Research Report (Health Effects Institute)[JOURNAL]
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200 papers
Assessing the National Health, Education, and Air Quality Benefits of the United States Environmental Protection Agency's School Bus Rebate Program: A Randomized Controlled Trial Design.
Adar SD, Pedde M, Hirth R … +1 more , Szpiro A
Res Rep Health Eff Inst · 2024 Oct · PMID 39582384
INTRODUCTION: Approximately 25 million children ride buses to school in the United States. While school buses remain the safest school transport from a traffic accident perspective, older buses can expose students to hig...
INTRODUCTION: Approximately 25 million children ride buses to school in the United States. While school buses remain the safest school transport from a traffic accident perspective, older buses can expose students to high levels of diesel exhaust. These exposures can adversely affect health, which might cause missed school days and reduced learning. To hasten the transition to cleaner, lower-emission vehicles, the US Environmental Protection Agency's (US EPA) ongoing School Bus Rebate Program randomly allocated over $27 million to replace older, higher-emission school buses with cleaner, lower-emission alternatives between 2012 and 2017. Here, we evaluated the effectiveness of this national program. METHODS: Leveraging the randomized allocation of rebate funding, we assessed the impacts of the US EPA's 2012-2017 School Bus Rebate Programs on attendance, educational achievement, emergency department (ED) visits for respiratory causes among children in Medicaid, and community air pollution levels. We analyzed all districts linked to applications with complete data using modified intention-to-treat (ITT) modeling for randomized controlled trials, comparing changes in school-district levels of each outcome, after versus before each lottery year, by funding selection status. We also examined the heterogeneity of effects by model years of the replaced buses and by quartiles of estimated ridership on applicant buses. RESULTS: Of the 3,019 applications that met our inclusion criteria, 406 were randomly selected for funding. The districts that were linked to these applications were similar in terms of size, demographic makeup, funding requests, and socioeconomic status to the districts linked to applications that were not selected for funding. The districts that were linked to applications selected for funding that replaced the oldest buses had improvements in attendance, educational performance, and ambient particulate matter ≤2.5 µm aerodynamic diameter (PM) concentrations in the year after the lottery, compared with districts linked to applications that were not selected for funding. Districts that replaced pre-1990 model year buses had the largest gains, with 0.45 percentage points (pp) and 95% confidence interval (CI) of 0.26 to 0.65 higher attendance (equivalent to 45 additional students attending school each day in an average-size school district of 10,000 students), 0.06 standard deviation (SD) higher reading and language arts (RLA) (95% CI: 0.05 to 0.07), 0.03 SD higher math test scores (0.01 to 0.04), and -1.0 µg/m (-1.5 to -0.5) lower ambient PM concentrations compared with districts not selected for funding. The replacement of model year 2000 and newer buses showed almost no effect on these outcomes. Districts replacing the oldest buses had suggestively higher ED visit rates, but these findings were not statistically distinguishable from no association and were sensitive to differing model specifications. UNLABELLED: Based on the attendance improvements observed alone, we estimate that the total investment of $27 million by the US EPA for the 2012-2017 lotteries may have resulted in $350 million of benefits per year, although these benefits could not be distinguished from no benefit. Further investment of funds to replace all school buses manufactured before the year 1990 could lead to an additional $400 million of economic benefits per year and replacing all school buses manufactured before the year 2000 could lead to an additional $1.3 billion of economic benefits per year. CONCLUSIONS: We conclude that the US EPA's School Bus Rebate Program investments to remove very old buses from the fleets have positively affected communities.
Birth Cohort Studies of Long-Term Exposure to Ambient Air Pollution in Early Life and Development of Asthma in Children and Adolescents from Denmark.
Pedersen M, Liu S, Andersen ZJ … +12 more , Nybo AA, Brandt J, Budtz-Jørgensen E, Bønnelykke K, Frohn LM, Ketzel M, Khan J, Tingskov PC, Stayner LT, Zhang J, Brunekreef B, Loft S
Res Rep Health Eff Inst · 2024 Sep · PMID 39469971
INTRODUCTION: Exposure to ambient air pollution from combustion-source emissions contributes to the prevalence of asthma, but the role of early-life exposure in asthma development is not well understood. The objective wa...
INTRODUCTION: Exposure to ambient air pollution from combustion-source emissions contributes to the prevalence of asthma, but the role of early-life exposure in asthma development is not well understood. The objective was to examine the effects of early-life exposure to multiple specific ambient air pollutants on incidence and prevalence of asthma and to determine the mechanistic basis for these effects. METHODS: The study included all live-born singletons in Denmark during 1998-2016 (N = 1,060,154), participants in the Danish National Birth Cohort (DNBC, N = 22,084), and participants in the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC, N = 803). We modeled the concentrations of particulate matter ≤2.5 and ≤10 μm in aerodynamic diameter (PM and PM), PM-related elemental carbon (EC), organic carbon (OC), sulfate (SO), nitrate (NO), ammonium (NH), secondary organic aerosols (SOA), and sea salt as well as nitrogen dioxide (NO), nitrogen oxides (NO), sulfur dioxide (SO), and ozone (O) - from all sources. Prenatal and postnatal time-weighted mean exposures were calculated for all residential addresses. UNLABELLED: We defined asthma incidence as the first registered asthma diagnosis for all and used parental recall at child aged 7 to determine the prevalence of doctor-diagnosed asthma ever and active asthma for the DNBC participants. For the COPSAC participants, we analyzed inflammatory markers in blood collected at 6 months of age; at 6 years of age, we analyzed nasal epithelial deoxyribonucleic acid (DNA) methylation, gene expression, immune mediators, and forced expiratory volume in 1 second (FEV). UNLABELLED: Cox proportional hazard models were fitted with fixed prenatal means and time-varying running annual means of a year before the event for the postnatal follow-up period for asthma incidence. Logistic regression models with cluster-robust standard errors and generalized estimating equations for dependence between women being included more than once were used for asthma prevalence. Mixed-effect linear regression models with random intercept for cohort were used to examine changes in lung function, and linear regression models were used to examine changes in biomarkers. RESULTS: The prenatal mean and interquartile range (IQR) concentrations of PM and NO were 10.5 (2.4) and 17.5 (8.7) μg/m. In the nationwide study the risk of asthma incidence increased with increasing prenatal exposure to all pollutants except for O and sea salt. An IQR increase in prenatal exposure was associated with an adjusted hazard ratio (HR) and 95% confidence interval (CI) of 1.06 (95% CI: 1.04-1.08) for PM and 1.04 (1.02-1.05) for NO. The corresponding estimates for postnatal exposures were 1.08 (1.05-1.10) and 1.02 (1.01-1.04), respectively. UNLABELLED: In the DNBC participants, the asthma incidence results from models further adjusted with cohort-specific covariates were similar to models adjusted for register-based covariates only. Prenatal exposure to PM, PM, NO, NO, EC, SO, and sea salt were weakly associated with elevated risk for asthma incidence. There was no evidence of associations with asthma prevalence. UNLABELLED: For the COPSAC children, an IQR of PM and of NH was each associated with a 2%-3% (95% CI: 1%-5%) reduction in mean FEV, consistently for prenatal and postnatal exposures. Prenatal exposure to PM and NO was associated with immunological changes in blood and the airways but not with DNA methylation or gene expression changes. CONCLUSIONS: The results of these studies strengthen the evidence that long-term exposure to ambient air pollution contributes to the development of asthma in early life through an altered immune profile, even at these relatively low concentrations.
Air Pollution in Relation to COVID-19 Morbidity and Mortality: A Large Population-Based Cohort Study in Catalonia, Spain (COVAIR-CAT).
Tonne C, Ranzani O, Alari A … +13 more , Ballester J, Basagaña X, Chaccour C, Dadvand P, Duarte T, Foraster M, Milà C, Nieuwenhuijsen MJ, Olmos S, Rico A, Sunyer J, Valentín A, Vivanco R
Res Rep Health Eff Inst · 2024 Sep · PMID 39468856
INTRODUCTION: Evidence from epidemiological studies based on individual-level data indicates that air pollution may be associated with coronavirus disease 2019 (COVID-19) severity. We aimed to test whether (1) long-term...
INTRODUCTION: Evidence from epidemiological studies based on individual-level data indicates that air pollution may be associated with coronavirus disease 2019 (COVID-19) severity. We aimed to test whether (1) long-term exposure to air pollution is associated with COVID-19-related hospital admission or mortality in the general population; (2) short-term exposure to air pollution is associated with COVID-19-related hospital admission following COVID-19 diagnosis; (3) there are vulnerable population subgroups; and (4) the influence of long-term air pollution exposure on COVID-19-related hospital admissions differed from that for other respiratory infections. METHODS: We constructed a cohort covering nearly the full population of Catalonia through registry linkage, with follow- up from January 1, 2015, to December 31, 2020. Exposures at residential addresses were estimated using newly developed spatiotemporal models of nitrogen dioxide (NO), particulate matter ≤2.5 μm in aerodynamic diameter (PM), particulate matter ≤10 μm in aerodynamic diameter (PM), and maximum 8-hr-average ozone (O) at a spatial resolution of 250 m for the period 2018-2020. RESULTS: The general population cohort included 4,660,502 individuals; in 2020 there were 340,608 COVID-19 diagnoses, 47,174 COVID-19-related hospital admissions, and 10,001 COVID-19 deaths. Mean (standard deviation) annual exposures were 26.2 (10.3) μg/m for NO, 13.8 (2.2) μg/m for PM, and 91.6 (8.2) μg/m for O. In Aim 1, an increase of 16.1 μg/m NO was associated with a 25% (95% confidence interval [CI]: 22%-29%) increase in hospitalizations and an 18% (10%-27%) increase in deaths. In Aim 2, cumulative air pollution exposure over the previous 7 days was positively associated with COVID-19-related hospital admission in the second pandemic wave (June 20 to December 31, 2020). Associations of exposure were driven by exposure on the day of the hospital admission (lag0). Associations between short-term exposure to air pollution and COVID-19-related hospital admission were similar in all population subgroups. In Aim 3, individuals with lower individual- and area-level socioeconomic status (SES) were identified as particularly vulnerable to the effects of long-term exposure to NO and PM on COVID-19-related hospital admission. In Aim 4, long-term exposure to air pollution was associated with hospital admission for influenza and pneumonia: (6%; 95% CI: 2-11 per 16.4-μg/m NO and 5%; 1-8 per 2.6-μg/m PM) as well as for all lower respiratory infections (LRIs) (18%; 14-22 per 16.4-μg/m NO and 14%; 11-17 per 2.6-μg/m PM) before the COVID-19 pandemic. Associations for COVID-19-related hospital admission were larger than those for influenza or pneumonia for NO, PM, and O when adjusted for NO. CONCLUSIONS: Linkage across several registries allowed the construction of a large population-based cohort, tracking COVID-19 cases from primary care and testing data to hospital admissions, and death. Long- and short-term exposure to ambient air pollution were positively associated with severe COVID-19 events. The effects of long-term air pollution exposure on COVID-19 severity were greater among those with lower individual- and area-level SES.
Estimating Model-Based Marginal Societal Health Benefits of Air Pollution Emission Reductions in the United States and Canada.
