Using prognostic model-generated meteorological output in the AERMOD dispersion model: an illustrative application in Philadelphia, PA

In this study, we introduce the prospect of using prognostic model-generated meteorological output as input to steady-state dispersion models by identifying possible advantages and disadvantages and by presenting a comparative analysis. Because output from prognostic meteorological models is now routinely available and is used for Eulerian and Lagrangian air quality modeling applications, we explore the possibility of using such data in lieu of traditional National Weather Service (NWS) data for dispersion models. We apply these data in an urban application where comparisons can be made between the two meteorological input data types. Using the U.S. Environment Protection Agency's American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) air quality dispersion model, hourly and annual average concentrations of benzene are estimated for the Philadelphia, PA, area using both hourly MM5 model-generated meteorological output and meteorological data taken from the NWS site at the Philadelphia International Airport. Our intent is to stimulate a discussion of the relevant issues and inspire future work that examines many of the questions raised in this paper.     

Ozone Air Quality Models

Abstract

The field of ozone air quality modeling, or as it is commonly referred to, photochemical air quality modeling, has undergone rapid change in recent years. Improvements in model components, as well as in methods of interpreting model performance, have contributed to this change. Attendant with this rapid change has been a growing need for those developing and using air quality models and policy makers to have a common understanding of the use and role of models in the decision making process. This Critical Review highlights recent advances and continuing problem areas in photochemical air quality modeling. Emphasis is placed on the components and input data for such models, model performance evaluation, and the implications for their use in regulatory decisions.     

Coupling air quality and land use modeling within an optimization framework

Land use regulations and air quality standards can be effective tools to control air pollution. Atmospheric transport/chemistry simulation models could be used to develop suitable regulations and standards; however, these models are not as efficient as air quality management models developed from embedding governing equations for atmospheric transport/chemistry into an optimization framework. Formulations of two steady-state air quality management models are presented to facilitate the development or evaluation of land use strategies to protect regional air quality from pollution generated from distributed point or nonpoint sources. Both models are linear programs constructed with equations that describe steady-state atmospheric pollutant fate and transport. The first model determines feasible pollutant loading patterns for multiple land use activities to accommodate the greatest regional population. The second model ascertains patterns of expanded land use which have a minimum impact on air quality. The primary goal of this paper is to explain how air pollution and land use modeling may be coupled to create an effective management tool to aid scientists and engineers with decisions affecting air quality and land use. The secondary goal is to show the types of air quality and regulatory information which could be obtained from these models. This latter goal is attained with general conclusions as consequence of applying ‘duality theory.’     

Application of genetic algorithms for the design of ozone control strategies

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practically of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Air quality modeling of hazardous pollutants: current status and future directions

The current requirements and status of air quality modeling of hazardous pollutants are reviewed. Many applications require the ability to predict the local impacts from industrial sources or large roadways as needed for community health characterization and evaluating environmental justice concerns. Such local-scale modeling assessments can be performed by using Gaussian dispersion models. However, these models have a limited ability to handle chemical transformations. A new generation of Eulerian grid-based models is now capable of comprehensively treating transport and chemical transformations of air toxics. However, they typically have coarse spatial resolution, and their computational requirements increase dramatically with finer spatial resolution. The authors present and discuss possible advanced approaches that can combine the grid-based models with local-scale information.     

Application of Genetic Algorithms for the Design of Ozone Control Strategies

ABSTRACT

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models.

We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practicality of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Recommendations on the use of satellite remote-sensing data for urban air quality

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.     

SAGA: a decision support system for air pollution management around a coal-fired power plant

Air quality models are currently feasible approaches to prevent air pollution episodes. From one of the first source-oriented modelling approaches for air pollution forecasting (Souto et al., 1994, 1996, 1998), a new decision support system for air quality management, SAGA, was developed to provide support to As Pontes Power Plant (APPP) staff. SAGA can provide air pollution forecasts and manage meteorological and air quality measurements. Power plant decisions are supported by the results of a non-hydrostatic meteorological model (ARPS, Xue et al., 2001) to produce Meteorological Forecasts (MFs), and to be coupled to different Lagrangian dispersion models.     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality

Abstract

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.     

Lagrangian sampling of 3-D air quality model results for regional transport contributions to sulfate aerosol concentrations at Baltimore,MD, in summer 2004

We use ensemble-mean Lagrangian sampling of a 3-D Eulerian air quality model, CMAQ, together with ground-based ambient monitors data from several air monitoring networks and satellite (MODIS) observations to provide source apportionment and regional transport vs. local contributions to sulfate aerosol and PM_2.5 concentrations at Baltimore, MD, for summer 2004. The Lagrangian method provides estimates of the chemical and physical evolution of air arriving in the daytime boundary layer at Baltimore. Study results indicate a dominant role for regional transport contributions on those days when sulfate air pollution is highest in Baltimore, with a principal transport pathway from the Ohio River Valley (ORV) through southern Pennsylvania and Maryland, consistent with earlier studies. Thus, reductions in sulfur emissions from the ORV under the EPA's Clean Air Interstate Rule may be expected to improve particulate air quality in Baltimore during summer. The Lagrangian sampling of CMAQ offers an inexpensive and complimentary approach to traditional methods of source apportionment based on multivariate observational data analysis, and air quality model emissions separation. This study serves as a prototype for the method applied to Baltimore. EPA is establishing a system to allow air quality planners to readily produce and access equivalent results for locations of their choice.     

