Changes in organic aerosol composition with aging inferred from aerosol mass spectra

Abstract. Recently, graphical representations of aerosol mass spectrometer (AMS) spectra and elemental composition have been developed to explain the oxidative and aging processes of secondary organic aerosol (SOA). It has been shown previously that oxygenated organic aerosol (OOA) components from ambient and laboratory data fall within a triangular region in the f44 vs. f43 space, where f44 and f43 are the ratios of the organic signal at m/z 44 and 43 to the total organic signal in AMS spectra, respectively; we refer to this graphical representation as the "triangle plot." Alternatively, the Van Krevelen diagram has been used to describe the evolution of functional groups in SOA. In this study we investigate the variability of SOA formed in chamber experiments from twelve different precursors in both "triangle plot" and Van Krevelen domains. Spectral and elemental data from the high-resolution Aerodyne aerosol mass spectrometer are compared to offline species identification analysis and FTIR filter analysis to better understand the changes in functional and elemental composition inherent in SOA formation and aging. We find that SOA formed under high- and low-NOx conditions occupy similar areas in the "triangle plot" and Van Krevelen diagram and that SOA generated from already oxidized precursors allows for the exploration of areas higher on the "triangle plot" not easily accessible with non-oxidized precursors. As SOA ages, it migrates toward the top of the triangle along a path largely dependent on the precursor identity, which suggests increasing organic acid content and decreasing mass spectral variability. The most oxidized SOA come from the photooxidation of methoxyphenol precursors which yielded SOA O/C ratios near unity. α-pinene ozonolysis and naphthalene photooxidation SOA systems have had the highest degree of mass closure in previous chemical characterization studies and also show the best agreement between AMS elemental composition measurements and elemental composition of identified species within the uncertainty of the AMS elemental analysis. In general, compared to their respective unsaturated SOA precursors, the elemental composition of chamber SOA follows a slope shallower than −1 on the Van Krevelen diagram, which is indicative of oxidation of the precursor without substantial losss of hydrogen, likely due to the unsaturated nature of the precursors. From the spectra of SOA studied here, we are able to reproduce the triangular region originally constructed with ambient OOA compents with chamber aerosol showing that SOA becomes more chemically similar as it ages. Ambient data in the middle of the triangle represent the ensemble average of many different SOA precursors, ages, and oxidative processes.

Abstract. In this study we compile and present results from the factor analysis of 43 Aerosol Mass Spectrometer (AMS) datasets (27 of the datasets are reanalyzed in this work). The components from all sites, when taken together, provide a holistic overview of Northern Hemisphere organic aerosol (OA) and its evolution in the atmosphere. At most sites, the OA can be separated into oxygenated OA (OOA), hydrocarbon-like OA (HOA), and sometimes other components such as biomass burning OA (BBOA). We focus on the OOA components in this work. In many analyses, the OOA can be further deconvolved into low-volatility OOA (LV-OOA) and semi-volatile OOA (SV-OOA). Differences in the mass spectra of these components are characterized in terms of the two main ions m/z 44 (CO2+) and m/z 43 (mostly C2H3O+), which are used to develop a new mass spectral diagnostic for following the aging of OA components in the atmosphere. The LV-OOA component spectra have higher f44 (ratio of m/z 44 to total signal in the component mass spectrum) and lower f43 (ratio of m/z 43 to total signal in the component mass spectrum) than SV-OOA. A wide range of f44 and O:C ratios are observed for both LV-OOA (0.17±0.04, 0.73±0.14) and SV-OOA (0.07±0.04, 0.35±0.14) components, reflecting the fact that there is a continuum of OOA properties in ambient aerosol. The OOA components (OOA, LV-OOA, and SV-OOA) from all sites cluster within a well-defined triangular region in the f44 vs. f43 space, which can be used as a standardized means for comparing and characterizing any OOA components (laboratory or ambient) observed with the AMS. Examination of the OOA components in this triangular space indicates that OOA component spectra become increasingly similar to each other and to fulvic acid and HULIS sample spectra as f44 (a surrogate for O:C and an indicator of photochemical aging) increases. This indicates that ambient OA converges towards highly aged LV-OOA with atmospheric oxidation. The common features of the transformation between SV-OOA and LV-OOA at multiple sites potentially enable a simplified description of the oxidation of OA in the atmosphere. Comparison of laboratory SOA data with ambient OOA indicates that laboratory SOA are more similar to SV-OOA and rarely become as oxidized as ambient LV-OOA, likely due to the higher loadings employed in the experiments and/or limited oxidant exposure in most chamber experiments.

