A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBC_tr) and the explained variation of $m/z$ 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of $m/z$ 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µg m⁻³ (7.8 %–9.0 %) and 0.7–1.2 µg m⁻³ (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µg m⁻³, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µg m⁻³, or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µg m⁻³, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µg m⁻³ (26.5 %) and 2.2 µg m⁻³ (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µg m⁻³ (3.4 %–15.9 %), from 0.6 to 2.2 µg m⁻³ (7.7 %–33.7 %) and from 0.9 to 3.1 µg m⁻³ (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30 %, ±11 %, ±25 % and ±12 %, respectively.

The oxidative potential (OP) of particulate matter (PM) is a major driver of PM-associated health effects. In India, the emission sources defining PM-OP, and their local/regional nature, are yet to be established. Here, to address this gap we determine the geographical origin, sources of PM, and its OP at five Indo-Gangetic Plain sites inside and outside Delhi. Our findings reveal that although uniformly high PM concentrations are recorded across the entire region, local emission sources and formation processes dominate PM pollution. Specifically, ammonium chloride, and organic aerosols (OA) from traffic exhaust, residential heating, and oxidation of unsaturated vapors from fossil fuels are the dominant PM sources inside Delhi. Ammonium sulfate and nitrate, and secondary OA from biomass burning vapors, are produced outside Delhi. Nevertheless, PM-OP is overwhelmingly driven by OA from incomplete combustion of biomass and fossil fuels, including traffic. These findings suggest that addressing local inefficient combustion processes can effectively mitigate PM health exposure in northern India.

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 (CO${}_{\mathrm{2}}^{+}$ ) 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.

This work describes results obtained from the 2016 Aerosol Chemical Speciation Monitor (ACSM) intercomparison exercise performed at the Aerosol Chemical Monitor Calibration Center (ACMCC, France). Fifteen quadrupole ACSMs (Q_ACSM) from the European Research Infrastructure for the observation of Aerosols, Clouds and Trace gases (ACTRIS) network were calibrated using a new procedure that acquires calibration data under the same operating conditions as those used during sampling and hence gets information representative of instrument performance. The new calibration procedure notably resulted in a decrease in the spread of the measured sulfate mass concentrations, improving the reproducibility of inorganic species measurements between ACSMs as well as the consistency with co-located independent instruments. Tested calibration procedures also allowed for the investigation of artifacts in individual instruments, such as the overestimation of m/z 44 from organic aerosol. This effect was quantified by the m/z (mass-to-charge) 44 to nitrate ratio measured during ammonium nitrate calibrations, with values ranging from 0.03 to 0.26, showing that it can be significant for some instruments. The fragmentation table correction previously proposed to account for this artifact was applied to the measurements acquired during this study. For some instruments (those with high artifacts), this fragmentation table adjustment led to an “overcorrection” of the f44 (m/z 44/Org) signal. This correction based on measurements made with pure NH₄NO₃, assumes that the magnitude of the artifact is independent of chemical composition. Using data acquired at different NH₄NO₃ mixing ratios (from solutions of NH₄NO₃ and (NH₄)₂SO₄) we observe that the magnitude of the artifact varies as a function of composition. Here we applied an updated correction, dependent on the ambient NO₃ mass fraction, which resulted in an improved agreement in organic signal among instruments. This work illustrates the benefits of integrating new calibration procedures and artifact corrections, but also highlights the benefits of these intercomparison exercises to continue to improve our knowledge of how these instruments operate, and assist us in interpreting atmospheric chemistry.

Kraków is routinely affected by very high air pollution levels, especially during the winter months. Although a lot of effort has been made to characterize ambient aerosol, there is a lack of online and long-term measurements of non-refractory aerosol. Our measurements at the AGH University of Science and Technology provide the online long-term chemical composition of ambient submicron particulate matter (PM₁) between January 2018 and April 2019. Here we report the chemical characterization of non-refractory submicron aerosol and source apportionment of the organic fraction by positive matrix factorization (PMF). In contrast to other long-term source apportionment studies, we let a small PMF window roll over the dataset instead of performing PMF over the full dataset or on separate seasons. In this way, the seasonal variation in the source profiles can be captured. The uncertainties in the PMF solutions are addressed by the bootstrap resampling strategy and the random a-value approach for constrained factors. We observe clear seasonal patterns in the concentration and composition of PM₁, with high concentrations during the winter months and lower concentrations during the summer months. Organics are the dominant species throughout the campaign. Five organic aerosol (OA) factors are resolved, of which three are of a primary nature (hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)) and two are of a secondary nature (more oxidized oxygenated OA (MO-OOA) and less oxidized oxygenated OA (LO-OOA)). While HOA contributes on average 8.6 % ± 2.3 % throughout the campaign, the solid-fuel-combustion-related BBOA and CCOA show a clear seasonal trend with average contributions of 10.4 % ± 2.7 % and 14.1 %, ±2.1 %, respectively. Not only BBOA but also CCOA is associated with residential heating because of the pronounced yearly cycle where the highest contributions are observed during wintertime. Throughout the campaign, the OOA can be separated into MO-OOA and LO-OOA with average contributions of 38.4 % ± 8.4 % and 28.5 % ± 11.2 %, respectively.

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.