Evaluating Efficiency-Equality Tradeoffs for Mobile Source Control Strategies in an Urban Area

In environmental risk management, there are often interests in maximizing public health benefits (efficiency) and addressing inequality in the distribution of health outcomes. However, both dimensions are not generally considered within a single analytical framework. In this study, we estimate both total population health benefits and changes in quantitative indicators of health inequality for a number of alternative spatial distributions of diesel particulate filter retrofits across half of an urban bus fleet in Boston, Massachusetts. We focus on the impact of emissions controls on primary fine particulate matter (PM_2.5) emissions, modeling the effect on PM_2.5 concentrations and premature mortality. Given spatial heterogeneity in baseline mortality rates, we apply the Atkinson index and other inequality indicators to quantify changes in the distribution of mortality risk. Across the different spatial distributions of control strategies, the public health benefits varied by more than a factor of two, related to factors such as mileage driven per day, population density near roadways, and baseline mortality rates in exposed populations. Changes in health inequality indicators varied across control strategies, with the subset of optimal strategies considering both efficiency and equality generally robust across different parametric assumptions and inequality indicators. Our analysis demonstrates the viability of formal analytical approaches to jointly address both efficiency and equality in risk assessment, providing a tool for decisionmakers who wish to consider both issues.     

Potential Health Effects of Light-Duty Diesel Exhaust

The purpose of this paper is to review briefly the evidence for potential human health effects that may result from increased dieselization of the nation's light-duty vehicle fleet. An effort is made to put the potential effects into perspective, both with regard to projected excess cancer deaths, should diesel exhaust be carcinogenic to humans, and in relation to past use of vehicles using leaded gasoline. Certain related research needs are highlighted. Available data concerning the relationship between diesel emissions, ambient air quality, and human health are summarized. On the basis of exposure estimates and relative potency factors, the authors conclude that the best estimate of the number of excess annual U.S. lung cancer deaths as a result of lifetime exposure to light-duty diesel particulate under 1990 conditions is between 80 and 1500. Available data suggest that the carcinogenic hazard of exhaust from vehicles burning leaded gasoline may be an order of magnitude greater, on a per mile basis, than that of diesel engines. The hazard of emissions from diesel are, in turn, probably an order of magnitude greater than that of gasoline engines with catalytic converters burning unleaded gasoline. Important research needs identified by the authors include determining whether diesel exhaust is in fact a human carcinogen, studying the effect of atmospheric chemical transformation of organics in diesel exhaust on the toxicity of the exhaust, making a better determination of the relative carcinogenicity of diesel and gasoline exhausts, and determining whether exposure to diesel exhaust contributes to the development or exacerbation of chronic lung disease or of respiratory illness, especially in the very young and the aged.     

Mixed-fleet single-terminal bus scheduling problem: Modelling,solution scheme and potential applications

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed Integer Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxembourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.     

Compressed Natural Gas Bus Safety: A Quantitative Risk Assessment

This study assesses the fire safety risks associated with compressed natural gas (CNG) vehicle systems, comprising primarily a typical school bus and supporting fuel infrastructure. The study determines the sensitivity of the results to variations in component failure rates and consequences of fire events. The components and subsystems that contribute most to fire safety risk are determined. Finally, the results are compared to fire risks of the present generation of diesel-fueled school buses. Direct computation of the safety risks associated with diesel-powered vehicles is possible because these are mature technologies for which historical performance data are available. Because of limited experience, fatal accident data for CNG bus fleets are minimal. Therefore, this study uses the probabilistic risk assessment (PRA) approach to model and predict fire safety risk of CNG buses. Generic failure data, engineering judgments, and assumptions are used in this study. This study predicts the mean fire fatality risk for typical CNG buses as approximately 0.23 fatalities per 100-million miles for all people involved, including bus passengers. The study estimates mean values of 0.16 fatalities per 100-million miles for bus passengers only. Based on historical data, diesel school bus mean fire fatality risk is 0.091 and 0.0007 per 100-million miles for all people and bus passengers, respectively. One can therefore conclude that CNG buses are more prone to fire fatality risk by 2.5 times that of diesel buses, with the bus passengers being more at risk by over two orders of magnitude. The study estimates a mean fire risk frequency of 2.2 x 10(-5) fatalities/bus per year. The 5% and 95% uncertainty bounds are 9.1 x 10(-6) and 4.0 x 10(-5), respectively. The risk result was found to be affected most by failure rates of pressure relief valves, CNG cylinders, and fuel piping.     

