Primary Organic Aerosol Factors Research Articles

Source characterization of volatile organic compounds in urban Beijing and its links to secondary organic aerosol formation

Volatile organic compounds (VOCs) are precursors for ozone and secondary organic aerosol (SOA) formation, thereby playing a vital role in atmospheric chemistry and urban air quality. To characterize the relationship between VOCs and SOA, organics both in gas and particulate phases were concurrently measured in urban Beijing. The VOCs and organic aerosol (OA) were apportioned into factors with different oxidation levels by applying the factorization analysis on their detailed mass spectra. Six factors of VOCs were identified, including four primary VOCs (PVOC) factors and two secondary VOCs (SVOC) factors. The PVOC factors dominated the total VOCs when the air mass originated in the cleaner northern areas, while SVOC factors dominated for polluted southern air masses. The normalized concentrations of PVOC and primary OA factors showed consistent diurnal variations regardless of air mass directions, owing to the relatively stable local emissions during the experimental period. This contrasted with the secondary factors due to more complex transformation processes. The traffic-related VOCs and solid fuel combustion VOCs negatively correlated with SOA, implying that they may have contributed to the SOA formation through photooxidation. The VOCs in lower oxidation levels were found to have poor correlations with the less oxidized SOA, whereas they correlated strongly to the more oxidized SOA. This implied that the less oxidized SOA may be in a transition state, where its production and loss rates were balanced. These served as products of VOCs oxidation and reactants of more oxidized SOA formation, playing important roles on the VOC to SOA transformation. The identified VOC emission sources and their photochemical production of SOA should be considered in air quality policy planning.

Combined organic and inorganic source apportionment on yearlong ToF-ACSM dataset at a suburban station in Athens

Abstract. The current improvements in aerosol mass spectrometers in resolution and sensitivity, and the analytical tools for mass spectra deconvolution, have enabled the in-depth analysis of ambient organic aerosol (OA) properties. Although OA constitutes a major fraction of ambient aerosol, its properties are determined to a great extent by the mixing characteristics of both organic and inorganic components of ambient aerosol. This work applies a new methodology to a year-long ACSM dataset to assess the sources of organic and total non-refractory species in the Athens background aerosol and provides insights into the interactions between organic and inorganic species. The use of innovative tools for applying positive matrix factorization (PMF, rolling window) enables the study of the temporal variability of the contribution of these sources and seasonal changes in their composition. The mass spectra of both organic and inorganic aerosol were obtained by a time-of-flight aerosol mass spectrometer (ToF-ACSM) for PMF analysis. The results revealed five factors when organic aerosol was analysed separately. Three of them were primary OA factors: hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA) and biomass burning organic aerosol (BBOA). The remaining two were secondary, less and more oxidized oxygenated organic aerosol (LO-OOA and MO-OOA respectively). The relative contributions of these factors were HOA 15 %, COA 18 %, BBOA 9 %, MO-OOA 34 % and LO-OOA 24 % (yearly averaged). When a combined organic and inorganic aerosol matrix was analysed, two additional factors were identified that were mainly composed of ammonium sulfate (83.5 %) and ammonium nitrate (73 %). Moreover, two secondary factors were resolved, containing both organics and inorganics and were named more (MOA) and less oxidized aerosol (LOA). The relative contributions on a yearly average of these factors were HOA 7 %, COA 9 %, BBOA 3 %, ammonium nitrate 3 %, ammonium sulfate 28 %, MOA 24 % and LOA 26 %.

Seasonality of Aerosol Sources Calls for Distinct Air Quality Mitigation Strategies.

An Aerosol Chemical Speciation Monitor (ACSM) was deployed to investigate the temporal variability of non-refractory particulate matter (NR-PM1) in the coastal city of Galway, Ireland, from February to July 2016. Source apportionment of the organic aerosol (OA) was performed using the newly developed rolling PMF strategy and was compared with the conventional seasonal PMF. Primary OA (POA) factors apportioned by rolling and seasonal PMF were similar. POA factors of hydrocarbon-like OA (HOA), peat, wood, and coal were associated with domestic heating, and with an increased contribution to the OA mass in winter. Even in summer, sporadic heating events occurred with similar diurnal patterns to that in winter. Two oxygenated OA (OOA) factors were resolved, including more-oxygenated OOA and less-oxygenated OOA (i.e., MO-OOA and LO-OOA, accordingly) which were found to be the dominant OA factors during summer. On average, MO-OOA accounted for 62% of OA and was associated with long-range transport in summer. In summer, compared to rolling PMF, the conventional seasonal PMF over-estimated LO-OOA by nearly 100% while it underestimated MO-OOA by 30%. The results from this study show residential heating and long-range transport alternately dominate the submicron aerosol concentrations in this coastal city, requiring different mitigation strategies in different seasons.

Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

Abstract. We collected 1 year of aerosol chemical speciation monitor (ACSM) data in Magadino, a village located in the south of the Swiss Alpine region, one of Switzerland's most polluted areas. We analysed the mass spectra of organic aerosol (OA) by positive matrix factorisation (PMF) using Source Finder Professional (SoFi Pro) to retrieve the origins of OA. Therein, we deployed a rolling algorithm, which is closer to the measurement, to account for the temporal changes in the source profiles. As the first-ever application of rolling PMF with multilinear engine (ME-2) analysis on a yearlong dataset that was collected from a rural site, we resolved two primary OA factors (traffic-related hydrocarbon-like OA (HOA) and biomass burning OA (BBOA)), one mass-to-charge ratio (m/z) 58-related OA (58-OA) factor, a less oxidised oxygenated OA (LO-OOA) factor, and a more oxidised oxygenated OA (MO-OOA) factor. HOA showed stable contributions to the total OA through the whole year ranging from 8.1 % to 10.1 %, while the contribution of BBOA showed an apparent seasonal variation with a range of 8.3 %–27.4 % (highest during winter, lowest during summer) and a yearly average of 17.1 %. OOA (sum of LO-OOA and MO-OOA) contributed 71.6 % of the OA mass, varying from 62.5 % (in winter) to 78 % (in spring and summer). The 58-OA factor mainly contained nitrogen-related variables which appeared to be pronounced only after the filament switched. However, since the contribution of this factor was insignificant (2.1 %), we did not attempt to interpolate its potential source in this work. The uncertainties (σ) for the modelled OA factors (i.e. rotational uncertainty and statistical variability in the sources) varied from ±4 % (58-OA) to a maximum of ±40 % (LO-OOA). Considering that BBOA and LO-OOA (showing influences of biomass burning in winter) had significant contributions to the total OA mass, we suggest reducing and controlling biomass-burning-related residential heating as a mitigation strategy for better air quality and lower PM levels in this region or similar locations. In Appendix A, we conduct a head-to-head comparison between the conventional seasonal PMF analysis and the rolling mechanism. We find similar or slightly improved results in terms of mass concentrations, correlations with external tracers, and factor profiles of the constrained POA factors. The rolling results show smaller scaled residuals and enhanced correlations between OOA factors and corresponding inorganic salts compared to those of the seasonal solutions, which was most likely because the rolling PMF analysis can capture the temporal variations in the oxidation processes for OOA components. Specifically, the time-dependent factor profiles of MO-OOA and LO-OOA can well explain the temporal viabilities of two main ions for OOA factors, m/z 44 (CO2+) and m/z 43 (mostly C2H3O+). Therefore, this rolling PMF analysis provides a more realistic source apportionment (SA) solution with time-dependent OA sources. The rolling results also show good agreement with offline Aerodyne aerosol mass spectrometer (AMS) SA results from filter samples, except for in winter. The latter discrepancy is likely because the online measurement can capture the fast oxidation processes of biomass burning sources, in contrast to the 24 h filter samples. This study demonstrates the strengths of the rolling mechanism, provides a comprehensive criterion list for ACSM users to obtain reproducible SA results, and is a role model for similar analyses of such worldwide available data.

Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter

National Capital Region (NCR) encompassing New Delhi is one of the most polluted urban metropolitan areas in the world. Real-time chemical characterization of fine particulate matter (PM1 and PM2.5) was carried out using three aerosol mass spectrometers, two aethalometers, and one single particle soot photometer (SP2) at two sites in Delhi (urban) and one site located ~40 km downwind of Delhi, during January–March 2018. The campaign mean PM2.5 (NR-PM2.5 + BC) concentrations at the two urban sites were 153.8 ± 109.4 μg.m−3 and 127.8 ± 83.2 μg.m−3, respectively, whereas PM1 (NR-PM1 + BC) was 72.3 ± 44.0 μg.m−3 at the downwind site. PM2.5 particles were composed mostly of organics (43–44)% followed by chloride (14–17)%, ammonium (9–11)%, nitrate (9%), sulfate (8–10)%, and black carbon (11–16)%, whereas PM1 particles were composed of 47% organics, 13% sulfate as well as ammonium, 11% nitrate as well as chloride, and 5% black carbon. Organic aerosol (OA) source apportionment was done using positive matrix factorization (PMF), solved using an advanced multi-linear engine (ME-2) model. Highly mass-resolved OA mass spectra at one urban and downwind site were factorized into three primary organic aerosol (POA) factors including one traffic-related and two solid-fuel combustion (SFC), and three oxidized OA (OOA) factors. Whereas unit mass resolution OA at the other urban site was factorized into two POA factors related to traffic and SFC, and one OOA factor. OOA constituted a majority of the total OA mass (45–55)% with maximum contribution during afternoon hours ~(70–80)%. Significant differences in the absolute OOA concentration between the two urban sites indicated the influence of local emissions on the oxidized OA formation. Similar PM chemical composition, diurnal and temporal variations at the three sites suggest similar type of sources affecting the particulate pollution in Delhi and adjoining cities, but variability in mass concentration suggest more local influence than regional.

