EPA/600/R-11/068 June 2011 www.epa.gov/ord vvEPA United States Environmental Protection Agency Summary Report of the Atmospheric Modeling and Analysis Division's Research Activities for 2010 Office of Research and Development National Exposure Research Laboratory ------- ------- EPA/600/R-11/068 June 2011 www.epa.gov/ord Summary Report of the Atmospheric Modeling and Analysis Division's Research Activities for 2010 S.T. Rao, Jesse Bash, Sherry Brown, Robert Gilliam, David Heist, David Mobley, Sergey Napelenok, Chris Nolte, and Tom Pierce Atmospheric Modeling and Analysis Division National Exposure Research Laboratory Office of Research and Development United States Environmental Protection Agency Research Triangle Park, North Carolina 27711 ------- Disclaimer The information in this document has been funded by the U.S. Environmental Protection Agency. It has been subjected to the Agency's peer and administrative review and has been approved for publication as an EPA document. Mention of trade names or commercial products docs not constitute endorsement or recommendation for use. ii ------- Abstract The research presented here was performed by the Atmospheric Modeling and Analysis Division (AM AD) of the National Exposure Research Laboratory in the U.S. Environmental Protection Agency's (EPA's) Office of Research and Development in Research Triangle Park. NC. The Division leads the development and evaluation of predictive atmospheric models on all spatial and temporal scales for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions, and for forecasting the Nation's air quality and reducing exposures to sensitive populations and ecosystems. A MAD is responsible for providing a sound scientific and technical basis for regulatory policies to improve ambient air quality. The models developed by A MAD arc being used by EPA and the air pollution community in understanding and forecasting the magnitude of the air pollution problem and also in developing emission control policies and regulations for air quality improvements. AMAD applies air quality models to support key integrated, interdisciplinary science research. This includes linking air quality models to other models in the sourcc-to-outcome continuum framework to effectively address issues involving human health and ecosystem exposure science. The Community Multiscale Air Quality Model is the flagship model of the Division. This report summarizes the research and operational activities of AMAD for calendar year 2010. iii ------- ------- Table of Contents Disclaimer Abstract List of Figures 1.0 Introduction 2.0 Summary of Accomplishments for the Division 2.1 Division-Wide Accomplishments 2.2 Model Development and Diagnostic Testing 2.3 Air Quality Model Evaluation 2.4 Air Quality-Global Climate Change 2.5 Linking Air Quality to Human Exposure 2.6 Linking Air Quality and Ecosystems 3.0 Model Development and Diagnostic Testing 3.1 Overview of Air Quality Model Development 3.2 CMAQ Aerosol Module 3.3 CMAQ Gas-Phase Chemistry 3.4 Planetary Boundary Layer Modeling 3.5 Meteorology Modeling for Air Quality 3.6 Integrated Meteorology-Chemistry Modeling 3.7 Mercury Modeling 3.8 Air Toxics 3.9 Nanoparticles Modeling 3.10 Emissions Modeling Research 4.0 Air Quality Model Evaluation 4.1 Introduction 4.2 Atmosphepheric Model Evaluation Tool 4.3 Air Quality Model Evaluation Initiative 4.4 Diagnostic Evaluation 4.5 Dynamic Evaluation 4.6 Probabilistic Model Evaluation 5.0 Climate and Air Quality Interactions 5.1 Introduction 5.2 Regional Climate Modeling: Dynamical Downscaling ------- 5.3 Regional Climate Modeling: Statistical Downscaling 33 5.4 Development of Coupled Regional Climate and Chemistry Modeling System 34 5.5 Decision Support Tools for Managing Air Quality and Mitigating Climate Change 36 5.6 Biosphere-Atmosphere Interactions: Improving the Treatment of Isoprene Oxidation 37 6.0 Linking Air Quality to Human Health 39 6.1 Introduction 39 6.2. Research Description 39 6.3. Accomplishments 40 6.4. Next Steps 42 7.0 Linking Air Quality and Ecosystem 45 7.1 Introduction 45 7.2 Air Deposition and Ecosystem Services Assessments 46 7.2.1 Introduction 46 7.2.2 Research Direction 46 7.2.3 Accomplishments 47 7.2.4 Next Steps 47 7.3 Air-Ecosystem Linkage Studies 48 7.3.1 Introduction 48 7.3.2 Research Direction 48 7.3.3 Accomplishments 48 7.3.4 Next Steps 48 7.4 Model and Tool Development 48 7.4.1 Introduction 48 7.4.2 Research Direction 49 7.4.3 Accomplishments 49 7.4.4 Next Steps 51 References 53 APPENDIX A: Atmospheric Modeling and Analysis Division Staff Roster (as of December 31, 2010) 57 APPENDIX B: Division and Branch Descriptions 58 APPENDIX C: 2010 Awards and Recognition 59 APPENDIX D: 2010 Publications (Division authors are in bold.) 60 APPENDIX E: Acronyms and Abbreviations 63 ------- List of Figures Figure 1-1. AMAD's role in the source-exposure-dose-effects continuum from the atmospheric science perspective. (Adapted from "A Conceptional Framework for U.S. EPA's National Exposure Research Laboratory," EPA/600/R-09/003, January 2009) 1 Figure 1-2. AMAD's structure and organization 2 Figure 1-3. AMAD's integrated interdisciplinary modeling research 2 Figure 4-1. Examples of two types of model evaluation techniques: (a) probabilistic—time series of daily maximum 8-h 03 concentrations from a 200-member CMAQ model ensemble at a monitoring site in an urban location; and (b) diagnostic—percent contribution of individual aerosol species comprising the total average regional PM25 mass concentrations predicted by CMAQ and measured by the Speciated Trends Network (STN) sites 25 Figure 4-2. Modeled (gray) and observed (rose) wind speed profiles averaged over the nocturnal periods of August 11-15, 2002, at Ft. Meade, MD. Boxes span the 25th to 75th percentiles, and whiskers extend from the 10th to 90th percentiles 27 Figure 4-3. Box/whisker plot of the percentage change in modeled (gray) and observed (green) maximum 8-h 03 concentrations (>95th percentile) for each summer period relative to a 5-year mean at the CASTNET monitoring sites in the eastern United States. The boxes span the 25th to 75th percentiles and whiskers extend from the 10th to 90th percentiles 28 Figure 4-4. Change in weekday 3-h average morning NOx concentrations. Model results (gray) and observations (white) are based on 42 urban sites. Each box/whisker plot shows the median values (line inside boxes). Boxes span the 25th to 75th percentiles, and whiskers extend from the 10th to the 90th percentiles of the concentration distributions 28 Figure 4-5. Base model, observational, and model ensemble empirical cumulative distributions of maximum daily average 8-h 03 concentrations for 2002 and 2005 at two AQS sites: (a) Terre Haute, IN; and (b) Detroit, Ml. All ensembles were constructed based on ±50% uncertainty in emissions of area and mobile NOx and ±3% uncertainty in emissions of point NOx. The wide spread of the ensemble at the Terre Haute site indicates greater sensitivity to NOx emissions in comparison with the site in Detroit 30 Figure 5-1. Example global telescoping grid structure 35 Figure 5-2. Schematic describing model linkages 36 Figure 5-3: Change in simulated mean surface-level 03 concentration because of updated chemical mechanism 38 Figure 6-1. Wind tunnel model of Las Vegas field study area built at 1:200 scale 40 Figure 6-2. Aerial photograph of study location in Raleigh, NC 41 vii ------- Figure 6-3. Photograph of bag samplers and anemometers downwind of a simulated noise barrier at the Idaho Falls field site 42 Figure 6-4. Example of a controlled burn of spilt oil in the Gulf of Mexico 43 Figure 6-5. Graphic on left shows high NOx emissions in Midwest (July 1997) and graphic on right shows transport of pollution from Midwest into New York State. Red squares are locations from which back-trajectories were initiated 43 Figure 7-1. Range of influence of NH3 emissions from a single, isolated Sampson County, NC cell in 2002 CMAQ simulations using unidirectional, the deposition velocity as a surrogate, and bidirectional NH3 surface exchange parameterizations, Dennis et al. (2010) 46 Figure 7-2. Monthly NH4 wet deposition bias when compared to NADP deposition for an annual 2002 simulation for a base case unidirectional exchange and a bidi case with bidirectional NH3 exchange. The red diamond in the box plot represents the mean monthly deposition bias. Monthly adjustments to precipitation biases have been applied where precipitation errors significantly correlate with deposition error; a star above the box plot indicates that this correction was not applied 47 Figure 7-3, Rainfed grain corn (A) example management schedule, (B) second fertilizer application date, and (C) second first fertilizer application rate 50 Figure 7-4. Watershed deposition tool output showing the average (per unit area) annual total (wet+dry) oxidized nitrogen deposition (kg-H/ha) estimated for each 12-digit HUC in the Albemarle-Pamlico Basin for 2002 51 ------- 1.0 Introduction The research presented here was performed by the Atmospheric Modeling and Analysis Division (AMAD) of the National Exposure Research Laboratory (NERL) in the U.S. Environmental Protection Agency's (EPA's) Office of Research and Development (ORD) in Research Triangle Park, NC. This report summarizes the research and operational activities of the Division for calendar year 2010. The Division structure includes the following four research branches. 1. Atmospheric Model Development Branch (AMDB) 2. Emissions and Model Evaluation Branch (EMEB) 3. Atmospheric Exposure Integration Branch (AEIB) 4. Applied Modeling Branch (AMB) Included in this report are a list of Division employees (Appendix A), missions of the Division and its branches (Appendix B), awards earned by Division personnel (Appendix C), citations for Division publications (Appendix D). and a list of acronyms and abbreviations used herein (Appendix E). The Division's role within the EPA NERL's "Exposure Framework" and ORD's source-to-outcome continuum is to conduct research that improves the Agency's understanding of the linkages from source to exposure (see Figure 1-1). Through its four research branches, the Division provides atmospheric sciences expertise, air quality forecasting support, and technical guidance on the meteorological and atmospheric pollution aspects of air quality management to various EPA offices (including the Office of Air Quality Planning and Standards [OAQPS] and regional offices), other Federal agencies, and State and local pollution control agencies. The Division provides this technical support and expertise using an interdisciplinary approach that emphasizes integration and partnership with EPA and public and private research communities. Specific research and development activities are conducted in-house and externally via external funding. The Division's research activities were subjected to a comprehensive peer review in January 2009. (Additional information from the peer review is available on the Division's Web site [www.epa.gov/amad/].) To present materials and programs for the peer review, the Division's activities were summarized with focuses on five outcome- oriented theme areas: 1. model development and diagnostic testing, 2. air quality model evaluation, 3. climate and air quality interactions, 4. linking air quality to human health, and 5. linking air quality and ecosystem health. Source-to-Outcome Adverse Outcome Source / Origin Division s Role Fate and Transport Early Signs of Effects Exposure Ambient ~ X Concentrations Figure 1-1. AMAD's role in the source-exposure-dose-effects continuum from the atmospheric science perspective. (Adapted from "A Conceptional Framework for U.S. EPA's National Exposure Research Laboratory," EPA/600/R-09/003, January 2009) ------- Sound Science tor Environmental Decisions Model Development and Diagnostic Testing Model Evaluation: Establishing Model s Credibility Climate Change and Air Quality Interactions Linking Air Quality and Human Health Linking Air Quality and Ecosystem Health ~o co m AIMAD Structure and Organization Figure 1-2. AMAD's structure and organization. Research tasks were developed within each theme area by considering the following questions. • Over the next 2 to 3 years, who are the major clients, and what are their needs? • What research investments are needed to further the science in ways that help the clients? How will we lead or influence the science in this area? • What personnel expertise, resources, and partners are needed to do this work? • Does the proposed work fall within the current scope and plans of existing projects, or would personnel resources need to be shifted from other projects to make this happen? The result is a research strategy for meeting user needs that is built around the above-mentioned five major theme areas and supported by the four branches of the Division as depicted in Figure 1-2. This report summarizes the research and operational activities of the Division for calendaryear 2010. The integration of research activities is illustrated in Figure 1-3. This report includes descriptions of research and operational efforts in air pollution meteorology, air quality model development, and model evaluation and applications. Chapters 2 through 6 of this report are organized according to the five major program themes listed above (and shown in Figure 1-2). AMAD's Intregrated Interdisciplinary Modeling Research Climate Change Air Quality Interactions Extreme Weather (heat waves, forest fires, floods,droughts) Climate Model Downscaling (clouds, aerosols precip) Impacts On Human Health Impacts On Ecosystems Hydrological modeling, precip prediction, atoms.deposition Air quality, i exposure I modeling Projected future air quality-climate (change effects Projected air qualityt conditions Integrated Atmospheric Modeling Systems • Meteorology, air quality modeling • Data analysis • CMAQ, AERMOD, other models \ Weather, Climate, Air Quality Observations Regulatory Policies Evaluation v Accountability of Regulations ™ Figure 1-3. AMAD's integrated interdisciplinary modeling research. ------- 2.0 Summary of Accomplis As a summary of and introduction to the annual report for 2010, the following Division accomplishments arc highlighted. 2.1 Division-Wide Accomplishments 1. Issue: Strategic thinking regarding integrated transdisciplinary approaches to solving environmental problems. Accomplishment: Coordination of various articles for the November 2010 issue of the Air and Waste Management Association's Environmental Manager (EM) on Integrated Transdisciplinary Research Findings: To properly address complex environmental problems, there is growing consensus among the research community that we must move away from the current single-pollutant, single- medium. and single-discipline approach to problem solving with a more integrated transdisciplinary approach. The articles in this issue address the need for integrated transdisciplinary research to solve the environmental problems of tomorrow. Impact: This EM special issue provides a thought -provoking set of articles for managers to consider integrated transdisciplinary research for problem solving—with examples of complex environmental problems confronting us now (e.g., climate change, transportation fuels in the 21st century, human exposure near roadways and in urban environments). 2. Issue: Developing AMAD's strategic research plan Accomplishment: A comprehensive Research Strategy was developed outlining the Division's core research in atmospheric model development and evaluation, as well as three major application areas in which air quality models arc used for human exposure, ecosystems exposure, and climate change air quality assessments. Findings: AMAD's research directions were articulated in three white papers on emerging application areas. These white papers were reviewed by an external panel of experts. The panel's report was complimentary of the Division's proposed research and included constructive comments and suggestions. Impact: AMAD's Strategic Plan was made publicly available to promote transparency in the Division's research planning process and facilitate collaborations both within the Laboratory and Agency as well as with external partners. The Strategic Plan positioned AMAD to adapt to emerging research directions within ORD. its for the Division 3. Issue: A key uncertainty in quantification of aerosol radiative effects and their impacts on climate change is the verification of the spatial and temporal variability in its magnitude and directionality and. consequently, its cumulative effect on the radiation balance of the earth-atmosphere system. Accomplishment: A research proposal was developed for a comprehensive observational- modeling study to investigate and verify aerosol radiative effects for past air quality conditions (e.g., changes in sulfate burden arising from sulfur dioxide | SO, | reductions as a result of Title IV of the Clean Air Act [CAA]). Findings: Based on a rigorous peer review, the proposal was recommended for funding as a Department of Energy-EPA Interagency Agreement (IAG). This project will support three post doctoral fellows. Impact: The successful attainment of the project goals will help quantify the impacts of troposplieric aerosol loading on atmospheric radiation budgets, and also help build confidence in the use of such modeling tools (e.g., the two-way WRF-CMAQ) for climate projection scenarios and emission management options. 4. Issue: Systematic intcrcomparisons and evaluations arc needed for regional air quality models over different continental regions. Accomplishment: The second Air Quality Model Evaluation International Initiative (AQMEII) Workshop was held September 26 and 27, 2010, in Turin, Italy. The meeting provided a venue for the 50 participants to share and discuss progress on model simulations and data analysis related to this modcl- intcrcomparison study. Findings: The workshop provided a venue to discuss and finali/c activities of the first phase of model intcrcomparison and evaluation. All participating groups made a commitment to submit their model data to a central site by December. Participants also developed plans for joint publications for a special issue in Atmospheric Environment, manuscripts arc due in May 2011. Impact: A model intcrcomparison exercise has been initiated for U.S., Canadian, and European air quality modeling systems to be applied on each continent for full-year simulations for operational and diagnostic evaluations. This is the first of its kind international collaborative effort in air quality modeling. 3 ------- 5. Issue: Understanding global air pollution issues to develop sustainable solutions for air quality and climate. Accomplishment: Developed a collaborative proposal with N.C. State University (NCSU) and the University of North Carolina-Chapel Hill (UNC-CH) to host a U.S.-India workshop on air quality; the proposal was funded by the National Science Foundation (NSF). Findings: The rapid growth of the Indian economy, spurred by industrial and urban expansion, has been accompanied by environmental stresses, particularly in air quality. The current capacity in India in research and operational aspects of air quality forecasting and regulatory management needs further enhancement. A workshop, to be held March 14-24, 2011, with partial support from the NSF and the U.S. Department of Energy, will bring together several experts from the United States and India with a common vision for identifying priority areas of research and development in the air quality and climate area, and a commitment for long-term collaboration. Impact: To engage participation by emerging scientists in the field, the workshop will include three days of invited lectures and presentations, followed by a 7-day hands-on training segment. This portion of the workshop will provide training to participants on a publicly available suite of numerical models (i.e., YVRF, CMAQ) that are used world wide in a variety of air quality applications, from basic research to local- and regional-level planning and management. The workshop will strive to improve understanding of the emission sources and meteorological conditions that contribute to regional-to-urban-scale air quality and climate issues of relevance to protecting public health and the environment. 6. Issue: Supporting international cooperation on air pollution modeling under the North Atlantic Treaty Organization (NATO). Accomplishment: Contributed substantially to the organization and program development for the September 27 to October 1, 2010, 31st NATO/ Science for Peace and Security (SPS) International Technical Meeting (ITM) on Air Pollution Modeling and its Applications, in Turin, Italy. Findings: The ITM has been broadened (Topic 7) from air quality and human health to cover ecosystems and economy (including air quality trends, cost-benefit analysis of regulatory programs and their effectiveness, and integrated modeling approaches). Impact: More than 130 participants from 35 countries attended the NATO/SPS meeting, presenting papers on a wide variety of air pollution modeling topics ranging from local- to global-scale applications. The meeting provided an important forum for synthesizing progress on air quality modeling programs around the world. All papers arc subject to peer review by the scientific community and will be published in a book under the NATO banner. 7. Issue: Promoting collaboration between U.S. and U.K. scientists on air pollution exposure research. Accomplishment: Nearly 50 international experts, including NERL scientists and others from the U.K. Department for Environment. Food, and Rural Affairs, and the Environmental Agency for England and Wales, met during December 6-10, 2010, in London to discuss progress in collaborations to improve and apply air quality models. Findings: NERL/AMAD scientists made several presentations at the workshop to promote collaborations in areas of air pollution modeling and its use to guide policy development, local-scale air pollution modeling and analysis, air pollution climate interactions, and air quality human health linkage studies. Impact: This collaboration is proposed in recognition of the common concerns for serious health, welfare, and economic impacts of atmospheric pollution. This collaboration recognizes the benefit of defining a research enterprise that involves joint interests and lays the foundation for a co-sponsored program that would improve efficiencies, and avoid redundant research activities and products through the leverage of resources and capabilities within each organization. 2.2 Model Development and Diagnostic Testing 1. Issue: Scientifically credible air quality models arc needed both for research purposes as well as regulatory applications in the implementation of the National Ambient Air Quality Standards. Accomplishment: An interim version of the Community Multiscale Air Quality (CMAQ) modeling system (CMAQv4.7.1) was released publicly. Findings: CMAQv4.7.1 incorporated scientific advancements in the representation of 3D advcctivc transport, in numerical solution of the aqueous- phase chemistry, incorporation of tools to assist in diagnostic analysis, and several code fixes recommended by the CMAQ user community. ------- Impact: CMAQv4.7.1 is being used by the OITicc of Air Quality Planning and Standards (OAQPS) in the regulatory impact analysis for the Transport Rule. Utility MACT Rule, and the Greenhouse Gas Rule. 2. Issue: As U.S. air quality improves, global background pollutant concentrations play an increasingly more important role in determining compliance with U.S. ambient air standards. Accomplishment: The CMAQ model was extended to hemispheric scales. Annual simulations for the year 2006 were completed, and model predictions were compared with a variety of measurements from surface, aircraft, and satellite-based platforms. Findings: Air quality modeling results for O,. PM, Hg, and other pollutants over the U nited States arc sensitive to the specification of boundary concentrations. Modeling over the hemispheric domain enables robust examination of modeled processes from an atmospheric budget perspective. Impact: CMAQ modeling capability has now been extended to the full Northern Hemisphere, enabling consistent specification of North American boundary concentrations and helping understand how the intercontinental transport of pollution a fleets air quality over the United States. 3. Issue: The combustion of engineered nanoniatcrials in diesel fuel can significantly alter ambient exposures to O,. fine PM, and hazardous air pollutants, with potential health consequences. Accomplishment: Completed Annual Performance Measure (APM) 366: Model the local-scale fate and transport of a combusted nanoniatcrial and its effect on regional-scale air quality. Findings: If nanoccriuin additives receive widespread use. cerium emissions from the United States will increase by a factor of 25. Ambient air concentrations of cerium could exceed 20 ng/m3 in major cities, representing a 100-fold increase from their current levels. Impact: Regulators in the Office of Transportation and Air Quality can now estimate the large-scale consequences of approving versus disapproving these additives in the E21 lb registration process. In addition. EPA health scientists, who arc seeking to develop realistic scenarios for assessing the health impacts of nanoccriuin additives, now have reasonable estimates of the level to which humans would be exposed in the future if the additives arc registered and widely used. 2.3 Air Quality Model Evaluation 1. Issue: Despite a reliance on regional air quality models by the international community, methods for evaluating the integrity of these models remain primitive. Accomplishment: Under the Air Quality Model Intcrnational Initiative (AQMEII), an international consortium of model evaluation experts representing 22 countries began to evaluate their models for the North American and European continents. AQMEII accepted an invitation to publish a scries of papers in a special issue of Atmospheric Environment. Findings: Preliminary results on regional air quality model evaluations across North America and Europe were presented at an AQMEII data analysis workshop in Turin. Italy, during September 2010. The importance of boundary conditions on the accuracy of model performance was stressed. Impact: AQMEII is facilitating the sharing of improved model evaluation techniques, which will lead to improvements in regional air quality model simulations and characterization of model uncertainties for communication to environmental decisionmakers. 2. Issue: Implementing effective emission control strategics to manage the cfleets of niultipollutant mixtures requires air quality models tliat can predict the response of pollutant levels to changes in emissions and meteorology. Accomplishment: Several peer-reviewed articles documenting the dynamic response of the CMAQ model in simulating the weekly and niultiannual variations of O, precursor emissions were published in the scientific literature. Findings: Although the CMAQ mode ling system showed skill at replicating the overall response of O, and Oxides of Nitrogen (NOx) concentrations because of cliangcs in emissions and meteorology. potential weaknesses were identified with the characterization of O, boundary conditions and the characterization of Volatile Organic Compound (VOC) and NOx emissions. Impact: The research findings arc informing the program office regarding the uncertainty of regional air quality models for O, attainment demo nst rat ions and arc helping shape priorities for model development, particularly in the areas of O, boundary conditions and weekly cycles in O, precursor emissions. 3. Issue: The quantification of uncertainty in air quality modeling results has been an important but elusive goal. 5 ------- Accomplishment: The Direct Decoupled Method (DDM) was used to provide a probabilistic-based dynamic evaluation of the CMAQ model, and the results were summarized in a manuscript submitted to Atmospheric Environment. Findings: In addition to uncertainties in NOx emissions, uncertainties in VOC emissions and boundary conditions were identified as areas needing further quantification. Impact: Characterizing the dynamic response of O, to changes in emissions suggests that uncertainties in key inputs, such as emissions and boundary conditions, should be explicitly considered in regional air quality modeling simulations. 4. Issue: The public release of CMAQvS.O needs to be evaluated. Accomplishment: The evaluation team began working closely with the development team and has developed a schedule and protocol for model evaluations. Findings: Improved communication between CMAQ model developers and evaluators will be accomplished via an internal Wiki site. Impact: It is anticipated that a robust model evaluation will be accomplished in an efficient and timely manner to support the release of CMAQvS.O to the air quality modeling community. 2.4 Air Quality-Global Climate Change 1. Issue: Traditional techniques for dynamical downscaling of global model results to the regional scale have relied only on specification of boundary conditions for the regional model. However, this specification in itself is insufficient to constrain the regional model. New techniques are needed to assure better consistency between global and regional model results. Accomplishment: Completed APM 284: Test model linkage approaches for downscaling global to regional climate with WRF. WRF downscaling has been tested using both global reanalysis data and output from the Goddard Institute for Space Studies (GISS) Model E Global Climate Model (GCM). Most of the testing thus far has focused on spectral and analysis nudging. Findings: Initial testing of dynamical downscaling from GISS Model E to WRF using various nudging techniques suggests that WRF is able to provide the needed regional texture to a simulated climate, while maintaining fidelity at larger scales to the driving fields. Results arc sensitive to the nudging parameters; thus, more testing is needed to determine best configuration for refined climate modeling. Impact: AMAD's experiments with data assimilation in the process of downscaling from global to regional climate models have shown much promise in moving this discipline forward. Initial results presented at recent conferences have generated much discussion and interest in the scientific community. 2. Issue: The WRF model originally was designed for short-term numerical weather prediction. It was not designed as a regional climate model. Certain aspects of the model, particularly its radiation budget and its treatment of soil heat and moisture fluxes, must be examined and possibly improved for long-term simulations. Accomplishment: Two 20-year continuous WRF simulations have been completed using different radiation parameterizations available in WRF. Tliese simulations did not use any nudging. Findings: The top-of-atmosphcre and surface radiation fields, a.s well as cloud cover, from these simulations will be compared with 5 years of satellite observations. Impact: This work will add confidence to future regional climate simulations conducted by WRF and will facilitate its use as a tool to understand climate-chemistry interactions. 3. Issue: Adjoint methods can be used to link the impacts of pollution to sources. Building the adjoint of a complex model like CMAQ requires a wide range of expertise and a community approach. Accomplishment: Convened a workshop with international participation on the development of CMAQ-adjoint methods for advanced sensitivity analysis. Findings: The CM AQ-adjoint workshop brought together model developers for a intense development session, where the components of the CM AQ-adjoint were assembled together for the first time. Second, a panel discussion was convened. Participants from OAQPS and acadeniia outlined the use of CMAQ- adjoint to address air pollution, ecosystem impact, and climate change mitigation needs. Impact: This meeting fostered the community of model developers who arc continuing to develop the model to better address air pollution, ecosystem impact, and climate change mitigation needs. 4. Issue: Thus far. AMAD's air quality-climate research has focused on the potential impacts of future global climate change on air quality . The reverse process (i.e., the impacts of local and regional air quality on climate) is also of intense scientific interest. Accomplishment: The WRF-CMAQ coupled meteorology-chemistry model has been tested, including direct aerosol feedback on shortwave ------- (SW) radiation and O, feedback on longwave (LW) radiation. Indirect feedback is under development. Findings: A prototype two-way coupled atmospheric modeling system, based on the WRF and CMAQ models, has been developed and further tested. Direct feedback of aerosols on SW radiation has been successfully implemented. Initial testing suggests that these effects can be large in regions with significant aerosol loading. Comparisons with limited measurements show improvements in simulation skill for SW radiation and 2-m temperature. Inclusion of direct effects leads to suppression of simulated Planetary Boundary Layer (PBL) heights which can then impact simulated air quality. Impact: The two-way coupled WRF-CMAQ system provides a framework to properly characterize the spatial heterogeneity in radiative forcing associated with short-lived aerosol and gases and. consequently, to better understand their aggregate influence on the earth's radiation budgets. This evolving system is expected to play a critical role in the Agency "s evolving research and regulatory applications exploring air quality-climate interactions. The flexible design of the system facilitates coupling meteorological and chemical calculations at finer temporal resolutions, which enables more consistent applications at fine spatial scales to better characterize variability in air quality and its linkage with health studies. 2.5 Linking Air Quality to Human Exposure 1. Issue: Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models frequently have been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. Accomplishment: Critically evaluated the LUR model being used by the community. Findings: A 2010 publication in Atmospheric Environment by M. Johnson, V. Isakov, J. Touma. S. Mukerjee, and H. O/kaynak presents the results of evaluation of land use regression models used to predict air quality concentrations in an urban area. Modeled hybrid air quality concentrations of PM, 5, NOx, and benzene in New Haven. CT, were used as pseudo-observations to develop and evaluate the different LUR models. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold-out (test) datasets. their performance diminished considerably. This indicated that that complex emissions and atmospheric processes resulting from meteorological, transport, and diffusion and chemical mechanisms can severely limit the predictive power of most LUR-based modeling applications. The paper also provided recommendations on future research to examine best ways to augment basic LUR models with site-specific source-receptor information generated from air quality models and to test how one might transfer LUR model results across different geographical locations or even countries. Impact: AMAD's results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially and temporally varying pollution levels using LUR techniques that arc site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts to improve the models' reliability or transferability. The information derived from this study will be used by EPA as a resource for developing appropriate tools in support of exposure assessments. Through this effort, the Divison has helped advance exposure science. 2. Issue: A principal route of human exposure to pollutants occurs for those living and working within several hundred meters of roadways. A better understanding of the mechanisms for near-road exposures is needed. Accomplishment: For the near-road research program, developed wind tunnel and field study databases to improve model algorithms for urban roadways in support of human exposure and health assessments. Findings: A 2009 Journal of the Air & Waste Management Association paper by V. Isakov and co-investigators presents an innovative methodology to link regional- and local-scale air quality models with human exposure models. It shows the presence of strong spatial gradients in exposures near roadways and industrial facilities that can vary by almost a factor of two across the urban area and much higher at the high end of the exposure distribution. Impact: A principal route of human exposure to pollutants occurs for those living and working within several hundred meters of roadways. A better understanding of the mechanisms of such exposure is needed. The importance of this work was recognized by EPA and external stakeholders by its inclusion ------- in the proposed nitrogen dioxide (NO,)NAAQS for near-road monitoring requirements. Results from this work also were used by the FHA in addressing near-road monitoring needs associated with their settlement agreement in the Sierra Club litigation. The Federal Highway Administration requested EPA's guidance and expertise in implementing their near road research requirements as part of this litigation, and an LAG has been established to that end. In addition to regulatory applications, the nominated papers have been cited in numerous other peer-reviewed journal articles related to near-road and local-scale dispersion topics. Through this work, the Division has helped to advance exposure science. 3. Issue: General consensus exists that populations spending significant amounts of time near major roads face increased risks for several adverse health effects. State and local agencies are interested in considering a roadway design that includes the presence of roadside vegetation as a means of mitigating air pollutant concentrations near roads. However, there are potential advantages and disadvantages of implementing vegetation to mitigate near-road air quality impacts. Vegetation in urban settings can provide numerous benefits beyond air quality improvements, such as ecosystem services, associated with improved phy sical and mental health and community vitality . Potential disbenefits include pollen production, water demand, channeling of invasive pests and fire into the urban environment, and exacerbation of sprawl by distancing buildings and other land use activities from roadways. Trees also may obstruct visibility on the road and reduce wind speed, cause damage or injury by falling, and create slippery conditions from dropped debris. Ideally, a large suite of costs and benefits should be evaluated in concert to optimize the use of urban vegetation to protect human health and promote sustainable communities. Clearly, more research is needed to assess the role of vegetation in mitigating air quality impacts from traffic emissions. Accomplishment: As a first step in evaluating this concept, in April 2010, A MAD scientists in collaboration with scientists from NERL, National Risk Management Research Laboratory (NRMRL), and National Health and Environmental Effects Research Laboratory (NHEERL) organized a workshop on the Role of Vegetation in Mitigating Air Quality Impacts from Traffic Emissions. The workshop included representatives from government agencies, academia. State and local agencies, and nongovernmental environmental organizations with expertise in air quality , urban forestry, ecosystem services, and environmental policy . The participants reviewed the current science and identified future activities in evaluating the potential role of vegetation in mitigating near-road air pollutant concentrations. Findings: A niultidisciplinary group of researchers and policy makers met to discuss the state of the science regarding the potential of roadside vegetation to mitigate near-road air quality impacts. The results are summarized in a journal article in the January 2011 issue of EM by Richard Baldauf. Laura Jackson. Gayle Hagler. Vlad Isakov. Greg McPherson, David Nowak, Thomas Caliill. Max Zhang. Rich Cook. Chad Bailey, and Pcriann Wood. Impact: This workshop was a first step in evaluating the potential role of vegetation in mitigating near- road air pollutant concentrations. A niultidisciplinary group of researchers and policymakers from government agencies, academia. State and local agencies, and nongovernmental environmental organizations with expertise in air quality, urban forestry, ecosystem services, and environmental policy reviewed the current science and identified future activities to further assess the role of vegetation in mitigating air quality impacts from traffic emissions. 4. Issue: In its mission to protect human health and the environment. EPA implemented the NOx Budget Trading Program (NBP) to reduce the emissions of NOx and the secondarily formed 0,. These pollutants and their precursors can be transported downwind, contributing to pollutant levels at locations far from the emission sources, potentially impacting human health in downwind areas. This study investigated the liealth impacts in New York State from exposure to polluted air parcels transported from the Midwest. Accomplishment: This study developed and applied a methodology to identify and target the transport of polluted air parcels and demonstrated that the risk for hospital admission resulting from respiratory-related illness was increased in New York State on those days that the air parcel originated over the polluted Ohio River Valley . Findings: The results of this analy sis indicate tliat the risk of being hospitalized for respiratory-related illness in New York State is greater on those days when air is transported from the Midwest into New York State as compared to days when air is transported from the North. Using a refined method to examine air parcels moving through a boundary drawn around high-emitting power plants in the midwestern United States resulted in stronger associations across more regions (significant odds ratios ranging from 1.06 to 1.16 for the entire study time period for six of the eight New York State regions). An assessment of temperature and its impact on the odds ratio calculation in the New York City metropolitan region indicates that temperature ------- alone does not explain the increased association between air pollution and respiratory-related hospital admissions. Impact: This technique is unique in that it examines the health impact of air pollution that can travel hundreds of kilometers downwind of its source region. It is one of the first studies to associate transported air pollution with a health end point in this manner. The approach developed by AMAD can be used as an indicator of exposure from transported air pollution. 2.6 Linking Air Quality and Ecosystems 1. Issue: Full implementation of the bi-directional flux approach for ammonia (NH3) in CMAQ requires grid-level information regarding fertiliser application timing, depth, and amount for each agricultural crop in the model domain. This information needs to be produced in a nationally consistent, process-based manner that can respond to changes in weather/ climate, atmospheric deposition, and land use/land cover conditions. Accomplishment: The Fertilizer Emission Scenario Tool for CMAQ (FEST-C) and associated agricultural fertilizer modeling system (AFMS) were developed to produce the input information needed for implementation of bi-directional CMAQ (APM 372). FEST-C is the interface that enables the user to identify the combination of preexisting weather, soil, land use/land cover, and agricultural management files that best describe the emissions setting of interest and to execute the programs that estimate the information needed by CMAQ. The tool produces a CMAQ-rcady input file for use in bi-directional CMAQ, as well as other supporting variables of interest to Ecological Services Research Program (ESRP) partners that can be read, analyzed, and displayed using the Visualization Environment for Rich Data Interpretation (VERDI) visualization and analysis tool. Findings: FEST-C was developed successfully, and a beta version was released in September 2010. The test client set indicated that the current capabilities appear to be adequate. However, the management input files need further improvement and evaluation to fully meet the needs of our ESRP partners and to reduce current emission uncertainties in the CMAQ NH, bi-directional model. Impact: Development of the FEST-C/AFMS system informed the design and execution of the CMAQ bi-directional NH, pilot study completed in FY 10. Completion of the beta version of FEST-C/ AFMS has laid the foundation for completing the full implementation of bi-directional CMAQ for NH, in 2011. Completion of the tool increased the visibility of AMAD within the ESRP. and requests for fertilizer and supplemental variable information have been received from scientists working with the National Atlas. Future Midwest Landscapes, and Albemarle-Pamlico Watershed Study teams to support their nitrogen budget, watershed, and water quality modeling work. A summary of the FEST-C/AFMS system was presented at the 31st International Meeting on Air Pollution Modeling and its Application. September 27 to October 1, 2010, in Turin, Italy. 2. Issue: Annual Mercury wet deposition from CMAQ 4.7 niultipollutant was biased high when compared with the Mercury Deposition Network (MDN) observations, and. when the CMAQ 4.7 was run at hcniisplieric scale, mercury was depleted during the period of the run. indicating that the mercury sinks were not balanced by emissions. Oxidized mercury (Hg[ 111) is soluble and reactive and removed from the atmosphere much more rapidly than elemental mercury (Hg[0]). The biases in mercury wet deposition and depletion of mercury in the hcniisplieric runs were the result of the parameterized oxidation rates of Hg|0|. Accomplishment: Hg|0| has an atmospheric lifetime ranging from 0.5 to 1 year, and Hg[II| has an atmospheric lifetime of days. The relatively slow oxidation rates of Hg|0| to Hg|ll| and differences in atmospheric lifetimes required the simulation and evaluation of this chemical nicclianisni on two scales. A model sensitivity study of mercury oxidation rates was run at the lieniispheric and continental United States (CONUS) scales. At the hcniisplieric scale, the budget of mercury sources and sinks and the model's ability to sustain ambient concentrations near the global background were used to evaluate changes to the mercury oxidation rates. Identical runs were conducted at the CONUS scale and evaluated against MDN and Ambient Mercury Network (AMNet) observations. Findings: A controversial gas phase reaction was found to be driving both the depletion of mercury at the hcniisplieric scale and model deposition biases at the regional scale. The reaction rates in the mercury chemical nicclianisni in CMAQ and all other chemical transport models remain uncertain and high-quality measurements of mercury oxidation and reduction rates from chamber studies and estimates using computational cliemistry arc needed to further reduce uncertainty in the model results. Impact: This refined chemical mechanism reduced the biases in the modeled wet deposition, improved the hcniisplieric mercury budget, and better captured 9 ------- the seasonal variation in mercury wet deposition. The mercury chemical mechanism was refined based on the sensitivity study that improved the hemispheric mercury budget and reduced model deposition biases to be included in CMAQ 5.0 and for use in model simulations in support of the EPA's toxics rule. 3. Issue: FY 10 research published by AMAD scientists suggests that full implementation of the bi-directional flux approach for NH, in CMAQ would alter the temporal and spatial simulation of ammonia deposition contributing to excess nutrient deposition to aquatic and terrestrial ecosystems and the formation of fine particulates. The research conclusions were based on a 1 -mo simulation, however. Full implementation of this approach into a research version of CMAQ will not be complete until 2011. An eastern U.S. pilot study was developed and executed for an annual simulation to inform the implementation process and to provide initial estimates of the potential value of the investment to incorporate bi-directional exchange of ammonia in CMAQ. Accomplishment: A pilot study was designed and executed for the eastern U.S. 12-kni CMAQ grid. Preliminary algorithms published in 2010 were added to CMAQ. Parameter time series were estimated offline for the full CMAQ domain and were then combined with these algorithms within CMAQ to estimate ammonia emissions from agricultural soils amended with inorganic nitrogen, canopy uptake, and net flux from the surface. Findings: The pilot study suggests that the bi-directional NH, flux CMAQ will reduce current unidirectional estimations of nitrogen dry deposition by a factor of two at background sites and by a factor of three for the model domain as a whole. Partitioning of nitrogen to the aerosol phase and wet deposition is increased. Transport of reduced nitrogen out of the modeling domain is increased by about 10%. Precipitation corrected wet deposition and ambient aerosol estimates arc improved relative to observations when this approach is used. Impact: Numerous "lessons learned" were transferred to the full model implementation process. Findings of the pilot were presented to O AQPS. which now is considering their implications for ongoing rulemaking and the possible contributions of bi-directional CMAQ results to the development of new secondary standards. Four manuscripts arc in development, and numerous presentations at professional meetings have been made to begin documenting the new algorithms. 4. Issue: To meet total maximum daily load (TMDL) targets to restore water quality, it is necessary to understand the sources of the deposition-driven loading of nitrogen to water bodies, including state and sector-level sources of atmospheric deposition. This requires a sensitivity analysis with an air quality model. "Brute force" sensitivity approaches do not work for pollutants with complex interactions and feedbacks, such as NH,. The capability to dcfensiblv estimate source attribution for NH, deposition has been lacking. Accomplishment: Adaptation of the Decoupled Direct Method-3D (DDM-3D) sensitivity technique for atmospheric deposition in CMAQ4.7.1 for reduced nitrogen stemming from NH, emissions. Findings: The NH, deposition from CMAQ could be partitioned successfully to state and sector-level sources of NH, emissions. However, the sensitivity algorithms do not consider all NH, nonlincarities. so the model sensitivity must be carefully set up to achieve an internally consistent interpretation of the budget partitioning. Impact: The foundation has been laid for source attribution analyses that will provide needed NH, atmospheric deposition source responsibility estimates to the Cliesapeake Bay TMDL process to provide guidance for management to consider to further reduce atmospheric deposition to the watershed and Bay. 10 ------- 3.0 Model Development and 3.1 Overview of Air Quality Model Development Introduction AMAD has a primary mission of developing air quality models in a "one-atmosphere" multipollutant. paradigm that are based on the current state of the science and directly applicable to EPA's air quality management and policy needs. The first release of the AMAD CMAQ model was in 1998, and AMAD has ongoing responsibilities to further develop, test, and evaluate the CMAQ model in an effort to identify model improvements and advance the science within CMAQ. The AMAD Model Development and Model Evaluation Program elements work closely to carry out these responsibilities. Within the AMAD Modeling Program, the Model Development Program element is responsible for the continued development, testing, and refinement of the CMAQ model for a variety of spatial (urban through hemispheric) and temporal (days to years) scales and for a variety of pollutants (03, PM, air toxics, visibility, and acid deposition). The one-atmosphere model concept enables the interaction of these pollutant regimes within one modeling construct. Through detailed treatment of physical and chemical processes affecting the fate of atmospheric pollutants, these modeling systems provide scientifically sound tools to understand the relationships between sources of air pollution and ambient concentrations over spatial scales ranging from urban to hemispheric and temporal scales ranging from hourly to annual. This is accomplished through an integrated niultidisciplinary approach involving physical, chemical, numerical, and computational science to develop a "numerical laboratory." wherein atmospheric physico-chemical interactions can be simulated effectively to guide development of air pollution abatement strategies. Through synthesis of laboratory and field measurements in paranictcri/ations included in the model and diagnostic testing against measurements over wider spatial and temporal scales, the models provide a framework to test and refine hypotheses and process formulations based on limited and controlled data, thereby improving our understanding of key processes regulating the atmospheric fate of pollutants. This comprehensive approach permits the testing of emissions control strategy impacts on the target pollutant, as well as collateral impacts on other pollutants. Model sensitivity and uncertainty tests arc conducted to understand the areas of the CMAQ model system that arc most in need of developmental focus and where the model response will be greatest. New experimental and theoretical knowledge of important chemical and physical processes in the atmosphere arc monitored, analyzed. and incorporated into the model when iagnostic Testing appropriate. New CMAQ model versions arc released for public access on an as needed basis. The Model Evaluation Program conducts evaluations to assess how well the CMAQ model is performing and to better understand the role of the model inputs and model processes in the air quality predictions. This requires comparisons against observational data from a variety of perspectives, where analyses consider different spatial and temporal scales to assess model performance. Interrelationships among different chemical species must also be considered, as well as the influence of uncertainties in meteorological predictions and emission estimates. Model evaluation serves dual purposes: (1) to characterize the accuracy of model predictions and (2) to identify needed improvements in modeled processes within the air quality model or model inputs. Research Activities and Accomplishments Several significant scientific and structural updates to the CMAQ modeling system were completed in FY08, resulting in the public release of CMAQv4.7. CMAQ v4.7 features a new aerosol module, that contains substantial scientific improvements over the aerosol modules released in previous versions of CMAQ. New pathways for secondary organic aerosol (SO A) formation from precursors, including isoprenc. sesquiterpenes, benzene, glyoxal, and nicthylglyoxal. were incorporated to improve the model's ability to represent the contribution of organic carbon (OC) to airborne fine PM. The model was updated to represent sea-salt emissions from wave-breaking in the coastal surf zone. A new parameterization to represent the heterogeneous dinitrogen pentoxide (N205) hydrolysis on particle surfaces, which includes dependence on temperature, relative humidity, inorganic PM composition, and phase state, was developed and incorporated in CMAQ. Modifications to the treatment of gas-phase chemistry in CMAQv4.7 were directed at improving the partitioning of airborne oxidi/cd nitrogen, as well as providing a consistent treatment for multipollutant applications (03, PM, air toxics, and mercury). In FY 10, an interim version of CMAQ (v4.7.1) was released publicly and included the following features: • Instrumented versions of CMAQ (including the sulfur tracking model, the primary carbon source apportionment model, and the direct decoupled sensitivity analysis method) to aid in diagnostic investigations • Updates to the vertical advcction scheme to reduce numerical diffusion associated with the original scheme 11 ------- • Updates to the numerical solver for aqueous chemistry to improve robustness and mass conservation characteristics • An on-line photolysis rate module that incorporates the radiative impacts of aerosol loading simulated by the model has been included; this enhancement enables investigation of potentially important impacts of scattering and absorbing aerosols in modulating photolysis rates and atmospheric photochemistry regulating the formation of secondary air pollutants. A prototype of a new modeling system that couples the meteorological model (based on the WRF) and chemistry - transport (CMAQ) calculations within a single executable, following a two-way coupled modeling paradigm, was developed successfully. Efforts to expand the applicability of the CMAQ modeling system to hemispheric scales were pursued. An annual simulation for 2006 and numerous sensitivity runs were conducted over a domain encompassing the Northern Hemisphere. Model predictions of O,. PM25 and constituents, and precursor species were compared with a variety of measure incuts from surface, aloft, and remote sensing platforms. Anew chemical mechanism. SAPRC-07TB, was developed and incorporated into CMAQ. This version was customized for EPA to include an explicit description of reactive H APs and high-emissions O, and PM precursors. In this mechanism, CMAQ uses an efficient operator technique to reduce the number of equations representing low-NO chemistry, which speeds up the run time while allowing more detail in the chemistry. Many chemical reactions in the atmosphere arc initiated by the photo-dissociation of trace gases. These reactions arc responsible for most of the smog buildup, which is detrimental to humans, animals, plant life, and materials. To accurately model and predict the effects of air pollution, good photo-dissociation reaction rate (or photolysis rate) estimates must be made. The CMAQv4.7 release included an optional inline photolysis rate module. This module allows for feedbacks of modeled atmospheric pollutants in the radiative transfer calculations. Several refinements arc planned for the inline photolysis module in preparation for the FY 11 CMAQ release, including: (1) specification of the O, column, (2) cloud attenuation effects, (3) incorporation of temperature (and possibly pressure) dependencies on the absorption cross sections/quantum yields, and (4) improvement to the surface albedo (including snow albedo and sea ice effects). In addition to these refinements, the module may be refined structurally to accomplish the following: (1) move cross-sections and quantum yields for photolysis rates from the module's source code to an input file. (2) use a preprocessor program to create the input file, and (3) enable photolysis rates to be shared with other CMAQ modules (e.g., the cloud module). Next Steps Two APMs related to CMAQ development arc required for FY 11. 1. Improved CMAQ mode ling system for use in urban- scale residual nonattainnicnt 2. An operational two-way coupled WRF-CMAQ mode ling system will be publicly released. Collectively, these require exploration of novel modeling methodologies to extend the CMAQ mode ling system to address emerging environmental problems at scales different from traditional CMAQ applications. Model applications to date have demonstrated clearly the continued need to account for and to improve the representation of interactions of atmospheric processes occurring at the various spatial and temporal scales. Model simulations over annual cycles have pointed to the need for more robust methods for specifying lateral boundary conditions. Although linking with global scale atinosplieric chemistry models has been pursued and will continue to be investigated, it is also recognized that biases in the global model can propagate and influence regional CMAQ calculations and often confound the interpretation of regional model results. The specifications of the lateral boundary conditions, to a large extent, dictate the simulated variability in the free troposphere, which in turn can impact the simulated surface-level background values for a variety of trace species. The tightening of the NAAQS to lower threshold values (e.g., the recent revisions to the O, NAAQS) places additional requirements on the ability of atinosplieric chemistry transport models to accurately represent the entire spectrum of ambient concentrations, including the background values. Expansion of the mode ling system to hemispheric scales provides opportunities to consistently represent processes at all scales and will improve the characterization of long-lived pollutants (e.g., mercury). The extension also supports future applications to study the linkage and interactions between global climate and air quality. On the other end of the spectrum, emerging Agency problems focusing on air quality-human exposure linkage will require application of the model at significantly finer resolutions to capture variability in ambient concentrations of a number of pollutants (03, PM, and air toxics) and resultant human exposure. These extensions will require further development and cnlianccnicnt of various physical and chemical process modules/algorithms included in the mode ling system. The two-way coupled WRF-CMAQ system will be essential for high resolution modeling because of the very high-frequency of meteorology data needed for air quality mode ling at urban scale resolutions. Many other aspects of mode ling science and techniques also need further development for urban-scale applications. Although both meteorology and air quality models have been applied at grid cell si/cs down to 1 km and even less, t lie re arc science issues at these fine scales that need further ------- development and analysis. For example, meteorology simulations at these scales have been shown to better capture local dynamical effects caused by complex terrain and coastal circulations; however, current nicsoscale models need considerable modifications to realistically capture the effects of urban environments on local wind fields, temperature and humidity, boundary layer dynamics, and local atmospheric dispersion. Also, there arc scale limitations for Eulcrian grid models. For example, the subgrid scale assumption of PBL models begins to break down as grid cell si/c is reduced toward 1 km because si/c of the largest eddies in the convcctivc boundary layer arc on the order of the PBL depth, which approaches or exceeds the 1-km grid scale. Wind field modeling in urban areas also presents a scale issue where the uiban buildings, which arc treated as roughness elements at larger grid resolutions, become obstacles to the flow at finer resolutions. What happens to pollutants emitted in such an environment is the key question. An alternate photolysis module is planned for the FY 11 release. This module will use a radiance module to calculate actinic flux at 27 wavelength bins (from 290nm to 800nm) at all altitudes within the troposphere. The radiance model will incorporate the independent variables of solar zenith angle, earth-sun distance as a function of Julian day. surface elevation, surface reflectance, cloud attenuation and reflectance, integrated aerosol optical depth, modulation of stratospheric aerosol depth, aerosol single scattering albedo and scattering phase function, and a latitude and season-specific model of atniosplieric gases and season-specific dependence for aerosol vertical profile. The radiance model is expected to reproduce the accuracy of a 64 stream calculation with a computational speed tliat is faster than a two-stream calculation. The radiance model will be coupled with a photochemistry model that will incorporate temperature and pressure dependencies in the cross-sections and quantum yields of currently modeled photochemical reactions. The coupled radiance and photochemistry models then will comprise a complete photolysis module. Clouds cover about 60% of the earth's surface and provide a means to efficiently transport constituents from the polluted boundary layer to the free troposphere, with substantial implications for long-range pollution transport and climate. Although CMAQ has hundreds of explicit or semi-explicit kinetic expressions to describe gas-pha.se chemistry, the aqueous chemical mechanism is limited to only a few (about seven) oxidation reactions. A more accurate and flexible solver for aqueous chemistry will be developed for the CMAQ model, which will enable clianging the reactions within the aqueous mechanism. The new solver will be bcnchniarkcd against the original by using box model and CMAQ simulations. Through collaborations with investigators at Rutgers University, new nitrogen-related chemistry will be added to the aqueous module. The new clieniistry will be evaluated with nitrogen deposition data collected during special studies (e.g., Bay Regional Atmospheric Chemistry Experiment |BRACE]) prior to public release. 3.2 CMAQ Aerosol Module Introduction The treatment of aerosol chemistry and physics is a critical component of the CMAQ modeling system. Ambient particles contain a mixture of numerous chemical species that originate from primary particle emissions and from secondary format ion pathways linked to gaseous precursors. Representation of this chemical diversity is an ongoing modeling challenge that requires the simulation of mass transfer between the gas and particle phases via condensation and evaporation, heterogeneous reactions of gaseous molecules on the particle surfaces, reactions that occur within the condensed particle phase, and homogeneous nuclcation of particles from low- vapor-pressure gaseous precursors. In addition to those processes, our ability to predict the ambient concentration, composition, and si/c distribution of atniosplieric PM is dependent on the accuracy of emission inventories, the numerical representation of aqucous-plia.se cloud and fog processes, and an accurate treatment of particle deposition. A correct simulation of atmospheric aerosol properties is vital for the prediction of PM, 5 concentration changes resulting from emission reductions and for calculations of regional haze. Two emerging priorities in the FY 10- FY11 time period will be (1) to accurately simulate the mass concentration and composition of coarse particles (PMCoaise), for which a new NAAQS is anticipated, and (2) to simulate cxcccdances of the new 24-h PM, 5 NAAQS (35 fig m 3), which occur predominantly in urban areas. Recent evaluation studies have revealed that the largest biases in CMAQ PM, 5 results arc driven by ovcrprcdictions of the unspeciatcd PM, 5 (Appel ct al.. 2008; Matliur ct al.. 2008), referred to hereafter as PM . Observations of PM , arc obtained from the Other. Other gravimetric PM, 5 mass measurement minus the sum of several chemically-speciatcd measurements (sulfatc|SO,|. NO,, ammonium, OC, and elemental carbonfEC]). Model predictions of PMother consist of unspeciatcd primary PM,, plus any noncarbonaccous material (e.g., oxygen and hydrogen atoms) associated with the organic aerosol. In FY09-FY11, three efforts will be undertaken to mitigate the incommensurability in PMofter evaluations and improve model performance for this important quantity. Research Activities and Accomplishments As new configurations have been added to the CM AQ modeling system (e.g., CMAQ-MP, CMAQ-TX, CM AQ-Hg. CMAQ-DDM. sulfate tracking, carbon apportionment), it has become increasingly difficult to maintain the code archive because of some subtle inefficiencies in the aerosol module architecture. 