Skip to main content
eScholarship
Open Access Publications from the University of California

Inhalation of Vehicle Emissions in Urban Environments

  • Author(s): Marshall, Julian David
  • et al.
Abstract

This dissertation explores the relationship between motor vehicle emissions and the human inhalation intake of these emissions. Motor vehicles are ubiquitous to urban areas throughout the world. In most urban areas, vehicle emission are a significant contributor to air pollution problems. Inhalation of vehicle emissions has been shown to cause a number of adverse health effects. Better understanding of the relationship between emissions and inhalation will aid in designing effective strategies to reduce air pollution health effects. Understanding the emission-to-inhalation relationship is also important for estimating the total health impacts attributable emissions from a specific air pollution source, such as motor vehicles in a specific city.

Three objectives of his dissertation are (1) to quantify the emission-to-inhalation relationship in a way that is useful to other air quality and health researchers and to policy analysts; (2) to employ a variety of analytic approaches, in order to understand better the relative strengths and weakness of each approach; and (3) to demonstrate that the conclusions one draws from air quality analyses may depend on whether one uses as the figure-of-merit inhalation of air pollution, as is done for analyses in this work, or other common air quality metrics such as mass emission rate, ambient concentrations, or concentration at the location of the maximally exposed individual.

The methods employed here include several data analysis and modeling approaches. The data analyses incorporate a range of inputs, including results from air dispersion models of varying sophistication, tracer-gas experiments, and “tracers of opportunity” (gases that are emitted primarily by one source or source category). The inhalation model that I develop in Chapter 6 simulates the movement of people through an urban area, tracking the individual or population inhalation rate during simulated activities (e.g., shopping, driving, cooking).

The specific research topics considered in this work are as follows. In the first portion of this dissertation (Chapters 1 – 5), I generate estimates for a summary inhalation metric, called intake fraction, for vehicle emissions in urban areas. In the second portion (Chapters 6 – 7), I first develop a mobility-based GIS inhalation model for urban air pollution, and then, separately, consider how changes in urban population and land area would influence population inhalation of private passenger emissions.

Intake fraction is the fraction of emissions from some source that are cumulative inhaled by an exposed population. As an example of how intake fraction values might be used, the health effects attributable to an emissions source or source category can be estimated as the product of the mass emission rate, the intake fraction (mass inhaled per mass emitted), and the toxicity (impact per mass inhaled). Intake fraction values will, in general, vary over time and among source categories, source locations, and pollutants. Intake fraction can be used as a potential basis to rank sources when prioritizing emission control strategies. All else being equal, the public health benefit per amount of emission reduced would be larger for an emission source with a large intake fraction than for a source with a small intake fraction.

In Chapter 1, I provide background and framing for the research. In Chapter 2, I explore the use of intake fraction. This chapter gives examples of how and why intake fraction varies among sources; discusses how this variability may be exploited to increase the health effectiveness of air pollution policy; and, illustrates types of intake and intake fraction analyses one might carry out, depending on the information available.

In Chapter 3, I provide an estimate of the intake fraction for nonreactive vehicle emissions in California’s South Coast Air Basin (SoCAB). This estimate incorporates several inputs, including (1) measured ambient concentrations of benzene and carbon monoxide (CO), two pollutants that are primarily emitted by motor vehicles; (2) US Census data indicating population densities throughout the SoCAB; (3) time-activity pattern data indicating the amount of time people spend indoors, outdoors, and inside vehicles; (4) microenvironment factors for benzene and CO, which indicate how concentrations may differ in specific microenvironments as compared to the nearby ambient concentration; and (5) the time-varying population-average breathing rate. The annual average intake fraction for nonreactive gaseous vehicle emissions in the SoCAB is estimated as 48 per million, with an uncertainty of ~33%. This value means that 48 g of emissions are collectively inhaled by the population per million g emitted.

The SoCAB is an important case study, in part because of its large population size (~15 million people, or 1 in 19 US residents). It is also atypical of US urban areas because of its high population density (~860 person km-2) and because of the typically poor air dispersion owing to relatively frequent low inversion heights. In Chapter 4, I estimate the central tendency and main range for the intake fraction of vehicle emissions in urban areas throughout the US. I employ three independent approaches. First, I use a one-compartment mass-balance model of an urban area to combine meteorological data on wind speed and mixing heights with demographic data on urban population and land area. Second, I use a statistical model that relates observed ambient concentrations of carbon monoxide (CO) to motor vehicle emission factors for CO. Third, I evaluate model input and output for the US EPA’s National-scale Air Toxics Assessment (NATA), the EPA’s main nationwide air dispersion and exposure model. These approaches incorporate measurements and models, and range in analytic complexity from straightforward to sophisticated. There is broad consistency among the results that these approaches yield. Combining the results of these three investigations, I estimate that the population-weighted annual-average mean intake fraction for nonreactive gaseous vehicle emissions in US urban areas is ~ 14 per million, with a confidence interval of ~ 50%. This value is about three times lower than the intake fraction value of 48 per million estimated for the SoCAB.

