Evaluation and Application of Current Methods Used to Estimate Exposure to Traffic-Related Air Pollutants at High Spatial or Temporal Resolutions.

Strategies for reducing exposure to ambient air pollution in urban areas may be less effective as pollutants and their sources have shifted from being dominated by large point sources to more complex mixtures that include a sizeable fraction of traffic-related air pollutants (TRAP). In past decades, urban air pollution management strategies were designed to control pollutant emissions from point sources, while traffic-related emissions primarily were controlled by federal regulations. This approach now may not address the exposures experienced by vulnerable individuals that can result in adverse health impacts and inequities in the distribution of health impacts. New tools and methods are needed to characterize exposures from emission sources including traffic. This dissertation aims to address this need by applying and evaluating several methods to estimate exposures, focusing on TRAP. The first aim examines trends in emissions, concentrations and source apportionments of fine particulate matter (PM2.5, particles with a diameter less than 2.5 µm) in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. The analysis reveals that the fraction of PM2.5 due to mobile sources and other local emissions have increased (Detroit) or stayed constant (Chicago), even as total PM2.5 concentrations have decreased in both cities. The methodology demonstrated in this aim could be used to compare trends in the share of PM2.5 contributed by vehicles across major cities; many cities have different local regulations and fleet mixes that may affect trends in vehicle-related PM2.5, and the methods in this aim could be used to identify potentially preferred pollution reduction strategies. The second aim provides an operational evaluation of RLINE, a research-level line-source dispersion model developed by the United States Environmental Protection Agency (EPA) for the near-road environment. Model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. Implications for regulatory, health impact and epidemiologic applications include the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty. The third aim examines the sensitivity of exposure estimates produced by the RLINE model to the model’s meteorological, emission and traffic allocation data. The application focuses on health studies examining near-road exposures to TRAP. Overall, results highlight the need for appropriate model inputs, especially meteorological inputs, in dispersion model applications designed to estimate near-road concentrations and exposures to TRAPs. The fourth aim quantifies source contributions to individual exposures and provides an apportionment of exposures. Results show that most of the exposure was derived from background levels, although contributions from non-commercial traffic sources provided important contributions during the evening and early morning periods in the “indoor-at-home” micro-environment. Using the presented methodology for exposure apportionment, interventions incorporating the temporal and spatial nature of exposure could be applied to potentially lower the exposure of individuals in vulnerable groups. This dissertation identified results that emphasize the need to target mobile sources of air pollutants in policies and regulations intended to decrease pollutant concentrations in urban areas, and it provides methods to estimate exposures. Predicting exposures of vulnerable and susceptible populations, including low-income and minority individuals living near major roads, may be particularly challenging, but these populations also are likely to suffer a disproportionate share of vehicle-related health impacts. The modeling approaches examined in this dissertation can help characterize exposures and evaluate strategies that reduce adverse impacts.PHDEnvironmental Health SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145852/1/cmilando_1.pd

Enhancing models and measurements of traffic-related air pollutants for health studies using dispersion modeling and Bayesian data fusion

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors. Dr. Batterman and colleagues sought to improve model estimations by applying and systematically comparing the performance of different statistical models. The study made extensive use of data collected in the Near-road EXposures and effects of Urban air pollutants Study (NEXUS), a cohort study designed to examine the relationship between near-roadway pollutant exposures and respiratory outcomes in children with asthma who live close to major roadways in Detroit, Michigan

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM2.5), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM2.5 and O3 exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO2 exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO2 exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level

Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas

MCR: Open-Source Software to Automate Compilation of Health Study Report-Back

Sharing individualized results with health study participants, a practice we and others refer to as “report-back,” ensures participant access to exposure and health information and may promote health equity. However, the practice of report-back and the content shared is often limited by the time-intensive process of personalizing reports. Software tools that automate creation of individualized reports have been built for specific studies, but are largely not open-source or broadly modifiable. We created an open-source and generalizable tool, called the Macro for the Compilation of Report-backs (MCR), to automate compilation of health study reports. We piloted MCR in two environmental exposure studies in Massachusetts, USA, and interviewed research team members (n = 7) about the impact of MCR on the report-back process. Researchers using MCR created more detailed reports than during manual report-back, including more individualized numerical, text, and graphical results. Using MCR, researchers saved time producing draft and final reports. Researchers also reported feeling more creative in the design process and more confident in report-back quality control. While MCR does not expedite the entire report-back process, we hope that this open-source tool reduces the barriers to personalizing health study reports, promotes more equitable access to individualized data, and advances self-determination among participants

Data from: Anthropogenic Perturbations to the Atmospheric Molybdenum Cycle

Molybdenum (Mo) is an essential trace element that is, important for terrestrial and aquatic ecosystems, as it is required for biological nitrogen fixation and uptake. Mo is carried in particles to the atmosphere from sources such as desert dust, sea spray, and volcanoes resulting in losses and sources to different ecosystems. Atmospheric Mo deposition is essential on long time scales for soils which have lost Mo due to soil weathering, with consequences for nitrogen cycling. Anthropogenic changes to the Mo cycle from combustion, motor vehicles, and agricultural dust, are likely to be large, and have more than doubled sources of Mo to the atmosphere. Locally, anthropogenic changes to Mo in industrialized regions can represent a 100‐fold increase in deposition, and may affect nitrogen cycling in nitrogen‐limited ecosystems. This dataset supports these findings.We acknowledge the Atkinson Center for funding for this project, and NSF CCF-1522054

Anthropogenic Perturbations to the Atmospheric Molybdenum Cycle

International audienceMolybdenum (Mo) is a key cofactor in enzymes used for nitrogen (N) fixation and nitrate reduction, and the low availability of Mo can constrain N inputs, affecting ecosystem productivity. Natural atmospheric Mo aerosolization and deposition from sources such as desert dust, sea salt spray, and volcanoes can affect ecosystem function across long timescales, but anthropogenic activities such as combustion, motor vehicles, and agricultural dust have accelerated the natural Mo cycle. Here we combined a synthesis of global atmospheric concentration observations and modeling to identify and estimate anthropogenic sources of atmospheric Mo. To project the impact of atmospheric Mo on terrestrial ecosystems, we synthesized soil Mo data and estimated the global distribution of soil Mo using two approaches to calculate turnover times. We estimated global emissions of atmospheric Mo in aerosols (-1, with 40%-75% from anthropogenic sources. We approximated that for the top meter of soil, Mo turnover times range between 1,000 and 1,000,000 years. In some industrialized regions, anthropogenic inputs have enhanced Mo deposition 100 fold, lowering the soil Mo turnover time considerably. Our synthesis of global observational data, modeling, and a mass balance comparison with riverine Mo exports suggest that anthropogenic activity has greatly accelerated the Mo cycle, with potential to influence N limited ecosystems