Douglas S Eisinger

Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data

Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concentration measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the observed concentrations. Local regression models were specified to the concentration-distance relationship and analysis of variance was used to determine the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more standard background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to underpredict (be lower than) actual background. Changes in pollutant concentrations with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concentration by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter number concentrations); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concentrations).

Predicting Near-Road PM2.5 Concentrations: Comparative Assessment of CALINE4, CAL3QHC, and AERMOD

Scientific evidence has increasingly shown an association between particulate matter (PM) and adverse human health impacts. Accurately predicting near-road PM2.5 concentrations is therefore important for project-level transportation conformity and health risk analysis. This study assessed the capability and performance of three dispersion models–-CALINE4, CAL3QHC, and AERMOD–-in predicting near-road PM2.5 concentrations. The comparative assessment included identifying differences among the three models in relation to methodology and data requirements. An intersection in Sacramento, California, and a busy road in London were used as sampling sites to evaluate how model predictions differed from observed PM2.5 concentrations. Screen plots and statistical tests indicated that, at the Sacramento site, CALINE4 and CAL3QHC performed moderately well, while AERMOD under-predicted PM2.5 concentrations. For the London site, both CALINE4 and CAL3QHC resulted in overpredictions when incremental concentrations due to on-road emission sources were low, while underpredictions occurred when incremental concentrations were high. The street canyon effect and receptor location likely contributed to the relatively poor performance of the models at the London site.

Evaluating inspection and maintenance programs: a policy-making framework.

Abstract This article presents a new analysis approach to design and evaluate motor vehicle inspection and maintenance (I/M) programs. The new approach, called I/M-Design, uses real-world data to provide two resources not previously available: (1) a transparent framework to quantitatively illustrate the range of emission reductions available from I/M, and (2) a sensitivity analysis tool to evaluate how key variables affect I/M performance. In addition, the approach satisfies a policy-making information need—how to convey, in a logical and straightforward manner, the expected benefits from I/M without relying on modeling tools inaccessible to those outside the air quality field. The material presented in this article illustrates the new approach by estimating hydrocarbon (HC) emission reduction benefits available from enhanced I/M in southern California’s South Coast Air Basin. I/M-Design estimates that enhanced I/M results in a 14–28% reduction in light-duty motor vehicle HC exhaust emissions; this estimate compares well to other California I/M program evaluations. Even more importantly, I/M-Design sensitivity analyses illustrate how I/M programs that implement stringent failure thresholds, motivate pre-test repair work, and improve repair effectiveness can provide emission reductions that substantially exceed the performance of existing programs.

A Reevaluation of Carbon Monoxide: Past Trends, Future Concentrations, and Implications for Conformity “;Hot-Spot” Policies

Abstract Control of CO is one of the great air-quality management success stories of the past 20 years. This paper evaluates whether past progress will continue into the future and whether changes in microscale CO concentrations are comparable to reductions observed at the regional scale. Neighborhood and microscale CO concentrations were evaluated at six northern and southern California monitoring sites. The study also included a review of CO emission, concentration, and exposure trends and on-road motor vehicle-based CO emission control programs for California and the United States. Consistent with California and national trends, CO concentrations declined at each of the six study locations from 1988 through 1998. Microscale concentrations declined at the same rate as did neighborhood-scale concentrations. Rollback analyses demonstrated that microscale concentrations will continue to decline through at least 2010–2020. Within a few years, microscale violations of the CO National Ambient Air Quality Standards (NAAQS) will be unlikely in California except under extraordinary circumstances.

Mobile Source Air Toxic Emissions: Sensitivity to Traffic Volume, Fleet Composition, and Average Speed

This study used a new emissions modeling tool, CT-EMFAC, to assess the sensitivity of mobile source air toxic (MSAT) emissions to changes in traffic volumes, speeds, and fleet composition. In addition, the choice of the speed calculation method, which is usually part of a postprocessing technique, was analyzed. The investigation employed a hypothetical 6.7-mi freeway segment located in southern California; activity data were derived from comparable real-world information obtained from the California Department of Transportation. Results show that emissions more than doubled in 2004 and increased by a factor of two to four in 2030 when traffic volumes increased 30% above base case conditions. The nonlinear shift in emissions was a function of decreased travel speeds and increased grams per mile emission rates that accompanied increased traffic volumes. Fleet composition (the proportion of trucks) was also shown to affect MSAT emissions, especially for diesel particulate matter and aldehydes. Under some scenarios, the choice of the speed calculation method had a greater effect on MSAT emissions than 26 years of fleet turnover. The analysis also showed that the type of speed calculation method used could result in large variations in MSAT emissions. Application of one speed calculation method resulted in calculated congested freeway speeds that did not fall below 10 mph. The speed calculation method chosen was highly influential in the 2030 case study when forecasted volumes reached levels at which speeds approached the 10 mph minimum allowed by the speed calculation method; this probably underpredicted emissions.