13 ------- In FY 10-FY11, problematic portions of the aerosol code will be incrementally updated and tested with a goal of removing the most egregious inefficiencies before the FY 11 release of CMAQ. In FY 10, the new secondary organic aerosol (SOA) treatment released in CMAQ v4.7 was described in a journal publication by Carlton et al. (in press). In FY 10- FY 11, the precursor-specific SOA results will be evaluated against organic-traccr-based measurements collected by HEASD in Research Triangle Park, the Midwest (LADCO sites), and Southeast (SEARCH sites). Based on our findings from those evaluations and reports from any ongoing National Center for Environmental Research (NCER)-funded extramural research, we will refine the model treatment of biogenic SOA. In FY 10. HE ASD delivered an APM summarizing their new experimental findings on anthropogenic SOA. Based on their report, combined with the results of ongoing extramural research, the anthropogenic SOA formation pathways in CMAQ will be ovcrliauled in FY 11-FY 12. Specifically, we expect to revise the treatment of alkane SOA and add new SOA formation pathways from light-weight PAHs (e.g., naphthalene, alky 1 iiapthalcnes). Coordinating with investigators from ENVIRON and the Electric Power Research Institute (EPRI), a new SOA module using the volatility basis set (VBS) will be implemented in CMAQ and tested against the base SOA module in CMAQ. If those tests arc successful, we will strive to release the VBS code as a user-specified option in FY 11. In the future, after sufficient testing, this may become the default option. A major focus of the FY 11 APM for CMAQ model development is improved PM, 5 predictions in "residual" nonattainnicnt areas, several do/en counties which arc forecasted to remain in nonattainnicnt of the NAAQS even after all of the national-scale emission control programs (e.g., Clean Air Interstate Rule [CAIR], NOx SIP Call, diesel retrofit, etc.) have taken effect. The vast majority of these residual problems arc likely to be dominated by primary PM,Thus, a detailed examination of the emission sources contributing to primary PM, 5 will be critical. In FY 10, the primary carbon source apportionment module tliat has been released with the past three public releases of CMAQ will be upgraded to CMAQ v4.7 and released to the public. In FY 10-FY 11. the source apportionment capability in CMAQ will be extended to treat other primary PM, 5 species, such as the trace metals emitted from industrial facilities. The resulting instrumented model will be used to assess and improve PM, 5 emission inventories in residual nonattainnicnt areas. In CMAQ, gas/particle equilibrium for inorganic species has been computed using the ISORROPIA module since the release of CM AQ \4.2 in 2002. The current version of that module, ISORROPI A v 1.7. treats the SOr/NO,-/ ' y 4 3 C1 -/NH4 +/Na+/H,0 system in a computationally efficient manner. but it suffers from numerical instabilities under certain extreme conditions. Over the past several years, three new thermodynamic modules have been developed as promising alternatives to ISORROPI A. Through a National Oceanic and Atmospheric Administration (NOAA)- funded contract with the Georgia Institute of Technology. ISORROPI A \ 2.0 was developed and released in FY09. The main advancement in ISORROPI A \ 2.0 is that it treats three additional species that arc abundant in sea salt and soil dust: (1) Ca,+, (2) K+, and (3) Mg,+. In FY 11, we will implement the latest ISORROPI A module in the FY 11 release of CM AQ. Next Steps A new CM AQ subroutine will be developed to produce model estimates of total PM,At present, model users who seek PM, 5 predictions from CMAQ must perform a linear combination of several do/en species in the model output. Inconsistencies in the postprocessing methodology can lead to discrepancies in the model results reported by different users, even when the exact same CM AQ configuration is applied to identical inputs. In addition to bringing consistency to the method of summing individual chemical components, the new subroutine will simulate changes in mass (i.e., artifacts) tliat occur when ambient PM, 5 is sampled and analyzed by the Federal Reference Method (FRM). The measurement artifacts to be simulated arc volatilization losses of ammonium nitrate during sampling, adsorption of semi-volatile organic gases during sampling, and retention of particle-phase water during gravimetric analysis of the filters (with consultation from Neil Frank [OAQPS]). The main output of this new subroutine will be gridded and time-resolved mass concentrations of PM,, FRM which, for the first time, can be compared directly with the large surface network of FRM monitors (i.e., State and Local Air Monitoring Stations [SLAMS]). Once the key measurement artifacts arc incorporated into the CM AQ calculations, the final modeled values of PM, 5 FRM can be used without hesitation in data fusion efforts (e.g., for CDC-PHASE). The SLAMS data are the basis for NAAQS attainment demonstrations so the modeled PM, 5 FRM output values should prove very useful to OAQPS. EPA regions, and the States. PM, 5 emission inputs will be augmented to explicitly speciate a number of chemical components that arc currently part of PMofter. At present, the Sparse Matrix Operator Kernel Emissions (SMOK.E) processor generates gridded files that include PM, 5 emissions of sulfate (S042 ), (NO} ), OC. and EC, where as all of the remaining mass is lumped together as PMother. The PMother component constitutes over half of the National Emission Inventory (NEI) for PM,5. Using detailed speciation profiles derived from our previous work (ReIT et al.. 2009), we will modify the SMOKE processor to subdivide the emissions of PM0ther into primary ammonium (NH4+), sodium (Na+), chloride (C1-), selected trace elements (niagncsiuni|Mg|. ------- aluminumfAl], silicon [Si], potassiumfK], caliuni |Ca|. titanium [Ti|, manganese [Mn], and iron [Fe|h non-carbon organic mass (NCOM), and a greatly-reduced quantity of P^other- Tlic CMAQ code will be modified to read these new species from the emission files and incorporate them into the chemistry and transport operators. The CMAQ aerosol module will be enhanced to treat the oxidation of OC that occurs in the atmosphere. It has been shown that OC oxidation increases NCOM (Turpin and Lim, 2001), thereby enhancing ambient PM2 5 mass concentrations. Our preliminary analyses of ambient data indicate that NCOM constitutes 10%-25% of PM2 5. However, the oxidation of secondary OC is treated very crudely in CMAQ v4.7 (using a zcrotli order oligomcrization rate constant) and the oxidation of primary OC is neglected entirely (a constant OM/OC ratio of 1.2 is assumed for all sources of OC). These shortcomings will be addressed in FY 11, and the resulting model predictions of NCOM will be evaluated against measurement-based estimates of the same quantity. The net result of the research described above will be 10 newly predicted quantities from the CMAQ model (PM2 5 FRM and fine-particle Mg, Al, Si. K, Ca, Ti, Mn, Fe, and NCOM) and more accurate predictions of NH4+, Na+, and CI- (because their direct emissions from anthropogenic sources will be accounted). When the total modeled concentrations of individual PM25 species (i.e., SO,:, NO,, NH4+, EC, and OC) arc subtracted from the new model estimates of PM2 5 FRM, we will obtain model results for PM„(, that arc commensurate with the difference-based Other observations of PMother collected across the Interagency Monitoring of Protected Visual Environment Network (IMPROVE) and Chemical Spcciation Networks. We then will be equipped to assess and address the CMAQ model biases for PMother in a rigorous manner. Building on the findings published in FY08 (Davis et al., 2008) plus recent experimental evidence from researchers at NO A A, the University of Washington, and the University of Illinois. CMAQ model treatment of the heterogeneous reaction between N205 and particle surfaces will be updated in FY 11 -FY 12 to account for the influence of organic content and/or organic coatings. EPA is considering a new ambient standard for PM ^ defined as PM10 minus PM,The largest sources of PMCoarse are sea salt, fugitive dust from anthropogenic activities (e.g., road traffic, agricultural tilling, construction), and windblown dust from arid land. All of these sources arc treated in CMAQ except for natural windblown dust. In FY 11, efforts will be made to incorporate new algorithms for estimating windblown dust emissions (Tong et al.. in preparation) directly into the CMAQ model and incorporate those new species into the chemistry and transport operators. With these advancements, the model infrastructure will be in place to numerically predict PM,, concentrations at various times J L Coarse and locations. CMAQ docs a reasonably good job of predicting fine particle mass concentrations (e.g., PM, 5), but a much poorer job representing the number concentration of atmospheric particles. In FY 11. we will coordinate with members of the external CMAQ user community to improve the treatment of nuclcation and primary particle emissions in the FY 11 release of CMAQ. These two updates will be the first steps toward refining CMAQ predictions particle number. 3.3 CMAQ Gas-Phase Chemistry Introduction The treatment of gas-phase chemistry, including the production of gas-phase pollutants; production of semi- volatile components, which can form aerosols; and the associated gas-phase chemistry solvers, will always be at the core of any advanced air quality model, particularly those that are formulated with the onc-atmosphcrc. niultipollutant concept. As such, periodic revisions to the representation of atmospheric chemistry in air quality models arc needed to incorporate new scientific findings as they become available and to address emerging air pollution issues that may arise. Also, it is often necessary to revise existing chemical mechanisms and solvers to be consistent with changes that arc made in other parts of the modeling system to enhance computational performance, case of use, and functionality . This is an ongoing process that is necessary to keep advanced air quality models, such as the CMAQ model, accurate, scientifically relevant, and fully operational. In the past, gas-phase chemical mechanism development focused on single-pollutant issues, but since largely it has become clear that it is more appropriate to treat chemistry in an integrated, multiphase. niultipollutant manner. The general goal of our research in this area is to develop, refine, and implement the gas-phase chemical mechanisms for use in the CMAQ model to accomplish the following objectives: • Ensure that CMAQ and other models tliat arc used for regulatory and research purposes have scientifically justifiable gas-phase chemical representations, arc appropriate for the application being studied, and arc consistent with our most up-to-date knowledge of atmospheric chemistry • Ensure that interactions between gas-phase chemistry and the chemistry occurring in aqueous- and particle- phases arc accounted for adequately, so that we can truly predict multimedia chemical effects of emissions changes • Develop techniques, tools, and strategics, so that we arc able to efficiently expand current mechanisms to predict the chemistry of additional atmospheric pollutants that we anticipate will become important in the future 15 ------- Research Activities and Accomplishments In FY 10, we completed the incorporation of one new, statc-of-thc-science atmospheric chemical mechanism into CMAQ, the SAPRC-07TB mechanism, and initiated incorporation of the Regional Atmospheric Chemistry Mechanism, version 2 (RACM2). The RACM2 mechanism is an updated and expanded version of RACM and includes more detailed aromatic and isoprcne chemistry. RACM2 was developed in 2007 and contains 349 chemical reactions. One hallmark of R ACM2 is its detailed representation of peroxv radical chemistry in rural and remote environments. By implementing RACM2 in CMAQ, we will provide a computational laboratory for examining the influence of gas and aerosol production in these types of environments and their influence on air quality. This quality of RACM2 also may provide a basis for future extensions to hemispheric simulations. The SAPRC-07 mechanism is an updated version of the SAPRC-99 mechanism, which has been used widely in CMAQ and other air quality models for many years. In FY 10, we have completed the incorporation of a customized, niultipollutant version of this mechanism, S APRC-07TB. which provides an explicit description of the most important species and reactions for a number of current EPA applications. SAPRC-07TB enables criteria. HAPs. and niultipollutant applications to give consistent results. In addition, the detailed organic chemistry in the original version of SAPRC-07 provides the capacity for changing the way that individual VOCs arc represented by our version of the mechanism. In the coming fiscal year, we will complete our analyses of the behavior of SAPRC- 07TB in CMAQ and incorporate any updates in inorganic and organic reactions. In addition, as we improve our ability to describe important SO A and cloud chemistry, we can use this information to develop more explicit versions of SAPRC-07TB that include these details. The CB05 mechanism will continue to be a major mechanism used in regulatory applications where many simulations are performed and for research studies done over long time periods. This nicclianisni uses structural lumping techniques to provide a highly compact representation, which makes it the most computationally efficient mechanism currently available for use in CMAQ niultipollutant studies. Recent published updates to the aromatic chemistry (in specific the toluene chemistry) were incorporated in the CB05 mechanisms, and extensive testing of the impacts of the update on O, and PM predictions was conducted. We will monitor research in atmospheric chemistry and perform periodic, limited updates to CB05 as appropriate, with a particular emphasis on CB06. Next Steps In the next 3 years, we will finalize inclusion of RACM2. We will make additional updates and improvements to the standard versions of these mechanisms to make them consistent with the latest research findings and provide details needed by the aqueous and aerosol computations, thus ensuring tliat the gas. aqueous, and aerosol chemistries arc correctly integrated. More importantly, we will start looking beyond the standard, fixed-species, fixed-coefficients mechanism suite to determine the best methodology for representing atniosplieric chemistry in the future. Given the rapidly changing research discoveries in atmospheric chemistry, we will examine whether a more flexible approach to mechanism creation is compatible with EPA's current mode ling applications. We will explore collaborations with groups in the United Kingdom, in particular the Master Chemical Mechanism (MCM) developers. This mechanism suite will allow CMAQ users to better match the type of application with an appropriate mechanism. In addition to these "base" mechanisms, we also arc developing extended versions for both special research purposes, and versions which will include the chemistry of additional H APs for use in full, niultipollutant analyses, described in section 3.8 (Air Toxics). Research versions of the SAPRC-07TB and CB05 mechanisms also will be constructed for special studies. Two versions, in particular (see just below), will be important components of our work during over the next few years: 1. SAPRC-07TB with detailed primary pollutants for reactivity studies—Current government-industry joint efforts to study VOC reactivity have identified the derivation of new reactivity scales as the most important need to improve the scientific basis of reactivity-based VOC regulations. To address this need, we arc initiating efforts to rede rive reactivity scales using CMAQ with the decoupled direct method (DDM). This is a large effort, which requires incrementally adding the more than 800 detailed species in SAPRC-07, in groups of 20 to 50, to a condensed version of SAPRC-07TB. The sensitivities for each VOC then will be used to calculate a wide variety of metrics that quantify potential contributions of individual VOCs to O, formation across the United States. The results of tliese simulations will be used by States, local air pollution agencies, and EPA to develop SIPs tliat better address the role of VOCs in O, formation. 2. SAPRC-07TB with detailed, updated isoprcne chemistry—Isoprcne is one of the most widespread VOCs emitted in most of the world. A robust and justifiable representation of isoprcne chemistry and its effect on hydroxy 1 and hydro peroxv radical formation, on cycling of nitrogen oxides and on production of formaldehyde arc all essential to have confidence that we adequately are representing O, and organic aerosol formation and sensitivity. Groups within AM AD arc collaborating arc reexamining and potentially revising the treatment of isoprcne ------- in SAPRC-07TB. Both a detailed and a condensed update to the current isoprene chemistry will be examined for potential inclusion in CMAQ-MP. All new and modified mechanisms will continue to be compatible with improved chemical representations for SO A modeling being developed within HEASD and will be linked closely with work performed under other AMAD tasks. The development of flexible, in-house chemical mechanisms will be one area that we will begin explore in FY 11. Under the U.S.-U.K.. collaborative agreement, we will study the feasibility of incorporating condensed representative versions of the MCM into a format appropriate for CMAQ applications. With this approach, we hope to more quickly incorporate new ground-breaking science into the mechanisms without the long time lag (6 to 10 years) between official fixed mechanism versions. We will monitor the scientific community for any new methods for representing gas-phase chemistry with an increased emphasis on interactions with aerosol formation and greater accuracy of condensations. These new methods arc at the frontiers of practice in the atmospheric chemistry modeling community. We will keep up to date with developments in this area and initiate efforts in the later years of this task to implement advanced chemistry representations in CMAQ. 3.4 Planetary Boundary Layer Modeling Introduction Air quality modeling systems arc essential tools for air quality regulation and research. These systems are based on Eulcrian grid models for both meteorology and atmospheric chemistry and transport. They arc used for a range of scales from continental to urban. A key process in both meteorology and air quality models is the treatment of subgrid-scale turbulent vertical transport and mixing of meteorological and chemical species. The most turbulent part of the atmosphere is the PBL. which extends from the ground up to about 1 to 3 km during the daytime but is only tens or hundreds of meters deep at night. The modeling of the atmospheric boundary layer, particularly during convcctivc conditions, long has been a major source of uncertainty in numerical modeling of meteorology and air quality. Much of the difficulty stems from the large range of turbulent scales that arc effective in the convcctivc boundary layer (CBL). Both small- scale turbulence tliat is subgrid-scale in most nicsoscale grid models and large-scale turbulence extending to the depth of the CBL arc important for vertical transport of atmosplieric properties and chemical species. Eddy diffusion schemes assume that all of the turbulence is subgrid-scale and, therefore, cannot simulate realistically convcctivc conditions. Simple nonlocal-closure PBL models, such as the Blackadar convcctivc model that has been a mainstay PBL option in Fifth-Pennsylvania State University (PSU) National Center Atmospheric Research (NCAR) Mcsoscale Model (MM5) for many years, and the original Asymmetric Convcctivc Model (ACM), also an option in MM5, represent large-scale transport driven by convcctivc plumes but neglect small-scale, subgrid-scale turbulent mixing. A new version of ACM (ACM2) has been developed that includes the nonlocal sclieme of the original ACM combined with an eddy diffusion scheme. Thus. ACM2 can represent both the supergrid-scale and subgrid- scale components of turbulent transport in the CBL. Testing ACM2 in onc-dinicnsional form and comparing with large-eddy simulations (LES) and field data from the second and third Global Energy and Water Cycle Experiment (GEWEX) Atmospheric Boundary Layer Study, known as the GABLS2 (CASES-99) and GABLS3 (Cabauvv. NL) experiments, demonstrate that the new scheme accurately simulates PBL heights, profiles of fluxes and mean quantities, and surface-level values. The ACM2 performs equally well for both meteorological parameters (e.g., potential temperature, moisture variables, winds) and trace cliemical concentrations, which is an advantage over eddy diffusion models tliat include a nonlocal term in the form of a gradient adjustment. Research Activities and Accomplishments The development and application of ACM2 in both WRF and CMAQ now has achieved consistent PBL treatment for meteorological and chemical species. Therefore, we will pursue further development of the ACM2, particularly for stable conditions. Very high concentrations of aerosols have been observed in areas of winter cold pools. Many western North American cities arc located in basins where cold air can collect, resulting in extremely stable inversion layers that trap locally emitted pollutants. Current meteorology and air quality models have insufficient vertical resolution and incomplete physical representations to accurately simulate these conditions. In FY09 and FY 10, AMAD was involved in a Regional Applied Research Effect (RARE) project to model winter air pollution episodes in Fairbanks. AK, where extreme surface inversion layers can result in very high concentrations of PM25. This modeling study uses a scries of nests with horizontal grid resolutions of 12, 4, and 1.33 km and very high vertical resolution, with the lowest layers of 4-m thickness. We will continue to use the Fairbanks modeling as a testbed for SBL model development. We have continuing involvement with the GEWEX GABLS. The ACM2 model was a participant in the GABLS2 experiment that was a comparison of singlc- colunin PBL models for a 2-day period of the CASES99 field experiment (Svcnsson. and Holtslag. 2006). The ACM2 was also a participant in the GABLS3 experiment which is a single diurnal cycle at the Cabauw meteorological tower in the Netherlands. The results of the G ABLS3 experiment will be published soon in Boundary Layer Meteorology. We have used tlie G ABLS3 experiment and the LES data created for G ABLS 1. which was a SBL case over sea ice in the arctic, to develop a new SBL model 17 ------- that was presented to the 19th Symposium on Boundary Layers and Turbulence in August 2010. The new SBL scheme also has been tested in WRF and evaluated for its simulation of low-level nocturnal jets. Next Steps AM AD will have continued involvement in the Fairbanks project through FY 11, in particular, an analysis of the results of the air quality simulations. Also, an evaluation of the new SBL scheme in WRF will continue in FY 11 and be extended to testing in CMAQ and the coupled WRF- CMAQ. 3.5 Meteorology Modeling for Air Quality Introduction Meteorology models arc very important components of air quality modeling systems because they describe the physical, dynamic, and thermodynamic state of the atmosphere. The Division, therefore, has a vested interest in developing components and capabilities that arc needed for air quality applications based on existing meteorology models. For example, the Division has been instrumental in the development of nudging-based four dimensional data assimilation (FDDA) in MM5 and more recently, in WRF Model. FDD A has been shown to significantly decrease the errors and biases of modeled meteorology that is a direct input to air quality models. We also have developed a land-surface model (LSM) and PBL schemes for MM5 and transferred them to WRF for better simulations of the PBL depth and surface fluxes and temperature, humidity, and wind within the PBL. which arc also critical for air quality modeling fidelity. MM5 has served as the Division's primary source of meteorologically modeled input to the air quality model for more than a decade, but. although MM5-based meteorology is still being used in some limited research, the Division now has transitioned fully to WRF for all of our new model developments and applications. Research Activities and Accomplishments From FY 10 and into FY 11, a total of five annual WRF simulations of the CON US at 12 km will be completed for various CM AQ applications, which represent the official transition away from MM5, as well as a transition from 36- kni CON US to a more refined 12-kin scale. Land-use and vegetation data arc important for (1) land surface modeling within the meteorology model (WRF), (2) dry deposition and bidirectional surface flux modeling, and (3) modeling biogenic emissions of photochcniically active chemical species. Thus, we have been working to upgrade and update the land-usc/vcgctation data used in our modeling systems. We already have accomplished the first stage of this effort by integrating higher quality and higher resolution land-use databases, the National Land Cover Database (NLCD) for the U nitcd States and the Moderate Resolution Imaging Spectroradiometer (MODIS) land-use data for outside the United States, into WRF to improve modeling of air-surface exchange processes. The NLCD is based on 30-ni resolution L-enhanced thematic mapping classified into 30 land use categories. The MODIS data has 20 land-use categories at 1 km resolution. The NLCD and MODIS datascts have been combined such that the higher resolution NLCD is used preferentially where available, with MODIS being used elsewhere. The Plcini- Xiu LSM in WRF and the M3dry dry deposition model in CM AQ have been modified to conform to this new hybrid land-use datasct. We have tested the new NLCD and MODIS data in a variety of applications in FY 10 and will continue to analyze the result in FY 11. Although significant differences in model performance between simulations with NLCD and the old U.S. Geological Survey (USGS) were not apparent in terms of surface meteorological variables and precipitation at the 12-km scale, some modest improvements were noted as the model grid scale is decreased to 4 km and 1 km. We now have moved almost exclusively to using NLCD (MODIS outside the United States) for almost all modeling applications. Another related area of research is the development of a new vegetation datasct for biogenic emission modeling also based on the NLCD/MODIS. The land-use data will be combined with forest inventory data (FIA) and agricultural data from the National Agricultural Statistical Service (NASS) to create a high resolution datasct that contains accurate information on tree species groups and crop types that could be used by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and Biogenic Emission Inventor System (BEIS) biogenic emission models. After testing and refinement of the paranictcri/ations in WRF and CM AQ that depend on land-use. the capability to use the hybrid NLCD/MODIS land-use database will be released to the WRF and CM AQ communities. Another research area concerns the improvement and application of data assimilation methods in the meteorological model. Data assimilation is used in the meteorology model to constrain the natural error growth in the meteorology and air-quality simulations. Typically for air-quality modeling, a nudging-based FDD A is applied throughout the simulation period to create dynamic analyses. We arc testing new FDDA techniques, including the utilization of both nonstandard surface and upper air observations that, in theory, will reduce the uncertainty of the analyses used to drive the FDDA and soil nudging. Tliese observation platforms include radar wind profilers, aircraft, satellite wind, satellite sea surface temperature, and a more dense set of surface observations from the numerous incsonct sites around the country. Furthermore, we have begun to explore using the 3D Variational Analysis (3DVar) in the WRF system, which has the potential to assimilate remotely sensed data (e.g., satellite radiance. Dopplcr precipitation estimates). We arc aggressively pursuing the testing of techniques begining in mid-FY 10. and arc analyzing the results, which ------- will be published in late 2011. The results, thus, far arc encouraging in that systematic biases in transport wind arc reduced through the use of some of these observational datasets. Next Steps The meteorological model fields arc processed into the CMAQ modeling system using the Meteorology- Chemistry Interface Processor (MCIP). MCIP prepares the meteorological fields in space and time for the emissions processing and the chemistry transport model (CTM). MCIP performs horizontal and vertical transformations, diagnoses additional fields, and produces the output in a format that is compatible with the CMAQ system. The community release of MCIP supports model output from MM5 and WRF-ARVV. In FY 11, MCIP will be updated on an as-needed basis to support internal Division modeling needs, as well as the CMAQ community. In particular, scientific changes to MCIP have been completed to support the NLCD/MODIS land-u.se. but further upgrades will be required especially for the more detailed vegetation work vet to be done. Furthermore. MCIP may need to be modified to support the urban canopy paranictcri/ations in WRF, to remove the dry deposition velocity calculations, and to support upgrades to the Geostationary Operational Environmental Satellites (GOES) processing. Releases of MCIP will occur in conjunction with major CMAQ releases and intermittently, as needed. A few projects in which analy sis will continue through FY 11 arc Northern Hemisphere annual simulations at a 108 km grid scale. We also arc using the high-resolution NLCD at 4 km and 1 km over Houston. Cleveland and Atlanta. In FY 11. we also will complete annual 2009 and 2010 CON US 12-km CMAQ simulations. New guidelines for FDDA-based meteorological simulations used for air quality models arc expected to be released as a result of this 2010-2011 research. We will rerun sonic of the longer term simulations to ensure the results of the limited 4-day test case hold for seasonal and annual simulations. Journal articles will document how the simulations of meteorology and regional transport were improved, which will build confidence in the inputs for CMAQ. Our meteorological modeling research program also focuses on improving finer scale meteorological mode ling simulations. The Division has embarked on a methodical study of the efficacy of nicso-ganinia scale meteorological mode ling simulations to address the suitability of the resultant fields for air quality modeling. Several simulations at fine scales have been or will be generated in FY 11. We have executed a base simulation that uses a very similar configuration as our 12-km scale simulations in terms of the way surface processes arc handled (PX LSM and ACM2 PBL schemes). We also have begun and will complete several simulations that use a new and much more advanced treatment of the urban canopy using the same modeling domain as the base 1 km. The urban canopy parameterization uses highly resolved urban morphology data, like building height, building to surface fraction, uiban fraction, and building area fraction, to represent a number of influences cities have on atmospheric flow. Not only will the meteorology of the simulations be compared with the base simulation, but equivalent CMAQ simulations will be executed to determine whether the air quality predictions arc improved. A seasonal Atlanta area 1 -km simulation is also another urban-related task for 2011. The air quality simulations driven by this urban meteorology will be used in a new technique to link air quality with human exposure. The outcome of this research will contribute toward the FY 11 APM "Improved CMAQ mode ling system for use in urban-scale residual non-attainment." as well as toward improving linkages with human exposure models within the Laboratory. This research also will help define a practical (horizontal scale) limit of predictability for deterministic meteorological models to be used for air quality modeling. 3.6 Integrated Meteorology-Chemistry Modeling Introduction Traditionally . 3-D CTMs that arc used for air quality research and regulation arc driven by 3-D meteorology fields provided by a priori runs of a meteorology model. Historically, the CTMs and meteorology models developed over several decades along independent tracks with little regard for computational, numerical, or even scientific consistency between the two systems. In recent years, however, there have been several efforts to combine meteorology and chemical transport models into single interactive systems. A primary driver for this trend has been the need to include the direct and indirect feedback effects of gases and aerosols on radiative forcing. Although effects arc mainly important for climate applications, it is becoming evident that they have substantial affects on local meteorology and air quality in regions of extreme air pollution. Zhang (2008) has provided an overview of several coupled meteorology-clieniistry models including the WRF/chem (Grcll, 2005) model in which chemistry has been added into the WRF model (Skamarock, 2008) at the science process level. Another approach is to couple historically independent meteorology and chemical transport models into a single executable. Advantages of this approach include maintaining consistency with existing separate sequential meteorology-chemistry systems that are being continuously and extensively applied and evaluated. Furthermore, the numerical and computational techniques employed in meteorology models and CTMs differ considerably because of the greater need for strict mass conservation and positivc- dcfinitcncss of transported scalars in the CTM. Also, CTMs generally use fractional integration of various processes, wheras meteorology models use generally integrate all prognostic variables and parameterizations each main time step. Because of these and other reasons we have 19 ------- coupled the WRF meteorology model and the CMAQ model to create a coupled modeling system with two-way interactions between chemistry and meteorology. Research Activities and Accomplishments A two-way meteorological and air quality coupled modeling system, WRF-CMAQ, has been developed in FY 10 to enhance model accuracy by eliminating excessive interpolation of hourly meteorological data and by incorporating aerosol feedback information from the air quality model to the meteorological model. The two-way coupled system consists of three components: (1) WRF, (2) CMAQ, and (3) the coupler. The coupler is encapsulated in Fortran 90 modules, so the details of the two-way coupling system arc transparent to the users. This design enables WRF or CMAQ to be detached from the system easily and executed in standalone mode. The single-source coding approach makes code maintenance less complicated. Next Steps Extensive testing of the two-way coupled WRF-CMAQ is underway and will continue into FY 11. The testing includes a wild fire episode in California in June 2008, where emitted smoke was concentrated enough in the central valley that SW radiation levels were much lower than normal. Initial tests show the model with SW feedback had lower radiation and daytime temperatures that agreed much better with observations than did the model with no feedbacks. Other tests arc being done on a hemispheric scale. A manuscript that documents these efforts is being prepared and will be published in FY 11- 2012. 