Research report (Health Effects Institute)最新文献

{"title":"Comparison of Long-Term Air Pollution Exposure from Mobile and Routine Monitoring, Low-Cost Sensors, and Dispersion Models.","authors":"G Hoek, F Bouma, N Janssen, J Wesseling, S van Ratingen, J Kerckhoffs, U Gehring, W Hendricx, R Vermeulen, K de Hoogh","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>Assessment of long-term exposure to outdoor air pollution remains a major challenge for epidemiological studies. One of these challenges is characterizing fine-scale spatial variation of the ambient concentrations of key traffic-related air pollutants - including ultrafine particles (UFPs), black carbon (BC), and nitrogen dioxide (NO<sub>2</sub>). Epidemiological studies have used widely different approaches to address these challenges, including empirical land use regression (LUR) models based on fixed-site routine or targeted monitoring, low-cost sensor networks, mobile monitoring, and deterministic dispersion models. Little information is available about the relative performance of these different approaches for assessing long-term exposure to traffic-related air pollution. Different methods may result in heterogeneity in health effect estimates from epidemiological studies applying different exposure-assessment approaches.</p><p><p>The Specific Aims of the study.</p><p><p>1. Develop long-term ambient air pollution exposure estimates for selected epidemiological studies based on low-cost sensors, mobile and fixed-site monitoring, and deterministic dispersion modeling.</p><p><p>2. Compare different exposure assessment methods in terms of their ability to predict spatial variation of long-term average concentrations using external validation data.</p><p><p>3. Compare different exposure assessment methods in terms of air pollution effect estimates in selected epidemiological studies.</p><p><p>We assessed UFPs, NO<sub>2</sub>, BC, and particulate matter ≤2.5 μm in aerodynamic diameter (PM<sub>2.5</sub>).</p><p><strong>Methods: </strong>We evaluated annual average air pollution concentrations across the Netherlands using a suite of different exposure models, which differed in modeling approach (empirical LUR, deterministic dispersion models) and monitoring data used (low-cost sensors, mobile monitoring, nationwide and Europewide routine monitoring, and study-specific targeted monitoring). For empirical models, we tested three model development algorithms: supervised linear regression (SLR), Random Forest, and least absolute shrinkage and selection operator (LASSO). The predictions of the models were compared at 20,000 addresses across the Netherlands. The performance was also tested on external validation data, which were obtained from a new campaign (2021-2023) and existing data from different years, allowing assessment of how well recent models predict past air pollution exposure. Epidemiological analyses in three cohort studies were conducted to compare health effect estimates of the different exposure models. We assessed associations of air pollution in a national administrative cohort with natural-cause and cause-specific mortality, in a cohort study that had detailed lifestyle data with natural-cause mortality and incidence of stroke and coronary events, and in a mature birth cohort with lung function and ast","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 226","pages":"1-101"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air Pollution Exposure, Prefrontal Connectivity, and Emotional Behavior in Early Adolescence.","authors":"M M Herting, E Burnor, H Ahmadi, S P Eckel, W Gauderman, J Schwartz, K Berhane, R McConnell, J-C Chen","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>Emerging evidence suggests that ambient air pollution may affect the developing brain and contribute to an increased risk of mental health problems. However, most studies have focused on prenatal or early postnatal periods of exposure, with less attention given to the dynamic neurodevelopment period of early adolescence. Moving forward, it is necessary to consider additional periods of exposure, such as adolescence, and the biological mechanisms that may drive potential neurotoxicological effects. This project aimed to investigate whether 1-year exposure to ambient fine particulate matter (PM<sub>2.5</sub>) and nitrogen dioxide (NO<sub>2</sub>) at 9-10 years of age was associated with (1) concurrent prefrontal white matter connectivity at ages 9-10 years and (2) emotional health problems at ages 9-10 years as well as 1 year later. Lastly, we hypothesized that poor prefrontal white matter connectivity might be an intermediate marker (i.e., mediator) for the association between 1-year ambient exposure and mental health outcomes.