Exposure Misclassification and Threshold Concentrations in Time Series Analyses of Air Pollution Health Effects

Linear, no-threshold relationships are typically reported for time series studies of air pollution and mortality. Since regulatory standards and economic valuations typically assume some threshold level, we evaluated the fundamental question of the impact of exposure misclassification on the persistence of underlying personal-level thresholds when personal data are aggregated to the population level in the assessment of exposure-response relationships. As an example, we measured personal exposures to two particle metrics, PM2.5 and sulfate (SO4(2-)), for a sample of lung disease patients and compared these with exposures estimated from ambient measurements Previous work has shown that ambient:personal correlations for PM2.5 are much lower than for SO4(2-), suggesting that ambient PM2.5 measurements misclassify exposures to PM2.5. We then developed a method by which the measured:estimated exposure relationships for these patients were used to simulate personal exposures for a larger population and then to estimate individual-level mortality risks under different threshold assumptions. These individual risks were combined to obtain the population risk of death, thereby exhibiting the prominence (and the value) of the threshold in the relationship between risk and estimated exposure. Our results indicated that for poorly classified exposures (PM2.5 in this example) population-level thresholds were apparent at lower ambient concentrations than specified common personal thresholds, while for well-classified exposures (e.g., SO4(2-)), the apparent thresholds were similar to these underlying personal thresholds. These results demonstrate that surrogate metrics that are not highly correlated with personal exposures obscure the presence of thresholds in epidemiological studies of larger populations, while exposure indicators that are highly correlated with personal exposures can accurately reflect underlying personal thresholds.     

Caveats for Causal Interpretations of Linear Regression Coefficients for Fine Particulate (PM2.5) Air Pollution Health Effects

Recent linear regression analyses have concluded that decreasing levels of fine particulate matter (PM2.5) air pollution have increased life expectancy in the United States. These findings have left unresolved questions about the causal relation between reductions in PM2.5 levels and changes in cause‐specific (especially, cardiovascular disease, CVD) mortality risks. Their robustness (e.g., sensitivity to deletion of a single data point) has also been questioned. We investigate these issues in the National Mortality and Morbidity Air Pollution Study database. Comparing changes in PM2.5 levels and cause‐specific mortality rates for elderly people in 24 cities between two periods separated by a decade (1987–1989 and 1999–2000) shows that reductions in PM2.5 were significantly associated with increases in respiratory mortality rates and with decreases in CVD mortality rates. CVD and all‐cause mortality risks fell equally for all months of the year over this period, but average PM2.5 levels increased significantly for winter months. This casts doubts on the causal interpretation that declines in PM2.5 over the decade caused reduced short‐term mortality risks. Nonlinear regression suggests that reduced or negative marginal health benefits are associated with reductions of PM2.5 below 1999–2000 levels (about 15 μg/m³). Such nonlinear relations imply that risk communication statements that project a constant incremental reduction in mortality risks per unit reduction in PM2.5 do not adequately reflect the realistic possibility of nonlinear exposure‐response relations and diminishing returns to further exposure reductions.     

