Single-source impact analysis using three-dimensional air quality models

Isolating the effects of an individual emissions source on secondary air pollutants such as ozone and some components of particulate matter must incorporate complex nonlinear processes, be sensitive to small emissions perturbations, and account for impacts that may occur hundreds of kilometers away. The ability to evaluate these impacts is becoming increasingly important for efficient air quality management. Here, as part of a recent compliance enforcement action for a violation of the Clean Air Act and as an evaluation of ozone response to single-source emissions plumes, two three-dimensional regional photochemical air quality models are used to assess the impact on ozone from approximately 2000 to 3000 excess t/month of nitrogen oxides emitted from a single power plant in Ohio. Periods in May, July, and August are evaluated. Two sensitivity methods are applied: the "brute-force" (B-F) method and the decoupled direct method (DDM). Using DDM, maximum 1-hr averaged ozone concentrations are found to increase by up to 1.8, 1.3, and 2.2 ppbv during May, July, and August episodes, respectively, and concentration increases greater than 0.5 ppbv occur in Ohio, Pennsylvania, Maryland, New York, West Virginia, Virginia, and North and South Carolina. B-F results for the August episode show a maximum 1-hr averaged ozone concentration increase of 2.3 ppbv. Significant localized decreases are also simulated, with a maximum of 3.6 ppbv in Ohio during the August episode and decreases of 0.50 ppbv and greater in Ohio, Pennsylvania, Maryland, West Virginia, and Virginia. Maximum increases are compared with maximum decreases for the August period using second-order DDM and are found, in aggregate, to be greater in magnitude by 42%. When evaluated during hours when ozone concentrations exceed 0.060 ppm, the maximum increases in ozone are higher than decreases by 82%. The spatial extent of ozone increase in both cases is about triple that of reduction.     

Integrated Assessment Modeling of Atmospheric Pollutants in the Southern Appalachian Mountains. Part I: Hourly and Seasonal Ozone

Abstract

Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI’s assessment.     

Airshed Model Evaluation of Reactivity Adjustment Factors Calculated with the Maximum Incremental Reactivity Scale for Transitional-Low Emission Vehicles

Abstract

The California Air Resources Board recently adopted regulations for light- and medium-duty vehicles that require reductions in the ozone-forming potential or “reactivity,” rather than the mass, of nonmethane organic gas (NMOG) emissions. The regulations allow sale of all alternatively fueled vehicles (AFVs) that meet NMOG exhaust emission standards equivalent in reactivity to those set for vehicles fueled with conventional gasoline. Reactivity adjustment factors (RAFs), the ratio of the reactivity (per gram) of the AFV exhaust to that of the conventionally fueled vehicle (CFV), are used to correct the stringent exhaust emission standards. Complete chemical speciation of the exhaust and conversion of each NMOG species to an appropriate mass of ozone using the maximum incremental reactivity (MIR) scale of Carter determines the RAF. The MIR approach defines reactivity where NMOG control is the most effective strategy in reducing ozone concentrations, and assumes it is not important to define reactivity at other conditions, i.e., where NO_x is the limiting precursor.

This study used the Carnegie/California Institute of Technology airshed model to evaluate whether the RAF-adjusted AFV emissions result in ozone impacts equivalent to those of CFV emissions. A matrix of two ozone episodes in the South Coast Air Basin (SoCAB) of California, two base emission inventories, and exhaust emissions from three alternative fuels that meet the first level of the low emission vehicle standards bounds the expected range of conditions. Although very good agreement was found previously for individual NMOG species,² this study noted deviations of up to ±15 percent from the equal ozone impacts for any vehicle/fuel combination required by the California regulations. These deviations appear to be attributable to differences in spatial and temporal patterns of emissions between vehicle fleets, rather than a problem with the MIR approach. The first formally adopted RAF, a value of 0.41 for 85 percent methanol/15 percent gasoline-fueled vehicles, includes a 10 percent increase based on the airshed modeling. The correction to the RAF is different for other fuels and may be different for air basins other than the SoCAB.     

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O₃), fine particulate matter (PM_2.5), and regional haze in the Southeastern United States. O₃ and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO₂, primary carbon (PC), NH₃, NO_x or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case.The sensitivity of summertime O₃ to VOC emissions is small in the Southeast and ground-level NO_x controls are generally more beneficial than elevated NO_x controls (per unit mass of emissions reduced). SO₂ emission reduction is the most beneficial control strategy in reducing summertime PM_2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO₂. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH₃ emissions is an effective strategy to reduce wintertime ammonium nitrate (NO₃NH₄) levels and improve visibility; NO_x emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.     

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains: Part II. Fine particulate matter and visibility

As part of the Southern Appalachian Mountains Initiative, a comprehensive air quality modeling system was developed to evaluate potential emission control strategies to reduce atmospheric pollutant levels at the Class I areas located in the Southern Appalachian Mountains. Six multiday episodes between 1991 and 1995 were simulated, and the skill of the modeling system was evaluated. Two papers comprise various parts of this study. Part I details the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. This paper (part II) addresses issues involved with modeling particulate matter (PM) and its relationship to visibility. For most of the episodes, the fractional error was approximately 50% or less for the major constituents of the fine PM (i.e., sulfate [SO4] and organics) in the region. The mean normalized errors and fractional errors are generally larger for the NO3 and soil components, but these components are relatively small. Variations in modeling bias with pollutant levels were also examined. The model showed a systematic overestimation for low levels and an underestimation for high levels for most PM species. For ammonium, the model showed better performance at lower SO4 concentrations when the measured SO4 was assumed to be completely neutralized (ammonium sulfate) and better performance at higher SO4 concentrations when the partially neutralized (ammonium bisulfate) assumption was made. The contributions of various components of PM to reductions in visibility were also calculated; SO4 was found to be the major contributor.     

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains. Part I: hourly and seasonal ozone

Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI's assessment.     

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.