Real-time, in situ molecular composition measurements of the organic fraction of fine particulate matter (PM_2.5) remain challenging, hindering a full understanding of the climate impacts and health effects of PM_2.5. In particular, the thermal decomposition and ionization-induced fragmentation affecting current techniques has limited a detailed investigation of secondary organic aerosol (SOA), which typically dominates OA. Here we deploy a novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS) during winter 2017 in downtown Zurich, Switzerland, which overcomes these limitations, together with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) and supporting instrumentation. Positive matrix factorization (PMF) implemented within the Multilinear Engine (ME-2) program was applied to the EESI-TOF-MS data to quantify the primary and secondary contributions to OA. An 11-factor solution was selected as the best representation of the data, including five primary and six secondary factors. Primary factors showed influence from cooking, cigarette smoke, biomass burning (two factors) and a special local unknown event occurred only during two nights. Secondary factors were affected by biomass burning (three factors, distinguished by temperature and/or wind direction), organonitrates, monoterpene oxidation, and undetermined regional processing, in particular the contributions of wood combustion. While the AMS attributed slightly over half the OA mass to SOA but did not identify its source, the EESI-TOF-MS showed that most (>70 %) of the SOA was derived from biomass burning. Together with significant contributions from less aged biomass burning factors identified by both AMS and EESI-TOF-MS, this firmly establishes biomass burning as the single most important contributor to OA mass at this site during winter. High correlation was obtained between EESI-TOF-MS and AMS PMF factors where specific analogues existed, as well as between total signal and POA–SOA apportionment. This suggests the EESI-TOF-MS apportionment in the current study can be approximately taken at face value, despite ion-by-ion differences in relative sensitivity. The apportionment of specific ions measured by the EESI-TOF-MS (e.g., levoglucosan, nitrocatechol, and selected organic acids) and utilization of a cluster analysis-based approach to identify key marker ions for the EESI-TOF-MS factors are investigated. The interpretability of the EESI-TOF-MS results and improved source separation relative to the AMS within this pilot campaign validate the EESI-TOF-MS as a promising approach to source apportionment and atmospheric composition research.

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 + 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.

Improving the understanding of the health and climate impacts of aerosols remains challenging and is restricted by the limitations of current measurement techniques. Detailed investigation of secondary organic aerosol (SOA), which is typically the dominating fraction of the organic aerosol (OA), requires instrumentation capable of real-time, in situ measurements of molecular composition. In this study, we present the first ambient measurements by a novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS). The EESI-TOF-MS was deployed along with a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) during summer 2016 at an urban location (Zurich, Switzerland). Positive matrix factorization (PMF), implemented within the Multilinear Engine (ME-2), was applied to the data from both instruments to quantify the primary and secondary contributions to OA. From the EESI-TOF-MS analysis, a six-factor solution was selected as the most representative and interpretable solution for the investigated dataset, including two primary and four secondary factors. The primary factors are dominated by cooking and cigarette smoke signatures while the secondary factors are discriminated according to their daytime (two factors) and night-time (two factors) chemistry. All four factors showed strong influence by biogenic emissions but exhibited significant day–night differences. Factors dominating during daytime showed predominantly ions characteristic of monoterpene and sesquiterpene oxidation while the night-time factors included less oxygenated terpene oxidation products, as well as organonitrates which were likely derived from NO₃ radical oxidation of monoterpenes. Overall, the signal measured by the EESI-TOF-MS and AMS showed a good correlation. Further, the two instruments were in excellent agreement in terms of both the mass contribution apportioned to the sum of POA and SOA factors and the total SOA signal. However, while the oxygenated organic aerosol (OOA) factors separated by AMS analysis exhibited a flat diurnal pattern, the EESI-TOF-MS factors illustrated significant chemical variation throughout the day. The captured variability, inaccessible from AMS PMF analysis, was shown to be consistent with the variations in the physiochemical processes influencing chemical composition and SOA formation. The improved source separation and interpretability of EESI-TOF-MS results suggest it to be a promising approach to source apportionment and atmospheric composition research.

