Air quality and individual-level academic performance in Brazil: A nationwide study of more than 15 million students between 2000 and 2020.

Studies have shown that living and studying in places with poor air quality is associated with cognitive deficits. However, there is still a limitation in the literature in terms of study design and geographic location. Also, only a few studies have looked at the effects of more than one air pollutant. To address this research gap, in this study we estimated the association between air pollution (considering three criteria air pollutants - PM2.5, NO2, and O3) and academic performance (a proxy of cognitive performance) at the student level in Brazil between 2000 and 2020. We assessed academic performance data from a nationwide high school exam. The data included 15,443,772 students who took this national test between 2000 and 2020 in Brazil. Air pollution data was derived from satellite remote sensing observations. We fit mixed-effects regression models with a state-specific random intercept and adjusted for school characteristics, spatio-temporal factors, and socioeconomic status. We performed sub-group analyses by stratifying the analysis by type of school management (private or public), location of the school (urban or rural), sex, and periods. Our findings suggest air pollution exposure was associated with drops in the students' marks varying from 0.13% to 5.39%. To our knowledge, this is the first study that estimates the association between air pollution and individual-level academic performance in Brazil. This study is of substantial environmental and educational importance by supporting policymakers to improve the air quality surrounding schools.

Association of low and high ambient temperature with mortality for cardiorespiratory diseases in Brazil.

Extreme temperatures are a major public health concern, as they have been linked to an increased risk of mortality from circulatory and respiratory diseases. Brazil, a country with vast geographic and climatic variations, is particularly vulnerable to the health impacts of extreme temperatures. In this study, we examined the nationwide (considering 5572 municipalities) association of low and high ambient temperature (1st and 99th percentiles) with daily mortality for circulatory and respiratory diseases in Brazil between 2003 and 2017. We used an extension of the two-stage time-series design. First, we applied a case time series design in combination with distributed lag non-linear modeling (DLMN) framework to assess the association by Brazilian region. Here, the analyses were stratified by sex, age group (15-45, 46-65, and >65 years), and cause of death (respiratory and circulatory mortality). In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions. Our study population included 1,071,090 death records due to cardiorespiratory diseases in Brazil over the study period. We found increased risk of respiratory and circulatory mortality associated with low and high ambient temperatures. The pooled national results for the whole population (all ages and sex) suggest a relative risk (RR) of 1.27 (95% CI: 1.16; 1.37) and 1.11 (95% CI: 1.01; 1.21) associated with circulatory mortality during cold and heat exposure, respectively. For respiratory mortality, we estimated a RR of 1.16 (95% CI: 1.08; 1.25) during cold exposure and a RR of 1.14 (95% CI: 0.99; 1.28) during heat exposure. The national meta-analysis indicated robust positive associations for circulatory mortality on cold days across several subgroups by sex and age, while only a few subgroups presented robust positive associations for circulatory mortality on warm days and respiratory mortality on both cold and warm days. These findings have important public health implications for Brazil and suggest the need for targeted interventions to mitigate the adverse effects of extreme temperatures on human health.

Low ambient temperature and hospitalization for cardiorespiratory diseases in Brazil.

Studies have shown that larger temperature-related health impacts may be associated with cold rather than with hot temperatures. Although it remains unclear the cold-related health burden in warmer regions, in particular at the national level in Brazil. We address this gap by examining the association between low ambient temperature and daily hospital admissions for cardiovascular and respiratory diseases in Brazil between 2008 and 2018. We first applied a case time series design in combination with distributed lag non-linear modeling (DLNM) framework to assess the association of low ambient temperature with daily hospital admissions by Brazilian region. Here, we also stratified the analyses by sex, age group (15-45, 46-65, and >65 years), and cause (respiratory and cardiovascular hospital admissions). In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions. Our sample included more than 23 million hospitalizations for cardiovascular and respiratory diseases nationwide between 2008 and 2018, of which 53% were admissions for respiratory diseases and 47% for cardiovascular diseases. Our findings suggest that low temperatures are associated with a relative risk of 1.17 (95% CI: 1.07; 1.27) and 1.07 (95% CI: 1.01; 1.14) for cardiovascular and respiratory admissions in Brazil, respectively. The pooled national results indicate robust positive associations for cardiovascular and respiratory hospital admissions in most of the subgroup analyses. In particular, for cardiovascular hospital admissions, men and older adults (>65 years old) were slightly more impacted by cold exposure. For respiratory admissions, the results did not indicate differences among the population groups by sex and age. This study can help decision-makers to create adaptive measures to protect public health from the effects of cold temperature.

