dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the recommended relative ionisation efficiency (RIE) calibration procedures have varied over the long-time span of these datasets  (2013) (2014) (2015) (2016) (2017) (2018) (2019). For instance, some of the datasets conducted RIE calibration only on specific m/z values (jump scan) as recommended earlier, instead of scanning the entire mass range (m/z 10 to 150) of the mass spectrometer  (Freney et al. , 2019). Considering the nitrate interference on the CO 2 + signal at m/z 44 (so-called Pieber effect) is time-dependent  (Freney et al. , 2019; Fröhlich et al. , 2015; Pieber et al. , 2016)  and m/z 44 is not measured in jump scan RIE calibrations, it is thus impossible to do a post-correction consistently. Therefore, none of the datasets was corrected for the Pieber Fig. 1. ACSM/AMS measurement periods considered in this study.
, 2008; Zotter et al. , 2017). The 22 sampling sites are classified based on their geographic locations as urban (13 sites, including four flagged as suburban: Athens DEM, Lille, Paris, and Bucharest) or non-urban (9 sites, Table  S1 ). The chemical composition of some of these 22 datasets have already been reported  (Barreira et al. , 2021; Bressi et al. , 2021). This study focuses on the overview of OA source apportionment results and includes new sites in our analysis. More details about source apportionment results at some of these sites can be found in the following published papers:  (Barreira et al. , 2021; Canonaco et al. , 2021; Chazeau et al. , 2022 Chazeau et al. , , 2021;; Chen et al. , 2021; Farah et al. , 2021; Heikkinen et al. , 2021; Lin et al. , 2019; Minguillón et al. , 2015; Petit et al.
g. , source profiles or time series of external data (i. e. , data external to the PMF input matrices)) to avoid unrealistic rotations and to generate more unique solutions. Using a priori information allows the user to guide the model towards an environmentally reasonable solution. Canonaco et al. (2013)  implemented an ME-2 solver  (Paatero, 1999)  into the Igor-based software package, Source Finder (SoFi). SoFi enables the users to have enhanced rotational control over the factor solutions by imposing constraints via, e. g. , the avalue approach, on one or more elements of F and/or G  (Paatero and Hopke, 2009). For instance, the a value (ranging from 0 to 1) is the tolerated relative deviation of a factor profile (f k ) or time series (g k ) from the chosen a priori input profile (F k ) or time series (G k ) during the iterative least-square minimization, as demonstrated in Equations (3a) and (3b)  (Canonaco et al.
Rolling PMF analysis with bootstrap and random a-value approach Conventional PMF is conducted over the whole dataset, with the assumption that the OA source profiles are static, which can lead to high errors when it comes to long-term datasets considering that OA chemical fingerprints are expected to vary over time  (Paatero et al. , 2014). For instance,  (Canonaco et al. , 2015)  showed a substantial seasonal variability of oxygenated organic aerosol (OOA) factor profiles. Parworth et al. (2015)  first proposed to run PMF analysis on a smaller time window (e. g. , 14 days) to roll over the whole dataset with a certain step. This technique was further refined by  Canonaco et al. (2021). The rolling PMF window mechanism allows the PMF model to adapt the temporal variations of the source profiles (e.
A standardised protocol of SA for a long-term dataset using SoFi pro This work presents a standardised protocol to identify main OA components. These guidelines work well for all 22 datasets despite the various pollution sources and OA levels at the different stations. All 22 datasets were analysed by following the protocol described in this study to minimise user subjectivity. Fig. S1  provides the general working flow of this protocol, while the more detailed step-by-step guideline is summarised in Table  1. Detailed explanations of each step are unfolded in the following subsections.
The COA factor is typically only present in urban environments close to residential and commercial areas. For an environment with potential coal combustion sources, looking for a "local" CCOA factor mass spectrum from unconstrained or HOAconstrained PMF runs is recommended. However, it is typically challenging to identify the CCOA factor with ACSM data when its contribution is not significant because of the relatively low m/z resolution and since the mass range (up to 100/120 for Q-ACSM) does not include polyaromatic hydrocarbons (PAHs). In addition, the similar spectral pattern of hydrocarbon ions (e. g. , C n H 2n-1 + and C n H 2n+1 + ) between HOA, BBOA and CCOA makes it challenging for PMF to resolve these two factors  (Sun et al. , 2016). Therefore, the existence of CCOA should be justified at least by the most significant contribution of m/z 115 (mainly C 9 H 7.
