Incident dementia and long-term exposure to constituents of fine particle air pollution: A national cohort study in the United States.

Growing evidence suggests that fine particulate matter (PM2.5) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+), and soil dust (DUST). To investigate the impact of long-term exposure to PM2.5 constituents on incident all-cause dementia and Alzheimer's disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration-response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM2.5 and its major constituents. An interquartile range increase in PM2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM2.5 constituents, associations remained significant for BC, OM, SO42-, and NH4+ for both end points (even after adjustments of other constituents), among which BC and SO42- showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM2.5 is significantly associated with higher rates of incident dementia and AD and that SO42-, BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.

The Association of Air Pollution Exposure With Glucose and Lipid Levels: The Role of an Extreme Air Pollution Event Alongside 2 Decades of Moderate Exposure.

Extreme air pollution events and moderate exposure to fine particulate matter (PM2.5) are associated with increased cardiometabolic risk. The World Trade Center (WTC) Health Program general responder cohort includes responders to the WTC disaster. We investigated whether their exposure to this extreme air pollution event (2001) was associated with long-term metabolic outcomes, independently from the associations of intermediate-term PM2.5 exposure later in life (2004-2019). We included 22,447 cohort members with cholesterol (n = 96,155) and glucose (n = 81,599) laboratory results. Self-reported WTC exposure was derived from a questionnaire. PM2.5 exposure was derived from a satellite-based model. We observed an increase of 0.78 mg/dL (95% confidence interval (CI): 0.30, 1.26) in glucose and 0.67 mg/dL (95% CI: 1.00, 2.35) in cholesterol levels associated with an interquartile range increase in PM2.5 averaged 6 months before the study visit. Higher WTC-exposure categories were also associated with higher cholesterol (0.99 mg/dL, 95% CI: 0.30, 1.67, for intermediate exposure) and glucose (0.82 mg/dL, 95% CI: 0.22, 1.43, for high exposure) levels. Most associations were larger among people with diabetes. Extreme air pollution events and intermediate PM2.5 exposure have independent metabolic consequences. These exposures contributed to higher glucose and lipids levels among WTC responders, which may be translated into increased cardiovascular risk.

The flip side of the coin: Exploring the environmental and health impacts of proof-of-work cryptocurrency mining.

Blockchain technology, the backbone of cryptocurrency, is under scrutiny due to the environmental and health hazards linked to its energy-consuming Proof-of-Work (PoW) mining process. This review study provides a comprehensive analysis of the global health implications of PoW mining and cryptocurrency, with a focus on environmental sustainability and human health. The research utilized both traditional databases (PubMed and Web of Science) and additional primary sources. The study underscores the high energy consumption and carbon emissions of Bitcoin mining, despite ongoing debates comparing cryptocurrency to conventional finance. The review calls for immediate interventions, including the exploration of renewable energy sources and a transition from PoW to more sustainable consensus mechanisms. A case study on China's carbon policies highlights the necessity for effective regulatory measures. The findings reiterate the environmental and health risks associated with PoW cryptocurrency mining, including its resource-intensive procedures, reliance on non-renewable energy, and emission of air pollutants. The review emphasizes the urgent need for global regulation and a transition to more sustainable consensus mechanisms, such as Proof-of-Stake (PoS), to reduce the industry's impact on climate and human health.

Ambient PM2.5 and specific sources increase inflammatory cytokine responses to stimulators and reduce sensitivity to inhibitors.

