WRAP Regional Modeling Center - Simulation Specifications

Scenario Name: 2018 Preliminary Reasonable Progress Simulation version A

RMC Code: “PRP18a”

Date Specifications Prepared: 4/2/2007; Last revised 11/3/2008

Time Window for Modeling/Analysis:

• Emissions modeling April 2 to May 18, 2007

• Air quality modeling May 18 to June 1, 2007

• Analysis results June7, 2007

• All PRP18a files to TSS June 7, 2007 – critical date

Description: 2018 Annual 36-km CMAQ air quality model simulation using PRP18a future year emissions.

Purpose/Objective: Evaluate the regional haze air quality for future year 2018 conditions with all known and expected controls as of March 2007. Provide predicted 2018 future year air quality and visibility conditions in the Western Class I areas for December 2007 regional haze plans.

Expected Analysis Methods: Specific analysis methods will generally be determined based on the nature of the simulation and the stated purpose and objectives. These typically involve a number of standard postprocessing products used to elucidate relevant modeli ng results and analyses a nd aid in summarizing and interpreting the simulation results. For the present simulation these would include:

• Standard displays of spatial distribution of concentr ations of relevant polluta nts (Sulfate, Nitrate, PM, OC, EC, etc..) for various temporal periods (daily, monthly, seas onal and annual totals)

• Spatial plots of differences between 2002 Plan Version D and model predictions

• Stacked-bar charts of contributions to light extinc tion for the 20% best and 20% worst days at each IMPROVE site

• Output files of SMOKE emissions and CMAQ air quality results to TSS

• SMOKE reports processed and uploaded to TSS

Input Data:

Emissions – emission data and QA found at: http://pah.cert.ucr.e du/aqm/308/qa_prp18a36.shtml

The following emissions data did not change from previous simulations:

• From Base02a

o BEIS3 biogenic emissions (includes soil NO)

o Windblown fugitive PM dust emissions – all components from WRAP WB Dust model

o WRAP NH 3 from WRAP NH 3 model

o Ontario, Canada point fires

o Mexico road dust

• From Plan02a2 o Eastern Pacific comme rcial shipping lanes

o WRAP baseline wildfires

o WRAP baseline wildland fire use

• From Base18a:

o WRAP non-road mobile (annual and seasonal)

• From Base18b:

o CENRAP on-road mobile

o WRAP baseline prescribed fire s (natural and anthropogenic)

o WRAP baseline agricultural fires

o WRAP baseline non-feder al rangeland fires

The following emission updates we re included in simulation PRP18a:

• Stationary Area

o WRAP: PRP2018 inventory

o CENRAP: 2018 inventory version October 16, 2006; included the area fire emissions in the stationary area sector (eliminated the area fire sector), using fire i nventory data from the stationary area inventory, not the area fire inventory; updates to temporal and gridding data for fire sources

o VISTAS: included the area fire emissions in the st ationary area sector (eliminated the area fire sector); 2018 vers ion G inventory

o MWRPO: 2018 versi on K inventory

o MANE-VU: 2018 version 3 inventory

o Mexico: Phase III inventory a nd updates to gridding data

o Canada: 2020 inventory

• Non-road Mobile

o WRAP: no change (see Base18a)

o CENRAP: new inventorie s (annual and monthly)

o VISTAS: 2018 version G inventory (annual)

o MWRPO: 2018 version K inventory (monthly)

o MANE-VU: 2018 version 3 inventory (annual)

o Mexico: Phase III inventory and upd ates to gridding data (annual)

o Canada: 2020 inventory (annual)

• On-road Mobile

o WRAP: corrected double counti ng of primary elemental a nd organic carbon by removing process-specific emissions from inventory; otherwise inventory same as in Base18a

o CENRAP: no change (see Base18b)

o VISTAS: new 2018 activities and M6 scenario inputs

o MWRPO: new 2018 activities and M6 scenario inputs

o MANE-VU: new 2018 activities and M6 scenario inputs

o Mexico: Phase III inventory a nd updates to gridding data

o Canada: 2020 inventory

• Road Dust (extracted from stationary area inventories)

o PM2.5/PM10 ratio adjustments and county-leve l (US)/state-level (Mexico)/provincelevel(Canada) vegetative cover transpor t factors applied to all dust sources