The impact of wildfires on air pollution and health across land use categories in Brazil over a 16-year period.

Forest fires cause many environmental impacts, including air pollution. Brazil is a very fire-prone region where few studies have investigated the impact of wildfires on air quality and health. We proposed to test two hypotheses in this study: i) the wildfires in Brazil have increased the levels of air pollution and posed a health hazard in 2003-2018, and ii) the magnitude of this phenomenon depends on the type of land use and land cover (e.g., forest area, agricultural area, etc.). Satellite and ensemble models derived data were used as input in our analyses. Wildfire events were retrieved from Fire Information for Resource Management System (FIRMS), provided by NASA; air pollution data from the Copernicus Atmosphere Monitoring Service (CAMS); meteorological variables from the ERA-Interim model; and land use/cover data were derived from pixel-based classification of Landsat satellite images by MapBiomas. We used a framework that infers the "wildfire penalty" by accounting for differences in linear pollutant annual trends (ß) between two models to test these hypotheses. The first model was adjusted for Wildfire-related Land Use activities (WLU), considered as an adjusted model. In the second model, defined as an unadjusted model, we removed the wildfire variable (WLU). Both models were controlled by meteorological variables. We used a generalized additive approach to fit these two models. To estimate mortality associated with wildfire penalties, we applied health impact function. Our findings suggest that wildfire events between 2003 and 2018 have increased the levels of air pollution and posed a significant health hazard in Brazil, supporting our first hypothesis. For example, in the Pampa biome, we estimated an annual wildfire penalty of 0.005 µg/m3 (95%CI: 0.001; 0.009) on PM2.5. Our results also confirm the second hypothesis. We observed that the greatest impact of wildfires on PM2.5 concentrations occurred in soybean areas in the Amazon biome. During the 16 years of the study period, wildfires originating from soybean areas in the Amazon biome were associated with a total penalty of 0.64 µg/m3 (95%CI: 0.32; 0.96) on PM2.5, causing an estimated 3872 (95%CI: 2560; 5168) excess deaths. Sugarcane crops were also a driver of deforestation-related wildfires in Brazil, mainly in Cerrado and Atlantic Forest biomes. Our findings suggest that between 2003 and 2018, fires originating from sugarcane crops were associated with a total penalty of 0.134 µg/m3 (95%CI: 0.037; 0.232) on PM2.5 in Atlantic Forest biome, resulting in an estimated 7600 (95%CI: 4400; 10,800) excess deaths during the study period, and 0.096 µg/m3 (95%CI: 0.048; 0.144) on PM2.5 in Cerrado biome, resulting in an estimated 1632 (95%CI: 1152; 2112) excess deaths during the study period. Considering that the wildfire penalties observed during our study period may continue to be a challenge in the future, this study should be of interest to policymakers to prepare future strategies related to forest protection, land use management, agricultural activities, environmental health, climate change, and sources of air pollution.

Short-term air pollution exposure and hospital admissions for cardiorespiratory diseases in Brazil: A nationwide time-series study between 2008 and 2018.

The established evidence associating air pollution with health is limited to populations from specific regions. Further large-scale studies in several regions worldwide are needed to support the literature to date and encourage national governments to act. Brazil is an example of these regions where little research has been performed on a large scale. To address this gap, we conducted a study looking at the relationship between daily PM2.5, NO2, and O3, and hospital admissions for circulatory and respiratory diseases across Brazil between 2008 and 2018. A time-series analytic approach was applied with a distributed lag modeling framework. We used a generalized conditional quasi-Poisson regression model to estimate relative risks (RRs) of the association of each air pollutant with the hospitalization for circulatory and respiratory diseases by sex, age group, and Brazilian regions. Our study population includes 23, 791, 093 hospital admissions for cardiorespiratory diseases in Brazil between 2008 and 2018. Among those, 53.1% are respiratory diseases, and 46.9% are circulatory diseases. Our findings suggest significant associations of ambient air pollution (PM2.5, NO2, and O3) with respiratory and circulatory hospital admissions in Brazil. The national meta-analysis for the whole population showed that for every increase of PM2.5 by 10 µg/m3, there is a 3.28% (95%CI: 2.61; 3.94) increase in the risk of hospital admission for respiratory diseases. For O3, we found positive associations only for some sub-group analyses by age and sex. For NO2, our findings suggest that a 10 ppb increase in this pollutant, there was a 35.26% (95%CI: 24.07; 46.44) increase in the risk of hospital admission for respiratory diseases. This study may better support policymakers to improve the air quality and public health in Brazil.