Mass spectral characterization of primary emissions and implications in source apportionment of organic aerosol

Abstract. Source apportionment of organic aerosol (OA) from aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) measurements relies largely upon mass spectral profiles from different source emissions. However, the changes in mass spectra of primary emissions from AMS–ACSM with the newly developed capture vaporizer (CV) are poorly understood. Here we conducted 21 cooking, crop straw, wood, and coal burning experiments to characterize the mass spectral features of OA and water-soluble OA (WSOA) using SV-AMS and CV-ACSM. Our results show overall similar spectral characteristics between SV-AMS and CV-ACSM for different primary emissions despite additional thermal decomposition in CV, and the previous spectral features for diagnostics of primary OA factors are generally well retained. However, the mass spectral differences between OA and WSOA can be substantial for both SV-AMS and CV-ACSM. The changes in f55 (fraction of m∕z 55 in OA) vs. f57, f44 vs. f60, and f44 vs. f43 in CV-ACSM are also observed, yet the evolving trends are similar to those of SV-AMS. By applying the source spectral profiles to a winter CV-ACSM study at a highly polluted rural site in the North China Plain, the source apportionment of primary OA was much improved, highlighting the two most important primary sources of biomass burning and coal combustion (32 % and 21 %). Considering the rapidly increasing deployments of CV-ACSM and WSOA studies worldwide, the mass spectral characterization has significant implications by providing essential constraints for more accurate source apportionment and making better strategies for air pollution control in regions with diverse primary emissions.

Six-year source apportionment of submicron organic aerosols from near-continuous highly time-resolved measurements at SIRTA (Paris area, France)

Abstract. Organic aerosol (OA) particles are recognized as key factors influencing air quality and climate change. However, highly time-resolved long-term characterizations of their composition and sources in ambient air are still very limited due to challenging continuous observations. Here, we present an analysis of long-term variability of submicron OA using the combination of an aerosol chemical speciation monitor (ACSM) and a multiwavelength Aethalometer from November 2011 to March 2018 at a peri-urban background site of the Paris region (France). Source apportionment of OA was achieved via partially constrained positive matrix factorization (PMF) using the multilinear engine (ME-2). Two primary OA (POA) and two oxygenated OA (OOA) factors were identified and quantified over the entire studied period. POA factors were designated as hydrocarbon-like OA (HOA) and biomass burning OA (BBOA). The latter factor presented a significant seasonality with higher concentrations in winter with significant monthly contributions to OA (18 %–33 %) due to enhanced residential wood burning emissions. HOA mainly originated from traffic emissions but was also influenced by biomass burning in cold periods. OOA factors were distinguished between their less- and more-oxidized fractions (LO-OOA and MO-OOA, respectively). These factors presented distinct seasonal patterns, associated with different atmospheric formation pathways. A pronounced increase in LO-OOA concentrations and contributions (50 %–66 %) was observed in summer, which may be mainly explained by secondary OA (SOA) formation processes involving biogenic gaseous precursors. Conversely, high concentrations and OA contributions (32 %–62 %) of MO-OOA during winter and spring seasons were partly associated with anthropogenic emissions and/or long-range transport from northeastern Europe. The contribution of the different OA factors as a function of OA mass loading highlighted the dominant roles of POA during pollution episodes in fall and winter and of SOA for highest springtime and summertime OA concentrations. Finally, long-term trend analyses indicated a decreasing feature (of about −175 ng m−3 yr−1) for MO-OOA, very limited or insignificant decreasing trends for primary anthropogenic carbonaceous aerosols (BBOA and HOA, along with the fossil-fuel and biomass-burning black carbon components) and no statistically significant trend for LO-OOA over the 6-year investigated period.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling.

This study presents land-use regression (LUR) models for submicron particulate matter (PM1) components from an urban area. Models are presented for mass concentrations of inorganic species (SO4, NO3, NH4), organic aerosol (OA) factors, and total PM1. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km2 area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R2 = 0.80, HOA R2 = 0.67) and lowest for secondary inorganic species (SO4 R2 = 0.47, NH4 R2 = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM1 components through a subsampling analysis.

