Magadino, where the sampling site is located, is in a Swiss alpine valley (46∘90′37′′ N, 85∘60′2′′ E; 204 m a.s.l.). This site belongs to the Swiss National Air Pollution Monitoring Network (NABEL, https://www.empa.ch/web/s503/nabel, last access: 20 July 2021). It is around 1.4 km away from the local train station, Cadenazzo; around 7 km away from Locarno Airport; and nearly 8 km away from Lake Maggiore. This station is surrounded by agricultural fields within a rural area and is considered a rural background site. It can be potentially affected by domestic wood burning, adjacent agricultural activity, and transit traffic through the valley. The site topography favours quite high PM levels due to stagnant meteorological conditions or boundary layer inversions, especially in winter. Magadino remains one of the most polluted regions in Switzerland, and it has often exceeded the annual average PM10 limit value for Switzerland (20 µgm-3) (Meteotest, 2017; The Swiss Federal Council, 2018). Therefore, there is an increasing need for a more effective mitigation strategy.

This study measured chemical composition and mass loadings of non-refractory constituents of ambient submicron aerosol particles (NR-PM1) by an Aerodyne quadrupole ACSM (Ng et al., 2011a). The ACSM uses the same sampling and detection technology as the AMS but is simplified and designated for long-term monitoring applications by reducing maintenance frequency at the cost of lower sensitivity, restriction to the integer mass resolution, and no size measurement. As for the AMS, sampled submicron particles enter the instrument through a critical orifice (100 µm i.d.) at a flow rate of 1.4 cm3 s-1 (at 20 ∘C and 1 atm). The sampling flow will pass either through a particle filter or directly into the system using an automated three-way switching valve that is switched every ∼ 30 s. An aerodynamic lens focuses the sampled particles into a narrow beam which impacts on a tungsten surface of around 600 ∘C, where the non-refractory particles vaporise and are subsequently ionised by an electron impact source (70 eV). A quadrupole mass spectrometer detects the resulting ions up to a mass-to-charge ratio (m/z) of 148 Th. The particle mass spectrum is represented by the difference between the total ambient air and particle-free signals.

The quantification of ACSM data requires an estimation of the fraction of NR-PM1 that bounces off the oven without being vaporised and therefore is not detected (Canagaratna et al., 2007; Matthew et al., 2008). In this study, a constant collection efficiency (CE) factor of 0.45 was applied to take it into account. The details of determinations of the CE value is described in Sect. S1 in the Supplement. In this study, we recorded the data with a time resolution of 30 min. During the campaign, the ACSM filament burnt out on 14 April 2014. This was addressed by switching to the backup filament installed within the instrument (no venting required). Calibration of the relative ionisation efficiencies (RIEs) of particulate nitrate, sulfate, and ammonium was conducted using size-selected (300 nm) pure NH4NO3 and pure (NH4)2SO4 particles. Calibrations of the RIE, m/z scale, and the sampling flow were performed every 2 months. In this study, we used the averaged RIEs for nitrate, sulfate, and ammonium. The exact values are shown in Fig. S1 of the Supplement.

Meteorological data, including temperature, precipitation, wind speed, wind direction, and solar radiation, are monitored at the NABEL station. In addition, concentrations of trace gases (SO2, O3, NOx), equivalent black carbon (eBC), and PM10 were measured with a time resolution of 10 min. We used an aethalometer (AE31 model by Magee Scientific) to measure eBC concentrations. Therefore, we conducted SA of eBC by following Zotter et al. (2017) using Ångström exponents for eBC from traffic αtr=0.9 and wood burning αwb=1.68. More details about eBC source apportionment are provided in Sect. S2 of the Supplement.

