Sources of organic aerosols in Europe: A modelling study using CAMx with modified volatility basis set scheme

Abstract. Source apportionment of organic aerosols (OA) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models act as potential tools to identify OA components and sources at high spatial and temporal resolution, however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional air quality model Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and contained more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modelled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in the previous studies which strengthens our confidence in our modelled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 μg m−3 to −0.4 μg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 %–42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %) while biogenic emissions contributed ~55 % to OA during summer in Europe on average. In both seasons, the other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (~5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modelling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

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Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-15247-2019 ◽

2019 ◽

Vol 19 (24) ◽

pp. 15247-15270 ◽

Cited By ~ 3

Author(s):

Jianhui Jiang ◽

Sebnem Aksoyoglu ◽

Imad El-Haddad ◽

Giancarlo Ciarelli ◽

Hugo A. C. Denier van der Gon ◽

...

Keyword(s):

Air Quality ◽

Source Apportionment ◽

Biomass Burning ◽

Agricultural Waste ◽

Organic Aerosols ◽

Basis Set ◽

Anthropogenic Sources ◽

Wood Burning ◽

Gasoline Vehicles

Abstract. Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 to −0.4 µg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 % to 42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼ 55 % to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼ 5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

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Modelling winter organic aerosol at the European scale with CAMx: evaluation and source apportionment with a VBS parameterization based on novel wood burning smog chamber experiments og

10.5194/acp-2016-785 ◽

2016 ◽

Author(s):

Giancarlo Ciarelli ◽

Sebnem Aksoyoglu ◽

Imad El Haddad ◽

Emily A. Bruns ◽

Monica Crippa ◽

...

Keyword(s):

Source Apportionment ◽

Biomass Burning ◽

Organic Aerosol ◽

Basis Set ◽

Formation Mechanisms ◽

Smog Chamber ◽

Air Quality Model ◽

Quality Model ◽

Residential Combustion ◽

Wood Burning

Abstract. We evaluated a modified VBS (Volatility Basis Set) scheme to treat biomass burning-like organic aerosol (BBOA) implemented in CAMx (Comprehensive Air Quality Model with extensions). The updated scheme was parameterized with novel wood combustion smog chamber experiments using a hybrid VBS framework that accounts for a mixture of wood burning organic aerosol precursors and their further functionalization and fragmentation in the atmosphere. The new scheme was evaluated for one of the winter EMEP intensive campaigns (February-March 2009) against aerosol mass spectrometer (AMS) measurements performed at 11 sites in Europe. We found a considerable improvement for the modelled organic aerosol (OA) mass compared to our previous model application with the mean fractional bias (MFB) reduced from −61 % to −29 %. We performed model-based source apportionment studies and compared results against positive matrix factorization (PMF) analysis performed on OA AMS data. Both model and observations suggest that OA was mainly of secondary origin at almost all sites. Modelled secondary organic aerosol (SOA) contributions to total OA varied from 32 to 88 % (with an average contribution of 62 %) and absolute concentrations were generally under-predicted. Modelled primary hydrocarbon-like organic aerosol (HOA) and primary biomass burning-like aerosol (BBOA) fractions contributed to a lesser extent (HOA from 3 to 30 %, and BBOA from 1 to 39 %) with average contributions of 13 and 25 %, respectively. Modelled BBOA fractions was found to represent 12 to 64 % of the total residential heating related OA, with increasing contributions at stations located in the northern part of the domain. Source apportionment studies were performed to assess the contribution of residential and non-residential combustion precursors to the total SOA. Non-residential combustion and transportation precursors contributed about 30–40 % to SOA formation (with increasing contributions at urban and near industrialized sites) whereas residential combustion (mainly related to wood burning) contributed to a larger extent, around 60–70 %. Contributions to OA from residential combustion precursors in different volatility ranges were also assessed: our results indicate that residential combustion gas-phase precursors in the semi-volatile range contributed from 6 to 30 %, with higher contributions predicted at stations located in the southern part of the domain. On the other hand, higher volatility residential combustion precursors contributed from 15 to 38 % with no specific gradient among the stations. The new retrieved parameterization, although leading to a better agreement between model and observations, still under-predicts the SOA fraction suggesting remaining uncertainties in the new scheme or that other sources and/or formation mechanisms need to be elucidated.

