Developing Multipollutant Exposure Indicators of Traffic Pollution: The Dorm Room Inhalation to Vehicle Emissions (DRIVE) Study.
Ontology highlight
ABSTRACT:
SUBMITTER: Sarnat JA
PROVIDER: S-EPMC7266376 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Developing Multipollutant Exposure Indicators of Traffic Pollution: The Dorm Room Inhalation to Vehicle Emissions (DRIVE) Study.
Sarnat J A JA Russell A A Liang D D Moutinho J L JL Golan R R Weber R J RJ Gao D D Sarnat S E SE Chang H H HH Greenwald R R Yu T T
Research report (Health Effects Institute) 20180401 196
<h4>Introduction</h4>The Dorm Room Inhalation to Vehicle Emissions (DRIVE<sup>2</sup>) study was conducted to measure traditional single-pollutant and novel multipollutant traffic indicators along a complete emission-to-exposure pathway. The overarching goal of the study was to evaluate the suitability of these indicators for use as primary traffic exposure metrics in panel-based and small-cohort epidemiological studies.<h4>Methods</h4>Intensive field sampling was conducted on the campus of the ...[more]
Similar Datasets
Project description:A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31-0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80-0.98). NOx correlations with PMF factors varied across cities (r = 0.29-0.67), while correlations with IMSIs were relatively consistent (r = 0.61-0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58-0.98) than with PMF-derived factors (r = 0.07-0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.
| S-EPMC4245641 | biostudies-literature
Project description:Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed-volume relationship, the California Line Source Dispersion Model, an empirical NO2-NOx relationship, estimated travel time changes during congestion, and concentration-response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, "U" shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2-NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties.
| S-EPMC4243514 | biostudies-literature
Project description:Accurately characterizing human exposures to traffic-related air pollutants (TRAPs) is critical to public health protection. However, quantifying exposure to this single source is challenging, given its extremely heterogeneous chemical composition. Efforts using single-species tracers of TRAP are, thus, lacking in their ability to accurately reflect exposures to this complex mixture. There have been recent discussions centered on adopting a multipollutant perspective for sources with many emitted pollutants to maximize the benefits of control expenditures as well as to minimize population and ecosystem exposure. As part of a larger study aimed to assess a complete emission-to-exposure pathway of primary traffic pollution and understand exposure of individuals in the near-road environment, an intensive field campaign measured TRAPs and related data (e.g., meteorology, traffic counts, and regional air pollutant levels) in Atlanta along one of the busiest highway corridors in the US. Given the dynamic nature of the near-road environment, a multipollutant exposure metric, the Integrated Mobile Source Indicator (IMSI), which was generated based on emissions-based ratios, was calculated and compared to traditional single-species methods for assessing exposure to mobile source emissions. The current analysis examined how both traditional and non-traditional metrics vary spatially and temporally in the near-road environment, how they compare with each other, and whether they have the potential to offer more accurate means of assigning exposures to primary traffic emissions. The results indicate that compared to the traditional single pollutant specie, the multipollutant IMSI metric provided a more spatially stable method for assessing exposure, though variations occurred based on location with varying results among the six sites within a kilometer of the highway.
| S-EPMC7202092 | biostudies-literature
Project description:BackgroundAmbient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes.ObjectiveTo determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature.DesignRandomized crossover trial. (ClinicalTrials.gov: NCT05454930).SettingIn-vehicle scripted commutes driven through traffic in Seattle, Washington, during 2014 to 2016.ParticipantsNormotensive persons aged 22 to 45 years (n = 16).InterventionOn 2 days, on-road air was entrained into the vehicle. On another day, the vehicle was equipped with high-efficiency particulate air (HEPA) filtration. Participants were blinded to the exposure and were randomly assigned to the sequence.MeasurementsFourteen 3-minute periods of blood pressure were recorded before, during, and up to 24 hours after a drive. Image-based central retinal arteriolar equivalents (CRAEs) were measured before and after. Brachial artery diameter and gene expression were also measured and will be reported separately.ResultsMean age was 29.7 years, predrive systolic blood pressure was 122.7 mm Hg, predrive diastolic blood pressure was 70.8 mm Hg, and drive duration was 122.3 minutes (IQR, 4 minutes). Filtration reduced particle count by 86%. Among persons with complete data (n = 13), at 1 hour, mean diastolic blood pressure, adjusted for predrive levels, order, and carryover, was 4.7 mm Hg higher (95% CI, 0.9 to 8.4 mm Hg) for unfiltered drives compared with filtered drives, and mean adjusted systolic blood pressure was 4.5 mm Hg higher (CI, -1.2 to 10.2 mm Hg). At 24 hours, adjusted mean diastolic blood pressure (unfiltered) was 3.8 mm Hg higher (CI, 0.02 to 7.5 mm Hg) and adjusted mean systolic blood pressure was 1.1 mm Hg higher (CI, -4.6 to 6.8 mm Hg). Adjusted mean CRAE (unfiltered) was 2.7 μm wider (CI, -1.5 to 6.8 μm).LimitationsImprecise estimates due to small sample size; seasonal imbalance by exposure order.ConclusionFiltration of TRAP may mitigate its adverse effects on blood pressure rapidly and at 24 hours. Validation is required in larger samples and different settings.Primary funding sourceU.S. Environmental Protection Agency and National Institutes of Health.