Ambient exposure to fine particulate matter (PM2.5) is associated with increased morbidity and mortality from multiple diseases. Recent observations suggest the hypothesis that trained immunity contributes to these risks, by demonstrating that ambient PM2.5 sensitizes innate immune cells to mount larger inflammatory response to subsequent bacterial stimuli. However, little is known about how general and durable this sensitization phenomenon is, and whether specific sources of PM2.5 are responsible. Here we consider these issues in a longitudinal study of children. The sample consisted of 277 children (mean age 13.92 years; 63.8% female; 38.4% Black; 32.2% Latinx) who completed baseline visits and were re-assessed two years later. Fasting whole blood was ex vivo incubated with 4 stimulating agents reflecting microbial and sterile triggers of inflammation, and with 2 inhibitory agents, followed by assays for IL-1ß, IL-6, IL-8, and TNF-α. Blood also was assayed for 6 circulating biomarkers of low-grade inflammation: C-reactive protein, interleukin-6, -8, and -10, tumor necrosis factor-α, and soluble urokinase-type plasminogen activator receptor. Using machine learning, levels of 15 p.m.2.5 constituents were estimated for a 50 m grid around children's homes. Models were adjusted for age, sex, race, pubertal status, and household income. In cross-sectional analyses, higher neighborhood PM2.5 was associated with larger cytokine responses to the four stimulating agents. These associations were strongest for constituents released by motor vehicles and soil/crustal dust. In longitudinal analyses, residential PM2.5 was associated with declining sensitivity to inhibitory agents; this pattern was strongest for constituents from fuel/biomass combustion and motor vehicles. By contrast, PM2.5 constituents were not associated with the circulating biomarkers of low-grade inflammation. Overall, these findings suggest the possibility of a trained immunity scenario, where PM2.5 heightens inflammatory cytokine responses to multiple stimulators, and dampens sensitivity to inhibitors which counter-regulate these responses.

Long-term exposure to PM2.5 species and all-cause mortality among Medicare patients using mixtures analyses.

BACKGROUND: The relationship between long-term exposure to PM2.5 and mortality is well-established; however, the role of individual species is less understood. OBJECTIVES: In this study, we assess the overall effect of long-term exposure to PM2.5 as a mixture of species and identify the most harmful of those species while controlling for the others. METHODS: We looked at changes in mortality among Medicare participants 65 years of age or older from 2000 to 2018 in response to changes in annual levels of 15 PM2.5 components, namely: organic carbon, elemental carbon, nickel, lead, zinc, sulfate, potassium, vanadium, nitrate, silicon, copper, iron, ammonium, calcium, and bromine. Data on exposure were derived from high-resolution, spatio-temporal models which were then aggregated to ZIP code. We used the rate of deaths in each ZIP code per year as the outcome of interest. Covariates included demographic, temperature, socioeconomic, and access-to-care variables. We used a mixtures approach, a weighted quantile sum, to analyze the joint effects of PM2.5 species on mortality. We further looked at the effects of the components when PM2.5 mass levels were at concentrations below 8 µg/m3, and effect modification by sex, race, Medicaid status, and Census division. RESULTS: We found that for each decile increase in the levels of the PM2.5 mixture, the rate of all-cause mortality increased by 1.4% (95% CI: 1.3%-1.4%), the rate of cardiovascular mortality increased by 2.1% (95% CI: 2.0%-2.2%), and the rate of respiratory mortality increased by 1.7% (95% CI: 1.5%-1.9%). These effects estimates remained significant and slightly higher when we restricted to lower concentrations. The highest weights for harmful effects were due to organic carbon, nickel, zinc, sulfate, and vanadium. CONCLUSIONS: Long-term exposure to PM2.5 species, as a mixture, increased the risk of all-cause, cardiovascular, and respiratory mortality.

National Cohort Study of Long-Term Exposure to PM2.5 Components and Mortality in Medicare American Older Adults.

