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

Sources and processes of organic aerosol in non-refractory PM1 and PM2.5 during foggy and haze episodes in an urban environment of the Yangtze River Delta, China

<strong class="journal-contentHeaderColor">Abstract.</strong> Atmospheric aerosol particles are a complex combination of primary emitted sources (biogenic and anthropogenic) and secondary aerosol resulting from the aging processes such as condensation, coagulation, and cloud processing. To better understand their sources, investigations have been focused on source identification in urban areas in the past, while rural background stations are normally less impacted by surrounding anthropogenic sources. Therefore, they are predisposed for studying the impact of long-range transport of anthropogenic aerosols. Moreover, long-term measurements can help to study the potential temporal changes in the sources. Here, the chemical composition and organic aerosol sources of submicron aerosol particles were investigated at the Central European rural-background research station, Melpitz, using a one yearlong dataset determined by an aerosol chemical speciation monitor (ACSM) and a multi-angle absorption photometer (MAAP) from September 2016 to August 2017. Melpitz represents due to its location the Central European aerosol. It is an ideal location to investigate the impact of long-range transport, since the location is influenced by less polluted air masses from westerly directions and more polluted continental air masses from Eastern Europe. The organic aerosol (OA) dominated the submicron particle mass concentration and showed strong seasonal variability ranging from 39 % (in winter) to 58 % (in summer). It was followed by sulphate (15 % and 20 %) and nitrate (24 % and 11 %). The OA source identification was performed using rolling positive matrix factorisation (PMF) approach to account for the potential temporal changes in the source profile (SoFi Pro). It was possible to split OA into five-factors with a distinct temporal variability and mass spectral signature. Three were associated to anthropogenic primary OA (POA) sources: hydrocarbon-like OA (HOA, 5.2 % of OA mass in winter and 6.8 % in summer), biomass burning OA (BBOA, 10.6 % and 6.1 %) and coal combustion OA (CCOA, 23 % and 8.7 %). Another two are secondary/processed oxygenated OA (OOA) sources: less-oxidized OOA (LO-OOA, 28.4 % and 36.7 %) and more-oxidized OOA (MO-OOA, 32.8 % and 41.8 %). Since equivalent black carbon (eBC) was clearly associated with the identified POA factors (sum of HOA, BBOA and CCOA, R²= 0. 87), eBC&rsquo;s contribution to each of the POA factors was achieved using a multi-linear regression model. Consequently, CCOA represented the main anthropogenic sources of carbonaceous aerosol (sum of OA and eBC) not only during winter (56 % of POA in winter) but also in summer (13 % of POA in summer), followed by BBOA (29 % and 69 % of POA in winter and summer, respectively) and HOA (15 % and 18 % of POA in winter and summer, respectively). A seasonal air mass cluster analysis was used to understand the geographical origins of the different aerosol types and show that during both winter and summer time, PM₁ (PM with aerodynamic diameter smaller than 1 &micro;m) air masses with eastern influence was always associated with the highest mass concentration and the highest coal combustion fraction. Since during winter time, CCOA is a combination of domestic heating and power plants emissions, the summer contribution of CCOA emphasises the critical importance of coal power plants emissions to rural background aerosols and its impact on air quality, through long-range transportation.

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.

Real-time non-refractory PM1 chemical composition, size distribution and source apportionment at a coastal industrial park in the Yangtze River Delta region, China

China experienced severe haze pollution in the past decades. However, systematic characterization of atmospheric fine particles with advanced measurement techniques has been very scarce in Shanghai, which is the largest megacity in China. Herein, we present the characterization of non-refractory submicron aerosol (NR-PM1) in urban Shanghai during winter 2017 by applying an Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research Inc.). The NR-PM1 is predominated by organics (43%), followed by nitrate (29%), sulfate (18%), ammonium (7%), and chloride (3%). Regarding the organic aerosol (OA) factors, three organic aerosol factors were identified and classified as hydrocarbon-like organic aerosol (HOA), less-oxygenated organic aerosol (LO-OOA), and more-oxygenated organic aerosol (MO-OOA), which contributed 32%, 22%, and 46% to total organic aerosol respectively. The NR-PM1 composition exhibited an increase in secondary inorganic aerosol (SIA) (sulfate, nitrate and ammonium) contribution from clean episodes (49%) to pollution episodes (59%), while organic aerosol contribution decreased accordingly from 48% to 39%. The strong increase of sulfate in high-relative humidity (RH) pollution episodes indicated that aqueous-phase oxidation of SO2 could be an important formation process for sulfate during particulate air pollution period. The contribution of nitrate was elevated from 25% during clean episodes to 32% during polluted episodes, likely owing to the increase of relative humidity which facilitates the hydrolysis of N2O5 and the gas-to-particle partitioning of hydrophilic NH4NO3 and. Further analysis of atmospheric formation relevance suggested that less-oxygenated organic aerosol formation was mainly driven by aqueous-phase chemistry reactions, whereas photochemical oxidation became an important process for more-oxygenated organic aerosol formation. Meanwhile, less-oxygenated organic aerosol formation may also be influenced by atmospheric oxidative tracer (i.e., Ox), as less-oxygenated organic aerosol exhibited a distinct peak at noon under high-relative humidity condition.

