scholarly journals Long-term chemical analysis and organic aerosol source apportionment at 9 sites in Central Europe: Source identification and uncertainty assessment

2017 ◽  
Author(s):  
Kaspar R. Daellenbach ◽  
Giulia Stefenelli ◽  
Carlo Bozzetti ◽  
Athanasia Vlachou ◽  
Paola Fermo ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Central Europe ◽  
Organic Aerosol ◽  
Epidemiological Studies ◽  
Alpine Valley ◽  
Enhanced Production ◽  
Inorganic Species ◽  
Yearly Average ◽  
Average Factor

Abstract. Long-term monitoring of the organic aerosol is important for epidemiological studies, validation of atmospheric models, and air quality management. In this study, we apply a recently developed filter-based offline methodology of the aerosol mass spectrometer to investigate the regional and seasonal differences of contributing organic aerosol sources. We present offline-AMS measurements for particulate matter smaller than 10 μm 9 stations in central Europe with different exposure characteristics for the entire year of 2013 (819 samples). The focus of this study is a detailed source apportionment analysis (using PMF) including in-depth assessment of the related uncertainties. Primary organic aerosol (POA) is separated in three components: hydrocarbon-like OA which is related to traffic emissions (HOA), cooking OA (COA), and biomass-burning OA (BBOA). We observe enhanced production of secondary organic aerosol (SOA) in summer, following the increase in biogenic emissions with temperature (summer oxygenated OA, SOOA). In addition, a SOA component was extracted that correlated with anthropogenic secondary inorganic species which is dominant in winter (winter oxygenated OA, WOOA). A factor (SC-OA) explaining sulfur-containing fragments (CH3SO2+), which has an event-driven temporal behavior, was also identified. The relative yearly average factor contributions range for HOA from 3 to 15 %, for COA from 3 to 31 %, for BBOA from 11 to 61 %, for SC-OA from 5 to 23 %, for WOOA from 14 to 28 %, and for SOOA from 14 to 40 %. The uncertainty of the relative average factor contribution lies between 5 and 9 % of OA. At the sites north of the alpine crest, the sum of HOA, COA, and BBOA (POA) contributes less to OA (POA/OA = 0.3) than at the southern alpine valley sites (0.6). BBOA is the main contributor to POA with 88 % in alpine valleys and 43 % north of the alpine crest. Furthermore, the influence of primary biological particles (PBOA), not resolved by PMF, is estimated and could contribute significantly to OA in PM10.

scholarly journals Long-term chemical analysis and organic aerosol source apportionment at nine sites in central Europe: source identification and uncertainty assessment

2017 ◽  
Vol 17 (21) ◽  
pp. 13265-13282 ◽  
Author(s):  
Kaspar R. Daellenbach ◽  
Giulia Stefenelli ◽  
Carlo Bozzetti ◽  
Athanasia Vlachou ◽  
Paola Fermo ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Central Europe ◽  
Organic Aerosol ◽  
Temporal Behaviour ◽  
Enhanced Production ◽  
Inorganic Species ◽  
Yearly Average ◽  
Average Factor ◽  
Sulfur Containing

