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

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

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-35645-2015 ◽

2015 ◽

Vol 15 (24) ◽

pp. 35645-35691 ◽

Cited By ~ 1

Author(s):

G. Ciarelli ◽

S. Aksoyoglu ◽

M. Crippa ◽

J. L. Jimenez ◽

E. 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 aerosols (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 (8.93 and 12.30 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 very well for all the four periods with average biases ranging from −2.13 to 1.04 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 over-predicts the inorganic aerosol fraction and under-predicts the organic one, such that the good agreement for PM2.5 is partly due to compensation of errors. The effect of the choice of volatility basis set scheme (VBS) 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 43 % on average. On the other hand, a test based on ambient measurement data increased OA concentrations by about 47 % 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 largely dominated by residential heating emissions during the February–March 2009 period and by biogenic precursors in June 2006.

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Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe

10.5194/acp-2018-920 ◽

2018 ◽

Author(s):

Jianhui Jiang ◽

Sebnem Aksoyoglu ◽

Giancarlo Ciarelli ◽

Emmanouil Oikonomakis ◽

Imad El-Haddad ◽

...

Keyword(s):

Air Quality ◽

Model Performance ◽

Organic Aerosol ◽

Air Quality Model ◽

Quality Model ◽

Inorganic Aerosols ◽

Aerosol Mass ◽

Emission Models ◽

Summer Time ◽

Paul Scherrer

Abstract. Biogenic volatile organic compound (BVOC) emissions are one of the essential inputs for chemical transport models (CTMs), but their estimates are associated with large uncertainties leading to significant influences on air quality modelling. This study aims at investigating the effects of using different BVOC emission models on the performance of a CTM in simulating secondary pollutants, i.e. ozone, organic and inorganic aerosols. The European air quality was simulated for the year 2011 by the regional air quality model Comprehensive Air Quality Model with Extensions (CAMx) version 6.3, using BVOC emissions calculated by two emission models: the Paul Scherrer Institute (PSI) model and the Model of Emissions of Gases and Aerosol from Nature (MEGAN) v2.1. Comparison of isoprene and monoterpene emissions from both models showed large differences in their general amounts as well as their spatial distribution both in summer and winter. MEGAN produced more isoprene emissions by a factor of 3 while the PSI model generated three times of monoterpene emissions in summer, while there was negligible difference (~ 4 %) in sesquiterpene emissions associated with the two models. Despite the large differences in isoprene emissions (i.e. 3-fold), the resulting impact in predicted summer-time ozone proved to be minor ( 60 ppb). A much larger effect of the different BVOC emissions was found for the secondary organic aerosol (SOA) concentrations. The higher monoterpene emissions (a factor of ~ 3) by the PSI model led to higher SOA by ~ 110 % on average in summer, compared to MEGAN, improving substantially the model performance for organic aerosol (OA): the mean bias between modelled and measured OA at 8 Aerodyne aerosol chemical speciation monitor (ACSM)/Aerodyne aerosol mass spectrometer (AMS) measurement stations was reduced by 21 %–83 % in rural/remote stations. Effects on inorganic aerosols (particulate nitrate, sulphate, and ammonia) were relatively smaller (

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Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-3747-2019 ◽

2019 ◽

Vol 19 (6) ◽

pp. 3747-3768 ◽

Cited By ~ 10

Author(s):

Jianhui Jiang ◽

Sebnem Aksoyoglu ◽

Giancarlo Ciarelli ◽

Emmanouil Oikonomakis ◽

Imad El-Haddad ◽

...

Keyword(s):

Air Quality ◽

Model Performance ◽

Organic Aerosol ◽

Air Quality Model ◽

Quality Model ◽

Mass Spectrometers ◽

Inorganic Aerosols ◽

Aerosol Mass ◽

Emission Models ◽

Paul Scherrer

Abstract. Biogenic volatile organic compound (BVOC) emissions are one of the essential inputs for chemical transport models (CTMs), but their estimates are associated with large uncertainties, leading to significant influence on air quality modelling. This study aims to investigate the effects of using different BVOC emission models on the performance of a CTM in simulating secondary pollutants, i.e. ozone, organic, and inorganic aerosols. European air quality was simulated for the year 2011 by the regional air quality model Comprehensive Air Quality Model with Extensions (CAMx) version 6.3, using BVOC emissions calculated by two emission models: the Paul Scherrer Institute (PSI) model and the Model of Emissions of Gases and Aerosol from Nature (MEGAN) version 2.1. Comparison of isoprene and monoterpene emissions from both models showed large differences in their general amounts, as well as their spatial distribution in both summer and winter. MEGAN produced more isoprene emissions by a factor of 3 while the PSI model generated 3 times the monoterpene emissions in summer, while there was negligible difference (∼4 %) in sesquiterpene emissions associated with the two models. Despite the large differences in isoprene emissions (i.e. 3-fold), the resulting impact in predicted summertime ozone proved to be minor (<10 %; MEGAN O3 was higher than PSI O3 by ∼7 ppb). Comparisons with measurements from the European air quality database (AirBase) indicated that PSI emissions might improve the model performance at low ozone concentrations but worsen performance at high ozone levels (>60 ppb). A much larger effect of the different BVOC emissions was found for the secondary organic aerosol (SOA) concentrations. The higher monoterpene emissions (a factor of ∼3) by the PSI model led to higher SOA by ∼110 % on average in summer, compared to MEGAN, and lead to better agreement between modelled and measured organic aerosol (OA): the mean bias between modelled and measured OA at nine measurement stations using Aerodyne aerosol chemical speciation monitors (ACSMs) or Aerodyne aerosol mass spectrometers (AMSs) was reduced by 21 %–83 % at rural or remote stations. Effects on inorganic aerosols (particulate nitrate, sulfate, and ammonia) were relatively small (<15 %).