</p><p><strong>Methods: </strong>We leveraged data from the multisite, nationwide Adolescent Brain Cognitive Development Study (ABCD Study; N = 11,880), with cross-sectional data on diffusion-weighted imaging at 9-10 years (baseline visit) and longitudinal emotional health outcomes at 9-10 (baseline visit) and 10-11 years (1-year follow-up). Based on residential addresses at ages 9-10 years, novel hybrid spatiotemporal exposure models were applied to estimate 1-year average ambient exposure to PM<sub>2.5</sub> and NO<sub>2</sub>. Diffusion tensor imaging (DTI) was used to measure white matter microstructure in tracts that innervate the prefrontal cortex. Emotional behavioral problems were measured based on caregiver reports using the Child Behavioral Checklist (CBCL). Mixed-effect two-pollutant models were fit using both PM<sub>2.5</sub> and NO<sub>2</sub> and adjusted for the study site, several potential sociodemographic and lifestyle characteristics, and magnetic resonance imaging (MRI) precision variables when necessary. For emotional health outcomes, longitudinal models included interaction terms for pollutant-by-time for both pollutants. Sensitivity analyses were conducted that also accounted for the number of years the child resided at the residential address, as well as adjusting for prenatal PM<sub>2.5</sub> and NO<sub>2</sub> exposures.</p><p><strong>Results: </strong>The final analytic sample included 7,546 participants with DTI data and 9,334 participants with emotional behavior data. The annual exposures to PM<sub>2.5</sub> and NO<sub>2</sub> across 21 study sites were 7.66 μg/m<sup>3</sup> [1.72-15.90 μg/m<sup>3</sup>] and 18.61 ppb [0.73-37.94 ppb], respectively. Annual exposure to PM<sub>2.5</sub> was found to be significantly related to prefrontal structural connectivity, including fractional anisotropy (FA) in the right superior longitudinal fasciculus and widespread differ","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 225","pages":"1-56"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of Vehicle Emission Regulations and Local Congestion Policies on Birth Outcomes Associated with Traffic Air Pollution.","authors":"P Hystad, M Willis, E Hill, D Schrank, J Molitor, A Larkin, B Ritz","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>In the United States, billions of dollars have been spent implementing interventions to reduce traffic-related air pollution (TRAP). These interventions are usually regulatory actions focused on reducing tailpipe emissions. However, they also include local programs to reduce traffic congestion and excess vehicle emissions, such as electronic tolls and roadway capacity improvements. Few health studies have empirically evaluated the direct impact of air pollution exposure reductions from these emission regulations and congestion reduction programs; no studies have examined infant health, an important population health outcome linked to air pollution exposures.</p><p><strong>Objective: </strong>Assess changes in birth outcomes for all recorded births in Texas from 1996 to 2016 associated with (1) long-term cumulative regulatory improvements of motor vehicle emissions and resulting TRAP change and (2) local congestion reduction programs that may yield localized TRAP changes over shorter time periods.</p><p><strong>Methods: </strong>We used Vital Statistics data in Texas from 1996 to 2016 (n = 8.1 million recorded births; n = 6,158,518 births analyzed after exclusions). We calculated diverse traffic-related exposure measures using residential addresses at the time of delivery. We implemented research triangulation methods using different study design and analysis approaches to test our primary hypotheses on the effects of long-term cumulative regulatory improvements and local congestion reduction programs on birth outcomes.</p><p><strong>Results: </strong>Traffic-related exposure measures (nitrogen dioxide [NO<sub>2</sub>] air pollution, traffic volume, congestion) were consistently associated with adverse birth outcomes over the 20-year study period. This finding is supported by an analysis of pregnant individuals living upwind versus downwind of the same major road, where living downwind within 500 m was associated with an 11.6-g decrease (95% CI: -18.01, -5.21) in term birth weight. For all pregnant individuals, NO<sub>2</sub> exposures decreased 59% from 1996 to 2016, while the total vehicle miles traveled (VMT) within 500 m of residential addresses (VMT<sub>500m</sub>) remained relatively stable. We observed marked differences in TRAP exposure for pregnant individuals by sociodemographic characteristics. While levels of air pollution disparities reduced in absolute terms over the 20 years, relative disparities persisted, and large differences in traffic levels remained. The magnitude of associations between VMT<sub>500m</sub> and adverse birth outcomes decreased for term low birth weight (-60%, OR in 1996: 1.08, OR in 2016: 1.03 for the highest vs. lowest quintile) and preterm (-65%) and very preterm (-61%) births, but not for term birth weight. A direct analysis of congestion exposure for 2015-2016 births, measured for all