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

Slides:



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

COMPARATIVE MODEL PERFORMANCE EVALUATION OF CMAQ-VISTAS, CMAQ-MADRID, AND CMAQ-MADRID-APT FOR A NITROGEN DEPOSITION ASSESSMENT OF THE ESCAMBIA BAY, FLORIDA.
Photochemical Model Performance for PM2.5 Sulfate, Nitrate, Ammonium, and pre-cursor species SO2, HNO3, and NH3 at Background Monitor Locations in the.
Title EMEP Unified model Importance of observations for model evaluation Svetlana Tsyro MSC-W / EMEP TFMM workshop, Lillestrøm, 19 October 2010.
Christian Seigneur AER San Ramon, CA
Atmospheric modelling activities inside the Danish AMAP program Jesper H. Christensen NERI-ATMI, Frederiksborgvej Roskilde.
Air Quality-Climate Interactions Aijun Xiu Carolina Environmental Program.
Atmospheric Mercury Simulation with CMAQ Version Russ Bullock - NOAA Air Resources Laboratory* Kathy Brehme - Computer Sciences Corp. 5 th Annual.
Jenny Stocker, Christina Hood, David Carruthers, Martin Seaton, Kate Johnson, Jimmy Fung The Development and Evaluation of an Automated System for Nesting.
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.
(work funded through the Great Lakes Restoration Initiative)
CMAQ (Community Multiscale Air Quality) pollutant Concentration change horizontal advection vertical advection horizontal dispersion vertical diffusion.
Modeling the Co-Benefits of Carbon Standards for Existing Power Plants STI-6102 Stephen Reid, Ken Craig, Garnet Erdakos Sonoma Technology, Inc. Jonathan.
The Sensitivity of Aerosol Sulfate to Changes in Nitrogen Oxides and Volatile Organic Compounds Ariel F. Stein Department of Meteorology The Pennsylvania.
1 CCOS Seasonal Modeling: The Computing Environment S.Tonse, N.J.Brown & R. Harley Lawrence Berkeley National Laboratory University Of California at Berkeley.
A Modeling Investigation of the Climate Effects of Air Pollutants Aijun Xiu 1, Rohit Mathur 2, Adel Hanna 1, Uma Shankar 1, Frank Binkowski 1, Carlie Coats.
Trans-Pacific Chemical Transport of Mercury: Sensitivity Analysis on Asian Emission Contribution to Mercury Deposition in North America Using CMAQ-Hg C.-J.
Ams/awma_ PROCESS-BASED ANALYSIS OF THE ROLE OF THE GULF BREEZE IN SIMULATING OZONE CONCENTRATIONS ALONG THE EASTERN GULF COAST Sharon G. Douglas.
Clinton MacDonald 1, Kenneth Craig 1, Jennifer DeWinter 1, Adam Pasch 1, Brigette Tollstrup 2, and Aleta Kennard 2 1 Sonoma Technology, Inc., Petaluma,
O. Russell Bullock, Jr. National Oceanic and Atmospheric Administration (NOAA) Atmospheric Sciences Modeling Division (in partnership with the U.S. Environmental.
2004 Workplan WRAP Regional Modeling Center Prepared by: Gail Tonnesen, University of California Riverside Ralph Morris, ENVIRON Corporation Zac Adelman,
Importance of Lightning NO for Regional Air Quality Modeling Thomas E. Pierce/NOAA Atmospheric Modeling Division National Exposure Research Laboratory.
The Impact of Biogenic VOC Emissions on Tropospheric Ozone Formation in the Mid-Atlantic Region Michelle L. Bell Yale University Hugh Ellis Johns Hopkins.
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.
Partnership for AiR Transportation Noise and Emission Reduction An FAA/NASA/TC-sponsored Center of Excellence A Comparison of CMAQ Predicted Contributions.
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.
1 Comparison of CAMx and CMAQ PM2.5 Source Apportionment Estimates Kirk Baker and Brian Timin U.S. Environmental Protection Agency, Research Triangle Park,
On the Model’s Ability to Capture Key Measures Relevant to Air Quality Policies through Analysis of Multi-Year O 3 Observations and CMAQ Simulations Daiwen.
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.
Preliminary Study: Direct and Emission-Induced Effects of Global Climate Change on Regional Ozone and Fine Particulate Matter K. Manomaiphiboon 1 *, A.
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.
Modeling the Atmospheric Deposition of Mercury to Lake Champlain (from Anthropogenic Sources in the U.S. and Canada) Dr. Mark Cohen NOAA Air Resources.