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Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50

Geoscientific Model Development ◽

10.5194/gmd-14-1681-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1681-1697

Author(s):

Jianhui Jiang ◽

Imad El Haddad ◽

Sebnem Aksoyoglu ◽

Giulia Stefenelli ◽

Amelie Bertrand ◽

...

Keyword(s):

Air Quality ◽

Biomass Burning ◽

Experimental Studies ◽

Organic Aerosol ◽

Basis Set ◽

Reference Scenario ◽

Air Quality Model ◽

Quality Model ◽

The Balkans ◽

Wall Loss

Abstract. Increasing evidence from experimental studies suggests that the losses of semi-volatile vapors to chamber walls could be responsible for the underestimation of organic aerosol (OA) in air quality models that use parameters obtained from chamber experiments. In this study, a box model with a volatility basis set (VBS) scheme was developed, and the secondary organic aerosol (SOA) yields with vapor wall loss correction were optimized by a genetic algorithm based on advanced chamber experimental data for biomass burning. The vapor wall loss correction increases the SOA yields by a factor of 1.9–4.9 and leads to better agreement with measured OA for 14 chamber experiments under different temperatures and emission loads. To investigate the influence of vapor wall loss correction on regional OA simulations, the optimized parameterizations (SOA yields, emissions of intermediate-volatility organic compounds from biomass burning, and enthalpy of vaporization) were implemented in the regional air quality model CAMx (Comprehensive Air Quality Model with extensions). The model results from the VBS schemes with standard (VBS_BASE) and vapor-wall-loss-corrected parameters (VBS_WLS), as well as the traditional two-product approach, were compared and evaluated by OA measurements from five Aerodyne aerosol chemical speciation monitor (ACSM) or aerosol mass spectrometer (AMS) stations in the winter of 2011. An additional reference scenario, VBS_noWLS, was also developed using the same parameterization as VBS_WLS except for the SOA yields, which were optimized by assuming there is no vapor wall loss. The VBS_WLS generally shows the best performance for predicting OA among all OA schemes and reduces the mean fractional bias from −72.9 % (VBS_BASE) to −1.6 % for the winter OA. In Europe, the VBS_WLS produces the highest domain average OA in winter (2.3 µg m−3), which is 106.6 % and 26.2 % higher than VBS_BASE and VBS_noWLS, respectively. Compared to VBS_noWLS, VBS_WLS leads to an increase in SOA by up to ∼80 % (in the Balkans). VBS_WLS also leads to better agreement between the modeled SOA fraction in OA (fSOA) and the estimated values in the literature. The substantial influence of vapor wall loss correction on modeled OA in Europe highlights the importance of further improvements in parameterizations based on laboratory studies for a wider range of chamber conditions and field observations with higher spatial and temporal coverage.

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Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50

10.5194/gmd-2020-274 ◽

2020 ◽

Author(s):

Jianhui Jiang ◽

Imad El-Haddad ◽

Sebnem Aksoyoglu ◽

Giulia Stefenelli ◽

Amelie Bertrand ◽

...

Keyword(s):

Air Quality ◽

Biomass Burning ◽

Experimental Studies ◽

Organic Aerosol ◽

Basis Set ◽

Reference Scenario ◽

Air Quality Model ◽

Quality Model ◽

Wall Loss ◽

Product Approach

Abstract. Increasing evidence from experimental studies suggests that the losses of semi-volatile vapors to the chamber walls could be responsible for the underestimation of organic aerosol (OA) in air quality models which use parameters obtained from the chamber experiments. In this study, a box model with volatility basis set (VBS) scheme was developed and the secondary organic aerosol (SOA) yields with vapor wall loss corrected were optimized by a genetic algorithm based on advanced chamber experimental data for biomass burning. The vapor wall loss correction increases the SOA yields by a factor of 1.9–4.9, and leads to a better agreement with the measured OA for 14 chamber experiments under different temperatures and emission loads. To investigate the influence of vapor wall loss correction on regional OA simulations, the optimized parameterizations (SOA yields, emissions of intermediate-volatility organic compounds from biomass burning, and enthalpy of vaporization) were implemented in the regional air quality model CAMx (Comprehensive Air Quality Model with extensions). The modeled results from the standard and vapor wall loss corrected VBS schemes, as well as the traditional two-product approach were compared and evaluated by OA measurements from five Aerodyne aerosol chemical speciation monitor (ACSM)/aerosol mass spectrometer (AMS) stations in the winter of 2011. The vapor wall loss corrected VBS (VBS_WLS) generally shows the best performance for predicting OA among all OA schemes, and reduces the mean fractional bias from −72.9 % (with the standard VBS (VBS_BASE)) to −1.6 % for the winter OA. In Europe, the VBS_WLS produces the highest domain average OA in winter (2.3 µg m−3), which is 106.6 % and 26.2 % higher than the standard VBS and the reference scenario (VBS_noWLS, same parameterization as VBS_WLS, except with the default yields without vapor wall loss correction), respectively. Compared to VBS_noWLS, VBS_WLS leads to an increase in SOA by up to ~ 80 % in Romania. VBS_WLS also leads to a better agreement between the modeled SOA fraction in OA (fSOA) and the estimated measured values in the literature. The substantial influence of vapor wall loss correction on modeled OA in Europe highlights the importance of further improvements in the parameterizations based on laboratory studies with a wider range of chamber conditions and field observations with higher spatial and temporal coverage.