Mitigating Diesel Truck Impacts in Environmental Justice Communities: Transportation Planning and Air Quality in Barrio Logan, San Diego, California

This paper describes a series of sequentially implemented policies to mitigate local diesel truck impacts resulting from goods movement activity at two port facilities and simultaneously to improve traffic operations in the communities of Barrio Logan in San Diego, California, and Old Town in National City, California, both low-income communities of color. The paper provides the first comprehensive documentation of the unique process and solutions that emerged following the collaboration of all major stakeholders. Local impacts in Barrio Logan comprised air pollution, noise, and decreased pedestrian safety, while traffic operations in both communities were affected by congestion on the main freeway access, interchanges with insufficient capacity, and heavily mixed land uses both within and adjacent to the communities. These issues provided the impetus for the mitigation effort, the final implementation of which involved a permanent rerouting of all trucks weighing more than 5 tons to roads external to the community. Previous assessments of the project have described the extent to which mitigation strategies are expected to improve traffic operations or have assumed air quality improvements without carrying out an air quality analysis. A local-scale analysis of diesel particulate matter (DPM) emissions in Barrio Logan is given. The results show that while the mitigation did not result in improved regional air quality, it did significantly improve air quality in the primary affected corridor and resulted in a 99% reduction in DPM emissions and an 87% reduction in diesel truck vehicle miles traveled.

Transportation Control Measures: Federal Requirements and State Implementation Plan Development Considerations

U.S. transportation control measure (TCM) policies have evolved over the past several years, especially with respect to the identification of reasonably available control measures (RACM). U.S. TCM experiences are used to examine transportation conformity in light of two important emerging policy issues: identification of TCMs that are RACM and the substitution of TCMs in existing state implementation plans (SIPs). The issues associated with forming TCMs into SIPs while buffering against conformity difficulties are outlined against the background of recent U.S. Environmental Protection Agency (EPA) rulemaking actions, court decisions, research findings, and various state SIP adoption efforts. Factors that are related to TCM substitution and take into consideration EPA approval actions and recent considerations raised by public interest groups and planning agencies are identified. A process for identifying candidate TCMs for RACM analysis, explicit criteria for determining RACM TCMs, insights concerning TCMs that will be most effective, and case study-derived recommendations on how to structure SIP TCM commitments are provided.

Emissions Modeling with MOVES and EMFAC to Assess the Potential for a Transportation Project to Create Particulate Matter Hot Spots

In particulate matter (PM) nonattainment and maintenance areas, quantitative hot-spot analyses are required to assess air quality impacts of transportation projects that are identified as projects of local air quality concern (POAQC). In its 2006 rulemaking, the U.S. Environmental Protection Agency identified sample projects that would likely be POAQCs, including a new highway project with annual average daily traffic (AADT) greater than 125,000 and at least 8% diesel truck traffic. The objective of this study was to identify project characteristics that could reasonably exclude the project from consideration as a POAQC. Scenario analyses were performed for a hypothetical project that featured a new freeway with four mixed-flow lanes and baseline traffic activity of 125,000 AADT and 8% diesel truck traffic. The MO Vehicle Emission Simulator and the Emission FACtors models were used to quantify PM10 and PM2.5 emissions for a 2006 analysis and to evaluate the impact of fleet turnover and truck percentages on project-level emissions from 2006 to 2035. Fleet turnover effects sharply reduce project-level PM2.5 emissions over time. For an analysis year of 2015, impacts from a highway project with 125,000 AADT and 8% trucks are approximately 50% less than impacts from such a project in 2006. In contrast, fleet turnover effects do not substantially reduce PM10 emissions, since re-entrained road dust emissions and tire wear and brake wear emissions increasingly dominate project-level inventories over time, and these emissions vary little by analysis year.

Modeling In-Use Construction Equipment Emissions for Highway Projects

Interest in estimating the air pollutant emissions that occur during highway project construction is increasing. This paper describes a construction emissions modeling framework with companion calculation methodologies. On the basis of bid data and field data collected from a range of highway construction projects, the modeling framework builds linkages between material quantities, equipment in-use hours, and pollutant emissions. The data calculation and processing methodologies can assist project analysts in creating emissions estimates across various construction operations at early environmental assessment stages when, typically, limited data are available for characterizing equipment activity for the future project. A hypothetical freeway-widening case study was used to demonstrate the application of the modeling framework and calculation methodologies. The example illustrates the frameworks ability to disaggregate emissions by construction operation; in the case analysis, hours of equipment use and emissions were greatest during roadway excavation and foundation work. Project analysts can also vary individual equipment characteristics that affect emissions, such as tier group (emissions certification standards), horsepower rating, equipment deterioration, and other factors in construction emissions analyses. For example, the emissions reduction benefits of shifting the equipment fleet to more stringent (Tier 4) emissions standards, especially as a strategy for reducing particulate emissions, can be quantified.