Rich Cook

In the 2014 National Air Toxics Assessment (NATA), the carbonyl compounds formaldehyde and acetaldehyde were identified as key cancer risk drivers and acrolein was identified as one of the three air toxics the drive most of the noncancer risk. In this assessment, averaged across the Continental United States, about 75% of ambient formaldehyde and acetaldehyde, and about 18% of acrolein, is formed secondarily. This study was conducted to estimate the potential contribution to these secondarily formed carbonyl compounds from mobile sources. To develop such estimates, we conducted several CMAQ runs, where emissions are set to zero for different mobile source sectors, to determine their potential contribution. Although zeroing out emissions from an individual sector can offer only a rough approximation of how the sector might contribute to overall secondary concentrations, our results suggest that across the U. S., mobile sources contribute about 6-18% to secondary formaldehyde, 0-10% to secondary acetaldehyde, and 0-70% to secondary acrolein, depending on location.

The composition of exhaust emissions from nonroad engines and equipment varies based on a number of parameters, including engine type, emission control technology, fuel composition, and operating conditions. Speciated emissions data which characterize the chemical composition of these emissions are needed to develop chemical speciation profiles used for air quality modeling and to develop air toxics inventories. In this paper, we present results of an extensive review and analysis of available exhaust speciation data for total organic gases (TOG) for spark ignition (SI) engines running on gasoline/ethanol blends now in widespread use, and compression ignition (CI) engines running on diesel fuel. We identified two data sets best suited for development of exhaust speciation profiles. Neither of these data sets have previously been published. We analyzed the resulting speciation profiles for differences in SI engine exhaust composition between 2-stroke and 4-stroke engines using E0 (0% ethanol) and E10 (10% ethanol) blends, and differences in CI engine exhaust composition among engines meeting different emission standards. Exhaust speciation profiles were also analyzed to compare differences in maximum incremental reactivity (MIR) values; this is a useful indicator for evaluating how organic gases may affect ozone formation for air quality modeling. Our analyses found significant differences in speciated emissions from 2-stroke and 4-stroke SI engines, and between engines running on E0 and E10 fuels. We found significant differences in profiles from pre-Tier 1 CI engines, engines meeting Tier 1 standards, and engines meeting Tier 2 standards. Although data for nonroad CI engines meeting tier 4 standards with control devices such as particulate filters and selective catalyst reduction (SCR) devices were not available, data from highway CI engines suggest these technologies will substantially change profiles for nonroad CI engines as well (EPA, 2014c). Implications: The nonroad engine data sets analyzed in this study will substantially improve exhaust speciation profiles used to characterize organic gas emissions from nonroad engines. Since nonroad engines are major contributors to ambient air pollution, these profiles can considerably improve U.S. emission inventories for gaseous air toxics emitted from nonroad engines. The speciation profiles developed in this paper can be used to develop more accurate emission inputs to chemical transport models, leading to more accurate air quality modeling.

This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants.

Modeling of inhalation exposure and risks resulting from exposure to mobile source air toxics can be used to evaluate impacts of reductions from control programs on overall risk, as well as changes in relative contributions of different source sectors to risk, changes in contributions of different pollutants to overall risk, and changes in geographic distributions of risk. Such analysis is useful in setting regulatory priorities, and informing the decision-making process. In this paper, we have conducted national-scale air quality, exposure, and risk modeling for the US in the years 2015, 2020, and 2030, using similar tools and methods as the 1999 National-Scale Air Toxics Assessment. Our results suggest that US Environmental Protection Agency emission control programs will substantially reduce average inhalation cancer risks and potential noncancer health risks from exposure to mobile source air toxics. However, cancer risk and noncancer hazard due to inhalation of air toxics will continue to be a public health concern.