Hakami A, Zhao S, Soltanzadeh M … +7 more , Vasilakos P, Alhusban A, Oztaner B, Fann N, Chang H, Krupnick A, Russell T
Res Rep Health Eff Inst · 2024 Aug · PMID 39397785
We developed spatially detailed source-impact estimates of population health burden measures of air pollution for the United States and Canada by quantifying sources-receptor relationships using the benefit-per-ton (BPT)...
We developed spatially detailed source-impact estimates of population health burden measures of air pollution for the United States and Canada by quantifying sources-receptor relationships using the benefit-per-ton (BPT) metric. We calculated BPTs as the valuations of premature mortality counts due to fine particulate matter (PM; particulate matter ≤2.5 μm in aerodynamic diameter) exposure resulting from emissions of one ton of a given pollutant. Our BPT estimates, while accounting for a large portion of societal impact, do not include morbidity, acute exposure mortality, or chronic exposure mortality due to exposure to other pollutants such as ozone. The adjoint version of a widely used chemical transport model (CTM) allowed us to calculate location-specific BPTs at a high level of granularity for source-impact characterization. Location-specific BPTs provides a means for exploiting the disparities in source impact of emissions at different locations. For instance, estimated BPTs show that 20% of primary PM and ammonia emissions in the United States account for approximately 50% and 60% of the burden of each species, respectively, for an estimated burden of $370B USD. Similarly, 10% of the most harmful emissions of primary PM and ammonia emissions in Canada account for approximately 60% and 50% of their burden, respectively. By delineating differences and disparities in source impacts, adjoint-based BPT provides a direct means for prioritizing and targeting emissions that are most damaging. Sensitivity analyses evaluated the impact of our assumptions and study design on the estimated BPTs. The choice of concentration-response function had a substantial impact on the estimated BPTs and is likely to constitute the largest source of uncertainty in those estimates. Our method for constructing annual BPT estimates based on episodic simulations introduces low uncertainty, while uncertainties associated with the spatial resolution of the CTM were evaluated to be of medium importance. Finally, while recognizing that the use of BPTs entails an implied assumption of linearity, we show that BPTs for primary PM emissions are stable across different emission levels in North America. While BPTs for precursors of secondary inorganic aerosols showed sensitivity to emission levels in the past, we found that those have stabilized with lower emissions and pollutant concentrations in the North American atmosphere. We used BPTs to provide location-specific and sectoral estimates for the cobenefits of reducing carbon dioxide emissions from a range of combustion sources. Cobenefit estimates rely heavily on the emission characteristics of the sector and therefore exhibit more pronounced sectoral fingerprints than do BPTs. We provide cobenefit estimates for various subsectors of on-road transportation, thermal electricity generation, and off-road engines. Off-road engines and various heavy-duty diesel vehicles had the largest cobenefits, which in most urban locations far exceeded estimates of the social cost of carbon. Based on our cobenefit estimations, we also provide per-vehicle burden estimates for different vintages of vehicle subsectors such as transit buses and short-haul trucks in major US cities.
Long-Term Exposure to Outdoor Ultrafine Particles and Black Carbon and Effects on Mortality in Montreal and Toronto, Canada.
Weichenthal S, Lloyd M, Ganji A … +12 more , Simon L, Xu J, Venuta A, Schmidt A, Apte J, Chen H, Lavigne E, Villeneuve P, Olaniyan T, Tjepkema M, Burnett RT, Hatzopoulou M
Res Rep Health Eff Inst · 2024 Jul · PMID 39392111
INTRODUCTION: Numerous studies support an important relationship between long-term exposure to outdoor fine particulate air pollution (PM) and both nonaccidental and cause-specific mortality. Less is known about the long...
INTRODUCTION: Numerous studies support an important relationship between long-term exposure to outdoor fine particulate air pollution (PM) and both nonaccidental and cause-specific mortality. Less is known about the long-term health consequences of other traffic pollutants, including ultrafine particles (UFPs, <0.1 μm) and black carbon (BC), which are often present at elevated concentrations in urban areas but are not currently regulated. Knowledge is lacking largely because these pollutants generally are not monitored by governments and vary greatly over small spatial scales, hindering the evaluation of long-term exposures in population-based studies. METHODS: We aimed to estimate associations between long-term exposures to outdoor UFPs and BC and nonaccidental and cause-specific mortality in Canada's two largest cities, Montreal and Toronto. We considered several approaches to exposure assessment: (1) land use regression (LUR) models based on large-scale year-long mobile monitoring campaigns combined with detailed land use and traffic information; (2) machine learning (i.e., convolutional neural networks [CNN]) models trained by combining mobile monitoring data with aerial images; and (3) the combined use of these two approaches. We also examined exposure models with and without backcasting based on historical trends in vehicle emissions (to capture potential trends in pollutant concentrations over time) and with and without accounting for neighborhood-level mobility patterns (based on travel demand surveys). These exposure models were linked to members of the Canadian Census Health and Environment Cohorts (CanCHEC) residing in Montreal or Toronto (including census years 1991, 1996, 2001, and 2006) with mortality follow-up from 2001 (or cohort entry for the 2006 cohort) to 2016. Cox proportional hazard models were used to estimate associations between long-term exposures to outdoor UFPs and BC, adjusting for sociodemographic factors and co-pollutants identified as potential confounding factors. Concentration-response relationships for outdoor UFPs and BC were also examined for nonaccidental and cause-specific mortality using smoothing splines. RESULTS: Our cohort study included approximately 1.5 million people with 174,200 nonaccidental deaths observed during the follow-up period. Combined LUR and machine learning model predictions performed slightly better than LUR models alone and were used as the main exposure models in all epidemiological analyses. Long-term exposures to outdoor UFP number concentrations were consistently positively associated with nonaccidental and cause-specific mortality. Importantly, hazard ratios (HRs) for outdoor UFP number concentrations were sensitive to adjustment for UFP size: UFP size was inversely related to number concentrations and independently associated with mortality, resulting in underestimation of mortality risk for outdoor UFP number concentrations when UFP size was excluded. HRs for outdoor UFP number concentrations were robust to backcasting and mobility weighting but varied slightly in analyses using LUR and machine learning models alone, with stronger associations typically observed for the machine learning models. Associations between outdoor BC concentrations and mortality were generally weak or null, but a positive association was observed for cardiovascular mortality. CONCLUSIONS: Outdoor UFP number concentrations were consistently associated with increased risks of nonaccidental and cause-specific mortality in Montreal and Toronto. Our results suggest that UFP size should be considered in epidemiological analyses of outdoor UFP number concentrations, as excluding size can lead to an underestimation of health risks. Our results suggest that outdoor UFP number concentrations are positively associated with mortality independent of other outdoor air pollutants, including PM mass concentrations and oxidant gases (i.e., nitrogen dioxide [NO] and ozone [O]). As outdoor UFPs are currently unregulated, interventions targeting these pollutants could significantly affect population health.
Scalable Multipollutant Exposure Assessment Using Routine Mobile Monitoring Platforms.
Apte JA, Chambliss SE, Messier KP … +4 more , Gani S, Upadhya AR, Kushwaha M, Sreekanth V
Res Rep Health Eff Inst · 2024 Jan · PMID 38482936
INTRODUCTION: The absence of spatially resolved air pollution measurements remains a major gap in health studies of air pollution, especially in disadvantaged communities in the United States and lower-income countries....
INTRODUCTION: The absence of spatially resolved air pollution measurements remains a major gap in health studies of air pollution, especially in disadvantaged communities in the United States and lower-income countries. Many urban air pollutants vary over short spatial scales, owing to unevenly distributed emissions sources, rapid dilution away from sources, and physicochemical transformations. Primary air pollutants from traffic have especially sharp spatial gradients, which lead to disparate effects on human health for populations who live near air pollution sources, with important consequences for environmental justice. Conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize these heterogeneous human exposures and localized pollution hotspots. In this study, we assessed the potential for repeated mobile air quality measurements to provide a scalable approach to developing high-resolution pollution exposure estimates. We assessed the utility and validity of mobile monitoring as an exposure assessment technique, compared the insights from this measurement approach against other widely accepted methods, and investigated the potential for mobile monitoring to be scaled up in the United States and low- and middle-income countries. METHODS: Our study had five key analysis modules (M1- M5). The core approach of the study revolved around repeated mobile monitoring to develop time-stable estimates of central-tendency air pollution exposures at high spatial resolution. All mobile monitoring campaigns in California were completed prior to beginning this study. In analysis M1, we conducted an intensive summerlong sampling campaign in West Oakland, California. In M2, we explored the dynamics of ultrafine particles (UFPs) in the San Francisco Bay Area. In analysis M3, we scaled up our multipollutant mobile monitoring approach to 13 different neighborhoods with ~450,000 inhabitants to evaluate within- and between-neighborhood heterogeneity. In M4, we evaluated the coupling of mobile monitoring with land use regression models to estimate intraurban variation. Finally, in M5, we reproduced our mobile monitoring approach in a pilot study in Bangalore, India. RESULTS: For M1, we found a moderate-to-high concordance in the time-averaged spatial patterns between mobile and fixed-site observations of black carbon (BC) in West Oakland. The dense fixed-site monitor network added substantial insight about spatial patterns and local hotspots. For M2, a seasonal divergence in the relationship between UFPs and other traffic-related air pollutants was evident from both approaches. In M3, we found distinct spatial distribution of exposures across the Bay Area for primary and secondary air pollutants. We found substantially unequal exposures by race and ethnicity, mostly driven by between-neighborhood concentration differences. In M4, we demonstrated that empirical modeling via land use regression could dramatically reduce the data requirements for building high-resolution air quality maps. In M5, we developed exposure maps of BC and UFPs in a Bangalore neighborhood and demonstrated that the measurement technique worked successfully. CONCLUSIONS: We demonstrated that mobile monitoring can produce insights about air pollution exposure that are externally validated against multiple other analysis approaches, while adding complementary information about spatial patterns and exposure heterogeneity and inequity that is not readily obtained with other methods.
Chemical and Cellular Formation of Reactive Oxygen Species from Secondary Organic Aerosols in Epithelial Lining Fluid.
Shiraiwa M, Fang T, Wei J … +10 more , Lakey P, Hwang B, Edwards KC, Kapur S, Mena J, Huang YK, Digman MA, Weichenthal SA, Nizkorodov S, Kleinman MT
Res Rep Health Eff Inst · 2023 Dec · PMID 38420854
INTRODUCTION: Oxidative stress mediated by reactive oxygen species (ROS) is a key process for adverse aerosol health effects. Secondary organic aerosols (SOA) account for a major fraction of particulate matter with aerod...
