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Volume 12, No. 4, August 2012, Pages 476-491 PDF(1.03 MB)  
doi: 10.4209/aaqr.2012.04.0084   

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Balakrishnaiah Gugamsetty1, Han Wei1, Chun-Nan Liu1, Amit Awasthi1, Shih-Chieh Hsu2, Chuen-Jinn Tsai1, Gwo-Dong Roam3, Yue-Chuen Wu3, Chung-Fang Chen3

1 Institute of Environmental Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
2 Research Center for Environmental Changes, Academia Sinica, Taipei 115, Taiwan
3 Environmental Analysis Laboratory, Environmental Protection Administration, Jhongli 320, Taiwan




Ambient Particulate Matters (PM10, PM2.5 and PM0.1) were investigated at Shinjung station in New Taipei City, Taiwan. Samples were collected simultaneously using a dichotomous sampler (Andersen Model SA-241) and a MOUDI (MSP Model 110) over a 24-h period from May 2011 to November 2011 at Shinjung station. Samples were analyzed for metallic trace elements using ion coupled plasma mass spectroscopy (ICP-MS) and ionic compounds by ion chromatography (IC). The average concentrations of PM10, PM2.5 and PM0.1 were found to be 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m3, respectively. Based on the chemical information, positive matrix factorization (PMF) was used to identify PM sources. A total of five source types were identified, soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols, and their contributions were estimated using PMF. The crustal enrichment factors (EF) were calculated using Al as a reference for the trace metal species to identify the sources. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis were used to identify local point sources. The results suggest a competitive relationship between anthropogenic and natural source processes over the monitoring station.



Keywords: Positive matrix factorization; Enrichment factor analysis; Conditional probability function analysis; PM10; PM2.5; PM0.1.



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