Gary S. Casuccio

Characterizing Mineral Dusts and Other Aerosols from the Middle East—Part 1: Ambient Sampling

The purpose of the Enhanced Particulate Matter Surveillance Program was to provide scientifically founded information on the chemical and physical properties of dust collected over a period of approximately 1 year in Djibouti, Afghanistan (Bagram, Khowst), Qatar, United Arab Emirates, Iraq (Balad, Baghdad, Tallil, Tikrit, Taji, Al Asad), and Kuwait (northern, central, coastal, and southern regions). Three collocated low-volume particulate samplers, one each for the total suspended particulate matter, < 10 μ m in aerodynamic diameter (PM10) particulate matter, and < 2.5 μ m in aerodynamic diameter (PM2.5) particulate matter, were deployed at each of the 15 sites, operating on a ‘1 in 6’ day sampling schedule. Trace-element analysis was performed to measure levels of potentially harmful metals, while major-element and ion-chemistry analyses provided an estimate of mineral components. Scanning electron microscopy with energy dispersive spectroscopy was used to analyze the chemical composition of small individual particles. Secondary electron images provided information on particle size and shape. This study shows the three main air pollutant types to be geological dust, smoke from burn pits, and heavy metal condensates (possibly from metals smelting and battery manufacturing facilities). Non-dust storm events resulted in elevated trace metal concentrations in Baghdad, Balad, and Taji in Iraq. Scanning-electron-microscopy secondary electron images of individual particles revealed no evidence of freshly fractured quartz grains. In all instances, quartz grains had rounded edges and mineral grains were generally coated by clay minerals and iron oxides.

Single-particle SEM-EDX analysis of iron-containing coarse particulate matter in an urban environment: sources and distribution of iron within Cleveland, Ohio.

The physicochemical properties of coarse-mode, iron-containing particles and their temporal and spatial distributions are poorly understood. Single-particle analysis combining X-ray elemental mapping and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively collected particles was used to investigate the physicochemical properties of iron-containing particles in Cleveland, OH, in summer 2008 (Aug-Sept), summer 2009 (July-Aug), and winter 2010 (Feb-March). The most abundant classes of iron-containing particles were iron oxide fly ash, mineral dust, NaCl-containing agglomerates (likely from road salt), and Ca-S containing agglomerates (likely from slag, a byproduct of steel production, or gypsum in road salt). The mass concentrations of anthropogenic fly ash particles were highest in the Flats region (downtown) and decreased with distance away from this region. The concentrations of fly ash in the Flats region were consistent with interannual changes in steel production. These particles were observed to be highly spherical in the Flats region, but less so after transport away from downtown. This change in morphology may be attributed to atmospheric processing. Overall, this work demonstrates that the method of passive collection with single-particle analysis by electron microscopy is a powerful tool to study spatial and temporal gradients in components of coarse particles. These gradients may correlate with human health effects associated with exposure to coarse-mode particulate matter.

Characterizing Mineral Dusts and Other Aerosols from the Middle East—Part 2: Grab Samples and Re-Suspensions

The purpose of the Enhanced Particulate Matter Surveillance Program was to provide scientifically founded information on the chemical and physical properties of dust collected during a period of approximately 1 year in Djibouti, Afghanistan (Bagram, Khowst), Qatar, United Arab Emirates, Iraq (Balad, Baghdad, Tallil, Tikrit, Taji, Al Asad), and Kuwait (northern, central, coastal, and southern regions). To fully understand mineral dusts, their chemical and physical properties, as well as mineralogical inter-relationships, were accurately established. In addition to the ambient samples, bulk soil samples were collected at each of the 15 sites. In each case, approximately 1kg of soil from the top 10 mm at a previously undisturbed area near the aerosol sampling site was collected. The samples were air-dried and sample splits taken for soil analysis. Further sample splits were sieved to separate the < 38 μ m particle fractions for mineralogical analysis. Examples of major-element and trace-element chemistry, mineralogy, and other physical properties of the 15 grab samples are presented. The purpose of the trace-element analysis was to measure levels of potentially harmful metals while the major-element and ion-chemistry analyses provided an estimate of mineral components. X-ray diffractometry provided a measure of the mineral content of the dust. Scanning electron microscopy with energy dispersive spectroscopy was used to analyze chemical composition of small individual particles. From similarities in the chemistry and mineralogy of re-suspended and ambient sample sets, it is evident that portions of the ambient dust are from local soils.