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. Organic aerosol (OA) represents a large fraction of submicron aerosols in the megacity of Beijing, yet long-term characterization of its sources and variations is very limited. Here we present an analysis of in situ measurements of OA in submicrometer particles with an aerosol chemical speciation monitor (ACSM) for 2 years from July 2011 to May 2013. The sources of OA are analyzed with a multilinear engine (ME-2) by constraining three primary OA factors including fossil-fuel-related OA (FFOA), cooking OA (COA), and biomass burning OA (BBOA). Two secondary OAs (SOA), representing a less oxidized oxygenated OA (LO-OOA) and a more oxidized (MO-OOA), are identified during all seasons. The monthly average concentration OA varied from 13.6 to 46.7 µg m−3 with a strong seasonal pattern that is usually highest in winter and lowest in summer. FFOA and BBOA show similarly pronounced seasonal variations with much higher concentrations and contributions in winter due to enhanced coal combustion and biomass burning emissions. The contribution of COA to OA, however, is relatively stable (10–15 %) across different seasons, yet presents significantly higher values at low relative humidity levels (RH &lt; 30 %), highlighting the important role of COA during clean periods. The two SOA factors present very different seasonal variations. The pronounced enhancement of LO-OOA concentrations in winter indicates that emissions from combustion-related primary emissions could be a considerable source of SOA under low-temperature (T) conditions. Comparatively, MO-OOA shows high concentrations consistently at high RH levels across different T levels, and the contribution of MO-OOA to OA is different seasonally with lower values occurring more in winter (30–34 %) than other seasons (47–64 %). Overall, SOA (= LO-OOA + MO-OOA) dominates OA composition during all seasons by contributing 52–64 % of the total OA mass in the heating season and 65–75 % in non-heating seasons. The variations in OA composition as a function of OA mass loading further illustrate the dominant role of SOA in OA across different mass loading scenarios during all seasons. However, we also observed a large increase in FFOA associated with a corresponding decrease in MO-OOA during periods with high OA mass loadings in the heating season, illustrating an enhanced role of coal combustion emissions during highly polluted episodes. Potential source contribution function analysis further shows that the transport from the regions located to the south and southwest of Beijing within ∼ 250 km can contribute substantially to high FFOA and BBOA concentrations in the heating season.

Abstract. PM1 (particulate matter with an aerodynamic diameter &lt;1 μm) non-refractory components and black carbon were measured continuously together with additional air quality and atmospheric parameters at an urban background site in Barcelona, Spain, during March 2009 (campaign DAURE, Determination of the sources of atmospheric Aerosols in Urban and Rural Environments in the western Mediterranean). Positive matrix factorization (PMF) was conducted on the organic aerosol (OA) data matrix measured by an aerosol mass spectrometer, on both unit mass (UMR) and high resolution (HR) data. Five factors or sources could be identified: LV-OOA (low-volatility oxygenated OA), related to regional, aged secondary OA; SV-OOA (semi-volatile oxygenated OA), a fresher oxygenated OA; HOA (hydrocarbon-like OA, related to traffic emissions); BBOA (biomass burning OA) from domestic heating or agricultural biomass burning activities; and COA (cooking OA). LV-OOA contributed 28% to OA, SV-OOA 27%, COA 17%, HOA 16%, and BBOA 11%. The COA HR spectrum contained substantial signal from oxygenated ions (O:C: 0.21) whereas the HR HOA spectrum had almost exclusively contributions from chemically reduced ions (O:C: 0.03). If we assume that the carbon in HOA is fossil while that in COA and BBOA is modern, primary OA in Barcelona contains a surprisingly high fraction (59%) of non-fossil carbon. This paper presents a method for estimating cooking organic aerosol in ambient datasets based on the fractions of organic mass fragments at m/z 55 and 57: their data points fall into a V-shape in a scatter plot, with strongly influenced HOA data aligned to the right arm and strongly influenced COA data points aligned to the left arm. HR data show that this differentiation is mainly driven by the oxygen-containing ions C3H3O+ and C3H5O+, even though their contributions to m/z 55 and 57 are low compared to the reduced ions C4H7+ and C4H9+. A simple estimation method based on the markers m/z 55, 57, and 44 is developed here and allows for a first-order-estimation of COA in urban air. This study emphasizes the importance of cooking activities for ambient air quality and confirms the importance of chemical composition measurements with a high mass and time resolution.

