Estimates of Anthropogenic Secondary Organic Aerosol Formation in Houston, Texas Special Issue of<i>Aerosol Science and Technology</i>on Findings from the Fine Particulate Matter Supersites Program

Dechapanya, Wipawee; Russell, Matthew; Allen, David T.

doi:10.1080/02786820390229462

Cited by 35 publications

(8 citation statements)

References 10 publications

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“…These characteristics and O : C and H : C ratios are typical of primary organic aerosol (Zhang et al, 2011;Aiken et al, 2008). This classification was further confirmed by spectral contrast angles (θ ) of ∼ 13 to 15 • between this factor and previously reported HOA factors (Aiken et al, 2009;Docherty et al, 2011;Mohr et al, 2012;Elser et al, 2016), indicating strong similarities in the mass spectra HOA (Kaltsonoudis et al, 2017).…”

Section: Composition Of Pm 25 Determined By Filter-based Measurementssupporting

confidence: 86%

“…4) exhibited a statistically significant moderate correlation with particulate sulfate levels (r = 0.5, p < 0.01), suggesting, to some extent, its formation on a regional scale (Peng et al, 2016). These observations, along with the resemblance of the mass signature of this factor with MO-OOA factors reported in previous studies (θ below 17 • ) (Docherty et al, 2011;Mohr et al, 2012), led to the classification of this factor as atmospherically processed OA resembling MO-OOA.…”

Section: Composition Of Pm 25 Determined By Filter-based Measurementssupporting

confidence: 58%

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Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

et al. 2018

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Abstract. Online and offline measurements of ambient particulate matter (PM) near the urban and industrial Houston Ship Channel in Houston, Texas, USA, during May 2015 were utilized to characterize its chemical composition and to evaluate the relative contributions of primary, secondary, biogenic, and anthropogenic sources. Aerosol mass spectrometry (AMS) on nonrefractory PM1 (PM ≤ 1 µm) indicated major contributions from sulfate (averaging 50 % by mass), organic aerosol (OA, 40 %), and ammonium (14 %). Positive matrix factorization (PMF) of AMS data categorized OA on average as 22 % hydrocarbon-like organic aerosol (HOA), 29 % cooking-influenced less-oxidized oxygenated organic aerosol (CI-LO-OOA), and 48 % more-oxidized oxygenated organic aerosol (MO-OOA), with the latter two sources indicative of secondary organic aerosol (SOA). Chemical analysis of PM2.5 (PM ≤ 2.5 µm) filter samples agreed that organic matter (35 %) and sulfate (21 %) were the most abundant components. Organic speciation of PM2.5 organic carbon (OC) focused on molecular markers of primary sources and SOA tracers derived from biogenic and anthropogenic volatile organic compounds (VOCs). The sources of PM2.5 OC were estimated using molecular marker-based positive matric factorization (MM-PMF) and chemical mass balance (CMB) models. MM-PMF resolved nine factors that were identified as diesel engines (11.5 %), gasoline engines (24.3 %), nontailpipe vehicle emissions (11.1 %), ship emissions (2.2 %), cooking (1.0 %), biomass burning (BB, 10.6 %), isoprene SOA (11.0 %), high-NOx anthropogenic SOA (6.6 %), and low-NOx anthropogenic SOA (21.7 %). Using available source profiles, CMB apportioned 41 % of OC to primary fossil sources (gasoline engines, diesel engines, and ship emissions), 5 % to BB, 15 % to SOA (including 7.4 % biogenic and 7.6 % anthropogenic), and 39 % to other sources that were not included in the model and are expected to be secondary. This study presents the first application of in situ AMS-PMF, MM-PMF, and CMB for OC source apportionment and the integration of these methods to evaluate the relative roles of biogenic, anthropogenic, and BB-SOA. The three source apportionment models agreed that ∼ 50 % of OC is associated with primary emissions from fossil fuel use, particularly motor vehicles. Differences among the models reflect their ability to resolve sources based upon the input chemical measurements, with molecular marker-based methods providing greater source specificity and resolution for minor sources. By combining results from MM-PMF and CMB, BB was estimated to contribute 11 % of OC, with 5 % primary emissions and 6 % BB-SOA. SOA was dominantly anthropogenic (28 %) rather than biogenic (11 %) or BB-derived. The three-model approach demonstrates significant contributions of anthropogenic SOA to fine PM. More broadly, the findings and methodologies presented herein can be used to advance local and regional understanding of anthropogenic contributions to SOA.

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Section: Composition Of Pm 25 Determined By Filter-based Measurementssupporting

confidence: 86%

Section: Composition Of Pm 25 Determined By Filter-based Measurementssupporting

confidence: 58%

Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

et al. 2018

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show abstract

“…These characteristics and O : C and H : C ratios are typical of primary organic aerosol Aiken et al, 2008). This classification was further confirmed by spectral contrast angles (θ ) of ∼ 13 to 15 • between this factor and previously reported HOA factors (Aiken et al, 2009;Docherty et al, 2011;Mohr et al, 2012;Elser et al, 2016), indicating strong similarities in the mass spectra HOA (Kaltsonoudis et al, 2017).…”