An Empirical Bayes Approach to Estimating the Relation of Mortality to Exposure to Particulate Matter

As part of its assessment of the health risks associated with exposure to particulate matter (PM), the U.S. Environmental Protection Agency analyzed the risks associated with current levels, and the risk reductions that might be achieved by attainment of alternative PM standards, in two locations in the United States, Philadelphia, and Los Angeles. The concentration-response function describing the relation between a health endpoint and ambient PM concentrations is an important component, and a source of substantial uncertainty, in such risk analyses. In the absence of location-specific estimates, the concentration-response functions necessary for risk assessments in Philadelphia and Los Angeles must be inferred from the available information in other locations. Although the functional form of the concentration-response relations is assumed to be the same everywhere, the value of the PM coefficient in that function may vary from one location to another. Under this model, a distribution describes the probability that the PM coefficient in a randomly selected location will lie in any range of interest. An empirical Bayes estimation technique was used to improve the estimation of location-specific concentration-response functions relating mortality to short-term exposure to particles of aerodynamic diameter less than or equal to 2.5 microm (PM-2.5), for which functions have previously been estimated in several locations. The empirical Bayes-adjusted parameter values and their SEs were used to derive an estimate of the distribution of PM-2.5 coefficients for mortality associated with short-term exposures. From this distribution, distributions of relative risks corresponding to different specified changes in PM-2.5 concentrations could be derived.     

Integrating risk assessment and life cycle assessment: a case study of insulation.

Increasing residential insulation can decrease energy consumption and provide public health benefits, given changes in emissions from fuel combustion, but also has cost implications and ancillary risks and benefits. Risk assessment or life cycle assessment can be used to calculate the net impacts and determine whether more stringent energy codes or other conservation policies would be warranted, but few analyses have combined the critical elements of both methodologies In this article, we present the first portion of a combined analysis, with the goal of estimating the net public health impacts of increasing residential insulation for new housing from current practice to the latest International Energy Conservation Code (IECC 2000). We model state-by-state residential energy savings and evaluate particulate matter less than 2.5 microm in diameter (PM2.5), NOx, and SO2 emission reductions. We use past dispersion modeling results to estimate reductions in exposure, and we apply concentration-response functions for premature mortality and selected morbidity outcomes using current epidemiological knowledge of effects of PM2.5 (primary and secondary). We find that an insulation policy shift would save 3 x 10(14) British thermal units or BTU (3 x 10(17) J) over a 10-year period, resulting in reduced emissions of 1,000 tons of PM2.5, 30,000 tons of NOx, and 40,000 tons of SO2. These emission reductions yield an estimated 60 fewer fatalities during this period, with the geographic distribution of health benefits differing from the distribution of energy savings because of differences in energy sources, population patterns, and meteorology. We discuss the methodology to be used to integrate life cycle calculations, which can ultimately yield estimates that can be compared with costs to determine the influence of external costs on benefit-cost calculations.     

Reassessing the human health benefits from cleaner air

Recent proposals to further reduce permitted levels of air pollution emissions are supported by high projected values of resulting public health benefits. For example, the Environmental Protection Agency recently estimated that the 1990 Clean Air Act Amendment (CAAA) will produce human health benefits in 2020, from reduced mortality rates, valued at nearly $2 trillion per year, compared to compliance costs of $65 billion ($0.065 trillion). However, while compliance costs can be measured, health benefits are unproved: they depend on a series of uncertain assumptions. Among these are that additional life expectancy gained by a beneficiary (with median age of about 80 years) should be valued at about $80,000 per month; that there is a 100% probability that a positive, linear, no-threshold, causal relation exists between PM(2.5) concentration and mortality risk; and that progress in medicine and disease prevention will not greatly diminish this relationship. We present an alternative uncertainty analysis that assigns a positive probability of error to each assumption. This discrete uncertainty analysis suggests (with probability >90% under plausible alternative assumptions) that the costs of CAAA exceed its benefits. Thus, instead of suggesting to policymakers that CAAA benefits are almost certainly far larger than its costs, we believe that accuracy requires acknowledging that the costs purchase a relatively uncertain, possibly much smaller, benefit. The difference between these contrasting conclusions is driven by different approaches to uncertainty analysis, that is, excluding or including discrete uncertainties about the main assumptions required for nonzero health benefits to exist at all.     

