Photochemical models are commonly used in regulatory and policy assessments to estimate pollutant concentrations and deposition of both inert and chemically reactive pollutants over large spatial scales. These models are generally run for horizontal grid resolutions of 36 and 12 km. However, several recent assessments have revealed the need for air quality predictions at resolutions finer than 12 km to resolve important local-scale gradients in pollutant concentrations. Given this need, we are undertaking a study to investigate several methods that can be used to obtain local-scale air quality concentrations This study looks at the application and evaluation of a variety of models including CMAQ, CAMx and AERMOD. In addition, we will also evaluate the use of a new method called the Multiplicative Approach to the Hybrid Method (MAHM), which combines CMAQ and AERMOD predicted concentrations to generate local-scale air quality predictions. To do this, these models/methods are applied at <= 4 km resolution for both a winter and summer month in the same local area: Detroit, MI. The study looks at model/method performance of PM2 5, O-3. and several toxic pollutants by comparing modeled versus ambient measured concentrations. Resources for implementation of each model/method are also evaluated

In response to the need to further explore and understand the technical needs and challenges presented by implementing a multi–pollutant, risk–based approach to air quality management, a case study was performed for the urban area of Detroit. As part of this case study, two contrasting air quality control strategies were assessed and compared. One strategy mimicked the “status quo”, where controls were selected separately to address ozone (O3) and fine particulate matter (PM2.5) nonattainment at monitor locations, while the other strategy reflected a “multi– pollutant, risk–based” approach aimed at further reducing population risk from exposure to ozone, PM2.5 and selected air toxics while still addressing ozone and PM2.5 nonattainment. This paper describes the technical framework used to apply and evaluate the two contrasting air quality control strategies and describes the relative benefits of each. Based on this case study, we found that the “multi–pollutant, risk–based” approach was able to: (1) achieve the same or greater reductions of PM2.5 and O3 at monitors; (2) improve air quality regionally and across the Detroit urban core for multiple pollutants; (3) produce approximately two times greater monetized benefits for PM2.5 and O3; (4) reduce non–cancer risk; and (5) result in greater net benefits and be more cost effective.

This paper discusses the need for critically evaluating regional-scale (~200-2000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.

The Detroit Exposure and Aerosol Research Study (DEARS) was designed to assess the impacts of local industrial and mobile sources on human exposures to air pollutants in and around Detroit, Michigan. Daily integrated measurements were made of personal exposure, and residential indoor and outdoor concentrations in six neighborhoods throughout the Detroit area. Concurrent data were collected for comparison at a central community ambient monitoring location and a regional background site. These data collected in DEARS can be used to evaluate local air quality and explore the application of air quality models to assess human exposure in an urban area.

Council (NRC) published a major assessment of air quality management practices, Air Quality Management in the United States. 1 The assessment resulted from a Congressional directive that the U.S. Environmental Protection Agency (EPA) commission the National Academy of Sciences to evaluate the effectiveness of the Clean Air Act (CAA) from a scientific and technical perspective and to provide recommendations for strengthening the nation’s air quality management system. In the report, the NRC recommends that the United States transition from a pollutant-by-pollutant approach to air quality management to a multipollutant, risk-based approach that emphasizes results over process, takes an airshed approach to controlling emissions, creates accountability for these results, and modifies air quality management actions as data on the effectiveness of these actions are obtained. The NRC report was the catalyst for a number of actions designed to explore a more efficient and consistent framework for comprehensive air pollution management practices addressing multipollutant categories and multimedia impacts. Paralleling these recommendations, the NRC also encouraged iterative program assessments—referred to as accountability—that incorporate human health and ecosystem indicators. Organizational responses to the NRC report have included restructuring within EPA, the addition of the Air Quality Management workgroup under the Clean Air Act Advisory Committee (CAAAC), and a new NARSTO assessment. This article discusses the underlying rationale driving a more comprehensive air management agenda and EPA’s efforts to improve the assessment basis for future management practices.

ISEE-332 Purpose: The U.S. Environmental Protection Agency (U.S. EPA) recently proposed regulations to reduce air pollution from diesel engines used in most kinds of construction, agricultural, and industrial equipment. This analysis reports the estimated health benefits of reductions in ambient particulate matter (PM) concentrations associated with those regulations based on the best available methods. Methods: Health impacts are estimated using EPA's environmental Benefits Mapping and Analysis Program (BenMAP), a customized geographic information system. Using outputs from the Regional Modeling System for Aerosols and Deposition (REMSAD), BenMAP calculates changes in air pollution metrics (e.g., daily averages) for input into health impact functions. BenMAP uses grid cell level population data, baseline health effect incidence rates, and changes in pollutant concentrations to estimate changes in health outcomes for each model grid cell. Economic values are then assigned to changes in health outcomes to generate monetized benefits. Using Monte Carlo methods, BenMAP also provides estimates of confidence intervals for health impacts and monetized benefits based on standard errors for effect estimates or other sources of uncertainty, such as meta-analyses of the epidemiological and economic literature. Results: The REMSAD modeling results suggest that when nonroad diesel engine emission reductions are fully realized in 2030, they will result in substantial, broad scale reductions in ambient fine particulate matter (population weighted reduction of over 0.5 μg/m3). This is associated with an estimated reduction in the incidence of premature mortality by 9,600, chronic bronchitis by 5,700, nonfatal myocardial infarctions by 16,000, and respiratory and cardiovascular hospital and emergency room admissions by 14,000. In addition, over 200,000 asthma exacerbations and millions of respiratory symptoms are predicted be avoided in 2030. The economic value of these health benefits is estimated at over $90 billion. Monetized benefits estimates are sensitive to choice of the effect estimate for premature mortality, valuation of mortality risk reductions, assumed lag/latency structure, and assumptions about the shape of the health impact function. However, under most reasonable assumptions, health benefits are substantial. Conclusions: This analysis estimated the health and welfare benefits of reductions in ambient concentrations of particulate matter resulting from reduced emissions of NOx, SO2, and diesel PM from nonroad diesel engines. The results suggests there will be significant health and welfare benefits arising from the regulation of emissions from nonroad engines in the U.S. and highlight the important role that pollution from the nonroad sector plays in the overall public health impacts of air pollution.