Rich Cook

Resolving Local-Scale Emissions for Modeling Air Quality near Roadways

Abstract A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a “bottom-up” approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.

Relationship between motor vehicle emissions of hazardous pollutants, roadway proximity, and ambient concentrations in Portland, Oregon

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. Recent air quality modeling in Portland, Oregon, using the CALPUFF dispersion model assigned emissions to specific roadway links. The resulting data were used to develop a regression model to approximate the CALPUFF predicted concentrations, determine the impacts of roadway proximity on ambient concentrations of three hazardous air pollutants, benzene, 1,3-butadiene, and diesel PM, and to estimate the zone of influence around roadways. Independent variables in the model included emission rates and traffic volumes for individual roadway links, distance and direction between roadway links and receptors, and distributions of wind speeds and directions. Dependent variables were derived from simulated annual average pollutant concentrations from motor vehicles at modeled receptor locations, predicted using CALPUFF. The regression model had limited capability to predict CALPUFF concentrations with an R-squared value of about 0.6. The model indicated the zone of influence around a roadway as between 200 and 400 m. The results support the thesis that in order to capture localized impacts of hazardous air pollutants in a dispersion model, there is a need to include individual roadway links.

Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM2.5 and its components (elemental carbon (EC), SO4), O3, carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NOx, and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM2.5, SO4, and O3); (ii) for all pollutants except NOx, temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

Creating locally-resolved mobile-source emissions inputs for air quality modeling in support of an exposure study in Detroit, Michigan, USA.

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.

A near-road modeling system for community-scale assessments of traffic-related air pollution in the United States

The Community Line Source (C-LINE) modeling system estimates emissions and dispersion of toxic air pollutants for roadways within the continental United States. It accesses publicly available traffic and meteorological datasets, and is optimized for use on community-sized areas (100-1000?km2). The user is not required to provide input data, but can provide their own if desired. C-LINE is a modeling and visualization system that access inputs, performs calculations, visualizes results, provides options to manipulate input variables, and performs basic data analysis. C-LINE was applied to an area in Detroit, Michigan to demonstrate its use in an urban environment. It was developed in ArcGIS, but a prototype web version is in development for wide-scale use. C-LINE is not intended for regulatory applications. Its local-scale focus and ability to quickly (run time?<?5?min) compare different roadway pollution scenarios supports community-based applications and help to identify areas for further research. Developed a near-road modeling system to estimate mobile-source emissions and dispersion.The modeling system automatically provides nationwide coverage for most major roadways.Users can manipulate input data on traffic and meteorology to compare differences in resulting air toxics concentrations.The modeling system is optimized for use in local-scale community-based types of scenarios.

Meeting report: Estimating the benefits of reducing hazardous air pollutants--summary of 2009 workshop and future considerations.

Background Quantifying the benefits of reducing hazardous air pollutants (HAPs, or air toxics) has been limited by gaps in toxicological data, uncertainties in extrapolating results from high-dose animal experiments to estimate human effects at lower doses, limited ambient and personal exposure monitoring data, and insufficient economic research to support valuation of the health impacts often associated with exposure to individual air toxics. Objectives To address some of these issues, the U.S. Environmental Protection Agency held the Workshop on Estimating the Benefits of Reducing Hazardous Air Pollutants (HAPs) in Washington, DC, from 30 April to 1 May 2009. Discussion Experts from multiple disciplines discussed how best to move forward on air toxics benefits assessment, with a focus on developing near-term capability to conduct quantitative benefits assessment. Proposed methodologies involved analysis of data-rich pollutants and application of this analysis to other pollutants, using dose–response modeling of animal data for estimating benefits to humans, determining dose-equivalence relationships for different chemicals with similar health effects, and analysis similar to that used for criteria pollutants. Limitations and uncertainties in economic valuation of benefits assessment for HAPS were discussed as well. Conclusions These discussions highlighted the complexities in estimating the benefits of reducing air toxics, and participants agreed that alternative methods for benefits assessment of HAPs are needed. Recommendations included clearly defining the key priorities of the Clean Air Act air toxics program to identify the most effective approaches for HAPs benefits analysis, focusing on susceptible and vulnerable populations, and improving dose–response estimation for quantification of benefits.

Development of organic gas exhaust speciation profiles for nonroad spark-ignition and compression-ignition engines and equipment

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.