There is increasing evidence linking long-term fine particulate matter (PM2.5) exposure to negative health effects. However, the relative influence of each component of PM2.5 on health risk is poorly understood. In a cohort study in the contiguous United States between 2000 and 2017, we examined the effect of long-term exposure to PM2.5 main components and all-cause mortality in older adults who had to be at least 65 years old and enrolled in Medicare. We estimated the yearly mean concentrations of six key PM2.5 compounds, including black carbon (BC), organic matter (OM), soil dust (DUST), nitrate (NO3-), sulfate (SO42-), and ammonium (NH4+), using two independently sourced well-validated prediction models. We applied Cox proportional hazard models to evaluate the hazard ratios for mortality and penalized splines for assessing potential nonlinear concentration-response associations. Results suggested that increased exposure to PM2.5 mass and its six main constituents were significantly linked to elevated all-cause mortality. All components showed linear concentration-response relationships in the low exposure concentration ranges. Our research indicates that long-term exposure to PM2.5 mass and its essential compounds are strongly connected to increased mortality risk. Reductions of fossil fuel burning may yield significant air quality and public health benefit.

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.

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study.

BACKGROUND: Fine particulate matter (PM2.5) is a well-recognized risk factor for premature death. However, evidence on which PM2.5 components are most relevant is unclear. METHODS: We evaluated the associations between mortality and long-term exposure to eight PM2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM2.5 mass. RESULTS: Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality. CONCLUSIONS: We present novel results of the relevance of different PM2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study.

BACKGROUND: Epigenome-wide association studies of ambient fine particulate matter (PM2.5) have been reported. However, few have examined PM2.5 components (PMCs) and sources or included repeated measures. The lack of high-resolution exposure measurements is the key limitation. We hypothesized that significant changes in DNA methylation might vary by PMCs and the sources. METHODS: We predicted the annual average of 14 PMCs using novel high-resolution exposure models across the contiguous U.S., between 2000-2018. The resolution was 50 m × 50 m in the Greater Boston Area. We also identified PM2.5 sources using positive matrix factorization. We repeatedly collected blood samples and measured leukocyte DNAm with the Illumina HumanMethylation450K BeadChip in the Normative Aging Study. We then used median regression with subject-specific intercepts to estimate the associations between long-term (one-year) exposure to PMCs / PM2.5 sources and DNA methylation at individual cytosine-phosphate-guanine CpG sites. Significant probes were identified by the number of independent degrees of freedom approach, using the number of principal components explaining > 95% of the variation of the DNA methylation data. We also performed regional and pathway analyses to identify significant regions and pathways. RESULTS: We included 669 men with 1,178 visits between 2000-2013. The subjects had a mean age of 75 years. The identified probes, regions, and pathways varied by PMCs and their sources. For example, iron was associated with 6 probes and 6 regions, whereas nitrate was associated with 15 probes and 3 regions. The identified pathways from biomass burning, coal burning, and heavy fuel oil combustion sources were associated with cancer, inflammation, and cardiovascular diseases, whereas there were no pathways associated with all traffic. CONCLUSIONS: Our findings showed that the effects of PM2.5 on DNAm varied by its PMCs and sources.

Socioeconomic and racial disparities in source-apportioned PM2.5 levels across urban areas in the contiguous US, 2010.

Fine particulate matter (PM2.5) air pollution exposure is associated with short and long-term health effects. Several studies found differences in PM2.5 exposure associated with neighborhood racial and socioeconomic composition. However, most focused on total PM2.5 mass rather than its chemical components and their sources. In this study, we describe the ZIP code characteristics that drive the disparities in exposure to PM2.5 chemical components attributed to source categories both nationally and regionally. We obtained annual mean predictions of PM2.5 and fourteen of its chemical components from spatiotemporal models and socioeconomic and racial predictor variables from the 2010 US Census, and the American Community Survey 5-year estimates. We used non-negative matrix factorization to attribute the chemical components to five source categories. We fit generalized nonlinear models to assess the associations between the neighborhood predictors and each PM2.5 source category in urban areas in the United States in 2010 (n=25,790 zip codes). We observed higher PM2.5 levels in ZIP codes with higher proportions of Black individuals and lower socioeconomic status. Racial exposure disparities were mainly attributed to Heavy Fuel, Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Economic disparities were mainly attributed to Soil and Crustal Dust, Heavy Fuel Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Upon further analysis through stratifying by regions within the United States, we found that the associations between ZIP code characteristics and source-attributed PM2.5 levels were generally greater in Western states. In conclusion, racial, socioeconomic, and geographic inequalities in exposure to PM2.5 and its components are driven by systematic differences in component sources that can inform air quality improvement strategies.

