**1**Single Source Modeling withPhotochemical Models**Kirk Baker U. S. Environmental Protection Agency,** **Research Triangle Park, NC Presented at the 2008 9** **th Modeling Conference** **2**Basic Components of Air Quality Modeling System**3 •** 1st-generation AQM(1970s - 1980s)**–**Dispersion Models (e.g., Gaussian Plume Models)**–**Photochemical Box Models (e.g. OZIP/EKMA)**•**2nd-generation AQM(1980s - 1990s)**–**Photochemical grid models (e.g., UAM, REMSAD)**•**3rd-generation AQM(1990s - 2000s)**–**Community-Based “One-Atmosphere” ModelingSystem (e.g., U.S. EPA’s Models-3/CMAQ)Evolution of Air Quality Models**4**Air ToxicsPMAcid RainVisibilityOzoneOne-Atmosphere ApproachMobileSourcesIndustrialSourcesAreaSources(Cars, trucks, planes,boats, etc.)(Power plants, refineries/chemical plants, etc.)(Residential, farmingcommercial, biogenic, etc.)NOx, VOC,PM, ToxicsNOx, VOC,SOx, PM,ToxicsNOx, VOC,PM, ToxicsChemistryMeteorologyAtmosphericDepositionClimateChange**5**



# ∂Ci 

# ∂t= −∇ •(VCi)+ ∇ •(K∇Ci)+Pi−LiCi+Si−RiAdvection ChemistryRemovalDiffusion Emissions**•**Chemical Transformations (Gas- & Aqueousphase and Heterogeneous Chemistry)**•**Advection (Horizontal & Vertical)**•**Diffusion (Horizontal & Vertical)**•**Removal Processes (Dry & Wet Deposition)Species Mass Continuity Equation:Major Atmospheric Processes Simulatedin Air Quality Models**6**Gaussian Dispersion ModelISC3, CALPUFF, AERMOD CMAQ, CAMx(for primary pollutant)(multi-pollutant one-atmosphere)First-Generation Air Quality ModelsPhotochemical Grid Model**7 PCM/Source Apportionment Advantages** **• Full state of the science gas-phase chemistry – Ability to estimate realistic ozone concentrations** **– No need for a constant ozone background value for PM** **• Advanced aqueous phase chemistry provides realisti c sulfate** **estimates; wet and dry deposition processes include d** **• Photochemical models generally have good temporal and spatial** **estimates of ammonia concentrations • Spatial/temporal representation of ammonia and nit** **ric acid concentrations and state of the science inorganic c** **hemistry (ISORROPIA) allow for realistic nitrate partitionin** **g between gas and particle phase** **• Source apportionment tools allow for tracking of s ingle emissions** **sources or groups of emissions sources 8** **PCM Source Apportionment Background • Photochemical models** **estimate [single] source contribution with** sourceapportionment**routines • Source apportionment tracks** **the formation and transport of PM2.5/ozone from emissions** **sources and allows the calculation of contributions at** **receptors • Chemically speciated PM2.5** **contribution can be converted to light extinction for visibility** **applications • Precursor emissions tracked to** **chemically speciated PM2.5, ozone, toxics** **NOX****NO3 -** **SOX****SO4 =** **NH3****NH4 +** **Primary OC****POC Primary EC** **PEC Primary Other** **POTH VOC** **SOA NOX** **O3 VOC** **O3 9** **Particulate Source Apportionment • Particle and Precursor Tagging Methodology (PPTM)** **has been implemented in CMAQ v4.6** **• Particulate Source Apportionment Technology (PSAT) has been** **implemented in CAMx v4.5 • Tracks contribution to mercury and PM sulfate, nit** **rate, ammonium, secondary organic aerosol, and inert species** **• Estimates contributions from emissions source grou ps, emissions** **source regions, and initial and boundary conditions to PM2.5 by** **adding duplicate model species for each contributin g source** **• These duplicate model species (tags) have the same properties and** **experience the same atmospheric processes as the bu lk chemical** **species • The tagged species are calculated using the regula** **r model solver for processes like dry deposition and advection as bulk** **species • Non-linear processes like gas and aqueous phase ch** **emistry are solved for bulk species and then apportioned to the** **tagged species 10** **Ozone Source Apportionment • Ozone source apportionment** **has been implemented in CAMx v4.5 (OSAT & APCA)** **and CMAQ v4.6 (OPTM) • Tracks ozone contribution from** **sources similarly to PM with reactive tracers** **• July maximum ozone contribution from a source** **shown at right • OSAT is simulated separately** **from particulate source apportionment** **11 Total Light Extinction** **Annual Maximum Light Extinction (1/Mm) Ammonium Nitrate Ammonium Sulfate** **Elemental Carbon Primary Organic Carbon** **Other: Metals/Soil/Etc 12** **Qualitative comparison to screening metric Emissions/Distance** **Total Light Extinction Emissions/Distance** **13 Issues using PCM for Single Source Apps.