A multivariate/chemical mass balance model for air pollution in China: A hybrid methodology
This research explores the possibility of using a two step method of identifying and quantifying air pollution emissions in an urban environment. The procedure uses a mathematical model called Target Transformation Factor Analysis (TTFA) to estimate source profiles using ambient trace element air concentration data. A source profile is analogous to a fingerprint since it is unique to each source of air pollution. It is important to use source profiles that are measured or estimated for the specific location under study. The profiles estimated by TTFA are then employed in a Chemical Mass Balance (CMB) source apportionment analysis for the airshed. Other known sources are estimated using source signatures from the literature. Applying the TTFA and CMB models in this fashion is called receptor modeling. Generically, a receptor model is the combination of measured air pollution concentration data with a numerical technique which apportions the measured air pollution among distinct source types. The results show that TTFA can be used to provide quantitative estimates of air pollution source profiles for an urban center in China. The number of profiles for unique source types was limited for this data set since emissions from certain types of sources co-varied during each sampling day. Consequently, the CMB analyses that applied the TTFA source profiles needed to be supplemented with standard US EPA source profiles. The application of TTFA for estimating source profiles from ambient data and the subsequent use of those profiles in CMB analyses with source profiles obtained from the EPA's source library can improve the statistical quality of the source apportionment analysis. TTFA can identify source categories of airborne pollution for specific cities, as well as give quantitative data on the composition of the emissions from those source types.
- Research Organization:
- Rutgers-the State Univ., New Brunswick, NJ (United States)
- OSTI ID:
- 5648177
- Resource Relation:
- Other Information: Thesis (Ph.D.)
- Country of Publication:
- United States
- Language:
- English
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