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
Organic aerosol (OA) accounts for a substantial fraction of atmospheric particulate matter (Jimenez et al., 2009), which is closely associated with human health impacts and climate change (Cohen et al., 2017; Kanakidou et al., 2005; Lelieveld et al., 2015). Organic aerosol originates from a variety of natural and anthropogenic sources (Hallquist et al., 2009), among which residential biomass burning has been recognized as the dominant source for both primary (POA) and secondary (SOA) organic aerosols in Europe during wintertime (Butt et al., 2016; Jiang et al., 2019b; Qi et al., 2019). Despite its substantial contribution to OA, biomass burning OA is largely underestimated by chemical transport models (CTMs) (Ciarelli et al., 2017a; Hallquist et al., 2009; Robinson et al., 2007; Theodoritsi and Pandis, 2019; Woody et al., 2016).
Many efforts have been devoted to understanding and diminishing the gap between
modeled and observed OA from biomass burning. One of the major reasons for
underestimated OA is the absence of semi-volatile organic compounds
(SVOCs) from residential biomass burning in the current emission inventories
(Denier van der Gon et al., 2015). A smog chamber
study showed that the precursors traditionally included in CTMs account
for only
Zhang et al. (2014) reported that vapor wall losses
may lead to an underestimation of SOA by a factor of 1.1–4.2, depending on
different NO
Here, we (1) developed a VBS-based box model and fit the vapor-wall-loss-corrected SOA yields of biomass burning IVOCs based on 14 chamber experiments under different temperature and emission loads, (2) implemented the vapor-wall-loss-corrected VBS parameters in the regional chemical transport model Comprehensive Air Quality Model with extensions (CAMx), and (3) investigated the role of vapor wall loss correction in model performance by comparing modeled organic aerosols from traditional and modified VBS OA schemes with ambient observations at multiple European sites. Biomass burning in this study refers to residential biomass burning, while wildfires and prescribed burning are not included.
The parameterization of the VBS scheme was based on experimental data from
two smog chamber campaigns in 2014–2015. It includes 14 experiments
conducted under various temperature conditions (
A VBS box model was developed to simulate the formation and evolution of
primary and secondary OA in the chamber. In the model, we assumed that the
condensable gases generated from oxidation of the precursors could (1) partition to the particle phase, (2) be lost on the chamber wall, and/or
(3) be diluted by other gases injected into the smog chamber. CAMx includes
four types of precursors from anthropogenic sources, i.e., toluene, xylene,
benzene, and IVOCs, which includes all the other unspeciated organic gases.
According to our measurements, the traditional anthropogenic precursors
toluene, xylene, and benzene only account for
The model is optimized to constrain the volatility distribution (as a
function of
The regional model CAMx version 6.50 (Ramboll, 2018) was used to model
organic aerosol in Europe (15
The meteorological parameters were prepared with the Weather Research and
Forecasting model (WRF version 3.7.1; Skamarock et al., 2008) based
on the 6 h European Centre for Medium-Range Weather Forecasts (ECMWF)
reanalysis global data (Dee et al., 2011). The meteorological parameters
were evaluated and reported in a previous study
(Jiang et al., 2019a), which showed that most
of the meteorological parameters met the criteria for meteorological model
performance by Emery (2001). The initial and boundary conditions were
obtained from the global model MOZART-4/GEOS-5
(Horowitz et al., 2003). Inputs of ozone
column densities were produced based on the Total Ozone Mapping Spectrometer
(TOMS) data by the National Aeronautics and Space Administration (NASA,
Description of the different OA schemes.
To investigate the effects of vapor-wall-loss-corrected yields and
to compare to other modifications and/or parameterizations that are currently
strongly debated in the community, five simulations with different OA
schemes were conducted in this study (Table 1). Besides VBS_WLS, which uses the optimized parameterization with vapor wall loss
correction for the biomass burning sector, SOAP and VBS_BASE
represent the two standard parameterizations in CAMx; VBS_3POA
represents a common approach to offset the missing SVOC emissions in recent
modeling studies without vapor wall loss, and VBS_noWLS is
another reference case without vapor wall loss, which uses exactly
the same parameters as VBS_WLS except for the SOA yields from
IVOCs. Details about each OA scheme are introduced below.
The general model performance for the major air pollutants (SO
Comparison between measured and modeled OA with an optimized
parameterization under
The optimized parameters were then applied to the box model to simulate OA
production for 14 chamber experiments. Figure 1 shows the comparison between
measured OA and modeled primary and secondary OA under the median chamber
conditions (
Dependence of the wall loss factor
To further understand the factors influencing
Optimized yield factors
The optimized volatility distribution for the secondary condensable gases
from biomass burning (ppm per ppm IVOC) based on different wall loss
assumptions (
Statistical results for model performance in simulating OA, SOA, and POA. The number of daily average observations from five ACSM/AMS stations is 216.
