[1] Size-segregated atmospheric aerosols (11 stages separating particles from <0.04 to >14.2 mu m) collected in the Arctic during the polar sunrise at Alert were analyzed for aerosol mass, dicarboxylic acids, and major inorganic ions. Oxalic, malonic, succinic, and glutaric acids were detected in all size ranges, with oxalic acid being dominant. Their concentrations maximized in the accumulation mode either at 0.24-0.40 or 0.40-0.8 mu m aerodynamic diameters, suggesting that diacids were mainly formed by gas-to-particle conversion via photochemical oxidation of nonmethane hydrocarbons and oxygenated organics originated from continental pollution sources. The relative abundances of oxalic acid were higher in the 0.24- to 0.4-mu m size particles (73-78%) than in supermicrometer particles (40-60%), indicating that oxalic acid is produced by gas phase oxidation of precursors followed by accumulation on preexisting particles. Mass size distributions of NH4+ and SO42- peaked in the accumulation mode similar to those of small diacids. The sea-salt enrichment factor of K+ (biomass burning tracer) relative to Na+ maximized in 0.1- to 0.8-mu m sizes, whereas those of Mg2+ and Ca2+ (dust tracers) in 0.4- to 7.8-mu m particles. Maximized chlorine loss and bromine enrichment were found at 0.4-0.8 and 0.24-0.4 mu m sizes, respectively. Concentrations of Br-, which typically showed a submicrometer maximum, increased significantly during an O-3 depletion event having a shift of size distribution to a supermicrometer mode. During this event, oxalic acid concentration relative to succinic acid increased in submicrometer mode (0.24-0.4 mu m), adding to a growing body of evidence supporting the hypothesis that halogen chemistry is important in the production and loss of oxalic acid in the arctic atmosphere.
The primary emission source contributions to fine organic carbon (OC) and fine particulate matter (PM2.5) mass concentrations on a daily basis in Atlanta, GA, are quantified for a summer (July 3 to August 4, 2001) and a winter (January 2-31, 2002) month. Thirty-one organic compounds in PM2.5 were identified and quantified by gas chromatography/mass spectrometry. These organic tracers, along with elemental carbon, aluminum, and silicon, were used in a chemical mass balance (CMB) receptor model. CMB source apportionment results revealed that major contributors to identified fine OC concentrations include meat cooking (7-68%; average: 36%), gasoline exhaust (7-45%; average: 21%), and diesel exhaust (6-41%; average: 20%) for the summer month, and wood combustion (0-77%; average: 50%); gasoline exhaust (14-69%; average: 33%), meat cooking (1-14%; average: 5%), and diesel exhaust (0-13%; average: 4%) for the winter month. Primary sources, as well as secondary ions, including sulfate, nitrate, and ammonium, accounted for 86 +/- 13% and 112 +/- 15% of the measured PM2.5 mass in summer and winter, respectively.
Based on measurements of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 μm or less) in January and August 2004, serious air pollution persists in Beijing. The chemical analysis included organic and elemental carbon, water-soluble ions, and elemental compositions. The positive matrix factorization (PMF) method was used to apportion the PM2.5 sources. The sources contributing dominantly to PM2.5 mass concentrations are coal combustion in winter and the secondary products in summer. Furthermore, the contributions from motor vehicles, road dusts and biomass burning could not be neglected. The products of biomass burning for winter heating in the area around Beijing could enter the urban area during quasi-quiescent weather conditions. In conclusion, some effective control measures were proposed to reduce the PM2.5 pollution in Beijing.
A substantial fraction of fine particulate matter (PM) across the United States is composed of carbon, which may be either emitted in particulate form (i.e., primary) or formed in the atmosphere through gas-to-particle conversion processes (i.e., secondary). Primary carbonaceous aerosol is emitted from numerous sources including motor vehicle exhaust, residential wood combustion, coal combustion, forest fires, agricultural burning, solid waste incineration, food cooking operations, and road dust. Quantifying the primary contributions from each major emission source category is a prerequisite to formulating an effective control strategy for the reduction of carbonaceous aerosol concentrations. A quantitative assessment of secondary carbonaceous aerosol concentrations also is required, but falls outside the scope of the present work.
