Sources of fine particulate species in ambient air over lake Champlain Basin, VT

This study is a part of an ongoing investigation of the types and locations of emission sources that contribute fine particulate air contaminants to Underhill, VT. The air quality monitoring data used for this study are from the Interagency Monitoring of Protected Visual Environments network for the period of 2001-2003 for the Underhill site. The main source-receptor modeling techniques used are the positive matrix factorization (PMF) and potential source contribution function (PSCF). This new study is intended as a comparison to a previous study of the 1988-1995 Underhill data that successfully revealed a total of 11 types of emission sources with significant contributions to this rural site. This new study has identified a total of nine sources: nitrate-rich secondary aerosol, wood smoke, East Coast oil combustion, automobile emission, metal working, soil/dust, sulfur-rich aerosol type I, sulfur-rich aerosol type II, and sea salt/road salt. Furthermore, the mass contributions from the PMF identified sources that correspond with sampling days with either good or poor visibility were analyzed to seek possible correlations. It has been shown that sulfur-rich aerosol type I, nitrate aerosol, and automobile emission are the most important contributors to visibility degradation. Soil/dust and sea salt/road salt also have an added effect.     

Assessment of source apportionment by Positive Matrix Factorization analysis on fine and coarse urban aerosol size fractions

This study was conducted in order to investigate the differences observed in source profiles in the urban environment, when chemical composition parameters from different aerosol size fractions are subjected to factor analysis. Source apportionment was performed in an urban area where representative types of emission sources are present. PM₁₀ and PM₂ samples were collected within the Athens Metropolitan area and analysed for trace elements, inorganic ions and black carbon. Analysis by two-way and three-way Positive Matrix Factorization was performed, in order to resolve sources from data obtained for the fine and coarse aerosol fractions. A difference was observed: seven factors describe the best solution in PMF3 while six factors in PMF2. Six factors derived from PMF3 analysis correspond to those described by the PMF2 solution for the fine and coarse particles separately. These sources were attributed to road dust, marine aerosol, soil, motor vehicles, biomass burning, and oil combustion. The additional source resolved by PMF3 was attributed to a different type of road dust. Combustion sources (oil combustion and biomass burning) were correctly attributed by PMF3 solely to the fine fraction and the soil source to the coarse fraction. However, a motor vehicle's contribution to the coarse fraction was found only by three-way PMF. When PMF2 was employed in PM₁₀ concentrations the optimum solution included six factors. Four source profiles corresponded to the previously identified as vehicles, road dust, biomass burning and marine aerosol, while two could not be clearly identified. Source apportionment by PMF2 analysis based solely on PM₁₀ aerosol composition data, yielded unclear results, compared to results from PMF2 and PMF3 analyses on fine and coarse aerosol composition data.     

Sources of Fine Particulate Species in Ambient Air over Lake Champlain Basin,VT

Abstract

This study is a part of an ongoing investigation of the types and locations of emission sources that contribute fine particulate air contaminants to Underhill, VT. The air quality monitoring data used for this study are from the Interagency Monitoring of Protected Visual Environments network for the period of 2001–2003 for the Underhill site. The main source-receptor modeling techniques used are the positive matrix factorization (PMF) and potential source contribution function (PSCF). This new study is intended as a comparison to a previous study of the 1988–1995 Underhill data that successfully revealed a total of 11 types of emission sources with significant contributions to this rural site. This new study has identified a total of nine sources: nitrate-rich secondary aerosol, wood smoke, East Coast oil combustion, automobile emission, metal working, soil/dust, sulfur-rich aerosol type I, sulfur-rich aerosol type II, and sea salt/road salt. Furthermore, the mass contributions from the PMF identified sources that correspond with sampling days with either good or poor visibility were analyzed to seek possible correlations. It has been shown that sulfur-rich aerosol type I, nitrate aerosol, and automobile emission are the most important contributors to visibility degradation. Soil/dust and sea salt/road salt also have an added effect.     