Lagrangian dispersion modeling of vehicular emissions from a highway in complex terrain

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

Implementation of a windblown dust parameterization into MODELS-3/CMAQ: Application to episodic PM events in the US/Mexico border

Windblown dust is known to impede visibility, deteriorate air quality and modify the radiation budget. Arid and semiarid areas with unpaved and unvegetated land cover are particularly prone to windblown dust, which is often attributed to high particulate matter (PM) pollution in such areas. Yet, windblown dust is poorly represented in existing regulatory air quality models. In a study by the authors on modeling episodic high PM events along the US/Mexico border using the state-of-the-art CMAQ/MM5/SMOKE air quality modeling system [Choi, Y.-J., Hyde, P., Fernando, H.J.S., 2006. Modeling of episodic particulate matter events using a 3D air quality model with fine grid: applications to a pair of cities in the US/Mexico border. Atmospheric Environment 40, 5181–5201], some of the observed PM₁₀ NAAQS exceedances were inferred as due to windblown dust, but the modeling system was incapable of dealing with time-dependent episodic dust entrainment during high wind periods. In this paper, a time-dependent entrainment parameterization for windblown dust is implemented in the CMAQ/MM5/SMOKE modeling system with the hope of improving PM predictions. An approach for realizing windblown dust emission flux for each grid cell over the study domain on an hourly basis, which accounts for the influence of factors such as soil moisture content, atmospheric stability and wind speed, is presented in detail. Comparison of model predictions with observational data taken at a pair of US/Mexico border towns shows a clear improvement of model performance upon implementation of the dust emission flux parameterization.     

Lagrangian Dispersion Modeling of Vehicular Emissions from a Highway in Complex Terrain

Abstract

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

Application of a scenario-based modeling system to evaluate the air quality impacts of future growth

The structure and design of future urban development can have significant adverse effects on air pollutant emissions as well as other environmental factors. When considering the future impact of growth on mobile source emissions, we generally model the increase in vehicle kilometers traveled (VKT) as a function of population growth. However, diverse and poorly planned urban development (i.e., urban sprawl) can force higher rates of motor vehicle use and in return increase levels of pollutant emissions than alternative land-use scenarios. The objective of this study is to develop and implement an air quality assessment tool that takes into account the influence of alternative growth and development scenarios on air quality.The use of scenario-based techniques in land use planning has been around since the late 1940s and been tested in many different applications to aid in decision-making. In this study, we introduce the development of an advanced interactive scenario-based land use and atmospheric chemistry modeling system coupled with a GIS (Geographical Information System) framework. The modeling system is designed to be modular and includes land use/land cover information, transportation, meteorological, emissions, and photochemical modeling components. The methods and modularity of the developed system allow its application to both broad areas and applications.To investigate the impact of possible land use change and urbanization, we evaluated a set of alternative future patterns of land use developed for a study area in Southwest California. Four land use and two population variants (increases of 500k and 1M) were considered. Overall, a Regional Low-Density Future was seen to have the highest pollutant emissions, largest increase in VKT, and the greatest impact on air quality. On the other hand, a Three-Centers Future appeared to be the most beneficial alternative future land-use scenario in terms of air quality. For all cases, the increase in population was the main factor leading to the change on predicted pollutant levels.     

Recommendations on statistics and benchmarks to assess photochemical model performance

Photochemical grid models are addressing an increasing variety of air quality related issues, yet procedures and metrics used to evaluate their performance remain inconsistent. This impacts the ability to place results in quantitative context relative to other models and applications, and to inform the user and affected community of model uncertainties and weaknesses. More consistent evaluations can serve to drive improvements in the modeling process as major weaknesses are identified and addressed. The large number of North American photochemical modeling studies published in the peer-reviewed literature over the past decade affords a rich data set from which to update previously established quantitative performance “benchmarks” for ozone and particulate matter (PM) concentrations. Here we exploit this information to develop new ozone and PM benchmarks (goals and criteria) for three well-established statistical metrics over spatial scales ranging from urban to regional and over temporal scales ranging from episodic to seasonal. We also recommend additional evaluation procedures, statistical metrics, and graphical methods for good practice. While we primarily address modeling and regulatory settings in the United States, these recommendations are relevant to any such applications of state-of-the-science photochemical models. Our primary objective is to promote quantitatively consistent evaluations across different applications, scales, models, model inputs, and configurations. The purpose of benchmarks is to understand how good or poor the results are relative to historical model applications of similar nature and to guide model performance improvements prior to using results for policy assessments. To that end, it also remains critical to evaluate all aspects of the model via diagnostic and dynamic methods. A second objective is to establish a means to assess model performance changes in the future. Statistical metrics and benchmarks need to be revisited periodically as model performance and the characteristics of air quality change in the future.

Implications: We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user’s ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.