INTRODUCTION: Oxidative stress mediated by reactive oxygen species (ROS) is a key process for adverse aerosol health effects. Secondary organic aerosols (SOA) account for a major fraction of particulate matter with aerodynamic diameter ≤2.5 µm (PM). PM inhalation and deposition into the respiratory tract causes the formation of ROS by chemical reactions and phagocytosis of macrophages in the epithelial lining fluid (ELF), but their relative contributions are not well quantified and their link to oxidative stress remains uncertain. The specific aims of this project were (1) elucidating the chemical mechanism and quantifying the formation kinetics of ROS in the ELF by SOA; (2) quantifying the relative importance of ROS formation by chemical reactions and macrophages in the ELF. METHODS: SOA particles were generated using reaction chambers from oxidation of various precursors including isoprene, terpenes, and aromatic compounds with or without nitrogen oxides (NO). We collected size-segregated PM at two highway sites in Anaheim, CA, and Long Beach, CA, and at an urban site in Irvine, CA, during two wildfire events. The collected particles were extracted into water or surrogate ELF that contained lung antioxidants. ROS generation was quantified using electron paramagnetic resonance (EPR) spectroscopy with a spin-trapping technique. PM oxidative potential (OP) was also quantified using the dithiothreitol assay. In addition, kinetic modeling was applied for analysis and interpretation of experimental data. Finally, we quantified cellular superoxide release by RAW264.7 macrophage cells upon exposure to quinones and isoprene SOA using a chemiluminescence assay as calibrated with an EPR spin-probing technique. We also applied cellular imaging techniques to study the cellular mechanism of superoxide release and oxidative damage on cell membranes. RESULTS: Superoxide radicals (·O) were formed from aqueous reactions of biogenic SOA generated by hydroxy radical (·OH) photooxidation of isoprene, β-pinene, α-terpineol, and d-limonene. The temporal evolution of ·OH and ·O formation was elucidated by kinetic modeling with a cascade of aqueous reactions, including the decomposition of organic hydroperoxides (ROOH), ·OH oxidation of primary or secondary alcohols, and unimolecular decomposition of α-hydroxyperoxyl radicals. Relative yields of various types of ROS reflected the relative abundance of ROOH and alcohols contained in SOA, which generated under high NO conditions, exhibited lower ROS yields. ROS formation by SOA was also affected by pH. Isoprene SOA had higher ·OH and organic radical yields at neutral than at acidic pH. At low pH ·O was the dominant species generated by all types of SOA. At neutral pH, α-terpineol SOA exhibited a substantial yield of carbon-centered organic radicals (R·), while no radical formation was observed by aromatic SOA. UNLABELLED: Organic radicals in the ELF were formed by mixtures of Fe and SOA generated from photooxidation of isoprene, α-terpineol, and toluene. The molar yields of organic radicals by SOA were 5-10 times higher in ELF than in water. Fe enhanced organic radical yields by a factor of 20-80. Ascorbate mediated redox cycling of iron ions and sustained organic peroxide decomposition, as supported by kinetic modeling reproducing time- and concentration-dependence of organic radical formation, as well as by additional experiments observing the formation of Fe and ascorbate radicals in mixtures of ascorbate and Fe. ·OH and superoxide were found to be efficiently scavenged by antioxidants. UNLABELLED: Wildfire PM mainly generated ·OH and R· with minor contributions from superoxide and oxygen-centered organic radicals (RO·). PM OP was high in wildfire PM, exhibiting very weak correlation with radical forms of ROS. These results were in stark contrast with PM collected at highway and urban sites, which generated much higher amounts of radicals dominated by ·OH radicals that correlated well with OP. By combining field measurements of size-segregated chemical composition, a human respiratory tract model, and kinetic modeling, we quantified production rates and concentrations of different types of ROS in different regions of the ELF by considering particle-size-dependent respiratory deposition. While hydrogen peroxide (HO) and ·O production were governed by Fe and Cu ions, ·OH radicals were mainly generated by organic compounds and Fenton-like reactions of metal ions. We obtained mixed results for correlations between PM OP and ROS formation, providing rationale and limitations of the use of oxidative potential as an indicator for PM toxicity in epidemiological and toxicological studies. UNLABELLED: Quinones and isoprene SOA activated nicotinamide adenine dinucleotide phosphate (NADPH) oxidase in macrophages, releasing massive amounts of superoxide via respiratory burst and overwhelming the superoxide formation by aqueous chemical reactions in the ELF. The threshold dose for macrophage activation was much smaller for quinones compared with isoprene SOA. The released ROS caused lipid peroxidation to increase cell membrane fluidity, inducing oxidative damage and stress. Further increases of doses led to the activation of antioxidant response elements, reducing the net cellular superoxide production. At very high doses and long exposure times, chemical production became comparably important or dominant if the escalation of oxidative stress led to cell death. CONCLUSIONS: The mechanistic understandings and quantitative information on ROS generation by SOA particles provided a basis for further elucidation of adverse aerosol health effects and oxidative stress by PM. For a comprehensive assessment of PM toxicity and health effects via oxidative stress, it is important to consider both chemical reactions and cellular processes for the formation of ROS in the ELF. Chemical composition of PM strongly influences ROS formation; further investigations are required to study ROS formation from various PM sources. Such research will provide critical information to environmental agencies and policymakers for the development of air quality policy and regulation.
Long-Term Exposure to AIR Pollution and COVID-19 Mortality and Morbidity in DENmark: Who Is Most Susceptible? (AIRCODEN).
Andersen ZJ, Zhang J, Lim YH … +14 more , So R, Jørgensen JT, Mortensen LH, Napolitano GM, Cole-Hunter T, Loft S, Bhatt S, Hoek G, Brunekreef B, Westendorp R, Ketzel M, Brandt J, Lange T, Kølsen-Fisher T
Res Rep Health Eff Inst · 2023 Nov · PMID 38286761
INTRODUCTION: Early ecological studies have suggested a link between air pollution and Coronavirus Diseases 2019 (COVID-19); however, the evidence from individual-level prospective cohort studies is still sparse. Here, w...
INTRODUCTION: Early ecological studies have suggested a link between air pollution and Coronavirus Diseases 2019 (COVID-19); however, the evidence from individual-level prospective cohort studies is still sparse. Here, we have examined, in a general population, whether long-term exposure to air pollution is associated with the risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and developing severe COVID-19, resulting in hospitalization or death and who is most susceptible. We also examined whether long-term exposure to air pollution is associated with hospitalization or death due to COVID-19 in those who have tested positive for SARS-CoV-2. METHODS: We included all Danish residents 30 years or older who resided in Denmark on March 1, 2020. and followed them in the National COVID-19 Surveillance System until first positive test (incidence), COVID-19 hospitalization, or death until April 26, 2021. We estimated mean levels of nitrogen dioxide (NO), particulate matter with an aerodynamic diameter <2.5 μm (PM), black carbon (BC), and ozone (O) at cohort participants' residence in 2019 by the Danish Eulerian Hemispheric Model/Urban Background Model. We used Cox proportional hazard models to estimate the associations of air pollutants with COVID-19 incidence, hospitalization, and mortality adjusting for age, sex, and socioeconomic status (SES) at the individual and area levels. We examined effect modification by age, sex, SES (education, income, wealth, employment), and comorbidities with cardiovascular disease, respiratory disease, acute lower respiratory infections, diabetes, lung cancer, and dementia. We used logistic regression to examine association of air pollutants with COVID-19-related hospitalization or death among SARS-CoV-2 positive patients, adjusting for age, sex, individual- and area-level SES. RESULTS: Of 3,721,810 people, 138,742 were infected, 11,270 hospitalized, and 2,557 died from COVID-19 during 14 months of follow-up. We detected strong positive associations with COVID-19 incidence, with hazard ratio (HR) and 95% confidence interval (CI) of 1.10 (CI: 1.05-1.14) per 0.5-μg/m increase in PM and 1.18 (CI: 1.14-1.23) per 3.6-μg/m increase in NO. For COVID-19 hospitalizations and for COVID-19 deaths, corresponding HRs and 95% CIs were 1.09 (CI: 1.01-1.17) and 1.19 (CI: 1.12-1.27), respectively for PM, and 1.23 (CI: 1.04-1.44) and 1.18 (CI: 1.03-1.34), respectively for NO. We also found strong positive and statistically significant associations with BC and negative associations with O. Associations were strongest in those aged 65 years old or older, participants with the lowest SES, and patients with chronic cardiovascular, respiratory, metabolic, lung cancer, and neurodegenerative disease. Among 138,742 individuals who have tested positive for SARS-Cov-2, we detected positive association with COVID-19 hospitalizations (N = 11,270) with odds ratio and 95% CI of 1.04 (CI: 1.01- 1.08) per 0.5-μg/m increase in PM and 1.06 (CI: 1.01-1.12) per 3.6-μg/m increase in NO, but no association with PM with an aerodynamic diameter <10 μm (PM), BC, or O and no association between any of the pollutants and COVID-19 mortality (N = 2,557). CONCLUSIONS: This large nationwide study provides strong new evidence in support of association between long-term exposure to air pollution and COVID-19.
Ambient Air Pollution and All-Cause and Cause-Specific Mortality in an Analysis of Asian Cohorts.
Downward GS, Vermeulen R, Asia Cohort Consortium Executive Board
Res Rep Health Eff Inst · 2023 May · PMID 37424069
INTRODUCTION: Much of what is currently known about the adverse effects of ambient air pollution comes from studies conducted in high-income regions, with relatively low air pollution levels. The aim of the current proje...
INTRODUCTION: Much of what is currently known about the adverse effects of ambient air pollution comes from studies conducted in high-income regions, with relatively low air pollution levels. The aim of the current project is to examine the relationship between exposure to ambient air pollution (as predicted from satellite-based models) and all-cause and cause-specific mortality in several Asian cohorts. METHODS: Cohorts were recruited from the Asia Cohort Consortium (ACC). The geocoded residences of participants were assigned levels of ambient particulate material with aerodynamic diameter of 2.5 μm or less (PM) and nitrogen dioxide (NO) utilizing global satellite-derived models and assigned for the year of enrollment (or closest available year). The association between ambient exposure and mortality was established with Cox proportional hazard models, after adjustment for common confounders. Both single- and two-pollutant models were generated. Model robustness was evaluated, and hazard ratios were calculated for each cohort separately and combined via random effect meta-analysis for pooled risk estimates. RESULTS: Six cohort studies from the ACC participated: the Community-based Cancer Screening Program (CBCSCP, Taiwan), the Golestan Cohort Study (Iran), the Health Effects for Arsenic Longitudinal Study (HEALS, Bangladesh), the Japan Public Health Center-based Prospective Study (JPHC), the Korean Multi-center Cancer Cohort Study (KMCC), and the Mumbai Cohort Study (MCS, India). The cohorts represented over 340,000 participants. UNLABELLED: Mean exposures to PM ranged from 8 to 58 μg/m. Mean exposures to NO ranged from 7 to 23 ppb. For PM, a positive, borderline nonsignificant relationship was observed between PM and cardiovascular mortality. Other relationships with PM tended toward the null in meta-analysis. For NO, an overall positive relationship was observed between exposure to NO and all cancers and lung cancer. A borderline association between NO and nonmalignant lung disease was also observed. The findings within individual cohorts remained consistent across a variety of subgroups and alternative analyses, including two-pollutant models. CONCLUSIONS: In a pooled examination of cohort studies across Asia, ambient PM exposure appears to be associated with an increased risk of cardiovascular mortality and ambient NO exposure is associated with an increased cancer (and lung cancer) mortality. This project has shown that satellite-derived models of pollution can be used in examinations of mortality risk in areas with either incomplete or missing air pollution monitoring.
Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site.