Development of the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup) and use of Monte Carlo simulation to predict internal exposure to 2.5 in Toronto.

Air pollution is a current and growing concern for Canadians, and there is evidence that ambient levels that meet current exposure standards may be associated with mortality and morbidity in Toronto, Canada. Evaluating exposure is an important step in understanding the relationship between particulate matter (PM) exposure and health outcomes. This report describes the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup), which predicts exposure distributions for 11 age-gender population subgroups in Toronto to PM2.5 (PM with a median aerodynamic diameter of 2.5 microm or less) using Monte Carlo simulation techniques. The model uses physiological and activity pattern characteristics of each subgroup to determine region-specific lung exposure to PM2.5, which is defined as the mass of PM2.5 deposited per unit time to each of five lung regions (two extrathoracic, bronchial, bronchiolar, and alveolar). The modeling results predict that children, toddlers, and infants have the broadest distributions of exposure, and the greatest chance of experiencing extreme exposures in the alveolar region of the lung. Importance analysis indicates that the most influential model variables are air exchange rate into indoor environments, time spent outdoors, and time spent at high activity levels. Additionally, a "critical point" was defined and introduced to the PEARLS to investigate the effects of possible threshold-pathogenic phenomena on subgroup exposure patterns. The analysis indicates that the subgroups initially predicted to be most highly exposed were likely to have the highest proportion of their population exposed above the critical point. Substantial exposures above the critical point were predicted in all subgroups for ambient concentrations of PM2.5 commonly observed in Toronto after continuous exposure of 24 hours or more.     

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on‐road PM_2.5‐related premature mortality at a national scale, previous approaches to estimate concentrations at a 12‐km × 12‐km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on‐road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on‐road PM_2.5‐related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36‐km × 36‐km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM_2.5 concentrations at a Census‐block level (～105,000 Census blocks). The hybrid modeling approach estimated 24% more on‐road PM_2.5‐related premature mortality than CMAQ. The major difference is from the primary on‐road PM_2.5 where the hybrid approach estimated 2.5 times more primary on‐road PM_2.5‐related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on‐road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near‐road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic‐related emissions.     

Assessment of Interindividual and Geographic Variability in Human Exposure to Fine Particulate Matter in Environmental Tobacco Smoke

Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures to fine particulate matter of 2.5 μm or smaller (PM_2.5). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS‐PM) Model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM_2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS‐PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass‐balance approach, which is comparable to best practices. The default inputs in SHEDS‐PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the proportion of smokers and “other smokers” and cigarette emission rate were found to be important. SHEDS‐PM does not currently account for in‐vehicle ETS exposure; however, in‐vehicle ETS‐related PM_2.5 levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass‐balance‐based methodology for estimating in‐vehicle ETS PM_2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS‐PM model. Interindividual variability for ETS exposure was quantified. Geographic variability in ETS exposure was quantified based on the varying prevalence of smokers in five selected locations in the United States.     

Diesel Emissions and Lung Cancer1

This paper uses two different methods to assess the potential risk of human lung cancer from exposure to diesel engine emissions. One method analyzes the best available epidemiological evidence on the lung cancer risks of persons exposed in their occupations to diesel engine emissions. The second conducts a comparative analysis of laboratory and epidemiological data on diesel engine emissions and two chemically related environmental exposures–coke oven emissions and roofing tar emissions. The estimates of potential risk derived from these two distinct methods are compared. The sources of uncertainty in each method are explicitly characterized. The value of these estimates for comparing the potential lung cancer risks from exposure to diesel engine emissions with other personal and societal risks are discussed. Also considered are the limitations of these results in predicting the possible excess incidence of lung cancer from ambient exposure to diesel emissions.     