Intermediate and long-term exposure to air pollution and temperature and the extracellular microRNA profile of participants in the normative aging study (NAS).

BACKGROUND: The molecular effects of intermediate and long-term exposure to air pollution and temperature, such as those on extracellular microRNA (ex-miRNA) are not well understood but may have clinical consequences. OBJECTIVES: To assess the association between exposure to ambient air pollution and temperature and ex-miRNA profiles. METHODS: Our study population consisted of 734 participants in the Normative Aging Study (NAS) between 1999 and 2015. We used high-resolution models to estimate four-week, eight-week, twelve-week, six-month, and one-year moving averages of PM2.5, O3, NO2, and ambient temperature based on geo-coded residential addresses. The outcome of interest was the extracellular microRNA (ex-miRNA) profile of each participant over time. We used a longitudinal quantile regression approach to estimate the association between the exposures and each ex-miRNA. Results were corrected for multiple comparisons and ex-miRNAs that were still significantly associated with the exposures were further analyzed using KEGG pathway analysis and Ingenuity Pathway Analysis. RESULTS: We found 151 significant associations between levels of PM2.5, O3, NO2, and ambient temperature and 82 unique ex-miRNAs across multiple quantiles. Most of the significant results were associations with intermediate-term exposure to O3, long-term exposure to PM2.5, and both intermediate and long-term exposure to ambient temperature. The exposures were most often associated with the 75th and 90th percentile of the outcomes. Pathway analyses of significant ex-miRNAs revealed their involvement in biological pathways involving cell function and communication as well as clinical diseases such as cardiovascular disease, respiratory disease, and neurological disease. CONCLUSION: Our results show that intermediate and long-term exposure to all our exposures of interest were associated with changes in the ex-miRNA profile of study participants. Further studies on environmental risk factors and ex-miRNAs are warranted.

Long-Term Association of Air Pollution and Hospital Admissions Among Medicare Participants Using a Doubly Robust Additive Model.

BACKGROUND: Studies examining the nonfatal health outcomes of exposure to air pollution have been limited by the number of pollutants studied and focus on short-term exposures. METHODS: We examined the relationship between long-term exposure to fine particulate matter with an aerodynamic diameter <2.5 micrometers (PM2.5), NO2, and tropospheric ozone and hospital admissions for 4 cardiovascular and respiratory outcomes (myocardial infarction, ischemic stroke, atrial fibrillation and flutter, and pneumonia) among the Medicare population of the United States. We used a doubly robust method for our statistical analysis, which relies on both inverse probability weighting and adjustment in the outcome model to account for confounding. The results from this regression are on an additive scale. We further looked at this relationship at lower pollutant concentrations, which are consistent with typical exposure levels in the United States, and among potentially susceptible subgroups. RESULTS: Long-term exposure to fine PM2.5 was associated with an increased risk of all outcomes with the highest effect seen for stroke with a 0.0091% (95% CI, 0.0086-0.0097) increase in the risk of stroke for each 1-µg/m3 increase in annual levels. This translated to 2536 (95% CI, 2383-2691) cases of hospital admissions with ischemic stroke per year, which can be attributed to each 1-unit increase in fine particulate matter levels among the study population. NO2 was associated with an increase in the risk of admission with stroke by 0.00059% (95% CI, 0.00039-0.00075) and atrial fibrillation by 0.00129% (95% CI, 0.00114-0.00148) per ppb and tropospheric ozone was associated with an increase in the risk of admission with pneumonia by 0.00413% (95% CI, 0.00376-0.00447) per parts per billion. At lower concentrations, all pollutants were consistently associated with an increased risk for all our studied outcomes. CONCLUSIONS: Long-term exposure to air pollutants poses a significant risk to cardiovascular and respiratory health among the elderly population in the United States, with the greatest increase in the association per unit of exposure occurring at lower concentrations.

Low-Concentration Air Pollution and Mortality in American Older Adults: A National Cohort Analysis (2001-2017).