o WRAP: corrected dropped PM2.5 from Ba se18b; affects all PM, including PMC

o CENRAP: new inventories

o VISTAS: 2018 versi on G inventory

o MWRPO: 2018 versi on K inventory3 o MANE-VU: 2018 version 3 inventory

o Mexico: no change (See Base02a)

o Canada: 2020 inventory

• Fugitive Dust

o PM2.5/PM10 ratio adjustments and county-leve l (US)/state-level (Mexico)/provincelevel(Canada) vegetative cover transpor t factors applied to all dust sources

o WRAP: PRP2018 inventory

o CENRAP: new inventories

o VISTAS: 2018 versi on G inventory

o MWRPO: 2018 versi on K inventory

o MANE-VU: 2018 version 3 inventory

o Mexico: Phase III inventory a nd updates to gridding data

o Canada: 2020 inventory

• Offshore Area, Gulf of Mexico

o Removed shipping lane portion of the inventory to keep from double counting with the gridding shipping inventory

o non-shipping emissions the same as Base18b

• Gridded Offshore Shipping

o Eastern Pacific: no change (see Plan02a)

o Gulf of Mexico and Atlan tic: Corbet/ENVIRON inventory

• Point Fires

o CENRAP: inventories same as Base02a , corrections to speciation profiles

o VISTAS: 2018 version Ba se G2 inventory

• WRAP Oil & Gas

o PRP2018 inventory

• Stationary Point

o WRAP: PRP2018 inventory

o CENRAP: new inventory

o VISTAS: 2018 vers ion G inventory

o MWRPO: 2018 versi on K inventory

o MANE-VU: 2018 version 3 inventory

o Mexico: Phase III inventory

o Canada: 2020 inventory

• Documentation on input data, mode l settings, and modeling/QA pro cedures provided through standard reporting products: PRP18_Inventory_summary_v{mmddyy}.xls

o Simulation schedule and input files

o Summary of inventory inputs to th e simulation with descriptions of the changes to each input

Other Inputs and Model Settings

• Initial and Boundary Conditions base d on natural GEOS-CHEM simulation

• Model version, settings and confi guration same as for 2002 Plan B

• Time period simulated – annual, typical year, meteorology for 2002 used

Results:

Summary tables and maps4 Table 1. PRP18a annual emissions summary by regions Region CO NOX VOC NH3 SO2 PM10 PM2

_ 5 PMC
US 90799132 13963994 66757755 5399684 8563099 16143203 5156474 11032520 Total WRAP 23936606 3535119 22640603 974946 990341 5049001 1666017 3403931 CENRAP 20167299 3713812 16034674 2423559 2232321 7157086 1783387 3551497 VISTAS 23286267 2813022 18837483 792990 2347095 2332697 935773 1330056 MWRPO 11298046 1704809 4716975 789173 1794155 664227 343924 201263 MANE-VU 11917915 1285561 4402945 418907 925065 871886 360126 493441 Canada 12346108 2175237 21050573 428767 2173766 3479869 887462 2592703 Mexico 9742629 1501699 14281680 1438928 1223375 972148 523137 449011 Pacific Shipping 40868 524797 367468 149 280561 36352 35220 1132 Atlantic/Gulf Shipping 30158 571769 12587 108 229651 63726 62756 971
Total **203565028 31789819 169102744 12667211 20759429 36770194 11754276 23056524**

Table 2. PRP18a WRAP total annual emissions by sector
Sector CO NOX VOC NH3 SO2 PM10 PM2

_ 5 PMC
Fires 8928013 202217 436714 45263 53856 726518 627618 119796 Area 1446307 320345 1860528 25430 110772 240738 211012 29765 Point 752028 895549 299700 14031 535055 190862 58607 132255 Non Road 4367813 594397 328247 1409 4074 43367 39320 4047 Onroad 5708505 519090 400626 71408 5933 36368 23618 12749 Biogenic 2624814 286996 18197340 0 0 0 0 0 WOG 68260 191728 749979 0 89 0 0 0 Road Dust 0 0 0 0 0 689599 72821 616779 Fugitive Dust 0 0 0 0 0 1123032 166125 956919 Wind-Blown Dust 0 0 0 0 0 1962165 431676 1530489 Offshore Platforms 0 0 0 0 0 0 0 0 Offshore Shipping 40868 524797 367468 149 280561 36352 35220 1132 Ag Ammonia 0 0 0 817256 0 0 0 0
Total **23936606 3535119 22640603 974946 990341 5049001 1666017 3403931**