Long-term Air Pollution Exposure and Pneumonia-related Mortality in a Large Pooled European Cohort.

Rationale: Ambient air pollution exposure has been linked to mortality from chronic cardiorespiratory diseases, while evidence on respiratory infections remains more limited. Objectives: We examined the association between long-term exposure to air pollution and pneumonia-related mortality in adults in a pool of eight European cohorts. Methods: Within the multicenter project ELAPSE (Effects of Low-Level Air Pollution: A Study in Europe), we pooled data from eight cohorts among six European countries. Annual mean residential concentrations in 2010 for fine particulate matter, nitrogen dioxide (NO2), black carbon (BC), and ozone were estimated using Europe-wide hybrid land-use regression models. We applied stratified Cox proportional hazard models to investigate the associations between air pollution and pneumonia, influenza, and acute lower respiratory infections (ALRI) mortality. Measurements and Main Results: Of 325,367 participants, 712 died from pneumonia and influenza combined, 682 from pneumonia, and 695 from ALRI during a mean follow-up of 19.5 years. NO2 and BC were associated with 10-12% increases in pneumonia and influenza combined mortality, but 95% confidence intervals included unity (hazard ratios, 1.12 [0.99-1.26] per 10 µg/m3 for NO2; 1.10 [0.97-1.24] per 0.5 10-5m-1 for BC). Associations with pneumonia and ALRI mortality were almost identical. We detected effect modification suggesting stronger associations with NO2 or BC in overweight, employed, or currently smoking participants compared with normal weight, unemployed, or nonsmoking participants. Conclusions: Long-term exposure to combustion-related air pollutants NO2 and BC may be associated with mortality from lower respiratory infections, but larger studies are needed to estimate these associations more precisely.

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.

Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration.

BACKGROUND: Numerous studies have documented PM2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM2.5 components. The lack of exposure measurements and high correlation among different PM2.5 components are two limitations. METHODS: We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO42-, NO3-, NH4+, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM2.5 components. Total PM2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models. RESULTS: Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM2.5 mass analysis, and single component associations. CONCLUSION: We have found positive associations between the mixture of 15 PM2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.

Long-term air pollution and road traffic noise exposure and COPD: the Danish Nurse Cohort.

BACKGROUND: While air pollution has been linked to the development of chronic obstructive pulmonary disease (COPD), evidence on the role of environmental noise is just emerging. We examined the associations of long-term exposure to air pollution and road traffic noise with COPD incidence. METHODS: We defined COPD incidence for 24 538 female nurses from the Danish Nurse Cohort (age >44 years) as the first hospital contact between baseline (1993 or 1999) and 2015. We estimated residential annual mean concentrations of particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) since 1990 and nitrogen dioxide (NO2) since 1970 using the Danish Eulerian Hemispheric Model/Urban Background Model/Air Geographic Information System modelling system, and road traffic noise (Lden) since 1970 using the Nord2000 model. Time-varying Cox regression models were applied to assess the associations of air pollution and road traffic noise with COPD incidence. RESULTS: 977 nurses developed COPD during a mean of 18.6 years' follow-up. We observed associations with COPD for all three exposures with HRs and 95% CIs of 1.19 (1.01-1.41) per 6.26 µg·m-3 for PM2.5, 1.13 (1.05-1.20) per 8.19 µg·m-3 for NO2 and 1.15 (1.06-1.25) per 10 dB for Lden. Associations with NO2 and Lden attenuated slightly after mutual adjustment, but were robust to adjustment for PM2.5. Associations with PM2.5 were attenuated to null after adjustment for either NO2 or Lden. No potential interaction effect was observed between air pollutants and noise. CONCLUSION: Long-term exposure to air pollution, especially traffic-related NO2, and to road traffic noise were independently associated with COPD.

