Implementation of the Particle & Precursor Tagging Methodology (PPTM) for the CMAQ Modeling System: Sulfur & Nitrogen Tagging 5 th Annual CMAS Conference.

Slides:

Advertisements

Similar presentations

Carey Jang, Air Quality Modeling Group USEPA/OAQPS CMAS Workshop, RTP, 10/20/2004 CMAS Workshop, RTP, 10/20/2004 Applications of CMAQ over the Pacific.

Advertisements

Emissions Reductions Beyond the Clean Smokestacks Act (CSA) Emissions Reductions Beyond the Clean Smokestacks Act (CSA) Environmental Management Commission.

Status and Changes to the US National Emissions Inventory (NEI) Thompson G. Pace, PE U.S. EPA Research Triangle Park, NC.

Inventory Issues and Modeling- Some Examples Brian Timin USEPA/OAQPS October 21, 2002.

COMPARATIVE MODEL PERFORMANCE EVALUATION OF CMAQ-VISTAS, CMAQ-MADRID, AND CMAQ-MADRID-APT FOR A NITROGEN DEPOSITION ASSESSMENT OF THE ESCAMBIA BAY, FLORIDA.

Template Development and Testing of PinG and VBS modules in CMAQ 5.01 Prakash Karamchandani, Bonyoung Koo, Greg Yarwood and Jeremiah Johnson ENVIRON International.

Christian Seigneur AER San Ramon, CA

Ozone and Aerosols in US and East Asia between 2001 and 2002 Yun-Fat Lam 1, Joshua S. Fu 1, Zuopan Li 1, Carey Jang 2, Rokjin Park 3 and Daniel J. Jacob.

Center for Environmental Research and Technology/Air Quality Modeling University of California at Riverside Modeling Source Apportionment Gail Tonnesen,

1 icfi.com | 1 HIGH-RESOLUTION AIR QUALITY MODELING OF NEW YORK CITY TO ASSESS THE EFFECTS OF CHANGES IN FUELS FOR BOILERS AND POWER GENERATION 13 th Annual.

Modeling the Co-Benefits of Carbon Standards for Existing Power Plants STI-6102 Stephen Reid, Ken Craig, Garnet Erdakos Sonoma Technology, Inc. Jonathan.

Beta Testing of the SCICHEM-2012 Reactive Plume Model James T. Kelly and Kirk R. Baker Office of Air Quality Planning & Standards US Environmental Protection.

Background Air Quality in the United States Under Current and Future Emissions Scenarios Zachariah Adelman, Meridith Fry, J. Jason West Department of Environmental.

North Carolina Division of Air Quality Report on Control of Mercury Emissions from Coal-Fired Electric Generating Units In response to 15 NCAC 02D.2509(b)

University of California Riverside, ENVIRON Corporation, MCNC WRAP Regional Modeling Center WRAP Regional Haze CMAQ 1996 Model Performance and for Section.

Examination of the impact of recent laboratory evidence of photoexcited NO 2 chemistry on simulated summer-time regional air quality Golam Sarwar, Robert.

Trans-Pacific Chemical Transport of Mercury: Sensitivity Analysis on Asian Emission Contribution to Mercury Deposition in North America Using CMAQ-Hg C.-J.

Sensitivity of top-down correction of 2004 black carbon emissions inventory in the United States to rural-sites versus urban-sites observational networks.

Lessons Learned: One-Atmosphere Photochemical Modeling in Southeastern U.S. Presentation from Southern Appalachian Mountains Initiative to Meeting of Regional.

Ams/awma_ PROCESS-BASED ANALYSIS OF THE ROLE OF THE GULF BREEZE IN SIMULATING OZONE CONCENTRATIONS ALONG THE EASTERN GULF COAST Sharon G. Douglas.

Did the recession impact recent decreases in observed sulfate concentrations? Shao-Hang Chu, US EPA/OAQPS/AQAD October, 2011.

O. Russell Bullock, Jr. National Oceanic and Atmospheric Administration (NOAA) Atmospheric Sciences Modeling Division (in partnership with the U.S. Environmental.

