Development and application of air quality modeling tools to determine uncertainty in biogenic emissions, source impacts, and area of influence.

摘要

Part I: Uncertainty in biogenic emissions estimates. Uncertainties in biogenic emissions have been estimated using a Monte Carlo technique. The impact that these uncertainties have on emissions control strategy choice have been examined. Also, the uncertainty in ozone predictions due to the uncertainties in biogenic emissions has been estimated. Biogenic emissions and their attendant uncertainties were estimated for the multiple episodes and for urban- and regional-scale air quality modeling domains in the United States.; Part II: The source apportionment and area of influence tools. (1) The Source Apportionment Tool . Using a new technique based on the DDM-3D, the culpability of various emissions source regions has been estimated for a number of air quality metrics at Whiteface Mountain and Long Island Sound in the state of New York. Also the impacts to the air quality metrics have been estimated due to the SO 2 and NOx emissions reductions that are required under the Pataki Initiative. (2) The Area of Influence Tool. The Area of Influence (AOI) method is described and applied. An AOI identifies the specific emissions sources that contribute to air quality degradation at a receptor. Further, an AOI identifies the magnitudes of the impacts that these sources have on an air quality metric at the receptor. The AOI technique is applied to elucidate both the magnitude and the location of the SO 2 emissions sources that impact 24 hour average aerosol sulfate levels at Great Smoky Mountains National Park (GSM).