Source Apportionment of Carbonaceous Aerosols Using Integrated Multi-variant and Source Tracer Techniques and a Unique Molecular Marker Data Set PDF Download

Author: James J. Schauer
Publisher:
ISBN:
Category : Aerosols
Languages : en
Pages : 212

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Author: Marissa Leigh Maier
Publisher:
ISBN:
Category : Aerosols
Languages : en
Pages :

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Four receptor models and a chemical transport model were used to quantify the sources of PM2.5 impacting the St. Louis Supersite (STL-SS) between June 2001 and May 2003. The receptor models utilized two independent datasets, one that included ions and trace elements and a second that incorporated 1-in-6 day organic molecular marker data. Since each source apportionment (SA) technique has its own limitations, this work compared the results of five different SA approaches to better understand the biases and limitations of each. The source impacts predicted by these five models were then integrated into an ensemble-trained SA methodology. The ensemble method offered several improvements over the five individual SA techniques. Primarily, the ensemble method calculated source impacts on days when individual models either did not converge to a solution or did not have adequate input data to develop source impact estimates. Additionally, the ensemble method resulted in fewer days on which major emissions sources (e.g., secondary organic carbon and diesel vehicles) were estimated to have either a zero or negative impact on PM2.5 concentrations at the STL-SS. When compared with a traditional chemical mass balance (CMB) approach using measurement-based source profiles (MBSPs), the ensemble method was associated with better fit statistics, including reduced chi-squared values and improved PM2.5 mass reconstruction. A comparison of the different modeling techniques also revealed some of the subjectivities associated with applying specific SA models to the STL-SS dataset. For instance, positive matrix factorization (PMF) results were very sensitive to both the fitting species and number of factors selected for the analysis, whereas source impacts predicted in CMB were sensitive to the selection of source profiles to represent local metals processing emissions. Additionally, the different SA approaches predicted different impacts for the same source on a given day, with correlation coefficients ranging from 0.03 to 0.66 for gasoline vehicle, -0.51 to 0.85 for diesel vehicles, -0.29 to 0.86 for dust, -0.34 to 0.76 for biomass burning, 0.22 to 0.72 for metals processing, and -0.70 to 0.68 for secondary organic carbon. These issues emphasized the value of using several different SA techniques at a given receptor site, either by comparing source impacts predicted by different models or by utilizing an ensemble-trained SA technique.

Author: Jeffrey Michael Jaeckels
Publisher:
ISBN:
Category :
Languages : en
Pages : 144

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Author: R. Subramanian
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Elizabeth Anne Stone
Publisher:
ISBN:
Category :
Languages : en
Pages : 279

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Author: David C. Coleman
Publisher: Academic Press
ISBN: 032315767X
Category : Science
Languages : en
Pages : 287

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Carbon Isotope Techniques deals with the use of carbon isotopes in studies of plant, soil, and aquatic biology. Topics covered include photosynthesis/translocation studies in terrestrial ecosystems; carbon relationships of plant-microbial symbioses; microbe/plant/soil interactions; and environmental and aquatic toxicology. Stable carbon isotope ratios of natural materials are also considered. Comprised of 15 chapters, this book begins with an introduction to radiation-counting instruments used in measuring the radioactivity in soil and plant samples containing carbon-14. The discussion then turns to the basic methods of 14C use in plant science, highlighted by three examples of applications in the field of plant physiology and ecology. Subsequent chapters explore the use of carbon isotope techniques for analyzing the carbon relationships of plant-microbial symbioses; the interactions of microbes, plants, and soils; and the degradation of herbicides and organic xenobiotics. Carbon dating and bomb carbon are also described. The final section is devoted to the uses and procedures for 13C and 11C. This monograph is intended for advanced undergraduate or graduate students, as well as generalist scientists who have not previously used radioisotopes or stable isotopes in their research.

Author: Caroline Parworth
Publisher:
ISBN: 9780355594157
Category :
Languages : en
Pages : 0