PM2.5 Model Performance Evaluation- Purpose and Goals PM Model Evaluation Workshop February 10, 2004 Chapel Hill, NC Brian Timin EPA/OAQPS.

Implementation of the Particle & Precursor Tagging Methodology (PPTM) for the CMAQ Modeling System: Mercury Tagging 5 th Annual CMAS Conference Research.

Brian Keaveny Climate and Energy Analyst2014 CMAS Conference NESCAUM October 29, 2014.

1 Using Hemispheric-CMAQ to Provide Initial and Boundary Conditions for Regional Modeling Joshua S. Fu 1, Xinyi Dong 1, Kan Huang 1, and Carey Jang 2 1.

Further Development and Application of the CMAQ Ozone and Particle Precursor Tagging Methodologies (OPTM & PPTM) 7 th Annual CMAS Conference Chapel Hill,

Community Multiscale Air Quality Modeling System CMAQ Air Quality Data Summit February 2008.

Predicting Effects of Point Source Controls on Escambia Bay using CMAQ and Watershed Models by Jo Ellen Brandmeyer, Steve Beaulieu, Randy Dodd, and Michele.

1 Comparison of CAMx and CMAQ PM2.5 Source Apportionment Estimates Kirk Baker and Brian Timin U.S. Environmental Protection Agency, Research Triangle Park,

Center for Environmental Research and Technology/Air Quality Modeling University of California at Riverside CMAQ Tagged Species Source Apportionment (TSSA)

Modeling of Ammonia and PM 2.5 Concentrations Associated with Emissions from Agriculture Megan Gore, D.Q. Tong, V.P. Aneja, and M. Houyoux Department of.

PM Model Performance in Southern California Using UAMAERO-LT Joseph Cassmassi Senior Meteorologist SCAQMD February 11, 2004.

Overview and Status of the Emissions Data Analysis and Modeling Portions of the Virginia Mercury Study 1 st Technical Meeting Richmond, VA 31 May 2007.

University of North Carolina at Chapel Hill Carolina Environmental Programs Models-3 Adel Hanna Carolina Environmental Program University of North Carolina.

8th annual CMAS conference, Chapel Hill, October 19-21, 2009 Eurasia Institute of Earth Sciences / ITU IMPACTS OF ISTANBUL EMISSIONS ON REGIONAL AIR QUALITY:

CMAQ APPLICATION TO OZONE POLLUTION IN THE PEARL RIVER DELTA OF CHINA Wei Zhou 1,2,Yuanghang Zhang 1,Xuesong Wang 1,Daniel Cohan 2 1.College of Environmental.

Operational Evaluation and Comparison of CMAQ and REMSAD- An Annual Simulation Brian Timin, Carey Jang, Pat Dolwick, Norm Possiel, Tom Braverman USEPA/OAQPS.

Application of the CMAQ Particle and Precursor Tagging Methodology (PPTM) to Support Water Quality Planning for the Virginia Mercury Study 6 th Annual.

Development and Application of Parallel Plume-in-Grid Models Prakash Karamchandani, Krish Vijayaraghavan, Shu-Yun Chen and Christian Seigneur AER, San.

Source Attribution Modeling to Identify Sources of Regional Haze in Western U.S. Class I Areas Gail Tonnesen, EPA Region 8 Pat Brewer, National Park Service.

Evaluation and Application of AMSTERDAM: Focus on Plume In Grid Prakash Karamchandani, Krish Vijayaraghavan, Shu-Yun Chen and Rochelle Balmori Atmospheric.

GEOS-CHEM Modeling for Boundary Conditions and Natural Background James W. Boylan Georgia Department of Natural Resources - VISTAS National RPO Modeling.

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division 16 October 2012 Integrating source.

Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Mercury, Ozone, and Particulate Matter US EPA / OAQPS / AQAD / AQMG Sharon Phillips, Kai Wang,

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division October 21, 2009 Evaluation of CMAQ.