Mounting epidemiological evidence has documented the associations between long-term exposure to multiple air pollutants and increased mortality. There is a pressing need to determine whether risks persist at low concentrations including below current national standards. Air pollution levels have decreased in the United States, and better understanding of the health effects of low-level air pollution is essential for the amendment of National Ambient Air Quality Standards (NAAQS). A nationwide, population-based, open cohort study was conducted to estimate the association between long-term exposure to low-level PM2.5, NO2, O3, and all-cause mortality. The study population included all Medicare enrollees (ages 65 years or older) in the contiguous U.S. from 2001 to 2017. We further defined three low-exposure subcohorts comprised of Medicare enrollees who were always exposed to low-level PM2.5 (annual mean ≤12-µg/m3), NO2 (annual mean ≤53-ppb), and O3 (warm-season mean ≤50-ppb), respectively, over the study period. Of the 68.7-million Medicare enrollees, 33.1% (22.8-million, mean age 75.9 years), 93.8% (64.5-million, mean age 76.2 years), and 65.0% (44.7-million, mean age 75.6 years) were always exposed to low-level annual PM2.5, annual NO2, and warm-season O3 over the study period, respectively. Among the low-exposure cohorts, a 10-µg/m3 increase in PM2.5, 10-ppb increase in NO2, and 10-ppb increase in warm-season O3, were, respectively, associated with an increase in mortality rate ranging between 10 and 13%, 2 and 4%, and 12 and 14% in single-pollutant models, and between 6 and 8%, 1 and 3%, and 9 and 11% in tripollutant models, using three statistical approaches. There was strong evidence of linearity in concentration-response relationships for PM2.5 and NO2 at levels below the current NAAQS, suggesting that no safe threshold exists for health-harmful pollution levels. For O3, the concentration-response relationship shows an increasingly positive association at levels above 40-ppb. In conclusion, exposure to low levels of PM2.5, NO2, and warm-season O3 was associated with an increased risk of all-cause mortality.

Stringency of COVID-19 Containment Response Policies and Air Quality Changes: A Global Analysis across 1851 Cities.

The COVID-19 containment response policies (CRPs) had a major impact on air quality (AQ). These CRPs have been time-varying and location-specific. So far, despite having numerous studies on the effect of COVID-19 lockdown on AQ, a knowledge gap remains on the association between stringency of CRPs and AQ changes across the world, regions, nations, and cities. Here, we show that globally across 1851 cities (each more than 300 000 people) in 149 countries, after controlling for the impacts of relevant covariates (e.g., meteorology), Sentinel-5P satellite-observed nitrogen dioxide (NO2) levels decreased by 4.9% (95% CI: 2.2, 7.6%) during lockdowns following stringent CRPs compared to pre-CRPs. The NO2 levels did not change significantly during moderate CRPs and even increased during mild CRPs by 2.3% (95% CI: 0.7, 4.0%), which was 6.8% (95% CI: 2.0, 12.0%) across Europe and Central Asia, possibly due to population avoidance of public transportation in favor of private transportation. Among 1768 cities implementing stringent CRPs, we observed the most NO2 reduction in more populated and polluted cities. Our results demonstrate that AQ improved when and where stringent COVID-19 CRPs were implemented, changed less under moderate CRPs, and even deteriorated under mild CRPs. These changes were location-, region-, and CRP-specific.

The association of maternal exposure to ambient temperature with low birth weight in term pregnancies varies by location: In Brazil, positive associations may occur only in the Amazon region.

Exposure to ambient temperature has been linked to adverse birth outcomes in several regions, including the USA, Australia, China, countries in the Middle East, and European countries. To date, no studies were performed in South America, a region with serious challenges related to climate change. Our investigation addresses this literature lack by examining the association between Low Birth Weight (LBW) and ambient temperature exposure in the largest county in South America, Brazil. We applied a nationwide case-control study design using a logistic regression model to estimate the odds ratio (OR) for LBW associated with ambient temperature during a specific trimester of pregnancy (1-3 trimester). Our sample size includes 5,790,713 birth records nationwide over 18 years (2001-2018), of which 264,967 infants were included in the model as cases of LBW, representing 4.6% of our total sample. We adjusted our model for several confounding variables, including weather factors, air pollution, seasonality, and SES variables at the individual level. Our findings indicate that North was the only region with positive and statistically significant associations in the primary analysis and most of the sensitivity analysis, which is the region where the Amazon is located. In this region, we estimated an increase of 5.16% (95%CI: 3.60; 6.74) in the odds of LBW per 1 °C increase in apparent temperature when the exposure occurred in the second trimester. Our results may be explained by the climate conditions in the Amazon region in the past years. A large body of literature indicates that the Amazon region has been facing serious climate challenges including issues related to policy, governance, and deforestation. Specifically, regarding deforestation, it is suggested that land use change and deforestation is projected to increase heat stress in the Amazon region, because of Amazon savannization, increasing the risk of heat stress exposure in Northern Brazil. Our study can assist public sectors and clinicians in mitigating the risk and vulnerability of the Amazonian population.