This study investigates the composition, sources, and transformation processes of organic aerosols (OA) in Xitun, a near-industrial urban area in Taichung City, Taiwan, during a field measurement campaign in November 2023. Using a real-time Aerosol Mass Spectrometer (AMS) combined with Positive Matrix Factorization analysis, five organic aerosol components are identified: hydrocarbon-like OA (HOA), aged hydrocarbon-like OA (aged-HOA), semi-volatile oxygenated OA (SV-OOA), low-volatility oxygenated OA (LV-OOA), and background species. Oxygenated OA (OOA), primarily comprising secondary organic aerosol (SOA) formed through the oxidation of gas-phase precursors, accounted for 43–60% of the total OA mass, while HOA and aged-HOA, mainly derived from primary organic aerosol (POA) emitted by traffic and industrial sources, contributed approximately 30% of the total OA mass. To simulate OA evolution, a two-box model is developed, incorporating physical processes, including advection and entrainment, which are characterized using CO concentration simulations. The results align closely with those from the Community Multiscale Air Quality (CMAQ) model. With the physical processes well-constrained, the chemical processes are added to the model to quantify the chemical production and loss of selected volatile organic compounds (VOCs) and the formation of semi-volatile organic compounds (SVOCs) species, distinguishing between anthropogenic and biogenic VOC sources contributing to SOA formation. The oxidation rates of OA will be further determined through model simulations constrained by AMS observations. This study offers valuable insights into the sources, oxidation processes, and evolution of organic aerosols, providing a basis for comprehensive modeling approaches and enhancing our understanding of their impacts on air quality and human health.

We use results from positive matrix factorization (PMF) analysis of 15 urban aerosol mass spectrometer (AMS) data sets to derive simple methods for estimating major organic aerosol (OA) component concentrations in real time. PMF analysis extracts mass spectral (MS) profiles and mass concentrations for key OA components such as hydrocarbon-like OA (HOA), oxygenated OA (OOA), low-volatility OOA (LV-OOA), semivolatile OOA (SV-OOA), and biomass burning OA (BBOA). The variability in the component MS across all sites is characterized and used to derive standard profiles for real-time estimation of component concentrations. Two methods for obtaining first-order estimates of the HOA and OOA mass concentrations are evaluated. The first approach is the tracer m/z method, in which the HOA and OOA concentrations are estimated from m/z 57 and m/z 44 as follows: HOA ∼ 13.4 × (C(57) - 0.1 × C(44)) and OOA ∼ 6.6 × C(44), where C(i) is the equivalent mass concentration of tracer ion m/z i. The second approach uses a chemical mass balance (CMB) method in which standard HOA and OOA profiles are used as a priori information for calculating their mass concentrations. The HOA and OOA mass concentrations obtained from the first-order estimates are evaluated by comparing with the corresponding PMF results for each site. Both methods reproduce the HOA and OOA concentrations to within ∼30% of the results from detailed PMF analysis at most sites, with the CMB method being slightly better. For hybrid CMB methods, we find that fixing the LV-OOA spectrum and not constraining the other spectra produces the best results.