Collaborative study of quantitative coal mineral analysis using computer-controlled scanning electron microscopy

Six laboratories collaborated in an international study of the computer-controlled scanning electron microscopy (CCSEM) method of quantitative coal mineral analysis. A total of five analyses were performed by most of the laboratories on three bituminous coal samples: Pittsburgh No. 8, Illinois No. 6 and Prince. Repeatability relative standard deviation (RSDr) was <20% for the four minerals analysed: calcite, kaolinite, pyrite and quartz. Reproducibility relative standard deviations (RSDR) ranged from 21 to 83%. Reproducibility of the kaolinite results was the poorest, with an average RSDR of 60%, and pyrite was the best, with an average RSDR of 22%. The reproducibility of calcite and quartz analysis results was similar, with an average RSDR of 38 and 36% respectively. Although pyrite content was determined the most precisely, normative mineral calculations indicate that the results are overbalanced. Improvement in the interlaboratory agreement of CCSEM results will require the development of a standardized calibration procedure.

Combining Raman Chemical Imaging and Scanning Electron Microscopy to Characterize Ambient Fine Particulate Matter

Raman chemical imaging and scanning electron microscopy (Raman/SEM) have been used in a preliminary study to determine the size, morphology, elemental and molecular composition, and molecular structure of fine particulate matter in several test samples and one ambient air sample. Raman chemical imaging and SEM, respectively, provide a way to spatially characterize a sample based on its molecular and elemental makeup. When combined, Raman chemical imaging and SEM provide detailed spatial, elemental, and molecular information for particulate matter as small as 250 nm. Initial studies demonstrate the potential of Raman/SEM for molecular and elemental determination of organic and inorganic fine particulate matter. This has been accomplished by analyzing samples with fine particulate matter using each method independently. Since both techniques are nondestructive, particles of interest can be relocated between instruments. Practical issues such as filter substrate compatibility and instrumentation compatibility are addressed. In addition, first results showing Raman/SEM chemical images from several standard materials, as well as ambient PM2.5 samples, are reported.

Heterogeneity of coarse particles in an urban area.

The variation in composition and concentration of coarse particles in Rochester, a medium-sized city in western New York, was studied using UNC passive samplers and computer-controlled scanning electron microscopy (CCSEM). The samplers were deployed in a 5 × 5 grid (2 km × 2 km per grid cell) for 2-3 week periods in two seasons (September 2008 and May 2009) at 25 different sites across Rochester. CCSEM analysis yielded size and elemental composition for individual particles and analyzed more than 800 coarse particles per sample. Based on the composition as reflected in the fluoresced X-ray spectrum, the particles were grouped into classes with similar chemical compositions using an adaptive resonance theory (ART) network. The mass fractions of particles in the identified classes were then used to assess the homogeneity of composition and concentration across the measurement domain. These results illustrate how particle sampling using the UNC passive sampler coupled with CCSEM/ART can be used to determine the concentration and source of the coarse particulate matter at multiple sites. The particle compositions were dominated by elements suggesting that the major particle sources are road dust and biological particles. Considerable heterogeneity in both composition and concentration were observed between adjacent sites as indicated by cofficient of divergence analyses.

Multivariate analysis of CCSEM auto emission data

Abstract Computer-controlled scanning electron microscopy (CCSEM) has proven to be a powerful tool in the characterization of individual particles and the source apportionment of ambient aerosol mass. The method can measure both physical properties of the particle including maximum and minimum diameters and area, as well as determine the major elemental composition of each particle. The particle volume and density can be estimated and the particle mass calculated. There are also powerful multivariate statistical procedures that can be directly applied to the individual particle data to assign any particular particle to the identified classes. The purpose of the current study was to explore the use of various hierarchical and non-hierarchical cluster analysis methods to define the number of distinct particle classes within an auto emission source sample. Each class was then separately modeled using a disjoint principal component procedure and membership of each particle in defined classes was tested. Mass fractions of each particle class and their uncertainties were calculated. These methods are being tested using auto emission source data from El Paso Quantitative Microscopy Study to identify sources of TSP and lead.

