Comment on egusphere-2022-269

Abstract. Particulate matter (PM) has become a major concern in terms of human health and climate impact. In particular, the source apportionment (SA) of organic aerosols (OA) present in submicron particles (PM1) has gained relevance as an atmospheric research field due to the diversity and complexity of its primary sources and secondary formation processes. Moreover, relatively simple but robust instruments such as the Aerosol Chemical Speciation Monitor (ACSM) are now widely available for the near-real-time online determination of the composition of the non-refractory PM1. One of the most used tools for SA purposes is the source-receptor positive matrix factorisation (PMF) model. Even though the recently developed rolling PMF technique has already been used for OA SA on ACSM datasets, no study has assessed its added value compared to the more common seasonal PMF method using a practical approach yet. In this paper, both techniques were applied to a synthetic dataset and to nine European ACSM datasets in order to spot the main output discrepancies between methods. The main advantage of the synthetic dataset approach was that the methods' outputs could be compared to the expected “true” values, i.e. the original synthetic dataset values. This approach revealed similar apportionment results amongst methods, although the rolling PMF profile's adaptability feature proved to be advantageous, as it generated output profiles that moved nearer to the truth points. Nevertheless, these results highlighted the impact of the profile anchor on the solution, as the use of a different anchor with respect to the truth led to significantly different results in both methods. In the multi-site study, while differences were generally not significant when considering year-long periods, their importance grew towards shorter time spans, as in intra-month or intra-day cycles. As far as correlation with external measurements is concerned, rolling PMF performed better than seasonal PMF globally for the ambient datasets investigated here, especially in periods between seasons. The results of this multi-site comparison coincide with the synthetic dataset in terms of rolling–seasonal similarity and rolling PMF reporting moderate improvements. Altogether, the results of this study provide solid evidence of the robustness of both methods and of the overall efficiency of the recently proposed rolling PMF approach.

Particulate Matter (PM) has become a major concern in terms of human health and climate impact. In particular, the source apportionment (SA) of organic aerosols (OA) present in submicron particles (PM1) has gained relevance as an atmospheric research field due to the diversity and complexity of its primary sources and secondary formation processes. Moreover, relatively simple but robust instruments such as the Aerosol Chemical Speciation Monitor (ACSM) are now widely available for the near real-time online determination of the composition of the non-refractory PM1. One of the most used tools for SA purposes is the source-receptor Positive Matrix Factorization (PMF) model. Even though the recently developed rolling PMF technique has already been used for OA SA on ACSM datasets, no study has assessed its added value concerning the more common seasonal PMF method from a practical approach yet. In this paper, both techniques were applied to a synthetic dataset and to nine European ACSM datasets in order to spot the main output discrepancies between methods. The main advantage of the synthetic dataset approach was that the methods’ outputs could be compared to the expected ‘true’ values, i.e. the original synthetic dataset values. This approach revealed similar apportionment results amongst methods, but differing with respect to the truth, although the rolling PMF profile adaptability feature has been proven advantageous. Also, these results highlighted the impact of the profile anchor on the solution. In the multi-site study, while differences were generally not significant when considering year-long periods, their importance grew towards shorter time spans, as in intra-month or intra-day cycles. Rolling PMF performed better than seasonal PMF globally for the ambient datasets investigated here as far correlation with external measurements is concerned, especially in periods between seasons. The results of this comparison also support rolling PMF benefits even though output discrepancies with seasonal PMF were scarce. Altogether, the results of this study provide solid evidence of the robustness of both methods and on the overall efficiency of the recently-proposed rolling PMF approach.