Abstract. Lanzhou, which is located in a steep alpine valley in western China, is one of the most polluted cities in China during the wintertime. In this study, an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), a seven-wavelength aethalometer, and a scanning mobility particle sizer (SMPS) were deployed during 10 January to 4 February 2014 to study the mass concentrations, chemical processes, and sources of submicrometer particulate matter (PM1). The average PM1 concentration during this study was 57.3 µg m−3 (ranging from 2.1 to 229.7 µg m−3 for hourly averages), with organic aerosol (OA) accounting for 51.2 %, followed by nitrate (16.5 %), sulfate (12.5 %), ammonium (10.3 %), black carbon (BC, 6.4 %), and chloride (3.0 %). The mass concentration of PM1 during winter was more than twice the average value observed at the same site in summer 2012 (24.5 µg m−3), but the mass fraction of OA was similar in the two seasons. Nitrate contributed a significantly higher fraction to the PM1 mass in winter than summer (16.5 % vs. 10 %), largely due to more favored partitioning to the particle phase at low air temperature. The mass fractions of both OA and nitrate increased by ∼ 5 % (47 to 52 for OA and 13 to 18 % for nitrate) with the increase of the total PM1 mass loading, while the average sulfate fraction decreased by 6 % (17 to 11 %), indicating the importance of OA and nitrate for the heavy air pollution events in Lanzhou. The size distributions of OA, nitrate, sulfate, ammonium, and chloride all peaked at ∼ 500 nm, with OA being slightly broader, suggesting that aerosol particles were internally mixed during winter, likely due to frequently calm and stagnant air conditions during wintertime in Lanzhou (average wind speed: 0.82 m s−1).The average mass spectrum of OA showed a medium oxidation degree (average O ∕ C ratio of 0.28), which was lower than that during summer 2012 (O ∕ C = 0.33). This is consistent with weaker photochemical processing during winter. Positive matrix factorization (PMF) with the multi-linear engine (ME-2) solver identified six OA sources, i.e., a hydrocarbon-like OA (HOA), a biomass burning OA (BBOA), a cooking-emitted OA (COA), a coal combustion OA (CCOA), and two oxygenated OA (OOA) factors. One of the OOAs was less oxidized (LO-OOA), and the other one more oxidized (MO-OOA). LO-OOA was the most abundant OA component (22.3 % of OA mass), followed by CCOA (22.0 %), COA (20.2 %), MO-OOA (14.9 %), BBOA (10.8 %), and HOA (9.8 %). The mass fraction of primary OA ( = HOA + BBOA + COA + CCOA) increased during high PM pollution periods, indicating that local primary emissions were a main reason for the formation of air pollution events in Lanzhou during winter. Radiocarbon (14C) measurement was conducted on four PM2.5 filter samples from this study, which allowed for a quantitative source apportionment of organic carbon (OC). The non-fossil sources on average accounted for 55 ± 3 % of OC, which could be mainly from biomass burning and cooking activities, suggesting the importance of non-fossil sources for the PM pollution in Lanzhou. Together with the PMF results, we also found that a large fraction (66 ± 10 %) of the secondary OC was from non-fossil OC.