In this study, we used acsm_local_1610 software (Aerodyne Research Inc.) to prepare the PMF input matrix. In total, this dataset includes 19 708 time points and 67 ions. Of these, CO2+-related variables (IO+ (m/z 16), IHO+ (m/z 17), and IH2O+ (m/z 18)) were excluded from the spectral matrix prior to a PMF analysis. They are reinserted into the OA factor mass spectra after the PMF analysis using the ratio from the fragmentation table (Allan et al., 2004); the factor concentrations are likewise adjusted. According to Allan et al. (2003, 2004), the measurement error matrix was calculated with a minimum error considered for the uncertainty in all variables in the data matrix as in Ulbrich et al. (2009). Following the recommendations in Paatero and Hopke (2003) and Ulbrich et al. (2009), the measurement uncertainty for variables (m/z) with a signal-to-noise ratio (S / N) < 2 (weak variables) and S / N < 0.2 (bad variables) was increased by a factor of 2 and 10, respectively. In total, 27 weak ACSM variables were down-weighted. Additionally, m/z 12 and m/z 13 were not considered during the PMF analyses due to being noisy and their overall negative signal. Moreover, m/z 15 was not only very noisy (S / N = 0.09) but maybe also affected by high biases due to potential interference with air signals.

In this study, we conducted a series of steps (Sect. S3.2 and S3.3 in the Supplement) to obtain the results we present in this paper. In summary, we first tested potential sources for each season with seasonal PMF pre-tests. Secondly, we obtained stable seasonal solutions from bootstrap seasonal analysis. Then, we conducted rolling PMF with certain settings (constraints, number of repeats, length of the window size, and step of rolling window). Lastly, we were able to retrieve robust results using specific criteria to define environmentally reasonable solutions. Please refer to Sect. S3.2 and S3.3 in the Supplement for more detailed description of each step. This section focuses on the general introduction of rolling PMF with ME-2, the differences between our method vs. the method developed by Canonaco et al. (2021), and the general settings of the rolling PMF analysis in this study.

Running PMF over the long-term ACSM datasets assumes that the OA source profiles are static within this time window. It can lead to large errors since OA chemical fingerprints are expected to vary over time (Paatero et al., 2014). For example, Canonaco et al. (2015) showed that summer and winter OOA variability cannot be accurately represented by a single pair of OOA profiles. A common way to reduce the model uncertainty arising from this source is to choose a proper number of OA factors (Sug Park et al., 2000) and then perform a PMF analysis on a subset of measurements to capture temporal features of OA chemical fingerprints. Such characterisation of OA sources on a seasonal basis has been demonstrated in several studies (Lanz et al., 2008; Crippa et al., 2014; Petit et al., 2014; Minguillón et al., 2015; Ripoll et al., 2015; Zhang et al., 2019). Parworth et al. (2015) introduced the rolling PMF by running PMF in a small window (14 d), which advanced with a step of 1 d. This novel technique enables the source profiles to adapt to the temporal variabilities. Canonaco et al. (2021) combined the rolling PMF technique with ME-2 (Sect. S3.1 in the Supplement) to deal with the rotational ambiguity of the PMF analysis. In addition, it also used the bootstrap resampling strategy (Efron, 1979) and random a values (Sect. S3.2.2 in the Supplement) to estimate the statistical and rotational uncertainties in the PMF analysis.

This study mostly followed the methods developed by Canonaco et al. (2021) but with some modifications. The settings of the rolling PMF window is explicitly explained in Sect. S3.2.3 of the Supplement). In addition, we also performed a test of the rolling window size (i.e. 1, 7, 14, and 28 d) using a similar approach (Sect. S4 in the Supplement). As Canonaco et al. (2021) did, we also used the criteria-based selection function developed by Canonaco et al. (2021) to evaluate our PMF runs. The settings of the criteria are provided in Sect. S3.2.4 of the Supplement.

