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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.
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.)
Anthropogenic emissions of submicron particulate matter (PM1) reduce the quality of life and life expectancy globally. The Kathmandu Valley in Nepal endures unmitigated aerosol pollution. The air quality index in this remote urban basin can be as hazardous as megacities’, like Delhi, which is eight-fold more populated. Ambient measurements in April 2015 and winter 2018 and mobile measurements in April 2018 characterized PM1 for the Nepal ambient monitoring and source testing experiment (NAMaSTE). Measurements of non-refractory PM1¬ (NR-PM1) size, concentration, and chemical composition by aerosol mass spectrometers (AMS) characterize the regional aerosols. Understanding ambient PM1 concentration, chemical composition, organic aerosol sources, and spatial variation is essential for pollution mitigation. The enhancement of PM1 from background to urban regions was 120%. Organic aerosol (OA, 50%) and black carbon (BC, 20%) dominate stationary PM1 mass. On-road PM1 had an elevated BC component of 60%. Carbonaceous aerosols were typically 70% or more of PM1 mass. Mobile measurements characterized an enhancement of sulfate aerosol (SO42- ) by 150% from background to industrial brick kilns regions. Source apportionment of OA by positive matrix factorization (PMF) identifies traffic, biomass, trash, and coal combustion, and a dominant fraction of secondary organic aerosols (57%). Traffic, the prominent primary source of PM1, contributed 18% of ambient OA and 36% of BC. In the city center the contribution of primary emissions was up to 60%. Brick kilns were a primary source of organic and inorganic sulfate emissions, and trash burning plumes a source of aerosol hydrogen chloride (HCl). The impact of distinct source factors varied by hour of day and measurement location. This study characterized significant ambient PM1 sources and the effect of measurement location on concentration and composition. Characterizing the impact from each emission source provides critical insight for local air quality management. The fast time response of ground-based mobile measurements and in-situ instrumentations allowed for the determination of distinct plumes and local sources, in addition to regional variation. This distinction is impossible from stationary or satellite measurement platforms. The combination of stationary and mobile measurements across the Kathmandu Valley characterizes the spatial and temporal impacts on PM1 concentration and chemical composition.