Frey HC, Grieshop AP, Khlystov A … +13 more , Bang JJ, Rouphail N, Guinness J, Rodriguez D, Fuentes M, Saha P, Brantley H, Snyder M, Tanvir S, Ko K, Noussi T, Delavarrafiee M, Singh S
Res Rep Health Eff Inst · 2022 Sep · PMID 36314577
INTRODUCTION: Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal va...
INTRODUCTION: Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development. UNLABELLED: The key pollutants that are the key focus of this work include nitrogen oxides (NO), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O). NO, CO, and BC are tracers of vehicle emissions and dispersion. PM is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM and UFP concentrations. O concentrations are influenced by interaction with NO near the roadway. Nitrogen dioxide (NO), CO, PM, and O are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work. METHODS: The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection. UNLABELLED: The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites. RESULTS: We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system. UNLABELLED: At the freeway site, we measured the spatial gradient of NO, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants. UNLABELLED: At the urban site, we measured daily average concentrations of nitric oxide (NO), NO, O, and PM at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O, PM, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution. UNLABELLED: Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R) was 0.61 for hourly NO concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NO at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NO at the freeway site and for UFPs and O at the urban site pedestrian transect. CONCLUSIONS: Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.
Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE): Phase 2.
Brauer M, Brook JR, Christidis T … +14 more , Chu Y, Crouse DL, Erickson A, Hystad P, Li C, Martin RV, Meng J, Pappin AJ, Pinault LL, Tjepkema M, van Donkelaar A, Weagle C, Weichenthal S, Burnett RT
Res Rep Health Eff Inst · 2022 Jul · PMID 36224709
INTRODUCTION: Mortality is associated with long-term exposure to fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter; PM), although the magnitude and form of these associations remain poorly under...
INTRODUCTION: Mortality is associated with long-term exposure to fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter; PM), although the magnitude and form of these associations remain poorly understood at lower concentrations. Knowledge gaps include the shape of concentration-response curves and the lowest levels of exposure at which increased risks are evident and the occurrence and extent of associations with specific causes of death. Here, we applied improved estimates of exposure to ambient PM to national population-based cohorts in Canada, including a stacked cohort of 7.1 million people who responded to census year 1991, 1996, or 2001. The characterization of the shape of the concentration-response relationship for nonaccidental mortality and several specific causes of death at low levels of exposure was the focus of the Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE) Phase 1 report. In the Phase 1 report we reported that associations between outdoor PM concentrations and nonaccidental mortality were attenuated with the addition of ozone (O) or a measure of gaseous pollutant oxidant capacity (O), which was estimated from O and nitrogen dioxide (NO) concentrations. This was motivated by our interests in understanding both the effects air pollutant mixtures may have on mortality and also the role of O as a copollutant that shares common sources and precursor emissions with those of PM. In this Phase 2 report, we further explore the sensitivity of these associations with O and O, evaluate sensitivity to other factors, such as regional variation, and present ambient PM concentration-response relationships for specific causes of death. METHODS: PM concentrations were estimated at 1 km spatial resolution across North America using remote sensing of aerosol optical depth (AOD) combined with chemical transport model (GEOS-Chem) simulations of the AOD:surface PM mass concentration relationship, land use information, and ground monitoring. These estimates were informed and further refined with collocated measurements of PM and AOD, including targeted measurements in areas of low PM concentrations collected at five locations across Canada. Ground measurements of PM and total suspended particulate matter (TSP) mass concentrations from 1981 to 1999 were used to backcast remote-sensing-based estimates over that same time period, resulting in modeled annual surfaces from 1981 to 2016. UNLABELLED: Annual exposures to PM were then estimated for subjects in several national population-based Canadian cohorts using residential histories derived from annual postal code entries in income tax files. These cohorts included three census-based cohorts: the 1991 Canadian Census Health and Environment Cohort (CanCHEC; 2.5 million respondents), the 1996 CanCHEC (3 million respondents), the 2001 CanCHEC (3 million respondents), and a Stacked CanCHEC where duplicate records of respondents were excluded (Stacked CanCHEC; 7.1 million respondents). The Canadian Community Health Survey (CCHS) mortality cohort (mCCHS), derived from several pooled cycles of the CCHS (540,900 respondents), included additional individual information about health behaviors. Follow-up periods were completed to the end of 2016 for all cohorts. Cox proportional hazard ratios (HRs) were estimated for nonaccidental and other major causes of death using a 10-year moving average exposure and 1-year lag. All models were stratified by age, sex, immigrant status, and where appropriate, census year or survey cycle. Models were further adjusted for income adequacy quintile, visible minority status, Indigenous identity, educational attainment, labor-force status, marital status, occupation, and ecological covariates of community size, airshed, urban form, and four dimensions of the Canadian Marginalization Index (Can-Marg; instability, deprivation, dependency, and ethnic concentration). The mCCHS analyses were also adjusted for individual-level measures of smoking, alcohol consumption, fruit and vegetable consumption, body mass index (BMI), and exercise behavior. UNLABELLED: In addition to linear models, the shape of the concentration-response function was investigated using restricted cubic splines (RCS). The number of knots were selected by minimizing the Bayesian Information Criterion (BIC). Two additional models were used to examine the association between nonaccidental mortality and PM. The first is the standard threshold model defined by a transformation of concentration equaling zero if the concentration was less than a specific threshold value and concentration minus the threshold value for concentrations above the threshold. The second additional model was an extension of the Shape Constrained Health Impact Function (SCHIF), the eSCHIF, which converts RCS predictions into functions potentially more suitable for use in health impact assessments. Given the RCS parameter estimates and their covariance matrix, 1,000 realizations of the RCS were simulated at concentrations from the minimum to the maximum concentration, by increments of 0.1 μg/m. An eSCHIF was then fit to each of these RCS realizations. Thus, 1,000 eSCHIF predictions and uncertainty intervals were determined at each concentration within the total range. UNLABELLED: Sensitivity analyses were conducted to examine associations between PM and mortality when in the presence of, or stratified by tertile of, O or O. Additionally, associations between PM and mortality were assessed for sensitivity to lower concentration thresholds, where person-years below a threshold value were assigned the mean exposure within that group. We also examined the sensitivity of the shape of the nonaccidental mortality-PM association to removal of person-years at or above 12 μg/m (the current U.S. National Ambient Air Quality Standard) and 10 μg/m (the current Canadian and former [2005] World Health Organization [WHO] guideline, and current WHO Interim Target-4). Finally, differences in the shapes of PM-mortality associations were assessed across broad geographic regions (airsheds) within Canada. RESULTS: The refined PM exposure estimates demonstrated improved performance relative to estimates applied previously and in the MAPLE Phase 1 report, with slightly reduced errors, including at lower ranges of concentrations (e.g., for PM <10 μg/m). UNLABELLED: Positive associations between outdoor PM concentrations and nonaccidental mortality were consistently observed in all cohorts. In the Stacked CanCHEC analyses (1.3 million deaths), each 10-μg/m increase in outdoor PM concentration corresponded to an HR of 1.084 (95% confidence interval [CI]: 1.073 to 1.096) for nonaccidental mortality. For an interquartile range (IQR) increase in PM mass concentration of 4.16 μg/m and for a mean annual nonaccidental death rate of 92.8 per 10,000 persons (over the 1991-2016 period for cohort participants ages 25-90), this HR corresponds to an additional 31.62 deaths per 100,000 people, which is equivalent to an additional 7,848 deaths per year in Canada, based on the 2016 population. In RCS models, mean HR predictions increased from the minimum concentration of 2.5 μg/m to 4.5 μg/m, flattened from 4.5 μg/m to 8.0 μg/m, then increased for concentrations above 8.0 μg/m. The threshold model results reflected this pattern with -2 log-likelihood values being equal at 2.5 μg/m and 8.0 μg/m. However, mean threshold model predictions monotonically increased over the concentration range with the lower 95% CI equal to one from 2.5 μg/m to 8.0 μg/m. The RCS model was a superior predictor compared with any of the threshold models, including the linear model. UNLABELLED: In the mCCHS cohort analyses inclusion of behavioral covariates did not substantially change the results for both linear and nonlinear models. We examined the sensitivity of the shape of the nonaccidental mortality-PM association to removal of person-years at or above the current U.S. and Canadian standards of 12 μg/m and 10 μg/m, respectively. In the full cohort and in both restricted cohorts, a steep increase was observed from the minimum concentration of 2.5 μg/m to 5 μg/m. For the full cohort and the <12 μg/m cohort the relationship flattened over the 5 to 9 μg/m range and then increased above 9 μg/m. A similar increase was observed for the <10 μg/m cohort followed by a clear decline in the magnitude of predictions over the 5 to 9 μg/m range and an increase above 9 μg/m. Together these results suggest that a positive association exists for concentrations >9 μg/m with indications of adverse effects on mortality at concentrations as low as 2.5 μg/m. UNLABELLED: Among the other causes of death examined, PM exposures were consistently associated with an increased hazard of mortality due to ischemic heart disease, respiratory disease, cardiovascular disease, and diabetes across all cohorts. Associations were observed in the Stacked CanCHEC but not in all other cohorts for cerebrovascular disease, pneumonia, and chronic obstructive pulmonary disease (COPD) mortality. No significant associations were observed between mortality and exposure to PM for heart failure, lung cancer, and kidney failure. UNLABELLED: In sensitivity analyses, the addition of O and O attenuated associations between PM and mortality. When analyses were stratified by tertiles of copollutants, associations between PM and mortality were only observed in the highest tertile of O or O. Across broad regions of Canada, linear HR estimates and the shape of the eSCHIF varied substantially, possibly reflecting underlying differences in air pollutant mixtures not characterized by PM mass concentrations or the included gaseous pollutants. Sensitivity analyses to assess regional variation in population characteristics and access to healthcare indicated that the observed regional differences in concentration-mortality relationships, specifically the flattening of the concentration-mortality relationship over the 5 to 9 μg/m range, was not likely related to variation in the makeup of the cohort or its access to healthcare, lending support to the potential role of spatially varying air pollutant mixtures not sufficiently characterized by PM mass concentrations. CONCLUSIONS: In several large, national Canadian cohorts, including a cohort of 7.1 million unique census respondents, associations were observed between exposure to PM with nonaccidental mortality and several specific causes of death. Associations with nonaccidental mortality were observed using the eSCHIF methodology at concentrations as low as 2.5 μg/m, and there was no clear evidence in the observed data of a lower threshold, below which PM was not associated with nonaccidental mortality.
Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods.
Dominici F, Zanobetti A, Schwartz J … +3 more , Braun D, Sabath B, Wu X
Res Rep Health Eff Inst · 2022 Jan · PMID 36193708
This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollut...
This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM), nitrogen dioxide (NO), and ozone (O) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project. Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses. Our work provides comprehensive evidence of associations between exposures to PM NO, and O and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM and mortality, even at PM levels below or equal to 12 μg/m, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure. For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m decrease in PM led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM levels lower than 12 μg/m (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]). Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM and slightly elevated the causal effects of NO exposure on all-cause mortality. The results for O remained almost unchanged. We found evidence of a harmful causal relationship between mortality and long-term PM exposures adjusted for NO and O across the range of annual averages between 2.77 and 17.16 μg/m (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards. There is, in general, a harmful causal impact of long-term NO exposures to mortality adjusted for PM and O across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO exposures on all-cause mortality are nonlinear with statistical uncertainty. The ERCs of long-term O exposures on all-cause mortality adjusted for PM and NO are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O exposures of 50 ppb versus those with 30 ppb. institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred. A list of abbreviations and other terms appears at the end of this volume.