However, instead of using published reference factor profiles like Canonaco et al. (2021) have done, we retrieved the reference profiles of primary and local factors from seasonal bootstrap analysis (Sect. S3.2 in the Supplement). Specifically, the reference profiles of the hydrocarbon-like OA (HOA) factor and biomass burning OA (BBOA) factor were retrieved from the winter (December, January, and February; DJF) bootstrapped PMF solution as shown in Fig. S4, and we obtained the m/z 58-related (58-OA) factor profile from the summer (June, July, and August; JJA) bootstrapped PMF solution (Fig. S4). The 58-OA factor was dominated by nitrogen-containing fragments (at m/z 58, m/z 84, and m/z 98). In general, the ACSM estimates the organic m/z 98 signal by dividing organic m/z 84 by a factor of 2 according to the fragmentation table of organic species that was provided by Allan et al. (2004). Thus, the intensity of m/z 98 is always half of the intensity of m/z 84 in each factor. This 58-OA factor appeared only after the filament was switched on 14 April 2014. The instrument setup thus strongly influenced the sensitivity of these components due to influences of surface ionisation. The nitrogen-containing ion, m/z 58, was also observed in Hildebrandt et al. (2011) due to the enhanced surface ionisation in a certain period. In addition, the potassium signal was enhanced at the same time, which further corroborated our hypothesis of the enhanced surface ionisation. Also, since this factor was constrained through the whole dataset, the PMF model overestimated the mass concentration of this factor significantly, which leads to high uncertainties for the 58-OA factor. Therefore, the time series of this source should be considered the upper limit, and the real mass concentration of it could be substantially lower. However, with the low mass concentration of the 58-OA factor during the whole campaign, we considered it a minor factor. Thus, this factor was considered in the PMF analysis, but no further interpretation of its potential source will be attempted in this paper. Moreover, we took a different path to define “good” PMF solutions by using a novel Student t-test approach to determine the environmentally reasonable solutions quantitatively with minimum subjective judgements (Sect. S3.3 in the Supplement). Overall, we provided a comprehensive analysis of a long-term ACSM dataset using this state-of-the-art technique in this work. The results are presented in the following section.

Considering that the major part of eBC is within PM1 (Schwarz et al., 2013), we added eBC to the total NR-PM1 from the ACSM to perform a mass closure analysis using independent measurements of PM2.5 and PM10 from filters. The gravimetric PM2.5 and PM10 show a high correlation with the total estimated PM1 (NR-PM1+eBC) (Fig. S1c). The slopes of the linear fits (±1 standard deviation) are 1.62 ± 0.05 (r2=0.81; N=79) for PM2.5 vs. PM1 and 1.84 ± 0.03 (r2=0.67; N=335) for PM10 vs. PM1. This means that the estimated PM1 comprised 62 % and 54 % of the PM2.5 and PM10 mass, respectively. The daily averages of inorganic species concentrations measured by the ACSM and those measured on the filters by ion chromatography showed a good correlation, with r2=0.83 for SO42-, r2=0.82 for NO3- and r2=0.50 for Cl-, with slopes close to 1 (Fig. S1a). The 2-week average of total ammonium and total nitrate measured by the offline AMS technique agreed rather well with the ACSM ammonium (r2=0.47) and nitrate (r2=0.79), as shown in the plots in Fig. S1b. The ion balance of particulate ammonium, sulfate, and nitrate measured by the ACSM showed that the measured aerosol particles were mostly neutral.

The daily average PM1 components are shown in Fig. 1a, with an annual average PM1 concentration (including eBC) from September 2013 to October 2014 equal to 10.2 µgm-3. In winter, the average PM1 concentration was highest (13.8 µgm-3), with OA contributing 54 % to the total PM1 mass. In summer, the average PM1 mass concentration was below 10 µgm-3, but the relative contribution of the OA fraction increased to 62 %.

Seasonally averaged diurnal cycles of NR-PM1 components and eBC are displayed in Fig. 2. In this study, all the data are based on local time (central European time). In autumn, spring, and summer, the diurnals of these pollutants seem to be mainly affected by the development of the boundary layer height (BLH). Most of the species show similar diurnal trends for these three seasons. In addition, summer has the highest sulfate concentration due to enhanced photochemical production. In winter, air pollutants accumulate during the evening and night due to thermal inversion. In general, eBC and organics have higher levels due to enhanced biomass burning emissions and a lower BLH. We observed distinct midday peaks of organics, sulfate, nitrate, ammonium, chloride, and NOx in the winter. Magadino experienced a series of windless and cold but sunny periods from December 2013 to January 2014, including such sharp peaks (Fig. S6a). This was due to advection within the shallow boundary layer as both primary and secondary pollutants increased simultaneously. At the same time, the local wind speed near the ground was very low. One potential explanation was that the locally and regionally induced orography-influenced winds, including vertical diffusion processes, caused these delayed midday peaks. However, these processes remain difficult to track without spatially distributed measurements. Such phenomena were not observed during cloudy, cold, and windless days (Fig. S6b) without thermally induced meteorological processes. Unlike in other seasons, the dilution process due to vertical mixing happened only after noon due to strong inversions during the night and late irradiation of the valley surface in the winter.

