A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Source apportionment of PM2.5 in Beijing by positive matrix factorization
AbstractAir pollution associated with atmospheric fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5μm or less) is a serious problem in Beijing, China. To provide a better understanding of the sources contributing to PM2.5, 24-h samples were collected at 6-day intervals in January, April, July, and October in 2000 at five locations in the Beijing metropolitan area. Both backward trajectory and elemental analyses identified two dust storm events; the distinctly low value of Ca:Si (<0.2) and high Al:Ca (>1.7) in Beijing PM2.5 appear indicative of contributions from dust storms. Positive matrix factorization (PMF) was used to apportion sources of PM2.5, and eight sources were identified: biomass burning (11%), secondary sulfates (17%), secondary nitrates (14%), coal combustion (19%), industry (6%), motor vehicles (6%), road dust (9%), and yellow dust. The lower organic carbon (OC), elemental carbon (EC), SO42−, and Ca values of yellow dust enable it to be distinguished from road dust. The PMF method resolved 82% of PM2.5 mass concentrations and showed excellent agreement with a previous calculation using organic tracers in a chemical mass balance (CMB) model. The present study is the first reported comparison between a PMF source apportionment model and a molecular marker-based CMB in Beijing.
Source apportionment of PM2.5 in Beijing by positive matrix factorization
AbstractAir pollution associated with atmospheric fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5μm or less) is a serious problem in Beijing, China. To provide a better understanding of the sources contributing to PM2.5, 24-h samples were collected at 6-day intervals in January, April, July, and October in 2000 at five locations in the Beijing metropolitan area. Both backward trajectory and elemental analyses identified two dust storm events; the distinctly low value of Ca:Si (<0.2) and high Al:Ca (>1.7) in Beijing PM2.5 appear indicative of contributions from dust storms. Positive matrix factorization (PMF) was used to apportion sources of PM2.5, and eight sources were identified: biomass burning (11%), secondary sulfates (17%), secondary nitrates (14%), coal combustion (19%), industry (6%), motor vehicles (6%), road dust (9%), and yellow dust. The lower organic carbon (OC), elemental carbon (EC), SO42−, and Ca values of yellow dust enable it to be distinguished from road dust. The PMF method resolved 82% of PM2.5 mass concentrations and showed excellent agreement with a previous calculation using organic tracers in a chemical mass balance (CMB) model. The present study is the first reported comparison between a PMF source apportionment model and a molecular marker-based CMB in Beijing.
Source apportionment of PM2.5 in Beijing by positive matrix factorization
Song, Yu (author) / Zhang, Yuanhang (author) / Xie, Shaodong (author) / Zeng, Limin (author) / Zheng, Mei (author) / Salmon, Lynn G. (author) / Shao, Min (author) / Slanina, Sjaak (author)
Atmospheric Environment ; 40 ; 1526-1537
2005-10-19
12 pages
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2010
| Taylor & Francis Verlag | 2004
Source Apportionment of Fine Particulate Matter in Phoenix, AZ, Using Positive Matrix Factorization
| Taylor & Francis Verlag | 2007