Abstract. Long-term monitoring of organic aerosol is important for epidemiological studies, validation of atmospheric models, and air quality management. In this study, we apply a recently developed filter-based offline methodology using an aerosol mass spectrometer (AMS) to investigate the regional and seasonal differences of contributing organic aerosol sources. We present offline AMS measurements for particulate matter smaller than 10 µm at nine stations in central Europe with different exposure characteristics for the entire year of 2013 (819 samples). The focus of this study is a detailed source apportionment analysis (using positive matrix factorization, PMF) including in-depth assessment of the related uncertainties. Primary organic aerosol (POA) is separated in three components: hydrocarbon-like OA related to traffic emissions (HOA), cooking OA (COA), and biomass burning OA (BBOA). We observe enhanced production of secondary organic aerosol (SOA) in summer, following the increase in biogenic emissions with temperature (summer oxygenated OA, SOOA). In addition, a SOA component was extracted that correlated with an anthropogenic secondary inorganic species that is dominant in winter (winter oxygenated OA, WOOA). A factor (sulfur-containing organic, SC-OA) explaining sulfur-containing fragments (CH3SO2+), which has an event-driven temporal behaviour, was also identified. The relative yearly average factor contributions range from 4 to 14 % for HOA, from 3 to 11 % for COA, from 11 to 59 % for BBOA, from 5 to 23 % for SC-OA, from 14 to 27 % for WOOA, and from 15 to 38 % for SOOA. The uncertainty of the relative average factor contribution lies between 2 and 12 % of OA. At the sites north of the alpine crest, the sum of HOA, COA, and BBOA (POA) contributes less to OA (POA / OA  =  0.3) than at the southern alpine valley sites (0.6). BBOA is the main contributor to POA with 87 % in alpine valleys and 42 % north of the alpine crest. Furthermore, the influence of primary biological particles (PBOAs), not resolved by PMF, is estimated and could contribute significantly to OA in PM10.

scholarly journals Advanced source apportionment of carbonaceous aerosols by coupling offline AMS and radiocarbon size segregated measurements over a nearly two-year period

2017 ◽  
Author(s):  
Athanasia Vlachou ◽  
Kaspar R. Daellenbach ◽  
Carlo Bozzetti ◽  
Benjamin Chazeau ◽  
Gary A. Salazar ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Biomass Burning ◽  
Size Fraction ◽  
Organic Aerosol ◽  
Anthropogenic Sources ◽  
Biogenic Emissions ◽  
Carbonaceous Aerosols ◽  
Alpine Valley ◽  
Yearly Average ◽  
Fine Mode

Abstract. Carbonaceous aerosols are related to adverse human health effects. Therefore, identification of their sources and analysis of their chemical composition is important. The offline AMS technique offers quantitative separation of organic aerosol (OA) factors that can be related to major OA sources either primary or secondary. While primary OA can be more clearly separated into sources, secondary (SOA) source apportionment is more challenging because different sources – anthropogenic or natural, fossil or non-fossil – can yield similar highly oxygenated mass spectra. Radiocarbon measurements provide unequivocal separation between fossil and non-fossil sources of carbon. Here we coupled these two offline methods and analysed the OA and organic carbon (OC) of different size fractions (particulate matter below 10 and 2.5 µm – PM10 and PM2.5, respectively) from the Alpine valley of Magadino (Switzerland) during the years 2013 and 2014 (219 samples). The combination of the techniques gave further insights into the characteristics of secondary OC (SOC) which rather based on the type of SOC precursor and not on the volatility or the oxidation state of OC, as typically considered. Out of the primary sources separated in this study, biomass burning OC was the dominant one in winter with average concentrations of 5.36 ± 2.64 µg m−3 for PM10 and 3.83 ± 1.81 µg m−3 for PM2.5, indicating that wood combustion particles were predominantly generated in the fine mode. The additional information from the size segregated measurements revealed a primary sulphur containing factor, mainly fossil, detected in the coarse size fraction and related to non-exhaust traffic emissions with average yearly PM10 (PM2.5) concentration of 0.20 ± 0.24 µg m−3 (0.05 ± 0.04 µg m−3). A primary biological OC was also detected in the coarse mode peaking in spring and summer with yearly average concentrations for PM10 (PM2.5) 0.79 ± 0.31 µg m−3 (0.24 ± 0.20 µg m−3). The secondary OC was separated into two oxygenated, non-fossil OC factors which were identified based on their seasonal variability (i.e. summer and winter OOC) and a third fossil OOC factor which correlated with fossil OC mainly peaking in winter and spring with PM10 (PM2.5) contributing on average 13 % ± 7 % (10 % ± 9 %) to the total OC. The winter OOC was connected to anthropogenic sources, with PM10 (PM2.5) contributing on average 13 % ± 13 % (6 % ± 6 %) to the total OC. The summer OOC, stemming from oxidation of biogenic emissions was more pronounced in the fine mode with PM10 (PM2.5) contributing on average 43%±12% (75 % ± 44 %) to the total OC. In total the non-fossil OC far dominated the fossil OC throughout all seasons, by contributing on average 75 % ± 24 % to the total OC. The results also suggested that during the cold period the prevailing source was residential biomass burning while during the warm period primary biological sources and secondary organic aerosol from the oxidation of biogenic emissions became important. However, SOC was also formed by aged fossil fuel combustion emissions not only in summer but also during the rest of the year.