Estimates of the Health Risk Reductions Associated with Attainment of Alternative Particulate Matter Standards in Two U.S. Cities

As part of its periodic re-evaluation of particulate matter (PM) standards, the U.S. Environmental Protection Agency estimated the health risk reductions associated with attainment of alternative PM standards in two locations in the United States with relatively complete air quality data: Philadelphia and Los Angeles. PM standards at the time of the analysis were defined for particles of aerodynamic diameter less than or equal to 10 microm, denoted as PM-10. The risk analyses estimated the risk reductions that would be associated with changing from attainment of the PM-10 standards then in place to attainment of alternative standards using an indicator measuring fine particles, defined as those particles of aerodynamic diameter less than or equal to 2.5 microm and denoted as PM-2.5. Annual average PM-2.5 standards of 12.5, 15, and 20 microg/m3 were considered in various combinations with daily PM-2.5 standards of 50 and 65 microg/m3. Attainment of a standard or set of standards was simulated by a proportional rollback of "as is" daily PM concentrations to daily PM concentrations that would just meet the standard(s). The predicted reductions in the incidence of health effects varied from zero, for those alternative standards already being met, to substantial reductions of over 88% of all PM-associated incidence (e.g., in mortality associated with long-term exposures in Los Angeles, under attainment of an annual standard of 12.5 microg/m3). Sensitivity analyses and integrated uncertainty analyses assessed the multiple-source uncertainty surrounding estimates of risk reduction.     

Uncertainty and Variability in Health-Related Damages from Coal-Fired Power Plants in the United States

The health‐related damages associated with emissions from coal‐fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal‐fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM_2.5). We applied a reduced‐form chemistry‐transport model accounting for primary PM_2.5 emissions and the influence of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions on secondary particulate formation. Outputs were linked with a concentration‐response function for PM_2.5‐related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant‐specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM_2.5, $6,000 to $50,000 per ton of SO₂, $500 to $15,000 per ton of NO_x, and $0.02 to $1.57 per kilowatt‐hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt‐hour was highly correlated with SO₂ emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.     

Air Pollution and Mortality: Quantification and Valuation of Years of Life Lost

To analyze the loss of life expectancy (LLE) due to air pollution and the associated social cost, a dynamic model was developed that took into account the decrease of risk after the termination of an exposure to pollution. A key parameter was the time constant for the decrease of risk, for which estimates from studies of smoking were used. A sensitivity analysis showed that the precise value of the time constant(s) was not critical for the resulting LLE. An interesting aspect of the model was that the relation between population total LLE and PM2.5 concentration was numerically almost indistinguishable from a straight line, even though the functional dependence was nonlinear. This essentially linear behavior implies that the detailed history of a change in concentration does not matter, except for the effects of discounting. This model was used to correct the data of the largest study of chronic mortality for variations in past exposure, performed by Pope et al. in 1995; the correction factor was shown to depend on assumptions about the relative toxicity of the components of PM2.5. In the European Union, an increment of 1 microg/m3 of PM2.5 for 1 year implies an average LLE of 0.22 days per person. With regard to the social cost of an air pollution pulse, it was found that for typical discount rates (3% to 8% real) the cost was reduced by a factor of about 0.4 to 0.6 relative to the case with zero discount rate, if the value of a life year was taken as given; if the value of a life year was calculated from the "value of statistical life" by assuming the latter as a series of discounted annual values, the cost varied by at most +/-20% relative to the case with zero discount rate. To assess the uncertainties, this study also examined how the LLE depended on the demographics (mortality and age pyramid) of a population, and how it would change if the relative risk varied with age, in the manner suggested by smoking studies. These points were found to have a relatively small effect (compared to the epidemiological uncertainties) on the calculated LLE.     