In recent years, intense haze events in megacities such as Beijing have received significant attention. Although secondary organic aerosol (SOA) has been identified as a major contributor to such events, knowledge of its sources and formation mechanisms remains uncertain. We investigate this question through the first field deployment of the extractive electrospray ionisation time-of-flight mass spectrometer (EESI-TOF) in Beijing, together with an Aerodyne long-time-of-flight aerosol mass spectrometer (L-TOF AMS). Measurements were performed during autumn and winter 2017, capturing the transition from non-heating to heating seasons. Source apportionment resolved four factors related to primary organic aerosols (traffic, cooking, biomass burning, and coal combustion), as well as four related to SOA. Of the SOA factors, two were related to solid fuel combustion (SFC), one to SOA generated from aqueous chemistry, and one to mixed/indeterminate sources. The SFC factors were identified from spectral signatures corresponding to aromatic oxidation products, while the aqueous SOA factor was characterised by signatures of small organic acids and diacids and unusually low ${\mathrm{CO}}^{+}/{\mathrm{CO}}_{\mathrm{2}}^{+}$ fragment ratios measured by the AMS. Solid fuel combustion was the dominant source of SOA during the heating season. However, a comparably intense haze event was also observed in the non-heating season and was dominated by the aqueous SOA factor. During this event, aqueous chemistry was promoted by the combination of high relative humidity and air masses passing over high-NO_x regions to the south and east of Beijing, leading to high particulate nitrate. The resulting high liquid water content was highly correlated with the concentration of the aqueous SOA factor. These results highlight the strong compositional variability between different haze events, indicating the need to consider multiple formation pathways and precursor sources to describe SOA during intense haze events in Beijing.

We present highly time-resolved (30 to 120 min) measurements of size-fractionated (PM₁₀ and PM_2.5) elements in two cities in Asia (Delhi and Beijing) and Europe (Krakow and London). For most elements, the mean concentrations in PM₁₀ and PM_2.5 are higher in the Asian cities (up to 24 and 28 times, respectively) than in Krakow and often higher in Delhi than in Beijing. Among European cities, Krakow shows higher elemental concentrations (up to 20 and 27 times, respectively) than London. Hourly maximum concentrations of Pb and Zn reach up to 1 µg m⁻³ in Delhi, substantially higher than at the other sites. The enrichment factor of an element together with the size distribution allows for a rough classification of elements by major source. We define five groups: (1) dust emissions, (2) non-exhaust traffic emissions, (3) solid fuel combustion, (4) mixed traffic/industrial emissions, and (5) industrial/coal/waste burning emissions, with the last group exhibiting the most site-to-site variability. We demonstrate that the high time resolution and size-segregated elemental dataset can be a powerful tool to assess aerosol composition and sources in urban environments. Our results highlight the need to consider the size distributions of toxic elements, diurnal patterns of targeted emissions, and local vs. regional effects in formulating effective environmental policies to protect public health.

Online characterization of aerosol composition at the near-molecular level is key to understanding chemical reaction mechanisms, kinetics, and sources under various atmospheric conditions. The recently developed extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) is capable of detecting a wide range of organic oxidation products in the particle phase in real time with minimal fragmentation. Quantification can sometimes be hindered by a lack of available commercial standards for aerosol constituents, however. Good correlations between the EESI-TOF and other aerosol speciation techniques have been reported, though no attempts have yet been made to parameterize the EESI-TOF response factor for different chemical species. Here, we report the first parameterization of the EESI-TOF response factor for secondary organic aerosol (SOA) at the near-molecular level based on its elemental composition. SOA was formed by ozonolysis of monoterpene or OH oxidation of aromatics inside an oxidation flow reactor (OFR) using ammonium nitrate as seed particles. A Vocus proton-transfer reaction mass spectrometer (Vocus-PTR) and a high-resolution aerosol mass spectrometer (AMS) were used to determine the gas-phase molecular composition and the particle-phase bulk chemical composition, respectively. The EESI response factors towards bulk SOA coating and the inorganic seed particle core were constrained by intercomparison with the AMS. The highest bulk EESI response factor was observed for SOA produced from 1,3,5-trimethylbenzene, followed by those produced from d-limonene and o-cresol, consistent with previous findings. The near-molecular EESI response factors were derived from intercomparisons with Vocus-PTR measurements and were found to vary from 10³ to 10⁶ ion counts s⁻¹ ppb⁻¹, mostly within ±1 order of magnitude of their geometric mean of 10^4.6 ion counts s⁻¹ ppb⁻¹. For aromatic SOA components, the EESI response factors correlated with molecular weight and oxygen content and inversely correlated with volatility. The near-molecular response factors mostly agreed within a factor of 20 for isomers observed across the aromatics and biogenic systems. Parameterization of the near-molecular response factors based on the measured elemental formulae could reproduce the empirically determined response factor for a single volatile organic compound (VOC) system to within a factor of 5 for the configuration of our mass spectrometers. The results demonstrate that standard-free quantification using the EESI-TOF is possible.