The automated rolling PMF analysis requires the knowledge of the reference profiles as well as the number of factors. This section presents how the number of factors was determined based on seasonal PMF pre-tests (refer to Sect. S3.2.1 in the Supplement for methodology). Initially, unconstrained PMF (three to six factors) was performed separately for the different seasons by following the SA guidelines provided by Crippa et al. (2014). Typically, the HOA profile is characterised by a high contribution of alkyl fragments (e.g. m/z 43, m/z 57) and the corresponding alkenyl carbocations (e.g. m/z 41, m/z 55), and the factor profile is relatively consistent over time and different locations. The BBOA profile exhibits significant signals at m/z 60 and m/z 73, which are well-known fragments arising from the fragmentation of anhydrous sugars present in biomass-related emissions (Alfarra et al., 2007). The HOA profile is present throughout the whole year for the unconstrained PMF runs, while the BBOA profile exists for all seasons except in summer. However, as shown in Fig. S2, the measured fraction of m/z 60 during summer was above the background level of 0.3 % ± 0.06 % for biomass-burning-related air masses (Aiken et al., 2009; Cubison et al., 2011; DeCarlo et al., 2008). In addition, the scaled residual at m/z 60 was decreased when a BBOA factor profile was constrained. Thus, we decided to constrain the BBOA factor for all seasons to potentially capture local events, such as open fires and barbecues in summer.

No evidence for the presence of a cooking-related OA (COA) factor was found based on the seasonal pre-analysis of the key fragments (m/z 55 and m/z 57). Figure S3 shows no difference in the slope of the absolute mass concentration of m/z 55 vs. m/z 57 for different hours of the day (Fig. S3a), while different seasons show different slopes (Fig. S3b). Therefore, a COA factor was not considered in the PMF model. Moreover, a rapid increase in the measured fraction of m/z 58, m/z 84, and m/z 98 together with m/z 39 (potassium signal) was observed after a filament exchange on 14 April 2014. It was likely that the ACSM's sensitivity towards those ions was changed by the filament exchange. Also, this 58-OA factor was present for spring, summer, and autumn in 2014 in unconstrained PMF runs all the time after the filament change. Therefore, we kept this factor for these three seasons.

For the factor(s) with a secondary origin, we performed PMF models with a different number of factors (three–six) to assess if the oxygenated OA (OOA) factor is separable without mixing with primary organic aerosol (POA) factors (with a high contribution of m/z 44 that is likely dominated by the CO2+ ion, derived from decomposition of carboxylic acids; Duplissy et al., 2011). We conducted these tests (with a different number of factors) independently for the different seasons (autumn 2013, winter, spring, summer, autumn 2014).

We analysed the winter data first by constraining an HOA factor profile (Crippa et al., 2013) with a tight a value of 0.05. The three-factor solution (with one OOA factor, i.e. less oxidised OOA (LO-OOA) and more oxidised OOA (MO-OOA)) showed similarly good agreement of HOA and BBOA with the external tracers (NOx, eBC from traffic sources (eBCtr), eBC from wood burning sources (eBCwb)) to that of the four-factor solution (with two OOA factors). However, the scaled residual of m/z 60 was reduced for the solution with two OOA factors. Moreover, the solution with one OOA factor was not sufficient to explain the variabilities in measured f44 vs. f43 (excluding the primary organic aerosol (POA) factors). For five- and six-factor solutions, the BBOA and LO-OOA factors started to split. Eventually, we selected the four-factor solution (HOA, BBOA, MO-OOA, LO-OOA) as the best representation of the winter data.