4. Atmospheric chemical transport models 4.1 Introduction 4.2 Box model 4.3 Three dimensional atmospheric chemical transport model.
Rick Saylor 1, Barry Baker 1, Pius Lee 2, Daniel Tong 2,3, Li Pan 2 and Youhua Tang 2 1 National Oceanic and Atmospheric Administration Air Resources Laboratory.
Georgia Institute of Technology Initial Application of the Adaptive Grid Air Quality Model Dr. M. Talat Odman, Maudood N. Khan Georgia Institute of Technology.
Overview of the tracer code in RegCM Tracers Aerosols ( droplets, smoke particles, dust, pollens, flying cats …) Gazeous phase, chemical species Evolution.
Application of the CMAQ Particle and Precursor Tagging Methodology (PPTM) to Support Water Quality Planning for the Virginia Mercury Study 6 th Annual.
Role of Air Quality Modeling in the RIA Norm Possiel & Pat Dolwick Air Quality Modeling Group EPA/OAQPS.
TEMIS user workshop, Frascati, 8-9 October 2007 TEMIS – VITO activities Felix Deutsch Koen De Ridder Jean Vankerkom VITO – Flemish Institute for Technological.
TEMPLATE DESIGN © A high-order accurate and monotonic advection scheme is used as a local interpolator to redistribute.
1 Modeling the Atmospheric Transport and Deposition of Mercury Dr. Mark Cohen NOAA Air Resources Laboratory Silver Spring, Maryland Mercury Workshop, Great.
OThree Chemistry Modeling of the Sept ’00 CCOS Ozone Episode: Diagnostic Experiments--Round 3 Central California Ozone Study: Bi-Weekly Presentation.
Session 5, CMAS 2004 INTRODUCTION: Fine scale modeling for Exposure and risk assessments.
CMAS Conference 2011 Comparative analysis of CMAQ simulations of a particulate matter episode over Germany Chapel Hill, October 26, 2011 V. Matthias, A.
U.S. EPA and WIST Rob Gilliam *NOAA/**U.S. EPA
William G. Benjey* Physical Scientist NOAA Air Resources Laboratory Atmospheric Sciences Modeling Division Research Triangle Park, NC Fifth Annual CMAS.
GEOS-CHEM Modeling for Boundary Conditions and Natural Background James W. Boylan Georgia Department of Natural Resources - VISTAS National RPO Modeling.
Evaluation of Models-3 CMAQ I. Results from the 2003 Release II. Plans for the 2004 Release Model Evaluation Team Members Prakash Bhave, Robin Dennis,
Seasonal Modeling of the Export of Pollutants from North America using the Multiscale Air Quality Simulation Platform (MAQSIP) Adel Hanna, 1 Rohit Mathur,
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division 16 October 2012 Integrating source.
Dr. Mark Cohen NOAA Air Resources Laboratory Silver Spring, Maryland
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,
Organization of Course INTRODUCTION 1.Course overview 2.Air Toxics overview 3.HYSPLIT overview HYSPLIT Theory and Practice 4.Meteorology 5.Back Trajectories.
Photochemical grid model estimates of lateral boundary contributions to ozone and particulate matter across the continental United States Kirk Baker U.S.
Operational Evaluation and Model Response Comparison of CAMx and CMAQ for Ozone & PM2.5 Kirk Baker, Brian Timin, Sharon Phillips U.S. Environmental Protection.
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.
MRPO Technical Approach “Nearer” Term Overview For: Emissions Modeling Meteorological Modeling Photochemical Modeling & Domain Model Performance Evaluation.
Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy Jonathan Pleim, Shawn Roselle,
ORIGIN OF BACKGROUND OZONE IN SURFACE AIR OVER THE UNITED STATES: CONTRIBUTION TO POLLUTION EPISODES Daniel J. Jacob and Arlene M. Fiore Atmospheric Chemistry.
1 REMSAD VERSION 7.10 WITH SOURCE TAGGING Inter-RPO Modeling Meeting May 25, 2004 Shan He, Emily Savelli, Jung-Hun Woo, John Graham and Gary Kleiman, NESCAUM.
7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often.
PREMAQ: A New Pre-Processor to CMAQ for Air Quality Forecasting Tanya L. Otte*, George Pouliot*, and Jonathan E. Pleim* Atmospheric Modeling Division U.S.
Development of a Multipollutant Version of the Community Multiscale Air Quality (CMAQ) Modeling System Shawn Roselle, Deborah Luecken, William Hutzell,
SELECTED RESULTS OF MODELING WITH THE CMAQ PLUME-IN-GRID APPROACH
Atmospheric modelling of HMs Sensitivity study
Presentation transcript:

Implementation of the Particle & Precursor Tagging Methodology (PPTM) for the CMAQ Modeling System: Mercury Tagging 5 th Annual CMAS Conference Research Triangle Park, NC 17 October 2006 Tom Braverman, EPA OAQPS Tom Myers, ICF International Dwight Atkinson, EPA OW EPA

Presentation Outline Background & objectives Overview of PPTM Implementation of PPTM for mercury in the CMAQ model Testing & example results Summary

Background & Objectives for Mercury Tagging Atmospheric deposition of mercury is a source of mercury contamination in surface waters As of 2005, more than 6,000 bodies of water were identified as mercury impaired and more than 2,000 were issued mercury fish advisories Key objective of mercury tagging is to quantify the contribution from selected sources/source categories to mercury deposition for bodies of water, hydrologic zones, and watershed regions

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

Overview of PPTM: Concepts Within the model, tagging is accomplished by the addition of duplicate species (e.g., HG_t1, HG_t2) Tagged species have the same properties and are subjected to the same processes (e.g., advection, chemical transformation, deposition) as the actual species Base simulation results not affected by tagging

Overview of PPTM: Attributes and Limitations Attributes: Straightforward and true to modeled results (limited normalization or partitioning assumptions) Technique has been extensively tested and refined in REMSAD (over a period of 7 years) before being incorporated into CMAQ Limitations: Currently number of tags is limited by # of output species allowed by CMAQ (hard-coded in libraries) Mercury tagging applied separately (currently) CMAQ run times and file sizes are increased

Overview of PPTM: Attributes and Limitations Other Notes: Provides information about contribution, and not response to changes in emissions Difference between sum of all tags and overall concentration gives an estimate of the uncertainty effects in the contribution estimates Tags are additional species in the model, which allows postprocessing of the outputs using standard methods (applicable for any species)

Implementation of PPTM for CMAQ: Mercury CMAQ version Tagged elements include: HG, HGIIGAS, HGIIAER, APHGI, APHGJ Key considerations/assumptions: Linear processes simulated directly (e.g., advection, dry deposition) Potentially non-linear processes (e.g., gas-phase chemistry, aqueous chemistry, particle dynamics) calculated for total species and apportioned to tags Simulation always includes the overall species tag and may include up to 7 additional tags; individual tags do not have to add up to the overall tag

Implementation of PPTM for CMAQ: Mercury CPU requirements increased by approximately 30 percent for 3 tags Documentation/user’s guide available from EPA or ICF as follows: Douglas, S., T. Myers and Y. Wei “ Implementation of Mercury Tagging in the Community Multiscale Air Quality (CMAQ) Model.” Prepared for EPA, OAQPS, Research Triangle Park, NC. ICF International, San Rafael, California (06-051).

Testing of PPTM for Mercury: Model Inputs 2001 Penn State mesoscale meteorological model version 5 (MM5) 1999 NEI mercury emissions inventory, except 2002 NEI for MWI 2001 criteria pollutant emissions 36 km horizontal grid square resolution 14 vertical layers (surface layer – 38 meters) Harvard’s GEOS-CHEM global model used for inflow of pollutants to the modeling domain (varied horizontally and vertically every three hour period)

Testing of PPTM for Mercury Limited period test runs used to confirm Base simulation results are the same w/ & w/o tagging Location and footprint of tags is reasonable (consistent with tag specifications & met conditions) Various types of tags (geographic, source category & combinations, per requested examples) work correctly One-month test runs (July 2001) w/o tagging source-category tags (T1=EGU, T2=other, T3=IC/BC)

Example CMAQ PPTM Mercury Tagging Results: Elemental Hg Tag 1: EGU Tag 2: Other CMAQ Base Tag 3: IC/BC

Example CMAQ PPTM Mercury Tagging Results: Divalent Hg Tag 1: EGU Tag 2: Other CMAQ Base Tag 3: IC/BC

Example CMAQ PPTM Mercury Tagging Results: Particulate Hg Tag 1: EGU Tag 2: Other CMAQ Base Tag 3: IC/BC

Example CMAQ PPTM Mercury Tagging Results: Dry Deposition Tag 1: EGU Tag 2: Other CMAQ Base Tag 3: IC/BC

Example CMAQ PPTM Mercury Tagging Results: Dry Deposition IN26 MD13 PA13 FL34 Difference/ uncertainty

Example CMAQ PPTM Mercury Tagging Results: Dry Deposition IN26 FL34 PA13 MD13 Results vary considerably by site

Summary Mercury tagging has been implemented in version of CMAQ Mercury tagging can be used to track the fate of mercury emissions from selected sources and to quantify the contribution of the emissions to CMAQ- derived concentration and deposition estimates Initial test results indicate that numerical effects (uncertainties) are small, compared to contribution estimates Plan to perform further mercury tagging work for other months and at 12 km grid square resolution