Elevated Concentrations of Lead in Particulate Matter on the Neighborhood-Scale in Delhi, India As Determined by Single Particle Analysis

High mass concentrations of atmospheric lead particles are frequently observed in the Delhi, India metropolitan area, although the sources of lead particles are poorly understood. In this study, particles sampled across Delhi (August - December 2008) were analyzed by computer-controlled scanning electron microscopy with energy dispersive X-ray spectroscopy (CCSEM-EDX) to improve our understanding of the spatial and physicochemical variability of lead-rich particles (>90% lead). The mean mass concentration of lead-rich particles smaller than 10 μm (PM10) was 0.7 μg/m(3) (1.5 μg/m(3) std. dev.) with high variability (range: 0-6.2 μg/m(3)). Four samples (16% of 25 samples) with PM10 lead-rich particle concentrations >1.4 μg/m(3) were defined as lead events and studied further. The temporal characteristics, heterogeneous spatial distribution, and wind patterns of events, excluded regional monsoon conditions or common anthropogenic sources from being the major causes of the lead events. Individual particle composition, size, and morphology analysis indicate informal recycling operations of used lead-acid batteries as the likely source of the lead events. This source is not typically included in emission inventories, and the observed isolated hotspots with high lead concentrations could represent an elevated exposure risk in certain neighborhoods of Delhi.

Development of a Transfer Function for a Personal, Thermophoretic Nanoparticle Sampler

Effective assessment of nanoparticle exposures requires accurate characterization of the aerosol. Of increasing concern is personal exposure to engineered nanoparticles that are specifically designed for use in the nanotechnology sector. This manuscript describes the operation and use of a personal sampler that utilizes thermophoretic force to collect nanoparticles onto a standard TEM (transmission electron microscope) grid. After collection, nanoparticles on the TEM grid are analyzed with an electron microscope, and the resultant data used to determine the characteristics of the nanoparticle aerosol sampled. Laboratory experiments were conducted to determine the inlet losses and collection efficiency of the thermophoretic sampler for particles between 20 and 600 nm in diameter. These results are used together with theory for thermophoretic velocity to form a transfer function that relates the properties of the collected particles to the properties of the sampled aerosol. The transfer function utilizes a normalization factor, F(d), which is larger than unity for very small particles but approaches unity for particles larger than about 70 nm. Copyright 2014 American Association for Aerosol Research

Source Apportionment of Soil Samples by the Combination of Two Neural Networks Based on Computer-Controlled Scanning Electron Microscopy

The apportionment of ambient aerosol mass to different sources of airborne soil is a difficult problem because of the similarity of the chemical composition of crustal sources. However, additional information can be obtained using individual particle analysis. A novel approach based on the combination of two neural networks, the adaptive resonance theory-based neural network (ART-2a) and the back-propagation (BP) neural network with electron microscopy data, has been developed to apportion the mass contributions of the crustal sources to ambient particle samples. The crustal source samples were analyzed using computer-controlled scanning electron microscopy (CCSEM). CCSEM provides elemental compositions and size parameters for individual particles as well as estimates of the shape and density from which the volume and mass of each particle can be estimated. The ART-2a neural network was first used to partition particles into homogeneous classes based on the elemental composition data. After the different particle type classes were produced by ART-2a, their mass fractions were calculated. In this way, the source profiles for the crustal dust sources can be obtained in terms of the mass fractions for different particle types. Then the BP neural network was applied to build the model between the mass fractions of different particle types and the mass contributions. Using the three physical source samples prepared for this study, artificial ambient samples were generated by randomly mixing particles from the three source samples. These samples were then used to examine the proposed method. Satisfactory predictions for the mass contributions of the three sources to the ambient samples have been obtained, indicating the proposed method is a promising tool for the source apportionment of chemically similar soil samples.