scholarly journals Long-term observational constraints of organic aerosol dependence on inorganic species in the southeast US

2020 ◽  
Author(s):  
Yiqi Zheng ◽  
Joel A. Thornton ◽  
Nga Lee Ng ◽  
Hansen Cao ◽  
Daven K. Henze ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Organic Aerosol ◽  
Chemical Transport Model ◽  
Anthropogenic Pollutants ◽  
Aerosol Acidity ◽  
Inorganic Species ◽  
Southeast Us

Abstract. Organic aerosol (OA), with a large biogenic fraction in summertime southeast US, adversely impacts on air quality and human health. Stringent air quality controls have recently reduced anthropogenic pollutants including sulfate, whose impact on OA remains unclear. Three filter measurement networks provide long-term constraints on the sensitivity of OA to changes in inorganic species, including sulfate and ammonia. The 2000–2013 summertime OA decreases by 1.7~1.9 %/year with little month-to-month variability, while sulfate declines rapidly with significant monthly difference in early 2000s. In contrast, modeled OA from a chemical-transport model (GEOS-Chem) decreases by 4.9 %/year with much larger month-to-month variability, largely due to the predominant role of acid-catalyzed reactive uptake of epoxydiols (IEPOX) onto sulfate. The overestimated modeled OA dependence on sulfate can be improved by implementing a coating effect and assuming constant aerosol acidity, suggesting the needs to revisit IEPOX reactive uptake in current models. Our work highlights the importance of secondary OA formation pathways that are weakly dependent on inorganic aerosol in a region that is heavily influenced by both biogenic and anthropogenic emissions.

scholarly journals A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

2021 ◽  
Vol 14 (2) ◽  
pp. 923-943
Author(s):  
Francesco Canonaco ◽  
Anna Tobler ◽  
Gang Chen ◽  
Yulia Sosedova ◽  
Jay Gates Slowik ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Organic Aerosol ◽  
Uncertainty Assessment ◽  
Aerosol Mass ◽  
Five Factors ◽  
Concentration Peak ◽  
Rolling Window ◽  
Improved Performance ◽  
Term Data

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.

scholarly journals Characterization of non-refractory (NR) PM<sub>1</sub> and source apportionment of organic aerosol in Kraków, Poland

2021 ◽  
Vol 21 (19) ◽  
pp. 14893-14906
Author(s):  
Anna K. Tobler ◽  
Alicja Skiba ◽  
Francesco Canonaco ◽  
Griša Močnik ◽  
Pragati Rai ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Chemical Characterization ◽  
Organic Aerosol ◽  
Pollution Levels ◽  
Full Dataset ◽  
Residential Heating ◽  
Solid Fuel Combustion ◽  
High Concentrations

Abstract. 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.

scholarly journals Advanced source apportionment of carbonaceous aerosols by coupling offline AMS and radiocarbon size-segregated measurements over a nearly 2-year period

2018 ◽  
Vol 18 (9) ◽  
pp. 6187-6206 ◽  
Author(s):  
Athanasia Vlachou ◽  
Kaspar R. Daellenbach ◽  
Carlo Bozzetti ◽  
Benjamin Chazeau ◽  
Gary A. Salazar ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Source Apportionment ◽  
Biomass Burning ◽  
Organic Aerosol ◽  
Anthropogenic Sources ◽  
Biogenic Emissions ◽  
Carbonaceous Aerosols ◽  
Yearly Average ◽  
Fine Mode ◽  
Pm2.5 Concentration

