Dust storms are significant contributors to ambient levels of particulate matter (PM) in many areas of the world. Central Asia, an area that is relatively understudied in this regard, is anticipated to be affected by dust storms due to its proximity to several major deserts that are in and generally surround Central Asia (e.g., the Aral Sea region, the Taklimakan desert in Western China). To investigate the relative importance of mineral dust (dust specifically composed of soil related minerals and oxides) in Central Asia, PM10 and PM2.5, and by difference, coarse particles (particles with diameters between 2.5 and 10 gm) were measured at two sites, Bishkek and Lidar Station Teplokluchenka (Lidar), in the Kyrgyz Republic. Samples were collected every other day from July 2008 to July 2009. Daily samples were analyzed for mass and organic and elemental carbon. Samples were also composited on a bi-weekly basis and analyzed for elemental constituents and ionic components. In addition, samples collected on days with relatively high and low PM concentrations were analyzed before, and separately, from the biweekly composites to investigate the chemical differences between the episodic events. Data from the episodic samples were averaged into the composited averages. Using the elemental component data, several observational models were examined to estimate the contribution of mineral dust to ambient PM levels. A mass balance was also conducted.Results indicate that at both sites, mineral dust (as approximated by the "dust oxide" model) and organic matter (OM) were the dominant contributors to PM10 and PM2.5. Mineral dust was a more significant contributor to the coarse PM (PM10-2.5) during high event samples at both sites, although the relative contribution is greater at the Lidar site (average +/- standard deviation = 42 +/- 29%) as compared with the Bishkek site (26 +/- 16%). Principal Components Analysis (PCA) was performed using data from both sites, and PCA indicated that mineral dust explained the majority of the variance in PM concentrations, and that the major apportioned factors of PM10 and PM2.5 were chemically similar between sites. (C) 2015 Elsevier Ltd. All rights reserved.

The contributions of anthropogenic and biogenic secondary organic carbon (SOC) to total PM2.5 mass are of interest to air quality management agencies required to demonstrate maintenance of the PM2.5 NAAQS. Reductions of SOC can be used in conjunction with the mitigation of other PM2.5 constituents to maintain PM2.5 concentrations below the regulatory limit. Currently, quantitative tools to understand the SOC source contributions to PM2.5 mass are not well developed, and the spatial variation of different types of SOC is not known.In this study concentrations of anthropogenic and biogenic SOC mass were determined using PM2.5 measurements made in Cleveland, OH and Mingo Junction, OH. Twenty-four hour averaged samples were collected on the EPA 1-in-6 day schedule over the course of one year between June 2007 and May of 2008. Organic molecular markers for anthropogenic and biogenic SOC were extracted from the PM2.5, silylated, and then analyzed by GC MS. Source apportionment calculations were conducted using the EPA CMB (v.8.2) software and organic molecular markers as source tracers.SOC concentrations calculated from SOC tracers measurements followed the expected seasonal patterns with maximum contributions during the summer and minimum contributions during the winter. Anthropogenic SOC constituted approximately 37% to the apportioned SOC and 6% to the measured OC, on average across both sites. Biogenic SOC contributed the 42% to the apportioned SOC, and 4% to the measured DC. Anthropogenic SOC contributed strongly to organic PM2.5 meaning that SOC may by partially controllable by reductions in VOC emissions from anthropogenic sources.Similarities in the month-to-month patterns in a-pinene markers were observed between Cleveland and Mingo Junction, suggesting a regional character to this type of SOC. However, such patterns were not readily apparent in the isoprene markers.Limitations were found in the current version of the model. Approximately half of the water soluble organic carbon unrelated to biomass burning (NB-WSOC) during spring, summer and early fall could not be apportioned by the CMB model with the SOC markers available during this study. This suggested that additional sources not included in the CMB model used in this study contributed to SOC, or that models using markers measured in chamber oxidations are not entirely representative of the study sites. The unapportioned OC did not correlate particularly well with any of the known OC sources. While performance of the model is limited due to uncertainties in the source profiles, the apportionments calculated still give a preliminary insight into the relative contributions to Soc from anthropogenic and biogenic emissions. (C) 2013 Elsevier Ltd. All rights reserved.

Considerable uncertainty still exists regarding the contribution of resuspended soil and road dust to PM2.5 organic carbon (OC) in US urban areas. Contributing factors are the limited knowledge of the OC content of resuspended soils and road dusts, and the variability of the ratio of OC to traditional soil markers such as silicon and aluminum. This study investigates the composition of resuspended soils and road dusts in the Midwestern US, and the contributions of these soils to atmospheric PM2.5 OC. Paved road dust and soil samples were resuspended in a residence chamber from which PM2.5 size fractions were collected and analyzed to generate source profiles. Differences significant to 1 standard deviation were observed in the mass ratios between OC, and silicon and aluminum across different soil types which were larger between soil types within each city (61–97%), than between samples of the same soil type collected in different cities (29–57%). CMB 8.2 source apportionment results revealed large biases in soil apportionments, but these did not greatly affect the overall OC apportionments in six Midwest cities due to the small contributions of soil to total OC (ranging between 0.01 and 0.1μgm−3). Apportionments of total PM2.5 mass were more greatly affected: biases up to 0.7μgm−3 for total PM2.5 masses ranging between 7 and 14μgm−3 were observed when soil profiles were interchanged.

Carbonaceous atmospheric particulate matter (PM2.5) collected in the midwestern United States revealed that soot emissions from incomplete coal combustion were important sources of several organic molecular markers used in source apportionment studies. Despite not constituting a major source of organic carbon in the PM2.5, coal soot was an important source of polyaromatic hydrocarbons, hopanes, and elemental carbon. These marker compounds are becoming widely used for source apportionment of atmospheric organic PM, meaning that significant emissions of these marker compounds from unaccounted sources such as Coal Soot Could bias apportionment results. This concept was demonstrated using measurements of atmospheric PM collected on a 1-in-6 day schedule at three monitoring sites in Ohio: Mingo Junction (near Steubenville), Cincinnati, and Cleveland. Impacts of coal soot were measured to be significant at Mingo Junction and small at Cleveland and Cincinnati. As a result biases in apportionment results were substantial at Mingo Junction and insignificant at Cleveland and Cincinnati. Misapportionments of organic carbon mass at Mingo Junction were significant when coal soot was detected in the particulate samples as identified by the presence of picene, but when coal soot was not included in the model: gasoline engines (+8% to +58% of OC), smoking engines (0% to -17% of OC), biomass combustion (+1% to +11% of OC), diesel engines (-1% to -2% of OC), natural gas combustion (0% to -2% of OC), and unapportioned OC (0% to -47% of OC). These results suggest that the role of coal soot in source apportionment, studies needs to be better examined in many parts of the United States and other parts of the world.