These annual average intake fraction values (48 per million for the SoCAB and 14 per million for the mean value among US urban areas) represent fleet-wide average values. The intake fraction of emissions from a specific vehicle or class of vehicles will, in general, differ from this fleet-wide average. For example, all else being equal, the intake fraction of nonreactive vehicle emissions will be higher if a vehicle operates and emits pollution near population centers (e.g., neighborhood delivery trucks) than if it operates and emits pollution along rural highways. Another reason why intake fraction may differ among vehicles is “self-pollution,” which occurs when a portion of the emissions from a vehicle migrate to inside that vehicle. Experiments were conducted by others, wherein a tracer gas (SF6) was injected at a known flow rate into the exhaust manifold of a school bus, and at the same time concentrations of SF6 were recorded inside the bus. Six buses, representing a range of vehicle ages, were tested with windows open and closed, along actual school bus routes in the SoCAB. In Chapter 5, I analyze results from these tracer gas experiments to estimate children’s school bus self-pollution intake fraction, i.e., the fraction of a school bus’s emissions that are inhaled by students riding on that bus. The average value across the six buses and all bus runs is 27 per million; values were higher with windows closed rather than open, and for older rather than newer buses. When considering the emission from a specific bus, the mass of pollution collectively inhaled by students on that bus is comparable to, and in many cases greater than, the mass of pollution collectively inhaled by all other residents in an urban area.

In Chapter 6, I develop a GIS-based inhalation model for the SoCAB, and investigate the importance of mobility on estimated inhalation rates for several air pollutants. This investigation represents a new and promising approach for inhalation intake analyses. The four main inputs to the model are (1) spatially and temporally disaggregated estimates of ambient concentrations of specific air pollutants; (2) geo-coded time-location-activity survey data indicating individuals’ location (latitude and longitude) throughout the day; (3) microenvironment factors, which account for differences between the estimated ambient concentration and the exposure concentration attributable to outdoor pollution in locations such as indoors, outdoors, and in-vehicle; and (4) breathing rates, which vary by age, gender, and activity level. Model output is the estimated inhalation intake rate (μg d-1) for each modeled pollutant and each simulated person-day. Results indicate that mobility influences daily intake rates by less than a factor of two for most individuals. I also explore how inhalation intake rates differ among ethnic and income groups. For the five pollutants considered in this chapter, differences in median intake rate vary by 10 – 60% among the four ethnic groups considered (White, Hispanic, African-American, and Asian/Pacific Islander).

In Chapter 7, I explore the impact of urban population and land area on inhalation intake of vehicle emissions. Changes in population density can impact emissions, owing to changes in average daily distance traveled per person, and also intake fraction, owing to changes in the proximity between people and emissions. The main research question I consider in this chapter is the following: if increasing population density reduces emissions but increases intake fraction, does per capita inhalation intake of vehicle emissions increase or decrease with increasing population density? The research approach employs a one-compartment mass-balance model as an archetypal representation of a hypothetical urban area. This approach clarifies underlying relationships, aids in elucidating causal connections, and permits the problem to be analytically tractable. I find that the impact of population density on inhalation intake of vehicle emissions depends on how much emissions change in response to a change in population density (the “density-emissions elasticity”). To use infill development (i.e., increase population density) to reduce inhalation of vehicle emissions, urban planners must strive to achieve large magnitude density-emissions elasticity values, so that vehicle emissions are significantly reduced by density increases.

In Chapter 8, I provide a summary of this dissertation, suggestions for future research, and concluding remarks. Overall, this dissertation presents new information and new ways of thinking about the relationship between vehicle emissions and inhalation of these emissions. The tools developed and results presented may be useful in health risk assessment, in policy and economic analyses such as cost-benefit and cost-effectiveness analyses, in considering the goals and impacts of transportation and land-use planning, and in designing effective intervention strategies to reduce the health effects of atmospheric pollutants. The emerging field of exposure science has developed tools, metrics, and approaches that are ready to be integrated more fully into air quality research and management. I make several specific suggestions are made for future investigations. For example, the diurnal profile of population breathing rate is important for determining daily inhalation intake of air pollution, yet there is little information available from which to estimate this parameter. Previous research has explored the influence of urban population density on distance traveled by private passenger vehicles. Given the importance of diesel emissions to air quality and health, this same parameter should be estimated for diesel vehicles.

Main Content
Current View