Green areas and students' academic performance in the Federal District, Brazil: An assessment of three greenness metrics.

Most of the epidemiological investigations looking at the health benefits of green spaces have measured the level of green areas by using only one approach, mainly the Normalized Difference Index - NDVI (a satellite-derived indicator). We hypothesized a difference in the association between health and green space depending on the metric used to measure green exposure. This study considers students' academic performance as a proxy of cognitive abilities (a health indicator). We estimated the relationship between green areas and students' academic performance in the Federal District (FD), Brazil, with three different greenness metrics: NDVI, distance to green spaces (m) - obtained from land use data, and quantity of green spaces (m2) - also from land use data. We assessed student-level academic performance data provided by the Department of the Education in the FD. The data includes students from the public schools in the FD for 256 schools (all the public schools in the FD) and 344,175 students (all the students enrolled in the public schools in the FD in 2017-2020).). For the first metric represented by the distance to green spaces, we estimated the straight-line distance between each school and the nearest green area. For NDVI and quantity of green spaces, we estimated the area of all green spaces within buffers of 500 m, 750 m, and 1 km around the schools. We applied a cross-sectional study design using mixed-effects regression models to analyze the association exposure to green areas around schools and student-level academic performance. Our results confirmed our hypothesis showing that the impact of green areas on students' performance varied significantly depending on the type of green metric. After adjustments for the covariates, we estimated that NDVI is positively associated with school-level academic performance, with an estimated coefficient of 0.91 (95%CI: 0.83; 0.99) for NDVI values at a school's centroid. Distance to green areas was negatively associated with academic performance [-2.09 × 10-5 (95CI: 3.91 × 10-5; -2.84 × 10-6]. The quantity of green areas was estimated with mixed results (direction of the association), depending on the buffer size. Results from this paper suggest that epidemiological investigations must consider the different effects of greenness measures when looking at the association between green space and academic performance. More studies on residual confounding from this association with a different study design are needed to promote public health by making schools healthier.

A Direct Estimate of the Impact of PM2.5, NO2, and O3 Exposure on Life Expectancy Using Propensity Scores.

BACKGROUND: Many studies have reported associations of air pollutants and death, but fewer examined multiple pollutants, or used causal methods. We present a method for directly estimating changes in the distribution of age at death using propensity scores. METHODS: We included all participants in Medicare from 2000 to 2016 (637,207,589 person-years of follow-up). We fit separate logistic regressions modeling the probability of death at each year of age from 65 to 98 or older as a function of exposure to particulate matter less tha 2.5 µM in diameter (PM2.5), NO2, and O3, using separate propensity scores for each age. We estimated the propensity score using gradient boosting. We estimated the distribution of life expectancy at three counterfactual exposures for each pollutant. RESULTS: The estimated increase in mean life expectancy had the population been exposed to 7 versus 12 µg/m3 PM2.5 was 0.29 years (95% CI = 0.28, 0.30). The change in life expectancy had the population been exposed to 10 versus 20 ppb of NO2 was -0.01 years (95% CI = -0.015, -0.006). The increase in mean life expectancy had the population been exposed to 35 versus 45 ppb of O3 was 0.15 years (95% CI = 0.14, 0.16). Each of these effects was independent and additive. CONCLUSIONS: We estimated that reducing PM2.5 and O3 concentrations to levels below current standards would increase life expectancy by substantial amounts compared with the recent increase of life expectancy at age 65 of 0.7 years in a decade. Our results are not consistent with the hypothesis that exposure to NO2 decreases life expectancy.

Proximity of schools to roads and students' academic performance: A cross-sectional study in the Federal District, Brazil.