Section: Pmf Of Ams Data: Factor Identity and Contribution To Oasupporting

confidence: 86%

Anthropogenic SOA tracers in molecular marker-based PMF

Stone¹

2018

Preprint

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Online and offline measurements of ambient particulate matter (PM) near the urban and industrial Houston Ship Channel in Houston, Texas, USA, during May 2015 were utilized to characterize its chemical composition and to evaluate the relative contributions of primary, secondary, biogenic, and anthropogenic sources. Aerosol mass spectrometry (AMS) on nonrefractory PM 1 (PM ≤ 1 µm) indicated major contributions from sulfate (averaging 50 % by mass), organic aerosol (OA, 40 %), and ammonium (14 %). Positive matrix factorization (PMF) of AMS data categorized OA on average as 22 % hydrocarbon-like organic aerosol (HOA), 29 % cooking-influenced less-oxidized oxygenated organic aerosol (CI-LO-OOA), and 48 % more-oxidized oxygenated organic aerosol (MO-OOA), with the latter two sources indicative of secondary organic aerosol (SOA). Chemical analysis of PM 2.5 (PM ≤ 2.5 µm) filter samples agreed that organic matter (35 %) and sulfate (21 %) were the most abundant components. Organic speciation of PM 2.5 organic carbon (OC) focused on molecular markers of primary sources and SOA tracers derived from biogenic and anthropogenic volatile organic compounds (VOCs). The sources of PM 2.5 OC were estimated using molecular marker-based positive matric factorization (MM-PMF) and chemical mass balance (CMB) models. MM-PMF resolved nine factors that were identified as diesel engines (11.5 %), gasoline engines (24.3 %), nontailpipe vehicle emissions (11.1 %), ship emissions (2.2 %), cooking (1.0 %), biomass burning (BB, 10.6 %), isoprene SOA (11.0 %), high-NO x anthropogenic SOA (6.6 %), and low-NO x anthropogenic SOA (21.7 %). Using available source profiles, CMB apportioned 41 % of OC to primary fossil sources (gasoline engines, diesel engines, and ship emissions), 5 % to BB, 15 % to SOA (including 7.4 % biogenic and 7.6 % anthropogenic), and 39 % to other sources that were not included in the model and are expected to be secondary.This study presents the first application of in situ AMS-PMF, MM-PMF, and CMB for OC source apportionment and the integration of these methods to evaluate the relative roles of biogenic, anthropogenic, and BB-SOA. The three source apportionment models agreed that ∼ 50 % of OC is associated with primary emissions from fossil fuel use, particularly motor vehicles. Differences among the models reflect their ability to resolve sources based upon the input chemical measurements, with molecular marker-based methods providing greater source specificity and resolution for minor sources. By combining results from MM-PMF and CMB, BB was estimated to contribute 11 % of OC, with 5 % primary emissions and 6 % BB-SOA. SOA was dominantly anthropogenic (28 %) rather than biogenic (11 %) or BB-derived. The threemodel approach demonstrates significant contributions of anthropogenic SOA to fine PM. More broadly, the findings and methodologies presented herein can be used to advance local and regional understanding of anthropogenic contributions to SOA.

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“…In these models, oxidation processes of VOCs are expressed simply by a mass-based stoichiometric coefficient (a i ) of SVOC production resulting from oxidation of precursor VOCs. Empirical models have been widely used for global and regional models owing to their computational efficiency [Andersson-Sköld and Simpson, 2001;Schell et al, 2001;Chung and Seinfeld, 2002;Tsigaridis and Kanakidou, 2003;Andreani-Aksoyoglu et al, 2004;Dechapanya et al, 2004;Lack et al, 2004;Fan et al, 2005;Russell and Allen, 2005;Heald et al, 2005].…”

Section: Introductionmentioning

confidence: 99%

Secondary organic aerosol formation in urban air: Temporal variations and possible contributions from unidentified hydrocarbons

et al. 2009

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[1] Quantitative evaluation of the performance of one of the most advanced mechanistic secondary organic aerosol (SOA) modules/models, the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution 2 (MADRID2) in the three-dimensional Models-3/ Community Multiscale Air Quality (CMAQ), in urban air is made. Model calculations are compared for the Tokyo, Japan, metropolitan area with measurements made using an Aerodyne quadrupole aerosol mass spectrometer (Q-AMS) at an urban site for 9 days in July and August 2003. In general, model calculations reproduced absolute values and temporal variations of meteorological parameters, C 2 -C 8 volatile organic compounds (VOCs), NO x (NO + NO 2 ), inorganic aerosols, and O 3 concentrations reasonably well at this site. However, model-calculated SOA concentrations are a factor of 5 smaller than observed oxygenated organic aerosol (OOA) concentrations, and calculated total organic aerosol (OA = SOA + primary organic aerosol) concentrations are smaller by a factor of 2, indicating missing processes or sources in the current organic aerosol model calculations. On the other hand, observed features of diurnal and day-to-day variations of OOA were captured by our model calculations. Because of the large quantity of unidentified total nonmethane VOCs (NMVOCs) in urban air, a possible contribution of SOA formation from high-molecular-weight VOCs is examined through simple sensitivity studies, in which emissions are increased to account for unidentified NMVOCs. It is found that they are potentially one of the missing SOA sources, demonstrating the importance of reliable measurements of high-molecular-weight VOCs and total NMVOCs. Relationships between SOA and O 3 , including regional ($150 Â 150 km 2 ) enhancements around the Tokyo metropolitan area, are also discussed.

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Estimates of Anthropogenic Secondary Organic Aerosol Formation in Houston, Texas Special Issue ofAerosol Science and Technologyon Findings from the Fine Particulate Matter Supersites Program

Cited by 35 publications

References 10 publications

Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

Anthropogenic SOA tracers in molecular marker-based PMF

Secondary organic aerosol formation in urban air: Temporal variations and possible contributions from unidentified hydrocarbons

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