Causes of haze in the Columbia River Gorge

Visibility impairment in the Columbia River Gorge National Scenic Area is an area of concern. A field study conducted from July 2003 to February 2005 was followed by data analysis and receptor modeling to better understand the temporal and spatial patterns of haze and the sources contributing to the haze in the Columbia River Gorge in the states of Washington and Oregon. The nephelometer light scattering and surface meteorological data at eight sites along the gorge showed five distinct wind patterns, each with its characteristic diurnal and spatial patterns in light scattering by particles (bsp). In summer, winds were nearly always from west to east (upgorge) and showed decreasing bsp with distance into the gorge and a pronounced effect of the Portland, OR, metropolitan area on haze, especially in the western portions of the gorge. Winter often had winds from the east with very high levels of bsp, especially at the eastern gorge sites, with sources east of the gorge responsible for much of the haze. The major chemical components responsible for haze were organic carbon, sulfate, and nitrate. Positive matrix factorization (PMF) using chemically speciated Interagency Monitoring of Protected Visual Environments data indicated seven source factors in the western gorge and five factors in the eastern gorge. Organic mass is a large contributor to haze in the gorge in all seasons, with a peak in fall. The PMF analysis suggests that approximately half of the organic mass is biomass smoke, with mobile sources as the second largest contributor. PMF analysis showed nitrates (important in fall and winter) mainly attributed to a generic secondary nitrate factor, with the next largest contributor being oil combustion at Mt. Zion, WA and mobile sources at Wishram, WA. Sulfate is a significant contributor in all seasons, with peak sulfate concentrations in summer.     

Causes of Haze in the Columbia River Gorge

Abstract

Visibility impairment in the Columbia River Gorge National Scenic Area is an area of concern. A field study conducted from July 2003 to February 2005 was followed by data analysis and receptor modeling to better understand the temporal and spatial patterns of haze and the sources contributing to the haze in the Columbia River Gorge in the states of Washington and Oregon. The nephelometer light scattering and surface meteorological data at eight sites along the gorge showed five distinct wind patterns, each with its characteristic diurnal and spatial patterns in light scattering by particles (b_sp). In summer, winds were nearly always from west to east (upgorge) and showed decreasing b_sp with distance into the gorge and a pronounced effect of the Portland, OR, metropolitan area on haze, especially in the western portions of the gorge. Winter often had winds from the east with very high levels of b_sp, especially at the eastern gorge sites, with sources east of the gorge responsible for much of the haze. The major chemical components responsible for haze were organic carbon, sulfate, and nitrate. Positive matrix factorization (PMF) using chemically speciated Inter-agency Monitoring of Protected Visual Environments data indicated seven source factors in the western gorge and five factors in the eastern gorge. Organic mass is a large contributor to haze in the gorge in all seasons, with a peak in fall. The PMF analysis suggests that approximately half of the organic mass is biomass smoke, with mobile sources as the second largest contributor. PMF analysis showed nitrates (important in fall and winter) mainly attributed to a generic secondary nitrate factor, with the next largest contributor being oil combustion at Mt. Zion, WA and mobile sources at Wishram, WA. Sulfate is a significant contributor in all seasons, with peak sulfate concentrations in summer.     

Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matrix factorization

As part of a large exposure assessment and health-effects panel study, 33 trace elements and light-absorbing carbon were measured on 24-hr fixed-site filter samples for particulate matter with an aerodynamic diameter <2.5 microm (PM2.5) collected between September 26, 2000, and May 25, 2001, at a central outdoor site, immediately outside each subject's residence, inside each residence, and on each subject (personal sample). Both two-way (PMF2) and three-way (PMF3) positive matrix factorization were used to deduce the sources contributing to PM2.5. Five sources contributing to the indoor and outdoor samples were identified: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and a source of crustal-derived material. Vegetative burning contributed more PM2.5 mass on average than any other source in all microenvironments, with average values estimated by PMF2 and PMF3, respectively, of 7.6 and 8.7 microg/m3 for the outdoor samples, 4 and 5.3 microg/m3 for the indoor samples, and 3.8 and 3.4 microg/m3 for the personal samples. Personal exposure to the combustion-related particles was correlated with outdoor sources, whereas exposure to the crustal and chlorine-rich particles was not. Personal exposures to crustal sources were strongly associated with personal activities, especially time spent at school among the child subjects.     