3.7 Mercury Modeling Introduction As methods to measure mercury in the laboratory and in the atmosphere continue to be developed, and as further experimentation is performed, the basic scientific understanding of atmospheric mercury behavior continues to improve. As new information becomes available from the peer-reviewed scientific literature, improved formulations for simulating atmospheric mercury behavior will be added to the CMAQ model. This likely will involve the addition of new phvsicochcmical species to the CMAQ model that arc found to have important reaction with atmospheric mercury. Mercury is emitted to air from a variety of environmental and industrial sources. Industrial sources will continue to be treated using model input data from emission inventories. However, treating environmental sources of mercury will require a multimedia modeling approach, where entirely new modeling structures arc added to simulate media other than the atmosphere, such as water bodies, soils, and vegetation. It is assumed that atmospheric mercury model development will be aided by the deployment of new ambient mercury concentration monitors, and that dry deposition will be added as a measured parameter of the Mercury Deposition Network. Research Activities and Accomplishments Parameterizations were implemented that estimate the surface water mercury oxidation and reduction reactions, vegetation uptake of elemental mercury (HgO), and reduction of soil-bound mercury to estimate the emission potential of the natural surfaces. This allows CMAQ to parameterize the air-surface water concentration gradient dependence on the elemental mercury flux (Foley ct al., 2010), the recycling of recently deposited divalent mercury, and the enrichment of elemental mercury concentrations in vegetation, soil, and surface waters that have been documented recently in the literature (Bash and Miller, 2009). These improvements capture observed pulses of emissions following precipitation in arid environments during measurement campaigns and are an inline estimation of natural mercury emissions that remains consistent with changes in modeled chemical mechanisms and boundary conditions, changes that would require updates to offline natural emission estimates (Bash. 2010). Next Steps The follow ing arc improvements and issues being considered for the FY 11 CMAQ release. • Chemical kinetics information—Kinetic rate constants for gaseous and aqueous mercury reactions will be updated as new information becomes available for reactions of mercury with any other chemical species included in the model provided there is sufficient time to test the updated model before release. • Multimedia treatment for air-surface exchanges of elemental mercury—The CMAQ model will be enhanced to include dry deposition and emission of mercury to soils and vegetation (in addition to water bodies) based on bi-directional flux treatments where dry deposition and emission arc treated as a combined process with cither an upward or downward net flux. This modeling advance will be based on new scientific information on the behavior of mercury within those new media. 3.8 Air Toxics Introduction In the past, chemical mechanism and air quality development have focused on O, and primary inorganic PM; we arc expanding the scope of the atmospheric photochemistry in CMAQ to include predictions for a large number of H APs. Research Activities and Accomplishments We have expanded the base mechanisms of CB05 and SAPRC-07, as described in the previous section, to characterize the production and decay of approximately 40 ------- additional HAPs, including VOCs, inorganics, and metals. These mechanisms arc used by the Agency to develop full, niultipollutant analyses that examine the consequences of emission control strategics on multiple types of pollutants: O,. PM, H APs. visibility, acid deposition, etc. Developing the chemistry for these H APs includes appropriately accounting for compounds with a wide range of atmospheric reactivity and source types, in all phases (gas, aerosol, and aqueous). Next Steps In the next 3 years, further efforts in this area will overlap with the developments in the base mechanism but with an emphasis on H APs. We anticipate that, over the next few years, the following efforts will be made. • Characterization of explicit PAHs—This class of compounds includes a large number of organics with multiple benzene rings, possessing a wide range of vapor pressures, toxicity, and reactivity. Because some PAHs and their by-products can be highly carcinogenic and/or produce SOA. we arc initiating efforts to include their chemistry in upcoming releases of CMAQ-MP. • Description of arsenic compounds—Arsenic is a major contributor to national air toxics assessments but has potentially complex chemistry involving aqueous transformations. We arc initiating efforts to better predict the complex transport and transformations of arsenic in CMAQ. • Mechanism improvements for the most toxic air pollutants—We will continue to monitor the scientific literature and refine our representation of H APs chemistry in CMAQ-MP. We work closely with EPA, the Office of Air and Radiation (OAR), and OAQPS to identify those compounds that pose the largest risks for acute and chronic health effects to the Unitcd States population, and we will focus on refining our chemical representation of these compounds. This will help us to ensure that the chemistry and transport of these important pollutants is characterized appropriately. In SAPRC-07TB, we have incorporated additional pathways for highly toxic acrolein formation that were not available in previous chemical mechanisms. In the next planning cycle, we will incorporate information from current research studies on aromatic chemistry to help us to refine our treatment of aromatic H APs and their toxic by products. • Description of new, emerging toxic air pollutants— There is increasing evidence that a large number of species, beyond those H APs listed in the Clean Air Act 112(b), also can cause serious, adverse health effects. For example, multifunctional carbonyl compounds, formed as secondary pollutants from a wide variety of nontoxic VOCs. may provide sources of additional toxicity in atmospheric mixtures. As more details on the exact structures of compounds that can cause inflammatory responses to uiban smog arc discovered, we will initiate efforts to better represent these classes of compounds in CMAQ-MP. • Incorporation of persistent organic pollutants. pesticides and hydrolluorocarbons—Tlierc arc many other pollutants that arc cither toxic per sc or have the potential to cause toxic effects in aquatic systems, and understanding the lifetime and distributions of these compounds is of worldwide concern. CMAQ has been used in past studies of two of tliese compounds (atrazinc and tetrafiuoropropene), and we anticipate that, as new information on emerging pollutants becomes available. CMAQ-MP will provide an ideal basis for studying these types of toxic pollutants. Our efforts in air toxics mode ling will continue to be closely coordinated with other gas. aqueous, and aerosol chemistry being performed by AMAD, as described in other sections of this report, as well as other efforts in NERL/HEASD and in OAQPS. 3.9 Nanoparticles Modeling As industrial production of nanoniatcrials increases, a capability to accurately simulate the transport and fate of these materials has risen to a high priority. As an initial case study of nanoniatcrials, we arc investigating air quality effects of doping dicscl fuel with nanoparticulatc cerium oxide (nCcOj. nCcO, is used as a dicscl fuel combustion cataly st to reduce fuel consumption and reduce emissions of greenhouse gases and PM. In FY09, we assembled a database of dicscl-cngine dynamometer studies tliat have been conducted with and without nCcO, in the fuel. Research Activities and Accomplishments In FY 10, we used the just-mentioned database to develop a national emission scenario in which dicscl fuel across the Unitcd States has been doped with a nCcO, additive. Those emission estimates were used to assess the additive's effect on regional-scale air quality using CMAQ. Next Steps In FY 11, we will simulate the dynamics of the particle population emitted in dicscl cxliaust (with and without nCcO,) near a major roadway to gain a detailed understanding about the atmospheric transport and fate of nanoniatcrials. Results from this 3-year effort will be presented to clients in the Office of Transportation and Air Quality (OTAQ) during FY 11. 3.10 Emissions Modeling Research Introduction Emissions arc among the most important drivers of the CMAQ mode ling system. However, their estimates arc subject to a large degree of uncertainty related to limited knowledge on sources, processes, chemistry, locations, and 21 ------- temporal variability. The NARSTO Emission Invcntory Assessment (www.narsto.org/section.src?SID=8) affirms the large degree of uncertainty in emission inventories, particularly for precursors of airborne fine PM and for sources of OC. EC, and NH,. Most anthropogenic emissions used in the CMAQ system arc derived from EPA's National Emission Inventory (NEI) (www.epa.gov/ ttn/chicf/ciinforniation.htnil). AMAD's emission modeling research focuses on the evaluation and improvement of emission categories that respond to meteorology and/or that arc natural or quasi- natural in character, and that arc not readily available from the NEI. As such, our research includes the development, evaluation, and implementation of emission models for biomass burning, fugitive dust, lightning, and biogenic sources. These sources emit O, precursors (VOCs and nitrogen oxides), particulate matter, and some air toxins. Work performed in this area also supports the Division's efforts in the Air Quality Model Evaluation International Initiative (AQMEII), nanoparticlcs. and the Fairbanks, AK, EPA R ARE project. Research Activities and Accomplishments During 2010, the Division focused on biomass burning, biogenics, fugitive dust, and improved spcciation. • Biomass burning—After working with EPA's O AQPS in helping the release of an operational satellite-based biomass burning emission estimation system for the NEI, the Division turned its attention to evaluating the emissions from this system in the context of air quality mode ling and in working with other researchers on improving areas of greatest uncertainty. Collaborations with the National Aeronautics and Space Administration (NASA) as well as with researchers at Michigan Technological University and the University of Kentucky, continued under a N AS A-funded grant. A 2-day workshop was held to continue the study of plume heights and agricultural burning emission estimates. Annual emission estimates being in 2003 have been provided by our collaborator, collaborators at the University of Louisville. These emission estimates arc being evaluated for inclusion in future versions of the NEI. • Biogenic emissions—For the 2011 release of CMAQ, we plan to offer two alternatives for biogenic emissions: NCAR's MEGAN and the BEIS version 3.14. Both models arc now in the modeling system, and emissions from each have been compared. In concert with the Division's ccosystcins-rclatcd research, we worked with UNC's Center for Environmental Programs (CEP) to incorporate updated agricultural data and information from the 30-in resolved National Land Cover Database (NLCD). We plan to create an update to the Biogenic Emission Landcovcr Database (BELD) that is based on the NLCD. Undcr a N AS A-ROSES-fundcd grant with the University of Maryland, we collaborated on the use of satellite imagery to evaluate soil NO emissions (sec presentation by H. Plata. "Soil NOx model/satellite measurement intcrconiparisons." at http://www.cmascenter.org/conference/2010/agenda. cfm). • Lightning NOx—Via a collaboration with NASA and the University of Maryland, the Division continued to explore the development and evaluation of an algorithm to estimate nitric oxide production from lightning using meteorological parameters available from the MM5 and WRF meteorological models. Results indicate that the NO profile simulated by CMAQ in the middle troposphere (which had been underestimated by CMAQ) compares much better with observations when lightning-generated NOx is included in the model. • Wind-blown and fugitive dust—The Division continued to interact with scientists in NOAA's air quality forecast model research program to develop and evaluate a wind-blown dust algorithm based on land cover data and meteorological variables (notably wind speed and precipitation). A wind-blown dust module is being incorporated into the 2011 CMAQ release. For fugitive dust from anthropogenic sources, we have revised the methodology and improved the spcciation of these emission sources. Specifically. we have included the spcciation of trace metals from all anthropogenic sources to better understand the emission inventory and available measurements. We have revised the transportable fraction applied to the fugitive dust emission inventory to be consistent across the entire inventory and have incorporated temporal allocation adjustments to better account for variations in the emission inventory. Finally, we have included rain and snow events into the emission processing to account for meteorological effects on dust in the emissions processing. Two of presentations on this research were given at the 2010 Community Modeling and Analysis System (CMAS) conference: H. Simon ct al.. "Modeling the trace-elemental composition of PM, 5 in CMAQ," and (2) G. Pouliot ct al.. "Assessing the anthropogenic fugitive dust emission inventory and temporal allocation using an updated spcciation of particulate matter." Both presentations may be viewed at http:// www.cmascenter.org/conference/2010/agenda.cfm. • Spcciatcd emissions—The Division continued to work with partners within EPA to improve the SPEC I ATE database, which is central to the spcciation of VOC and PM gas and aerosols for emissions used in the CMAQ mode ling system. Portions of this work arc summarized in Simon ct al. (2010) and were presented by Simon and colleagues at the 2010 CMAS conference. ------- Next Steps The Division plans to continue improving and evaluating those components of the emission modeling system used in CMAQ and areas in which other organizations, such as OAQPS. arc able to provide support. The Division's research is organized around several model evaluation studies addressing O, and PM predictions of CMAQ and characterization of CMAQ performance for client groups, particularly OAQPS. Work is planned to improve process-based emission algorithms and the use of geographical data. Many of these improvements likely will depend on outside funding and continued collaboration with OAQPS and NRMRL. The NARSTO Emission Inventory Assessment recommends that inventory builders. "Develop and/or improve source profiles and emission factors plus the related activity data to estimate emissions for particulate matter, volatile organic compounds, ammonia, and air toxics." Outputs from this research will create tools for directly modeling hourly values from dust and wild fires, VOCs from biogenic sources, and from lightning NOx. The Division plans to further develop and test emission modeling tools for episodic modeling (hourly) of the emissions of biogenic emissions, wildland fires, lightning NOx, and fugitive dust. In collaboration with OAQPS, these advances will be incorporated into the SMOKE modeling system. SMOKE was developed to provide emission data to CMAQ. The emission modeling advances arc in direct support of the major update of CMAQ planned for FY 11. Bio mass burning—We plan to continue our work with OAQPS and the U.S. Forest Service to evaluate information on fire activity, fuel loadings, and climatological patterns associated with biomass burning emission estimates. Sensitivity tests and model evaluation of CMAQ arc planned to examine whether improvements in the fire emission est i mat ion methods will improve air quality model simulations. We plan to prepare one or more publications for submittal to a pccr-rcvicwcd journal related to this effort. We also plan to continue our collaboration with scientists at NASA Langlcy, as well as with NERL's Environmental Sciences Division, to evaluate and possibly improve plume-rise estimates for biomass burning events and to improve temporal/spatial estimates of rangcland/cropland burn emissions. Biogenic emissions—We plan to continue work with NRMRL and scientists at NCAR to integrate and evaluate MEGAN in the CMAQ modeling system. Building off previous progress, we plan to evaluate model performance with MEGAN and submit a publication for consideration to a pccr-rcvicwcd journal to report our findings and recommendations. We intend to release the next version of CMAQ with MEGAN and BEISv3.14. Working with scientists at the University of North Carolina, we will continue to explore updates of the vegetation landcovcr with the 30-m resolved land cover classes in the EPA/USGS NLCD. During 2011, we plan to focus on collaborating with NCAR via UNC contract to harnioni/c the vegetation cover datascts in MEGAN and BEIS. Time and resources permitting, we will include an updated vegetation cover datasct in BEIS after the fall 2011 release of CMAQ. Lightning NOx—In collaboration with NASA, an algorithm for estimating NO production from lightning in the CMAQ modeling system will continue to be refined and tested. NASA has indicated that a draft journal article on this work is in preparation. We plan to incorporate the lightning algorithm into the fall 2011 release of CMAQ. Gcogcnic dust—As time permits, we will continue to interact with NOAAs air quality mode ling forecast research team to prepare a publication and to include an algorithm for improved estimates of fugitive dust to be integrated into the CMAQ modeling system. The Division will continue to assess alternative temporal profiles and to provide appropriate recommendations to OAQPS to improve the NEI. We arc also testing an inline wind- sblown fugitive dust emission algorithm in the CMAQ code. 23 ------- 24 ------- 4.0 Air Quality Model Evaluation 4.1 introduction To ensure the integrity of regional air quality models for environmental decision-making, the Division conducts extensive evaluation studies to rigorously assess air quality model performance in simulating the spatial and temporal features embedded in air quality observations. We analyze the performance of meteorology, emissions, and chemical transport models to characterize model performance and also to identify what model improvements (inputs or processes) are needed. Thus, the Division's model evaluation efforts are tied tightly to its model development research activities. Our evaluations seek to answer four fundamental questions: (1) Is the model getting the right answer? (2) Is the model getting the right answer for the right reasons? (3) How well does the model respond to changes in emissions and/or meteorology? and (4) What is the uncertainty associated with the performance of the model? We have used these questions to construct a model evaluation framework (Dennis et al., 2010) consisting of operational, diagnostic, dynamic, and probabilistic techniques. Operational evaluation is a comparison of model predicted and observed concentrations of the end-point pollutant(s) of interest and is a fundamental first phase of any model evaluation study (Foley et al., 2010) Diagnostic evaluation (b) ¦ Other ~ TC ~ NH/ ¦ NO-," ~ S042 20-1 15- Northeast c O (J 10- 5- (H 21HI 12% 37ft I52S •4% '16=,'= Ł ± .1 c _ - n = O STN CM AO 20- 15- 10- 5- 0- Atlantic 32% 1 . ——— ¦31% 49Vi STN CWAQ — TO Figure 4-1. Examples of two types of model evaluation techniques: (a) probabilistic—time series of daily maximum 8-h 03 concentrations from a 200-member CMAQ model ensemble at a monitoring site in an urban location; and (b) diagnostic—percent contribution of individual aerosol species comprising the total average regional PM25 mass concentrations predicted by CMAQ and measured by the Speciated Trends Network (STN) sites. 25 ------- investigates the processes and input drivers that a fleet model performance (Bhavc et al.. 2007, see Figure4-lb below). Dynamic evaluation focuses on assessing the model's air quality response to changes in emissions and meteorology, which is central to applications in air quality management (Godovvitch et al., 2010). Probabilistic evaluation characterizes the uncertainty of air quality model predictions and is used to provide a credible range of predicted values rather than a single "best-estimate" (Pinder et al., 2009, see Figure 4-1 a). Because these four types of model evaluation are not necessarily mutually exclusive, the Division's model evaluation studies often incorporate aspects from more than one technique of evaluation. Information on selected aspects of the Division's model evaluation research program follows just below. 4.2 Atmosphepheric Model Evaluation Tool Introduction To conduct and communicate model evaluation requires the availability of statistical and graphical software. Over the past several years, the Division has been working on the development and application of the Atmospheric Model Evaluation Tool (AMET). A MET is an open- source software package designed to aid in the evaluation of meteorological and air quality models. AMET is a combination of several publically available software packages: specifically, the MySQL database software, PERL, and R statistical software. The database is used to store observed and modeled data, whereas the R software is used to perform various statistical analyses and create a number of different statistical plots. An example of AMET's capability is shown above in Figure 4-lb. Research Activities Recent activities have focused on communicating and releasing the software to the air quality modeling community. Accomplishments Appel et al. (2011) reported on the design and public release of AMET. AMETvl.l was made available through the CM AS center Website at www.cmascenter.org. Next Steps Work is ongoing to create AMETv2.0, which should include enhancements to the database and analysis codes to provide greater support of new data sources (e.g., satellite data). It is likely that the next release of AMET will occur in late 2011 or early 2012. 4.3 Air Quality Model Evaluation Initiative Introduction The Air Quality Model Evaluation International Initiative (AQMEII) is a joint research project between air quality modeling groups in North America (U.S. and Canada) and Europe (numerous European modeling groups from various countries). AQMEII aims to promote research on regional air quality model evaluation across the European and North American atmospheric modeling communities through the exchange of information on practices, the realization of intercommunity activities and the identification of research priorities, keeping policy needs in focus. The activity is organized around the concept of Operational, diagnostic, dynamic, and probabilistic evaluation defined and discussed at the joint EPA/American Meteorological Society workshop in Research Triangle Park in 2007 and the first AQMEII workshop in Stresa, Italy, in 2009. AQMEII is coordinated by two co-chairs. Dr. Rao from AMAD and Dr. Galmarini from the European Union's Joint Research Center in Italy. In addition. Environment Canada serves as a regional focal point with the U.S. and Europe. The results of AQMEII will be available to the scientific community in general at academic, institutional, and private sector levels. Research Activities The Division is responsible for performing and evaluating the CMAQ model simulation for North America. As one of the coordinating members of AQMEII, AMAD has been responsible for coordinating the availability of the input data (e.g., meteorology, emissions) that were used for the modeling applications for North America. Recent activities have focused on communicating and releasing the software to the air quality modeling community. Accomplishments Over the course of the year, Division scientists have been working closely with groups in Canada and Europe to compare the results of their modeling efforts and identify the strengths and weakness of each air quality model, with the ultimate goal of the research project being to learn how to improve those models and the methods of evaluating results from air quality models. By the end of 2010, the Division had completed the CM AQ modeling for North America and submitted all output data to the European Union's Joint Research Center for further analysis. Next Steps A special issue of Atmospheric Environment will be dedicated to results from the AQMEII project, and AMAD has committed to submitting a number of manuscripts to the special issue on topics ranging from meteorology, emissions, and boundary conditions to analyses of the operational performance of the AQMEII CMAQ modeling 26 ------- results to principal component analysis (PCA) of the modeling results. All of these papers are joint research projects with many international groups contributing to each of the manuscripts. Submission of manuscripts to the special issue of Atmospheric Environment is due in May 2011. In addition, results from the AQMEII project will be presented at the 2011 CMAS conference and the AQMEII workshop to be held in Chapel Hill, NC, in October 2011. 4.4 Diagnostic Evaluation Introduction Diagnostic evaluation research during the year examined the interaction of meteorology on chemical transport modeling. Research Activities Our focus has been on photochemical O, formation and horizontal transport processes because they strongly influence the temporal evolution and spatial variability of the simulated 3-D O, distribution within the lower troposphere. Results from the CMAQ with the Carbon Bond CB05 chemical mechanism are being evaluated against surface-based routine measurements, as well as 1800 1600 1400 3" O 1200 < E """ 1000 o lu 800 X 600 400 200 0 0 2 4 6 8 10 12 14 16 WIND SPEED (m/s) Figure 4-2. Modeled (gray) and observed (rose) wind speed profiles averaged over the nocturnal periods of August 11-15,2002, at Ft. Meade, MD. Boxes span the 25th to 75th percentiles, and whiskers extend from the 10th to 90th percentiles. upper air profile measurements obtained by research aircraft during intensive field studies from three summer months in 2002, when several high-O, episodes occurred in the eastern United States. Net O, production efficiency (OPE) results suggested photochemical O, formation was comparable between the model and observations, with OPE values of 6.7 and 7.6, respectively, within the afternoon PBL. Evaluation of wind profiles revealed wind speeds generated by the WRF model with the FDD A base approach displayed greater underestimation of observed speeds in the nocturnal residual layer aloft. During a multiday O, episode, when a low-level jet occurred nightly in the Mid-Atlantic region modeled results with the base FDDA underestimated the maximum jet speed by up to 3 m/s (Figure 4-2), and wind directions exhibited a southerly bias of 20 or more relative to observed directions. Trajectory analysis demonstrated that these differences in winds can introduce large spatial displacements in modeled and observed O, patterns within the region. A follow-up sensitivity study with the WRF meteorological model has been performed with different FDDA options, which also included the use of additional available wind profile data (i.e., boundary layer wind profiler measurements and Doppler radar wind profiles). Results demonstrated that model simulation of the Mid-Atlantic nocturnal low-level jet winds was improved when more observed wind profiles were assimilated. Accomplishments More accurate transport patterns based on these findings are expected to lead to model improvement in estimating air quality concentrations because wind fields generated by the WRF model are directly applied in the CMAQ chemical transport model simulations for retrospective periods. Next Steps Journal manuscripts presenting results of this diagnostic evaluation study and WRF/FDDA sensitivity effort are being prepared for journal submission in 2011. 4.5 Dynamic Evaluation Introduction Dynamic evaluation examines a model's concentration response caused by changes in emissions and/ or meteorology through comparisons to observed concentration change. Therefore, historical periods must be identified that provide observable changes in pollutant concentrations that can be related closely to known and appreciable changes in emissions and/or meteorology. The time period spanning EPA's NOx SIP call program provides a valuable test of model response using the dynamic evaluation approach since substantial reductions 27 ------- in NOx emissions occurred in the major point source sector during 2003-2004. Gego et al. (2007) and U.S. EPA (2007) show examples of how observed O, levels exhibited notable decreases after implementation of the NOx SIP Call. Gilliland et al. (2008) documented the results of an initial dynamic evaluation study based on CMAQ model simulations with three different chemical mechanisms to assess maximum 8-h O, changes between summer 2002 (pre-NOx SIP Call) and summers 2004 and 2005 (post-NOx SIP Call). Recognizing the large reduction in anthropogenic activity that naturally occurs on weekends relative to weekdays, we performed a dynamic evaluation of CMAQ modeling system for the so-called "weekend O, effect" to determine if observed changes in O, because of weekday-to- weekend (WDWE) reductions in precursor emissions could be accurately simulated. The weekend O, effect offered another opportunity for dynamic evaluation, as it is a widely documented historical phenomenon that lias persisted since the 1970s. Research Activities During the past year, the Division expanded on the NOx- SIP Call analysis by assessing the changes in maximum 8-h O, concentrations (> 95th%) from CMAQ simulations spanning five consecutive summer periods (2002 to 2006) against concentration changes at the rural-based Clean Air Status and Trends Network (CASTNET) sites. Figure 4-3 below depicts variations in the modeled and observed O, changes for each summer period relative to 5-year mean values. The modeled results track the observed changes rather closely, and considerable overlap exists during most periods. However, the largest differences between the modeled and observed values occur for the summer 2002 period, which are attributable to the notable model underestimates of maximum O, levels during several high O, episodes. A diagnostic study has been underway to investigate horizontal transport and O, chemical formation to examine whether either of these processes is contributing to the underestimation. In addition. Modeled and Observed Relative Changes in 8-h 03 Departure from Average over Five Summer Periods ifi * 2002 2003 2004 2005 2006 2007 Figure 4-3. Box/whisker plot of the percentage change in modeled (gray) and observed (green) maximum 8-h O, concentrations (>95th percentile) for each summer period relative to a 5-year mean at the CASTNET monitoring sites in the eastern United States. The boxes span the 25th to 75th percentiles and whiskers extend from the 10th to 90th percentiles. (U O) c co -C O sz o "¦M 2 -*—< sz CD o c o o i r 2002 2003 2004 2005 2006 2007 YEAR Figure 4-4. Change in weekday 3-h average morning NOx concentrations. Model results (gray) and observations (white) are based on 42 urban sites. Each box/whisker plot shows the median values (line inside boxes). Boxes span the 25th to 75th percentiles, and whiskers extend from the 10th to the 90th percentiles of the concentration distributions. 28 ------- analyses arc proceeding with modeled and observed O, profiles over multiple summers to determine the extent of concentration change aloft. Another dynamic model evaluation effort examined weekday morning (high-traffic period) concentrations of NOx to assess changes in modeled and observed NOx levels that could be attributable to mobile emission changes over this multi-year period. Although modeled mobile emissions dropped by 25% over this period. CMAQ results were governed by the 15% decrease evident in total NO emissions, which also include notable contributions x 7 from area, nonroad. and the minor point-source sectors. Using 18 years (1988 to 2005) of observed and modeled O, and temperature data across the northeastern United States, we reexamined the long-term trends in the weekend effect and confounding factors that could complicate the interpretation of this trend and explored whether CMAQ could replicate the temporal features of the observed weekend effect. This work was performed in collaboration with researchers from the State University of New York and the University of Idaho. Accomplishments Three journal articles on dynamic evaluation were published during 2010. Godovvitch and Rao (2010) revealed that spatial patterns of percentage decreases in O, between 2002 and 2006 showed strong similarities between the modeled and observed results, although the modeled changes were somewhat less than observed changes. Godowitch et al. (2010) reported that observed urban NOx concentrations decreased by nearly 25%, whereas modeled concentrations declined by about 15% from 2002 to 2006 (see Figure 4-4). Pierce et al. (2010) reported that CMAQ could replicate the decrease in amplitudes of the weekly O, cycle that occurred during an 18-year period. However, similar to the two other dynamic evaluation studies, the modeled response of O, to weekday-weekend differences in emissions was somewhat less than that observed. These studies suggest that more attention should be given toprobing uncertainties in emission estimates, reducing the grid cell si/c in the lowest layer of CMAQ, and using time- dependent and more realistic boundary conditions for the CMAQ simulations. 