Global Burden of Disease from Major Air Pollution Sources (GBD MAPS): A Global Approach.
McDuffie E, Martin R, Yin H … +1 more , Brauer M
Res Rep Health Eff Inst · 2021 Dec · PMID 36148817
Ambient fine particulate matter (particles <2.5 μm in aerodynamic diameter [PM]) is the world's leading environmental health risk factor. Reducing the PM disease burden requires specific strategies that target dominant s...
Ambient fine particulate matter (particles <2.5 μm in aerodynamic diameter [PM]) is the world's leading environmental health risk factor. Reducing the PM disease burden requires specific strategies that target dominant sources across multiple spatial scales. The Global Burden of Disease from Major Air Pollution Sources (GBD MAPS) project provides a contemporary and comprehensive evaluation of contributions to the ambient PM disease burden from source sectors and fuels across 21 regions, 204 countries, and 200 subnational areas. We first derived quantitative contributions from 24 emission sensitivity simulations using an updated global atmospheric chemistry-transport model, input with a newly developed detailed anthropogenic emissions dataset that includes emissions specific to source sector and fuels. These simulation results were integrated with newly available high-resolution satellite-derived PM exposure estimates and disease-specific concentration-response relationships consistent with the GBD project to quantify contributions of specific source sector and fuel to the ambient PM disease burden across all regions, countries, and subnational areas. To improve the transparency and reproducibility of this and future work, we publicly provided the global atmospheric chemistry-transport model source code, emissions dataset and emissions model source code, analysis scripts, and source sensitivity results, and further described the emissions dataset and source contribution results in two publications. We found that nearly 1.05 million (95% uncertainty interval [UI]: 0.74-1.36 million) deaths worldwide (27.3% of the total mortality attributable to PM) would be avoidable by eliminating fossil fuel combustion, with coal contributing over half of that burden. Residential (19.2%; 736,000 deaths [95% UI: 521,000-955,000]), industrial (11.7%; 448,000 deaths [95% UI: 318,000-582,000]), and energy (10.2%; 391,000 deaths [95% UI: 277,000-507,000]) sector emissions are among the dominant global sources Uncertainty in these estimates reflects those of the input datasets. Regions with the largest anthropogenic contributions generally have the highest numbers of attributable deaths, which clearly demonstrates the importance of reducing these emissions to realize reductions in global air pollution and its disease burden.
Associations of Air Pollution on the Brain in Children: A Brain Imaging Study.
Guxens M, Lubczynska MJ, Perez-Crespo L … +5 more , Muetzel RL, El Marroun H, Basagana X, Hoek G, Tiemeier H
Res Rep Health Eff Inst · 2022 Feb · PMID 36106707
INTRODUCTION: Epidemiological studies are highlighting the negative effects of the exposure to air pollution on children's neurodevelopment. However, most studies assessed children's neurodevelopment using neuropsycholog...
INTRODUCTION: Epidemiological studies are highlighting the negative effects of the exposure to air pollution on children's neurodevelopment. However, most studies assessed children's neurodevelopment using neuropsychological tests or questionnaires. Using magnetic resonance imaging (MRI) to precisely measure global and region-specific brain development would provide details of brain morphology and connectivity. This would help us understand the observed cognitive and behavioral changes related to air pollution exposure. Moreover, most studies assessed only a few air pollutants. This project investigates whether air pollution exposure to many pollutants during pregnancy and childhood is associated with the morphology and connectivity of the brain in school-age children and pre-adolescents. METHODS: We used data from the Generation R Study, a population-based birth cohort set up in Rotterdam, the Netherlands in 2002-2006 (n = 9,610). We used land-use regression (LUR) models to estimate the levels of 14 air pollutants at participant's homes during pregnancy and childhood: nitrogen oxides (NO), nitrogen dioxide (NO), particulate matter with aerodynamic diameter ≤10 μm (PM) or ≤2.5 μm (PM), PM between 10 μm and 2.5 μm (PM), absorbance of the PM fraction - a measure of soot (PMabsorbance), the composition of PM such as polycyclic aromatic hydrocarbons (PAHs), organic carbon (OC), copper (Cu), iron (Fe), silicon (Si), zinc (Zn), and the oxidative potential of PM evaluated using two acellular methods: dithiothreitol (OP) and electron spin resonance (OP). We performed MRI measurements of structural morphology (i.e., brain volumes, cortical thickness, and cortical surface area) using T-weighted images in 6- to 10-year-old school-age children and 9- to 12-year-old pre-adolescents, structural connectivity (i.e., white matter microstructure) using diffusion tensor imaging (DTI) in pre-adolescents, and functional connectivity (i.e., connectivity score between brain areas) using resting-state functional MRI (rs-fMRI) in pre-adolescents. We assessed cognitive function using the Developmental Neuropsychological Assessment test (NEPSY-II) in school-age children. For each outcome, we ran regression analysis adjusted for several socioeconomic and lifestyle characteristics. We performed single-pollutant analyses followed by multipollutant analyses using the deletion/substitution/addition (DSA) approach. RESULTS: The project has air pollution and brain MRI data for 783 school-age children and 3,857 pre-adolescents. First, exposure to air pollution during pregnancy or childhood was not associated with global brain volumes (e.g., total brain, cortical gray matter, and cortical white matter) in school-age children or pre-adolescents. However, higher pregnancy or childhood exposure to several air pollutants was associated with a smaller corpus callosum and hippocampus, and a larger amygdala, nucleus accumbens, and cerebellum in pre-adolescents, but not in school-age children. Second, higher exposure to several air pollutants during pregnancy was associated with a thinner cortex in various regions of the brain in both school-age children and pre-adolescents. Higher exposure to air pollution during childhood was also associated with a thinner cortex in a single region in pre-adolescents. A thinner cortex in two regions mediated the association between higher exposure to air pollution during pregnancy and an impaired inhibitory control in school-age children. Third, higher exposure to air pollution during childhood was associated with smaller cortical surface areas in various regions of the brain except in a region where we observed a larger cortical surface area in pre-adolescents. In relation to brain structural connectivity, higher exposure to air pollution during pregnancy and childhood was associated with an alteration in white matter microstructure in pre-adolescents. In relation to brain functional connectivity, a higher exposure to air pollution, mainly during pregnancy and early childhood, was associated with a higher brain functional connectivity among several brain regions in pre-adolescents. Overall, we identified several air pollutants associated with brain structural morphology, structural connectivity, and functional connectivity, such as NO, NO, PM of various size fractions (i.e., PM, PM, and PM), PMabsorbance, PAHs, OC, three elemental components of PM (i.e., Cu, Si, Zn), and the oxidative potential of PM CONCLUSIONS: The results of this project suggest that exposure to air pollution during pregnancy and childhood play an adverse role in brain development. We observed this relationship even at levels of exposure that were below the European Union legislations. We acknowledge that identifying the independent effects of specific pollutants was particularly challenging. Most of our conclusions generally refer to traffic-related air pollutants. However, we did identify pollutants specifically originating from brake linings, tire wear, and tailpipe emissions from diesel combustion. The current direction toward innovative solutions for cleaner energy vehicles is a step in the right direction. However, our findings indicate that these measures might not be completely adequate to mitigate health problems attributable to traffic-related air pollution, as we also observed associations with markers of brake linings and tire wear.
Mortality and Morbidity Effects of Long-Term Exposure to Low-Level PM, BC, NO, and O: An Analysis of European Cohorts in the ELAPSE Project.
Brunekreef B, Strak M, Chen J … +40 more , Andersen ZJ, Atkinson R, Bauwelinck M, Bellander T, Boutron MC, Brandt J, Carey I, Cesaroni G, Forastiere F, Fecht D, Gulliver J, Hertel O, Hoffmann B, de Hoogh K, Houthuijs D, Hvidtfeldt U, Janssen N, Jorgensen J, Katsouyanni K, Ketzel M, Klompmaker J, Hjertager Krog N, Liu S, Ljungman P, Mehta A, Nagel G, Oftedal B, Pershagen G, Peters A, Raaschou-Nielsen O, Renzi M, Rodopoulou S, Samoli E, Schwarze P, Sigsgaard T, Stafoggia M, Vienneau D, Weinmayr G, Wolf K, Hoek G
Res Rep Health Eff Inst · 2021 Sep · PMID 36106702
INTRODUCTION: Epidemiological cohort studies have consistently found associations between long-term exposure to outdoor air pollution and a range of morbidity and mortality endpoints. Recent evaluations by the World Heal...