The accuracy of two- and three-way positive matrix factorization models: Applying simulated multisite data sets

The application of three-way data sets (combined multisite data sets) for source apportionment has become common, but its influence on the performance of receptor modeling techniques has not yet been explored systematically. To study the influence of site-to-site correlations of source contributions and the spatial variability of source profiles on two- and three-way positive matrix factorization (PMF), simulated three-way data sets were constructed and modeled by different applications of PMF (PMF2 for each site individually, PMF2 for data sets combining all sites together, and PMF3 for all sites). In addition, the performance of PMF was evaluated under conditions of collinearity and different source categories at two sites. The results indicated that if the sites were contributed by sources with identical profiles, the site-to-site correlations of source contributions would not influence the PMF2, and the three-way blocks could be used by PMF2. However, the PMF2 using three-way data sets was sensitive to the spatial variability of source profiles. For the three-way model, PMF3 could perform well only when all of the sources exhibited strong site-to-site associations among all sites, and at the same time, the spatial variability of source profiles were sufficiently small. It might due to the algorithm that, for each source, PMF3 produces the same source profile and the same temporal variation in daily contributions among all sites.

Implications:?The application of multisite data sets for source apportionment has become common. However, limited work investigated the accuracy of two- and three-way PMFs when using multisite data sets. If the application of PMFs using multisite data sets were not appropriate, the results would be unreasonable. The unreasonable results would supply confused information for PM control strategies. In this work, simulated multisite data sets were modeled by different applications of PMFs. The effort to assess and compare the performance of two- and three-way PMFs using multisite data sets is very limited. The findings could provide information for multisite source apportionment.     

Sources of organic aerosol: Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models

This paper presents results from positive matrix factorization (PMF) of organic molecular marker data to investigate the sources of organic carbon (OC) in Pittsburgh, Pennsylvania. PMF analysis of 21 different combinations of input species found essentially the same seven factors with distinctive source-class-specific groupings of molecular markers. To link factors with source classes we directly compare PMF factor profiles with actual source profiles. Six of the PMF factors appear related to primary emissions from sources such as motor vehicles, biomass combustion, and food cooking. Each primary factor contributed between 5% and 10% of the annual-average OC with the exception of the cooking related factor which contributed 20% of the OC. However, the contribution of the cooking factor was sensitive to the specific combinations of input species. PMF could not differentiate between gasoline and diesel emissions, but the aggregate contribution of primary emissions from these two source classes is estimated to be less than 10% of the annual-average OC. One factor appears related to secondary organic aerosol based on its substantial contribution to biogenic oxidation products. This secondary factor contributed more than 50% of the summertime average OC. Reasonable agreement was observed between the PMF results and those of a previously published chemical mass balance (CMB) analysis of the same molecular marker dataset. Individual PMF factors are correlated with specific CMB sources, but systematic biases exist between the two estimates. These biases were generally within the uncertainty of the two estimates, but there is also evidence that PMF is not cleanly differentiating between source classes.     

Characterization of size segregated particles collected over Alaska and the CAnadian high Arctic,AGASP-II flights 204–206

Ten aircraft-collected cascade impactor samples from the North American Arctic were analyzed using analytical electron microscopy. Morphological, mineralogical and elemental information were obtained from individual particles, as well as compositional data and size distribution estimates of the bulk aerosol. Categorization of carbonaceous material into organic-type and combustion-type carbon particles was performed in this study. This was accomplished through the use of a new ultra-thin window X-ray spectrometer, which can directly detect carbon X-rays emitted from particles, and through interpretation of morphological and electron diffraction data. Verification of graphite as a specific carbon mineral phase present in Arctic soot particles was performed in this manner.Several classes of particles were present in most of the aerosol samples and size fractions. These included liquid H₂SO₄ droplets, which were always present in the highest numbers, and crustal-type and composite SO₄⁻² particles. A small fraction (0–30%) of a random sampling of SO²⁻₄particles from all impactor stages were found to contain detectable nitrogen, suggesting that partial neutralization by NH₃ may have occurred in this minority of the SO²⁻₄ droplets. Particles rich in non-combustion carbon and thought to be composed of organic material were also observed in most samples. Haze samples collected off the coast of Alert, NWT, show moderate loadings of H₂SO₄ droplets. Judging from these loadings and those from higher-altitude samples, ambient aerosol particle concentrations must have been considerably higher in the haze. The extent to which local activity at Alert has influenced these haze samples is not known, although a major contribution is not expected. Stratospheric samples did not contain several classes of particles thought to have major anthropogenic source inputs to the Arctic, such as black carbon and coal-fired combustion spheres. The lightest particle loadings in any samples were collected in the upper troposphere near the tropopause, where condensation nuclei counts during sampling fell to as low as 10 cm⁻³.     