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.

Outdoor light at night and breast cancer incidence in the Danish Nurse Cohort.

BACKGROUND: Knowledge of the role of melatonin, xenograft experiments, and epidemiological studies suggests that exposure to light at night (LAN) may disturb circadian rhythms, possibly increasing the risk of developing breast cancer. OBJECTIVES: We examined the association between residential outdoor LAN and the incidence of breast cancer: overall and subtypes classified by estrogen (ER) and progesterone (PR) receptor status. METHODS: We used data on 16,941 nurses from the Danish Nurse Cohort who were followed-up from the cohort baseline in 1993 or 1999 through 2012 in the Danish Cancer Registry for breast cancer incidence and the Danish Breast Cancer Cooperative Group for breast cancer ER and PR status. LAN exposure data were obtained from the U.S. Defense Meteorological Satellite Program (DMSP) available for 1996, 1999, 2000, 2003, 2004, 2006, and 2010 in nW/cm2/sr unit, and assigned to the study participants' residence addresses during the follow-up. Time-varying Cox regression models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between LAN and breast cancer, adjusting for individual characteristics, road traffic noise, and air pollution. RESULTS: Of 16,941 nurses, 745 developed breast cancer in total during 320,289 person-years of follow-up. We found no association between exposure to LAN and overall breast cancer. In the fully adjusted models, HRs for the highest (65.8-446.4 nW/cm2/sr) and medium (22.0-65.7 nW/cm2/sr) LAN tertiles were 0.97 (95% CI: 0.77, 1.23) and 1.09 (95% CI: 0.90, 1.31), respectively, compared to the lowest tertile of LAN exposure (0-21.9 nW/cm2/sr). We found a suggestive association between LAN and ER-breast cancer. CONCLUSION: This large cohort study of Danish female nurses suggests weak evidence of the association between LAN and breast cancer incidence.

Long-term exposure to road traffic noise and stroke incidence: a Danish Nurse Cohort study.

BACKGROUND: Road traffic noise has been linked to increased risk of ischemic heart disease, yet evidence on stroke shows mixed results. We examine the association between long-term exposure to road traffic noise and incidence of stroke, overall and by subtype (ischemic or hemorrhagic), after adjustment for air pollution. METHODS: Twenty-five thousand six hundred and sixty female nurses from the Danish Nurse Cohort recruited in 1993 or 1999 were followed for stroke-related first-ever hospital contact until December 31st, 2014. Full residential address histories since 1970 were obtained and annual means of road traffic noise (Lden [dB]) and air pollutants (particulate matter with diameter < 2.5 µm and < 10 µm [PM2.5 and PM10], nitrogen dioxide [NO2], nitrogen oxides [NOx]) were determined using validated models. Time-varying Cox regression models were used to estimate hazard ratios (HR) (95% confidence intervals [CI]) for the associations of one-, three-, and 23-year running means of Lden preceding stroke (all, ischemic or hemorrhagic), adjusting for stroke risk factors and air pollutants. The World Health Organization and the Danish government's maximum exposure recommendations of 53 and 58 dB, respectively, were explored as potential Lden thresholds. RESULTS: Of 25,660 nurses, 1237 developed their first stroke (1089 ischemic, 148 hemorrhagic) during 16 years mean follow-up. For associations between a 1-year mean of Lden and overall stroke incidence, the estimated HR (95% CI) in the fully adjusted model was 1.06 (0.98-1.14) per 10 dB, which attenuated to 1.01 (0.93-1.09) and 1.00 (0.91-1.09) in models further adjusted for PM2.5 or NO2, respectively. Associations for other exposure periods or separately for ischemic or hemorrhagic stroke were similar. There was no evidence of a threshold association between Lden and stroke. CONCLUSIONS: Long-term exposure to road traffic noise was suggestively positively associated with the risk of overall stroke, although not after adjusting for air pollution.

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.