Abstract. Carbonaceous aerosols are related to adverse human health effects. Therefore, identification of their sources and analysis of their chemical composition is important. The offline AMS (aerosol mass spectrometer) technique offers quantitative separation of organic aerosol (OA) factors which can be related to major OA sources, either primary or secondary. While primary OA can be more clearly separated into sources, secondary (SOA) source apportionment is more challenging because different sources – anthropogenic or natural, fossil or non-fossil – can yield similar highly oxygenated mass spectra. Radiocarbon measurements provide unequivocal separation between fossil and non-fossil sources of carbon. Here we coupled these two offline methods and analysed the OA and organic carbon (OC) of different size fractions (particulate matter below 10 and 2.5 µm – PM10 and PM2.5, respectively) from the Alpine valley of Magadino (Switzerland) during the years 2013 and 2014 (219 samples). The combination of the techniques gave further insight into the characteristics of secondary OC (SOC) which was rather based on the type of SOC precursor and not on the volatility or the oxidation state of OC, as typically considered. Out of the primary sources separated in this study, biomass burning OC was the dominant one in winter, with average concentrations of 5.36 ± 2.64 µg m−3 for PM10 and 3.83 ± 1.81 µg m−3 for PM2.5, indicating that wood combustion particles were predominantly generated in the fine mode. The additional information from the size-segregated measurements revealed a primary sulfur-containing factor, mainly fossil, detected in the coarse size fraction and related to non-exhaust traffic emissions with a yearly average PM10 (PM2.5) concentration of 0.20 ± 0.24 µg m−3 (0.05 ± 0.04 µg m−3). A primary biological OC (PBOC) was also detected in the coarse mode peaking in spring and summer with a yearly average PM10 (PM2.5) concentration of 0.79 ± 0.31 µg m−3 (0.24 ± 0.20 µg m−3). The secondary OC was separated into two oxygenated, non-fossil OC factors which were identified based on their seasonal variability (i.e. summer and winter oxygenated organic carbon, OOC) and a third anthropogenic OOC factor which correlated with fossil OC mainly peaking in winter and spring, contributing on average 13 % ± 7 % (10 % ± 9 %) to the total OC in PM10 (PM2.5). The winter OOC was also connected to anthropogenic sources, contributing on average 13 % ± 13 % (6 % ± 6 %) to the total OC in PM10 (PM2.5). The summer OOC (SOOC), stemming from oxidation of biogenic emissions, was more pronounced in the fine mode, contributing on average 43 % ± 12 % (75 % ± 44 %) to the total OC in PM10 (PM2.5). In total the non-fossil OC significantly dominated the fossil OC throughout all seasons, by contributing on average 75 % ± 24 % to the total OC. The results also suggested that during the cold period the prevailing source was residential biomass burning while during the warm period primary biological sources and secondary organic aerosol from the oxidation of biogenic emissions became important. However, SOC was also formed by aged fossil fuel combustion emissions not only in summer but also during the rest of the year.

scholarly journals A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to one year of organic aerosol data

2020 ◽  
Author(s):  
Francesco Canonaco ◽  
Anna Tobler ◽  
Gang Chen ◽  
Yulia Sosedova ◽  
Jay Gates Slowik ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Biomass Burning ◽  
Organic Aerosol ◽  
Uncertainty Assessment ◽  
Aerosol Mass ◽  
Concentration Peak ◽  
Rolling Window ◽  
Improved Performance ◽  
One Year

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 one-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 days) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainty 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 leads 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 biomass burning (BBOA) were the correlation with equivalent black carbon (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 fraction of m/z 43 and m/z 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 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 0.3–1.1 μg m−3 (3.4–15.9 %), 0.6–2.2 μg m−3 (7.7–33.7 %) and 0.9–3.1 μg m−3 (13.7–39.9 %), respectively. The relative PMF errors modelled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30, ±11 %, ±25 % and ±12 %, respectively.

Sign in / Sign up

Export Citation Format

Share Document