INTRODUCTION: Epidemiological cohort studies have consistently found associations between long-term exposure to outdoor air pollution and a range of morbidity and mortality endpoints. Recent evaluations by the World Health Organization and the Global Burden of Disease study have suggested that these associations may be nonlinear and may persist at very low concentrations. Studies conducted in North America in particular have suggested that associations with mortality persisted at concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM) well below current air quality standards and guidelines. The uncertainty about the shape of the concentration-response function at the low end of the concentration distribution, related to the scarcity of observations in the lowest range, was the basis of the current project. Previous studies have focused on PM, but increasingly associations with nitrogen dioxide (NO) are being reported, particularly in studies that accounted for the fine spatial scale variation of NO. Very few studies have evaluated the effects of long-term exposure to low concentrations of ozone (O). Health effects of black carbon (BC), representing primary combustion particles, have not been studied in most large cohort studies of PM. Cohort studies assessing health effects of particle composition, including elements from nontailpipe traffic emissions (iron, copper, and zinc) and secondary aerosol (sulfur) have been few in number and reported inconsistent results. The overall objective of our study was to investigate the shape of the relationship between long-term exposure to four pollutants (PM, NO, BC, and O) and four broad health effect categories using a number of different methods to characterize the concentration-response function (i.e., linear, nonlinear, or threshold). The four health effect categories were (1) natural- and cause-specific mortality including cardiovascular and nonmalignant as well as malignant respiratory and diabetes mortality; and morbidity measured as (2) coronary and cerebrovascular events; (3) lung cancer incidence; and (4) asthma and chronic obstructive pulmonary disease (COPD) incidence. We additionally assessed health effects of PM composition, specifically the copper, iron, zinc, and sulfur content of PM. METHODS: We focused on analyses of health effects of air pollutants at low concentrations, defined as less than current European Union (EU) Limit Values, U.S. Environmental Protection Agency (U.S. EPA), National Ambient Air Quality Standards (NAAQS), and/or World Health Organization (WHO) Air Quality Guideline values for PM, NO, and O. We address the health effects at low air pollution levels by performing new analyses within selected cohorts of the ESCAPE study (European Study of Cohorts for Air Pollution Effects; Beelen et al. 2014a) and within seven very large European administrative cohorts. By combining well-characterized ESCAPE cohorts and large administrative cohorts in one study the strengths and weaknesses of each approach can be addressed. The large administrative cohorts are more representative of national or citywide populations, have higher statistical power, and can efficiently control for area-level confounders, but have fewer possibilities to control for individual-level confounders. The ESCAPE cohorts have detailed information on individual confounders, as well as country-specific information on area-level confounding. The data from the seven included ESCAPE cohorts and one additional non-ESCAPE cohort have been pooled and analyzed centrally. More than 300,000 adults were included in the pooled cohort from existing cohorts in Sweden, Denmark, Germany, the Netherlands, Austria, France, and Italy. Data from the administrative cohorts have been analyzed locally, without transfer to a central database. Privacy regulations prevented transfer of data from administrative cohorts to a central database. More than 28 million adults were included from national administrative cohorts in Belgium, Denmark, England, the Netherlands, Norway, and Switzerland as well as an administrative cohort in Rome, Italy. We developed central exposure assessment using Europewide hybrid land use regression (LUR) models, which incorporated European routine monitoring data for PM, NO, and O, and ESCAPE monitoring data for BC and PM composition, land use, and traffic data supplemented with satellite observations and chemical transport model estimates. For all pollutants, we assessed exposure at a fine spatial scale, 100 × 100 m grids. These models have been applied to individual addresses of all cohorts including the administrative cohorts. In sensitivity analyses, we applied the PM models developed within the companion HEI-funded Canadian MAPLE study (Brauer et al. 2019) and O exposures on a larger spatial scale for comparison with previous studies. Identification of outcomes included linkage with mortality, cancer incidence, hospital discharge registries, and physician-based adjudication of cases. We analyzed natural-cause, cardiovascular, ischemic heart disease, stroke, diabetes, cardiometabolic, respiratory, and COPD mortality. We also analyzed lung cancer incidence, incidence of coronary and cerebrovascular events, and incidence of asthma and COPD (pooled cohort only). We applied the Cox proportional hazard model with increasing control for individual- and area-level covariates to analyze the associations between air pollution and mortality and/or morbidity for both the pooled cohort and the individual administrative cohorts. Age was used as the timescale because of evidence that this results in better adjustment for potential confounding by age. Censoring occurred at the time of the event of interest, death from other causes, emigration, loss to follow-up for other reasons, or at the end of follow-up, whichever came first. A priori we specified three confounder models, following the modeling methods of the ESCAPE study. Model 1 included only age (time axis), sex (as strata), and calendar year of enrollment. Model 2 added individual-level variables that were consistently available in the cohorts contributing to the pooled cohort or all variables available in the administrative cohorts, respectively. Model 3 further added area-level socioeconomic status (SES) variables. A priori model 3 was selected as the main model. All analyses in the pooled cohort were stratified by subcohort. All analyses in the administrative cohorts accounted for clustering of the data in neighborhoods by adjusting the variance of the effect estimates. The main exposure variable we analyzed was derived from the Europewide hybrid models based on 2010 monitoring data. Sensitivity analyses were conducted using earlier time periods, time-varying exposure analyses, local exposure models, and the PM models from the Canadian MAPLE project. We first specified linear single-pollutant models. Two-pollutant models were specified for all combinations of the four main pollutants. Two-pollutant models for particle composition were analyzed with PM and NO as the second pollutant. We then investigated the shape of the concentration-response function using natural splines with two, three, and four degrees of freedom; penalized splines with the degrees of freedom determined by the algorithm and shape-constrained health impact functions (SCHIF) using confounder model 3. Additionally, we specified linear models in subsets of the concentration range, defined by removing concentrations above a certain value from the analysis, such as for PM 25 μg/m (EU limit value), 20, 15, 12 μg/m (U.S. EPA National Ambient Air Quality Standard), and 10 μg/m (WHO Air Quality Guideline value). Finally, threshold models were evaluated to investigate whether the associations persisted below specific concentration values. For PM, we evaluated 10, 7.5, and 5 μg/m as potential thresholds. Performance of threshold models versus the corresponding no-threshold linear model were evaluated using the Akaike information criterion (AIC). RESULTS: In the pooled cohort, virtually all subjects in 2010 had PM and NO annual average exposures below the EU limit values (25 μg/m and 40 μg/m, respectively). More than 50,000 had a residential PM exposure below the U.S. EPA NAAQS (12 μg/m). More than 25,000 subjects had a residential PM exposure below the WHO guideline (10 μg/m). We found significant positive associations between PM, NO, and BC and natural-cause, respiratory, cardiovascular, and diabetes mortality. In our main model, the hazard ratios (HRs) (95% [confidence interval] CI) were 1.13 (CI = 1.11, 1.16) for an increase of 5 μg/m PM, 1.09 (CI = 1.07, 1.10) for an increase of 10 μg/m NO, and 1.08 (CI = 1.06, 1.10) for an increase of 0.5 × 10/m BC for natural-cause mortality. The highest HRs were found for diabetes mortality. Associations with O were negative, both in the fine spatial scale of the main ELAPSE model and in large spatial scale exposure models. For PM, NO, and BC, we generally observed a supralinear association with steeper slopes at low exposures and no evidence of a concentration below which no association was found. Subset analyses further confirmed that these associations remained at low levels: below 10 μg/m for PM and 20 μg/m for NO. HRs were similar to the full cohort HRs for subjects with exposures below the EU limit values for PM and NO, the U.S. NAAQS values for PM, and the WHO guidelines for PM and NO. The mortality associations were robust to alternative specifications of exposure, including different time periods, PM from the MAPLE project, and estimates from the local ESCAPE model. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. HRs in two-pollutant models were attenuated but remained elevated and statistically significant for PM and NO. In two-pollutant models of PM and NO HRs for natural-cause mortality were 1.08 (CI = 1.05, 1.11) for PM and 1.05 (CI = 1.03, 1.07) for NO. Associations with O were attenuated but remained negative in two-pollutant models with NO, BC, and PM. We found significant positive associations between PM, NO, and BC and incidence of stroke and asthma and COPD hospital admissions. Furthermore, NO was significantly related to acute coronary heart disease and PM was significantly related to lung cancer incidence. We generally observed linear to supralinear associations with no evidence of a threshold, with the exception of the association between NO and acute coronary heart disease, which was sublinear. Subset analyses documented that associations remained even with PM below 20 μg/m and possibly 12 μg/m. Associations remained even when NO was below 30 μg/m and in some cases 20 μg/m. In two-pollutant models, NO was most consistently associated with acute coronary heart disease, stroke, asthma, and COPD hospital admissions. PM was not associated with these outcomes in two-pollutant models with NO. PM was the only pollutant that was associated with lung cancer incidence in two-pollutant models. Associations with O were negative though generally not statistically significant. In the administrative cohorts, virtually all subjects in 2010 had PM and NO annual average exposures below the EU limit values. More than 3.9 million subjects had a residential PM exposure below the U.S. EPA NAAQS (12 μg/m) and more than 1.9 million had residential PM exposures below the WHO guideline (10 μg/m). We found significant positive associations between PM, NO, and BC and natural-cause, respiratory, cardiovascular, and lung cancer mortality, with moderate to high heterogeneity between cohorts. We found positive but statistically nonsignificant associations with diabetes mortality. In our main model meta-analysis, the HRs (95% CI) for natural-cause mortality were 1.05 (CI = 1.02, 1.09) for an increase of 5 μg/m PM, 1.04 (CI = 1.02, 1.07) for an increase of 10 μg/m NO, and 1.04 (CI = 1.02, 1.06) for an increase of 0.5 × 10/m BC, and 0.95 (CI = 0.93, 0.98) for an increase of 10 μg/m O. The shape of the concentration-response functions differed between cohorts, though the associations were generally linear to supralinear, with no indication of a level below which no associations were found. Subset analyses documented that these associations remained at low levels: below 10 μg/m for PM and 20 μg/m for NO. BC and NO remained significantly associated with mortality in two-pollutant models with PM and O. The PM HR attenuated to unity in a two-pollutant model with NO. The negative O association was attenuated to unity and became nonsignificant. The mortality associations were robust to alternative specifications of exposure, including time-varying exposure analyses. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. Effect estimates in the youngest participants (<65 years at baseline) were much larger than in the elderly (>65 years at baseline). Effect estimates obtained with the ELAPSE PM model did not differ from the MAPLE PM model on average, but in individual cohorts, substantial differences were found. CONCLUSIONS: Long-term exposure to PM, NO, and BC was positively associated with natural-cause and cause-specific mortality in the pooled cohort and the administrative cohorts. Associations were found well below current limit values and guidelines for PM and NO. Associations tended to be supralinear, with steeper slopes at low exposures with no indication of a threshold. Two-pollutant models documented the importance of characterizing the ambient mixture with both NO and PM. We mostly found negative associations with O. In two-pollutant models with NO, the negative associations with O were attenuated to essentially unity in the mortality analysis of the administrative cohorts and the incidence analyses in the pooled cohort. In the mortality analysis of the pooled cohort, significant negative associations with O remained in two-pollutant models. Long-term exposure to PM, NO, and BC was also positively associated with morbidity outcomes in the pooled cohort. For stroke, asthma, and COPD, positive associations were found for PM, NO, and BC. For acute coronary heart disease, an increased HR was observed for NO. For lung cancer, an increased HR was found only for PM. Associations mostly showed steeper slopes at low exposures with no indication of a threshold.
Social Susceptibility to Multiple Air Pollutants in Cardiovascular Disease.
Clougherty JE, Humphrey JL, Kinnee EJ … +4 more , Robinson LF, McClure LA, Kubzansky LD, Reid CE
Res Rep Health Eff Inst · 2021 Jul · PMID 36004603
INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research...
INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research identified increased effects of air pollution in lower socioeconomic position (SEP) communities, where pollution exposures are also often higher. The complex spatial confounding between air pollution and SEP makes it very challenging, however, to disentangle the impacts of these very different exposure types and to accurately assess their interactions. The specific causal components (i.e., specific social stressors) underlying this SEP-related susceptibility remain unknown, because there are myriad pathways through which poverty and/or lower-SEP conditions may influence pollution susceptibility - including diet, smoking, coexposures in the home and occupational environments, health behaviors, and healthcare access. Growing evidence suggests that a substantial portion of SEP-related susceptibility may be due to chronic psychosocial stress - given the known wide-ranging impacts of chronic stress on immune, endocrine, and metabolic function - and to a higher prevalence of unpredictable chronic stressors in many lower-SEP communities, including violence, job insecurity, and housing instability. As such, elucidating susceptibility to pollution in the etiology of CVD, and in the risk of CVD events, has been identified as a research priority. This interplay among social and environmental conditions may be particularly relevant for CVD, because pollution and chronic stress both impact inflammation, metabolic function, oxidative stress, hypertension, atherosclerosis, and other processes relevant to CVD etiology. Because pollution exposures are often spatially patterned by SEP, disentangling their effects - and quantifying any interplay - is especially challenging. Doing so, however, would help to improve our ability to identify and characterize susceptible populations and to improve our understanding of how community stressors may alter responses to multiple air pollutants. More clearly characterizing susceptible populations will improve our ability to design and target interventions more effectively (and cost-effectively) and may reveal greater benefits of pollution reduction in susceptible communities, strengthening cost-benefit and accountability analyses, ultimately reducing the disproportionate burden of CVD and reducing health disparities. METHODS: In the current study, we aimed to quantify combined effects of multiple pollutants and stressor exposures on CVD events, using a number of unique datasets we have compiled and verified, including the following. 1. Poverty metrics, violent crime rates, a composite socioeconomic deprivation index (SDI), an index of racial and economic segregation, noise disturbance metrics, and three composite spatial factors produced from a factor analysis of 27 community stressors. All indicators have citywide coverage and were verified against individual reports of stress and stressor exposure, in citywide focus groups and surveys. 2. Spatial surfaces for multiple pollutants from the New York City (NYC) Community Air Survey (NYCCAS), which monitored multiple pollutants year-round at 150 sites and used land use regression (LUR) modeling to estimate fine-scale (100-m) intra-urban spatial variance in fine particles (PM), nitrogen dioxide (NO), sulfur dioxide (SO), and ozone (O). 3. Daily data and time-trends derived from all U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitors in NYC for 2005-2011, which we combined with NYCCAS surfaces to create residence- and day-specific spatiotemporal exposure estimates. 4. Complete data on in- and out-patient unscheduled CVD events presented in NYC hospitals for 2005-2011 (n = 1,113,185) from the New York State (NYS) Department of Health's Statewide Planning and Research Cooperative System (SPARCS). In the study, we quantified relationships between multiple pollutant exposures and both community CVD event rates and individual risk of CVD events in NYC and tested whether pollution-CVD associations varied by community SEP and social stressor exposures. We hypothesized (1) that greater chronic community-level SEP, stressor, and pollution exposures would be associated with higher community CVD rates; (2) that spatiotemporal variations in multiple pollutants would be associated with excess risk of CVD events; and (3) that pollution-CVD associations would be stronger in communities of lower SEP or higher stressor exposures. RESULTS: To first understand the separate and combined associations with CVD for both stressors and pollutants measured at the same spatial and temporal scale of resolution, we used ecological cross-sectional models to examine spatial relationships between multiple chronic pollutant and stressor exposures and age-adjusted community CVD rates. Using census-tract-level annual averages (n = 2,167), we compared associations with CVD rates for multiple pollutant concentrations and social stressors. We found that associations with community CVD rates were consistently stronger for social stressors than for pollutants, in terms of both magnitude and significance. We note, however, that this result may be driven by the relatively greater variation (on a proportional basis) for stressors than for pollutants in NYC. We also tested effect modification of pollutant-CVD associations by each social stressor and found evidence of stronger associations for NO, PM, and wintertime SO with CVD rates, particularly across quintiles of increasing community violence or assault rates (P trend < 0.0001). To examine individual-level associations between spatiotemporal exposures to multiple pollutants and the risk of CVD events, across multiple lag days, we examined the combined effects of multiple pollutant exposures, using spatiotemporal (day- and residence-specific) pollution exposure estimates and hospital data on individual CVD events in case-crossover models, which inherently adjust for nontime-varying individual confounders (e.g., sex and race) and comorbidities. We found consistent significant relationships only for pollutant exposures and the risk of CVD events, suggesting very acute impacts of pollution on CVD risk. Associations with CVD were positive for NO, PM, and SO, as hypothesized, and we found inverse associations for O (a secondary pollutant chemically decreased ["scavenged"] by fresh emissions that, in NYC, displays spatial and temporal patterns opposite those of NO). Finally, to test effect modification by chronic community social stressors on the relationships between spatiotemporal pollution measures and the risk of CVD events, we used individual-level case-crossover models, adding interaction terms with categorical versions of each social stressor. We found that associations between NO and the risk of CVD events were significantly elevated only in communities with the highest exposures to social stressors (i.e., in the highest quintiles of poverty, socioeconomic deprivation, violence, or assault). The largest positive associations for PM and winter SO were generally found in the highest-stressor communities but were not significant in any quintile. We again found inverse associations for O, which were likewise stronger for individuals living in communities with greater stressor exposures. CONCLUSIONS: In ecological models, we found stronger relationships with community CVD rates for social stressors than for pollutant exposures. In case-crossover analyses, higher exposures to NO, PM, and SO were associated with greater excess risk of CVD events but only on the case day (there were no consistent significant lagged-day effects). In effect-modification analyses at both the community and individual level, we found evidence of stronger pollution-CVD associations in communities with higher stressor exposures. Given substantial spatial confounding across multiple social stressors, further research is needed to disentangle these effects in order to identify the predominant social stressors driving this observed differential susceptibility.
Novel Mechanisms of Ozone-Induced Pulmonary Inflammation and Resolution, and the Potential Protective Role of Scavenger Receptor BI.
Gowdy KM, Kilburg-Basnyat B, Hodge MX … +8 more , Reece SW, Yermalitsk V, Davies SS, Manke J, Armstrong ML, Reisdorph N, Tighe RM, Shaikh SR
Res Rep Health Eff Inst · 2021 Mar · PMID 33998222
INTRODUCTION: Increases in ambient levels of ozone (O), a criteria air pollutant, have been associated with increased susceptibility and exacerbations of chronic pulmonary diseases through lung injury and inflammation. O...
INTRODUCTION: Increases in ambient levels of ozone (O), a criteria air pollutant, have been associated with increased susceptibility and exacerbations of chronic pulmonary diseases through lung injury and inflammation. O induces pulmonary inflammation, in part by generating damage-associated molecular patterns (DAMPs), which are recognized by pattern recognition receptors (PRRs), such as toll-like receptors (TLRs) and scavenger receptors (SRs). This inflammatory response is mediated in part by alveolar macrophages (AMs), which highly express PRRs, including scavenger receptor BI (SR-BI). Once pulmonary inflammation has been induced, an active process of resolution occurs in order to prevent secondary necrosis and to restore tissue homeostasis. The processes known to promote the resolution of inflammation include the clearance by macrophages of apoptotic cells, known as efferocytosis, and the production of specialized pro-resolving mediators (SPMs). Impaired efferocytosis and production of SPMs have been associated with the pathogenesis of chronic lung diseases; however, these impairments have yet to be linked with exposure to air pollutants. SPECIFIC AIMS: The primary goals of this study were: Aim 1 - to define the role of SR-BI in O-derived pulmonary inflammation and resolution of injury; and Aim 2 - to determine if O exposure alters pulmonary production of SPMs and processes known to promote the resolution of pulmonary inflammation and injury. METHODS: To address Aim 1, female wild-type (WT) and SR-BI-deficient, or knock-out (SR-BI KO), mice were exposed to either O or filtered air. In one set of experiments mice were instilled with an oxidized phospholipid (oxPL). Bronchoalveolar lavage fluid (BALF) and lung tissue were collected for the analyses of inflammatory and injury markers and oxPL. To estimate efferocytosis, mice were administered apoptotic cells (derived from the Jurkat T cell line) after O or filtered air exposure. UNLABELLED: To address Aim 2, male WT mice were exposed to either O or filtered air, and levels of SPMs were assessed in the lung, as well as markers of inflammation and injury in BALF. In some experiments SPMs were administered before exposure to Oor filtered air, to determine whether SPMs could mitigate inflammatory or resolution responses. Efferocytosis was measured as in Aim 1. RESULTS: For Aim 1, SR-BI protein levels increased in the lung tissue of mice exposed to O, compared with mice exposed to filtered air. Compared with WT controls, SR-BI KO mice had a significant increase in the number of neutrophils in their airspace 24 hours post O exposure. The oxPL levels increased in the airspace of both WT and SR-BI KO mice after O exposure, compared with filtered air controls. Four hours after instillation of an oxPL, SR-BI KO mice had an increase in BALF neutrophils and total protein, and a nonsignificant increase in macrophages compared with WT controls. O exposure decreased efferocytosis in both WT and SR-BI KO female mice. UNLABELLED: For Aim 2, mice given SPM supplementation before O exposure showed significantly increased AM efferocytosis when compared with the Oexposure control mice and also showed some mitigation of the effects of O on inflammation and injury. Several SPMs and their precursors were measured in lung tissue using reverse-phase high performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS). At 24 hours after O exposure 14R-hydroxydocosahexaenoic acid (HDHA) and 10,17-dihydroxydocosahexaenoic acid (diHDoHE) were significantly decreased in lung tissue, but at 6 hours after exposure, levels of these SPMs increased. CONCLUSIONS: Our findings identify novel mechanisms by which O may induce pulmonary inflammation and also increase susceptibility to and exacerbations of chronic lung diseases.
Improvements in Air Quality and Health Outcomes Among California Medicaid Enrollees Due to Goods Movement Actions.
Meng YY, Su JG, Chen X … +3 more , Molitor J, Yue D, Jerrett M
Res Rep Health Eff Inst · 2021 May · PMID 35869754
INTRODUCTION: In 2006, the California Air Resources Board (CARB) and local air quality management districts implemented an Emission Reduction Plan for Ports and Goods Movement program (referred to hereinafter as GM polic...