After the bootstrap seasonal PMF runs of the winter data (details in Sect. S3.2.2 of the Supplement), we extracted the HOA and BBOA profiles to use them as the reference factor profiles (Fig. S4) for the pre-tests of other seasons. For the spring, summer, and autumn seasons, three- to six-factor PMF solutions were modelled separately for each season by constraining the HOA (a value = 0.1) and BBOA (a value = 0.3) profiles. For the three-factor solution, we observed an OOA factor with some signals at m/z 58, m/z 84, and m/z 98, which we could not relate to a specific source or process. Also, the scaled residuals of variables showed significant levels for these three ions. In addition, the time series and factor profile of 58-OA were so distinct that PMF could easily resolve it. When we increased the number of OA factors from three to four, a factor dominated by m/z 58, m/z 84, and m/z 98 emerged, named the 58-OA factor. However, the OOA factor still showed slight signals at m/z 58, m/z 84, and m/z 98. An increase in the number of factors from four to five resulted not only in a decrease in QQexp but also in “clean” OOA factors without mixing with the 58-OA factor. A further increase in the number of factors did not change QQexp substantially (< 1 %), and the sixth factor was a mathematical split of the 58-OA factor with m/z 58 as the dominating variable. Thus, the five-factor PMF model was chosen as the most appropriate for the spring, summer, and autumn 2014 to isolate this instrumental artefact via PMF. We did not add the 58-OA factor for the autumn season in 2013 since it appeared only after the filament exchange on 14 April 2014. This 58-OA factor was included while running PMF because of the rapid drop of the QQexp from four to five factors in the PMF model, but the source of this factor will not be discussed in the paper.

Here we present the optimised time window size (14 d) (details of the time window optimisation are given in Sect. S4 of the Supplement and in Fig. S10). In total, we considered 53.4 % of the PMF runs (11 087 out of 20 750) with only 11 non-modelled data points. The results of the full-year PMF analysis of the 30 min resolved ACSM data are summarised in Fig. 3. The relative contributions of the OA factors are in addition shown in Fig. 3b. The primary traffic-related HOA had tiny variation (seasonal averages between 8.1 % and 10.1 %) throughout the year (Fig. 4). In contrast, BBOA showed a distinct yearly cycle (8.3 %–27.4 %) with a yearly averaged contribution of 17.1 %. They increased significantly (to 27.4 %) in winter which is typical of Alpine valleys (Szidat et al., 2007). This means that biomass burning was the most important primary OA source during the cold season in Magadino. The eBCwb showed similar trends to those of the BBOA factor time series during the cold seasons (Fig. 3c). The contribution of the 58-OA factor remained small before the filament was changed on 14 April 2014, which was expected because we could not retrieve this factor in seasonal, unconstrained PMF runs before April 2014.

In this study, we retrieved two OOA factors, LO-OOA and MO-OOA. Total OOA (LO-OOA + MO-OOA) contributed substantially to the total OA mass throughout the whole year, with an average contribution of 71.6 % (Figs. 3b, 4). In general, the contribution of OOA to the total OA mass did not vary distinctly over the seasons but reached a maximum of 90.1 % on 12 June 2014, the day with the highest daily average temperature (30.7 ∘C).

In this work, we made head-to-head comparisons between the seasonal bootstrap solutions and the rolling PMF results (see Figs. A1–A3 and Table A1 in the Appendix) in terms of mass concentrations, factor profiles, scaled residuals, and correlations between time series for each factor and corresponding external tracers. We found consistent factor profiles and mass concentrations for the constrained factors (i.e. HOA, BBOA, and 58-OA), while OOA factors showed some noticeable differences in both mass concentrations and factor profiles. Rolling PMF provided slightly better correlations and smaller scaled residuals. Therefore, we consider rolling PMF results to be more environmentally reasonable than those of the seasonal PMF (more details in Appendix A).