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

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Presentation on theme: "Inventory Issues and Modeling- Some Examples Brian Timin USEPA/OAQPS October 21, 2002."— Presentation transcript:

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

2 Purpose Show examples of how certain inventory issues affect modeling results Will focus on: – Ammonia – Crustal/fugitive – Fires – PM2.5 speciation profiles

3 Ammonia Particulate nitrate is generally overpredicted in CMAQ Ammonia emissions play a key role in the nitrate overpredictions – ORD has completed ammonia “inverse modeling” based on measurements of ammonium wet deposition 1990 ammonia inventory appears to be overestimated Reduced ammonia emissions from livestock by 20-60% in our 1996 modeling inventory for each month (from previous seasonal profiles) in our latest model runs We have found that nitrate is still overpredicted with reduced ammonia emissions

4 CMAQ Ammonia Sensitivity Runs- 50% NH3 Reduction- January Basecase NitrateNitrate with 50% Ammonia Reduction

5 Crustal/Fugitive Emissions and Speciation Profiles Crustal/other primary PM2.5 – SCC specific speciation profiles are used to speciate the primary PM2.5 emissions into organic carbon, elemental carbon, primary nitrate, primary sulfate, and “unspeciated PM2.5”. – Many of the profiles have a large percentage of unspeciated PM2.5 – The unspeciated mass is tracked in the model as other/crustal (PMFINE in REMSAD and A25 in CMAQ) – In urban areas, the annual average modeled unspeciated PM2.5 concentrations can be as high as 5-10 ug/m3.

6 Crustal/Fugitive Emissions and Speciation Profiles – The measured “other PM” in urban areas is generally < 1 ug/m3 What is unspeciated PM? Does it really belong in another category? Why is there so much of it predicted in urban areas? – Largest sources are paved roads, construction, and open burning – Updates to speciation profiles may reduce unspeciated portion of PM2.5 and may lead to improved primary carbon inventories – The largest contributors to “other PM2.5” should be closely examined to see where estimation improvements can be made

7 January Average Crustal/Other PM2.5

8 Fire Emissions Burning emissions – Separate SCC’s for wildfires, prescribed burning, agricultural burning, slash burning, and open burning Wildfires- we removed them from our modeling – We do not know when and where wildfires occurred in 1996 WRAP has a new inventory for 1996 that we may be able to use – Lack of wildfires is likely contributing to an underestimate in organic carbon (especially in the West) Prescribed burning- included in current modeling – Relatively large amount of prescribed burning emissions in certain parts of the country Some States have large prescribed burning emissions (based on State submitted data), some States have none (based on the lack of State submitted data)

9 Modeling/Inventory Issues Burning Emissions Seasonal factors for prescribed burning need to be examined – We are currently allocating 65% of the prescribed burning to the spring – Seasonal factors should probably vary by region – This creates unrealistic model results when transitioning between seasons

10 Effect of Prescribed Burning on Primary Organics

11 Link Between Emissions Modeling and Meteorology Emissions of many species are strongly linked with meteorology – Currently incorporate meteorological variables into biogenic and mobile models All of the previous examples are influenced by meteorology – Ammonia Temperature, wind speed – Fugitive dust Moisture, wind speed – Fires Winds, mixing In the long term, many of these emissions types may need to be incorporated into models which account for meteorology

12 Summary There are many existing uncertainties in inventory categories that can have large impacts on the modeling results The inventory community is beginning to address many of these “issues” New emissions models that incorporate meteorological variables may be necessary to adequately characterize spatial and temporal emissions patterns The modeling community can help identify and prioritize issues as they impact modeling

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