INTRODUCTION: In 2006, the California Air Resources Board (CARB) and local air quality management districts implemented an Emission Reduction Plan for Ports and Goods Movement program (referred to hereinafter as GM policy actions) (CARB 2006). The GM policy actions comprise approximately 200 actions with an estimated investment value of $6 to $10 billion. These actions targeted the major sources and polluters related to goods movements, such as highways; ports and railyard trucks; ship fuel and shore power; cargo equipment; and locomotives. These actions aimed to reduce total statewide domestic GM emissions to 2001 levels or lower by the year 2010; to reduce the statewide diesel particulate matter (DPM) health risk from GM by 85% by the year 2020; and to reduce the nitrogen oxides (NO) emissions from international GM in the South Coast Air Basin by 30% from projected 2015 levels and 50% from projected 2020 levels. The years 2006 and 2007 marked an important milestone in starting to regulate GM polluters and adopting stricter standards for traffic-related air pollution. UNLABELLED: This project aimed to examine the impact of the GM policy actions on reductions in ambient air pollution and subsequent improvements in health outcomes of Medi-Cal fee-for-service (FFS) beneficiaries with chronic conditions in 10 counties in California. Specifically, we examined whether the GM policy actions reduced air pollution near GMC corridors more than in control areas. We subsequently assessed whether there were greater decreases in emergency room (ER) visits and hospitalizations for enrollees with chronic conditions who lived in the GM corridors (GMCs) than for those who lived in other areas. METHODS: The study used a quasi-experimental design. We defined areas within 500 m of truck-permitted freeways and ports as GMCs. We further defined non-goods movement corridors (NGMCs) as locations within 500 m of truck-prohibited freeways or 300 m of a connecting roadway, and areas out of GMCs and NGMCs as controls (CTRLs). We defined years 2004-2007 as the pre-policy period and years 2008-2010 as the post-policy period. We developed linear mixed-effects land use regression models and created annual air pollution surfaces for nitrogen dioxide (NO), fine particulate matter (PM), and ozone (O) across California for years 2004-2010 at a spatial resolution of 30 m, then assigned them to enrollees' home addresses. UNLABELLED: We used a retrospective cohort of 23,000 California Medicaid (Medi-Cal) FFS adult beneficiaries living in 10 California counties with six years of data (September 1, 2004, to August 31, 2010). Cohort beneficiaries had at least one of four chronic conditions, including asthma, chronic obstructive pulmonary disease (COPD), diabetes, and heart disease. UNLABELLED: We used a difference-in-differences (DiD) model to assess whether air pollutant concentration and health care utilization (ER visits and hospitalizations) for cohort beneficiaries declined more for those living in intervention corridors (GMCs, NGMCs) than those living in CTRLs. All the models controlled for age, sex, language spoken, race/ethnicity, number of comorbidities in baseline years, county, time-varying health indicator variables, and several neighborhood variables. UNLABELLED: To facilitate interpretation, we calculated the DiD estimates in each of the three years after the policy intervention. The DiD was used to assess the causal impact of regulatory policy on reductions of air pollution, as well as for the improvements in health outcomes. UNLABELLED: We explored whether improvements in health outcomes were due to the air pollution reduction by using a multi- level mediation model, in which the effect of GM actions on health outcomes was mediated through the effect of actual air pollution reductions in the post-policy years. We used the Generalized Structural Equation Models for the estimation and combined the effects of NO and PM in the model. To further verify the causal inferences of the GM actions on reductions of exposures and improvements in health outcomes, we performed sensitivity analyses with propensity score weighting. RESULTS: We observed statistically significant reductions in pollutant NO and PM concentrations for enrollees in all 10 counties. The enrollees in GMCs experienced greater reductions in NO and PM from the pre- to the post-policy periods than those in CTRLs. Greater reductions were also observed among beneficiaries living in NGMCs versus those in CTRLs, but those reductions were smaller than among beneficiaries living in GMCs. For O concentrations, an opposite trend was observed. UNLABELLED: Furthermore, we observed significantly greater reductions in ER visits for patients with asthma and COPD living in GMCs than those in CTRLS in the post-policy years. For example, we saw in the DiD modeling results there were 170 fewer ER visits for 1,000 beneficiaries with asthma per year in GMCs if the regionwide trend in the CTRL group was considered not related to the GM policy. Similarly, among the beneficiaries with COPD, there were 180 fewer ER visits per 1,000 patients estimated in the GMCs for the third year after the implementation of the policy. UNLABELLED: We also observed greater reductions in ER visits among those with asthma, when comparing NGMCs with CTRLs, but reductions were smaller than comparisons between GMCs and CTRLs. The ER visits for those with COPD, diabetes, and the total sample in NGMCs also had downward trends in the post-policy year in comparison with those in CTRLs but the differences were not statistically significant; similar phenomena were also observed for the ER visits among those with diabetes and heart diseases and in the total sample when GMCs versus CTRLs and GMCs versus NGMCs were compared. Although hospitalizations also decreased more in GMCs than in NGMCs and more in NGMCs than in CTRLs in the post-policy period, results were not statistically significant. UNLABELLED: Using the mediation models, we observed 0.129 more reductions in the expected number of ER visits among individuals with asthma for a composite reduction in one unit NO and one unit PM (DiD = -0.129, < 0.05) from the pre-policy years to the post-policy years. The reductions in NO and PM due to policy change estimated by the mediation model are essentially the same as shown in the respective DiD models. Mediation analyses suggested that the effects of GM policy interventions on health improvements were largely due to exposure reductions. Finally, sensitivity analyses with propensity scores produced similar DiD results. CONCLUSIONS: This project has produced empirical evidence that air pollution control actions reduced pollution exposures among disadvantaged and susceptible populations. More importantly, our findings suggest that the reductions in air pollution led to health outcome improvements among low-income people with chronic conditions. Our investigation also contributed to scientific methods for assessing the health effects of long-term, large-scale, and complex regulatory actions with routinely collected pollutants and medical claims data. Therefore, the results strongly support both short-term and long-term efforts to improve air quality for all members of society and future studies on the impact of air pollution control policies.
Understanding the Functional Impact of VOC-Ozone Mixtures on the Chemistry of RNA in Epithelial Lung Cells.
Contreras LM, Gonzalez-Rivera JC, Baldridge KC … +3 more , Wang DS, Chuvalo-Abraham J, Ruiz LH
Res Rep Health Eff Inst · 2020 Jul · PMID 32845096
INTRODUCTION: Ambient air pollution is associated with premature death caused by heart disease, stroke, chronic obstructive pulmonary disease (COPD), and lung cancer. Recent studies have suggested that ribonucleic acid (...
INTRODUCTION: Ambient air pollution is associated with premature death caused by heart disease, stroke, chronic obstructive pulmonary disease (COPD), and lung cancer. Recent studies have suggested that ribonucleic acid (RNA) oxidation is a sensitive environment-related biomarker that is implicated in pathogenesis. AIMS AND METHODS: We used a novel approach that integrated RNA-Seq analysis with detection by immunoprecipitation techniques of the prominent RNA oxidative modification 8-oxo-7,8-dihydroguanine (8-oxoG). Our goal was to uncover specific messenger RNA (mRNA) oxidation induced by mixtures of volatile organic compounds (VOCs) and ozone in healthy human epithelial lung cells. To this end, we exposed the BEAS-2B human epithelial lung cell line to the gas- and particle-phase products formed from reactions of 790 ppb acrolein (ACR) and 670 ppb methacrolein (MACR) with 4 ppm ozone. RESULTS: Using this approach, we identified 222 potential direct targets of oxidation belonging to previously described pathways, as well as uncharacterized pathways, after air pollution exposures. We demonstrated the effect of our VOC-ozone mixtures on the morphology and actin cytoskeleton of lung cells, suggesting the influence of selective mRNA oxidation in members of pathways regulating physical components of the cells. In addition, we observed the influence of the VOC-ozone mixtures on metabolic cholesterol synthesis, likely implicated as a result of the incidence of mRNA oxidation and the deregulation of protein levels of squalene synthase (farnesyl-diphosphate farnesyltransferase 1 [FDFT1]), a key enzyme in endogenous cholesterol biosynthesis. CONCLUSIONS: Overall, our findings indicate that air pollution influences the accumulation of 8-oxoG in transcripts of epithelial lung cells that largely belong to stress-induced signaling and metabolic and structural pathways. A strength of the study was that it combined traditional transcriptome analysis with transcriptome-wide 8-oxoG mapping to facilitate the discovery of underlying processes not characterized by earlier approaches. Investigation of the processes mediated by air pollution oxidation of RNA molecules in primary cells and animal models needs to be explored in future studies. Our research has thus opened new avenues to further inform the relationship between atmospheric agents on the one hand and cellular responses on the other that are implicated in diseases.
Enhancing Models and Measurements of Traffic-Related Air Pollutants for Health Studies Using Dispersion Modeling and Bayesian Data Fusion.
Batterman S, Berrocal VJ, Milando C … +3 more , Gilani O, Arunachalam S, Zhang KM
Res Rep Health Eff Inst · 2020 Mar · PMID 32239871
INTRODUCTION: The adverse health effects associated with exposure to traffic-related air pollutants (TRAPs) remain a key public health issue. Often, exposure assessments have not represented the small-scale variation and...
INTRODUCTION: The adverse health effects associated with exposure to traffic-related air pollutants (TRAPs) remain a key public health issue. Often, exposure assessments have not represented the small-scale variation and elevated concentrations found near major roads and in urban settings. This research explores approaches aimed at improving exposure estimates of TRAPs that can reduce exposure measurement error when used in health studies. We consider dispersion models designed specifically for the near-road environment, as well as spatiotemporal and data fusion models. These approaches are implemented and evaluated utilizing data collected in recent modeling, monitoring, and epidemiological studies conducted in Detroit, Michigan. APPROACH: Dispersion models, which estimate near-road pollutant concentrations and individual exposures based on first principles - and in particular, high fidelity models - can provide great flexibility and theoretical strength. They can represent the spatial variability of TRAP concentrations at locations not measured by conventional and spatially sparse air quality monitoring networks. A number of enhancements to dispersion modeling and mobile on-road emissions inventories were considered, including the representation of link-based road networks and updated estimates of temporal allocation of traffic activity, emission factors, and meteorological inputs. The recently developed Research LINE-source model (RLINE), a Gaussian line-source dispersion model specifically designed for the near-road environment, was used in an operational evaluation that compared predicted concentrations of nitrogen oxides (NO), carbon monoxide (CO), and PM (particulate matter ≤ 2.5 µm in aerodynamic diameter) with observed concentrations at air quality monitoring stations located near high-traffic roads. Spatiotemporal and data fusion models provided additional and complementary approaches for estimating TRAP exposures. We formulated both nonstationary universal kriging models that exploit the spatial correlation in the monitoring data, and data fusion models that leverage the information contained in both the monitoring data and the output of numerical models, specifically RLINE. These models were evaluated using observations of nitric oxide (NO), NO, black carbon (BC), and PM monitored along transects crossing major roads in Detroit. We also examined model assumptions, including the appropriateness of the covariance functions, errors in RLINE outputs, and the effects of jointly modeling two pollutants and using an updated emission inventory. RESULTS: For CO and NO, dispersion model performance was best when monitoring sites were close to major roads, during downwind conditions, during weekdays, and during certain seasons. The ability to discern local and particularly the traffic-related portion of PM was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain sources (e.g., area, fugitive) and some processes (e.g., formation of secondary aerosols). Sensitivity analyses of alternative meteorological inputs and updated emission factors showed some performance gain when using local (on-site) meteorological data and updated inventories. Overall, the operational evaluation suggested RLINE's usefulness for estimating spatially and temporally resolved exposure estimates. The application of the universal kriging models confirmed that wind speed and direction are important drivers of nonstationarity in pollutant concentrations, and that these models can predict exposure estimates that have lower prediction errors than do stationary model counterparts. The application of the Bayesian data fusion models suggested that the RLINE output had a spatially varying additive bias for NO and PM and provided little additional information for NO, besides what is already contained in traffic and geographical information system (GIS) covariates, but had improved estimates of PM concentrations. Results of the nonstationary Bayesian data fusion model that used RLINE output across a field spanning the measurement sites were similar to a regression-based Bayesian data fusion approach that used only RLINE output at the monitoring locations, with the latter being computationally less burdensome. Using the regression-based Bayesian data fusion model, we found that RLINE with the updated emission inventory provided results that were more useful for estimating NO concentration at unmonitored sites, but the updated emission inventory did not improve predictions of PM concentrations. Joint modeling of NO and PM was not useful, a result of differences in RLINE's utility in predicting PM and NO - useful for the former, but not for the latter - and differences in the spatial dependence structures of the two pollutants. Overall, information provided by RLINE was shown to have the potential to improve spatiotemporal estimates of TRAP concentrations. CONCLUSIONS: The study results should be interpreted and generalized cautiously given the limitations of the data used. Similar analyses in other settings are recommended for confirming and extending our findings. Still, the study highlights considerations that are relevant for exposure estimates used in health studies. The ability of a dispersion model to accurately reproduce and predict a pollutant depends on the pollutant as well as on spatial and temporal factors, such as the distance and direction from the road, time-of-day, and day-of-week. The nature and source of exposure measurement errors should be taken into consideration, particularly in health studies that take advantage of time- activity information that describes where and when individuals are exposed to pollution. Efforts to refine model inputs and improve model performance can be helpful; meteorological inputs may be the most critical. For both dispersion and spatiotemporal statistical models, sufficient and high-quality monitoring data are essential for developing and evaluating these models. Our analyses using Bayesian data fusion models confirm the presence of spatially varying errors in dispersion model outputs and allow quantification of both the magnitude and the spatial nature of these errors. This valuable information can be leveraged in health studies examining air pollution exposure as well as in studies informing regulatory responses.
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