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Application of WRF/Chem over North America under the AQMEII Phase 2 – Part 2: Evaluation of 2010 application and responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010

Geoscientific Model Development ◽

10.5194/gmd-8-2095-2015 ◽

2015 ◽

Vol 8 (7) ◽

pp. 2095-2117 ◽

Cited By ~ 11

Author(s):

K. Yahya ◽

K. Wang ◽

Y. Zhang ◽

T. E. Kleindienst

Keyword(s):

Air Quality ◽

North America ◽

Model Performance ◽

Organic Aerosol ◽

Basis Set ◽

Meteorological Variables ◽

Phase 2 ◽

Air Quality Model ◽

Quality Model ◽

Coupled Models

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) simulation with the 2005 Carbon Bond (CB05) gas-phase mechanism coupled to the Modal for Aerosol Dynamics for Europe (MADE) and the volatility basis set approach for secondary organic aerosol (SOA) are conducted over a domain in North America for 2006 and 2010 as part of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 project. Following the Part 1 paper that focuses on the evaluation of the 2006 simulations, this Part 2 paper focuses on a comparison of model performance in 2006 and 2010 as well as analysis of the responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010. In general, emissions for gaseous and aerosol species decrease from 2006 to 2010, leading to a reduction in gaseous and aerosol concentrations and associated changes in radiation and cloud variables due to various feedback mechanisms. WRF/Chem is able to reproduce most observations and the observed variation trends from 2006 to 2010, despite its slightly worse performance than WRF that is likely due to inaccurate chemistry feedbacks resulting from less accurate emissions and chemical boundary conditions (BCONs) in 2010. Compared to 2006, the performance for most meteorological variables in 2010 gives lower normalized mean biases but higher normalized mean errors and lower correlation coefficients. The model also shows poorer performance for most chemical variables in 2010. This could be attributed to underestimations in emissions of some species, such as primary organic aerosol in some areas of the US in 2010, and inaccurate chemical BCONs and meteorological predictions. The inclusion of chemical feedbacks in WRF/Chem reduces biases in meteorological predictions in 2010; however, it increases errors and weakens correlations comparing to WRF simulations. Sensitivity simulations show that the net changes in meteorological variables from 2006 to 2010 are mostly influenced by changes in meteorology and those of ozone and fine particulate matter are influenced to a large extent by emissions and/or chemical BCONs and to a lesser extent by changes in meteorology. Using a different set of emissions and/or chemical BCONs helps improve the performance of individual variables, although it does not improve the degree of agreement with observed interannual trends. These results indicate a need to further improve the accuracy and consistency of emissions and chemical BCONs, the representations of SOA and chemistry–meteorology feedbacks in the online-coupled models.

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Application of WRF/Chem version 3.4.1 over North America under the AQMEII Phase 2: evaluation of 2010 application and responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-1639-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 1639-1686 ◽

Cited By ~ 8

Author(s):

K. Yahya ◽

K. Wang ◽

Y. Zhang ◽

T. E. Kleindienst

Keyword(s):

Air Quality ◽

North America ◽

Model Performance ◽

Organic Aerosol ◽

Basis Set ◽

Meteorological Variables ◽

Phase 2 ◽

Air Quality Model ◽

Quality Model ◽

Coupled Models

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) simulation with the 2005 Carbon Bond gas-phase mechanism coupled to the Modal for Aerosol Dynamics for Europe and the Volatility Basis Set approach for Secondary Organic Aerosol (SOA) are conducted over a domain in North America for 2006 and 2010 as part of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 project. This paper focuses on comparison of model performance in 2006 and 2010 as well as analysis of the responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010. In general, emissions for gaseous and aerosol species decrease from 2006 to 2010, leading to a reduction in gaseous and aerosol concentrations and associated changes in radiation and cloud variables due to various feedback mechanisms. WRF/Chem is able to reproduce most observations and the observed variation trends from 2006 to 2010, despite its slightly worse performance than WRF that is likely due to inaccurate chemistry feedbacks resulted from less accurate emissions and chemical boundary conditions (BCONs) in 2010. Compared to 2006, the performance for most meteorological variables in 2010 gives lower normalized mean biases but higher normalized mean errors and lower correlation coefficients. The model also shows worse performance for most chemical variables in 2010. This could be attributed to underestimations in emissions of some species such as primary organic aerosol in some areas of the US in 2010, and inaccurate chemical BCONs and meteorological predictions. The inclusion of chemical feedbacks in WRF/Chem reduces biases in meteorological predictions in 2010; however, it increases errors and weakens correlations comparing to WRF simulation. Sensitivity simulations show that the net changes in meteorological variables from 2006 to 2010 are mostly influenced by changes in meteorology and those of ozone and fine particulate matter are influenced to a large extent by emissions and/or chemical BCONs and to a lesser extent by changes in meteorology. These results indicate a need to further improve the accuracy of emissions and chemical BCONs, the representations of SOA and chemistry–meteorology feedbacks in the online-coupled models.

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Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

10.5194/acp-2017-182 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jianlin Hu ◽

Xun Li ◽

Lin Huang ◽

Qi Ying ◽

Qiang Zhang ◽

...

Keyword(s):

Air Quality ◽

Health Effects ◽

Air Pollutants ◽

Emission Inventory ◽

Model Performance ◽

Observation Data ◽

Emission Inventories ◽

Air Quality Model ◽

Quality Model ◽

Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

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