4.6 Probabilistic Model Evaluation Introduction Most model evaluation studies have focused on assessing the performance of a single set of deterministic modeling results. However, it is well known that air quality predictions arc sensitive to uncertainties in meteorology, emissions, and processes represented within the chemical transport model itself. Research Activities To support probabilistic model evaluations and the application of air quality models in a probabilistic manner, research is underway to achieve the following accomplishments. • Estimate the sensitivity of O, and aerosol predictions to different model configurations and inputs (an AQMEII activity) • Develop methods to assess the uncertainty in CMAQ predictions of O, and aerosols using an ensemble of model configurations and input sets • Apply these methods to assess the uncertainty in CM AQ-predicted changes in O, and aerosols resulting from emission changes • Use statistical methods to postprocess the ensemble of model runs based on observed pollutant levels ® Use the uncertainty information to help prioritize key model development needs In previous years, this work was based on a 12-mcmber ensemble of CMAQ simulations that included six different MM5 configurations and two different chemical mechanisms. We expanded the membership of the ensemble to include uncertainty from emissions by using the CMAQ-DDM-3D to generate large member ensembles, while avoiding the major computational cost of running a regional air quality model multiple times. The resulting ensemble of CMAQ-cstimated O, concentrations was used to fit a probability distribution function, and the quality of the ensemble-derived pdf was assessed using observations and parametric (confidence interval) and nonparanictric (ranked-histogram) methods. A journal article of this work was published in Environmental Science and Technology (Pinder et al.. 2009) Accomplishments During 2010, we began exploring statistical postprocessing methods to weight the ensemble members and improve estimates of model uncertainty. We applied a Baycsian model averaging (BMA) technique to calibrate the ensemble predictions by weighting each individual ensemble member based on how closely it matches observed O, values. We evaluated these methods based 29 ------- (A) Maximum Daily Average 8-h 03 Concentration atTerre Haute, IN (AQS#181670024) 8- 8- O _ CN ^ 2CC2 base mode 2005 base mode 2002 node dUMDJQDfi 2CCS mojei dtstribJBon ~ ~ 2002 ooier.attcns * * 200S odise^vaflnni -| 1 1 r 40 50 60 70 ppb 80 1 90 (B) Maximum Daily Average 8-h 03 Concentration at Detroit, Ml (AQS#261630019) CDIL base r-cdei 2Tji base r-ccei|_ Q0Q2 rcoel clrtrfcullor rcacl clsfr fcultor ^^^2302 observation^ 233z obsservatcrs ppb Figure 4-5. Base model, observational, and model ensemble empirical cumulative distributions of maximum daily average 8-h O, concentrations for 2002 and 2005 at two AQS sites: (a) Terre Haute, IN; and (b) Detroit, MI. All ensembles were constructed based on ±50% uncertainty in emissions of area and mobile NO and ±3% uncertainty in emissions of point NOx. The wide spread of the ensemble at the Terre Haute site indicates greater sensitivity to NOx emissions in comparison with the site in Detroit. on observed O, in the Southeast in the summer of 2005. A manuscript describing these results currently is being drafted and is expected to be submitted for journal review in 2011. As we continue to develop and improve these methods for creating probabilistic model outputs, we are also investigating how these tools can be used in policy- relevant applications. Specifically, we are combining probabilistic evaluation techniques with dynamic evaluation of model-predicted O, concentrations. Using DDM-produced sensitivity coefficients and building on our previous model evaluation efforts, we estimated the dynamic response of the model because of NOx reductions between 2002 and 2006, while accounting for uncertainties in NOx emission inputs (Figure 4-5). This work has been described in a journal article accepted for publication by Napelenok et al. (2011). Next Steps We currently are collaborating with Rice University and N.C. State University to explore other Bayesian estimation techniques for quantifying the uncertainty in model inputs and outputs. In addition, we are collaborating with OAQPS on a pilot project to compare a DDM-based approach for examining emission control options with response surface modeling methods that have been developed and used by the regulatory community for the past several years. This project will enable us to explore the strengths and weaknesses of different probabilistic approaches for supporting EPA's air quality management and policy needs. 30 ------- 5.0 Climate and Air Quality Interactions 5.1 Introduction Energy generation from fossil fuels contributes to global warming and degrades air quality, both of which profoundly impact human and ecosystem health. Atmospheric levels of carbon dioxide (C02) and other GHGs have increased dramatically since the Industrial Revolution, and emissions from fossil fuel combustion have been linked to human disease since the London smog event in 1952. The Intcrgovcrninental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) (Solomon et al.. 2007) concluded that the continued rise in GHGs from human activity is the primary cause of the temperature increases observed over the 20th century, and that global warming is likely to continue over the next century even with significant mitigation of greenhouse gas emissions. These temperature increases also lead to changes in other climatic conditions, such as changes in precipitation intensity and duration. Extreme weather conditions could become more frequent, which, in turn, can affect adversely human and ecosystem health. Emissions from anthropogenic combustion sources arc also the largest sources of PM, O,. CO, and NOx and sulfur. These pollutants arc known to contribute to respiratory and cardiovascular effects in human populations, as well as ecological effects to aquatic (acidification and eutrophication) and terrestrial ecosystems (damage to agricultural and other vegetation). The scientific issues of global climate change have been studied for decades. However, in response to the 2007 U.S. Supreme Court decision in Massachusetts vs. EPA and the EPA Administrator's recent Endangcrnicnt Finding under the CAA Act (U.S. EPA, 2009), the EPA must rapidly prepare for rulemaking regarding control of emissions. Additionally, the EPA Office of Air and Radiation (O AR) recognizes that some "traditional" pollutants regulated under the Clean Air Act also have radiative forcing properties (e.g., the black carbon component of PM,,, O,) tliat impact climate. The Agency has a need for integrated policy approaches to both mitigate climate change and manage air quality. The urgency also has increased greatly to understand the implications of climate change on local and regional scales, given the numerous anticipated impacts on environmental protection and regulatory responsibilities of the Agency. For example, research already has demonstrated that future climate conditions very likely will increase air quality risks and decrease the effectiveness of emission control efforts (e.g., Nolte et al.. 2008; Weaver et al.. 2009). In addition to enhanced photochemical production in a generally warmer environment, greater frequency of stagnation events could exacerbate air quality problems. It also is anticipated that increases in extreme precipitation events could lead to additional water-borne disease outbreaks and degradation of water quality conditions. Anticipated damages to aquatic and terrestrial ecosystems could include impacts such as encroachment by invasive species and bionie migration. The Agency's needs fall into two broad questions from the air. climate, and energy integrated transdisciplinary research (ITR) plan that will be used to organize and focus AMAD research activities described in this chapter. The first regards risk assessment and adaptation: How will climate change impact air and water quality, water availability, and ecosystems'.' Then, there is risk mitigation: How can the Agency best contribute to climate change mitigation through U.S. controls of both long-lived GHGs and short-lived pollutants'.' Accomplishments Because this research area represents a significant broadening of scope beyond AMAD's traditional domain of air quality, a white paper. "Integration of air quality and climate change—mode ling connections from global to regional scales." was undertaken to help plan and guide the research. This white paper, together with white papers on interactions between air quality and human health and between air quality and ecosystems, was reviewed by an external panel in March 2010. The white papers contributed to the development of AMAD's 5-year Strategic Plan and were published on the Division's Web site in October 2010. 5.2 Regional Climate Modeling: Dynamical Downscaling Introduction Global climate models (GCMs) arc used to simulate gridded climate conditions with worldwide coverage. Because GCMs often arc run for niulticcntury simulations. GCMs typically generate output at fairly coarse temporal (e.g., monthly or daily) and spatial (e.g., 2° latitude x 2.5° longitude or 1° latitude x 1° longitude) intervals. To investigate and understand regional effects of climate change (and particularly to capture extreme events), higher temporal and spatial frequency of the gridded fields is required. One broad category of methods to create regional climate fields that arc influenced by GCM simulations is "downscaling." Dow nscaling involves using the fields from a GCM, in conibination with additional information about the regional scale (e.g., topography, land use, historical regional climate data) and physical, mathematical, and/or statistical models, to extend the GCM simulation to finer spatial and temporal granularity. With increased horizontal texture, the downscaled fields arc more sensitive than the GCMs to local spatial 31 ------- heterogeneities in weather and climate that result from topographical changes, land/water interfaces, vegetation, and population. Downscaled climate fields can then be used to simulate the regional impacts of climate change on water quality and availability, agriculture, ecosystems, human health, air quality resulting from emissions control strategies, and energy demand. Dynamical downscaling involves using a regional climate model (RCM), where the initial and lateral boundary conditions arc obtained from a GCM. Dynamical dow nscaling is fundamentally similar to regional weather forecasting, except that a GCM is used for lateral boundary conditions; there arc no observations to assimilate, and simulation periods arc years or decades rather than days or weeks. Furthermore, rather than evaluating the model based on its skill in representing day-to-day weather observations, the model's performance is evaluated in terms of how well it represents climatic features, such as seasonal means, the magnitude of extremes, or the frequency of events exceeding some threshold. Research Activities For AMAD's dynamical downscaling, the WRF model is being used as an RCM. Initial work is using coarse- scale reanalysis fields (i.e., 2.5° x 2.5°) as input, which is comparable in resolution to the GCM that will be used for downscaling future climate. Focusing first on reanalysis data enables comparison of the RCM output to a wide variety of observational data, including both gridded data at smaller spatial scale and point observations, to examine the extent to which the RCM added value over the coarse- scale input data. Simulations over the continental United States have been conducted using grid cells that arc 36-km x 36-km in si/c. A key goal of this research is to develop optimal techniques for downscaling the large-scale forcing resolved by coarse grid global data while allow ing effective small-scale forcing and dynamical response to be well represented by the RCM. Comparative analysis of scale- selective techniques for data assimilation (nudging) and boundary condition specification arc important aspects of this research. Thus, modeling experiments will compare the effects of using no nudging (other than through the lateral boundaries), analysis nudging, and spectral nudging. Evaluation is planned against means and extremes of near- surface fields, cloud and radiation fields, and spectra. Accomplishments During 2010, AM AD completed an initial study on the use of WRF as an RCM. Three simulations of the year 1988 were conducted, and the results obtained using analysis nudging, spectral nudging, and without any nudging were compared to each other and to data from the North American Regional Reanalysis (NARR) product. A manuscript describing this study (Bow den ct al.. 2011) has been completed and will be submitted for publication in 2011. 32 Also during 2010, AMAD created its first continuous, inultidccadal WRF simulations. Simulations were a.s described above but covered the period from 1988 to 2007. These simulations will be used for several evaluation studies that arc planned for 2011. Concurrent with the RCM simulations conducted using reanalysis data, we have developed a dynamical downscaling linkage between WRF and the NASA GISS Model E GCM. A 6-vcar test datasct was acquired from collaborators at GISS to establish the logistical connections between Model E and WRF (i.e., vertical coordinates, state variables, linkage of surface fields) and to test the downscaling techniques. Next Steps Evaluation of the 20-ycar WRF simulations is in progress. In addition to analyzing how well WRF represents mean climatic variables, such as surface temperature, precipitation, and radiation fields, we will focus on the effect of analysis and spectral nudging on those mean variables and on the simulated atmospheric circulation. In addition, we plan to examine the ability of WRF to capture the frequency of extreme events, such as droughts and floods. As GISS develops Model E fields for the IPCC AR5, those data will be made available at high temporal frequency (i.e., three-hourly or six-hourly) for "time slice intervals" (i.e., approximately decadal subsets) of the GCM simulation. Using knowledge gained from the reanalysis testing, evaluated dynamical downscaling techniques will be employed to create regional downscaled climate fields from current and future decadal time slices that will be used to analyze high-resolution regional climate change effects. Several aspects of dynamical downscaling need to be refined to adapt WRF, which is a numerical weather prediction model, to regional climate applications. For example, the model must be able to realistically simulate the surface energy budget over inultidccadal simulations. Initial testing with different radiation sclieincs in WRF has show n a persistent undcrprcdiction of cloud cover that results in overheating of the land surface. Clearly, improvements in the cloud and radiation physics components of WRF arc needed for regional climate applications. In 2011. we will be evaluating the modeled radiation fields from the 20-ycar retrospective WRF simulations against satellite retrievals from the Clouds and Earth's Radiant Energy System (CERES) and MOD IS instruments, which arc both aboard the Aqua and Terra satellites, and against observations taken at the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility. These efforts to evaluate and improve the cloud and radiation budgets in WRF arc being coordinated with research to implement indirect aerosol ------- radiative forcing in the WRF-CMAQ system (described below and in Chapter 3), which also involves evaluation using CERES and MODIS data. These 20-vcar WRF simulations also will help with understanding how well extremes and variability arc simulated by WRF, helping build confidence in future-year estimates of extreme events and variability under climate change conditions. 5.3 Regional Climate Modeling: Statistical Downscaling Introduction Statistical downscaling methods use correlations among historically observed and modeled meteorological variables under current climate conditions to predict future regional and/or local patterns and events from the broader-scale GCM simulations. Typically, these approaches do not use the same detailed information that is used in dynamical downscaling, such as physical equations, orographic data, or extensive land-use information. The advantages of statistical downscaling methods lie in their efficiency and speed, and these methods could be particularly attractive if numerous climate scenarios need to be investigated. Statistical methods arc not limited by the resolution achievable by the nested regional dynamical model. Thus, statistical methods possibly could be used to gain a better understanding of fine-scale variability, even down to point locations, given high-resolution training data. It has been reported in the literature that the performances of dynamical and statistical downscaling arc comparable for current climatic conditions. However, it is questionable whether statistical models can perform as well under future conditions (Wilbv ct al., 2002) because statistical downscaling methods rely on associations among meteorological variables. These relationships do not explain all of the inherent variability in atmospheric phenomena; in fact, the choice of variables to be used as the "predictors" in such approaches can be a difficult part of the statistical downscaling process. Once a statistical model has been developed for a particular time period (e.g., using current climate), it is unclear whether the relationships it incorporates will remain the same under different climatic conditions (e.g., in future decades). (Sec Schmith. 2008, for more discussion.) However, statistical downscaling docs make this "stationarity" assumption as it extrapolates to future conditions. Research Activities During the past year, AMAD has been reviewing, improving, and testing statistical downscaling methods, with an emphasis on evaluation and calibration. Most of this research is being done using a regression-based downscaling technique proposed by Hoar and Nychka (2008), which models the response variable (i.e., surface temperature) as a function of fine-scale temperature estimates obtained by applying a thin-plate spline interpolation technique to the output of a GCM (or other coarse-resolution model). This enables us to bypass the difficulties associated with predictor selection (a difficult issue with many regression-based methods), so that we can focus on issues associated with calibration, training period selection, and quality and quantity of training data. If the efficiency and accuracy of this method proves suitable, this method could be adapted to use different response variables or different predictors. Accomplishments In the past year, AMAD has implemented a regression- based statistical downscaling method proposed by Hoar and Nychka (2008). We currently arc finishing a period of extensive testing, with a particular focus on understanding the impact of such assumptions as stationarity and temporal correlation. The downscaling met hod has been applied for temperature and has been trained and evaluated using finc- scalc output from the PRISM model, encompassing the continental United States during the period. 1907 to 1996. In tests with simulated data, we found that temporal correlation docs have an impact on the downscaling estimates and should be accounted for in the downscaling model. Two statistical techniques to aid in detecting and estimating temporal correlation, the Durbin-Watson test and restricted maximum likelihood (REML), performed well in our simulation studies. With tliesc adjustments, the Hoar and Nychka method produced confidence intervals for future years' temperature that were well calibrated, with only a small loss in efficiency because of the autocorrelation. These simulation studies provided benchmarks of how well we could expect the method to perforin in "ideal" circumstances, in which stationarity and type of correlation structure can be assumed. AMAD also applied the downcaling technique to the PRISM temperature data. With a 30-year training period (1907 to 1936), the met hod was used to obtain temperature estimates for individual years (1937, 1996) and interval averages (1937 to 1966 and 1967 to 1996). In contrast to the simulated cases, the confidence intervals generated by the technique were calibrated poorly. The magnitude of this discrepancy (between simulated cases and cases using PRISM) suggests that there arc fundamental problems with the assumptions of the method when applied to "real- world" cases and. particularly, with the assumption that the statistical associations remain constant over time. Work is currently underway to determine whether this problem can be ameliorated (e.g., additional covariatcs. other data sources, etc.). 33 ------- Next Steps AMAD is planning future work to better understand the relative strengths and weaknesses of statistical and dynamical downscaling for various applications. Research questions of particular interest include those that follow. • Developing at least a basic understanding of how the uncertainty may change when applied to future-year GCM simulations • Identifying the relative strengths/weaknesses of the dynamical and statistical approaches to downscaling • Determining whether hybrid downscaling approaches may be able to capitalize on the strengths of both dynamical and statistical methods 5.4 Development of Coupled Regional Climate and Chemistry Modeling System Introduction Climate change is influenced by long-lived GHG and also by short-lived radiativcly active gases and aerosols that typically arc modeled by regional-scale air quality models such as the CMAQ model. The direct and indirect feedback effects of gases and aerosols on radiative forcing and cloud microphysics have important climate impacts, particularly at regional scales. Thus, both GCM and RCM components of the climate modeling system need to include these air quality feedback effects. Therefore, AMAD is developing a coupled regional climate and chemistry modeling (RCCM) system that can downscale climate and chemistry from the GCM through a series of nested grids from hemispheric to continental to local scales. Research Activities A two-way coupled WRF-CMAQ modeling system is being developed and tested for air quality and climate applications at scales ranging from hemispheric to local. There are several advantages to an integrated meteorology and chemistry modeling approach, including more frequent data exchange for high-resolution modeling; better integration of physical processes, such as cloud-chemistry interactions; and feedback effects of gases and aerosols on radiation and cloud microphysics. Thus, the WRF-CMAQ system is an important development for air quality research and assessment over multiple scales and regional climate modeling, where direct and indirect feedbacks on radiation, clouds, aerosols, and precipitation arc important. Further research and development of more comprehensive treatments of direct and indirect feedback of short-lived gases and aerosols on radiation and cloud properties arc needed. WRF-CMAQ currently includes direct feedback of aerosols, accounting for si/c distribution and chemical composition, on SW radiation in the Community Atmospheric Model (CAM) radiation scheme and the Rapid Radiative Transfer Model for GCMs (RRTMG) SW scheme (lacono et al., 2008). The direct effects of troposplieric O, on LW radiation in the CAM and RRTMG LW schemes also have been implemented. Some initial evaluation studies have shown an expected response to these direct feedback effects for both typical summer conditions in the eastern Unitcd States and for an outbreak of multiple wildfires in California in June 2008. Aerosol direct radiative effects associated with scattering and absorption of incoming radiation result in a reduction of SW radiation reaching the surface, which then translates to reduction in temperature at the surface, as well as a reduction in PBL height. For moderate pollution levels, such as during typical summer conditions in the eastern United States, impacts on SW reduction and subsequent suppression in PBL heights arc relatively modest. Significantly, larger changes in simulated surface radiation were seen for the California wildfire case where smoke from the wildfires resulted in much higher aerosol loading. The reduced SW radiation caused much cooler surface air temperatures (up to 4 to 5 K in the smoke plumes), lower PBL heights (up to 400 m), and significantly higher ground level concentrations of O, and PM25 (Mathur et al.. 2009). Evaluation of direct aerosol forcing is continuing in 2011 by extending the initial 10-day test case for the eastern United States to the full month of August 2006 using the RRTMG radiation scheme and the current versions of both WRF (v3.2) and CMAQ (v5.0). The California wildfire case also is being updated with the latest model versions and improved estimates of wild fire emissions. New research aimed at improved modeling of aerosol mixing state and optical properties has been initiated through collaboration with UNC. The work will lead to more accurate direct radiative effects of mixed composition aerosols, particularly involving black carbon aerosol, in the RRTMG SW scheme. Through this collaborative research, we also arc investigating the feedback effects of aerosols on LW radiation. It is hypothesized that some aerosols, especially black carbon, may absorb LW radiation as well as docs SW radiation. Aerosols also affect radiation through their role as cloud condensation nuclei (CCN). Greater concentrations of CCN lead to a greater number of smaller cloud droplets, causing greater SW cloud albedo. Activation of aerosols as CCN alters cloud microphysics such that warm rain processes arc less efficient, thereby extending cloud lifetime. These effects of increased aerosol concentrations on clouds arc known as the first and second indirect effects, respectively. Development and evaluation of the direct and indirect feedback processes in the two-way coupled WRF- CMAQ modeling system, and eventually the RCCM, will advance through comparison with the latest release version of the online coupled WRF/Chem model. WRF/ Clicni currently simulates aerosol, cloud, and radiative interactions when employing its CBM-Z photochemical mechanism and MOSAIC aerosol scheme (Chapman et al., 2009). Sensitivity tests using present-day and future ------- emission scenarios oriented toward climate change studies will be conducted with both the WRF/Chem and the two- way WRF-CMAQ modeling systems, and subsequent simulation results will be analyzed to evaluate their feedback processes. Recent forecasting and retrospective CMAQ applications over continental scales and for annual cycles have pointed to the need for more robust methods to specify chemical lateral boundary conditions (LBC). Although linking with global-scale atmospheric chemistry models has been pursued, it also is recognized that biases in the global model can propagate and influence regional CMAQ calculations and often confound interpretation of model results. The successful extension of CMAQ to hemispheric scales would provide several advantages: (1) consistent LBCs for regional calculations focusing on air quality issues in the United States (i.e., the biases arising from process formulations are consistent at all scales); (2) opportunities to explore emerging challenges related to the processes regulating "background" levels (The recent revisions to the O, NAAQS and possible further reductions in the O, standard make the accurate representation of background O, in the model even more imperative than before); (3) lay the foundation for future applications of the integrated WRF-CMAQ model to explore radiative impacts of aerosols and radiatively active gases, such as 03, on the earth's energy budget and the indirect feedbacks of aerosols on cloud microphysics; and (4) a framework for assessing uncertainties in modeling regional distributions of long-lived HAPs and toxics (e.g., mercury). When the WRF-CMAQ model is configured for climate studies, the hemispheric grid domain will be an important link between the global climate model and the regional nested grids over North America and local areas Accomplishments During 2010, an initial implementation of aerosol indirect effects using the CCN activation scheme from the two- way, or online, coupled WRF/Chem model (Abdul-Razzak and Ghan, 2000) was adapted to WRF-CMAQ. Initial testing of the WRF-CMAQ over the Northern Hemisphere lias shown realistic ground-level concentration distributions over the major emissions regions and realistic aerosol optical depth (AOD) compared with MODIS satellite retrievals. Preliminary analysis of the hemispheric simulation for March 2006 showed remarkably good agreement of modeled AOD with AERONET observations in Beijing and Greenbelt. MD. The proposal, "Development of a Next-Generation Comprehensive Global Environmental Modeling System for Assessing Air and Water Quality in Current and Future Climates," was selected for funding as one of the initial EPA ORD Pathfinder Innovation Projects. The proposal is to extend CMAQ to the global scale by coupling it to the Ocean-Land-Atmosphere Model (OLAM) GCM. OLAM uses a telescopic finite-volume discretization scheme that divides the globe into regular polygons of arbitrary size. It lias the capability to provide greater spatial resolution over areas of interest, while simulating the entire globe Los Angeles Focus Region Houston Focus Region Figure 5-1. Example global telescoping grid structure. 35 ------- (see Figure 5-1). Simulating the entire globe at once would avoid problems that can arise in downscaling a GCM with an RCM. such as poor temporal resolution of lateral boundary conditions, inconsistent physics treatments between different modeling platforms, and the inability of the RCM to feed back on the GCM solution. 3-year collaborative project with the Department of Energy (DOE) entitled: Evaluation of the Interactions Among Tropospheric Aerosol Loading. Radiative Balance, Clouds, and Precipitation. This observational-modeling study aims to address this issue by systematically investigating changes in anthropogenic emissions of S02 and NO over the past two decades in the United States, their impacts on anthropogenic aerosol loading over the North American troposphere, and subsequent impacts on regional radiation budgets. The DOE funding of this interagency agreement will be used to support three new postdoctoral fellows to work on this project. 5.5 Decision Support Tools for Managing Air Quality and Mitigating Climate Change Introduction Emissions of long-lived and short-lived GHGs and aerosols have a substantial impact on both climate and air quality. The goal of this effort is to develop an integrated suite of models to examine the impacts and understand the key uncertainties of emission mitigation strategies on both climate and air quality. Ultimately, the goal is to answer the question: What is the impact of U.S. emission control strategies on air quality and climate? Figure 5-2 describes the two classifications of decision-support tools. On the left side of the diagram are screening tools to enable decisionmakers to rapidly identify emission scenarios and policy options of interest. On the right is the series of models, spanning global and regional scales, needed to fully assess the impacts of a given policy option. The screening tools can be used to identify a narrow but critical subset of emission scenarios or policy options that then can serve as input to the more complete models. The work in this section describes AMAD efforts relevant to the screening tools and the Atmosphere-Ocean General Circulation Modeling. This schematic describes the models and linkages from changes in policy decisions to changes in SLCF, GHG, and CAPs, and their local impacts on human health and ecosystems. Research Activities Decision support tools for scenario analysis. A key finding of the Climate Impacts on Regional Air Quality (CIRAQ) project is that future air quality largely is driven by changes in future emissions. Forecasting emission changes over decades is impossible, so decision-makers rely on scenarios to help guide their choices. However, scenarios are comprised of many assumptions. Discovering policy options that deliver robust results across a range of assumptions requires investigating many scenarios. Unfortunately, state-of-the-science global and regional climate models require tremendous computational resources. Exploring more than a handful of scenarios is not tenable. Decisionmakers need approximation tools that can be used to rapidly screen the costs and benefits of climate mitigation strategies. Emission & Land Use Scenarios Reflecting U.S. Policy Options for GHGs, SLCF, and CAPs Atmosphere Ocean General Circulation Model GISS ModelE & other models with online chemistry and aerosols It Screening Tools MAGIC C GHG A Global T Adjoint GEOS-Chem SLCF -> ARad. Forcing Adjoint CMAQ CAPs AAir Quality 1 GHG & SLCF A Climate (Temperature, precipitation, etc.) 2° * 2.5° horizontal scale Weather Research and Forecasting Model (WRF) ~ WRF-CMAQ with online chemistry and aerosols Downscaling to regional climate at finer horizontal scale Assessments: Human Health, Air Quality, Water Resources, Ecosystem Impacts Figure 5-2. Schematic describing model linkages. 