WRAP Regional Modeling Center, Attribution of Haze Meeting, Denver CO 7/22/04 Introduction to the the RMC Source Apportionment Modeling Effort Gail Tonnesen,

Georgia Institute of Technology SUPPORTING INTEX THROUGH INTEGRATED ANALYSIS OF SATELLITE AND SUB-ORBITAL MEASUREMENTS WITH GLOBAL AND REGIONAL 3-D MODELS:

2018 Emission Reductions from the Base 18b Emission Inventory Lee Gribovicz Fire Emissions Joint Forum Meeting San Diego, California February 22-23, 2007.

Photochemical grid model estimates of lateral boundary contributions to ozone and particulate matter across the continental United States Kirk Baker U.S.

V:\corporate\marketing\overview.ppt CRGAQS: CAMx 2004 PSAT Results Presentation to the Gorge Study Technical Team By ENVIRON International Corporation.

Template Comparison of PM Source Apportionment and Sensitivity Analysis in CAMx Bonyoung Koo, Gary Wilson, Ralph Morris, Greg Yarwood ENVIRON Alan Dunker.

W. T. Hutzell 1, G. Pouliot 2, and D. J. Luecken 1 1 Atmospheric Modeling Division, U. S. Environmental Protection Agency 2 Atmospheric Sciences Modeling.

Response of fine particles to the reduction of precursor emissions in Yangtze River Delta (YRD), China Juan Li 1, Joshua S. Fu 1, Yang Gao 1, Yun-Fat Lam.

Fairbanks PM 2.5 Source Apportionment Using the Chemical Mass Balance (CMB) Model Tony Ward, Ph.D. The University of Montana Center for Environmental Health.

Krish Vijayaraghavan, Rochelle Balmori, Shu-Yun Chen, Prakash Karamchandani and Christian Seigneur AER, San Ramon, CA Justin T. Walters and John J. Jansen.

1 Long Range Transport of Air Pollution Air pollution can travel hundreds of miles and cause multiple health and environmental problems on regional or.

Mobile Source Contributions to Ambient PM2.5 and Ozone in 2025

Development of a Multipollutant Version of the Community Multiscale Air Quality (CMAQ) Modeling System Shawn Roselle, Deborah Luecken, William Hutzell,

Kenneth Craig, Garnet Erdakos, Lynn Baringer, and Stephen Reid

Source Apportionment Modeling to Investigate Background, Regional, and Local Contributions to Ozone Concentrations in Denver, Phoenix, Detroit, and Atlanta.

EASIUR: A Reduced-Complexity Model Derived from CAMx

Using CMAQ to Interpolate Among CASTNET Measurements

Stephen Mueller & Jonathan Mallard Tennessee Valley Authority

SELECTED RESULTS OF MODELING WITH THE CMAQ PLUME-IN-GRID APPROACH

M. Samaali, M. Sassi, V. Bouchet

CAIR Update WESTAR October 2, 2008.

U.S. Perspective on Particulate Matter and Ozone

RMC Activity Update Emissions Forum July 1, 2003.

Results from 2018 Preliminary Reasonable Progress Modeling

Presentation transcript:

Implementation of the Particle & Precursor Tagging Methodology (PPTM) for the CMAQ Modeling System: Sulfur & Nitrogen Tagging 5 th Annual CMAS Conference Friday Center, UNC-Chapel Hill, NC 17 October 2006 Tom Myers, Sharon Douglas & Jay Haney ICF International, San Rafael, CA

Presentation Outline Conceptual overview of PPTM Implementation of PPTM for sulfur and nitrogen in the CMAQ model Testing & example results S tagging application for pulp & paper sector

Overview of PPTM: Concepts Emissions (or initial/boundary condition) species are tagged in the emissions (or IC/BC) files and continuously tracked throughout the simulation Tags can be applied to source regions, source categories, individual sources, initial conditions, and/or boundary conditions PPTM quantifies the contribution of tagged sources to simulated species concentrations & deposition Base simulation results not affected by tagging