Abstract. There are limited measurements of the chemical composition, abundance and sources of atmospheric particles in the High Arctic To address this, we report 93 d of soot particle aerosol mass spectrometer (SP-AMS) data collected from 20 February to 23 May 2015 at Villum Research Station (VRS) in northern Greenland (81∘36′ N). During this period, we observed the Arctic haze phenomenon with elevated PM1 concentrations ranging from an average of 2.3, 2.3 and 3.3 µg m−3 in February, March and April, respectively, to 1.2 µg m−3 in May. Particulate sulfate (SO42-) accounted for 66 % of the non-refractory PM1 with the highest concentration until the end of April and decreasing in May. The second most abundant species was organic aerosol (OA) (24 %). Both OA and PM1, estimated from the sum of all collected species, showed a marked decrease throughout May in accordance with the polar front moving north, together with changes in aerosol removal processes. The highest refractory black carbon (rBC) concentrations were found in the first month of the campaign, averaging 0.2 µg m−3. In March and April, rBC averaged 0.1 µg m−3 while decreasing to 0.02 µg m−3 in May. Positive matrix factorization (PMF) of the OA mass spectra yielded three factors: (1) a hydrocarbon-like organic aerosol (HOA) factor, which was dominated by primary aerosols and accounted for 12 % of OA mass, (2) an Arctic haze organic aerosol (AOA) factor and (3) a more oxygenated marine organic aerosol (MOA) factor. AOA dominated until mid-April (64 %–81 % of OA), while being nearly absent from the end of May and correlated significantly with SO42-, suggesting the main part of that factor is secondary OA. The MOA emerged late at the end of March, where it increased with solar radiation and reduced sea ice extent and dominated OA for the rest of the campaign until the end of May (24 %–74 % of OA), while AOA was nearly absent. The highest O∕C ratio (0.95) and S∕C ratio (0.011) was found for MOA. Our data support the current understanding that Arctic aerosols are highly influenced by secondary aerosol formation and receives an important contribution from marine emissions during Arctic spring in remote High Arctic areas. In view of a changing Arctic climate with changing sea-ice extent, biogenic processes and corresponding source strengths, highly time-resolved data are needed in order to elucidate the components dominating aerosol concentrations and enhance the understanding of the processes taking place.

Characterization of submicron aerosol volatility in the regional atmosphere in Southern China

Characterization of submicron particles during autumn in Beijing, China

In situ investigation results of aerosol optical properties (absorption and scattering) and chemical composition at an urban background site in Lithuania (Vilnius) are presented. Investigation was performed in May-June 2017 using an aerosol chemical speciation monitor (ACSM), a 7-wavelength Aethalometer and a 3-wavelength integrating Nephelometer. A positive matrix factorisation (PMF) was used for the organic aerosol mass spectra analysis to characterise the sources of ambient organic aerosol (OA). Five OA factors were identified: hydrocarbon-like OA (HOA), biomass-burning OA (BBOA), more and less oxygenated OA (LVOOA and SVOOA, respectively), and local hydrocarbon-like OA (LOA). The average absorption (at 470 nm) and scattering (at 450 nm) coefficients during the entire measurement campaign were 16.59 Mm−1 (standard deviation (SD) = 17.23 Mm−1) and 29.83 Mm−1 (SD = 20.45 Mm−1), respectively. Furthermore, the absorption and scattering Angström exponents (AAE and SAE, respectively) and single-scattering albedo (SSA) were calculated. The average AAE value at 470/660 nm was 0.97 (SD = 0.16) indicating traffic-related black carbon (BCtr) dominance. The average value of SAE (at 450/700 nm) was 1.93 (SD = 0.32) and could be determined by the submicron particle (PM1) dominance versus the supermicron ones (PM &gt; 1 µm). The average value of SSA was 0.62 (SD = 0.13). Several aerosol types showed specific segregation in the SAE versus SSA plot, which underlines different optical properties due to various chemical compositions.

