AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
Improving Particulate Matter Source Apportionment: A Hybrid Approach Utilizing Chemical Transport and Receptor Models with Geostatistical Methods
CESUNICA IVEY, Heather Holmes, Yongtao Hu, Armistead Russell, James Mulholland, Georgia Institute of Technology
Abstract Number: 136 Working Group: Source Apportionment
Abstract An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter. Further, there is a growing desire to directly use source impacts in health studies [1, 2]. However, source impacts cannot be directly measured. Traditionally, observed concentrations have been utilized in source apportionment methods such as receptor-oriented modeling (RM). Several challenges are presented by this method, leading to the development of a novel hybrid approach that is used to determine source impacts by combining the capabilities of RM and chemical transport modeling (CTM). The hybrid method calculates an adjustment factor (R) for estimated impact of each source at each monitor location using observations and results of CTM sensitivity analysis [3]. R is a scaling factor applied to the original CTM source impacts and is obtained using a chemical mass balance approach that incorporates measurement, modeling and emission uncertainties. Previously, R was calculated only at monitoring locations [3]. That approach is extended to produce an R value for every grid cell in the CTM domain for source impact adjustments at locations beyond observation locations. The interpolation can also be done in time. In this study, kriging is the primary method to spatially interpolate R values calculated using data from Speciation Trends Network (STN) locations. An urban-rural analysis is completed using data from the SEARCH monitoring network [4] to determine the performance of the hybrid and kriging methods. Results are evaluated by comparing observed values to reconstructed species concentrations, which are derived using R values. Typically, the STN data are limited temporally, as the monitoring networks report data one in every three or six days, so temporal interpolation is assessed as well. Data from additional monitoring sites with daily observations are used to assess the performance of temporally interpolated R values.
References
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3. Hu, Y.; Balachandran, S.; Pachon, J.; Jaemeen, B.; Odman, M. T.; Mulholland, J. A.; Russell, A. G. 2012 (in review)
4. Edgerton, E. S.; Hartsell, B. E.; Saylor, R. D.; Jansen, J. J.; Hansen, D. A.; Hidy, G. M. J. Air Waste Manage. Assoc. 2006, 56, 1325-1341.