Discussion:

Emissions Summary

The objective of emissions simulation PR P18a was to incorporate new emissions data and corrections to errors discovered during the QA/QC of previous WRAP-RMC em issions simulations to create a new future year, annual 36-km grid resolution emissions dataset for air quality modeling. As summar ized in the Input Data Æ Emissions section of this specifica tion sheet, we received 2018 PRP inve ntories for the WRAP states and collected updated 2018 inventories for the rest of the U.S. We also obtained year 2020 inventories for Canada5 and new 1999 national inventories for Mexico. We combined these updated inventory da ta with both corrected and existing emissions data to develop the PRP18a emissions. We will summarize simulation PRP18a by RPO, Canada, and Mexico in terms of new inventories, emi ssions data and modeling corre ctions, and emissions that we reused from previous simulations. This section will conclude with a discussion of the overall impacts of these changes on the emissions modeling results.

The RMC received new data for the WRAP states and tribes that in cluded 2018 PRP inventories for the stationary area, stationary point, and oil and gas sectors. We extracted f ugitive dust PM emissions sources from the stationary area inventory using th e list of sources consistent with si mulation Base18b. We expanded the list of sources in the stationary area inventory that we cat egorize as fugitive dust betw een the Plan02b/c series and Base18b simulations. The reason for categorizing these emi ssions as fugitive dust rather than leaving them as stationary area sources is to apply revised PM 10 /PM 2.5 ratios and vegetative scavengi ng reduction factors to the PM emissions from dust sources. The result of using the expanded list of fugitive dust emissions sources was an increase in the PM emissions in the fugitive dust sector and a decrease in the PM emissions in the stationary area sector in simulation PRP18a relative to simulation Pl an02c. The net result of the expanded dust list is an overall reduction of PM 2.5 emissions through the applic ation of scavenging factor s to a larger number of sources. As with previous simulatio ns the updated oil and gas inventorie s consisted of both area and point sources. The explicit WRAP oil and gas emissions sector contains only the area source oil and gas inventories. The point oil and gas sources are cont ained in the stationary point invent ory. The rest of the changes to the WRAP emissions in simulation PRP18a resulted from corrections to modeli ng and data errors discovered since the completion of simulation Base18b.

We corrected errors in the WRAP on-road mobile and road dust sectors that we discovered during the QA of simulation Base18b. We found that we were double counting primary elemen tal and organic carbon emissions from brake wear, tire wear, and rest ing-exhaust on-road mobile emissions processes. The net result of this correction was to decrease the PM 2.5 emissions, specifically in the form of elemental and organic carbon, from the WRAP on-road mobile sources in simulation PRP 18a relative to Base18b. We also discovered that we inadvertently dropped all PM 2.5 from WRAP road dust sources in simulation Base18b. Although the road dust inventory technically was not updated for simulation PRP18a, we did experience a net increase in PM 2.5 emissions and a decrease in PMC emissions from road dust relative to simulation Ba se18b after correcting this error. The rest of the inventory se ctors in the WRAP region remained unc hanged in simulation PRP18a relative to Base18b and Plan02b/c. We also made several updates and corrections to the emissions data for the other RPOs in simulation PRP18a.