Abstract. Atmospheric aerosol particles are a complex combination of primary emitted sources (biogenic and anthropogenic) and secondary aerosol resulting from aging processes such as condensation, coagulation, and cloud processing. To better understand their sources, investigations have been focused on urban areas in the past, whereas rural-background stations are normally less impacted by surrounding anthropogenic sources. Therefore, they are predisposed for studying the impact of long-range transport of anthropogenic aerosols. Here, the chemical composition and organic aerosol (OA) sources of submicron aerosol particles measured by an aerosol chemical speciation monitor (ACSM) and a multi-angle absorption photometer (MAAP) were investigated at Melpitz from September 2016 to August 2017. The location of the station at the frontier between western and eastern Europe makes it the ideal place to investigate the impact of long-range transport over Europe. Indeed, the station is under the influence of less polluted air masses from westerly directions and more polluted continental air masses from eastern Europe. The OA dominated the submicron particle mass concentration and showed strong seasonal variability ranging from 39 % (in winter) to 58 % (in summer). It was followed by sulfate (15 % and 20 %) and nitrate (24 % and 11 %). The OA source identification was performed using the rolling positive matrix factorization (PMF) approach to account for the potential temporal changes in the source profile. It was possible to split OA into five factors with a distinct temporal variability and mass spectral signature. Three were associated with anthropogenic primary OA (POA) sources: hydrocarbon-like OA (HOA; 5.2 % of OA mass in winter and 6.8 % in summer), biomass burning OA (BBOA; 10.6 % and 6.1 %) and coal combustion OA (CCOA; 23 % and 8.7 %). Another two are secondary and processed oxygenated OA (OOA) sources: less oxidized OOA (LO-OOA; 28.4 % and 36.7 %) and more oxidized OOA (MO-OOA; 32.8 % and 41.8 %). Since equivalent black carbon (eBC) was clearly associated with the identified POA factors (sum of HOA, BBOA, and CCOA; R2= 0. 87), eBC's contribution to each of the POA factors was achieved using a multilinear regression model. Consequently, CCOA represented the main anthropogenic sources of carbonaceous aerosol (sum of OA and eBC) not only during winter (56 % of POA in winter) but also in summer (13 % of POA in summer), followed by BBOA (29 % and 69 % of POA in winter and summer, respectively) and HOA (15 % and 18 % of POA in winter and summer, respectively). A seasonal air mass cluster analysis was used to understand the geographical origins of the different aerosol types and showed that during both winter and summer time, PM1 (PM with an aerodynamic diameter smaller than 1 µm) air masses with eastern influence were always associated with the highest mass concentration and the highest coal combustion fraction. Since during wintertime CCOA is a combination of domestic heating and power plant emissions, the summer contribution of CCOA emphasizes the critical importance of coal power plant emissions to rural-background aerosols and its impact on air quality, through long-range transportation.

Abstract. Air pollution in urban environments has been shown to have a negative impact on air quality and human health, particularly in megacities. Over recent decades, Delhi, India, has suffered high atmospheric pollution, with significant particulate matter (PM) concentrations as a result of anthropogenic activities. Organic aerosols (OAs) are composed of thousands of different chemical species and are one of the main constituents of submicron particles. However, quantitative knowledge of OA composition, their sources and their processes in urban environments is still limited. This is important particularly in India, as Delhi is a massive, inhomogeneous conurbation, where we would expect the apportionment and concentrations to vary depending on where in Delhi the measurements/source apportionment is performed, indicating the need for multisite measurements. This study presents the first multisite analysis carried out in India over different seasons, with a focus on identifying OA sources. The measurements were taken during 2018 at two sites in Delhi, India. One site was located at the India Meteorological Department, New Delhi (ND). The other site was located at the Indira Gandhi Delhi Technical University for Women, Old Delhi (OD). Non-refractory submicron aerosol (NR-PM1) concentrations (ammonium, nitrate, sulfate, chloride and organic aerosols) of four aerosol mass spectrometers were analysed. Collocated measurements of volatile organic compounds, black carbon, NOx and CO were performed. Positive matrix factorisation (PMF) analysis was performed to separate the organic fraction, identifying a number of conventional factors: hydrocarbon-like OAs (HOAs) related to traffic emissions, biomass burning OAs (BBOAs), cooking OAs (COAs) and secondary OAs (SOAs). A composition-based estimate of PM1 is defined by combining black carbon (BC) and NR-PM1 (C-PM1= BC + NR-PM1). No significant difference was observed in C-PM1 concentrations between sites, OD (142 ± 117 µg m−3) compared to ND (123 ± 71 µg m3), from post-monsoon measurements. A wider variability was observed between seasons, where pre-monsoon and monsoon showed C-PM1 concentrations lower than 60 µg m−3. A seasonal variation in C-PM1 composition was observed; SO42- showed a high contribution over pre-monsoon and monsoon seasons, while NO3- and Cl− had a higher contribution in winter and post-monsoon. The main primary aerosol source was from traffic, which is consistent with the PMF analysis and Aethalometer model analysis. Thus, in order to reduce PM1 concentrations in Delhi through local emission controls, traffic emission control offers the greatest opportunity. PMF–aerosol mass spectrometer (AMS) mass spectra will help to improve future aerosol source apportionment studies. The information generated in this study increases our understanding of PM1 composition and OA sources in Delhi, India. Furthermore, the scientific findings provide significant information to strengthen legislation that aims to improve air quality in India.