Identification of sources affecting fog formation using receptor modeling approaches and inventory estimates of sectoral emissions

Positive matrix factorization (PMF) was used to identify factors affecting fog formation in Kanpur during the ISRO-GBP land campaign-II (LC-II) in December 2004. PMF predicted factors were validated by contrasting the emission strength of sources in the foggy and clear periods, using a combination of potential source contribution function (PSCF) analysis and quantitative emission inventory information. A time series aerosol chemical data set of 29 days and 12 species was decomposed to identify 4-factors: Secondary species, Biomass burning, Dust and Sea salt. PMF predicted particle mass with a satisfactory goodness-of-fit (slope of 0.83 ± 0.17 and R² of 0.8), and strong species within 11–12% relative standard deviation. Mean contributions of anthropogenic factors were significantly higher during the foggy period for secondary species (2.9 ± 0.3) and biomass burning (1.2 ± 0.09) compared to the clear period. Local sources contributing to aerosols that mediated fog events at Kanpur, based on emissions in a 200 km × 200 km area around Kanpur city were thermal power plants and transportation (SO₂) and biofuel combustion (BC and OM). Regional scale sources influencing emissions during the foggy period, in probable source regions identified by PSCF included thermal power plants, transportation, brick kilns and biofuel combustion. While biofuel combustion and transportation are distributed area sources, individual point sources include coal-fired thermal power plants located in Aligarh, Delhi, Ghaziabad, Jhansi, Kanpur, Rae Bareli and Rupnagar and brick kilns located in Allahabad, Agra, Farrukhabad, Ghaziabad, Kanpur, Ludhiana, Lucknow and Rae Bareli. Additionally, in the foggy period, large areas of probable source regions lay outside India, implying the significance of aerosol incursion from outside India.     

Improving source apportionment of fine particles in the eastern United States utilizing temperature-resolved carbon fractions

The objectives of this study were to examine the use of carbon fractions to identify particulate matter (PM) sources, especially traffic-related carbonaceous particle sources, and to estimate their contributions to the particle mass concentrations. In recent studies, positive matrix factorization (PMF) was applied to ambient fine PM (PM2.5) compositional data sets of 24-hr integrated samples including eight individual carbon fractions collected at three monitoring sites in the eastern United States: Atlanta, GA, Washington, DC, and Brigantine, NJ. Particulate carbon was analyzed using the Interagency Monitoring of Protected Visual Environments/Thermal Optical Reflectance method that divides carbon into four organic carbons (OC): pyrolized OC and three elemental carbon (EC) fractions. In contrast to earlier PMF studies that included only the total OC and EC concentrations, gasoline emissions could be distinguished from diesel emissions based on the differences in the abundances of the carbon fractions between the two sources. The compositional profiles for these two major source types show similarities among the three sites. Temperature-resolved carbon fractions also enhanced separations of carbon-rich secondary sulfate aerosols. Potential source contribution function analyses show the potential source areas and pathways of sulfate-rich secondary aerosols, especially the regional influences of the biogenic, as well as anthropogenic secondary aerosol. This study indicates that temperature-resolved carbon fractions can be used to enhance the source apportionment of ambient PM2.5.     

Source apportionment of particulate matter in a large city of southeastern Po Valley (Bologna, Italy)

This study reports the results of an experimental research project carried out in Bologna, a midsize town in central Po valley, with the aim at characterizing local aerosol chemistry and tracking the main source emissions of airborne particulate matter. Chemical speciation based upon ions, trace elements, and carbonaceous matter is discussed on the basis of seasonal variation and enrichment factors. For the first time, source apportionment was achieved at this location using two widely used receptor models (principal component analysis/multi-linear regression analysis (PCA/MLRA) and positive matrix factorization (PMF)). Four main aerosol sources were identified by PCA/MLRA and interpreted as: resuspended particulate and a pseudo-marine factor (winter street management), both related to the coarse fraction, plus mixed combustions and secondary aerosol largely associated to traffic and long-lived species typical of the fine fraction. The PMF model resolved six main aerosol sources, interpreted as: mineral dust, road dust, traffic, secondary aerosol, biomass burning and again a pseudo-marine factor. Source apportionment results from both models are in good agreement providing a 30 and a 33 % by weight respectively for PCA-MLRA and PMF for the coarse fraction and 70 % (PCA-MLRA) and 67 % (PMF) for the fine fraction. The episodic influence of Saharan dust transport on PM₁₀ exceedances in Bologna was identified and discussed in term of meteorological framework, composition, and quantitative contribution.     