Overview of PPTM: Concepts Within the model, tagging is accomplished by the addition of duplicate species (e.g., SO2_t1, SO2_t2) Tagged species have the same properties and are subjected to the same processes (e.g., advection, chemical transformation, deposition) as the actual species Mercury (Hg), sulfur (S), and nitrogen (N) tagging completed; tagging for elemental carbon (EC) and organic carbon (OC) under development/testing

Example Uses of PPTM for Sulfur and Nitrogen Determine the contribution of SO2 emissions from an individual source, group of sources, or source category (e.g., electric generating units (EGUs), pulp and paper facilities) to sulfate and overall PM2.5 concentrations Quantify/compare the contribution of NOx emissions from transportation sources, area sources (e.g., wood burning stoves) & offroad sources (e.g., IC engines, construction equipment)

Implementation of PPTM for CMAQ: Sulfur & Nitrogen CMAQ version 4.5 Tagged S elements include: SO2, ASO4I, ASO4J, SULF (for internal tracking) Tagged N species include: ANH4I, ANH4J, ANO3I, ANO3J, NO, NO2, NO3, N2O5, HNO3, HONO, PNA, PAN, NTR, NH3

Implementation of PPTM for CMAQ: Sulfur & Nitrogen Key considerations/assumptions: Linear processes simulated directly Potentially non-linear processes calculated for total species and apportioned to tags Additional terms from some processes saved to ensure proper allocation Gas-phase chemistry routines have additional production & loss terms added; NO, NO2, NO3, N2O5 changes apportioned based on NOx totals for each tag

Implementation of PPTM for CMAQ: Sulfur & Nitrogen Key considerations/assumptions: Other N species calculated with additional chemistry terms; adjusted so net production/loss for sum of tags matches that for the whole Simulation calculates the overall species value as the sum of all tags; tags must be defined to add up to the whole

Implementation of PPTM for CMAQ: Sulfur & Nitrogen CPU requirements increased by approximately 15% for 3 S tags and 50% for 3 N tags Documentation/user’s guide available from EPA or ICF as follows: Douglas, S., T. Myers and Y. Wei “ Implementation of Sulfur and Nitrogen Tagging in the Community Multiscale Air Quality (CMAQ) Model.” Prepared for EPA, OAQPS, Research Triangle Park, NC. ICF International, San Rafael, California (06-076).

Example Sulfur Tagging Results

CMAQ PPTM: Contribution to Monthly Average Sulfate

CMAQ PPTM: Site-specific SO4 Contributions (IMPROVE Sites) ugm-3 CMAQ simulated monthly average contribution to sulfate from three example EGU facilities.

Sulfur Tagging Application for the Pulp & Paper Sector Based on CAIR 2010 modeling & emission inventory 1 month simulation period (July) (Annual simulation ongoing) Tags applied for 10 pulp & paper sources and IC/BCs

CMAQ PPTM: Monthly Average Sulfate for July 2010

CMAQ PPTM: Contribution to Monthly Average Sulfate

CMAQ PPTM: Site-specific SO4 Contributions (IMPROVE Sites) ugm-3 CMAQ simulated monthly average contribution to sulfate from regional pulp & paper facilities.

CMAQ PPTM: Site-specific SO4 Contributions (Urban Sites) ugm-3 CMAQ simulated monthly average contribution to sulfate from regional pulp & paper facilities.

CMAQ PPTM: Site-specific SO4 Contributions (Urban Sites) ugm-3 CMAQ simulated monthly average contribution to sulfate from regional pulp & paper facilities.

Example Nitrogen Tagging Results

Example CMAQ PPTM Nitrogen Tagging Results: Great Smoky Mtns NH4 NO3 NOx

Example CMAQ PPTM Nitrogen Tagging Results: Great Smoky Mtns NH4 NO3 NOx

Summary of CMAQ PPTM Status and Next Steps PPTM has been successfully implemented in the CMAQ model for S, N (and Hg) and quantifies the contribution of tagged sources to simulated PM Application & interpretation of results is straightforward (no hidden assumptions) Initial test results indicate that numerical effects (uncertainties) are small, compared to contribution estimates Next steps include Complete implementation for OC & EC Add OPTM (for ozone)