Associations Between 1980 U.S. Mortality Rates and Alternative Measures of Airborne Particle Concentration

We analyzed the 1980 U.S. vital statistics and available ambient air pollution data bases for sulfates and fine, inhalable, and total suspended particles. Using multiple regression analyses, we conducted a cross-sectional analysis of the association between various particle measures and total mortality. Results from the various analyses indicated the importance of considering particle size, composition, and source information in modeling of particle pollution health effects. Of the independent mortality predictors considered, particle exposure measures related to the respirable and/or toxic fraction of the aerosols, such as fine particles and sulfates, were most consistently and significantly associated with the reported SMSA-specific total annual mortality rates. On the other hand, particle mass measures that included coarse particles (e.g., total suspended particles and inhalable particles) were often found to be nonsignificant predictors of total mortality. Furthermore, based on the application of fine particle source apportionment, particles from industrial sources (e.g., from iron/steel emissions) and from coal combustion were suggested to be more significant contributors to human mortality than soil-derived particles.     

Multi-Stage Model Estimates of Lung Cancer Risk from Exposure to Diesel Exhaust, Based on a U.S. Railroad Worker Cohort

A California Environmental Protection Agency (Cal/EPA) report concluded that a reasonable and likely explanation for the increased lung cancer rates in numerous epidemiological studies is a causal association between diesel exhaust exposure and lung cancer. A version of the present analysis, based on a retrospective study of a U.S. railroad worker cohort, provided the Cal/EPA report with some of its estimates of lung cancer risk associated with diesel exhaust. The individual data for that cohort study furnish information on age, employment, and mortality for 56,000 workers over 22 years. Related studies provide information on exposure concentrations. Other analyses of the original cohort data reported finding no relation between measures of diesel exhaust and lung cancer mortality, while a Health Effects Institute report found the data unsuitable for quantitative risk assessment. None of those three works used multistage models, which this article uses in finding a likely quantitative, positive relations between lung cancer and diesel exhaust. A seven-stage model that has the last or next-to-last stage sensitive to diesel exhaust provides best estimates of increase in annual mortality rate due to each unit of concentration, for bracketing assumptions on exposure. Using relative increases of risk and multiplying by the background lung cancer mortality rates for California, the 95% upper confidence limit of the 70-year unit risks for lung cancer is estimated to be in the range 2.1 x 10(-4) (microg/m3)(-1) to 5.5 x 10(-4) (microg/m3)(-1). These risks constitute the low end of those in the Cal/EPA report and are below those reported by previous investigators whose estimates were positive using human data.     

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh,North Carolina

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM_2.5) concentrations in ambient air and associated health risks from PM_2.5 exposure in North Carolina's Raleigh‐Durham‐Chapel Hill area. We integrate transportation demand, land‐use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (?0.2%) point estimates of regional annual average PM_2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (?33%) PM_2.5‐attributable mortality. Furthermore, compactness increases local variation in PM_2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM_2.5 in local hotspots and increase population exposure to PM_2.5. Health effects may be magnified if compact neighborhoods and PM_2.5 hotspots are spatially co‐located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile‐dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.     

基于系统最优的城市公交专用道网络设计模型及算法

设置公交专用道是实现"公交优先"的重要手段,然而,专用道设置将改变不同交通方式的道路通行能力,进而影响交通网络的整体性能。本文试图提出一种基于系统最优思想的公交专用道网络设计方法,既保证出行者的利益,又能满足交通系统总费用最优的目标。首先,本文分析了公交专用道设置对公交车辆和社会车辆这两种交通方式道路通行能力的影响,基于经典的BPR函数,构造了考虑专用道设置的不同方式的路段阻抗函数。其次,分析了出行者在多方式交通网络中的模式选择和路径选择问题,采用用户平衡理论分析了城市多方式交通平衡配流问题,给出了相应的变分不等式模型。更进一步,采用双层规划方法构造了基于系统最优的城市公交专用道网络设计模型,该模型以交通网络总费用最小为优化目标,并考虑了不同交通方式的平衡流量约束,采用分支定界算法对该双层规划模型进行求解。最后,通过一个简单算例对模型及算法的可行性和有效性进行了分析和验证。