Identification of sources of Phoenix aerosol by positive matrix factorization

Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavy-duty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO4(-2) and OC that may represent coal-fired power plant emissions. For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous particles, and they have higher contributions in winter. For the coarse particles, the major source contributions were soil and construction (high Ca). These sources also peaked in winter.     

Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site

Particle composition data for PM_2.5 samples collected at Kalmiopsis Interagency Monitoring of Protected Visual Environments (IMPROVE) site in southwestern Oregon from March 2000 to May 2004 were analyzed to provide source identification and apportionment. A total of 493 samples were collected and 32 species were analyzed by particle induced X-ray emission, proton elastic scattering analysis, photon-induced X-ray fluorescence, ion chromatography, and thermal optical reflectance methods. Positive matrix factorization (PMF) was used to estimate the source profiles and their mass contributions. The PMF modeling identified nine sources. In the Kalmiopsis site, the average mass was apportioned to wood/field burning (38.4%), secondary sulfate (26.9%), airborne soil including Asian dust (8.6 %), secondary nitrate (7.6%), fresh sea salt (5.8%), OP-rich sulfate (4.9%), aged sea salt (4.5 %), gasoline vehicle (1.9%), and diesel emission (1.4%). The potential source contribution function (PSCF) was then used to help identify likely locations of the regional sources of pollution. The PSCF map for wood/field burning indicates there is a major potential source area in the Siskiyou County and eastern Oregon. The potential source locations for secondary sulfate are found in western Washington, northwestern Oregon, and the near shore Pacific Ocean where there are extensive shipping lanes. It was not possible to extract a profile directly attributable to ship emissions, but indications of their influence are seen in the secondary sulfate and aged sea salt compositions.     

Source identification of atmospheric PCBs in Philadelphia/Camden using positive matrix factorization followed by the potential source contribution function

The concentrations of gas-phase polychlorinated biphenyls (PCBs) in the atmosphere of the Camden, NJ, USA are elevated by as much as 20 times over regional background. These high PCB levels are a concern because they lead to atmospheric deposition loadings of PCBs to the tidal Delaware River that exceed the entire total maximum daily load (TMDL). Two models were applied to the atmospheric PCB concentration data from Camden in an attempt to identify the PCB source types and regions. Positive matrix factorization (PMF) was used to identify the source types. Four factors were identified which are thought to represent sources such as volatilized Aroclors and particle-phase PCBs. The potential source contribution function (PSCF) model was then used to identify the geographic source regions by examining the origination points for air parcels that result in high PCB concentrations at the Camden receptor site. The PSCF model for ΣPCBs indicates PCB source regions throughout the Philadelphia–Camden metro area, including portions of both Pennsylvania and New Jersey. The PSCF plots for the resolved PMF factors suggest that factors 1–4 show fewer distinct source regions, indicating that their sources are diffuse and/or lie very close to the receptor site. The PSCF plots for factors 2 and 3 reveal very different source regions. Factor 2 primarily arises from the city of Philadelphia, whereas factor 3 originates in southern New Jersey and south of Philadelphia. This study demonstrates the utility of the combined PMF/PSCF approach in identifying atmospheric PCB source types and regions.     

A study on the potential sources of air pollutants observed at Tjörn,Sweden

The Potential Source Contribution Function (PSCF) receptor model combines both chemical and meteorological information. In this study, PSCF was employed to identify the potential source emission regions for aerosol compositions measured at Tjörn, Sweden (58.01 °N, 11.36 °E). PSCF was for the first time applied on a European scale. One hundred and fifty-two four-day air parcel backward trajectories were combined with concentrations of sixteen elements determined in 33 coarse and fine aerosol samples. The observations were made between February 17 and March 26, 1985. The modeling results of the heavy metals V, Pb, Zn, and As are presented and compared with available emission inventory data. A number of known industrialized regions in the former USSR and Europe are found of high potential to be the emission source areas. These areas are in good agreement with the known emission information. The PSCF maps of total sulfur, Non-Seasalt-Sulfur (N.S.S.) and chlorine are also presented. High potential regions in the Arctic area exist in the PSCF map for total sulfur wheres they do not occur in that for N.S.S.     

