Development & Application ofAdvanced Plume-in-Grid (PiG)Multi-Pollutant ModelsPrakash Karamchandani, Krish Vijayaraghavan,Shu-Yun Chen & Christian SeigneurAER, Inc., San Ramon, CA9th Conference on Air Quality ModelingOctober 9 & 10, 2008EPA, RTP, NCWhy Use Plume-in-Grid Approach?Plume Size vs Grid Size (from Godowitch, 2004)**• Artificial dilution of stack** **emissions •** **Unrealistic near-stack plume concentrations** **• Incorrect representation of** **plume chemistry •** **Incorrect representation of plume transport** Limitations of PurelyGrid-Based ApproachSubgrid-scale representationof plumes addresses theselimitationsPlume Chemistry & Relevance toOzone & PM Modeling**Early Plume Dispersion** **NO/NO 2** **/O 3** **chemistry**12**Mid-range Plume Dispersion Reduced VOC/NO** **x /O** **3 chemistry —** **acid formation from OH and NO** **3 /N** **2 O** **5 chemistry** **Long-range Plume Dispersion** 3**Full VOC/NO x** **/O 3** **chemistry — acid and O** **3 formation** PiG Modeling• PiG model consists of a reactive plume modelembedded within a 3-D grid model– Plume model captures local variability inconcentrations near sources with fulltreatment of chemistry– Grid model provides continuously evolvingbackground concentrations– Grid model concentrations are adjusted atlarge downwind distances when the plumesize is commensurate with the grid size: plumematerial is “handed over” to grid modelHistory of PiG Modeling• Began in the 1980s, focusing on ozone (PiG versionof UAM was called PARIS - Plume-Airshed ReactiveInteracting System)-Seigneur et al., 1983, Atmos.Environ.• Early models were overly simplified– No treatment of wind shear or plume overlaps– No treatment of effect of atmospheric turbulenceon chemical kinetics– Simplified treatment of chemistry in some models• The development of a state-of-the-science PiGmodel for ozone was initiated in 1997 under EPRIsponsorshipAdvanced PiG Model• Embedded Plume Model: SCICHEM (state-of-thescience treatment of stack plumes at the sub-gridscale)-developed by L-3 Communications/Titan andAER (Karamchandani et al., 2000, ES&T).– SCICHEM is based on SCIPUFF, an alternativemodel recommended by EPA on a case-by-casebasis for regulatory applications (also used byDTRA and referred to as HPAC)– Three-dimensional puff-based model, with secondorder closure approach for plume dispersion andtreatment of puff splitting and merging– SCICHEM adds full chemistry mechanism (e.g.,CBM-IV) to SCIPUFFAdvanced PiG Model• SCICHEM was first embedded in MAQSIP, theprecursor to the U.S. EPA Model, CMAQ• In 2000, AER incorporated SCICHEM into CMAQ(Karamchandani et al., 2002, JGR)• The model is called CMAQ-APT (Advanced PlumeTreatment)CMAQ-APT Applications for Ozone• Eastern United States with two nested griddomains (12 and 4 km resolution), July 1995(Karamchandani et al., 2002, JGR)• Central California (4 km resolution), JulyAugust 2000 (Vijayaraghavan et al., 2006,Atmos. Environ.)• Key conclusion from Eastern U.S.application: for isolated point sources,CMAQ-APT predicts lower O3and HNO3formation compared to the base modelAddition of PM Treatment in thePiG Model• PM and aqueous-phase chemistry treatmentswere added in 2004-2005 (Karamchandani et al.,2006, Atmos. Environ.)• Two versions:– EPA treatment of PM (CMAQ-AERO3-APT)– MADRID treatment of PM (CMAQ-MADRID-APT),developed by AERMADRID: Model of Aerosol Dynamics, Reaction,Ionization and Dissolution (Zhang et al., 2004,JGR)Model ComponentsCMAQ v. 4.6MADRID PM TreatmentCMAQ-MADRIDSCICHEM-AERO3PM Treatment based on EPA CMAQSCICHEM-MADRIDPM Treatment based on CMAQ-MADRIDCMAQ-MADRID-APTCMAQ-AERO3-APTApplication to Southeastern U.S.