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Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results
AbstractThe chemical mass balance (CMB) model was applied for source apportionment of PM2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.
Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results
AbstractThe chemical mass balance (CMB) model was applied for source apportionment of PM2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.
Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results
Lee, Sangil (author) / Russell, Armistead G. (author)
Atmospheric Environment ; 41 ; 9616-9624
2007-08-14
9 pages
Article (Journal)
Electronic Resource
English
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