The source apportionment of organic aerosols (OA) in background locations is one of the important issues in contemporary air quality protection. A better understanding of the atmospheric aerosols origin and their source apportionment in different locations is essential for improving air quality.Measurements of non-refractory PM1 (NR-PM1) were simultaneously performed during winter (1st February – 10th March) and summer (1st July – 20th August)  2021 at three background sites, National Atmospheric Observatory Košetice (N 49°35′, E 15°05′; 534 m a.s.l.) and Frýdlant (N 50°94′, E 15°07′; 366 m a.s.l.) in Czech Republic, and Melpitz (N 51°32', E 12°56'; 86 m a.s.l.) in Germany. Each site was equipped with an Aerosol Mass Spectrometer: a ToF- Aerosol Chemical Speciation Monitor (ToF-ACSM) at Košetice, a Compact Time-of-Flight Aerosol Mass Spectrometer C-ToF-AMS) at Frýdlant, and a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) at Melpitz.A Positive Matrix Factorization (PMF) with multi-linear engine (ME-2) using Source Finder (SoFi) (Canonaco et al., 2013) was applied to OA data sets to separate OA into different factors in terms of their mass spectra and time series. Four sources of OA were identified for the winter season. Three of these sources, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and coal combustion organic aerosol (CCOA), belong to the primary sources. The last factor, designated oxidised organic aerosol (OOA), belongs to the secondary sources. Four sources of OA have also been identified for the summer season. Two of these sources were designated as primary sources (HOA and BBOA) and two as secondary sources, more oxidized organic aerosol (MO-OOA) and less oxidized organic aerosol (LO-OOA). Acknowledgements:This work was supported within a German-Czech cooperation in the TRACE project funded by the GACR under grant 20-08304J and by DFG under grant 431895563, also by the MEYS of the Czech Republic under grants ACTRIS-CZ LM2018122 and ACTRIS-CZ RI (CZ.02.1 .01 / 0.0 / 0.0 / 16_013 / 0001315), and European Union's Horizon 2020 research and innovation program ACTRIS IMP (871115). Reference:Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., Prévôt, A. S.: SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data, Atmos. Measur. Tech., 6(12), 3649-3661, 2013.

<strong class="journal-contentHeaderColor">Abstract.</strong> Delhi, India, experiences extremely high concentrations of primary organic aerosol (POA). Few prior source apportionment studies on Delhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol (COA) on POA. In a companion paper, we develop a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements of this approach over traditional PMF. Here, we quantify the contributions of BBOA, COA, and hydrocarbon-like organic aerosol (HOA) by applying positive matrix factorization (PMF) resolved by time of day on two seasons (winter and monsoon seasons of 2017) using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. We also conduct detailed uncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition to HOA, COA is found to be a major constituent of POA in the monsoon, and BBOA is found to be a major constituent of POA in the winter. Neither COA nor the different types of BBOA were resolved in the seasonal (not time-resolved) analysis. The COA mass spectra (MS) profiles are consistent with mass spectral profiles from Delhi and around the world, particularly resembling MS of heated cooking oils with a high <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>m</mi><mo>/</mo><mi>z</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="23pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="91270dba487782af7360c80516416e4b"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-13631-2022-ie00001.svg" width="23pt" height="14pt" src="acp-22-13631-2022-ie00001.png"/></svg:svg></span></span> 41. The BBOA MS have a very prominent <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>m</mi><mo>/</mo><mi>z</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="23pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="63c7f3946b001d4e8415de9c4a1834dd"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-13631-2022-ie00002.svg" width="23pt" height="14pt" src="acp-22-13631-2022-ie00002.png"/></svg:svg></span></span> 29 in addition to the characteristic peak at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>m</mi><mo>/</mo><mi>z</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="23pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="d797e7418bb082ad5eec13189d6e5a75"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-13631-2022-ie00003.svg" width="23pt" height="14pt" src="acp-22-13631-2022-ie00003.png"/></svg:svg></span></span> 60, consistent with previous MS observed in Delhi and from wood burning sources. In addition to separating the POA, our technique also captures changes in MS profiles with the time of day, a unique feature among source apportionment approaches available. In addition to the primary factors, we separate two to three oxygenated organic aerosol (OOA) components. When all factors are recombined to total POA and OOA, our results are consistent with seasonal PMF analysis conducted using EPA PMF. Results from this work can be used to better design policies that target relevant primary sources of organic aerosols in Delhi.

Abstract. 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 (eBCtr) 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.

Abstract. Online non-refractory submicron aerosol mass spectrometer (AMS) measurements in urban areas have successfully allowed the apportionment of specific sources and/or physical and chemical properties of the organic fraction. However, in order to be fully representative of PM pollution, a comprehensive source apportionment analysis is needed by taking into account all major components of submicron aerosols, creating strengthened bonds between the organic components and pollution sources. We present here a novel two-step methodology to perform such an analysis, by taking advantage of high time resolution of monitoring instruments: the aerosol chemical speciation monitor (ACSM) and the multi-wavelength absorption measurements (Aethalometer AE31) in Paris, France. As a first step, organic aerosols (OA) were deconvolved to hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and oxygenated OA (OOA) with positive matrix factorization (PMF), and black carbon was deconvolved into its wood burning and fossil fuel combustion fractions. A second PMF analysis was then carried out with organic factors, BC fractions and inorganic species (nitrate, sulfate, ammonium, chloride), leading to a four-factor solution allowing highly time-resolved characterization of the major sources of PM1. Outputs of this PMF2 include two dominant combustion sources (wood burning and traffic) as well as semi-volatile and low-volatile secondary aerosols. While HOA is found to be emitted by both wood burning and traffic, the latter sources occurred to significantly contribute also to OOA.