Rolling PMF The mass spectra from bootstrapped solutions are recommended as the reference profiles to constrain POA and site-specific factors during rolling PMF. BBOA is known as the most spatiotemporally variable factor compared to all other POA factors  (Crippa et al. , 2014). Also, considering that the highest mass concentrations of BBOA occur in winter, the BBOA mass spectrum from the bootstrapped winter solution is recommended as the constraint, as it is more representative of the dataset. Alternatively, the published profiles (HOA and COA from  Crippa et al. (2013)  and BBOA from  Ng et al. (2011a)  or  Crippa et al. (2013) ) could be used  (Canonaco et al. , 2021). Canonaco et al. (2021)  suggested using a random a-value technique (randomly select a values for each constraint within a certain range) and bootstrap resampling strategy to estimate the rotational uncertainties of rolling PMF.
Based on the seasonal bootstrap analysis, the upper a value for the site-specific factors can be determined by the seasonal variation and uncertainties. Canonaco et al. (2021)  showed that an upper value of 0. 4 for POAs is sufficient to cover the temporal variabilities. However, an upper a value of 0. 5 for BBOA is suggested when high temporal variabilities of the BBOA mass spectrum are expected. When the number of factors is not identical for all the seasons, the rolling PMF should be conducted with both n and n + 1 factors over the entire dataset.
Criteria-based selection for PMF runs With the large number of PMF runs expected for rolling analysis (e. g. , >15,000 runs for a one-year dataset with 30-min time-resolution), inspecting each single PMF run is not feasible. Therefore, a criteriabased selection should be used to (i) evaluate the quality of the PMF runs quantitatively and relatively objectively and (ii) sort out the unconstrained factors in the same order for further averaging. The criteriabased selection has been explicitly explained in  Canonaco et al. (2021). In short, SoFi Pro enables the user to define criteria based on the time series and/or factor profiles to select environmentally reasonable solutions. In addition, this criteria-based selection function can also serve to reposition unconstrained factors as unconstrained factors can appear in random order in a different iteration of the PMF.
Special cases and limitations of the current protocol For all the 22 ACSM/AMS datasets, this standardised protocol works well in general. However, different numbers of factors at different periods remain challenging for rolling PMF. There are three special cases that this protocol could not cover. Specifically, the BBOA factor is not present in the warm period for the Barcelona, Cyprus, and Marseille datasets. However, this protocol could not cover such situations with a proper criterion to objectively include/exclude certain OA factors (e. g. , BBOA) in certain time periods. The distribution of the correlation between BBOA and eBC wb was utilised for the Marseille data, which appears to have a bimodal Fisher distribution. Thus, the 10th percentile results from the separated distributions were used as thresholds to define the existence of BBOA  (Chazeau et al.
, 2022). For the Barcelona and Cyprus datasets, the criterion to decide the existence of BBOA is the explained variation of f 60 by BBOA. A t-test was conducted with the null hypothesis that the variation of f 60 explained by BBOA is not significantly larger than that of other factors. The presence of BBOA is only considered when the p value was ≤ 0. 05. In addition, as discussed before, a different number of factors often suffers from more uncertainties at the edge of the transition period by averaging different numbers of factor solutions simultaneously. One strategy to avoid averaging over different numbers of factor solutions is to unselect any data point in a range of edge ± window length/2. However, it could potentially lead to relatively more non-modelled points during the transition period.
Overview of the primary and secondary OA Primary and secondary OA factors are identified for each station in Fig. 2. In this study, all the OA factors except MO-OOA and LO-OOA are considered as POA factors. Overall, the well-known POA factors have been resolved, including HOA and BBOA. All datasets identify HOA except Hyytiälä (non-urban). BBOA is resolved in 19 datasets (12 urban and 7 non-urban sites) with the exceptions being Hyytiälä, Puy de Dôme, and Helsinki. In general, both HOA and BBOA present considerable fractions of the total submicron aerosol with average contributions of 5. 0% and 5. 6%, respectively. Also, they both show higher contributions at the urban sites (HOA: 5. 7% and BBOA: 6. 3%) than at the non-urban ones (HOA: 4. 0% and BBOA 4. 5%). COA is identified in three southern European cities (i.