roadways in Texas using connected device data, showed that congestion was associated with ","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 223","pages":"1-88"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiometabolic Health Effects of Air Pollution, Noise, Green Space, and Socioeconomic Status: The HERMES Study.","authors":"O Raaschou-Nielsen, A H Poulsen, M Ketzel, L M Frohn, N Roswall, U A Hvidtfeldt, J H Christensen, J Brandt, M Sørensen","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>We conducted the HERMES study to address the role of source-specific air pollution and the independent effects of air pollution, noise, and green space as well as the identification of susceptible subgroups defined by sociodemographic characteristics, stress conditions, and comorbidity in relation to cardiometabolic health. We studied three cohorts, a chemistry transport model (CTM) system, a noise model, a high-resolution land use map, and Danish registries on health and sociodemographic variables at individual and small-area levels.</p><p><strong>Methods: </strong>Using Danish registries we defined a cohort of about 2 million persons living in Denmark. We also used data from the Danish National Health Survey (DNHS) (<i>n =</i> 246,766) and the Diet Cancer and Health - Next Generations cohort (DCH-NG) (<i>n</i> = 32,851). The Danish registries provided sociodemographic data at individual and small-area levels and allowed identification of medical diagnoses, comorbidity, and financial stress. The other two cohorts included information on lifestyle habits and measurements of blood pressure and biomarkers. We used Cox models for analyses of associations between exposures and type 2 diabetes, myocardial infarction (MI), and stroke. For analyses of interactions, we used both Cox and Aalen models and multivariate linear regression models for the analyses of air pollution and biomarkers.</p><p><strong>Results: </strong>Air pollution concentrations correlated well with measurements. Analyses of associations between air pollution and type 2 diabetes, MI, and stroke adjusted for individual and area-level sociodemographic variables showed that further adjustment for individual lifestyle had minimal effect on the risk estimates. All four air pollutants were associated with a higher risk of each of the three endpoints. The local traffic contribution to air pollution seemed more important for risk of type 2 diabetes than the contribution from all other sources combined, whereas for MI and stroke, the contribution from all other sources seemed most important. The most consistent interaction was a stronger association between air pollution and type 2 diabetes, MI, and stroke among those with comorbidity. For MI and stroke, we found several interactions on the absolute scale that could not be detected on the relative scale. In multiexposure analyses, we found that particulate matter ≤2.5 μm in aerodynamic diameter (PM<sub>2.5</sub>) was most important for cardiovascular diseases, and ultrafine particles (UFPs) were most important for type 2 diabetes. We also found that noise and lack of green space were associated with all three endpoints. Analyses of the DCH-NG cohort showed associations between exposure to air pollution and higher concentrations of non-high-density lipoprotein, lower concentrations of high-density lipoprotein, and higher blood pressure. The contribution to air pollution from sources other than local traffic se","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 222","pages":"1-62"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11816022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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.","authors":"S D Adar, M Pedde, R Hirth, A Szpiro","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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<sub>2.5</sub>) 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<sup>3</sup> (-1.5 to -0.5) lower ambient PM<sub>2.5</sub> 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 n","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 221","pages":"1-44"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11587696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air Pollution in Relation to COVID-19 Morbidity and Mortality: A Large Population-Based Cohort Study in Catalonia, Spain (COVAIR-CAT).","authors":"C Tonne, O Ranzani, A Alari, J Ballester, X Basagaña, C Chaccour, P Dadvand, T Duarte, M Foraster, C Milà, M J Nieuwenhuijsen, S Olmos, A Rico, J Sunyer, A Valentín, R Vivanco","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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<sub>2</sub><sup>3</sup>), particulate matter ≤2.5 μm in aerodynamic diameter (PM<sub>2.5</sub>), particulate matter ≤10 μm in aerodynamic diameter (PM<sub>10</sub>), and maximum 8-hr-average ozone (O<sub>3</sub>) at a spatial resolution of 250 m for the period 2018-2020.