Organic aerosol in the summertime Southeastern United States: Components and 
their link to volatility distribution, oxidation state and hygroscopicity

Kostenidou, Evangelia; Karnezi, Eleni; Hite, J. R.; Bougiatioti, Aikaterini; Cerully, K. M.; Xu, Lu; Ng, N. L.; Nenes, Athanasios; Pandis, Spyros N.

doi:10.5194/acp-2017-1010

Cited by 4 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Dall'Osto et al (2015) argued that the interpretation of the COA factor may be problematic as it may include OA from other 70 sources and not just cooking. Kostenidou et al (2018) also argued that the bbOA factor determined in the South US by Xu et al (2017) may include oxygenated OA from other sources. Yuan et al (2012) suggested that PMF factors may correspond to different stages of photochemical processing, rather than to independent sources.…”

Section: Introductionmentioning

confidence: 99%

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Drosatou¹,

Skyllakou²,

Theodoritsi³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Factor analysis of Aerosol Mass Spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is <q>chemically different</q> from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30&#8201;% and of the other primary sources less than 40&#8201;%, when these sources contribute more than 20&#8201;% to the total OA. For contributions between 10 and 20&#8201;% the corresponding uncertainties increase to 50&#8201;%. Finally, when these sources are small (less than 10&#8201;% of the OA) the corresponding error is a factor of two or even three. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low and high volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as low-volatility factor is indeed lower than that of the other (high volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly-aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher SOA from volatile, semi-volatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.

show abstract

Section: Introductionmentioning

confidence: 99%

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Drosatou¹,

Skyllakou²,

Theodoritsi³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The TD used in this campaign is a commercial product from ARI and has been applied widely in laboratory experiments (Saha et al, 2015;Huffman et al, 2009b;Grieshop et al, 2009b;Lee et al, 2011;May et al, 2013c;Saha and Grieshop, 2016;Sato et al, 2018;Champion et al, 2019 and reference therein) and field studies (Huffman et al, 2009a;Hu et al, 2016c;Paciga et al, 2016;Louvaris et al, 2017b;Cao et al, 2018;Kostenidou et al, 2018;Xu et al, 2021;Zhu et al, 2021 and reference therein). The TD consists of two parts: a heated section and a denuder section.…”

Section: Thermodenuder and Volatility Estimationmentioning

confidence: 99%

“…The volatility distribution of OA can be estimated based on either an empirical method (Faulhaber et al, 2009) or an evaporation kinetic model (Riipinen et al, 2010;Saha et al, 2015;Cappa, 2010a;Karnezi et al, 2014;Saha et al, 2017;Park et al, 2012;Epstein et al, 2009;Louvaris et al, 2017a). Based on the TD-AMS system, volatilities of ambient OA from urban/suburban (Xu et al, 2021;Louvaris et al, 2017b;Cao et al, 2018;Huffman et al, 2009a;Cappa and Jimenez, 2010;Saha et al, 2018;Paciga et al, 2016;Kang et al, 2022), rural (Zhu et al, 2021) and forest sites (Hu et al, 2016c;Saha et al, 2017;Kostenidou et al, 2018) have been explored. Huffman et al (2009a) first directly characterized chemically resolved volatility of ambient aerosols in urban areas, supporting the notion that OA components are semivolatile and primary organic aerosol (POA) is more volatile than secondary organic aerosol (SOA).…”

Section: Introductionmentioning

confidence: 99%

Impact of aging on the sources, volatility, and viscosity of organic aerosols in Chinese outflows

