Browsing by Subject "ORGANIC-AEROSOL"

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  • Brown, Steven G.; Eberly, Shelly; Paatero, Pentti; Norris, Gary A. (2015)
    The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to randomerrors and rotational ambiguity. To demonstrate the utility of the EEmethods, results are presented for three data sets: (1) speciated PM2.5 data froma chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species inwater quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high- resolution aerosolmass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results fromsensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying toomany factors. DISP diagnosticswere consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (
  • Hyttinen, Noora; Kupiainen-Maatta, Oona; Rissanen, Matti P.; Muuronen, Mikko; Ehn, Mikael; Kurten, Theo (2015)
    Several extremely low volatility organic compounds (ELVOCs) formed in the ozonolysis of endocyclic alkenes have recently been detected in laboratory and field studies. These experiments have been carried out with chemical ionization atmospheric pressure interface time-of-flight mass spectrometers (CI-APi-TOP) with nitrate ions as reagent ions. The nitrate ion binds to the detected species through hydrogen bonds, but it also binds very strongly to one or two neutral nitric acid molecules. This makes the measurement highly selective when there is an excess amount of neutral nitric acid in the instrument. In this work, we used quantum-chemical methods to calculate the binding energies between a nitrate ion and several highly oxidized ozonolysis products of cydohexene. These were then compared with the binding energies of nitrate ion nitric acid clusters. Systematic configurational sampling of the molecules and clusters was carried out at the B3LYP/6-31+G* and omega B97xD/aug-cc-pVTZ levels, and the final single-point energies were calculated with DLPNO-CCSD(T)/def2-QZVPP. The binding energies were used in a kinetic simulation of the measurement system to determine the relative ratios of the detected signals. Our results indicate that at least two hydrogen bond donor functional groups (in this case, hydroperoxide, OOH) are needed for an ELVOC molecule to be detected in a nitrate ion CI-APi-TOP. Also, a double bond in the carbon backbone makes the nitrate cluster formation less favorable.
  • Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prevot, Andre S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael (2017)
    Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statisticsbased data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k -means C C, for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral simi-larity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.
  • Hakala, Jani; Mikkilä, Jyri; Hong, Juan; Ehn, Mikael; Petäjä, Tuukka (2016)
    The aim of this study was to provide a description and characterize the performance of a volatility hygroscopicity tandem differential mobility analyzer (VH-TDMA) by calibration measurements. In our investigations, we used the two calibration standards most often used by the TDMA community: ammonium sulfate ((NH4)(2)SO4) and sodium chloride (NaCl) particles. The hygroscopic growth factors, volatility factors, and the hygroscopic growth factors of the nonvolatile particle core were measured in different relative humidity and thermal denuder temperature conditions. The measured hygroscopic growth and deliquescence relative humidities for ammonium sulfate and sodium chloride particles are in line with theory and results from previous measurements, as are the measurements for volatility and hygroscopicity of the particle core. We summarize the measurement campaigns, where the VH-TDMA has been deployed, and show the instrument is capable of measuring high quality data of atmospheric particle properties in various environments, in the laboratory and in the field.