, 2019) ; and a mixed ship-industry factor in Marseille (2. 2%,  Chazeau et al. , 2022). Finally, two secondary OOA factors (MO-OOA and LO-OOA) are present at all sites except for Birkenes (MO-OOA only). The MO-OOA and LO-OOA contribute to the total PM 1 with an average of 22. 0% and 13. 1%, respectively. Both MO-OOA and LO-OOA show drastic differences among urban (16. 7% and 10. 6%) and non-urban sites (29. 6% and 17. 2%), which is expected since more primary sources are present in the urban environment. When summing up MO-OOA and LO-OOA (Total OOA), Fig. 2  suggests that secondary OA is the main contributor to total submicron PM (average = 34. 5%, range from 11. 7 to 62. 4%) and dominates OA (average = 71. 1%, range from 47. 3 to 100%) across Europe. In addition, the resolved OA factors have been validated using data external to the PMF input matrices as well as the mass conc.
of m/z 60 in the organic matrix (Table  S4 ). These external data include NH 4 , NO 3 , SO 4 , which were obtained from ACSM/AMS, and eBC was measured by AE33/AE31/MAAP. Regarding the PMF errors (Equation (  6 ) in Canonaco et al. (  2021 )), they are estimated by logarithmic probability density functions (pdf) of the standard deviations of each time point i divided by the mean concentration of each time point i for corresponding OA factors. The PMF errors of major OA components are presented in Table  S6. In general, POAs often have smaller PMF errors than OOA factors since they are always constrained.
OA composition changes as a function of OA loading To understand how the OA composition changes under different loadings, each dataset is divided into ten bins containing the same number of points based on the OA mass concentrations. As shown in Fig. 3 , apart from Zürich and Helsinki, all urban as well as two nonurban sites (Magadino and Melpitz) report larger POA contributions under high OA loadings. It is most likely because primary emissions substantially contribute to OA mass concentrations in relatively polluted areas under stagnant conditions  (Tobler et al. , 2021; Zhang et al. , 2019). Specifically, HOA shows a relatively constant contribution even when the OA mass concentration increases at non-urban sites, while it increases as the OA mass concentration increases at urban sites.
Diel cycles for resolved OA components The highly time-resolved long-term ACSM/AMS data allow investigating the diel cycles of the OA components. Figure  4  shows the averages (solid lines) and standard deviations (shaded areas) of diel profiles of the major OA components (HOA, BBOA, COA-like, MO-OOA, LO-OOA and Total OOA), normalised by the annual average of the corresponding OA component mass concentration for each site at both non-urban and urban sites. Birkenes and Hyytiälä datasets are not included because they only have 4-hour and 3-hour time resolutions, respectively. Overall, all OA components exhibit increases during the evening since accumulation occurs in a shallow boundary layer during that time of the day. However, the decreasing trends from midnight to sunrise are caused by decreased emissions and relatively higher wind speed, and the dilution of OA components after sunrise is caused by the rising boundary layer which introduces clean airmasses  (Stull, 1988).
Urban LO-OOA shows much stronger evening peaks than at non-urban sites, potentially caused by nighttime chemistry yielding urban OOA from POA oxidation (where POA includes both HOA, BBOA, and other primary sources). Kodros et al. (2020)  and  Tiitta et al. (2016)  demonstrated how dark ageing of BBOA could potentially yield substantial amounts of OOA. Such ageing is likely to explain some of our observations at the urban sites  (Zhang et al. , 2020). Both urban and non-urban sites show small spatial variability for the Total OOA, but the diel cycle for urban sites has a more substantial evening peak again due to the strong LO-OOA increase. In general, the POA factors show more temporal variability than the OOA factors at both urban and non-urban sites. In addition, Fig. S5  shows the averages of normalized weekly cycles for the non-urban and urban sites separately (with Birkenes and Hyytiälä datasets included among the non-urban sites).
Spatial and seasonal variability of OA contributions The time series of daily-averaged OA fractions for each site is shown in Fig. S2 , which presents an overall picture of the entire source apportionment result. It indicates significant spatial and temporal variabilities of OA contributions across Europe. To study the seasonal variation of OA and its sources, data was divided into four seasons: winter (DJF: December, January, and February), spring (MAM: March, April, and May), summer (JJA: June, July, and August), and autumn (SON: September, October, and November). Figure  5  indicates a relatively small spatial variability of the relative OA contributions at both urban and non-urban datasets, and there is no clear pattern between the OA fraction and PM 1 loading. Urban sites have higher POA contributions in OA than non-urban sites.