Investigations of the educational implications of children's exposure to air pollutants at school are crucial to enhance our understanding of the hazards for children. Most of the existing literature is based on studies performed in North America and Europe. Further investigation is required in low- and middle-income countries, where there are important challenges related to public health, transportation, environment, and education sector. In response, in this present study, we studied the association between proximity of schools to roads and the academic achievement of the students in the Federal District, Brazil. We accessed academic achievement data at the student level. The data consist of 256 schools (all the public schools in the FD) and a total of 344,175 students (all the students enrolled in the public schools in the FD in 2017-2020). We analyzed the association between the length of all roads within buffers around schools and student-level academic performance using mixed-effects regression models. After adjustments for several covariates, the results of the primary analysis indicate that the presence of roads surrounding schools is negatively associated with student-level academic performance in the FD. This association varies significantly depending on the buffer size surrounding schools. We found that the highest effects occur in the first buffer, with 250 m. While in the first buffer we estimated that an increase of 1 km of length of roads around schools was associated with a statistically significant decrease of 0.011 (95%CI: 0.008; 0.013) points in students' grades (students' academic performance varies from 0 to 10), in the buffer of 1 km we found a decrease of 0.002 (95%CI: 0.002; 0.002) points in the student-level academic performance. Findings from our investigation provide support for the creation of effective health, educational and urban planning policies for local intervention in the FD. This is essential to improve the environmental quality surrounding schools to protect children from exposure to environmental hazards.

Emulating causal dose-response relations between air pollutants and mortality in the Medicare population.

BACKGROUND: Fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are major air pollutants that pose considerable threats to human health. However, what has been mostly missing in air pollution epidemiology is causal dose-response (D-R) relations between those exposures and mortality. Such causal D-R relations can provide profound implications in predicting health impact at a target level of air pollution concentration. METHODS: Using national Medicare cohort during 2000-2016, we simultaneously emulated causal D-R relations between chronic exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and all-cause mortality. To relax the contentious assumptions of inverse probability weighting for continuous exposures, including distributional form of the exposure and heteroscedasticity, we proposed a decile binning approach which divided each exposure into ten equal-sized groups by deciles, treated the lowest decile group as reference, and estimated the effects for the other groups. Binning continuous exposures also makes the inverse probability weights robust against outliers. RESULTS: Assuming the causal framework was valid, we found that higher levels of PM2.5, O3, and NO2 were causally associated with greater risk of mortality and that PM2.5 posed the greatest risk. For PM2.5, the relative risk (RR) of mortality monotonically increased from the 2nd (RR, 1.022; 95% confidence interval [CI], 1.018-1.025) to the 10th decile group (RR, 1.207; 95% CI, 1.203-1.210); for O3, the RR increased from the 2nd (RR, 1.050; 95% CI, 1.047-1.053) to the 9th decile group (RR, 1.107; 95% CI, 1.104-1.110); for NO2, the DR curve wiggled at low levels and started rising from the 6th (RR, 1.005; 95% CI, 1.002-1.018) till the highest decile group (RR, 1.024; 95% CI, 1.021-1.027). CONCLUSIONS: This study provided more robust evidence of the causal relations between air pollution exposures and mortality. The emulated causal D-R relations provided significant implications for reviewing the national air quality standards, as they inferred the number of potential early deaths prevented if air pollutants were reduced to specific levels; for example, lowering each air pollutant concentration from the 70th to 60th percentiles would prevent 65,935 early deaths per year.

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States.

In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O3 with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R2 (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R2, 0.93, and performance was weakest for the western mountainous regions (R2 of 0.86) and New England (R2 of 0.87). For the cross validation by season, our model had the best performance during summer with an R2 of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O3 over space and time, especially in health studies at an intra-urban scale.

Environmental and health impacts of transportation and land use scenarios in 2061.

We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76%-62% in suburbs; 55%-34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061. Emissions of nitrogen dioxide (NO2) and carbon dioxide (CO2) were estimated and NO2 used with dispersion modeling to estimate concentrations. Walking and Public Transit (PT) use and corresponding distances walked in Y2061 were >70% higher for the Ideal scenario vs the BAU, while car share and distances were <40% lower. NO2 levels were slightly lower in the Ideal scenario vs the BAU, but always higher in the urban core. Health impacts, summarized with disability adjusted life years (DALY), differed between urban and suburb areas but globally, the Ideal scenario reduced the impacts of the Y2061 BAU by 33% DALY. Percentages of car and PT trips were similar for the Y2031 and Y2061 BAU but kms travelled by car, CO2 and NO2 increased, due to increased populations. Drastic measures to decrease car share appear necessary to substantially reduce impacts of transportation.

Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space.