• Study designed to supplement RPO modeling beingconducted by the Visibility Improvement State andTribal Association of the Southeast (VISTAS)• 2 months simulated (January and July 2002) withBase CMAQ v 4.4 and CMAQ-APT-PM• 14 power plant plumes explicitly simulated withplume-in-grid approach• Model performance: Base CMAQ vs. CMAQ-APT-PM• Power plant contributions to PM2.5componentscalculated and compared for Base CMAQ andCMAQ-APT-PMModeling Domain and Locationsof PiG sourcesPower-Plant Contributions to Average JulyPM2.5Sulfate ConcentrationsBase CMAQCMAQ-AERO3-APTChange in Power-Plant Contributions to PM2.5Sulfate Concentrations When a Plume-in-GridApproach is Used%Predicted powerplant contributionsto sulfate are lowerwhen a PiGtreatment is usedConclusions from CMAQAERO3-APT Application• Using a purely gridded approach will typicallyoverestimate power plant contributions to PMbecause SO2to sulfate and NOxto nitrateconversion rates are overestimated• Plume-in-grid PM modeling provides a betterrepresentation of the near-source transport andchemistry of point source emissions and theircontributions to PM2.5concentrations• CMAQ-AERO3-APT predicts lower power plantcontributions than base CMAQ to local andregional sulfate and total nitrate, particularly insummerAddition of MercuryTreatment in the PiG Model• Implementation of mercury modules in CMAQMADRID-APT was completed in 2006(Karamchandani et al., 2006, 5thAnnual CMASConference)• Application of CMAQ-MADRID-APT (with Hg) tothe southeastern U.S. (12 km grid resolution) for2002• Application of CMAQ-MADRID-APT (with Hg) tocontinental U.S. (36 km grid resolution) for 2001(Vijayaraghavan et al., 2008, JGR)Continental U.S. Application for2001**• 30 power plants** **with highest Hg** **II emissions** **• 36 km grid**Mercury Wet Deposition Fluxin Aug-Sep. 2001Grid Model % Change due to APT**The model over-predicts wet deposition in** **Pennsylvania The advanced plume treatment** **corrects some of the overprediction**Sub-Grid Scale Modeling of Air ToxicsConcentrations Near Roadways• Population exposure to hazardous air pollutants(HAPs) is an important health concern• Exposure levels near roadways are factors of 10larger than in the background–models need tocapture spatial variability in exposure levels• Many of the species of interest are chemicallyreactive–e.g., formaldehyde, 1,3-butadiene,acetaldehyde–models need to treat the chemistry ofthese species• Traditional modeling approaches are inadequate toprovide both chemistry treatment and fine spatialresolutionPiG Modeling for RoadwayEmissions• Based on CMAQ-APT• Prototype version developed in 2007 (Karamchandaniet al., 2008, Env. Fluid Mech.):– simulates near-source CO and benzeneconcentrations from roadway emissions– chemistry is switched off– roadway emissions treated as series of areasources along the roadway with initial size equal tothe roadway width• Concentrations calculated at discrete receptorlocations by combining incremental puffconcentrations with the grid-cell average backgroundconcentrationModel Application**• Busy interstate highway in New York City (I278)** **• July 11-15, 1999 period of NARSTO/Northeast** **Program • Grid model domain** Qualitative Evaluation of COConcentrations**• Results compared with CO** **concentration profiles measured in Los** **Angeles by Zhu et al. (2002), Atmos.** **Environ.**PiG Modeling Constraints• Can be computationally expensive if a large numberof point sources are treated with the puff model –computational requirements increase by a factor oftwo to three for 50 to 100 sources• Point sources have to be selected carefully to limitthe number of sources treated• To obtain results in a reasonable amount of time,annual simulations are usually conducted bydividing the calendar year into quarters andsimulating each quarter on different processors ormachines• Parallel version of code can address theseconstraintsParallelization of PiG Model• Development of parallel version of CMAQ-MADRIDAPT completed in late 2007• On a 4-processor machine, the parallel version isabout 2.5 times faster than the single-processorversion• On-going project to apply the model to the centraland eastern United States at 12 km resolution and toevaluate it with available data– Over 150 point sources explicitly treated with APT– Annual actual and typical simulations for 2002– Future year emission scenarios– Other emission sensitivity scenariosOngoing Application of ParallelPiG Model• 12 km grid resolution• 243 x 246 x 19 grid cells• Over 150 PiG sourcesAcknowledgments• Funding:– Electric Power Research Institute (EPRI)– Southern Company– California Energy Commission (CEC)– Atmospheric & Environmental Research, Inc.• Collaboration in Model Development: L-3 COM• Parallelization Insights: David Wong, EPA• Data Sources:– VISTAS– Atmospheric Research & Analysis, Inc. (ARA)– Georgia Environmental Protection Division (GEPD)