| S-EPMC11259360 | biostudies-literature
Project description:BACKGROUND: Long-term exposures to particulate matter air pollution (PM2.5 and PM10) and high traffic load have been associated with markers of systemic inflammation. Epidemiological investigations have focused primarily on total PM, which represents a mixture of pollutants originating from different sources. OBJECTIVE: We investigated associations between source-specific PM and high-sensitive C-reactive protein (hs-CRP), an independent predictor of cardiovascular disease. METHODS: We used data from the first (2000-2003) and second examination (2006-2008) of the Heinz Nixdorf Recall study, a prospective population-based German cohort of initially 4,814 participants (45-75 years of age). We estimated residential long-term exposure to local traffic- and industry-specific fine particulate matter (PM2.5) at participants' residences using a chemistry transport model. We used a linear mixed model with a random participant intercept to estimate associations of source-specific PM and natural log-transformed hs-CRP, controlling for age, sex, education, body mass index, low- and high-density lipoprotein cholesterol, smoking variables, physical activity, season, humidity, and city (8,204 total observations). RESULTS: A 1-?g/m3 increase in total PM2.5 was associated with a 4.53% increase in hs-CRP concentration (95% CI: 2.76, 6.33%). hs-CRP was 17.89% (95% CI: 7.66, 29.09%) and 7.96% (95% CI: 3.45, 12.67%) higher in association with 1-?g/m3 increases in traffic- and industry-specific PM2.5, respectively. RESULTS for PM10 were similar. CONCLUSIONS: Long-term exposure to local traffic-specific PM (PM2.5, PM10) was more strongly associated with systemic inflammation than total PM. Associations of local industry-specific PM were slightly stronger but not significantly different from associations with total PM.
| S-EPMC4080540 | biostudies-literature
Project description:Mortality in non-cystic fibrosis bronchiectasis (NCFB) is known to be influenced by a number of factors such as gender, age, smoking history and Pseudomonas aeruginosa, but the impact of traffic related air pollution indicators on NCFB mortality is unknown.We followed 183 patients aged 18 to 65 years with a HRCT proven diagnosis of NCFB and typical symptoms, who had visited the outpatient clinic at the University Hospital of Leuven, Belgium, between June 2006 and October 2012. We estimated hazard ratios (HR) for mortality in relation to proximity of the home to major roads and traffic load, adjusting for relevant covariables (age, gender, disease severity, chronic macrolide use, smoking history, socioeconomic status and Pseudomonas aeruginosa colonization status).Fifteen out of the 183 included patients died during the observation period. Residential proximity to a major road was associated with the risk of dying with a HR 0.28 (CI 95% 0.10-0.77; p?=?0.013) for a tenfold increase in distance to a major road. Mortality was also associated with distance-weighted traffic density within 100 meters (HR for each tenfold increase in traffic density 3.80; CI 95% 1.07-13.51; p?=?0.04) and 200 meters from the patient's home address (HR for each tenfold increase in traffic density 4.14; CI 95% 1.13-15.22; p?=?0.032).Traffic-related air pollution appears to increase the risk of dying in patients with NCFB.The study was approved by the local ethical committee of the UZ Leuven, Belgium (ML-5028), registered at ClinicalTrial.gov (NCT01906047).