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.

<strong class="journal-contentHeaderColor">Abstract.</strong> Haze pollution is a severe environmental problem, caused by elevation of fine particles (aerodynamic diameter <span class="inline-formula">&lt;2.5</span> <span class="inline-formula">µm</span>, PM<span class="inline-formula">_2.5</span>), which is related to secondary aerosol formation, unfavourable synoptic conditions and regional transport, etc. The regional haze formation in basin areas, along with intensive emission of precursors, high relative humidity and poor dispersion conditions, is still limitedly understood. In this study, a field campaign was conducted to investigate the factors resulting in haze formation in the Sichuan Basin (SCB) during winter in 2021. The fine aerosol chemical composition was characterised using a time-of-flight aerosol chemical speciation monitor (ToF-ACSM), which also provided detailed information on the sources for organic aerosols (OAs). The average concentration of non-refractory fine particles (NR-PM<span class="inline-formula">_2.5</span>) was <span class="inline-formula">98.5±38.7</span> <span class="inline-formula">µg m⁻³</span>, and organics aerosols, nitrate, sulfate, ammonium and chloride took up 40.3 %, 28.8 %, 10.6 %, 15.3 % and 5.1 % of PM<span class="inline-formula">_2.5</span>. Three factors, including a hydrocarbon-like OA (HOA), a biomass burning OA (BBOA) and an oxygenated OA (OOA), were identified by applying the positive matrix factorisation (PMF) analysis, and they constituted 24.2 %, 24.2 % and 51.6 % of OA on average, respectively. Nitrate formation was promoted by gas-phase and aqueous-phase oxidation, while sulfate was mainly formed through aqueous-phase process. OOA showed strong dependence on O<span class="inline-formula">_<i>x</i></span>, demonstrating the contribution of photooxidation to OOA formation. OOA concentration increased as aerosol liquid water content (ALWC) increased within 200 <span class="inline-formula">µg m⁻³</span> and kept relatively constant when ALWC <span class="inline-formula">&gt;200</span> <span class="inline-formula">µg m⁻³</span>, suggesting the insignificant effect of aqueous-phase reactions on OOA formation. Among the three haze episodes identified during the whole campaign, the driving factors were different: the first haze episode (H1) was driven by nitrate formation through photochemical and aqueous-phase reactions, and the second haze episode (H2) was mainly driven by the intense emission of primary organic aerosols from biomass burning and vehicle exhaust, while the third haze episode (H3) was mainly driven by reactions involving nitrate formation and biomass burning emission. HOA and BBOA were scavenged, while OOA, nitrate and sulfate formation was enhanced by aqueous-phase reactions during fog periods, which resulted in the increase of <span class="inline-formula">O:C</span> from pre-fog to post-fog periods. This study revealed the factors driving severe haze formation in the SCB and implied the benefit of controlling nitrate as well as intense biomass burning and vehicle exhaust emission for the mitigation of heavy aerosol pollution in this region.

