhood context may influence lung cancer risk.

Background Land-use regression (LUR) is a cost-effective approach for predicting spatial variability in ambient air pollutant concentrations with high resolution. Models have been widely used in epidemiological studies and are often applied to time periods before or after the period of air quality monitoring used in model development. However, it is unclear how well such models perform when extrapolated over time. Objective The objective of this study was to assess the temporal stability of LUR models over a period of 7 years in Metro Vancouver, Canada. Methods A set of NO and NO 2 LUR models based on 116 measurements were developed in 2003. In 2010, we made 116 measurements again, of which 73 were made at the exact same location as in 2003. We then developed 2010 models using updated data for the same predictor variables used in 2003, and also explored additional variables. Four methods were used to derive model predictions over 7 years, and predictions were compared with measurements to assess the temporal stability of LUR models. Results The correlation between 2003 NO and 2010 NO measurements was 0.87 with a mean (sd) decrease of 11.3 (9.9) ppb. For NO 2 , the correlation was 0.74, with a mean (sd) decrease of 2.4 (3.2) ppb. 2003 and 2010 LUR models explained similar amounts of spatial variation ( R 2 = 0.59 and R 2 = 0.58 for NO; R 2 = 0.52 and R 2 = 0.63 for NO 2 , in 2003 and in 2010 respectively). The 2003 models explained more variability in the 2010 measurements ( R 2 = 0.58–0.60 for NO; R 2 = 0.52–0.61 for NO 2 ) than the 2010 models explained in the 2003 measurements ( R 2 = 0.50–0.55 for NO; R 2 = 0.44–0.49 for NO 2 ), and the 2003 models explained as much variability in the 2010 measurements as they did in the 2003 measurements. Conclusion LUR models are able to provide reliable estimates over a period of 7 years in Metro Vancouver. When concentrations and their variability are decreasing over time, the predictive power of LUR models is likely to remain the same or to improve in forecasting scenarios, but to decrease in hind-casting scenarios.

Epidemiologic studies have shown that both air pollution and community noise are associated with cardiovascular disease mortality. Because road traffic is a major contributor to these environmental pollutants in metropolitan areas, it is plausible that the observed associations may be confounded by coexistent pollutants. As part of a large population-based cohort study to address this concern, we used a noise prediction model to assess annual average community noise levels from transportation sources in metropolitan Vancouver, Canada. The modeled annual average noise level was 64 (inter quartile range 60-68) dB(A) for the region. This model was evaluated by comparing modeled annual daytime A-weighted equivalent continuous noise levels (L(day)) with measured 5-min daytime A-weighted equivalent continuous noise levels (L(eq,day,5 min)) at 103 selected roadside sites in the study region. On average, L(day) was 6.2 (95% CI, 6.0-7.9) dB(A) higher than, but highly correlated (r=0.62; 95% CI, 0.48-0.72) with, L(eq,day,5 min). These results suggest that our model-based noise exposure assessment could approximately reflect actual noise exposure in the study region. Overall, modeled noise levels were not strongly correlated with land use regression estimates of traffic-related air pollutants including black carbon, particulate matter with aerodynamic diameter ≤2.5 μm (PM(2.5)), NO(2) and NO; the highest correlation was with black carbon (r=0.48), whereas the lowest correlation was with PM(2.5) (r=0.18). There was no consistent effect of traffic proximity on the correlations between community noise levels and traffic-related air pollutant concentrations. These results, consistent with previous studies, suggest that it is possible to assess potential adverse cardiovascular effects from long-term exposures to community noise and traffic-related air pollution in prospective epidemiologic studies.