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Source Apportionment of PM2.5 Using Positive Matrix Factorization (PMF) and PMF with Factor Selection

Artikel i vetenskaplig tidskrift
Författare Peter Molnár
Sandra Johannesson
U. Quass
Publicerad i Aerosol and Air Quality Research
Volym 14
Nummer/häfte 3
Sidor 725-U711
ISSN 1680-8584
Publiceringsår 2014
Publicerad vid Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa
Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa, enheten för arbets-och miljömedicin
Sidor 725-U711
Språk en
Länkar dx.doi.org/10.4209/aaqr.2013.11.033...
Ämnesord Personal exposure, Indoor, Residential outdoor, Urban background, PARTICULATE AIR-POLLUTION, NEW-YORK-CITY, PERSONAL EXPOSURES, FINE, PARTICLES, BLACK SMOKE, TRACE-ELEMENTS, RESIDENTIAL OUTDOOR, MASS, CONCENTRATIONS, GENERAL-POPULATION, INDOOR
Ämneskategorier Miljömedicin och yrkesmedicin

Sammanfattning

Personal exposure, indoor, residential outdoor and urban background particulate matter (PM2.5) samples were collected in parallel, for 30 participants and analyzed for their chemical content. Source apportionments for the separate microenvironments were performed using conventional positive matrix factorization (PMF), and for the combined dataset, applying a new PMF method with factor selection. Regional sources were the largest contributor to the sampled PM2.5 in all microenvironments and accounted for 69% in urban background; 55% and 54% in residential outdoor and indoor environment, respectively; and 40% of personal exposure. For personal exposure, personal activities accounted for 21% (2.2 mu g/m(3)), and constituted the main difference in total mass concentration between personal exposure and the other microenvironments. The PMF method with factor selection was found to be a useful tool in the PMF analysis of multiple microenvironments, since ambient contributions to indoor and personal exposure are less likely to be distorted or misinterpreted. The possibility to more correctly estimate the source contributions will increase by combining the datasets for the different microenvironments into a larger dataset and using the PMF with factor selection method.

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