36 ------- For long-lived GHGs. the emissions arc proportional to the climate impacts. However, short-lived cliniatc- forcing gases and aerosols undergo chemical and physical transformations that substantially change their climate impacts. Radiative forcing can be used as an approximate surrogate for the impact of these species on climate. We arc developing an integrated assessment model composed of four parts: (1) the GEOS-Chem global chemical transport model, (2) the LIDORT radiative transfer model, (3) the adjoint of these two models coupled together, and (4) the market allocation (MARKAL) model. This project is named GLIMPSE. GEOS-Chem and LIDORT can estimate the impact of emissions, chemical fate, and transport on direct radiative forcing. The adjoint of these models can be used to build a reduced form model that can rapidly assess the impacts of a change in emissions on radiative forcing. Finally. MARKAL connects technologies and energy demands to emissions. With GLIMPSE it is possible to examine combined constraints of GHG emissions, short-lived species radiative forcing, and relative cost to examine the trade-oils between different policy options. This tool is being developed in collaboration with our colleagues in NRMRL who arc extending MARKAL to include more sources of short-lived, radiativcly active gases and aerosols. This is in collaboration with University of Colorado professor Davcn Henze, who is the original developer of GEOS-Chem Adjoint.. Finally, successful completion will require frequent interaction with our colleagues in the EPA program offices to match the decision-support tool to their needs. The first application of the tool will be to examine impacts of emission reductions from black carbon and sulfate. Next, it will be extended to include radiative forcing and chemical precursor emissions for Ov Finally, the adjoint version of CMAQ will be included as part of GLIMPSE to simultaneously assess air quality and radiative forcing. Global modeling of U.S. Emission control strategies; impacts of short-lived, radiativety active gases and aerosols. Although greenhouse gas emissions and short- lived species radiative forcing arc useful approximations, they do not fully prescribe the change in climate. Scenarios and policy options that merit further consideration will be used as input to the global climate model for a complete assessment of the global climate impacts of U.S. climate mitigation strategics. The first phase of this work will use the GISS Mode IE simulations prepared for the regional climate downscaling ta.sk. These climate simulations simulate present day and future climate under multiple Representative Concentration Pathways (RCPs). the scenarios under development for the IPCC AR5. GISS Model E simulations will provide detailed diagnostic information for 10-ycar timcsliccs, beginning in 1980, 2000, 2030, and 2100. In the second phase, a few scenarios informed by policy options developed using GLIMPSE will be used as input to additional climate simulations using GISS Model E coupled with online chemistry to resolve the climate impacts of U.S. emission control strategics on global climate. A key purpose of these simulations is to contrast the relative impact of GHG mitigation with emission reductions of short-lived climate-forcing species. The results from these simulations will be used with the regional downscaling methods described in Task AMAD11-108 to determine the regional climate impacts of U.S. emission changes. Accomplishments We have developed a prototype of the GLIMPSE system, presented the results at scientific meetings and conferences, met with stakeholders in the program offices, and incorporated their feedback. Next Steps In 2011, we will use GLIMPSE as a tool to analyze the impacts of U.S. emission control strategics on concentrations and global radiative forcing of short-lived, radiativcly active aerosols, including S04, black carbon, and OC and to assess the impact of possible climate change mitigation policies on radiative forcing and air quality. Also in 2011, we will conduct a review of proposed feedback mechanism to assess the impacts of reactive nitrogen emissions on climate cliangc. and we will conduct a bounding study using models and observations to examine the extent to which anthropogenic emissions enhance O, in the upper troposphere. 5.6 Biosphere-Atmosphere Interactions: Improving the Treatment of Isoprene Oxidation Introduction The scenarios described above characterize emissions trajectories for sources that can be controlled directly. However, climate and land management changes arc likely to impact future emissions of VOCs as well. With warming temperatures and increased CO;. isoprene emissions arc expected to increase. A review of the NCER STAR-fundcd projects on climate and air quality identified a key uncertainty in the future O, production efficiency as the chemical treatment of isoprene and the formation of isoprene nitrate compounds. Isoprene is emitted naturally by many species of trees and shrubs. With the exception of methane, which reacts much more slowly in the atmosphere, isoprene is the most highly emitted VOC, both worldwide and within the United States. It is an important precursor for O, formation and is thought to be a substantial contributor to SO A. These species arc critical for understanding interactions between 37 ------- air quality and climate for two reasons. First, in the future, we expect there to be much more isoprene relative to NOx. As demonstrated by recent field measurements, the isoprene, chemistn and formation of O , under these conditions is highly uncertain. Second, unlike the rest of the United States, the climate in the Southeast has not wanned. To what extent have light-scattering aerosols from isoprene contributed to this trend? We are partnering with collaborators making field measurements and conducting laboratory studies to improve our understanding of isoprene oxidation and to assess the relative impacts of uncertainties in isoprene emissions, chemical mechanism and reaction rates, and dry and wet deposition rates. Research Activities Recent laboratory and theoretical studies have provided new insight into the oxidation of isoprene. including more detailed understanding of the formation and fate of isoprene nitrates (INs; Paulot et al., 2009), formation of epoxides under low-NOx conditions, and reformation of HOx via isomerization reactions. We have incorporated these reactions into the SAPRC07 chemical mechanism and simulated the time period of the INTEX-NA field campaign to investigate the impact of INs on 03 production and the oxidized nitrogen budget. Accomplishments The new scheme has been evaluated against smog chamber experiments from three different laboratories. The results show improved predictions of O, for the experiments with the lowest NOx concentrations. We also have implemented the new scheme into the CMAQ model and have conducted simulations for the INTEX-NA field campaign period during summer 2004. When compared with ambient measurements, we find this updated chemistry provides a more accurate simulation of the isoprene oxidation products methacrolein, methyl vinyl ketone, and alkyl nitrates. Including limited HOx radical regeneration in the oxidation of isoprene improves simulated hydroxy radical (OI-I) concentrations, especially when isoprene concentrations are high. Over the southeastern United States, a region of high isoprene emissions, simulations including this chemistry show a 2 to 4-ppb increase in O, (see Figure 5-3) and 30% increase in SOA. Next Steps We now are testing updates to the nitrate radical oxidation pathway for isoprene, as well as experiments to examine the sensitivity of simulated O, concentrations to key reaction rates in the chemical mechanism. A follow-on study is planned for 2011 that will focus on assessing the relative impact of chemical mechanism uncertainties with uncertainties in emissions and deposition rates on simulated O, concentrations. n 4.00 112 \ 0.00 1 ppbV 148 July 5, 2004 0:00:00 Min*=-0.00 at (147,9). Max=4.32 at (103,40) Figure 5-3: Change in simulated mean surface-level 03 concentration because of updated chemical mechanism. 38 ------- 6.0 Linking Air Quality to Human Health 6.1 Introduction The need for improved dispersion models to support human exposure assessments in urban areas has led to a significant effort to design and build a new modeling const met to efficiently provide near-field concentration estimates to support exposure and health related studies. Included in this effort is the ongoing near-roadway research program that has been working toward improving the urban-scale line-source (mobile source) dispersion algorithms. Additionally, this task involves development and application of model evaluation techniques and acquiring, assessing, and processing databases for model development and evaluation purposes. The new modeling system will be applied in ongoing exposure and health studies in collaboration with clients within and outside of EPA. Along with the need for better models is the need to understand the optimal accuracy and precision in exposure estimates needed to conduct human exposure and health studies. In the absence of personal exposure measurements, epidemiology studies traditionally have relied on imperfect surrogates of personal exposures, such as area-wide ambient air pollution levels based on readily available outdoor concentrations from central monitoring sites. The direct use of monitoring data inherently assumes that they are representative of the air quality over a broad area. However, there is increasing evidence tliat the current monitoring network is not capturing the sharp gradients in exposure caused by high concentrations near, for example, major roadways. Yet, providing more detailed exposure information can be resource intensive and may result in greater (or undefined) uncertainty. Research in the Division has focused on improving the understanding of the optimal level of accuracy and precision for use in exposure estimates. 6.2. Research Description EPA's Clean Air Research niultivcar plan increases the emphasis on research to better understand the linkages between sources and health outcomes. This research area is focused on the critical modeling link between sources of air pollutants in urban areas and health outcomes. Modeling the wide variety of pollutant sources in the meteorologically and topographically complex urban environment will require the development of an improved mode ling system tliat is both complete in its handling of the urban setting yet computationally efficient because of the hundreds and possibly thousands of sources tliat arc considered in exposure analyses. Additionally, model development and evaluation databases and model evaluation techniques will be developed to accomplish this task. The EPA Fluid Modeling Facility is uniquely designed for conducting laboratory studies characterizing the often steep concentration gradients expected in urban areas and is a major source of development and evaluation data. The new dispersion modeling system will be based in a significant way on the previous development of the American Meteorological Sociey/EPA Regulatory Model (AERMOD) urban-scale dispersion model. Although AERMOD is designed for a wide variety of regulatory applications, the new model will benefit from the science and algorithm basis of AERMOD without the computational burden often required for regulatory analyses. The new model will be an integral part of a hybrid mode ling system tliat links observations with both regional- and urban-scale modeled concentrations to provide the necessary concentration distributions required for human exposure modeling and associated liealtli risk assessments. Although the new dispersion model is not expected to account for complex chemistry, it is anticipated that, for selected pollutants (e.g., NO), near-field chemistry may be accounted for with simple paranictcri/ations. In urban areas, mobile sources arc a significant contributor to air pollution exposure. This research area includes an effort to further understand the atmosplieric transport and dispersion of mobile source pollutants within the first few hundred meters of the roadway, a region often characterized by complex flow (e.g., vehicle-induced turbulence, noise barriers, road cuts, buildings, vegetation). Examination of traffic emissions and potential population exposures near roadways includes field and laboratory measure incuts of concentration distributions near simulated roadways. This task involves the investigation of near- road dispersion through numerical, wind tunnel, and tracer studies, with the goal of characterizing the near-road spatial distribution of mobile source pollutants and the development of improved modeling algorithms within the new urban-scale dispersion model. Research in the Atmospheric Exposure Integration Branch (AEIB) also includes a project to compile the results of a suite of tiered exposure studies to capture the critical elements relevant to characterizing exposure (e.g., variability of pollutant. Ileal tli study design), common findings resulting from the studies, and remaining challenges. This work requires close coordination across NERL's air quality mode ling and measurement groups (AMAD and HEASD) to tap their collective knowledge of the chemistry and physical processes involved with the spatial and temporal properties of multiple pollutants. In addition, close collaboration with the epidemiology community (HEASD, the National Center for Environmental Asscssniant [NCEA], NHEERL 39 ------- and academia) is critical in understanding the different demands of the various health study designs and to demonstrate the impact of the tiered exposure definitions on the health effect measure. This research provides critical information on the level of spatial and temporal data needed to support human exposure and health studies. It will result in greater efficiencies as more refined exposure information can require more resources, yet result in more (or undefined) uncertainty. 6.3. Accomplishments Wind Tunnel Modeling of Flow and Dispersion near Roadways A series of physical modeling experiments was performed in the Division's Meteorological Wind Tunnel, investigating 14 common roadway configurations, including a flat roadway with no surrounding obstacles (base case), noise barriers of varied height and distance from the roadway, two different porous barriers intended to simulate the dispersive effects of roadside vegetation, and depressed and elevated roadways. Results show that the configuration of the roadway can have a substantial effect on the concentrations both on and downwind of the roadway (Heist et al., 2009a). The results are being used to develop algorithms for inclusion in dispersion models to account for these effects. As part of the EPA and FHA field study of mobile source pollutant dispersion near Interstate 15 in Las Vegas, NV, a laboratory study was conducted in the Meteorological Wind Tunnel on a 1:200 scale model of the study area to examine the effect of the road depression associated with a railroad overpass at the field site and to explicitly define the flow and concentration fields surrounding this major roadway (Figure 6-1). Wind tunnel data show that the roadway depression (of about 5.5 m maximum depth) results in a decrease in maximum ground-level concentration of about 25% over that observed with a flat roadway (Heist et al.. 2009b). Dispersion Model Algorithm Development A new line-source algorithm (AERL1NE) has been developed that efficiently computes dispersion from sources, such as roadways, considering multiple lanes of traffic, low wind, meander conditions and varying wind directions (including those parallel to the traffic flow) (Venkatram et al., 2009). Comparison of AERLINE against the Raleigh 2006 highway field study for both downwind and upwind concentrations is in progress. A draft algorithm formulation was developed that accounts for the influence of roadways that are depressed below grade. This algorithm is currently undergoing evaluation against wind tunnel data and will be included in AERLINE in FY 11. Analysis of the Raleigh 2006 Pilot Field Study The 2006 Raleigh field stud}', conducted in the summer 2006, was designed to provide data to characterize the influence of traffic-generated emissions in the near-road environment, especially to help assess the impact on air quality and particle toxicity near a heavily traveled highway. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices Figure 6-1. Wind tunnel model of Las Vegas field study area built at 1:200 scale. 40 ------- Sonie-100 Smiic-Zl Traffic ram Figure 6-2. Aerial photograph of study location in Raleigh, NC. to provide continuous and integrated assessments of the variation of near-road pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road (Figure 6-2). This research helped to demonstrate the temporal and spatial impact of traffic emissions on near-road air quality (Isakov et al., 2007; Venkatrain et al., 2007). Tracer Field Study of Flow and Dispersion near Roadway Noise Barriers An interagency agreement was developed between NERL and NOAA's Field Research Laboratory to conduct a field study in Idaho Falls of flow fields and tracer dispersion from a line source (simulating roadway emissions) immediately upwind of a 6-m-high barrier (typical of near-road noise barriers found in urban and suburban areas) (Figure 6-3). Simultaneous line-source tracer release and measurements were conducted without the barrier. The results currently are being analyzed to be used for model algorithm development to account for the effects of noise barriers in AERLINE. Unlike the associated wind tunnel studies, which are limited to neutral conditions, the tracer studies were designed to examine dispersion for all atmospheric stabilities (Finn et al.. 2010). Computational Fluid Dynamics (CFD) Modeling of Near-Road Configurations To complement the wind tunnel and tracer studies of near-road dispersion, a series of CFD simulations has been initiated in collaboration between NERL (AMAD and HEASD) and NRMRL. In FY09, the FLUENT CFD code was used to simulate the six-lane flat roadway scenario, with conditions identical to the wind tunnel study. The wind tunnel results were used to suggest the most appropriate on-road initial dispersion conditions and the most appropriate turbulence parameterization, as well as other flow parameters to be used for other roadway scenarios. Gulf of Mexico Oil Fire Plume Modeling During remediation efforts following the British Petroleum oil spill, more than 400 separate controlled burns were conducted over a 2-m period, resulting in the removal of approximately 11 million gallons of crude oil from the Gulf (see Figure 6-4). AEIB scientists co-authored a journal article describing a risk assessment of the airborne dioxin that was a by-product of these controlled burns (Schaum et al., 2010). The paper describes a screening- level assessment of the exposure and potential cancer risk to nearby workers and residents potentially exposed 41 ------- Figure 6-3. Photograph of bag samplers and anemomete Falls field site. to dioxin via inhalation. The authors also examined the potential for this population to be exposed to dioxins in fish harvested for consumption. Using available information about the size and rate of each burn, meteorology from nearby buoys, and the NOAA North American meso- meteorological model, scientists determined that the worst case concentrations occurred within 50 m of burns when wind speeds were at their highest (10 m per s). These dispersion modeling results and the limited on- site monitoring provided critical input to the dioxin risk assessment. The estimated cancer risks from the predicted air concentrations and deposition were found to be lower than the EPA's typical range when consideration is given to require additional actions, such as cleanup or the establishment of regulatory policy. Accountability Indicators Project In its mission to protect human health and the environment, the EPA implemented the NO Budget Trading Program (NBP) to reduce the emissions of NOx and secondarily- formed 0,. These pollutants and their precursors can be transported downwind, contributing to pollutant levels at locations much farther from the emission sources, potentially impacting human health in downwind areas. In this study, we investigated the health impacts in New York State from exposure to polluted air parcels transported from the Midwest. We developed and applied a methodology to identify and target the transport of polluted air parcels and demonstrated that the risk for hospital admission because of respiratory-related illness was increased in New York State on those days that the air parcel originated over the Ohio River Valley, where several high-emitting power plants are located (Figure downwind of a simulated noise barrier at the Idaho 6-5). More specifically, we examined air parcels moving through a boundary drawn around high-emitting power plants in the Midwest using back-trajectories and found significant associations with these transported air parcels (risk estimates ranged from 1.06 to 1.16 for the entire study time period) for six of the eight New York State regions). The approach developed by this research can be used as an indicator of exposure for transported air pollution. 6.4. Next Steps Model Development AEIB will focus on the design and development of a new urban-scale modeling construct that is similar in nature to AERMOD but without the regulatory structure. Instead, the new model will address the specific needs of human exposure and health analyses. The overall model design will emphasize computational efficiency, user friendliness, use of readily available inputs, and the characterization of uncertainty, and it will make use of existing model algorithms in existing dispersion models. Wind tunnel and field studies will feed into the development and testing of near-road algorithms in relation to atmospheric stability, wind direction, and vehicle-induced turbulence. Model Evaluation The new dispersion model will be the air quality core of the human exposure modeling system (including observations and regional modeling) in the approach to link urban sources and human exposure assessments and human health outcomes. Evaluation of both individual model algorithms and the overall modeling approach in human exposure and health-related scenarios is needed. ------- Figure 6-4. Example of a controlled bum of spilt oil in the Gulf of Mexico. Databases for use in model evaluation will include the MESA AIR (Baltimore) study, the NEXUS (Detroit) study. Urban 2003 (Oklahoma City), and the California Energy- Commission urban tracer study (Southern California). In addition, wind tunnel study data and the Idaho Falls tracer study also can serve in an evaluation capacity, particularly for individual model algorithms. Through collaboration with U.K. scientists, the DAPPLE urban study data-base in London also will be considered for the evaluations. Applications of New Modeling System and Client Collaboration Once the new modeling system lias been developed and tested, inclusion of it or its components will be considered in the HEASD's Community and Environmental Justice modeling effort (i.e., C-FERST/STREETS software). Additionally the new dispersion model will be used to support the design and interpretation of the air quality and health study planned for the Research Triangle Park, area in 2013 or 2014. Collaboration with the Federal Aviation Administration on the development of specific applications at airports will be initiated because of the significance of these urban source areas. Finally, collaborations with the Office of Transportation and Air Quality and the FHA relative to characterization of mobile source emissions and the FHA/EPA field studies will continue. A large part of AEIB's local-scale modeling application efforts will be focused on characterizing the concentration distributions related to the NEXUS research project currently underway to characterize the impact of mobile sources on near-road air quality and exposures for children with persistent asthma who live near major roadways in Detroit. Exposure metrics developed in this project will be coupled with the Childhood Health Effects from Roadway and Urban Pollutant Burden Study (CHERUBS). Modeled and monitored air quality and exposure data will be coupled with assessments of respiratory effects to investigate the relationships between traffic-related exposures and observed health effects. Air quality modeling is being conducted with the current AERMOD dispersion model for a screening level analysis. AERLINE and the new dispersion model will be used in a refined analysis once actual measurements of traffic and air quality- are available from the study. Synthesis Analysis Various levels of air pollution characterization (e.g., statistical combination of observed and modeled values, hybrid modeling approaches) will be provided for several tiered exposure studies, including the Cooperative Agreements with the University of Washington. Rutgers University, and Emory University, and the collaborative effort with the New York State Department of Health. A synthesis analysis will be conducted to summarize the results from the tiered studies described above, as well as other studies, to dociunent the findings in a systematic way. The analysis will identify the spatial and temporal resolution needed for the various human exposure and health study designs commonly used, the characteristics of the various air pollutants of concern (e.g., variability, reactivity), and our current knowledge regarding the state- of-science estimates that currently can be provided and the uncertainty surrounding these estimates. Demonstration Project The concepts summarized and examined in the synthesis analysis will be incorporated into a dispersion model being developed specifically for use in human exposure and health studies. For example, pollutants that must be characterized at finer spatial resolutions also might need simple chemistry (e.g., NO,), as well as line-source capability. Thus, such approaches will be integrated into the new model and applied in studies such as NEXUS through C-FERST. C-FERST is a framework developed for use by communities and individuals to estimate human exposure to toxic substances and to identify and prioritize risks from these substances. . 1,440 — Til in t~r Figure 6-5. Graphic on left shows high NO. emissions in Midwest (July 1997) and graphic on right shows transport of pollution from Midwest into New York State. Red squares are locations from which back- trajectories were initiated. 43 ------- ------- 7.0 Linking Air Quality and Ecosystem 7.1 Introduction EPA is charged with protecting the health and welfare of the Nation. The CAA definition of welfare effects includes, but is not limited to, effects on soils, water, wildlife, vegetation, visibility, weather, and climate. Terrestrial and aquatic ecosystems represent an integration of all these factors. Atmospheric deposition is an important nonpoint source of pollution impacting ecosystems. Deposition of both nitrogen and sulfur is the main source of acidification of fresh water and terrestrial ecosystems. Atmospheric deposition of nitrogen is a contributor to nitrogen- nutrient enrichment affecting Western ecosystems and eutrophication affecting estuaries. From 15% to 40% of the nitrogen load to coastal estuaries is estimated to come from atmospheric deposition. Atmospheric deposition is a major source of mercury that is methylated and subsequently bio- accumulated in food chains, affecting wildlife and humans. AMAD's goal for air-ecosystem linkage is the consistent interfacing of weather, climate and air quality models with aquatic and terrestrial ecosystem models to provide the local atmosphere-biogeochemical drivers of ecosystem exposure and resultant effects. A goal is also to harmoni/c the connection of the local ecosystem scale (tens of square kilometers) with the regional airshed scale (thousands to millions of square kilometers). The physically consistent linkage of atmospheric deposition and exposure with aquatic/watershed and terrestrial models is central, has not received adequate attention to date, and needs further development. Achieving these goals requires a coordinated research strategy covering a variety of activities to best use AMAD's limited resources. The strategy involves: (1) improvements in CMAQ's bi-directional and uni- directional air-surface exchange characterizations, (2) improvements in the WRF/CMAQ land surface characterizations and air-surface exchange in complex terrain. (3) improvements in precipitation fields. (4) linkage of hydrologic models with WRF/CMAQ to provide a consistent connection between precipitation and hydrology for a system-consistent linkage to ecosystem models, and (5) application of expanded model capability to support assessments of management scenarios The research is organized into three interrelated tasks. 1. Air Deposition and Ecosystem Services Assessments 2. Air-Ecosystem Linkage Studies 3. Model and Tool Development Relevance. The National Research Council (NRC) and the Clean Air Act Advisory Committee (CAA AC) have urged EPA to pay increased attention to ecosystem protection and develop its capacity in this direction. In response, the Agency has undertaken a joint review of the existing secondary (welfare-based) NAAQS for O,. NOx and sulfur oxides (SOX). This is the first time secondary standards have been addressed separately from the primary standards. The secondary NAAQS review is focused on ecological effects from exposure to O, concentrations and from atmospheric deposition associated with acidification sulfur and nitrogen and nutrient enrichment nitrogen. Because the CAA stipulates that the NAAQS are for ambient air concentrations, not deposition, the air quality models serve a critical function to connect air quality and deposition to support the secondary NAAQS. In addition, the NRC and CAAAC recommended that EPA explore the use of critical loads in the development of the secondary NAAQS. The Office of Air Programs (OAP) is exploring the use of critical loads (associated with atmospheric deposition) as a management tool. In addition, a niultiagcncy group on critical loads as an approach to ecosystem management for land management agencies has been established. The agencies arc the National Park Service (NPS), the United States Forest Service (USFS), USGS. and EPA. Modeling of critical loads by these agencies is focusing in the eastern United States on acidification and is focusing on acidification in the eastern United States and on nitrogen nutrient enrichment nationally, but with a focus on the western United States on nitrogen nutrient enrichment. The climate change research program at EPA has begun to be equally concerned about ecosystem response, as well as air quality and human health. The goal is to identify and evaluate mitigation and adaptation strategies that will sustain or improve current ecosystem functioning and provision of services. This requires that changes in climate and land use arc expressed in ways that can be translated by models to changes in such ecosystem functions and services. EPAs climate change program also puts a priority on assessing and predicting water quality, not just water quantity. Using models to investigate alternative future scenarios facilitates comparing and contrasting the effects of land use cliange with the effects of climate cliange on air-surface exchange of nutrients and pollutants and. ultimately, ecosystem health. In parallel. EPAs ESRP has a focus on ecosystem services and their relationship to human health and well-being. The overarching ESRP research questions arc; What arc the effects of multiple stressors on ecosystem services, at multiple scales, over time; and What is the impact of different plausible changes in these services on human well-being and on the value of the services. Land use change and climate change arc considered to be two 45 ------- important drivers to be addressed in the context of changing ecosystem sendees. Tansdisciplinary research programs that explore interactions between air, water, and land are central to addressing these questions and developing the science and modeling tools necessary to simulate alternative future scenarios and mitigation strategies. The objective of the ecology theme research is to advance the development and application of state-of-the-scienee atmospheric tools to determine the exposure of ecosystem receptors to atmospheric pollution causing and contributing to ecological and ecosystem sendee degradation. The intent is to provide an improved ability to connect forecasts of the effect of air management actions on future atmospheric deposition to ecosystem models. The air-ecosystem research within AMAD aims to provide better atmospheric connections for three major ecosystem assessment areas described below. 1. Atmospheric deposition that better supports SOx-NOx welfare standards and critical load assessments and determinations 2. Air-ecosystem exposure linkage of atmospheric stressors to ecosystem models to support ecosystem assessments coupled with air management drivers, including the assessment of ecosystem services 3. Atmospheric/meteorological-ecosystem exposure linkage to support ecosystem impact assessment of climate change 7.2 Air Deposition and Ecosystem Services Assessments 7.2.1 Introduction The thrust of this research is to define, develop and investigate targeted CMAQ linkage applications for study and learning and to develop guidance on how to best establish chemical and meteorological linkages with ecosystem models, including improvement of linkages to address climate impacts on ecosystems. Three major areas of research are (1) connection of WRF precipitation to consistent surface hydrology. (2) linkage to water quality/watershed models, and (3) linkage to ecosystem welfare and critical load models. Target ecological end points include fresh water aquatic, estuarine aquatic, and terrestrial systems. A key focal point is to develop a model representation of the hydrologic overland flow with other agency partners that is consistent with the precipitation from WRF/CMAQ. This focal point addresses a critical issue identified by the most recent linkage research. The thrust of linkage to water quality model research is to address air-water model cross-media (linkage/ coupling) connection issues and develop approaches to reconcile space-time mismatches for management support. Development of guidance for critical load estimation is the third part of the research thrust. Accuracy requirements for effective model-to-model communication are explored and defined and approaches are developed to more effectively provide atmospheric deposition to ecosystem models for stressor relationships for critical load management support. 