However, each dataset shows an apparent seasonal variability with higher POA contributions and mass concentrations in cold seasons than in warm ones (Fig. 5  and Fig. S6 ). The contributions of POA also appear to be higher when the total PM 1 mass concentration increases at all non-urban sites, as shown in Fig. 5. Specifically, urban sites show higher HOA contributions (overall average contribution of 12. 7 ± 2. 9%) than non-urban sites (7. 4 ± 2. 7%), which is expected due to more traffic emissions in urban areas. Moreover, for both urban and non-urban sites, the HOA contribution shows a distinct seasonality with the lowest contribution in summer (8. 8 ± 3. 4%) and the highest in winter (12. 0 ± 4. 8%), and similar contributions in spring (10. 9 ± 4. 3%) and autumn (11. 5 ± 4. 6%). This might be due to the lower boundary layer with stagnant conditions in the cold season favouring the accumulation of primary and local pollutants and/or reduced photochemistry.
, 2021) ). In Melpitz, coal combustion is less dependent on local sources, but likely more affected by emissions from Poland or other eastern European countries that are rapidly transported by advection in winter, leading to the observed seasonality. For the Dublin dataset, the SFOA (heavily affected by both peat and coal combustion sources  (Lin et al. , 2019 (Lin et al. , , 2018)) ) shows an enhanced contribution in winter (32. 9%) and a decreased contribution in summer (13. 2%). The SFOA in Carnsore Point station shows less seasonality because of the absence of local sources but still has a higher contribution in winter (13. 3%) than in summer (9. 2%). The COA contribution shows almost no seasonality, ranging from 14. 3 ± 2. 7% in summer to 15. 4 ± 3. 3% in autumn at the six urban sites where this factor is resolved, suggesting that the cooking emission contribution is constant and non-negligible in European cities in all seasons.
The specific contributions of all OA components for all datasets and all seasons are summarised in Table  S6 , in which all POA except HOA, BBOA and COA are summed up as "other OA". Canonaco et al. (2015)  analysed the seasonal variability of major ion intensities (i. e. , m/z 44 and m/z 43) in the OOA factors in Zürich. They suggested that biomass burning emissions, the most important precursor of SOA in that city during winter, cause the LO-OOA factor to be at the left half of the triangular region shown by  (Ng et al. , 2010)  within the f 44 vs f 43 space, consistent with biomass burning emissions as shown by  Heringa et al. (2011). In contrast, biogenic SOA from terpene oxidation may "push" the LO-OOA factors to the right side of the triangle in summer  (Pfaffenberger et al. , 2013).
Spatial and seasonal variabilities of f 44 vs f 43 in the OOA factors This study explores the seasonality of f 44 vs f 43 for MO-OOA, LO-OOA and the Total OOA factors for both seasons at all sites, as shown in Fig. 6. It shows that the rolling PMF provides a good separation between MO-OOA and LO-OOA in both winter and summer (Fig. 6a ), which is consistent with what Canonaco et al. (  2021 ) and Chen et al. (  2021 ) reported for two Swiss datasets. However, the positions of MO-OOA for the different stations in the f 44 vs f 43 space show large spatial variability (Fig. 6a ), mainly attributed to the complex and various ageing processes in different locations under different meteorological conditions. In general, all LO-OOAs during the summer (JJA) shift to the right side of the triangle by a different extent when compared to the winter (DJF) seasons (Fig.
6a ), which is most likely due to the enhanced biogenic emissions with higher temperatures during summer seasons  (Canonaco et al. , 2015). The right-side shift from summer to winter is more apparent when summing up the LO-OOA and MO-OOA into the Total OOA factors (Fig. 6b ). At the same time, some site-specific ions could significantly influence the LO-OOA position. For instance, due to the pronounced biomass burning influence in Bucharest during summer, the LO-OOA factor has considerable contributions of m/z 55, 57, 60 and 73, which is accompanied by a low f 43 in LO-OOA during summer. The LO-OOA factors of the Tartu and Hyytiälä datasets stay on the right side of the triangle even during the winter season. Tartu appears to have a significant BBOA contribution throughout the year (18. 2% in summer).