** **• Photochemical models resource intensive (computational, disk space, staff) for multi-year** **applications, especially at grid resolutions <= 12k m** **• Additional level of staff expertise • Existing community emissions inputs (from States,** **RPOs, etc) for photochemical models are actual emissions and may need to be modified if more** **conservative emissions estimates are necessary • Useful for near-field applications?** **14 Near Field & Long Range Transport** **• Further research is necessary to determine how useful PCMs are** **for near-field modeling (<50 km) • Currently, some photochemical** **models support full science sub grid plume treatment, sub-cell** **receptor locations, and 2-way nesting capability** **• Review existing near-field applications using PCMs, evaluate** **tracer studies 15** **Other Work • Midwest RPO did some preliminary testing (not** **an evaluation of CAMx PSAT or CALPUFF) of single source modeling with CAMx PSAT to** **compare with CALPUFF visibility estimates • Several States did single source visibility** **modeling for sources less than 50 km from Class I areas; used sub-grid plume treatment** **16 MRPO: CALPUFF (left) and CAMx PSAT (right)** **• Annual 2002 Simulations using latest version of CALPUFF** **(incl. POSTUTIL and CALPOST)** **• Meteorological input data is hourly and from an annual 2002 MM5 simulation** **• Grid is consistent with photochemical model grid: 97 X 90 x 16 (36km grid** **cells) over Eastern U.S. • Results show the** **number of times each grid cell exceeds the 24hr** **average .5 DV degradation over “background” visibility** **• Results are the combined visibility degradation from sulfate and nitrate** **• These runs were facility total actual emissions** **• Applied CAMx4 PSAT sulfate for April Sept 2002** **• These results do not show the impact from nitrate; Assume that visibility** **degradation is dominated by summer sulfate** **• Results for each facility are shown similar to CALPUFF results: counts of** **> .5 DV change over natural background conditions** **• fRH values derived using daily average relative humidity in the grid** **cell as predicted by MM5 and calculated using the exact same look** **up table that is used in CALPOST; the maximum daily average RH is 90** **17**Total 24hr avg .5 DV ExceedancesCALPUFF (top) CAMx4 PSAT (bottom)*CALPUFF includes sulfate+nitrate and CAMx4 includessulfate**18***CALPUFF includes sulfate+nitrate and CAMx4 includessulfateTotal 24hr avg .5 DV ExceedancesCALPUFF (top) CAMx4 PSAT (bottom)**19 MRPO Conclusions** **• CAMx4 PSAT results do not show visibility degradation as far downwind as CALPUFF** **• Need to consider nitrate PSAT runs for better comparison although visibility degradation is** **expected to be dominated by summer sulfate • The tools agree qualitatively for certain faciliti** **es but not all of them** **20 Final Remarks** **• Photochemical grid models provide an opportunity for credible single source modeling** **with source apportionment methodology • These models have the advantage of state of** **the science chemistry, but that comes with increased resource burden** **• These models are routinely used for other regulatory purposes like O3/PM2.5/Regional** **Haze State Implementation Plans 21** **END OF PRESENTATION 22** **Post-Processing • Estimate .5 DV changes over natural background** **conditions • Methodology equivalent to CALPOST visibility post** **processing • A natural background value of 18 1/Mm is used for** **the Eastern U.S.** (see CALPUFF manual)**• A count of > .5 DV change over natural background is** **kept for each grid cell and compared to Class I are as** **• Bext Total = Bext Modeled + Bext Background****• Delta DV = 10*ln(Bext Total/10) – 10*ln(Bext****Background/10) 23** Dailyavg RHPSAT Results:fRH calc from 24-hr RH v monthly gridded fRH valuesMonthlyavg fRHMonthlyavg fRHDailyavg RH**24 Soil** **EC Primary OC** **Anthropogenic Biogenic** **VOC VOC, NO** **x O** **3 , OH, NO** **3 Secondary OC** **OC PM** **2.5 Chemical Composition** **Sulfate SO** **2 OH, H** **2 O** **2 ,** **O 3** **VOC, NO x** **O 2** **, Fe, Mn Oxidizing Agent** **H 2** **SO 4** **NO x** **O 3** **and OH HNO** **3 VOC, NO** **x NH** **3 Nitrate** **Others •** PM2.5 Nitrate formation depends on temperature, humidity, and theconcentration of other nearby species**•**PM2.5 Nitrate formation is favored by lower temperatures and higherhumidity (winter and night-time)**•**PM2.5 Sulfate and Organic Carbon is higher during the summer whenthere is higher availability of by-products of photochemical activity**25**SURFACETOPRegional Photochemical Models:Tool for O3, PM2.5 (and Haze)~ 9 miles**26 CALPUFF Sensitivity** **• Sensitivity simulation for visibility calculation parameter BCKNH3** **• Background ammonia concentrations (same value for entire domain for entire** **year) • Important parameter in CALPUFF** **estimation of PM2.5 nitrate 27** CALPUFF Sensitivity:BCKNH3 = 1.0 (basecase/default)BCKNH3 = 0.5 and 1.5Difference Plot SENS-BASE Difference Plot SENS-BASE