Fine particle of organic aerosol (OA), mostly arising from pollution, are abundant in Beijing. To achieve a better understanding of the difference in OA in summer and autumn, a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, Aerodyne Research Inc., USA) was deployed in urban Beijing in August and October 2012. The mean OA mass concentration in autumn was 30±30 μg m−3, which was higher than in summer (13±6.9 μg m−3). The elemental analysis found that OA was more aged in summer (oxygen-to-carbon (O/C) ratios were 0.41 and 0.32 for summer and autumn, respectively). Positive matrix factorization (PMF) analysis identified three and five components in summer and autumn, respectively. In summer, an oxygenated OA (OOA), a cooking-emission-related OA (COA), and a hydrocarbon-like OA (HOA) were indentified. Meanwhile, the OOA was separated into LV-OOA (low-volatility OOA) and SV-OOA (semi-volatile OOA); and in autumn, a nitrogen-containing OA (NOA) was also found. The SOA (secondary OA) was always the most important OA component, accounting for 55% of the OA in the two seasons. Back trajectory clustering analysis found that the origin of the air masses was more complex in summer. Southerly air masses in both seasons were associated with the highest OA loading, while northerly air masses were associated with the lowest OA loading. A preliminary study of OA components, especially the POA (primary OA), in different periods found that the HOA and COA all decreased during the National Day holiday period, and HOA decreased at weekends compared with weekdays.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role, but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/2019, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and 2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 local time). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA dominated factors were related to the total AMS SOA (i.e., MO-OOA + LO-OOA) by multi-linear regression (MLR). Aromatic SOA was the major SOA component during the day-time, with 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during night-time. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during day-time and 36.1 % of total SOA mass (11.2 % of total OA) during night-time. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass ( 11.7 % and 8.5 % of total OA mass) during day-time respectively and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass) during night-time, respectively. A simple dilution/partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed day-time concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted VOCs. In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the night-time high concentrations are caused by POA emissions led by traffic and biomass burning, and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the day-time suggests an increased OA toxicity and health-related consequences for the general public.

<strong class="journal-contentHeaderColor">Abstract.</strong> In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winter. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA <span class="inline-formula">+</span> LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role, but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/2019, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and 2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 local time). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA dominated factors were related to the total AMS SOA (i.e., MO-OOA + LO-OOA) by multi-linear regression (MLR). Aromatic SOA was the major SOA component during the day-time, with 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during night-time. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during day-time and 36.1 % of total SOA mass (11.2 % of total OA) during night-time. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass ( 11.7 % and 8.5 % of total OA mass) during day-time respectively and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass) during night-time, respectively. A simple dilution/partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed day-time concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted VOCs. In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the night-time high concentrations are caused by POA emissions led by traffic and biomass burning, and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the day-time suggests an increased OA toxicity and health-related consequences for the general public.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role, but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/2019, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and 2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 local time). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA dominated factors were related to the total AMS SOA (i.e., MO-OOA + LO-OOA) by multi-linear regression (MLR). Aromatic SOA was the major SOA component during the day-time, with 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during night-time. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during day-time and 36.1 % of total SOA mass (11.2 % of total OA) during night-time. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass ( 11.7 % and 8.5 % of total OA mass) during day-time respectively and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass) during night-time, respectively. A simple dilution/partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed day-time concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted VOCs. In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the night-time high concentrations are caused by POA emissions led by traffic and biomass burning, and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the day-time suggests an increased OA toxicity and health-related consequences for the general public.

Abstract. An Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research Inc.) was deployed at the Montseny (MSY; 41° 46'46" N, 02° 21'29" E, 720 m a.s.l.) regional background site in the western Mediterranean, Spain, from June 2012 to July 2013 to measure real-time inorganic (nitrate, sulfate, ammonium and chloride) and organic submicron aerosol concentrations. Co-located measurements, including real-time submicron particulate matter (PM1) and black carbon (BC) concentrations, and off-line PM1 chemical analysis were also carried out. This is one of the few studies that compare ACSM data with off-line PM1 measurements, avoiding the tail of the coarse mode included in the PM2.5 fraction. The ACSM + BC concentrations agreed with the PM1 measurements, and a strong correlation was found between the concentrations of ACSM species and the off-line measurements, although some discrepancies remain unexplained. Results point to a current underestimation of the relative ionization efficiency (RIE) established for organic aerosol (OA), which should be revised in the future. The OA was the major component of submicron aerosol (53% of PM1), with a higher contribution in summer (58% of PM1) than in winter (45% of PM1). Source apportionment of OA was carried out by applying positive matrix factorization (PMF), using the multilinear engine (ME-2) to the organic mass spectral data matrix. Three sources were identified in summer: hydrocarbon-like OA (HOA), low-volatile oxygenated OA (LV-OOA), and semi-volatile oxygenated OA (SV-OOA). The secondary OA (SOA; 4.8 μg m−3, sum of LV-OOA and SV-OOA) accounted for 85% of the total OA, and its formation during daytime (mainly SV-OOA) was estimated to be 1.1 μg m−3. In winter, HOA was also identified (12% of OA), a contribution from biomass burning OA (BBOA) was included and it was not possible to differentiate between two different SOA factors, but a single oxygenated OA (OOA) factor was resolved. The OOA contribution represented 60% of the total OA, with a degree of oxidation higher than both OOA summer factors. An intense wildfire episode was studied, obtaining a region-specific BBOA profile.