7.2.2 Research Direction The objectives of this research are: (1) to develop a linkage between meteorological-model-produced precipitation and associated modeled surface hydrology to address air-water paradigm mismatches and support ecosystem exposure and loading estimates under climate change. (2) to actively use the CMAQ model to research and explore climate and air-ecosystem linkage issues to develop guidance on how to improve the linkage, (3) to illuminate model functioning and examine areas of model uncertainty to identify areas for further model development, and (4) to improve CMAQ and tool development and inform application approaches in support of multimedia modeling and ecosystem management. Primary attention is focused on the elements of the hydrological cycle that impact ecosystem loading and exposure to atmospheric pollutants. Two focal areas are the NOx-SOx air quality standard review and the study of critical loads (deposition-based tipping points). Aspects of weather and climate beyond that can combine with the hydrological cycle to impact ecosystem loading and exposure to atmospheric pollutants also will be explored. The lessons are to be synthesized to inform research planning for linkage research, CMAQ and ecosystem model development (including ecosystem services assessment), and integrated multimedia model development and application. Base {solid kre) S0?v3 (dash line) Bi-directional (circles) 1 Advection 0.8 0.6 Total 0 4 Dm, 0.2 SO.Vrf 0 100 200 300 400 500 Distance (km) Figure 7-1. Range of influence of NH3 emissions from a single, isolated Sampson County, NC cell in 2002 CMAQ simulations using unidirectional, the deposition velocity as a surrogate, and bidirectional NH3 surface exchange parameterizations, Dennis et al. (2010) ------- N = 146 N = 159 N = 164 N = 161 N=159 N=158 N = 163 N = 1SS N=170 N=I64 N=167 N = 160 ~ Base Case ~ Bidi Case ~t 00 0 i I m 00 nl QQ uu 0 I 01 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 7-2. Monthly NH4 wet deposition bias when compared to NADP deposition for an annual 2002 simulation for a base case unidirectional exchange and a bidi case with bidirectional NH3 exchange. The red diamond in the box plot represents the mean monthly deposition bias. Monthly adjustments to precipitation biases have been applied where precipitation errors significantly correlate with deposition error; a star above the box plot indicates that this correction was not applied. 7.2.3 Accomplishments CMAQ sensitivity runs for Chesapeake Bay with the new DDM-3D capability for NOy deposition were completed for an emissions sector analysis and a six-Bay-State relative-contribution analysis for the 2020 CAIR scenario. The Chesapeake Bay Modeling Subcommittee was briefed on the results. Using the DDM-3D corrected earlier problems with the brute-force approach. A new NH, budget analysis at 12 km, using a prototype CMAQ with NH, bi-directional air-surface exchange incorporated, showed that incorporating bi-directional exchange of NH, will have an important impact of reducing the local dry deposition and an impact on the estimates of the range of influence of NH, emissions, almost doubling the range (Figure 7-1). These results underline the importance of incorporating NH, bi-directional exchange in CMAQ (Dennis et al., 2010). WRF meteorology for 12-km CONUS domain for 2002 was completed in FY 10 for ESRP and general use. Preliminary annual CMAQ runs with inline lightning NOx algorithm simulations were completed in FY 10 for 2002 using WRF 12-km CONUS 2002 meteorology. A pilot simulation of the bi-directional CMAQ was completed to show proof of capability to meet the FY11 deliverable to the Nitrogen Team of bidirectional CMAQ total nitrogen deposition fields. Preliminary evaluation against limited ambient NH3 observations and NADP wet deposition estimates suggests that the changes from the unidirectional CMAQ are reasonable and result in reduced CMAQ simulation bias (Figure 7-2). Preliminary estimates of county-level nitrogen-fertilizer input uncertainty, expressed as a coefficient of variation and preliminary FEST-C denitrification estimates were shared with Nitrogen Team members assembling an N-input meta-database. 7.2.4 Next Steps The objectives of future research in multimedia modeling for air-ecosystem linkages and ecosystem service assessments are: (1) to study and improve the connection between meteorological and atmospheric models and ecosystem models to address air-water paradigm mismatches; (2) to actively use the CMAQ model to research and explore climate and air-ecosystem linkage issues to develop guidance on how to improve transdisciplinary modeling; (3) to illuminate model functioning and examine areas of model uncertainty to identify areas for further model development to inform future research focusing on the development and evaluation of future modeling techniques and tools for CMAQ; and (4) to improve the capacity of CMAQ to address drivers of change of particular interest to the ESRP assessments of ecosystem services, such as land use and land cover change in response to population growth or bio-energy policy and climate change in support of transdisciplinary modeling and ecosystem management. Two focal areas are the NOx-SOx secondary air quality standard review and the study of critical loads (deposition- based tipping points). The lessons are to be synthesized to inform research planning for CMAQ and ecosystem model development and for integrated transdisciplinary 47 ------- model development and application. The final objective is to provide atmospheric inputs (e.g., wet and dry deposition of nitrogen and ambient particulate concentrations) to the ESRP national and place-based research teams to support overall ecosystem service assessment activities. 7.3 Air-Ecosystem Linkage Studies 7.3.1 introduction The thrust of this research is to develop CMAQ modeling research and applications in multimedia modeling assessments associated with ESRP. using CMAQ as a laboratory. The ESRP teams with which AMAD is interacting most arc: the Future Midwest Landscapes (FML) place-based team, the Nitrogen Team involving national and regional assessments, the Mapping Team involving provision of deposition-related data layers, and the Coastal Carolinas place-based team. Provision of CMAQ physical and chemical outputs to link to ecosystem models for scenarios representing current and future conditions arc major objectives. 7.3.2 Research Direction An underlying objective is to identify and to elucidate atmosphere-ecosystem services of relevance to the ESRP. in collaboration with O AQPS. The role of these services is then communicated to the broader ecosystem community through participation in national and place-based ESRP teams, research planning committees, and peer reviewed publications. A fundamental objective is to assess the relevance and capacity of CMAQ to address and respond to drivers of change of particular interest to ESRP. such as land use and land cover change in response to population growth or bio-energy policy and climate change. Coupled with this is the objective to provide atmospheric inputs (e.g., wet and dry deposition of nitrogen and ambient particulate) concentrations to the ESRP national and place- based research teams to support overall ecosystem service assessment activities. A final objective is to develop the capacity of CMAQ to address secondary welfare NAAQS for SO, and NOx. 7.3.3 Accomplishments Activities in FY 10 focused on continued development of the APVVS implementation plan and coordination with watershed model development for the Cape Fear basin. Planning focused on extending the coordination of air-water linkage testing with air-uatcr/tcrrestrial research being performed for the Cape Fear basin with the Ecosystem Research Division (ERD). A beta version of FEST-C was completed and was transferred to the ESRP Nitrogen, mapping, and FML Team scientists (Cooter et al., 2010). We continued to interact with FML SPARROW, SWAT, and BMP modelers regarding their use of CMAQ deposition and FEST-C Nitrogen budget estimates. We participated in regular research planning conference calls and coordinated FEST-C output plans with FML modelers. The FML was identified by AMAD as a pilot for improved cross-divisional cooperation. A NERL pool postdoctoral position was developed with ESD to explore the more effective use of their planned and on-going field Nitrogen and landu.sc/landcovcr research in CMAQ model development and evaluation for the ESRP. Chesapeake Bay place-based research activities were aimed at contributing to an overall understanding of Chesapeake Bay nitrogen issues for development of a Bay implementation plan. 7.3.4 Next Steps Several research topics arc planned for the future research in multimedia modeling for air-ecosystem linkages and ecosystem service assessments organized under the three headings below. 1. Multimedia Linkage and Transdisciplinary Studies - Linking to water quality and terrestrial models - Linking to ecosystem welfare and tipping points - Linking hydrology model to WRF/CMAQ system - Linking climate, air quality and ecosystem services 2. Using CMAQ as a Laboratory for Deposition Research - Gulf hypoxia and regional watershed modeling with SPARROW - Chesapeake Bay TMDL and presidential directive - Secondary N A AQS-drivcn research 3. Ecosystem Services Research Program - National pollutant-driven research (Nitrogen Team) - Place-based driven research - FML - Albemarle-Pamlico Watershed and Estuary Study (APWES) - Chesapeake Bay ESRP research 7.4 Model and Tool Development 7.4.1 Introduction The thrust of this research area is to further develop and advance CMAQ and supporting tools for ecosystem applications. The research is subdivided into five key areas: (1) to improve the parameterizations of the air-surface exchange of atniosplieric pollutants, both bi-directional and unidirectional for aquatic and terrestrial landscapes; (2) to develop subgrid scale land-usc-specific deposition estimates and to develop parameterizations of subgrid variation in dry deposition because of complex terrain; (3) to advance the WRF/CMAQ land surface parameterizations and leaf area index (LAI) estimates to improve connections to ecosystem models; (4) in coordination with the model ------- development team, to improve the modeling of the amount, location, and duration of precipitation by WRF to reduce the error in wet deposition estimates from CMAQ as well as to extend WRF precipitation predictions to finer grid scales in complex terrain; and. finally. (5) to develop and advance tools to facilitate the linkage of CMAQ with ecosystem. 7.4.2 Research Direction A major objective is to improve unidirectional and bi-directional exchange process descriptions in CMAQ, with an emphasis on incorporation of bi-directional exchange algorithms in CMAQ for NH, and mercury. This objective also includes evaluation of CMAQ deposition predictions to test improvements; to suggest further model improvements, including the search for missing pathways in CMAQ; and to reduce uncertainty in deposition calculations. A second major objective is also to develop subgrid-scale approaches to better represent dry deposition variability within a grid. The third major objective is to advance the land surface paranictcri/ations used by WRF/CMAQ. A final major objective is identification, development, and enhancement of air-surface interfaces and tools needed to facilitate the use of CMAQ deposition outputs by analysts and researchers not familiar with the atmospheric deposition model and enable CMAQ to better support ecosystem studies and support CMAQ process enhancements. 7.4.3 Accomplishments To improve the understanding of bidirectional exchange processes. AMAD collaborated with field scientists from EPA/NRMRL and the NO A A ARL in planning and conducting a field campaign where NH, surface exchange was measured over corn, unfertilized grassland, and forest canopies. Data from the field studies have been used in several model development efforts, during including first-order and siniplificd-first-order closure models for flux from a vegetative canopy (Bash et al.. 2010) and a process-based soil NH, emissions model (Cooter et al.. 2010). A bi-directional flux pilot study for the eastern United States was conducted by Robin Dennis. Ellen Cooter. Megan Gore (NCSU graduate student). Viney Aiieja (graduate students supervising professor), and Jon Plcini. The purpose of the pilot is to facilitate the step-wise implementation of the new bi-directional flux algorithms developed and constrained by observations collected in collaboration with NRMRL and NO A A into CMAQ. As a first step, the new canopy model and emission potential from the soil emission model were incorporated into CMAQ 4.7.1. Testing and evaluation began in 2010, and several manuscripts arc being prepared for submission in 2011. The CMAQ deposition model was changed to enable mercury-specific deposition processes. Consideration of the nicsophyll resistance was added, which is important for modeling elemental mercury. For reactive gaseous mercury (RGM), the chemical parameters were changed to be consistent with HgC,,. Previously. RGM had been modeled using HNO, as a surrogate. This enables CMAQ to parameterize the air-surface water concentration gradient dependence on the elemental mercury flux (Foley et al.. 2010), the recycling of recently deposited divalent mercury, and the enrichment of elemental mercury concentrations in vegetation, soil, and surface waters tliat have been documented recently in the literature (Bash and Miller 2009). Tliese improvements capture pulses of emissions observed following precipitation in arid environments observed during measurement campaigns and arc an inline estimation of natural mercury emissions that remains consistent with changes in modeled chemical mechanisms and boundary conditions, clianges that would require updates to offline natural emission estimates (Bash 2010). AMAD scientists arc collaborating with USD A to build CMAQ modeling capability to simulate pesticide transport in a project that was initiated in FY 10. CMAQ with pesticide fate and transport capability will be evaluated with special observations collected by the U.S. Department of Agriculture (USDA). A coordination meeting was held in March 9, 2010, in Research Triangle Park among the USDA Principal Investigators (Cathlccn Hapenian and Laura McConncll). the USDA postdoctoral fellow assigned to the project (Cody Howard), and interested AMAD scientists. The first phase of the project was to design a field study to be conducted during spring/summer 2011. Dr. Howard was provided Pesticide Emissions Model (PEM) code by AMAD which he is now using on USDA machines. He completed both the SMOKE and CMAQ training offered by CM AS. Subsequent to training, he requested and was provided AAA connection to the HPCC to facilitate his continued exploration of CMAQ. Unidirectional Exchange In FY09, as part of a collaborative effort with NRMRL, deposition velocity estimates from CMAQ for ultrafine particles were compared against field measurements taken over a loblolly pine forest stand in FY06 and FY07. The results were presented at the 2009 NADP meeting. In FY09-10, staff collaborated with scientists from the U nitcd States and Canada on a study of sulfur budgets of watersheds in Southeastern Canada and the Northeastern Unitcd States Wet deposition is measured at these sites, but dry deposition estimates arc not available. A number of approaches for estimating the contribution of dry deposition were investigated. The wet deposition results and ambient NH, concentrations from CMAQ version 4.7.1, with the changes made during implementation of the NH, bi-directional cxcliange pilot project were evaluated against network observations. 49 ------- Grain Corn Managment Schedule 3.5 3 2.5 2 1.5 0.5 0 ^ >$> & ^ K ^ ^ ^ ^ ft ft ft ft ft ft ft ft f\ Day of Year Aug : JJy Jun May Apr . Mar ¦ Feb 1 Jan _ Grain Corn Second Fertilizer Application Date Grain Corn Second Fertilizer Application Rate Figure 7-3, Rainfed grain corn (A) example management schedule, (B) second fertilizer application date, and (C) second first fertilizer application rate. Subgrid-Scale Variability In FY07, work was begun on implementing the capability in CMAQ to output land-use-category specific deposition velocities (referred to as the mosaic approach) to examine the effects of subgrid-scale variability in land use on calculated deposition fluxes. Initial efforts focused on disaggregating the CMAQ deposition velocities by accounting for the effects of subgrid-scale surface roughness. InFY08, a prototype of the mosaic approach was put into the full CMAQ model for further testing and analysis. This prototype was expanded in FY09, and the CMAQ mosaic was run for a 2-week period in May 2002 to test the capability of the code to capture the temporal trend in leaf area index and deposition velocity. A subset of the results from this analysis was presented at the 2009 NADP meeting. In FY 10. the mosaic approach was further adapted to enable output of stomatal flux for each land use category. This new capability is being utilized in a study of (). exposure. Modeling for CASTNet Concerns about the comparability of deposition estimates between the U.S. CASTNET and the Canadian monitoring network, CAPMoN, have been raised, and a comparison of the deposition velocity model approaches was done in FY07-09. A manuscript describing this analysis lias been submitted to Atmospheric Em'ironment for publication. Data gaps in the meteorological data collected at the CASTNET sites result in missing values for the predicted deposition velocity and, therefore, the flux. InFY09-10, a study was done to design a method for imputing missing flux values to provide a more complete historical dataset. A manuscript describing this study lias been submitted to Water, Air, and Soil Pollution. Fertilizer Emission Scenario Tool During FY09, a work assignment was initiated and a formal work plan was developed and approved for development of the Fertilizer Emission Scenario Tool (FEST-C). FEST-C provides information on fertilizer application timing, amount, and mode of application, as well as soil characteristics and surface losses in runoff for use by CMAQ (Figure 7-3). During FY10, a beta version of FEST-C was delivered as part of APM372. Contractor code was transferred from the AMAD CMAS vehicle to the OAQPS emissions modeling contract. As part of this delivery, a briefing seminar was given and access and training was provided to OAQPS and ESRP/mapping and FML theme scientists. Quality Assurance (QA) of initial FEST-C results was initiated, and a code outline for full implementation in bi-directional CMAQ was developed. An overview of FEST-C was presented at the 2010 CMAS meeting. Additionally, the Spatial Allocator tool was enhanced to process data produced by the FEST-C tool and the Environmental Policy Integrated Climate (EPIC) for CMAQ NH , bi-directional flux modeling. This includes handling of transformed WRF input weather files, as well as EPIC output files. A poster describing this effort was presented at the 2010 CMAS meeting. Updated Land Cover Data A new land cover data-set was developed for use with CMAQ that consists of information from the National Land Cover Dataset (NLCD) for the United States and MODIS data for Mexico and Canada. The Spatial Allocator tool was revised to include raster tools for converting NLCD data to the gridded format required for CMAQ modeling. During FY10, MCIP was modified to accept 2001 NLCD as land cover input, and a test case of the bi-directional CMAQ pilot was rim using NLCD. ------- During FY09, 2002 USD A county-level crop data were assembled and provided to the contractor (UNC) for update of BEIS agricultural crop fractions. This work was a collaborative effort with the emissions modeling team. Results of the updated crop information have been QA'd and accepted. During FY 10, the updated BELD agricultural data was used to distribute crop area in the beta version of the FEST-C tool, and issues were identified that will be resolved during FY 11. Work plan delays have pushed completion of the BELD update (BELD 4.0) into FY11. Watershed Deposition Tool The Watershed Deposition Tool (WDT) was developed to provide an easy to use tool for mapping the deposition estimates from CMAQ to watersheds to provide the linkage between air and water needed for TMDL (total maximum daily load) and related nonpoint-source watershed analyses. This software tool takes gridded atmospheric deposition estimates from NOAA/EPA's regional, multipollutant air quality model, CMAQ, and allocates them to 8-digit HUCs (hydrologic cataloging units of rivers and streams) within a watershed. State, or region. WDT was delivered to AMAD in FY07 and released to the public via the AMAD Web site at http://www.epa.gov/asmdnerl/ EcoExposure/depositionMapping.html. The software and sample CMAQ data files can be downloaded from the Web site. In FY08. the WDT was updated and continues to be used widely by the community to map CMAQ deposition estimates to watershed delineations, (Figure 7-4). The WDT was developed by Argonne National Laboratory and contains proprietary code. In keeping with EPAs efforts to provide open source products to the extent possible, the capabilities of the WDT were migrated into the Visualization Environment for Rich Data Interpretation Figure 7-4. Watershed deposition tool output showing the average (per unit area) annual total (wet+dry) oxidized nitrogen deposition (kg-H/ha) estimated for each 12-digit HUC in the Albemarle-Pamlico Basin for 2002. (VERDI) during FY08 and FY09. A description of the WDT and example application of the tool was published in Schwede et al. (2009). In FY10, files for 2002-2006 from the CDC PHASE study were made available for download and use with the WDT Visualization Environment for Rich Data interpretation In FY07, the initial version of VERDI was delivered to AMAD. VERDI is an open source Java tool for visualizing CMAQ and other environmental data. It is designed as an update/replacement for the Package for Analysis and Visualization of Environmental Data (PAVE), which currently is used for visualizing CMAQ data. An initial version of the VERDI was released formally to the public in FY08 for use in visualizing CMAQ output. The software and associated documentation are available from littp:// verdi-tool.org. The distribution of the tool is managed by CMAS. In FY08-09, additional capabilities were added to VERDI, including an alternate tile plot routine, an areal interpolation plot that provides the capability of the WDT, and the ability to display CMAQ data in polar stereograpliic and lat-long projections. A demonstration of this enhanced version of VERDI was given at the 2009 CMAS meeting. Numerous enhancements were made to VERDI in FY10, including improving the scripting capability and overlay capabilities, adding a remote file browser, and adding the capability to export shapefiles. A poster describing the enhancements was presented at the 2010 CMAS meeting. Spatial Allocator The Spatial Allocator tool was revised to include raster tools for converting NLCD data to the gridded format required for CMAQ modeling and to process data produced by the FEST-C tool. 7.4.4 Next Steps The objectives in air-surface exchange model and tool development is to further improve and advance CMAQ and supporting tools for ecosystem applications. The research is subdivided into six key areas: (1) to improve the parameterizations of the air-surface exchange of atmospheric pollutants, both bi-directional and unidirectional for aquatic and terrestrial landscapes; (2) to develop subgrid-scale land-use-specific deposition estimates, to develop parameterizations of subgrid variation in dry deposition because of complex terrain, and to develop techniques to account for deposition of pollutants via cloud water deposition; (3) to advance and modernize the WRF/CMAQ land surface parameterizations to improve connections to ecosystem models; (4) to develop a model representation of the hydrologic overland How, with other Agency partners that is consistent with the precipitation from WRF/CMAQ; (5) in coordination with the model development team, to improve the modeling of amount, location, and duration of precipitation by WRF to 51 ------- reduce the error in wet deposition estimates from CMAQ as well as to extend WRF precipitation predictions to finer grid scales in complex terrain; and. finally, (6) to develop and advance tools to facilitate the linkage of CMAQ, with ecosystem models based on lessons learned from the model applications. Several research topics are planned for the future research in the air-surface exchange and model tool development organized under the two headings below. 1. Air-Surface Exchange - Bi-directional exchange oNH3 o Mercury o Pesticides - Unidirectional exchange oNO 0S0J - Subgrid variability o Land use variability o Terrain variability and occult deposition 2. Data and Tool Development - Fertilizer Emissions Scenario Tool for CMAQ (FEST-C) - Watershed Deposition Tool (VVDT) - Visualization Environment for Rich Data Interpretation (VERDI) 52 ------- References Abdul-Razzak, H.. and S. Ghan. 2000. A parameterization of aerosol activation. 2. Multiple aerosol types. J. Geophys. Res., 105(D6), 6837-6844, doi: 10.1029/1999JD901161. Appcl. K.W., Gilliam. R.C.. Davis. N., andZubrow, A.: Overview of the Atmospheric Model Evaluation Tool (AMET) vl.l for evaluating meteorological and air quality models. Environ. Modell. Softw., 26, 4, 434-443, 2011. Appcl. K.W., P.V. Bhavc, A.B. Gilliland, G. Sarvvar, and S.J.Roscllc. Evaluation of the community multiscalc air quality (CMAQ) model version 4.5: Sensitivities impacing model performance; Part 11 - particulate matter. Atmospheric ENvironment, 42(24): 6057-066, (2008). Araujo. R.. R.S. Dyer. R.C. Fortniann. F.A. Fulk, F.S. Hauchnian, D.T. Hcggcni. S.T. Rao. M R. Rodgcrs. L.S. Sheldon and E.J. Weber. A Conceptual Framework for U.S. EPAs National Exposure Research Laboratory. U.S. Environmental Protection Agency. Washington. DC. EPA/600/R-09/003 (NTIS PB2010-103951) (2009). Bash. J O. 2010. Description and initial simulation of a dynamic bi-directional air-surface exchange model for mercury in CMAQ. J. Geophys. Res.. Vol 115. Issue D06305, doi: 10.1029/2009/JD012834. Bash, J.O, J.T. Walker, G.G. Katul, M.R. Jones, E. Nemitz, and W. Robarge. 2010. Estimation of in-canopy ammonia sources and sinks in a ZeaMays Canopy. Environmental Science and Technology, 44:1683-1689. Bash. J.O. and D R. Miller; 2009. Growing season total gaseous mercury (TGM) flux measurements over an Acer nib rum L. stand. Atmospheric Environment. 43: 5953-5961. Bhavc. P. V., Pouliot. G. A., and Zheng. M. 2007. Diagnostic model evaluation for carbonaceous PM, 5 using organic markers measured in the southeastern US. Environ. Sci. Technol, 41(5): 1577-1583. Bowden, J.H., T.L. Otte. and C.G. Nolte. An examination of mean error and added variability for interior grid nudging techniques over an annual cycle using the WRF model for regional climate modeling (to be submitted to J. Clim., 2011). Chapman, E.G., W.I. Gustafson. Jr.. R.C. Easter, J.C. Barnard. S.J. Ghan, M.S. Pckour, and J.D. Fast. 2009. Coupling acrosol-cloud-radiativc processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources. Atmos. Chem. Phys., 9, 945-964. Cooter, E.J., J.O. Bash, J.T. Walker. M R. Jones, and W. Robarge. 2010. Estimation of NH3 bi-directional flux from managed agricultural soils. Atmospheric Environment, 44:2 107-2 115, doi: 10.1016/j. atmosenv.2010.02.044. Dennis. R.L., R. Matliur. J. Plcini. and J. Walker. 2010. Fate and transport of ammonia at the local to regional scale as simulated by the Community Multiscalc Air Quality model. Atmospheric Pollution Research, 1. 207-214. Finn. D.. K. L. Clawson, R. G. Carter. J. D. Rich. R. M. Ecknian. S. G. Perry, V. Isakov, and D. Heist. 2010. Tracer Studies to Characterize the Effects of Roadside Noise Barriers on Near-Road Pollutant Dispersion under Varying Atmospheric Stability Conditions. Atmos. Environ., 44(2):204-214. Foley, K. M„ S. J. Roscllc. K. W. Appcl. P. V. Bhavc, J. E. Plcini. T. L. Otte. R. Matliur, G. Sarwar, J. O. Young. R. C. Gilliam. C. G. Nolte, J. T. Kelly, A. B. Gilliland. and J. O. Bash. 2010. Incremental testing of the Community Multiscalc Air Quality (CMAQ) mode ling system version 4.7. Gcosci. Model Dev., 3, 205-226. Gcgo, E., PS Porter, A. Gilliland. S.T. Rao. Observation- based assessment of the impact of nitrogen oxides emissions reductions on O, air quality over the eastern United States. Journal of Applied Meteorology and Climatology. 46, 994-1008, 2007. Gilliland. A. B.. Hogrcfc, C, Pindcr. R. W., Godowitch, J. M., Foley, K. L., Rao. S. T. (2008). Dynamic evaluation of regional air quality models: Assessing changes in 0-3 stemming from changes in emissions and meteorology. Atmos. Environ. 42(20): 5110-5123. Godowitch, J.M., and S.T. Rao. Assessing multi-year changes in modeled and observed maximum 8-hour o/onc with a dynamic evaluation approach. 12th Conference on Atniosplieric Chemistry. Paper #163, on- line at http://anis.confcx.coni/anis/htscarch.cgi. 2010. Godowitch, J. M„ Pouliot. G. A., Rao, S. T. (2010). Assessing multi-year changes in modeled and observed urban NOX concentrations from a dynamic model evaluation perspective. Atmos. Environ. 44(24): 2894- 2901. doi:1016/j.atmosenv.2010.04.040 Golden. H E., C D. Knightcs. E.J. Cooter. R.L. Dennis, R.C. Gilliam. K.M. Foley, 2010. Linking air quality and watershed models for environmental assessments: Analysis of the effects of model-specific precipitation estimates on calculated water flux, Environmental Modelling & Software, 25, 1722-1737. 53 ------- Grell ct al.. 2011: WRF/Chem Version 3.3 User's Guide. http://ruc.noaa.gov/wrl7WG 11/Users_guide.pdf Heist. D.. S. Perry, L. Brixev. 2009a. A Wind Tunnel Study of the Effect of Roadway Configurations on the Dispersion of Traffic-Related Pollution. Atmos. Environ., 43: 5101-5111. Heist. D.K., Perry, S C.. Brixev, L.A., 2009b. Dispersion of Traffic-Related Pollutants in a Wind-Tunncl Model of a Las Vegas Field Site. International Workshop on Physical Modeling of Flow and Dispersion Phenomena. Brussels. Belgium. Aug. 24-26. Hoar. T., Nychka, D., 2008. Statistical downscaling of the community climate system model (CCSM) monthly temperature and precipitation projections. htto://www. gisclimatechange.org/Downscaling.pdf. lacono. M.J., IS. Delaine re. E.J. Mlawer, MVV. Shephard, S.A. Clough. and W.D. Collins. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res.. 113, D13 103. doi: 10.1029/2008JD009944, 2008. Isakov, V., J. Touma. and A. Khlystov. 2007. A method of assessing air toxics concentrations in urban areas using mobile platform measurements. J. Air & Waste Manage. Assoc. 57:1286-1295, DOI: 10.3155/1047- 3289.57.11.1286. Mathur. R., J. Plcim. D. Wong, T. Otte. R. Gilliam, S. Rosclle. J. Young, F. Binkowski, and A. Xiu, 2009. The WRF-CMAQ Integrated On-line Modeling System: Development, Testing, and Initial Applications, Air Pollution Modeling and Its Application XX, Springer, The Netherlands, in press. Mathur, R.. S. Yu, D. Kang. and K.L. Schere. Assessment of the winter-time performance of developmental particulate matter forecasts witht the Eta-CMAQ modeling system. Journal of Geophysical Research- Stmospheres (JGR-Atmospheres). 113(D02303): 1-15, (2008) Napelenok, S.L, Foley, K.M., Kang. D„ Mathur, R.. Pierce, T.„ Rao, S.T., 2011. Dynamic evaluation of regional air quality model's response to emission reductions in the presence of uncertain emission inventories (in press). Nolte, C.G., A.B. Gi Hi land, C. Hogrefe. L.J. Mickley, 2008, Linking global to regional models to assess future climate impacts on surface o/one levels in the United States. J. Geophys. Res.. 113, D14307. doi: 10.1029/2007JD008497. Paulot. F., J.D. Croun.se. H.G. Kjaergaard. J.H. Kroll. J.H. Seinfeld. P.O. Wcnnberg, 2009, Isoprene photooxidation: New insights into the production of acids and organic nitrates, Atmos. Chem. Phys. 9, 4, 1479-1501. Pierce, T„ C. Hogrefe, S.T. Rao. PS. Porter. J. Ku. 2010. Dynamic evaluation of a regional air quality model: assessing the emissions-induced weekly o/one cycle, Atmos. Environ. 44:3583-3596. Pinder. R. W„ Gilliam. R. C., Appel. K. W., Napelenok, S. L„ Foley, K. M„ Gilliland. A. B. (2009). Efficient Probabilistic Estimates of Surface O/one Concentration Using an Ensemble of Model Configurations and Direct Sensitivity Calculations. Environ. Sci. Technol. 43(7): 2388-2393. Schaum. J., M. Cohen. S. Perry. R. Art/.. R. Draxler, J. B. Frithsen. D. Heist. M. Lorber. and L. Phillips . 2010. Screening level assessment of risks due to dioxin emissions from burning oil from the BP Deepwater Horizon Gulf of Mexico spill. Environ. Sci. Technol. 44: 9383-9389. Schwede, D.. R. Dennis. M. Bit/. 2009. The Watershed Deposition Tool: A tool for incorporating atmospheric deposition in watershed analyses. Journal of the American Water Resources Association, 45(4), 973-985. Schmith. T„ 2008. Stationarity of regression relationships: Application to empirical downscaling. J. Clim., 21, 4529-4537. Skamarock. W. C, and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech Note NCAR/TN-4751STR, 125 pp. [ Available from UCAR Communications, P.O. Box 3000, Boulder. CO 80307.] U.S. EPA, 2009, Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act. http://www.epa.gov/climatechange/ endangernient.html. US EPA, 2007. NOx Budget Trading Program. EPA- 430-R-07-009. http://www.epa.gov/airmarkets Venkatram, A., V. Isakov. E. Thoma, and R. Baldauf. 2007. Analysis of air quality data near roadways using a dispersion model. Atmos. Environ., 41(40): 9481-9497. Venkatram. A., V. Isakov, R. Seila. and R. Baldauf. 2009. Modeling the impacts of traffic emissions on air toxics concentrations near roadways. Atmos. Environ.. 43(20): 3191-3199. Weaver, C., X. Liang. J. Zhu. P. Adams, P. A mar. J.C. Avise, M. Caughey. J. Chen, R.C. Cohen, E. Cooter. J. Dawson. R.C. Gilliam. A. Gilliland. A.H. Goldstein. A.E. Grambsch. A. Guenther. W.I. Gustafson. R.A. Harley. S. He. B.L. Hemming, C. Hogrefe, H. Huang. S. Hunt. D.J. Jacob. PL. Kenny, K. Kunkel. J. Lamarque. B. Lamb. N.K. Larkin. L.R. Leung. K. Liao. J. Lin. B.H. Lynn, K. Manomaiphiboon, C.F. Mass. D. McKinzie, L.J. Mickley. S. O'Neill. C.G. Nolte. S.N. Pandis. P.N. Racherla. C. Roscnzwcig. A. Russell. E. Salathe. A.L. Steiner. E. Tagaris. Z. Tao. S. Ton.se. C. Wiedinmver, A. Williams, D. Winner, J. Woo. S. Wu, and D.J. Wuebbles. A Preliminary Synthesis of Modeled Climate Change ------- Impacts on U.S. Regional O/onc Concentrations. Bulletin of the American Meteorological Society. American Meteorological Society, Boston, MA, 90(12):1843-1863 (2009) Wilby, R. L„ Dawson, C. W., and Barrow, E. M„ 2002. SDSM - A decision support tool for the assessment of regional climate change impacts. Environ. Modelling Software, 17. 147-159. Zhang, Y, 2008: Online-coupled meteorology and chemistry models: history, current status, and outlook. Atmos Chein and Phys.. 11. 