Sources of carbonaceous aerosols collected from three sites of Chattanooga, TN (CH), Muscle Shoals, AL (MS), and Look Rock, TN (LR) in the Tennessee Valley Region (TVR) were apportioned using both organic tracer-based chemical mass balance (CMB) modeling and radiocarbon (14C) measurement and the results were compared. Eight sources were resolved by CMB, among which wood combustion (averaging 0.92μgm−3) was the largest contributor to primary organic carbon (OC) concentrations, followed by gasoline exhaust (0.35μgm−3), and diesel exhaust (0.18μgm−3). The identified primary sources accounted for 43%, 71%, and 14% of measured OC at CH, MS, and LR, respectively. Contributions from the eight primary sources resolved by CMB could explain 107±10% of ambient elemental carbon (EC) concentrations, with diesel exhaust (66±32%) and wood combustion (37±33%) as the most important contributors. The fossil fractions in total carbon determined by 14C measurements were in reasonably good agreement with that in primary (OC+EC) carbon apportioned by CMB in the MS winter samples. The comparison between the 14C and CMB results revealed that contemporary sources dominated other OC in the TVR, especially in summertime (84% contemporary).
A geographic information system (GIS)-based chemometric approach was applied to investigate the spatial distribution patterns of heavy metals in marine sediments and to identify spatial human impacts on global and local scales. Twelve metals (Zn, V, Ni, Mn. Pb, Cu Cd, Ba, Ha, Fe, Cr and Al) were surveyed twice annually at 59 sites in Hong Kong from 1998 to 2004. Cluster analysis classified the entire coastal area into three areas on a global scale, representing different pollution levels. Backward discriminant analysis, with 84.5% correct assignments, identified Zn, Pb, Cu, Cd, V, and Fe as significant variables affecting spatial variation on a local scale. Enrichment factors indicated that Cu. Cr, and Zn were derived from human impacts while Al, Ba, Mn, V and Fe originated from rock weathering. Principal component analysis further subdivided human impacts and their affected areas in each area, explaining 87%, 84% and 87% of the total variances, respectively. The primary anthropogenic sources in the three areas were (i) anti-fouling paint and domestic sewage, (ii) surface runoff, wastewater, vehicle emissions and marine transportation, and (iii) ship repainting, dental clinics, electronic/chemical industries and leaded fuel, respectively. Moreover, GIS-based spatial analysis facilitated chemometric methods. (c) 2007 Elsevier Ltd. All rights reserved.
The comprehensive application of different multivariate methods and geographic information systems (GIS) was used to evaluate the spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong. Fourteen variables were surveyed at 27 sites monthly from 2000 to 2004. After data pretreatment, cluster analysis grouped the 12 months into two groups, June-September and the remaining months, and divided the entire area into two parts, representing different pollution levels. Discriminant analysis determined that NO3- -N, DO, and temperature and TN, SD, pO(4)(3-) -P, and VSS were 4 significant variables affecting temporal and spatial variations with 84% and 90% correct assignments, respectively Five potential pollution sources were identified for each part by rotated principal component analysis, explaining 71% and 68% of the total variances, respectively. Receptor-based source apportionment revealed that most of the variables were primarily influenced by soil weathering and organic pollution, nutrient pollution (or agricultural runoff), and mineral pollution. Furthermore, GIS further facilitated and supported multivariate analysis results. (C) 2007 Elsevier Ltd. All rights reserved.
[1] Fine particle organic carbon in Delhi, Mumbai, Kolkata, and Chandigarh is speciated to quantify sources contributing to fine particle pollution. Gas chromatography/mass spectrometry of 29 particle-phase organic compounds, including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and levoglucosan along with quantification of silicon, aluminum, and elemental carbon are used in a molecular-marker based source apportionment model to quantify the primary source contributions to the PM2.5 mass concentrations for four seasons in three sites and for the summer in Chandigarh. Five primary sources are identified and quantified: diesel engine exhaust, gasoline engine exhaust, road dust, coal combustion, and biomass combustion. Important trends in the seasonal and spatial patterns of the impact of these five sources are observed. On average, primary emissions from fossil fuel combustion (coal, diesel, and gasoline) are responsible for about 25–33% of PM2.5 mass in Delhi, 21–36% in Mumbai, 37–57% in Kolkata, and 28% in Chandigarh. These figures can be compared to the biomass combustion contributions to ambient PM2.5 of 7–20% for Delhi, 7–20% for Mumbai, 13–18% for Kolkata, and 8% for Chandigarh. These measurements provide important information about the seasonal and spatial distribution of fine particle phase organic compounds in Indian cities as well as quantifying source contributions leading to the fine particle air pollution in those cities.
