Utilizing the Chemical Mass Balance and Positive Matrix Factorization models to determine influential species and examine possible rotations in receptor modeling results

Atmospheric Environment

Volumn 41, Issue 33, 2007, Pages 6986-6998

  Rizzo, Michael J ^a   Scheff, Peter A ^b  

^a U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^b UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

Chemical Mass Balance; Influential species; PM2.5; Positive Matrix Factorization; Receptor modeling; Rotations; Source apportionment; Speciation Trends Network (STN)

Indexed keywords

DATA ACQUISITION; FACTORIZATION; MATHEMATICAL MODELS; SULFATE MINERALS; VEHICLES;

CHEMICAL MASS BALANCE; POSITIVE MATRIX FACTORIZATION; RECEPTOR MODELING; SPECIATION TRENDS NETWORK;

PARTICLES (PARTICULATE MATTER);

SULFATE;

AUTOMOBILE; CHEMICAL MASS BALANCE; MODELING; PARTICULATE MATTER; POINT SOURCE; SULFATE;

ARTICLE; CHEMICAL ANALYSIS; MOTOR VEHICLE; POSITIVE MATRIX FACTORIZATION; PRIORITY JOURNAL; RECYCLING; ROTATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

CHICAGO; ILLINOIS; NORTH AMERICA; UNITED STATES;

EID: 34748887159     PISSN: 13522310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2007.05.008     Document Type: Article

References (10)

1. Henry R.C. Receptor model applied to patterns in space (RMAPS) Part I-model description. Journal of the Air & Waste Management Association 47 (1997) 216-219
2. Henry R.C. Receptor model applied to patterns in space (RMAPS) Part II-apportionment of airborne particulate sulfur from project MOHAVE. Journal of the Air & Waste Management Association 47 (1997) 220-225
3. Henry R.C. Receptor model applied to patterns in space (RMAPS) Part III-apportionment of airborne particulate sulfur in Western Washington state. Journal of the Air & Waste Management Association 47 (1997) 226-230
4. Kim E., Hopke P.K., and Edgerton E.S. Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization. Atmospheric Environment 38 (2004) 3349-3362
5. Lee E., Chan C.K., and Paatero P. Application of positive matrix factorization in source apportionment of particulate pollutants. Atmospheric Environment 33 (1999) 3201-3312
6. Paatero, P. User's guide for the Multilinear Engine program ''ME2'' for fitting multilinear and quasi-multilinear models. 〈ftp://rock.helsinki.fi/pub/misc/pmf/me2/me2guid.pdf〉.
7. Paatero P., Hopke P.K., Song X.-H., and Ramadan Z. Understanding and controlling rotations in factor analytic models. Chemometrics and Intelligent Laboratory Systems 60 (2002) 253-264
8. Paatero P., Hopke P.K., Begum B.A., and Biswas S.K. A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution. Atmospheric Environment 39 (2005) 193-201
9. Rizzo, M.J., Scheff, P.A. Fine particulate source apportionment using data from the USEPA speciation trends network in Chicago, Illinois: comparison of two source apportionment models. Atmospheric Environment, in press, doi:10.1016/j.atmosenv.2007.03.055.
10. Zhao W., and Hopke P.K. Source apportionment for ambient particles in the San Gorgonio wilderness. Atmospheric Environment 38 (2004) 5901-5910