Secondary organic aerosol (SOA) contributes a substantial fraction to total ambient particulate mass. SOA is a complex mixture of different organic species formed via many gas- and particle-phase reaction pathways. The Aerodyne aerosol mass spectrometer (AMS) has become a standard tool in measuring the bulk chemical composition of SOA in realtime. In addition to acquiring mass spectra of SOA, the high-resolution time-of-flight AMS, or HR-ToF-AMS, can distinguish and quantify ions with the same nominal mass but different elemental compositions. This thesis presents results from several studies in which the HR-ToF-AMS is used to chemically characterize SOA generated in chamber experiments. Glyoxal is a common oxidation product of both biogenic and anthropogenic volatile organic compounds (VOCs) and is known to partition into wet inorganic aerosol. Chamber studies of glyoxal uptake onto ammonium sulfate aerosol are conducted to better understand the mechanisms controlling glyoxal uptake onto ambient aerosol. Organic growth due to glyoxal uptake was found to be reversible under dark conditions. HR-ToF-AMS spectra provide evidence for glyoxal dimers and trimers existing in the particle phase. HR-ToF-AMS spectra indicate the irreversible formation of carbon-nitrogen compounds in the aerosol. Organosulfates are not detected under dark conditions; however, active photochemistry was found to occur within aerosol during irradiated experiments. Carboxylic acids and organic esters are identified within the aerosol. An organosulfate, which had been previously assigned as glyoxal sulfate in ambient samples and chamber studies of isoprene oxidation, is observed only in the irradiated experiments. Comparison with a laboratory-synthesized standard and chemical considerations strongly suggest that this organosulfate is glycolic acid sulfate, an isomer of the previously proposed glyoxal sulfate. Developments in HR-ToF-AMS data analysis have allowed for the measurement of the elemental composition of SOA. Additional graphical representations of AMS spectra and elemental composition have been developed to explain the oxidative and aging processes of SOA. It has been shown previously that oxygenated organic aerosol (OOA) components from ambient and laboratory data fall within a triangular region in the f44 vs. f43 space, where f44 and f43 are the ratios of the organic signal at m/z 44 and 43 to the total organic signal, respectively; we refer to this model as the plot. Alternatively, the Van Krevelen diagram has been used to the elemental composition of SOA and describe the evolution of functional groups in SOA. The variability of SOA formed in chamber experiments from twelve different precursors in both plot and Van Krevelen domains are investigated. Spectral and elemental data from the high-resolution Aerodyne aerosol mass spectrometer are compared to offline species identification analysis and FTIR filter analysis to better understand the changes in functional and elemental composition inherent in SOA formation and aging. SOA formed under high- and low-NOx conditions occupy similar areas in the plot and Van Krevelen diagram, and SOA generated from already-oxidized precursors starts higher on the plot. The most oxidized SOA come from the photooxidation of methoxyphenol precursors which yielded SOA O/C ratios near unity. ∝-pinene ozonolysis and naphthalene photooxidation SOA systems have had the highest degree of mass closure in previous chemical characterization studies and also show the best agreement between AMS elemental composition measurements and elemental composition of identified species. In general the elemental composition of chamber SOA follows a slope shallower than -1 on the Van Krevelen diagram. From the spectra of SOA studied, the triangular region originally constructed with ambient OOA components with chamber aerosol can be reproduced. Ambient data in the middle of the triangle represent the ensemble average of many different SOA precursors, ages, and oxidative processes.