Sources and source variations for aerosol at Mace Head,Ireland

The sources and source variations for aerosol at Mace Head, Ireland, were studied by applying positive matrix factorization (PMF), a variant of factor analysis, to a 5-yr data set for bulk aerosol. Signals for the following six sources were evident year round: (1) mineral dust, (2) sea salt, (3) general pollution, (4) a secondary SO₄²⁻–Se signal that is composed of both natural (marine) and pollution (coal) components, (5) ferrous industries, (6) and a second marine (possibly biogenic) source. Analyses of seasonally stratified data suggested additional sources for iodine and oil emissions but these were present only in certain seasons, respectively. The marine signal is particularly strong in winter. The main pollution transport from Europe to Mace Head occurs in May, but the influence of continental European emissions is evident throughout the year. Mineral aerosol evidently follows a transport pathway similar to that of pollution aerosol, i.e., recirculation via the westerlies brings pollutants mixed with dust to the site from nearby land, i.e., Ireland, the United Kingdom, and the Belgium, Netherlands, and Luxemburg (Benelux) region, with some inputs from Scandinavia, Western Europe, Eastern Europe, and even the Mediterranean region. Compared with Bermuda, aerosol at Mace Head has stronger marine sources (especially marine-derived secondary SO₄²⁻ and Se) but weaker crustal and oil signals. Transport across the North Atlantic, especially in winter, cannot be ruled out.     

Sources,distribution, and acidity of sulfate–ammonium aerosol in the Arctic in winter–spring

We use GEOS-Chem chemical transport model simulations of sulfate–ammonium aerosol data from the NASA ARCTAS and NOAA ARCPAC aircraft campaigns in the North American Arctic in April 2008, together with longer-term data from surface sites, to better understand aerosol sources in the Arctic in winter–spring and the implications for aerosol acidity. Arctic pollution is dominated by transport from mid-latitudes, and we test the relevant ammonia and sulfur dioxide emission inventories in the model by comparison with wet deposition flux data over the source continents. We find that a complicated mix of natural and anthropogenic sources with different vertical signatures is responsible for sulfate concentrations in the Arctic. East Asian pollution influence is weak in winter but becomes important in spring through transport in the free troposphere. European influence is important at all altitudes but never dominant. West Asia (non-Arctic Russia and Kazakhstan) is the largest contributor to Arctic sulfate in surface air in winter, reflecting a southward extension of the Arctic front over that region. Ammonium in Arctic spring mostly originates from anthropogenic sources in East Asia and Europe, with added contribution from boreal fires, resulting in a more neutralized aerosol in the free troposphere than at the surface. The ARCTAS and ARCPAC data indicate a median aerosol neutralization fraction [NH₄⁺]/(2[SO₄^2?] + [NO₃^?]) of 0.5 mol mol^?1 below 2 km and 0.7 mol mol^?1 above. We find that East Asian and European aerosol transported to the Arctic is mostly neutralized, whereas West Asian and North American aerosol is highly acidic. Growth of sulfur emissions in West Asia may be responsible for the observed increase in aerosol acidity at Barrow over the past decade. As global sulfur emissions decline over the next decades, increasing aerosol neutralization in the Arctic is expected, potentially accelerating Arctic warming through indirect radiative forcing and feedbacks.     

Cloud condensation nuclei associated with arctic haze

Measurements are reported of the cloud condensation nucleus spectrum of Arctic haze from which it has been possible to deduce that a large fraction of the cloud active nuclei are soluble salts. On the basis of these findings, maximum supersaturation during the formation of Arctic stratiform clouds is expected to be around a third of a percent (for updrafts of 10 cm s⁻¹). Soluble nuclei down to 3–4 × 10⁻⁶ radius would be nucleated under these conditions. These inferences suggest that clouds forming in polluted Arctic air may contain relatively small (i.e. 10–30 cm⁻³) cloud droplet concentrations.     

Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

ABSTRACT

Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavy-duty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO₄ ^-2 and OC that may represent coal-fired power plant emissions.

For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous particles, and they have higher contributions in winter. For the coarse particles, the major source contributions were soil and construction (high Ca). These sources also peaked in winter.