Numerous modeling approaches to estimate concentrations of PM2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM2.5 components, with an R2 under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R2 value increased to 0.62 and 0.59, respectively. These elements presented the highest R2 value from the hybrid model. The forest model presented the best performance, with R2 values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R2 value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R2 value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM2.5 constituents over space.

Short-term effects of particle gamma radiation activities on pulmonary function in COPD patients.

BACKGROUND: It is not known whether environmental gamma radiation measured in US cities has detectable adverse health effects. We assessed whether short-term exposure to gamma radiation emitted from ambient air particles [gamma particle activity (PRγ)] is associated with reduced pulmonary function in chronic obstructive pulmonary disease (COPD) patients. OBJECTIVE: We hypothesize that the inhalation of gamma radiation emitted from ambient air particles may be associated with reduced pulmonary function in individuals with COPD. METHODS: In 125 patients with COPD from Eastern Massachusetts who had up to 4 seasonal one-week assessments of particulate matter ≤2.5 µm (PM2.5), black carbon (BC), and sulfur followed by spirometry. The US EPA continuously monitors ambient gamma (γ) radiation including γ released from radionuclides attached to particulate matter that is recorded as 9 γ-energy spectra classes (i = 3-9) in counts per minute (CPMγ) in the Boston area (USA). We analyzed the associations between ambient and indoor PRγi (up to one week) and pre and post-bronchodilator (BD) forced expiratory volume in 1 s (FEV1) and with forced vital capacity (FVC) using mixed-effects regression models. We estimated indoor PRγi using the ratio of the indoor-to-outdoor sulfur in PM2.5 as a proxy for infiltration of ambient radionuclide-associated particles. RESULTS: Overall, exposures to ambient and indoor PRγi were associated with a similar decrease in pre- and post-BD FEV1 and FVC. For example, ambient PRγ3 exposure averaged from the day of pulmonary function testing through the previous 3 days [IQR of 55.1 counts per minute (CPMγ)] was associated with a decrease in pre-BD FEV1 of 21.0 ml (95%CI: -38.5 to -3.0 ml; p < 0.01) and pre-BD FVC of 27.5 ml [95% confidence interval (CI): -50.7 to -5.0 ml; p < 0.01] with similar effects adjusting for indoor and outdoor BC and PM2.5. CONCLUSION: Our results show that short-term ambient and indoor exposures to environmental gamma radiation associated with particulate matter are associated with reduced pre- and post-BD pulmonary function in patients with COPD.

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables.

BACKGROUND: Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors. OBJECTIVES: We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality. SEARCH METHODS: We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016. SELECTION CRITERIA: We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender. DATA COLLECTION AND ANALYSIS: We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies. MAIN RESULTS: We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and respiratory diseases ( Figure 1 , we observed a risk equal to 2.7% (95% confidence interval = 0.9%, 7.7%). Our results showed statistical significance in the test of moderators for all pollutants, suggesting that the modifier variables influence the average cardiorespiratory disease risk and may explain the varying effects of air pollution. CONCLUSIONS: Variables related to aspects of energy, transportation, and socioeconomic status may explain the varying effect size of the association between air pollution and cardiorespiratory diseases. Public Health Implications. Our study provides a transferable model to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention.

Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches.

Ground-level ozone (O3) is a powerful oxidizing agent and a harmful pollutant affecting human health, forests and crops. Estimating O3 exposure is a challenge because it exhibits complex spatiotemporal patterns. The aim in this study was to provide high-resolution maps (100 × 100 m) of O3 for the metropolitan area of Montreal, Canada. We assessed the kriging with external drift (KED) model to estimate O3 concentration by synoptic weather classes for 2010. We compared these results with ordinary kriging (OK), and a simple average of 12 monitoring stations. We also compared the estimates obtained for the 2010 summer with those from a Bayesian maximum entropy (BME) model reported in the literature (Adam-Poupart et al., 2014). The KED model with road and vegetation density as covariates showed good performance for all six synoptic classes (daily R2 estimates ranging from 0.77 to 0.92 and RMSE from 2.79 to 3.37 ppb). For the summer of 2010, the model using KED demonstrated the best results (R2 = 0.92; RMSE = 3.14 ppb), followed by the OK model (R2 = 0.85, RMSE = 4 ppb). Our results showed that errors appear to be substantially reduced with the KED model. This may increase our capacity of linking O3 levels to health problems by means of improved assessments of ambient exposures. However, future work integrating the temporal dependency in the data is needed to not overstate the performance of the KED model.