1680-7316. ------- ------- APPENDIX A Atmospheric Modeling and Analysis Division Staff Roster (as of December 31, 2010) Office of the Director S.T. Rao. Director David Moblcv, Deputy Director Patricia McGhee, Assistant to the Director Sherry Brown Wanda Payne (SEEP1) Ken Schere, Science Advisor Gary Walter, IT Manager Emissions and Model Evaluation Branch Tom Pierce. Chief Wyat Appcl Brian Eder Kristen Foley Jim Godowitch Steve Howard Roger Kwok (NRC2 Postdoctoral Fellow) Sergey Napclenok George Pouliot Havala Pye (ORISE3 Postdoctoral Fellow) Alfrcida Torian Atmospheric Exposure Integration Branch Val Garcia. Chief Jesse Bash Jason Ching Jim Crooks (Postdoctoral Fellow) Robin Dennis Megan Gore (Contractor) Vlad Isakov Gill-Ran Jeong (Visiting Scientist) Donna Schwede Myrto Valari (NRC2 Postdoctoral Fellow) David Heist. Fluid Modeling Facility Asliok Patel (SEEP1), Fluid Modeling Facility Steve Perry, Fluid Modeling Facility Bill Petersen (Contractor). Fluid Modeling Facility John Rose (SEEP1), Fluid Modeling Facility Atmospheric Model Development Branch Roliit Matliur. Chief Shirley Long (SEEP1), Secretary Prakash Bliavc Garnet Erdakos (NRC2 Postdoctoral Fellow) Rob Gilliam Bill Hut/ell Deborah Lucckcn Martin Otte (Postdoctoral Fellow) Shawn Roselle Golam Sarwar John Streicher David Wong Jeff Young Shaocai Yu (Postdoctoral Fellow) Applied Modeling Branch Jon Pleini, Acting Chief Mclanie Ratteray (SEEP,), Secretary Farhan Aklitar (ORISE3 Postdoctoral Fellow) Bill Bcnjcy Jared Bowden (NRC2 Postdoctoral Fellow) Russ Bullock Dan Cohan (Visiting Scientist) Barron Henderson (ORISE3) Jerry Hcrwchc Chris Nolte Tanya Otte Rob Pinder Jenise Swall Ben Wells (Contractor) Ying Xie (NRC2 Postdoctoral Fellow) 'SEEP - Senior Environmental Employee Program -NRC - National Research Council 'ORISE - Oak Ridge Science and Education Program 57 ------- APPENDIX B Division and Branch Descriptions Atmospheric Modeling Analysis Division The Division leads the development and evaluation of atmospheric models on all spatial and temporal scales for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions, and for forecasting the Nation's air quality . AMAD is responsible for providing a sound scientific and technical basis for regulatory policies to improve ambient air quality . The models developed by AMAD arc being used by EPA, NOAA, and the air pollution community in understanding and forecasting the magnitude of the air pollution problem and also in developing emission control policies and regulations for air quality improvements. AMAD applies air quality models to support key integrated, niultidisciplinary science research. This includes linking air quality models to other models in the source-to-outcome continuum to effectively address issues involving human health and ecosystem exposure science. Atmospheric Model Development Branch AMDB develops, tests, and refines analy tical, statistical, and numerical models used to describe and assess relationships between air pollutant source emissions and resultant air quality , deposition, and pollutant exposures to humans and ecosystems. The models arc applicable to spatial scales ranging from local/urban and nicsoscale through continental, including linkage with global models. AMDB adapts and extends meteorological models to couple effectively with chemical-transport models to create comprehensive air quality modeling systems, including the capability for two-way communication and feedback between the models. The Branch conducts studies to describe the atmospheric processes affecting the transport, diffusion, transformation, and removal of pollutants in and from the atmosphere using theoretical approaches, as well as from analyses of monitoring and field study data. AMDB converts these and other study results into models for simulating the relevant phy sical and chemical processes and for characterizing pollutant transport and fate in the atmosphere. AMDB conducts model exercises to assess the sensitivity and uncertainty associated with model input databases and applications results. AMDB's modeling research is designed to produce tools to serve the Nation's need for science-based air quality decision- support systems. Emissions and Model Evaluation Branch EMEB develops and applies advanced methods for evaluating the performance of air quality simulation models to establish their scientific credibility. Model evaluation includes diagnostic assessments of modeled atmospheric processes to guide the Division's research in arcas such as land-use and land cover characterization, emissions, meteorology, atmospheric chemistry, and atmospheric deposition. The Branch also advances the use of dynamic and probabilistic model evaluation techniques to examine whether the predicted changes in air quality arc consistent with the observations. By collaborating with other EPA offices that provide data and algorithms on emissions cliaractcri/ation and source apportionment and the scientific community, the Branch evaluates the quality of emissions used for air quality modeling and. if warranted, develops emission algorithms that properly reflect the effects of changing meteorological conditions. Atmospheric Exposure Integration Branch AEIB develops methods and tools to integrate air quality process-based models with human liealtli and ecosystems exposure models and studies. The three major focus arcas of this Branch arc (1) linkage of air quality with human exposure. (2) deposition of ambient pollutants onto sensitive ecosystems, and (3) assessment of the impact of air quality regulations (accountability ). AEIB's research to link air quality to human exposure includes urban-scale modeling, atmospheric dispersion studies, and support of exposure field studies and epidemiological studies. The urban-scale modeling program (which includes collection and integration of experimental data from its Fluid Modeling Facility ) is focused on building "hot-spot" air toxic analy sis algorithms and linkages to human exposure models. The deposition research program develops tools for assessing nutrient loadings and ecosystem vulnerability, and the accountability program develops techniques to evaluate the impact of the regulatory strategics that have been implemented on air quality and conducts research to link emissions and ambient pollutant concentrations with exposure and human and ecological health end points. Applied Modeling Branch AMB uses atmosplieric mode ling tools to address emerging issues related to air quality and atmospheric influences on ecosystems. Climate change, growing demand for biofucls. emission control programs, and growth all affect air quality and ecosystems in various ways that require integrated assessment. Fundamental to these studies is the development of credible scenarios of current and future conditions on a regional scale and careful consideration of global-scale influences to air pollution and climate. Scenarios of climate, growth and development, and regulations will be used with regional atmospheric models to investigate potential changes in exposure risks related to air quality and meteorological conditions. 58 ------- APPENDIX C 2010 Awards and Recognition EPA Gold Medal William Bcnjcy, Ellen Cootcr. Alice Gilland. and Robert Gilliam—Climate Change and Air Quality Assessment Team ORD Bronze Medal VIad Isakov. Joe Touma and others—Cummulative Air Accountability Team ORD Bronze Medal Prakash Bhavc. Ann Carlton, Rohit Mathur. Sergey Napelenok. Robert Pinder, George Pouliot. Golam Sarwar, and others-Organic Aerosol Science Team ORD Technical Organic Assistance to the Regions or Program Offices Award Kenneth Schere, Jonathan Plcim. Rohit Mathur. George Pouliot. Rogcrt Gilliam, and Thomas Pierce—Fairbanks Modeling Team ORD Diversity Leadership Award Prakash Bhavc—For recognition of his various approaches to model diversity ideals and for his active support of diversity programs NERL Special Achievement Award Wyat Appel and Robert Gilliam—Goal 1, Support the Agency "s Mission: Developing the Atmospheric Model Evaluation Tool (AMET) and overseeing its successful transfer to the meteorological and air quality modeling community S.T. Rao and David Mob ley—Goal 2, Be a High Performing Organization: Efforts to improve integration and coordination of research across HEASD Jonathan Plcim—Goal 3, Be a Leader in the Environmental Research Community: Demonstrating excellence in advancing the state of the science in atmospheric modeling Prakash Bhavc, Ann Carlton. Deborah Lucckcn, Rohit Mathur and Golam Sarwar-Goal 4, Integrate Environmental Science and Technology to Solve Environmental Problems: Recognizing NERL's Atmospheric Chemistry and Modeling Team, who effectively integrated research across research disciplines and organizations Kristen Foley—Meritorious Research Support Award: Providing much needed statistical and programming support toward the advancement of credible air quality models Ken Schere, Alice Gilliland and others—Teamwork Award: Modeling Linkage Team David Heist and Steven Perry—Blue Ribbon Paper Award: The Effect of a Tall Tower on Flow and Dispersion Through a Model Urban Neighborhood. Part 1: Flow Characteristics and Part 2: Pollutant Dispersion Robert Pinder. Robert Gilliam. Wyat Appel. Sergey Napelenok, Kristen Foley, and Alice Gilliland - Blue Ribbon Paper Award: Efficient Probabilistic Estimates of Surface Ozone Concentration Using an Ensemble of Model Configurations and Direct Sensitivity Calculation Prakash Bhavc. Heather Simon. George Pouliot. and David Mobley - Blue Ribbon Paper Award: Emissions Inventory of PM25 Trace Elements Across the U.S. Shawn Rosclle—Leadership Award: Demonstrating leadership and assembly of various CMAQ versions and for providing enthusiastic support of its use by the program offices, contract stall, and internal division stall Bill Hut/ell—Collaboration Award: Essential role in the improvement and maintenance of the computer code for the Community Multiscale Air Quality Model AMAD Awards Blue Ribbon Paper: David K. Heist and Steven Perry— Effect of a Tall Tower On Flow and Dispersion Through a Model Urban Neighborhood. Part 1: Flow Characteristics and Part 2: Pollutant Dispersion Blue Ribbon Paper: Robert Pinder. Robert Gilliam. Wyat Appel. Sergey Napelenok. Kristen Foley, and Alice Gilliland—Efficient Probalisitic Estimates of Surface Ozone Concentration Using an Ensemble of Model Configurations and Direct Sensitivity Calculation Blue Ribbon Paper: Prakash V. Bhavc. Heather Simon. George A. Pouliot. and David Mobley—Emissions Inventory PM, 5 Trace Elements Across the Unitcd States. Leadership Award: Shawn Rosclle—Demonstrating leadership in coordinating the development and assembly of various CM AQ versions and for providing enthusiastic support of its use by the program offices, contract stall, and internal division staff. Collaboration Award: Bill Hutzcll. For his essential role in the improvement and maintenance of the computer code for the Community Multiscale Air Quality Model 59 ------- APPENDIX D 2010 Publications(Division authors are in bold.) Journal Articles Appel, W., S.J. Rosette, R.C. Gilliam, J.E. Pleim and R.C. Gilliam. Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern Unitcd States to MM5 and WRF Meteorological Drivers. Geoscientific Model Development, Copernicus Publications. Katlenburg-Lindau. Germany. 3(1): 169-188, (2009). Bao. H„ S. Yu and D.Q. Tong. Massive Volcanic SO, Oxidation and Sulphate Aerosol Deposition in Ceno/oic North America. Nature, Macmillan Publishers Ltd.. London. UK, 465(7300):845-974, (2010). Bash, J.O., J.T. Walker, G.G. Katul. M.R. Jones, E. Nemitz and W. P. Robarge. Estimation of In-Canopy Ammonia Sources and Sinks in a Fertilised Zea mays Field. Environmental Science & Technology, American Chemical Society. Washington. DC. 44(5): 1683-1689, (2010). Bash, J.O. Description and Initial Simulation of a Dynamic Bidirectional Air-Surface Exchange Model for Mercury in Community Multiscale Air Quality Model. Journal of Geophysical Research-Atmospheres, American Geophysical Union, Washington. DC. 115(D06305): 1-15, (2010). Carlton, A.G., R.W. Finder, P. Bhave and G. Pouliot. To What Extent Can Biogenic SO A Be Controlled? Environmental Science & Technology, American Chemical Society. Washington. DC. 44(9):3201-3646, (2010). Carlton, A.G., P. Bhave, S. Napelenok, E.O. Edney, G. Sarwar, R.W. Finder, G. Pouliot and M. Houyoux. Model Representation of Secondary Organic Aerosol in CMAQ v4.7. Environmental Science & Technology, American Chemical Society, Washington, DC. 44(221:8553-8560, (2010). Cooter, E., J. O. Bash. J.T. Walker. Jr., M R. Jones and W. Robarge. Estimation of NH3 Bi-Directional Flux from Managed Agricultural Soils. Atmospheric Environment, Elsevier Science Ltd.. New York. NY, 44( 17):2107-2115. (2010). Dennis, R.L., R. Mathur, J.E. Pleim and J.T. Walker. Jr. Fate of Ammonia Emissions at the Local to Regional Scale as Simulated by the Community Multiscale Air Quality Model. Atmospheric Pollution Research, Turkish National Committee for Air Pollution Research and Control. Izmir, Turkey. 1(4):207-214, (2010). Dennie, R.L., T. Fox, M. Fucntes. A. Gilliland, S. Hanna. C. Hogrefe, J. Irwin, S.T. Rao, R. Scheffe, K.L. Sehere, D. Steyn and A. Venkatram. A Framework for Evaluating Regional-Scale Numerical Photochemical Modeling Systems . Environmental Fluid Mechanics, Springer, New York. NY, 10(4):471-489, (2010). Djalalova. I., J.M. Wilczak. S. McK.een, G. Grell. S. Pcckhani. M. Pagowski. L. DellcMonachc. J. McQueen, P. Lee, Y. Tang. J. McHenry, W. Gong. V. Bouchet and R. Mathur. Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O, and PM, 5 during the TEXAQS-II Experiment of 2006. Atmospheric Environment, Elsevier Science Ltd.. New York. NY, 44(41:455-467, (2010). Driscoll. C.T., E.B. Cowling, P. Grcnnfelt, J.M. Galloway and R.L. Dennis. Integrated Assessment of Ecosystem Effects of Atmospheric Deposition. EM: Air and Waste Management Associations Magazine for Environmental Managers, Air & Waste Management Association, Pittsburgh, PA, (11):6-13, (2010). Finn. D.. K.L. Clawson. R.G. Carter, J.D. Rich. R.M. Ecknian. S.G. Perry, V. Isakov and D. Heist. Tracer Studies to Characteri/e the EITects of Roadside Noise Barriers on Near-Road Pollutant Dispersion under Varying Atmospheric Stability Conditions. Atmospheric Environment, Elsevier Science Ltd.. New York. NY, 44(2):204-214, (2010). Foley, K., S.J. Roselle, W. Appel, P. Bhave, J.E. Pleim, T.L. Otte, R. Mathur, G. Sarwar, J.O. Young, R.C. Gilliam, C.G. Nolte, J. T. Kelly, A. Gilliland and J.O. Bash. Incremental Testing of the Community Multiscale Air Quality (CMAQ) Modeling System Version 4.7. Geoscientific Model Development, Copernicus Publications. Katlenburg-Lindau. Germany. 3(l):205-226, (2010). Gantt, B.. N. Mcskhidzc and A.G. Carlton. The Contribution of Marine Organics to the Air Quality of the Western Unitcd States. Atmospheric Chemistry and Physics, Copernicus Publications, Katlenburg-Lindau. Germany, 10( 15):7415-7423, (2010). Ghosh. S.K., H. Lee, J. Davis and P. Bhave. Spatio- Teniporal Analysis of Total Nitrate Concentrations Using Dynamic Statistical Models. Journal of the American Statistical Association, American Statistical Association, Alexandria. VA, 105(490):538-551, (2010). Gilliam, R.C. and J.E. Pleim. Performance Assessment of New Land-Surface and Planetary Boundary Layer Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology, American Meteorological Society, Boston, MA, 49(4):760-774, (2010). 60 ------- Godowitch, J.M., G. Pouliot and S.T. Rao. Assessing Multi-year Changes in Modeled and Observed Urban NOx Concentrations from a Dynamic Model Evaluation Perspective. Atmospheric Environment, Elsevier Science Ltd.. New York. NY. 44(24) :2894-2901, (2010). Golden. H E., C D. Knights. E.J. Cooter, R.L. Dennis, R.C. Gilliam, K.M. Foley. Linking Air Quality and Watershed Models for Environmental Assessments: Analysis of the Effects of Model-Specific Precipitation Estimates on Calculated Water Flux. Environmental Modelling & Software, 25:1722-1737 (2010). Horton, R.. C. Roscn/weig. R. Ramaswamv, PL. Kinney, R. Mathur and J.E. Pleini. Integrated Climate Change Information for Resilient Adaptation Planning. EM: Air and Waste Management Associations Magazine for Environmental Managers, Air & Waste Management Association. Pittsburgh. PA, (11): 14-25, (2010). Johnson, M.M., V. Isakov, J. Tounia. S. Mukcrjec and H.A. O/kavnak. Evaluation of Land Use Regression Models Used to Predict Air Quality Concentrations in an Urban Area. Atmospheric Environment, Elsevier Science Ltd. New York, NY, 44(30):3660-3668, (2010). Kang. D., R. Mathur and S.T. Rao. Real-Time Bias- Adjusted O, and PM2 5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States. Atmospheric Environment, Elsevier Science Ltd. New York. NY. 44(18):2203-2212, (2010). Kang G. D.. R. Mathur and S.T. Rao. Assessment of Bias-Adjusted PM Air Quality Forecasts over the Continental United States During 2007. Geoscientific Model Development, Copernicus Publications. Katlenburg- Lindau. Germany. 3(l):309-320, (2010). Kelly, J., P. Bhave. C.G. Nolte, U. Shankar and K. Foley. Simulating Emission and Chemical Evolution of Coarse Sea-Salt Particles in the Community Multiscale Air Quality (CMAQ) Model. Geoscientific Model Development, Copernicus Publications. Katlenburg-Lindau. Germany, 3(l):257-273, (2010). Lim. C.Y, M. Stein, J.K. Ching and R. Tang. Statistical Properties of Differences between Low and High Resolution CMAQ Runs with Matched Initial and Boundary Conditions. Environmental Modelling & Software, Elsevier Science. New York, NY, 25( 1): 158-169. (2010). Luecken, D.J., R.L. Waterland. S. Papasav va, K. Taddonio, W.T. Hutzell, J.P. Rugh and S. Andersen. O/one and TFA Impacts in North America for Degradation of 2,3,3,3-Tetrafluoropropene (HFO-1234yf), a Potential Greenhouse Ga.s Replacement. Environmental Science & Technology, American Chemical Society. Washington. DC, 44(11:343-348, (2010). Nielsen-Gammon, J.W., X. Hu. F. Zhang and J.E. Pleini. Evaluation of Planetary Boundary Layer Scheme Sensitivities for the Purpose of Parameter Estimation. American Meteorological Society Monthly Weather Review, American Meteorological Society. Boston. MA, 138(9):3400-3417, (2010). Otte, T.L. and J.E. Pleini. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ Modeling System: Updates through MCIP\ 3.4.1. Geoscientific Model Development, Copernicus Publications, Katlenburg- Lindau. Germany, 3(l):243-256, (2010). Pierce, T.E., C. Hogrefe, S.T. Rao. P. Porter and J. Ku. Dynamic Evaluation of a Regional Air Quality Model: Assessing the Emissions-Induced Weekly O/one Cycle. Atmospheric Environment, Elsevier Science Ltd, New York, NY. 44(29):3583-3596, (2010). Rao, S.T. and D. Mobley. Moving Toward an Integrated Transdisciplinary Approach to Solving Environmental Problems. EM: Air and Waste Management Associations Magazine for Environmental Managers, Air & Waste Management Association. Pittsburgh. PA, (11):5, (2010). Simon, EL, L. Beck. P. Bhave, F. Divita. Y. Hsu. D.J. Luecken, D. Mobley, G. Pouliot. A.H. Re IT. G. Sarwar and M. Strum. The Development and Uses of EPA's SPEC I ATE Database. Atmospheric Pollution Research, Turkish National Committee for Air Pollution Research and Control. Izmir, Turkey, 1(4): 196-206, (2010). Simon, EL, Y. Kimura, G. McGaughey, D. Allen. S. S. Brown, D. Coffman, J. Dibb. H. D. OstholT, P. Quinn, J.M. Roberts. G. Yarwood. S. Kemball-Cook, D.W. Byun and D. Lee. Modeling Heterogeneous CINO Formation. Chloride Availability, and Chlorine Cycling in Southeast Texas. Atmospheric Environment, Elsevier Science Ltd. New York. NY, 44(40):5476-5488, (2010). Tian, D.. D. Cohan, Y. Hu. S. Napelenok, M.E. Chang and A. Russell. Uncertainty Analysis of O/one Formation and Response to Emission Controls Using Higher-Order Sensitivities. Journal of the Air & Waste Management Association, Air & Waste Management Association, Pittsburgh. PA, 60(7):797-804, (2010). Yu, S., R. Mathur, G. Sarwar, D. Kang, D. Tong, G. Pouliot and J.E. Pleini. Eta-CMAQ Air Quality Forecasts for O, and Related Species Using Three Different Photochemical Mechanisms (CB4, CB05, SAPRC-99): Comparisons with Measurements During the 2004 IC ARTT Study. Atmospheric Chemistry and Physics, Copernicus Publications, Katlenburg-Lindau. Germany, 10(6):3001-3025, (2010). Zhang. Y, P. Liu. X. Liu. M.Z. Jacobson. PH. McMurry, F. Yu, S. Yu and K.L. Schere. A Comparative Study of NucleationParameterizations: 2. Thrce-Dimensional Model Application and Evaluation. Journal of Geophysical Research, American Geophysical Union, Washington, DC. 115(020213): 1-26, (2010). 61 ------- Book Chapters Clawson. K.L.. D. Finn, R.G. Carter. J.D. Rich. R.M. Eckman. S.G. Perry, V. Isakov, D. Heist and T.E. Pierce. NOAA EPA Near-Roadway Sound Barrier Atmospheric Tracer Study 2008. Chapter 1, Douw G. Steyn, S.T. Rao (ed.), Air Pollution Modeling and its Applications XX . Springer Netherlands, Netherlands. C(1.15):27-31, (2010). Garcia, V.. E. Gego, R. Jones, S. Lin. C. Pantea. S.T. Rao and A. Woottcn. Examining the Impact of Regional-Scale Air Quality Regulations on Human Health Outcomes. Chapter 7, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands. 7.5(C):545-548, (2010). Godowitch, J.M., G. Pouliot and S.T. Rao. On the Use of a Dynamic Evaluation Approach to Assess Multi- year Change in Modeled and Observed Urban NOx Concentrations. Chapter 4, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and Its Application XX. Springer Netherlands, Netherlands. C(4.3):337-341, (2009). Kang. D„ R. Mathur and S.T. Rao. Implementation of Real-Time Bias-Adjusted O, and PM, 5 Air Quality Forecasts and their Performance Evaluations during 2008 over the Continental United States. Chapter 3, Douw G. Steyn and S. T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands, C(3.1):283-288, (2010). Lin. S., R. Jones, C. Pantea. V. Garcia, S.T. Rao. S. Hwang and N. Kim. Impact of the NOx SIP Call on Respiratory Hospitalizations in New York State. Chapter 7, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and its Application XX. Springer Netherlands. Netherlands. 7.6(0:549-552, (2010). Mathur, R., J.E. Pleim, B.C. Wong, T.L. Otte, R.C. Gilliam, S.J. Roselle, J.O. Young, F.S. Binkowski and A. Xiu. The WRF-CMAQ Integrated On-Line Modeling System: Development. Testing, and Initial Applications. Chapter 2, Douw G. Steyn and S. T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands. C(2.9): 155-159. (2010). Napelenok, S., J. Arnold. K. Foley and D. K. Henze. Regional Background Fine Particulate Matter. Chapter 2, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands, Netherlands. 2.32(C):277-280, (2010). Pierce, T.E., D. Heist, V. Isakov, S.G. Perry, K. Clawson and R. Eckman. Towards an Improved Characterization of Dispersion Near Major Roadways. Chapter 1, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands. C(1.16):95-98, (2010). Pleim, J.E., R.C. Gilliam and S. Yu. Atmospheric Boundary Layer Modeling for Combined Meteorology and Air Quality Systems. Chapter 1, Douw G Steyn, S.T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands. C(1.8):45-49, (2010). Porter. S., C. Hogrefe. E. Gego. K. Foley, J.M. Godowitch and S.T. Rao. Application of Wavelet Filters in an Evaluation of Photochemical Model Performance. Chapter 4, Air Pollution Modeling and its Applications XX. Springer Netherlands. Netlierlands. 4.17(0:415-420, (2010). Ran. L„ J.E. Pleim and R.C. Gilliam. Impact of High Resolution Land-Use Data in Meteorology and Air Quality Modeling Systems. Chapter 1, Douw G. Steyn and S. Trivikrania Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands. C(l.l):3-7, (2010). Rao, S.T., K.L. Schere, S. Galniarini and D. Steyn. AQMEII: A New International Initiativc on Air Quality Model Evaluation. Chapter 4, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netlierlands, 4.11(0:385-389, (2010). Sarwar, G., R. Joseph and R. Mathur. Influence of Chlorine Emissions on Ozone Levels in the Troposphere. Chapter 2, Douw G. Steyn and S.T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands. Netherlands. 2.23(C):237-240, (2010). Published Reports Rao, S.T., J.O. Bash, S. Brown, R.C. Gilliam, D. Mob ley, S. Napelenok, C.G. Nolte, T.E. Pierce and R.W. Finder. Summary Report of the Atmospheric Modeling and Analysis Division's Research Activities for 2009. U.S. Environmental Protection Agency. Washington. DC. EPA/600/R-10/058 (NTIS PB2011-100862). 2010. Rao S.T., J.O. Bash, R.C. Gilliam, D. Mobley, S. Napelenok, C.G. Nolte, T.E. Pierce, R.W. Finder and C.G. Nolte. Summary Report of Atmospheric Modeling and Analysis Divisions Research Activities for 2008. U.S. Environmental Protection Agency. Washington, DC. EPA/600/R-10/014, 2010. Other Hamilton, W.J., D.W. Byun, W. Chan, J.K. Ching, Y. Han, R. A. Lope/. V.F. Coarfa and D. Lee. A Pilot Study using EPA's CM AQ Model and Hospital Admission Data to Identify Multipollutant "Hot Spots" of Concerns in Harris County. Texas. Mickey Leland National Urban Air Toxics Research Center (NUATRC), Houston, TX, 2010. 62 ------- APPENDIX E Acronyms and Abbreviations 3D 3-Dimensional 4D 4-Dimensional AAA Authentication Authorization Audit ACM Asymmetric Convective Model AEIB Atmospheric Exposure Integration Branch AERLINE AERMOD American Meteorological Society/EPA Regulatory Model AERDNET AErosol RObotic NETwork AFMS Agricultural fertilizer modeling system AMAD Atmospheric Modeling and Analysis Division AMB Applied Modeling Branch AMDB Atmospheric Model Development Branch AMET Atmospheric Model Evaluation Tool AMNet Ambient Mercury Network AOD Aerosol Optical Depth APM Annual Performance Measure APWS Albemarle-Pamlico Water Shed AQMEII Air Quality Model Evaluation International Initiative AQS Air Quality System AR4 Fourth Assessment Report AR5 Fifth Assessment Report ARL Air Resources Laboratory ARM Atmospheric Radiation Measurement BEIS Biogenic Emission Inventory System BELD Biogenic Emissions Landuse Data Bidi Bidirectional BMA Bayesian Model Averaging BMP Best Management Practice BRACE Bay Regional Atmospheric Chemistry Experiment C-FERST/ Community-Focused Exposure and Risk STREETS Screening Tool / Screening Tool for Roadway Emissions and Exposure to Toxics CAA Clean Air Act CAAAC Clean Air Act Advisory Committee CAIR Clean Air Interstate Rule CAM Community Atmospheric Model CMAQ-MP CAP Criteria Air Pollutants CAPMON Canadian Air and Precipitation Monitoring Network CAST NET EPA's Clean Air Status and Trends Network CB05 Carbon Bond 2005 CBL Convective Boundary Layer CBM-Z Carbon Bond Mechanism-Zaveri CCN Clouds and the Earth's Radiant Energy System CDC Centers for Disease Control and Prevention CEP UNC's Center for Environmental Programs CERES Clouds and Earth's Radiant Energy System CFD Computational Fluid Dynamics CHERUBS Childhood Health Effects from Roadway and Urban Pollutant Burden Study CIRAQ Climate Impacts on Regional Air Quality CMAQ Community Multiscale Air Quality Model CMAQ-TX Community Multiscale Air Quality Model- Texas CMAS Community Modeling and Analysis System CO Carbon Monoxide C02 Carbon Dioxide CONUS Continental US CTM Chemical Transport Model DAPPLE Dispersion of Air Pollution and Penetration into the Local Environment DDM Decoupled Direct Method DOE Department of Energy DDM-3D Decoupled Direct Method-3D EC elemental carbon EMEB Emissions and Model Evaluation Branch EPA U.S. Environmental Protection Agency EPIC Environmental Policy Integrated Climate Model EPRI Electric Power Research Institute ERD Ecosystem Research Division ESD Environmental Sciences Division ESRP Ecological Services Research Program FDDA 4D Data Assimilation FEST-C Fertilizer Emissions Scenario Tool for CMAQ FIA Forest Inventory Data FLUENT Computational Fluid Dynamics Sofware produced by ANSYS, Inc. (its not an acronym, just the name of the software) FML Future Midwestern Landscapes FRM Federal Reference Method FY Fiscal Year GCM Global Climate Model 63 ------- GABLS GEWEX Atmospheric Boundary Layer Study GEOS-Chem Global 3-D chemical transport model (CTM) for atmospheric composition driven by meteorological input from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling and Assimilation Office GEWEX Global Energy and Water Cycle Experiment GHG Greenhouse Gas GISS Goddard Institute for Space Studies GLIMPSE Geos-CHEM LIDORT Integrated with MARKAL for the Purpose of Scenario Exploration GEOS Goddard Earth Observing System HAP Hazardous Air Pollutant HEASD Human Exposure & Atmospheric Sciences Division Hg[ll] Oxidized Mercury HgC12 Mercuric Chloride HgtO] Elemental Mercury HN03 Nitric Acid HONO Nitrous Acid H02 Hydroperoxyl Radical H202 Hydrogen Peroxide HUC Hydrologic Unit Code HPCC High Performance Computers and Communication I AG Interagency Agreement ICARTT International Consortium for Atmospheric Research on Transport and Transformation IE Institute for the Environment (UNC-CH) IMPROVE Interagency Monitoring of Protected Visual Environment Network INs Isoprene Nitrates INTEX Intercontinental Chemical Transport Experiment INTEX-NA Intercontinental Chemical Transport Experiment-North America IPCC International Panel On Climate Change ISORROPIA Thermodynamics Partitioning Module ITM International Technical Meeting ITR Integrated Transdisciplinary Research LADCO Lake Michigan Air Directors Consortium LAI Leaf Area Index LBC Lateral Boundary Condition LES Large-Eddy Simulations LIDAR Light Detection And Ranging LIDORT Linearized Discreet Ordinate Radiative Transfer LSM Land Surface Model Lur Land-Use Regression LW Longwave M3dry MACT MAE MCIP MARKAL MCM MDA MDN MEGAN MESA AIR MLBC MM5 MODIS MOSAIC MP MPI MYSQL NAAQS NADP NAM NAMMIS NARR NARSTO NAS NASA NASS NATO NBP NCAR NCEA nCe02 NCER NCOM NCSU NEI NERL NEXUS NGA NH, Models 3 Dry Deposition Model Maximum Achievable Control Technology Mean Absolute Error Meteorology-Chemistry Interface Processor Maximum Achievable Control Technology Master Chemical Mechanism Maximum Daily Average Mercury Deposition Network Model Of Emissions Of Gases And Aerosols From Nature Multi-Ethnic Study of Atherosclerosis and Air Pollution Multilayer Biochemical Model Fifth Generation Of The Penn State/Ucar Mesoscale Model Moderate Resolution Imaging Spectroradiometer Model for Simulating Aerosol Interactions and Chemistry Multipollutant message passing interface open source database software National Ambient Air Quality Standard National Acid Deposition Program North American Mesoscale North American Mercury Model Intercomparison Study North American Regional Reanalysis Formerly the North American Research Strategy for Tropospheric Ozone National Academy of Sciences National Aeronautics and Space Administration National Agricultural Statistical Service North Atlantic Treaty Organization NO Budget Trading Program National Center for Atmospheric Research National Center for Environmental Assessment Nanoparticulate Cerium Oxide National Center for Environmental Research Graphically Reduced Carbon Organic Mass North Carolina State University National Emission Inventory National Exposure Research Laboratory Near-road Exposure to Urban air pollutants Study National Geospatial Agency Ammonia 64 ------- NH4 Ammonium OTHER Model Species Representing Pm25 That Is NHEERL National Health & Environmental Effects Not Chemically Speciated Research Laboratory PMML Predictive Model Markup Language NLCD National Land Cover Data PRISM Parameter-Elevation Regressions on NMM Nonhydrostatic Mesoscale Model Independent Slopes Model NO Nitrogen Oxide PX LSM Pleim-Xiu Land Surface Model no2 Nitrogen Dioxide QUIC Quick Urban Industrial Complex N°3 Nitrate Qv Water vapor mixing ratio N 0 Dinitrogen Pentoxide RACM2 Regional Atmospheric Chemistry 2 5 Mechanism, version 2 NOx Oxides Of Nitrogen RARE Regional Applied Research Effort NO Oxidized Nitrogen y RCCM Regional Climate and Chemistry Modeling NOAA National Oceanic And Atmospheric System Administration RCM Regional Climate Model NOAH Noaa's Land Surface Model RCP Representative Concentration Pathways NPS National Park Service RELMAP Regional Lagranian of Air Pollution Nr Reactive Nitrogen REML Durbin-WatsDon test and restricted NRC National Research Council maximum likelihood NRMRL National Risk Management Research Laboratory REMSAD Regional Modeling System for Aerosols and Deposition NSF National Science Foundation RGM reactive gaseous mercury NUDAPT National Urban Database And Access RMSE root mean squared error Portal Tool ROSES Research Opportunities in Space and Earth 03 Ozone Sciences OAP Office Of Air Programs RRTMG Rapid Radiative Transfer Model for GCMs OAQPS Office Of Air Quality Planning And Standards SCIAMACHY Scanning Imaging Absorption Spectrometer for Atmospheric Cartography OAR Office Of Air & Radiation SEARCH South Eastern Aerosol Research and OC Organic Carbon Characterization Study OH Hydroxy Radical SGV subgrid variability OLAM Ocean-Land-Atmosphere Model SHEDS Stochastic Human Exposure and Dose Simulation OM Organic Mass SIP State Implementation Plan OPE ozone production efficiency SLAMS State or Local Air Monitoring Station ORD Office of Research and Development SLCF short-lived climate forcers OTAQ Office of Transportation and Air Quality SMOKE Sparse Matrix Operator Kernel Emissions PAH Polycyclic Aromatic Hydrocarbon SO, sulfur dioxide PAN Peroxyacyl Nitrate S04 sulfate PAVE Package For Analysis And Visualization Of 4 sulfur oxides Environmental Data sox PBL Planetary Boundary Layer SOA secondary organic aerosol PCA Principal Component Analysis SOAcld secondary organic aerosol formed in clouds PEM Pesticides Emissions Model SPARROW Spatially Referenced Regressions on Watershed Attributes PM Particulate Matter SPS Science for Peace and Security PM,, Particulate Matter Smaller Than 2.5 2.5 Microns In Diameter SST Sea Surface Temperature PM2.5FRM PM25 is defined above and FRM is: Federal Reference Method STAR Science To Achieve Results STENEX Stencil Exchange PM10 Particulate Matter Smaller Than 10 STN Speciated Trends Network Microns In Diameter SW Shortwave ^ COARSE Particulate Matter Between 2.5 And 10 Microns In Diameter SWAT TBEP Soil & Water Assessment Tool Tampa Bay Estuary Program ------- TEAM Trace Element Analysis Model TES Tropospheric Emission Spectrometer TexAQS Texas Air Quality Study TM Thematic Mapper TMDL Total Maximum Daily Load UCP Urban Canopy Parameter UNC-CH University of North Carolina at Chapel Hill USDA United States Department of Agriculture USFS United States Forest Service USGS U.S. Geological Survey VBS Volatility Basis Set VERDI Visualization Environment For Rich Data Interpretation VOC Volatile Organic Compound WDT Watershed Deposition Tool WDWE Weekday-To-Weekend WRF Weather Research And Forecasting WSOC Water Soluble Organic Compound YSU Yonsei University 66 ------- SCIENCE SEPA United States Environmental Protection Agency Office of Research and Development (8101R) Washington, DC 20460 Official Business Penalty for Private Use $300 Recycled/Recyclable Printed on paper that contains a minimum of 50% posteonsumer fiber content processed chlorine free PRESORTED STANDARD POSTAGE & FEES PAID EPA PERMIT NO.G-35 ------- |