scholarly journals Supplementary material to "Positive Matrix Factorization of Organic Aerosol: Insights from a Chemical Transport Model"

2018 ◽  
Author(s):  
Anthoula D. Drosatou ◽  
Ksakousti Skyllakou ◽  
Georgia N. Theodoritsi ◽  
Spyros N. Pandis
Keyword(s):  
Matrix Factorization ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Positive Matrix Factorization ◽  
Chemical Transport Model ◽  
Positive Matrix ◽  
Supplementary Material

scholarly journals Positive matrix factorization of organic aerosol: insights from a chemical transport model

2019 ◽  
Vol 19 (2) ◽  
pp. 973-986 ◽  
Author(s):  
Anthoula D. Drosatou ◽  
Ksakousti Skyllakou ◽  
Georgia N. Theodoritsi ◽  
Spyros N. Pandis
Keyword(s):  
Matrix Factorization ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Positive Matrix Factorization ◽  
The Other ◽  
Chemical Transport Model ◽  
Positive Matrix ◽  
Aerosol Mass ◽  
High Volatility

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, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) 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 “chemically different” 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 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10 % to the OA. 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 a 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 secondary organic aerosol from volatile, semivolatile, 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.

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

2018 ◽  
Author(s):  
Anthoula D. Drosatou ◽  
Ksakousti Skyllakou ◽  
Georgia N. Theodoritsi ◽  
Spyros N. Pandis
Keyword(s):  
Matrix Factorization ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Positive Matrix Factorization ◽  
The Other ◽  
Chemical Transport Model ◽  
Positive Matrix ◽  
Aerosol Mass ◽  
High Volatility

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 chemically different 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 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. For contributions between 10 and 20 % the corresponding uncertainties increase to 50 %. Finally, when these sources are small (less than 10 % 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.

scholarly journals Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

2019 ◽  
Vol 19 (11) ◽  
pp. 7279-7295 ◽  
Author(s):  
Athanasia Vlachou ◽  
Anna Tobler ◽  
Houssni Lamkaddam ◽  
Francesco Canonaco ◽  
Kaspar R. Daellenbach ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Organic Aerosol ◽  
Positive Matrix Factorization ◽  
Water Soluble ◽  
Oxidation Products ◽  
Positive Matrix ◽  
Bootstrap Analysis ◽  
Seasonal Behavior ◽  
Yearly Cycle ◽  
Aerosol Mass

Abstract. Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.

scholarly journals Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

2012 ◽  
Vol 12 (2) ◽  
pp. 5939-6018
Author(s):  
C. A. Stroud ◽  
M. D. Moran ◽  
P. A. Makar ◽  
S. Gong ◽  
W. Gong ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Point Sources ◽  
Urban Site ◽  
Stable Model ◽  
Chemical Transport Model ◽  
The Us ◽  
Model Predictions

Abstract. Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in southern Ontario (ON), Canada, were used to evaluate Environment Canada's regional chemical transport model predictions of primary organic aerosol (POA). Environment Canada's operational numerical weather prediction model and the 2006 Canadian and 2005 US national emissions inventories were used as input to the chemical transport model (named AURAMS). Particle-component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON) and two rural sites (Harrow and Bear Creek, ON) to derive hydrocarbon-like organic aerosol (HOA) factors. Co-located carbon monoxide (CO), PM2.5 black carbon (BC), and PM1 SO4 measurements were also used for evaluation and interpretation, permitting a detailed diagnostic model evaluation. At the urban site, good agreement was observed for the comparison of daytime campaign PM1 POA and HOA mean values: 1.1 μg m−3 vs. 1.2 μg m−3, respectively. However, a POA overprediction was evident on calm nights due to an overly-stable model surface layer. Biases in model POA predictions trended from positive to negative with increasing HOA values. This trend has several possible explanations, including (1) underweighting of urban locations in particulate matter (PM) spatial surrogate fields, (2) overly-coarse model grid spacing for resolving urban-scale sources, and (3) lack of a model particle POA evaporation process during dilution of vehicular POA tail-pipe emissions to urban scales. Furthermore, a trend in POA bias was observed at the urban site as a function of the BC/HOA ratio, suggesting a possible association of POA underprediction for diesel combustion sources. For several time periods, POA overprediction was also observed for sulphate-rich plumes, suggesting that our model POA fractions for the PM2.5 chemical speciation profiles may be too high for these point sources. At the rural Harrow site, significant underpredictions in PM1 POA concentration were found compared to observed HOA concentration and were associated, based on back-trajectory analysis, with (1) transport from the Detroit/Windsor urban complex, (2) longer-range transport from the US Midwest, and (3) biomass burning. Daytime CO concentrations were significantly overpredicted at Windsor but were unbiased at Harrow. Collectively, these biases provide support for a hypothesis that combines a current underweighting of PM spatial surrogate fields for urban locations with insufficient model vertical mixing for sources close to the urban measurement sites. The magnitude of the area POA emissions sources in the US and Canadian inventories (e.g., food cooking, road and soil dust, waste disposal burning) suggests that more effort should be placed at reducing uncertainties in these sectors, especially spatial and temporal surrogates.

scholarly journals Mixing times of organic molecules within secondary organic aerosol particles: a global planetary boundary layer perspective

2017 ◽  
Author(s):  
Adrian M. Maclean ◽  
Christopher L. Butenhoff ◽  
James W. Grayson ◽  
Kelley Barsanti ◽  
Jose L. Jimenez ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Secondary Organic Aerosol ◽  
Transport Model ◽  
Laboratory Data ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Mixing Times ◽  
Chemical Transport Model ◽  
Transport Models ◽  
Lack Of Information

Abstract. When simulating the formation and life cycle of secondary organic aerosol (SOA) with chemical transport models, it is often assumed that organic molecules are well mixed within SOA particles on the time scale of 1 h. While this assumption has been debated vigorously in the literature, the issue remains unresolved in part due to a lack of information on the mixing times within SOA particles as a function of both temperature and relative humidity. Using laboratory data, meteorological fields and a chemical transport model, we determine how often mixing times are

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