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Aerosols, or particulate matter (PM), can affect climate through scattering and absorption of radiation and influence the radiative properties, precipitation efficiency, thickness, and lifetime of clouds. Aerosols are one of the greatest sources of uncertainty in climate model predictions of radiative forcing. To fully understand the sources of uncertainty contributing to the radiative properties of aerosols, measurements of PM mass, composition, and size distribution are needed globally and seasonally. To add to the current understanding of the seasonal and temporal variations in aerosol composition and chemistry, this study has focused on the quantification, speciation, and characterization of atmospheric PM in urban and rural regions of the United States (US) for short and long periods of time. In the first two chapters, we focus on 1 month of aerosol and gas-phase measurements taken in Fresno, CA, an urban and agricultural area, during the National Aeronautics and Space Administration's (NASA) field study called DISCOVER-AQ. This air quality measurement supersite included a plethora of highly detailed chemical measurements of aerosols and gases, which were made at the same time as similar aircraft column measurements of aerosols and gases. The goal of DISCOVER-AQ is to improve the interpretation of satellite observations to approximate surface conditions relating to air quality, which can be achieved by making concurrent ground- and aircraft-based measurements of aerosols and gases. We begin in chapter 2 by exploring the urban aerosol and gas-phase dataset from the NASA DISCOVER-AQ study in California. Specifically, we discuss the chemical composition and mass concentration of water-soluble PM2.5 that were measured using a particle-into-liquid sampler with ion chromatography (PILS-IC) in Fresno, California from January 13–February 10, 2013. This data was analyzed for ionic inorganic species, organic acids and amines. Gas-phase species including HNO3 and NH3 were collected with annular denuders and analyzed using ion chromatography. Using the thermodynamic E-AIM model, inorganic particle water mass concentration and pH were calculated for the first time in this area. Organic particle water mass concentration was calculated from [kappa]-Köhler theory. In chapter 3 further analysis of the aerosol- and gas-phase data measured during DISCOVER-AQ was performed to determine the effectiveness of a local residential wood burning curtailment program in improving air quality. Using aerosol speciation and concentration measurements from the 2013 winter DISCOVER-AQ study in Fresno, CA, we investigate the impact of residential wood burning restrictions on fine particulate mass concentration and composition. Key species associated with biomass burning in this region include K+, acetonitrile, black carbon, and biomass burning organic aerosol (BBOA), which represents primary organic aerosol associated with residential wood burning. Reductions in acetonitrile associated with wood burning restrictions even at night were not observed and most likely associated with stagnant conditions during curtailment periods that led to the buildup of this long-lived gas. In chapter 4 we transition to the rural aerosol dataset from the DOE SGP site. We discuss the chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) that were measured with an aerosol chemical speciation monitor (ACSM) at the DOE SGP site from November 2010 through June 2012. Positive matrix factorization (PMF) was performed on the measured organic aerosol (OA) mass spectral matrix using a newly developed rolling window technique to derive factors associated with distinct sources, evolution processes, and physiochemical properties. The rolling window approach captured the dynamic variations of the chemical properties of the OA factors over time. Three OA factors were obtained including two oxygenated OA (OOA) factors, differing in degrees of oxidation, and a BBOA factor. Sources of NR-PM1 species at the SGP site were determined from back trajectory analyses. NR-PM1 mass concentration was dominated by organics for the majority of the study with the exception of winter, when NH4N33 increased due to transport of precursor species from surrounding urban and agricultural regions and also due to cooler temperatures. Chapter 5 is a continuation of chapter 4, where we will explore the use of the multilinear engine (ME-2) as a factor analysis technique, which is an algorithm used for solving the bilinear model called positive matrix factorization (PMF). The importance of ME-2 and its potential application on the long-term aerosol chemical speciation monitor (ACSM) data collected from the Department of Energy (DOE) Southern Great Plains (SPG) site will be discussed. ME-2 was performed on 19 months of OA mass spectral data obtained from the ACSM at the SGP site. Evaluation of ME-2 results are presented, followed by comparison of ME-2 factor results with corresponding OACOMP factor results reported in chapter 4. We show that ME-2 can determine a biomass burning organic aerosol (BBOA) factor during periods when OACOMP cannot. (Abstract shortened by ProQuest.)

Author: Philip Rund
Publisher:
ISBN:
Category :
Languages : en
Pages : 36

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Improvements in organic aerosol (OA) source apportionment techniques are investigated based on field measurements made in the Southeast US by a Chemical Ionization Mass Spectrometer (CIMS) equipped with a custom Filter Inlet for Gases and AEROsols (FIGAERO), as part of the Southern Oxidant and Aerosol Study (SOAS). Non-Negative Matrix Factorisation (NNMF) is applied to the particulate data in the form of both resolved thermograms and concentration timeseries. Assessments of the variance explained in the input data sets by the NNMF reconstructed approximation are used as a statistical tool for a less subjective choice of the number of factors. Linear correlation coefficients and vector phase angle are also used to produce a quantitative measure of the relative similarity between the output factors both temporally and in regards to composition. Each factor contains specific thermogram behavior (from which volatility information can be derived), unique weights for individual ions corresponding to individual molecular components of measured OA, and diurnal cycles. All three pieces of information were used to assign a specific source to each factor, ultimately showing that the dominant component of OA captured by the FIGAERO-CIMS stems from the oxidation of monoterpenes. Individual molecular components were permitted to belong to multiple and potentially all groupings of OA determined by NNMF, revealing certain factors with similar composition but remarkably different volatility and temporal trends. The median mass contribution determined from each factor produced by this factorisation routine, with no a priori information used as input, align well with those determined by an independent study of particle data during SOAS using a spectral basis set produced from several laboratory chamber experiments. The factorisation routine is shown to be more robust using resolved thermograms as input rather than the concentration timeseries. Of the seven factors given for the thermogram data, three were attributed to monoterpene-derived OA with respective extremely low, low, and semi-volatile behavior. These factors combined represent 68% of the total organic aerosol mass examined. Additionally, two factors were sourced to isoprene chemistry, one correlating with IEPOX-derived SOA, and the other likely relating to photochemistry and exhibiting relatively low volatility. The isporene-related factors accounted for 22% of OA mass. Notably absent in the factorisation of OA is a category exclusively capturing the behavior of particulate organic nitrates (PON). While this may be consistent with relatively low local concentrations of this class of particles, a separate factorisation was performed on only the PON in order to examine the volatility and temporal trends of these potentially important compounds. The added layer of volatility information and molecular level identification of OA composition provided by the FIGAERO-CIMS shows potential with the NNMF algorithm to reproduce atmospherically relevant sources from observations as well as providing framework to further identify chemical processes that lead to these categories based on volatility.

