Factors influencing mobile source particulate matter emissions-to-exposure relationships in the Boston urban area (original) (raw)
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Atmospheric Environment, 2007
Assessing the public health benefits from air pollution control measures is assisted by understanding the relationship between mobile source emissions and subsequent fine particulate matter (PM 2.5 ) exposure. Since this relationship varies by location, we characterized its magnitude and geographic distribution using the intake fraction (iF) concept. We considered emissions of primary PM 2.5 as well as particle precursors SO 2 and NO x from each of 3080 counties in the US. We modeled the relationship between these emissions and total US population exposure to PM 2.5 , making use of a source-receptor matrix developed for health risk assessment. For primary PM 2.5 , we found a median iF of 1.2 per million, with a range of 0.12-25. Half of the total exposure was reached by a median distance of 150 km from the county where mobile source emissions originated, though this spatial extent varied across counties from within the county borders to 1800 km away. For secondary ammonium sulfate from SO 2 emissions, the median iF was 0.41 per million (range: 0.050-10), versus 0.068 per million for secondary ammonium nitrate from NO x emissions (range: 0.00092-1.3). The median distance to half of the total exposure was greater for secondary PM 2.5 (450 km for sulfate, 390 km for nitrate). Regression analyses using exhaustive population predictors explained much of the variation in primary PM 2.5 iF (R 2 ¼ 0.83) as well as secondary sulfate and nitrate iF (R 2 ¼ 0.74 and 0.60), with greater near-source contribution for primary than for secondary PM 2.5 . We conclude that long-range dispersion models with coarse geographic resolution are appropriate for risk assessments of secondary PM 2.5 or primary PM 2.5 emitted from mobile sources in rural areas, but that more resolved dispersion models are warranted for primary PM 2.5 in urban areas due to the substantial contribution of near-source populations. r
Spatial variation in particulate matter components over a large urban area
Atmospheric Environment, 2014
To characterize exposures to particulate matter (PM) and its components, we performed a large sampling study of small-scale spatial variation in size-resolved particle mass and composition. PM was collected in size ranges of < 0.2, 0.2-to-2.5, and 2.5-to-10 μm on a scale of 100s to 1000s of meters to capture local sources. Within each of eight Southern California communities, up to 29 locations were sampled for rotating, month-long integrated periods at two different times of the year, six months apart, from Nov 2008 through Dec 2009. Additional sampling was conducted at each community's regional monitoring station to provide temporal coverage over the sampling campaign duration. Residential sampling locations were selected based on a novel design stratified by high-and low-predicted traffic emissions and locations over-and under-predicted from previous dispersion model and sampling comparisons. Primary vehicle emissions constituents, such as elemental carbon (EC), showed much stronger patterns of association with traffic than pollutants with significant secondary formation, such as PM 2.5 or water soluble organic carbon. Associations were also stronger during cooler times of the year (Oct through Mar). Primary pollutants also showed greater within-community spatial variation compared to pollutants with secondary formation contributions. For example, the average cool-season community mean and standard deviation (SD) for EC were 1.1 and 0.17 μg/m 3 , respectively, giving a coefficient of variation (CV) of 18%. For PM 2.5 , average mean and SD were 14 and 1.3 μg/m 3 , respectively, with a CV of 9%. We conclude that within-community spatial differences are important for accurate exposure assessment of traffic-related pollutants.
Formal health impact assessment (HIA), currently underused in the United States, is a relatively new process for assisting decision-makers in non-health sectors by estimating the expected public health impacts of policy and planning decisions. In this paper we quantify the expected air quality impacts of increased traffic due to a proposed new university campus extension in Chapel Hill, North Carolina. In so doing, we build the evidence base for quantitative HIA in the United States and develop an improved approach for forecasting traffic effects on exposure to ambient fine particulate matter (PM2.5) in air. Very few previous US HIAs have quantified health impacts and instead have relied on stakeholder intuition to decide whether effects will be positive, negative, or neutral. Our method uses an air dispersion model known as CAL3QHCR to predict changes in exposure to airborne, traffic-related PM2.5 that could occur due to the proposed new campus development. We employ CAL3QHCR in a new way to better represent variability in road grade, vehicle driving patterns (speed, acceleration, deceleration, and idling), and meteorology. In a comparison of model predictions to measured PM2.5 concentrations, we found that the model estimated PM2.5 dispersion to within a factor of two for 75% of data points, which is within the typical benchmark used for model performance evaluation. Applying the model to present-day conditions in the study area, we found that current traffic contributes a relatively small amount to ambient PM2.5 concentrations: about 0.14 μg/m3 in the most exposed neighborhood—relatively low in comparison to the current US National Ambient Air Quality Standard of 12 μg/m3. Notably, even though the new campus is expected to bring an additional 40,000 daily trips to the study community by the year 2025, vehicle-related PM2.5 emissions are expected to decrease compared to current conditions due to anticipated improvements in vehicle technologies and cleaner fuels.
Spatial and temporal variability in urban fine particulate matter concentrations
Environmental Pollution, 2011
Identification of hot spots for urban fine particulate matter (PM 2.5 ) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM 2.5 patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM 2.5 concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM 2.5 variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations.
International journal of environmental research and public health, 2014
Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model...