</p><p><strong>Results: </strong>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<sup>3</sup> for NO<sub>2</sub>, 13.8 (2.2) μg/m<sup>3</sup> for PM<sub>2.5</sub>, and 91.6 (8.2) μg/m<sup>3</sup> for O<sub>3</sub>. In Aim 1, an increase of 16.1 μg/m<sup>3</sup> NO<sub>2</sub> 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<sub>2</sub> and PM<sub>2.5</sub> 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<sup>3</sup> NO<sub>2</sub> and 5%; 1-8 per 2.6-μg/m<sup>3</sup> PM<sub>2.5</sub>) as well as for all lower respiratory infections (LRIs) (18%; 14-22 per 16.4-μg/m<sup>3</sup> NO<sub>2</sub> and 14%; 11-17 per 2.6-μg/m<sup>3</sup> PM<sub>2.5</sub>) before the COVID-19 pandemic. Associations for COVID-1","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 220","pages":"1-48"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Birth Cohort Studies of Long-Term Exposure to Ambient Air Pollution in Early Life and Development of Asthma in Children and Adolescents from Denmark.","authors":"M Pedersen, S Liu, Z J Andersen, Andersen Am Nybo, J Brandt, E Budtz-Jørgensen, K Bønnelykke, L M Frohn, M Ketzel, J Khan, Pedersen C Tingskov, L T Stayner, J Zhang, B Brunekreef, S Loft","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>The study included all live-born singletons in Denmark during 1998-2016 (N = 1,060,154), participants in the Danish National Birth Cohort (DNBC<sup>3</sup>, 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<sub>2.5</sub> and PM<sub>10</sub>), PM-related elemental carbon (EC), organic carbon (OC), sulfate (SO<sub>4</sub><sup>2-</sup>), nitrate (NO<sub>3</sub><sup>-</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), secondary organic aerosols (SOA), and sea salt as well as nitrogen dioxide (NO<sub>2</sub>), nitrogen oxides (NO<sub>x</sub>), sulfur dioxide (SO<sub>2</sub>), and ozone (O<sub>3</sub>) - from all sources. Prenatal and postnatal time-weighted mean exposures were calculated for all residential addresses.</p><p><p>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<sub>1</sub>).</p><p><p>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.</p><p><strong>Results: </strong>The prenatal mean and interquartile range (IQR) concentrations of PM<sub>2.5</sub> and NO<sub>2</sub> were 10.5 (2.4) and 17.5 (8.7) μg/m<sup>3</sup>. In the nationwide study the risk of asthma incidence increased with increasing prenatal exposure to all pollutants except for O<sub>3</sub> 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<sub>2.5</sub> and 1.04 (1.02-1.05) for NO<sub>2</sub>. The corresponding ","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 219","pages":"1-63"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Model-Based Marginal Societal Health Benefits of Air Pollution Emission Reductions in the United States and Canada.","authors":"A Hakami, S Zhao, M Soltanzadeh, P Vasilakos, A Alhusban, B Oztaner, N Fann, H Chang, A Krupnick, T Russell","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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<sup>1</sup>) metric. We calculated BPTs as the valuations of premature mortality counts due to fine particulate matter (PM<sub>2.5</sub>; 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.</p><p><p>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<sub>2.5</sub> 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<sub>2.5</sub> 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.</p><p><p>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<sub>2.5</sub> 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.</p><p><p>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 engine","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 218","pages":"1-63"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Term Exposure to Outdoor Ultrafine Particles and Black Carbon and Effects on Mortality in Montreal and Toronto, Canada.","authors":"S Weichenthal, M Lloyd, A Ganji, L Simon, J Xu, A Venuta, A Schmidt, J Apte, H Chen, E Lavigne, P Villeneuve, T Olaniyan, M Tjepkema, R T Burnett, M Hatzopoulou","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>Numerous studies support an important relationship between long-term exposure to outdoor fine particulate air pollution (PM<sub>2.5</sub>) 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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 U","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 217","pages":"1-63"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable Multipollutant Exposure Assessment Using Routine Mobile Monitoring Platforms.","authors":"J A Apte, S E Chambliss, K P Messier, S Gani, A R Upadhya, M Kushwaha, V Sreekanth","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 empiri","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 216","pages":"1-54"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10957117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140121561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}