CCN activity and organic hygroscopicity of aerosols downwind of an urban region in central Amazonia: seasonal and diel variations and impact of anthropogenic emissions

Abstract. During the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign, size-resolved cloud condensation nuclei (CCN) spectra were characterized at a research site (T3) 60 km downwind of the city of Manaus, Brazil, in central Amazonia for 1 year (12 March 2014 to 3 March 2015). Particle hygroscopicity (κCCN) and mixing state were derived from the size-resolved CCN spectra, and the hygroscopicity of the organic component of the aerosol (κorg) was then calculated from κCCN and concurrent chemical composition measurements. The annual average κCCN increased from 0.13 at 75 nm to 0.17 at 171 nm, and the increase was largely due to an increase in sulfate volume fraction. During both wet and dry seasons, κCCN, κorg, and particle composition under background conditions exhibited essentially no diel variations. The constant κorg of ∼ 0. 15 is consistent with the largely uniform and high O : C value (∼ 0. 8), indicating that the aerosols under background conditions are dominated by the aged regional aerosol particles consisting of highly oxygenated organic compounds. For air masses strongly influenced by urban pollution and/or local biomass burning, lower values of κorg and organic O : C atomic ratio were observed during night, due to accumulation of freshly emitted particles, dominated by primary organic aerosol (POA) with low hygroscopicity, within a shallow nocturnal boundary layer. The O : C, κorg, and κCCN increased from the early morning hours and peaked around noon, driven by the formation and aging of secondary organic aerosol (SOA) and dilution of POA emissions into a deeper boundary layer, while the development of the boundary layer, which leads to mixing with aged particles from the residual layer aloft, likely also contributed to the increases. The hygroscopicities associated with individual organic factors, derived from PMF (positive matrix factorization) analysis of AMS (aerosol mass spectrometry) spectra, were estimated through multivariable linear regression. For the SOA factors, the variation of the κ value with O : C agrees well with the linear relationship reported from earlier laboratory studies of SOA hygroscopicity. On the other hand, the variation in O : C of ambient aerosol organics is largely driven by the variation in the volume fractions of POA and SOA factors, which have very different O : C values. As POA factors have hygroscopicity values well below the linear relationship between SOA hygroscopicity and O : C, mixtures with different POA and SOA fractions exhibit a steeper slope for the increase in κorg with O : C, as observed during this and earlier field studies. This finding helps better understand and reconcile the differences in the relationships between κorg and O : C observed in laboratory and field studies, therefore providing a basis for improved parameterization in global models, especially in a tropical context.

Primary to secondary organic aerosol: evolution of organic emissions from mobile combustion sources

Abstract. A series of smog chamber experiments were conducted to investigate the transformation of primary organic aerosol (POA) and formation of secondary organic aerosol (SOA) during the photooxidation of dilute exhaust from a fleet of gasoline and diesel motor vehicles and two gas-turbine engines. In experiments where POA was present in the chamber at the onset of photooxidation, positive matrix factorization (PMF) was used to determine separate POA and SOA factors from aerosol mass spectrometer data. A 2-factor solution, with one POA factor and one SOA factor, was sufficient to describe the organic aerosol for gasoline vehicles, diesel vehicles, and one of the gas-turbine engines. Experiments with the second gas-turbine engine required a 3-factor PMF solution with a POA factor and two SOA factors. Results from the PMF analysis were compared to the residual method for determining SOA and POA mass concentrations. The residual method apportioned a larger fraction of the organic aerosol mass as POA because it assumes that all mass at m / z 57 is associated with POA. The POA mass spectrum for the gasoline and diesel vehicles exhibited high abundances of the CnH2n+1 series of ions (m / z 43, 57, etc.) and was similar to the mass spectra of the hydrocarbon-like organic aerosol factor determined from ambient data sets with one exception, a diesel vehicle equipped with a diesel oxidation catalyst. POA mass spectra for the gas-turbine engines are enriched in the CnH2n−1 series of ions (m / z 41, 55, etc.), consistent with the composition of the lubricating oil used in these engines. The SOA formed from the three sources exhibits high abundances of m / z 44 and 43, indicative of mild oxidation. The SOA mass spectra are consistent with less-oxidized ambient SV-OOA (semivolatile oxygenated organic aerosols) and fall within the triangular region of f44 versus f43 defined by ambient measurements. However there is poor absolute agreement between the experimentally derived SOA mass spectra and ambient OOA factors, though this poor agreement should be expected based on the variability of ambient OOA factors. Van Krevelen analysis of the POA and SOA factors for gasoline and diesel experiments reveal slopes of −0.50 and −0.40, respectively. This suggests that the oxidation chemistry in these experiments is a combination of carboxylic acid and alcohol/peroxide formation, consistent with ambient oxidation chemistry.