| S-EPMC4156957 | biostudies-literature
Project description:BackgroundThe importance of describing, understanding and regulating multi-pollutant mixtures has been highlighted by the US National Academy of Science and the Environmental Protection Agency. Furthering our understanding of the health effects associated with exposure to mixtures of pollutants will lead to the development of new multi-pollutant National Air Quality Standards.ObjectivesIntroduce a framework within which diagnostic methods that are based on our understanding of air pollution mixtures are used to validate the distinct air pollutant mixtures identified using cluster analysis.MethodsSix years of daily gaseous and particulate air pollution data collected in Boston, MA were classified solely on their concentration profiles. Classification was performed using k-means partitioning and hierarchical clustering. Diagnostic strategies were developed to identify the most optimal clustering.ResultsThe optimal solution used k-means analysis and contained five distinct groups of days. Pollutant concentrations and elemental ratios were computed in order to characterize the differences between clusters. Time-series regression confirmed that the groups differed in their chemical compositions. The mean values of meteorological parameters were estimated for each group and air mass origin between clusters was examined using back-trajectory analysis. This allowed us to link the distinct physico-chemical characteristics of each cluster to characteristic weather patterns and show that different clusters were associated with distinct air mass origins.ConclusionsThis analysis yielded a solution that was robust to outlier points and interpretable based on chemical, physical and meteorological characteristics. This novel method provides an exciting tool with which to identify and further investigate multi-pollutant mixtures and link them directly to health effects studies.
| S-EPMC3774277 | biostudies-literature
Project description:Environmental health literacy is particularly relevant to racial/ethnic and linguistic minority populations who are likely to live near major roadways and highways. We conducted exploratory research to develop ways to communicate the risks of traffic-related air pollution to Puerto Rican adults living in and near Boston, Massachusetts. We held two initial focus groups with Spanish-speaking Puerto Rican adults (N = 16) enrolled in the Boston Puerto Rican Health Study (BPRHS). Most had only a high school education or less and earned a low income. We used thematic analysis of transcripts to suggest ways to improve three fact sheets designed to communicate BPRHS findings to the community. Based on these results, we made substantial revisions. We then conducted a second set of two focus groups with the same participants to assess revisions. Participants viewed the revised fact sheets more favorably and indicated that they found them easier to read. Lessons learned about improving readability and relevance included increasing text size, adding more graphics, chunking text, and providing more technical details. Designing successful environmental health communication tools that retain scientific accuracy is not a simple task. There is need for systematic attempts to evaluate and report on health literacy and community engagement processes for developing materials that are easy to read, culturally relevant, and that communicate complex environmental health information and concepts in ways people can understand and act on.
| S-EPMC5830854 | biostudies-literature
Project description:Epidemiological and toxicological studies continue to demonstrate correlative and causal relationships between exposure to traffic-related air pollution and various metrics of adverse pulmonary, cardiovascular, and neurological health effects. The key challenge for in vivo studies is replicating real-world, near-roadway exposure dynamics in laboratory animal models that mimic true human exposures. The advantage of animal models is the accelerated time scales to show statistically significant physiological and/or behavioral response. This work describes a novel exposure facility adjacent to a major freeway tunnel system that provides a platform for real-time chronic exposure studies. The primary conclusion is that particulate matter (PM) concentrations at this facility are routinely well below the National Ambient Air Quality Standards (NAAQS), but studies completed to date still demonstrate significant neurological and cardiovascular effects. Internal combustion engines produce large numbers of ultrafine particles that contribute negligible mass to the atmosphere relative to NAAQS regulated PM2.5 but have high surface area and mobility in the body. It is posited here that current federal and state air quality standards are thus insufficient to fully protect human health, most notably the developing and aging brain, due to regulatory gaps for ultrafine particles.
| S-EPMC9437142 | biostudies-literature
Project description:The demand for transportation, driven by an increasing global population, is continuously rising. This has led to a higher number of vehicles on the road and an increased reliance on fossil fuels. Consequently, the rise in atmospheric carbon dioxide (CO2) levels has contributed to global warming. Therefore, it is important to consider sustainable transportation practices to meet climate change mitigation targets. In this research paper, a non-linear mathematical model is developed to study the dynamics of atmospheric CO2 concentration in relation to human population, economic activities, forest biomass, and vehicle population. The developed model is analyzed qualitatively to understand the long-term behavior of the system's dynamics. Model parameters are fitted to actual data of world population, human economic activities, atmospheric CO2, forest biomass, and vehicle population. It is shown that increased vehicular CO2 emissions have a potential contribution to the increase in atmospheric CO2 and the decline of human population. Numerical simulations are carried out to verify the analytical findings and we performed global sensitivity analysis to explore the impacts of different sensitive parameters on the CO2 dynamics.
| S-EPMC10825272 | biostudies-literature