We incorporated updated 2018 inventories and some corr ections for all of the other RPOs in simulation PRP18a. We updated the stationary ar ea, stationary point, and non-road m obile inventories for all non-WRAP RPO’s; we also updated the on-road mobile inventories for all RPOs ex cept CENRAP. As the fugitive dust and road dust inventories are derived fr om the stationary area inventory, the updates to the stationary area inventories also impacted these emissions sectors. As with the WRAP inventories, we treated the fugitive and road dust inventories explicitly in order to apply revised PM 10 /PM 2.5 ratios and vegetative scavenging reduction factors. We selected the fugitive dust sources from the stationary area inventory using the expanded list of sources that we applied for the WRAP states. We also eliminated the non-WRAP area -source fire sector, which included CENRAP and VISTAS fires that are inventoried at the county-leve l and get modeled as area sources. We folded this category into the stat ionary area sector for simulation PRP 18a. The impetus for this change was the discovery that we double counted the area source fi re category for the CENRAP states in all previous simulations. To maintain consistency with the updated CENRAP 2018 inventories, we discarded the previous set of area fire inventories and used the estimates c ontained in the updated 2018 stationary area inventory for these sources. For the point fire category we updated the VISTAS data to a new 2018 inventory and corrected errors in the application of speciation profiles to the fires in the CENRAP region. In addition to these updates and corrections to sources in the continental U.S., we also included updated the offshore, Canadian, and Mexican emissions in simulation PRP18a.6 We added a new source in commercial shipping lane emissi ons in the Gulf of Mexico and the Atlantic Ocean and updated the Mexico and Canadian inventories in simulation PRP18a. We received a new gridded commercial shipping inventory that c ontained emissions for the Pacific, Gulf of Mexico, and Atlantic. We discarded the Pacific shippi ng lane emissions in this new inventory and combined the Pacific shipping lane emissions that we used in the Plan02b/c and Base18b simulations with the Gulf of Mexico and Atlantic emissions from the new inventory. With the addition of th e shipping lane inve ntory for the Gulf of Mexico, we modified the offshore area source inve ntory for this region by removing th e shipping sources to keep from double counting these emissions. Otherwis e the Gulf of Mexico area and point source inventories remained unchanged. We also removed some shipping sources from the Canadian inventory to keep from double counting the offshore shipping emissions in the Pacific. We used new 2020 Canadian inventories for all emissions sectors. The Canadian inventories in cluded pre-processed elevat ed point source emissions prepared by the U.S. EPA for the RPO 36-km modeling grid. Finally we us ed updated Phase III 1999 Me xican inventories that covered the entire country for all em issions sectors. Along with the updated inventories we used revised spatial surrogates developed from Shapefiles provided by Mexico’s Instituto Nacional de Ecología. We prepared the fugitive dust inventories from Canada and Me xico through the application of revised PM 10 /PM 2.5 ratios and vegetative scavenging reduction factors in a manner consistent with the U. S. inventories. As the Phase III Mexican inventory did not contain road dust estimates, we used the same Mexican road dust inventory that we used in all previous modeling runs. The rest of the s ources for Mexico and Canada, as well as the domain-wide biogenic emissions did not ch ange in simulation PRP18a.

Overall the WRAP emissions on an annua l basis (annual totals for all WRAP states) decreased for all inventory pollutants for simulation PRP18a rela tive to simulation Base18b. The larges t reductions for the PM pollutants came from the fugitive dust, on-road mobile, and stationary point sectors. The addition of PM 2.5 emissions in the road dust sector that resulted from correcting the dropped emissions in simulation Base18b was the only major offset to the PM 2.5 emissions reductions in PRP18a. The NO x emissions reductions in simulation PRP18a relative to Base18b were driven by large emissions reductions in the oil and gas sector and smaller reductions in the stationary point sector. Th e only sector th at exhibited NO x increases in comparing these two simulations was the stationary area sector. The SO 2 reductions in simulation PRP18a relative to Base18b were driven primarily by the stationary point sect or with smaller reductions in the stat ionary area sector. A small reduction in NH 3 emissions was the result of change s in the stationary point sector. The oil and gas sector contributed the largest VOC reductions, while the re duction of VOC from stationary poi nt sources was all but offset by increases in the stationary point se ctor. Large CO reductions from stati onary point sources were offset by smaller increases in the stationa ry area and oil and gas sectors.