Author: Yaping Zhang
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 207

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Atmospheric organic aerosols are composed of thousands of individual compounds, interacting with climate through changes in aerosol optical properties and cloud interactions, and can be detrimental to human health. Aerosol mass spectrometry (MS) and gas chromatography (GC)-separated MS measurements have been utilized to better characterize the chemical composition of this material that comes from a variety of sources and experiences continuous oxidation while in the atmosphere. This dissertation describes the development of a novel rapid data analysis method for grouping of major components within chromatography-separated measurements and first application using thermal desorption aerosol gas chromatograph (TAG) -- MS data. Chromatograms are binned and inserted directly into a positive matrix factorization (PMF) analysis to determine major contributing components, eliminating the need for manual compound integrations of hundreds of resolved molecules, and incorporating the entirety of the eluting MS signal, including Unresolved Complex Mixtures (UCM) and decomposition products that are often ignored in traditional GC-MS analysis. Binned GC-MS data has three dimensions: (1) mass spectra index m/z, (2) bin number, and (3) sample number. PMF output is composed of two dimensions; factor profiles and factor time series. The specific arrangement of the input data (three dimensions of variation structured as a two dimensional matrix) in a two dimensional PMF analysis affects the structure of the PMF profiles and time series output. If mass spectra index is in the profile dimension, and bin number and sample number are in the time series dimension, PMF groups components into factors with similar mass spectra, such as major contributing individual compounds, UCM with similar functional composition, and homologous compound series. This type of PMF analysis is described as the binning method for chromatogram deconvolution, and is presented in Chapter 2. If the sample number is in the time series dimension, and the bin number and mass spectra index, arranged as mass spectra resolved retention time/chromatogram (bin number), are in the profile dimension, PMF groups components with similar time series trends. This type of PMF analysis is described as binning method for source apportionment, and is described in Chapter 3. The binning methods are compared to traditional compound integration methods using previously-collected hourly ambient samples from Riverside, CA during the 2005 Study of Organic Aerosols at Riverside (SOAR) field campaign, as discussed in Chapters 2-3. Further application of the binning method for source apportionment is performed on newly acquired hourly TAG data from East St. Louis, IL, operated as part of the 2013 St. Louis Air Quality Regional Study (SLAQRS). Major sources of biogenic secondary organic aerosol (SOA), anthropogenic primary organic aerosol (POA) were identified, as described in detail in Chapter 4. Finally, our PMF separation method was tested for reliability using primary and secondary sources in a controlled laboratory system. As shown in Chapter 5, we find that for application of PMF on receptor measurements, high signal intensity and unique measurement profiles, like those found in TAG chromatograms, are keys to successful source apportionment. The binning method with component separation by PMF may be a valuable analysis technique for other complex data sets that incorporate measurements (e.g., mass spectrometry, spectroscopy, etc.) with additional separations (e.g., volatility, hygroscopicity, electrical mobility, etc.).

Author: Stephen Mark Toner
Publisher:
ISBN:
Category :
Languages : en
Pages : 288

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Book Description
Methods of measuring the chemical and physical properties of aerosols as well as proper source apportionment of ambient particles are necessary to provide insight as to the roles they play in the environment and their impact on human health. In addition, the ability to apportion ambient particles quickly and accurately will be very helpful for environmental and health agencies and for monitoring and enforcing emission standards by allowing such agencies to determine the primary source of aerosols in their monitoring areas. The goal of this dissertation is to provide a new approach for aerosol source apportionment using aerosol time-of-flight mass spectrometry (ATOFMS) single particle data. This goal was accomplished by determining unique mass spectral signatures for specific aerosol sources and by developing these signatures into a source signature library in which ambient ATOFMS data can be matched and apportioned. The creation of the source signature library (SSL) began with the characterization of specific sources themselves. Heavy duty diesel vehicle (HDDV) emissions were characterized using ATOFMS from a dynamometer study. The particle types detected for HDDVs were compared to those from a previous dynamometer study of gasoline powered light duty vehicles (LDV) to see if HDDV and LDV particles can be distinguished. A SSL was then created for the HDDV and LDV emissions to test the ability to properly apportion between the two sources on ambient ATOFMS data collected next to a major freeway using a SSL matching technique. This work demonstrated that the two sources are readily distinguishable in a fresh emission environment, and that the matching method is a valid means for apportioning ATOFMS data. The SSL was then extended for multiple specific sources as well as for non-source specific particles and was used to apportion the same freeway study particles; showing that the source matching method is able to accurately distinguish different particle sources and that there can be a large contribution from sources other than vehicles near a major freeway. Lastly, the SSL matching method was used to apportion ambient aerosols for two major non-US cities to show that the SSL matching technique is applicable to worldwide ambient ATOFMS data.