Abstract. Positive matrix factorization (PMF) was applied to the merged high resolution mass spectra of organic and inorganic aerosols from aerosol mass spectrometer (AMS) measurements to investigate the sources and evolution processes of submicron aerosols in New York City in summer 2009. This new approach is able to study the distribution of organic and inorganic species in different types of aerosols, the acidity of organic aerosol (OA) factors, and the fragment ion patterns related to photochemical processing. In this study, PMF analysis of the unified AMS spectral matrix resolved 8 factors. The hydrocarbon-like OA (HOA) and cooking OA (COA) factors contain negligible amounts of inorganic species. The two factors that are primarily ammonium sulfate (SO4-OA) and ammonium nitrate (NO3-OA), respectively, are overall neutralized. Among all OA factors the organic fraction of SO4-OA shows the highest degree of oxidation (O/C = 0.69). Two semi-volatile oxygenated OA (OOA) factors, i.e., a less oxidized (LO-OOA) and a more oxidized (MO-OOA), were also identified. MO-OOA represents local photochemical products with a diurnal profile exhibiting a pronounced noon peak, consistent with those of formaldehyde (HCHO) and Ox(= O3 + NO2). The NO+/NO2+ ion ratio in MO-OOA is much higher than that in NO3-OA and in pure ammonium nitrate, indicating the formation of organic nitrates. The nitrogen-enriched OA (NOA) factor contains ~25% of acidic inorganic salts, suggesting the formation of secondary OA via acid-base reactions of amines. The size distributions of OA factors derived from the size-resolved mass spectra show distinct diurnal evolving behaviors but overall a progressing evolution from smaller to larger particle mode as the oxidation degree of OA increases. Our results demonstrate that PMF analysis of the unified aerosol mass spectral matrix which contains both inorganic and organic aerosol signals may enable the deconvolution of more OA factors and gain more insights into the sources, processes, and chemical characteristics of OA in the atmosphere.

Organic aerosol (OA) data acquired by the Aerosol Mass Spectrometer (AMS) in 37 field campaigns were deconvolved into hydrocarbon‐like OA (HOA) and several types of oxygenated OA (OOA) components. HOA has been linked to primary combustion emissions (mainly from fossil fuel) and other primary sources such as meat cooking. OOA is ubiquitous in various atmospheric environments, on average accounting for 64%, 83% and 95% of the total OA in urban, urban downwind, and rural/remote sites, respectively. A case study analysis of a rural site shows that the OOA concentration is much greater than the advected HOA, indicating that HOA oxidation is not an important source of OOA, and that OOA increases are mainly due to SOA. Most global models lack an explicit representation of SOA which may lead to significant biases in the magnitude, spatial and temporal distributions of OA, and in aerosol hygroscopic properties.

Chemical composition and sources of organic aerosol on the Adriatic coast in Croatia

Abstract. The composition of PM1 (particulate matter with diameter less than 1 µm) in the greater London area was characterized during the Clean Air for London (ClearfLo) project in winter 2012. Two high-resolution time-of-flight aerosol mass spectrometers (HR-ToF-AMS) were deployed at a rural site (Detling, Kent) and an urban site (North Kensington, London). The simultaneous and high-temporal resolution measurements at the two sites provide a unique opportunity to investigate the spatial distribution of PM1. We find that the organic aerosol (OA) concentration is comparable between the rural and urban sites, but the contribution from different sources is distinctly different between the two sites. The concentration of solid fuel OA at the urban site is about twice as high as at the rural site, due to elevated domestic heating in the urban area. While the concentrations of oxygenated OA (OOA) are well-correlated between the two sites, the OOA concentration at the rural site is almost twice that of the urban site. At the rural site, more than 70 % of the carbon in OOA is estimated to be non-fossil, which suggests that OOA is likely related to aged biomass burning considering the small amount of biogenic SOA in winter. Thus, it is possible that the biomass burning OA contributes a larger fraction of ambient OA in wintertime than what previous field studies have suggested. A suite of instruments was deployed downstream of a thermal denuder (TD) to investigate the volatility of PM1 species at the rural Detling site. After heating at 250 °C in the TD, 40 % of the residual mass is OA, indicating the presence of non-volatile organics in the aerosol. Although the OA associated with refractory black carbon (rBC; measured by a soot-particle aerosol mass spectrometer) only accounts for &lt; 10 % of the total OA (measured by a HR-ToF-AMS) at 250 °C, the two measurements are well-correlated, suggesting that the non-volatile organics have similar sources or have undergone similar chemical processing as rBC in the atmosphere. Although the atomic O : C ratio of OOA is substantially larger than that of solid fuel OA and hydrocarbon-like OA, these three factors have similar volatility, which is inferred from the change in mass concentration after heating at 120 °C. Finally, we discuss the relationship between the mass fraction remaining (MFR) of OA after heating in the TD and atomic O : C of OA and find that particles with a wide range of O : C could have similar MFR after heating. This analysis emphasizes the importance of understanding the distribution of volatility and O : C in bulk OA.