Spatially differentiated and source-specific population exposure to ambient urban air pollution
Models assessing exposure to air pollution often focus on macro-scale estimates of exposure to all types of sources for a particular pollutant across an urban study area. While results based on these models may aid policy makers in identifying larger areas of elevated exposure risk, they often do not differentiate the proportion of population exposure attributable to different polluting sources (e.g. traffic or industrial). In this paper, we introduce a population exposure modeling system that integrates air dispersion modeling, Geographic Information Systems (GIS), and population exposure techniques to spatially characterize a source-specific exposure to ambient air pollution for an entire urban population at a fine geographical scale. By area, total population exposure in Dallas County in 2000 was more attributable to vehicle polluting sources than industrial polluting sources at all levels of exposure. Population exposure was moderately correlated with vehicle sources (r = 0.440, p < 0.001) and weakly with industrial sources (r = 0.069, p = 0.004). Population density was strongly correlated with total exposure (r = 0.896, p < 0.001) but was not significantly correlated with individual or combined sources. The results of this study indicate that air quality assessments must incorporate more than industrial or vehicle polluting sources-based population exposure values alone, but should consider multiple sources. The population exposure modeling system proposed in this study shows promise for use by municipal authorities, policy makers, and epidemiologists in evaluating and controlling the quality of the air in the process of urban planning and mitigation measures.
Neighborhood-scale air quality impacts of emissions from motor vehicles and aircraft
Atmospheric Environment, 2013
Large inter-community variations in traffic-related pollutant levels were observed. Intra-community variations in pollutants were also observed. Disproportionate contributions of high-emitting vehicles to UFP levels were examined. UFP emissions appeared to have decreased over the past decade. On the closure day, particulate pollution was conspicuously reduced area-wide. a b s t r a c t A mobile monitoring platform (MMP) was used to measure real-time air pollutant concentrations in different built environments of Boyle Heights (BH, a lower-income community enclosed by several freeways); Downtown Los Angeles (DTLA, adjacent to BH with taller buildings and surrounded by several freeways); and West Los Angeles (WLA, an affluent community traversed by two freeways) in summer afternoons of 2008 and 2011 (only for WLA). Significant inter-community and less significant but observable intra-community differences in traffic-related pollutant concentrations were observed both in the residential neighborhoods studied and on their arterial roadways between BH, DTLA, and WLA, particularly for ultrafine particles (UFP). HEV, defined as vehicles creating plumes with concentrations more than three standard deviations from the adjusted local baseline, were encountered during 6e13% of sampling time, during which they accounted for 17e55% of total UFP concentrations both on arterial roadways and in residential neighborhoods. If instead a single threshold value is used to define HEVs in all areas, HEV's were calculated to make larger contributions to UFP concentrations in BH than other communities by factors of 2e10 or more. Santa Monica Airport located in WLA appears to be a significant source for elevated UFP concentrations in nearby residential neighborhoods 80e400 m downwind. In the WLA area, we also showed, on a neighborhood scale, striking and immediate reductions in particulate pollution (w70% reductions in both UFP and, somewhat surprisingly, PM 2.5 ), corresponding to dramatic decreases in traffic densities during an I-405 closure event ("Carmageddon") compared to non-closure Saturday levels. Although pollution reduction due to decreased traffic is not unexpected, this dramatic improvement in particulate pollution provides clear evidence air quality can be improved through strategies such as heavy-duty-diesel vehicle retrofits, earlier retirement of HEV, and transition to electric vehicles and alternative fuels, with corresponding benefits for public health.
International Journal of Environmental Research and Public Health
The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors—individuals’ routine travel patterns and the local variations of air pollution fields. We investigated whether individuals’ routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time–activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual’s mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estima...
Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area
Transportation Research Part D: Transport and Environment, 2004
Dispersion models are useful tools for setting emission control priorities and developing strategies for reducing air toxics emissions. Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. High local concentrations may be underestimated near major roadways, which are often clustered in urban centers. Here, we describe a methodology which utilizes a Geographic Information System to allocate benzene emissions to major road segments in an urban area and model the segments as elongated area sources. The Industrial Source Complex Short Term dispersion model is run using both gridded and link-based emissions to evaluate the effect of improved spatial allocation of emissions on ambient modeled benzene concentrations. Allocating onroad mobile emissions to road segments improves the agreement between modeled concentrations when compared with monitor observations, and also results in higher estimated concentrations in the urban center.
Transportation Research Record: Journal of the Transportation Research Board, 2014
Vehicular traffic is a major source of ambient air pollution in urban areas. Traffic-related air pollutants, including carbon monoxide, nitrogen oxides, particulate matter less than 2.5 μm in diameter, and diesel exhaust emissions, have been associated with adverse human health effects, especially in areas near major roads. In addition to emissions from vehicles, ambient concentrations of air pollutants include contributions from stationary sources and background (or regional) sources. Although dispersion models have been widely used to evaluate air quality strategies and policies and can represent the spatial and temporal variation in environments near roads, the use of these models in health studies to estimate air pollutant exposures has been relatively limited. This paper summarizes the modeling system used to estimate exposures in the Near-Roadway Exposure and Urban Air Pollutant Study, an epidemiological study that examined 139 children with asthma or symptoms consistent with ...