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Modeling organic aerosols over east China using a volatility basis-set approach with aging mechanism in a regional air quality model

Atmospheric Environment ◽

10.1016/j.atmosenv.2015.05.045 ◽

2016 ◽

Vol 124 ◽

pp. 186-198 ◽

Cited By ~ 28

Author(s):

Zhiwei Han ◽

Zuxin Xie ◽

Gehui Wang ◽

Renjian Zhang ◽

Jun Tao

Keyword(s):

Air Quality ◽

Organic Aerosols ◽

Basis Set ◽

East China ◽

Air Quality Model ◽

Quality Model ◽

Aging Mechanism ◽

Regional Air Quality

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Evaluation of European air quality modelled by CAMx including the volatility basis set scheme

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-10313-2016 ◽

2016 ◽

Vol 16 (16) ◽

pp. 10313-10332 ◽

Cited By ~ 25

Author(s):

Giancarlo Ciarelli ◽

Sebnem Aksoyoglu ◽

Monica Crippa ◽

Jose-Luis Jimenez ◽

Eriko Nemitz ◽

...

Keyword(s):

Air Quality ◽

Measurement Data ◽

Model Performance ◽

Monitoring And Evaluation ◽

Basis Set ◽

Air Quality Model ◽

Quality Model ◽

Aerosol Mass ◽

Sensitivity Tests ◽

Compensation Of Errors

Abstract. Four periods of EMEP (European Monitoring and Evaluation Programme) intensive measurement campaigns (June 2006, January 2007, September–October 2008 and February–March 2009) were modelled using the regional air quality model CAMx with VBS (volatility basis set) approach for the first time in Europe within the framework of the EURODELTA-III model intercomparison exercise. More detailed analysis and sensitivity tests were performed for the period of February–March 2009 and June 2006 to investigate the uncertainties in emissions as well as to improve the modelling of organic aerosol (OA). Model performance for selected gas phase species and PM2.5 was evaluated using the European air quality database AirBase. Sulfur dioxide (SO2) and ozone (O3) were found to be overestimated for all the four periods, with O3 having the largest mean bias during June 2006 and January–February 2007 periods (8.9 pbb and 12.3 ppb mean biases respectively). In contrast, nitrogen dioxide (NO2) and carbon monoxide (CO) were found to be underestimated for all the four periods. CAMx reproduced both total concentrations and monthly variations of PM2.5 for all the four periods with average biases ranging from −2.1 to 1.0 µg m−3. Comparisons with AMS (aerosol mass spectrometer) measurements at different sites in Europe during February–March 2009 showed that in general the model overpredicts the inorganic aerosol fraction and underpredicts the organic one, such that the good agreement for PM2.5 is partly due to compensation of errors. The effect of the choice of VBS scheme on OA was investigated as well. Two sensitivity tests with volatility distributions based on previous chamber and ambient measurements data were performed. For February–March 2009 the chamber case reduced the total OA concentrations by about 42 % on average. In contrast, a test based on ambient measurement data increased OA concentrations by about 42 % for the same period bringing model and observations into better agreement. Comparison with the AMS data at the rural Swiss site Payerne in June 2006 shows no significant improvement in modelled OA concentration. Further sensitivity tests with increased biogenic and anthropogenic emissions suggest that OA in Payerne was affected by changes in emissions from residential heating during the February–March 2009 whereas it was more sensitive to biogenic precursors in June 2006.

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