Considering biomass burning OA oxidises rapidly, the biomass burning influence is more pronounced in the MO-OOA factor than in the LO-OOA factor during the whole year. That is why the LO-OOA factor in winter remains on the right side of the triangle. Hyytiälä is located in the boreal forest with high biogenic SOA formation in summer  (Heikkinen et al. , 2021; Yli-Juuti et al. , 2021) , which explains the high f 43 in summer. In addition, no POA factors were deconvolved from this dataset following the presented protocol. By utilizing machine learning techniques, Heikkinen et al. (  2021 ) resolved a slightly aged POA factor that could neither be further separated into HOA nor BBOA. This factor appeared only in winter and coincided with a LO-OOA drop to near-zero loadings when utilizing k-mean clustering approach  (Heikkinen et al.
Spatial and temporal variabilities of key ions in resolved OA factors This study also investigates key ions' spatial and monthly variabilities in common OA factors (i. e. , m/z 55 and m/z 57 for HOA and COA; m/z 60 and m/z 73 for BBOA; m/z 44 and m/z 43 for MO-OOA, LO-OOA, and the Total OOA). All monthly intensities of these key ions in these OA factors were averaged across the 22 datasets to see possible monthly trends (Fig. 7 ). Overall, the key ions for HOA, COA, MO-OOA and Total OOA factors barely show a monthly trend. In contrast, f 60 and f 73 in BBOA are substantially higher in the cold months compared to the warmer months. The main reason is likely that levoglucosan and thus m/z 60 is less stable in the aerosol with higher temperature as well as the change in the biomass burning source during different seasons (e.
, 2018; Bougiatioti et al. , 2014; Grieshop et al. , 2009; Heringa et al. , 2011; Weimer et al. , 2008; Xie et al. , 2014). Thus, as discussed previously, retrieving site-specific BBOA factor profiles using unconstrained PMF analysis instead of published BBOA factor profiles is strongly recommended. The most dominating ion in MO-OOA, f 44 , has a somewhat smaller IQR/median ratio, ranging from 0. 16 to 0. 34, and the f 43 in MO-OOA has an IQR/median ratio in a range of 0. 45-0. 83. This is because different datasets could have significantly variable ageing processes, precursors, and meteorological conditions, which appear to affect the degrees of oxygenation (the relative intensities of m/z 44 and m/z 43). In addition, MO-OOA remains dominated mainly by f 44 (average f 44 = 0. 25), but with a much smaller f 43 intensity (average f 43 = 0.
Conclusion A state-of-the-art standardised protocol for source apportionment of long-term ACSM/AMS organic aerosol mass spectrum datasets was developed. Our protocol was validated systematically and strictly applied to 22 sites with year-long measurements. It demonstrates the consistency of this protocol with comprehensive source apportionment results, even though each dataset was analysed by each research group individually. Our source apportionment strategy has been significantly improved compared to conventional seasonal PMF by utilising rolling windows, bootstrap, and ME-2 techniques, which were first introduced by  Canonaco et al. (2021). As addressed by  Chen et al. (2021)  and  Tobler et al. (2021) , this strategy allows us to retrieve robust source apportionment results by considering temporal variations of source profiles.
e. , MO-OOA and LO-OOA in these 22 datasets. Moreover, some local OA components have been identified at specific stations, like a coffee roastery OA factor in Helsinki, a ship industry OA factor in Marseille, a sea salt factor in Carnsore Point, and a cigarette smoke OA factor in Zürich. The OOA factors together constitute the main contributor (47. 3-100%) to OA and generally show more stable diel and weekly cycles than POA factors. The contributions of POA increase with increasing total OA mass concentration in most of the polluted regions. It suggests that the control of primary emissions could help mitigate OA mass concentration or at least decrease the likelihood of highly polluted episodes. Also, most POA factors show enhanced contribution/mass concentrations during cold seasons compared to warm seasons due to residential heating.
Lower boundary layer heights (lower temperature) combined with stagnant This study reveals the spatial and seasonal concentration variability of the MO-OOA, LO-OOA, and Total OOA factors. MO-OOA and Total OOA show little variability, while LO-OOA varies considerably across spatial and seasonal domains. This is expected since the ageing processes, abundances/types of precursors and meteorological conditions can differ both temporally and spatially. In particular, the seasonality of the LO-OOA factor in most of the datasets agrees well with the findings reported by  Canonaco et al. 2015) , with increasing f 43 (thus, decreasing f 44 ) intensity in warm seasons likely due to the enhanced biogenic emissions. Moreover, with the help of the rolling PMF technique, timedependent OA factor profiles have been retrieved.