Abstract. The ChArMEx (Chemistry and Aerosols Mediterranean Experiments) SOP2 (special observation period 2) field campaign took place from 15 July to 5 August 2013 in the western Mediterranean Basin at Ersa, a remote site in Cape Corse. During the campaign more than 80 volatile organic compounds (VOCs), including oxygenated species, were measured by different online and offline techniques. At the same time, an exhaustive description of the chemical composition of fine aerosols was performed with an aerosol chemical speciation monitor (ACSM). Low levels of anthropogenic VOCs (typically tens to hundreds of parts per trillion for individual species) and black carbon (0.1–0.9 µg m−3) were observed, while significant levels of biogenic species (peaking at the ppb level) were measured. Furthermore, secondary oxygenated VOCs (OVOCs) largely dominated the VOC speciation during the campaign, while organic matter (OM) dominated the aerosol chemical composition, representing 55 % of the total mass of non-refractory PM1 on average (average of 3.74 ± 1.80 µg m−3), followed by sulfate (27 %, 1.83 ± 1.06 µg m−3), ammonium (13 %, 0.90 ± 0.55 µg m−3) and nitrate (5 %, 0.31 ± 0.18 µg m−3). Positive matrix factorization (PMF) and concentration field (CF) analyses were performed on a database containing 42 VOCs (or grouped VOCs), including OVOCs, to identify the covariation factors of compounds that are representative of primary emissions or chemical transformation processes. A six-factor solution was found for the PMF analysis, including a primary and secondary biogenic factor correlated with temperature and exhibiting a clear diurnal profile. In addition, three anthropogenic factors characterized by compounds with various lifetimes and/or sources have been identified (long-lived, medium-lived and short-lived anthropogenic factors). The anthropogenic nature of these factors was confirmed by the CF analysis, which identified potential source areas known for intense anthropogenic emissions (north of Italy and southeast of France). Finally, a factor characterized by OVOCs of both biogenic and anthropogenic origin was found. This factor was well correlated with submicron organic aerosol (OA) measured by an aerosol chemical speciation monitor (ACSM), highlighting the close link between OVOCs and organic aerosols; the latter is mainly associated (96 %) with the secondary OA fraction. The source apportionment of OA measured by ACSM led to a three-factor solution identified as hydrogen-like OA (HOA), semi-volatile oxygenated OA (SV-OOA) and low volatility OOA (LV-OOA) for averaged mass concentrations of 0.13, 1.59 and 1.92 µg m−3, respectively. A combined analysis of gaseous PMF factors with inorganic and organic fractions of aerosols helped distinguish between anthropogenic continental and biogenic influences on the aerosol- and gas-phase compositions.

The key problems with Positive Matrix Factorization (PMF) model for PM2.5 source apportionment were inconsistent results with different species selections and a lack of evaluation criteria for results accuracy. Moreover, high proportions of secondary inorganic aerosols sources (SNA) were identified by PMF without corresponding primary sources. This study develops a new method that combines multi‐isotopes (34S, 15N, 18O and 14C) and PMF model to optimize source apportionment. Data sets A–F, constructed from PM2.5 components, were input into PMF model to obtain optimal results (3–9 factors), which changed with the selection of species. Specifically, the contributions of coal combustion (CC, 3%–36%), biomass burning (BB, 11%–38%), and vehicle sources (VS, 4%–15%) showed significant differences in data sets, indicating that conventional methods cannot obtain accurate results. Then, 15N, 34S, 18O were introduced to restrict and reallocate identified SNA sources to primary sources, overcoming the influence of species on results. Additionally, 14C was used to evaluate data sets results, which showed that the combination of PMF model with more markers (data set F, 9‐factor) and multi‐isotopes techniques obtained optimized results that aligned with 14C results. Compared with the initial results, the contributions of CC, VS, and BB in the allocated 9‐factor increased by 26.4%, 5%, and 19.5%, respectively, becoming main sources of PM2.5. This study represents the first time that combination of PMF model and multi‐isotopes achieves SNA sources reapportionment and results evaluation, improving source apportionment methods.

Particulate matter was measured in Conroe, Texas (~60 km north of downtown Houston, Texas) during the September 2013 DISCOVER-AQ campaign to determine the sources of particulate matter in the region. The measurement site is influenced by high biogenic emission rates as well as transport of anthropogenic pollutants from the Houston metropolitan area and is therefore an ideal location to study anthropogenic-biogenic interactions. Data from an Aerosol Chemical Speciation Monitor (ACSM) suggest that on average 64 percent of non-refractory PM1 was organic material, including a high fraction (27%–41%) of organic nitrates. There was little diurnal variation in the concentrations of ammonium sulfate; however, concentrations of organic and organic nitrate aerosol were consistently higher at night than during the day. Potential explanations for the higher organic aerosol loadings at night include changing boundary layer height, increased partitioning to the particle phase at lower temperatures, and differences between daytime and nighttime chemical processes such as nitrate radical chemistry. Positive matrix factorization was applied to the organic aerosol mass spectra measured by the ACSM and three factors were resolved—two factors representing oxygenated organic aerosol and one factor representing hydrocarbon-like organic aerosol. The factors suggest that the measured aerosol was well mixed and highly processed, consistent with the distance from the site to major aerosol sources, as well as the high photochemical activity.

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.

Characteristics of the chemical processes of organic aerosols by time-of-flight aerosol chemical speciation monitor (TOF-ACSM) in winter in a site of Fenhe Valley, northern 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. 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.