Source apportionment of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 μm or less) in Beijing, China, was determined using two eigenvector models, principal component analysis/absolute principal component scores (PCA/APCS) and UNMIX. The data used in this study were from the chemical analysis of 24-h samples, which were collected at 6-day intervals in January, April, July, and October 2000 in the Beijing metropolitan area. Both models identified five sources of PM2.5: secondary sulfate and secondary nitrate, a mixed source of coal combustion and biomass burning, industrial emission, motor vehicles exhaust, and road dust. On average, the PCA/APCS and UNMIX models resolved 73% and 85% of the PM2.5 mass concentrations, respectively. The results were comparable to previous estimate using the positive matrix factorization (PMF) and chemical mass balance (CMB) receptor models. Secondary products and the emissions from coal combustion and biomass burning dominated PM2.5. Such comparison among various receptor models, which contain different physical constraints, is important for better understanding PM2.5 sources.
Air 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 fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 μm or less) in Beijing, China, was determined using two eigenvector models, principal component analysis/absolute principal component scores (PCA/APCS) and UNMIX. The data used in this study were from the chemical analysis of 24-h samples, which were collected at 6-day intervals in January, April, July, and October 2000 in the Beijing metropolitan area. Both models identified five sources of PM2.5: secondary sulfate and secondary nitrate, a mixed source of coal combustion and biomass burning, industrial emission, motor vehicles exhaust, and road dust. On average, the PCA/APCS and UNMIX models resolved 73% and 85% of the PM2.5 mass concentrations, respectively. The results were comparable to previous estimate using the positive matrix factorization (PMF) and chemical mass balance (CMB) receptor models. Secondary products and the emissions from coal combustion and biomass burning dominated PM2.5. Such comparison among various receptor models, which contain different physical constraints, is important for better understanding PM2.5 sources.
Fine particulate matter (PM2.5) was measured for 4 months during 2002–2003 at seven sites located in the rapidly developing Pearl River Delta region of China, an area encompassing the major cities of Hong Kong, Shenzhen and Guangzhou. The 4-month average fine particulate matter concentration ranged from 37 to 71μgm−3 in Guangdong province and from 29 to 34μgm−3 in Hong Kong. Main constituents of fine particulate mass were organic compounds (24–35% by mass) and sulfate (21–32%). With sampling sites strategically located to monitor the regional air shed patterns and urban areas, specific source-related fine particulate species (sulfate, organic mass, elemental carbon, potassium and lead) and daily surface winds were analyzed to estimate influential source locations. The impact of transport was investigated by categorizing 13 (of 20 total) sampling days by prevailing wind direction (southerly, northerly or low wind-speed mixed flow). The vicinity of Guangzhou is determined to be a major source area influencing regional concentrations of PM2.5, with levels observed to increase by 18–34μgm−3 (accounting for 46–56% of resulting particulate levels) at sites immediately downwind of Guangzhou. The area near Guangzhou is also observed to heavily impact downwind concentrations of lead. Potassium levels, related to biomass burning, appear to be controlled by sources in the northern part of the Pearl River Delta, near rural Conghua and urban Guangzhou. Guangzhou appears to contribute 5–6μgm−3 of sulfate to downwind locations. Guangzhou also stands out as a significant regional source of organic mass (OM), adding 8.5–14.5μgm−3 to downwind concentrations. Elemental carbon is observed to be strongly influenced by local sources, with highest levels found in urban regions. In addition, it appears that sources outside of the Pearl River Delta contribute a significant fraction of overall fine particulate matter in Hong Kong and Guangdong province. This is evident in the relatively high PM2.5 concentrations observed at the background sites of 18μgm−3 (Tap Mun, southerly flow conditions) and 27μgm−3 (Conghua, northerly flow conditions).
Ninety daytime/nighttime PM2.5 aerosol samples were collected at 5 sites in forest, tunnel, urban, rural, and mixed forest/urban areas in the Lower Fraser Valley during the Pacific 2001 Air Quality Study. Solvent-extractable organic matter, such as n-alkanes (C-14-C-33), n-alkan-2-ones (C-10-C-31), and 6, 10, 14- trimethylpentadecan-2-one on the fine aerosols, were quantified. The concentrations of total n-alkanes from primary sources were 45.5-112 ng m(-3) at the tunnel site, 3.3-34.6 ng m(-3) at the urban site, 0.6-18.1 ng m-3 at the rural site, and 1.7-16.9 ng m(-3) at the forest and the mixed areas. The homologue distributions of the n-alkanes displayed different patterns at the 5 sites, showing day-night differences and reflecting their primary source types and impacts of episodes. The carbon preference index (CPI) values of the n-alkanes showed highest value (average of 2.39 +/- 0.47) at the forest, lowest (1.15 +/- 0.11) at the tunnel. The CPI showed higher values in night samples at all sites except the urban site which was impacted by specific episodes such as biomass burning and/or fuel burning occurring during the nighttimes, the higher nighttime values of CPI, with consistent lower n-alkane concentrations, suggested that weaker anthropogenic emissions during night were a more likely cause. The total n-alkan-2-ones on the aerosols were 1.8-12.6ng m(-3) at the tunnel site and 0.2-7.2ng m-3 at the other 4 sites. Low-molecular weight n-alkan-2-ones (< C-22) were observed at all sites with the highest level at the tunnel. High molecular weight n-alkan-2-one (> C-23) were consistently higher at the tunnel but varied with date at the forest site. The n-alkan-2ones, both low and high molecular weight, could have multiple sources including vehicular emissions and oxidation processes. The branched ketone, 6, 10, 14- trimethylpentadecan-2-one, an oxidative product of phytol on the fine aerosols, 3 was enriched in the forest with the average of 7.6 +/- 7.1 ng mg(-3) (c) 2006 Elsevier Ltd. All rights reserved.
Spatial variations of source contributions to fine organic carbon (OC) and fine particles in the southeastern United States were investigated using molecular marker-based chemical mass balance modeling (CMB-MM) and carbon isotope analysis. Nine primary emission sources were resolved with wood combustion (average 1.73 μg m−3, 23 ± 14% of measured OC) being the most dominant contributor to OC, followed by gasoline engine exhaust (0.45 μg m−3, 6.1 ± 6.2% of OC), diesel engine exhaust (0.43 μg m−3, 4.8 ± 4.1% of OC), and meat cooking (0.30 μg m−3, 4.1 ± 2.6% of OC). Measurable contributions from vegetative detritus, cigarette smoke, road dust, and natural gas exhaust were found. The impact of coke facilities was estimated for the first time in Birmingham, Alabama, and contributed 0.52 μg m−3 on average to fine OC. The unexplained OC accounted for 54 ± 26% of measured OC, possibly because of contributions from secondary OC, other unidentified primary sources and the possible positive artifact of OC. The urban excess of OC from diesel exhaust, gasoline exhaust and meat cooking can be seen from the results of the urban-rural pair in Alabama. Detailed chemical analysis revealed the wood burning episode at the rural site and an episode of secondary formation in the study region. The 14C analysis, a tool to study the relative contributions of contemporary and fossil carbon contents of fine particles, agreed well with the CMB-MM analysis. Both reflected a higher fossil fraction of carbon at urban sites especially in Birmingham, Alabama.