Relative to simulation Plan02c, the PRP 18a WRAP emissions decreased for PM 2.5 , NO x , SO 2 , and CO and increased for PMC, PM 10 , and VOC. The PM 2.5 reductions were driven by the on-road mobile, non-road mobile, and stationary area sectors and offset by increases in the stationary point, road dust and fugitive dust sectors. The largest increase in PM 2.5 is from the fugitive dust sector and reflects the expanded list of dust sources that we applied after simulation Plan02c. The NO x emissions decreased as a result of the on-road mobile, non-road mobile, and stationary point sectors. Th ese decreases were offset slightly by NO x increases in the stationary area and oil and gas sectors. The SO 2 reductions were driven by stationary point, on-road mobile, and non-road mobile sectors. A large CO reduction in the on-road mobile sector was offset by increases in the stationary area, stationary point, on-road mobile, and oil and gas sectors. PMC and PM 10 increased in simulation PRP18a relative to Plan02c primarily because of the fugitive dust and road dust sector s. The only major offset to these increases were reductions in PMC and PM 10 emissions from stationary area sources. Increases in NH 3 emissions were driven by the on-road mobile and stationary point sectors. Large increases in VOC emissions from the oil and gas and stationary area sectors were offset by smaller decreases in the on-road and non-road mobile sectors.7 Visibility Modeling Results

The objective of the PRP18a simulation was to evalua te the regional haze air qua lity for future year 2018 conditions with all known and expected controls as of March 2007 and to provide predicted 2018 future year air quality and visibility conditions in the Western Class I areas for December 2007 regional haze plans. The PRP18a case should be compared to the 2002 Plan Case Version D (Plan02d) to evaluate changes in model simulated visibility from 2002 to 2018.

Relevant Output Products

Analysis products for the PRP18a simulation can be found at: http://pah.cert.ucr.edu/aq m/308/cmaq.shtml#prp18avsplan02d

• Relevant model output products include daily, seas onal and annual spatial di stributions of Regional Haze contributing pollutants.

• Model output concentrations will serve as indication of future year 2018 baseli ne visibility conditions

• Results of this simulation have b een compared with and 2002 Planni ng (Plan02d) simulation to evaluate changes in model simulated visibi lity using both spatial plots of the differences between the two simulations for each PM species, and as the bar plot s of the change in visibility from each PM component at the individual monitoring sites Comp arisons with other simulations as appropriate

Summary of Key Findings

Figures 1 through 4 show the annual average model concen trations of the PM species for the PRP18a case in the left panels, and the change in concentrations relati ve to the Plan02d case are s hown in the right panels. There were small reductions in sulfate in PRP19a and larger reductions in nitrate, ammonium and elemental carbon throughout large regions in the WR AP states. Figure 2 shows that there were small reductions in ozone of about 1 to 3 ppb though much of the WRAP domain, excluding some ma jor urban areas were low ozone levels increased because of reduced NOx emissions a nd reduced titration of ozone by NO. While the annual average plots are useful for summar izing the trends in PM concentra tions, there is substantial seasonal variability in the results, and monthly average results are also availa ble at the RMC webpage at this ink: http://pah.cert.ucr.edu/aq m/308/cmaq.shtml#prp18avsplan02d

Figure 5 shows an example of a stac ked bar plot of the modeled contri bution of individual PM species to visibility impairment at Rocky M ountain National Park for each day dur ing the PRP18a simulation. Complete results for all IMPROVE sites are available at this link: http://pah.cert.ucr.edu/aqm/308/results_prp18a_vs_p lan02d/visibility/wrap.prp18a_vs_plan02d.IMPRO VE.aero_bext.pdf8 Figure 1. Left panel shows annual average sulfate (top) and nitrate (bottom) CMAQ simulated concentrations for Plan02d. Right panel shows the change in concentration for Plan02d minus Plan02c.9 Figure 2. Left panel shows annual average amm onium (top) and ozone (bottom) CMAQ simulated concentrations for Plan02d. Right panel shows the change in concentration for Plan02d minus Plan02c.10 Figure 3. Left panel shows annual average elementa l carbon (top) and organic carbon (bottom) CMAQ simulated concentrations for Plan02d. Right panel s hows the change in concen tration for Plan02d minus Plan02c.11 Figure 4. Left panel shows annual average coarse matter (top) and total PM2.5 (bottom) CMAQ simulated concentrations for Plan02d. Right panel shows the change in concentration for Plan02d minus Plan02c.12 Figure 5. Stacked bar plot showing mode led contribution of individual PM speci es to visibility impairment at Rocky Mountain National Park for each day during the 2018 Preliminary Reasona ble Progress simulation.