Feng

Wang

et al. 2023

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. To investigate the impact of aging on the sources, volatility, and viscosity of organic aerosol (OA) in Chinese outflows, a high-resolution time-of-flight aerosol mass spectrometer (HR-AMS) coupled with a thermodenuder (TD) was deployed in the spring of 2018 in Dongying, which is a regional receptor site of metropolitan emissions in the North China Plain (NCP). The average mass concentration of PM1 is 31.5±22.7 µg m−3, which is mainly composed of nitrate (33 %) and OA (25 %). The source apportionment results show that the OA is mainly contributed by oxygenated OA (OOA) from secondary sources, including background-OOA (33 %) representing a background concentration of OA (2.6 µg m−3) in the NCP area, and transported-OOA (33 %) oxidized from urban emissions. The other two factors include aged hydrocarbon-liked OA (aged-HOA, 28 %) from transported vehicle emissions and biomass burning OA (BBOA, 5 %) from local open burning. The volatility of total OA (average C*=3.2×10-4 µg m−3) in this study is generally lower than that reported in previous field studies, which is mainly due to the high OA oxidation level resulting from aging processes during transport. The volatilities of OA factors follow the order of background-OOA (average C*=2.7×10-5 µg m−3) < transported-OOA (3.7×10-4µgm-3)< aged-HOA (8.1×10-4µgm-3)< BBOA (0.012 µg m−3). Extremely low volatilities in ambient air indicate that oligomers may exist in aged plumes. The viscosity estimation suggests that the majority of ambient OA in this study behaves as semisolid (60 %), liquifies at higher relative humidity (RH) (21 %), and solidifies (19 %) during noon when the RH is low and the oxidation level is high. Finally, the estimated mixing time of molecules in 200 nm OA varies dramatically from minutes at night to years in the afternoon, emphasizing the need to consider its dynamic kinetic limits when modeling OA. In general, the overall results of this study improve our understanding of the impact of aging on OA volatility and viscosity.

show abstract

“…This differentiation was recommended in the peer review of Ulbrich et al (2009), due to the limitations of Pearson R for slowly varying time series concentrations. Similar to the seasonal PMF approach typically adopted, the time-of-day PMF approach does not account for uncertainties and time variations in relative ionization efficiencies of the detected species (Xu et al, 2018;Katz et al, 2021). Additionally, the sensitivity of the time-of-day PMF approach to the changing influence of outliers and the number of zeros has not been evaluated (Sect.…”

Section: Pmf (Me-2) Analysismentioning

confidence: 99%

Contributions of primary sources to submicron organic aerosols in Delhi, India

et al. 2022

View full text Add to dashboard Cite

Abstract. Delhi, India, experiences extremely high concentrations of primary organic aerosol (POA). Few prior source apportionment studies on Delhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol (COA) on POA. In a companion paper, we develop a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements of this approach over traditional PMF. Here, we quantify the contributions of BBOA, COA, and hydrocarbon-like organic aerosol (HOA) by applying positive matrix factorization (PMF) resolved by time of day on two seasons (winter and monsoon seasons of 2017) using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. We also conduct detailed uncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition to HOA, COA is found to be a major constituent of POA in the monsoon, and BBOA is found to be a major constituent of POA in the winter. Neither COA nor the different types of BBOA were resolved in the seasonal (not time-resolved) analysis. The COA mass spectra (MS) profiles are consistent with mass spectral profiles from Delhi and around the world, particularly resembling MS of heated cooking oils with a high m/z 41. The BBOA MS have a very prominent m/z 29 in addition to the characteristic peak at m/z 60, consistent with previous MS observed in Delhi and from wood burning sources. In addition to separating the POA, our technique also captures changes in MS profiles with the time of day, a unique feature among source apportionment approaches available. In addition to the primary factors, we separate two to three oxygenated organic aerosol (OOA) components. When all factors are recombined to total POA and OOA, our results are consistent with seasonal PMF analysis conducted using EPA PMF. Results from this work can be used to better design policies that target relevant primary sources of organic aerosols in Delhi.

show abstract

Organic aerosol in the summertime Southeastern United States: Components and their link to volatility distribution, oxidation state and hygroscopicity

Cited by 4 publications

References 3 publications

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model

Impact of aging on the sources, volatility, and viscosity of organic aerosols in Chinese outflows

Contributions of primary sources to submicron organic aerosols in Delhi, India

Contact Info

Product

Resources

About