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NONROAD Model Sensitivity to Temperature

This document presents a detailed analysis of the sensitivity of the EPA's NONROAD Model to temperature variations, assessing its impact on various nonroad emission sources, including recreational, construction, industrial, and agricultural equipment. It encompasses over 260 specific equipment types and highlights how temperature influences emission factors such as PM, HC, and NOx. Through a case study in Georgia, the analysis demonstrates the significance of spatial resolution on modeling emissions and provides insights into modeling practices, assumptions, and potential caveats.

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NONROAD Model Sensitivity to Temperature

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  1. NONROAD Model Sensitivity to Temperature 12/1/2003 Rosa Chi EAS6792

  2. EPA NONROAD Model • Includes the following major emission source categories: • recreational equipment • construction and mining equipment • industrial • lawn and garden • agricultural • commercial • logging • airport ground support • railway maintenance • recreational marine vessels • More than 260 specific equipment types modeled!

  3. Emissions Conversion Factors PM Fractions HC Conversions Population Emission Factors Activity Geographic Allocation Deterioration Rated Power Load Factor Hours per Year Age Distribution EF Corrections Activity Corrections Growth Scrappage Seasonal/ Monthly Corrections Weekend/ Weekday Corrections Average Life Temperature RVP Oxygenate Fuel Sulfur

  4. Emissions Calculations • Emissions = (Population) x (Rated Power) x (Load Factor) x (Activity) x (Emission Factor) • (Emission Factor) = (Base EF) * (Temperature Correction Factor) * (Other Factors)

  5. Temperature Corrections • Pollutants: HC, CO, NOx • Evaporative Emissions: Ideal Gas Law • 4-stroke Exhaust Emissions: TCF = exp [ A * (Tamb-75) ] • “A” depends on Tamb and pollutant • No corrections for 2-stroke, diesel • How important is spatial resolution?

  6. Sensitivity Analysis • Scenario • State of Georgia • August 18, 2000 • State-wide average, min, max temperatures based on 97 NWS weather stations covering 86 counties.

  7. Assumptions • Vary temperatures up and down by 10% • Sensitivity = 5 * (E110% - E90%)/E100% • Assume linear!

  8. Aug. 18, 2000 Base Emissions

  9. Emissions Sensitivities

  10. Summary of Results

  11. Conclusions • Finer spatial temperature resolution not needed • Caveats: • May not apply for large temperature ranges • Effect in winter not studied • Shows importance of modeling spatial temperature difference in model as is, not importance of temperature difference in real life!

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