Concentrations of measured and modeled OA, POA, and SOA at
five ACSM or AMS stations in winter
The modeled OA concentrations with different OA schemes were compared with
measurements from five ACSM/AMS stations in winter. The statistical results
are shown in Table 2, and the distributions of OA concentrations and the mean
bias between modeled and measured primary and secondary OA are displayed in
Fig. 4. OA is underestimated overall with all OA schemes. The VBS
schemes lead to a better model performance than the two-product approach
SOAP, except for VBS_BASE with the default VBS parameterization.
These results are consistent with a previous study using CAMx (Meroni et
al., 2017), in which the better performance of SOAP compared to the default
VBS was reported as a result of error compensation. The improved performance
of modified VBS (3POA, noWLS, WLS) for OA mainly comes from the contribution
of SOA (Table 2). The modeled SOA by 3POA and noWLS is very similar, and
therefore the analysis below will focus on the comparison between noWLS and
WLS, for which the only difference is that WLS uses vapor-wall-loss-corrected yields for IVOCs from biomass burning, while noWLS uses the fitted
yields assuming no vapor wall loss (
Measured and modeled daily average OA using different OA schemes in winter. ZRH: Zurich, BLQ: Bologna, MRS: Marseille, SIRTA: Paris SIRTA, SPC: San Pietro Capofiume.
Limited by the availability of OA measurements, the effects of vapor wall
loss correction on model performance present a clear site dependence in this
study. The modeled and measured daily average OA concentrations at each site
are shown in Fig. 5. The temporal variations of primary and secondary OA at
these sites can be found in Fig. S4. VBS_WLS leads to the
best performance for both OA and SOA in Marseille and SIRTA, in spite of an
overall underestimation (Fig. S4b, c). In Zurich, the vapor-wall-loss-corrected yields for biomass burning improve the model performance in
February and March, while there is an overestimation of OA and SOA for
all the OA schemes in November (Fig. S4a). The largest contribution to OA
during this period was found to be from biogenic SOA, which was
potentially overestimated due to overestimated temperatures during the
same time period (Jiang et al.,
2019b). Bologna and SPC are located in the Po Valley where biomass burning
contributes most to winter OA
(Jiang et al., 2019b), and therefore
higher effects from vapor wall loss correction on SOA are observed compared
to other sites. At SPC, fog scavenging processes played an important
role in OA during the measurements (Gilardoni et
al., 2014); however, the meteorological model failed to reproduce the fog
events due to the coarse resolution in this study
(Jiang et al., 2019b). Consequently,
both VBS_WLS and noWLS lead to an overestimation of OA and
SOA, while SOAP and VBS_BASE show better performance, probably
due to compensation for errors (Fig. S4e). In Bologna, a significant
overestimation of temperature was found on 2 to 6 December
(Jiang et al., 2019b), leading to a
significant underestimation of SOA for all the OA schemes (Fig. S4d).
Excluding this period, the modeled SOA by VBS_WLS is 89 %
higher than the measurements, while the modeled SOA concentrations by the
other schemes are closer to the measurements, with relative differences of
The distinct performance of vapor-wall-loss-corrected VBS at different sites could arise from various factors. It might come from the high uncertainties of SVOC and IVOC emissions from biomass burning, which were estimated by the same factor for the whole domain but were reported to have substantial inter-country variations (Denier van der Gon et al., 2015). Missing formation and removal processes such as photolytic and heterogeneous oxidation in the model could also result in different model performance for specific sites. In addition, in spite of the advanced chamber measurements we used to optimize the yield parameters covering a wide range of precursor species and multiple temperature and chamber conditions, the fitted vapor-wall-loss-corrected parameterization is still highly uncertain. To achieve a more robust parameterization and to further improve the model performance for OA, more studies on SVOC and IVOC emissions, as well as the formation and removal mechanisms of SOA based on extensive laboratory studies and field observations with higher spatial and temporal coverage, are needed.
Modeled OA, SOA, and POA in winter (DJF, December–January–February) by different OA schemes.
The modeled OA results in Europe for the whole year of 2011 with different OA
schemes were compared to investigate the effects of OA schemes and the vapor
wall loss correction. Among all the sources, residential biomass burning
contributed 16.3 %–52.6 % POA and 5.9 %–28.9 % SOA in winter
(Jiang et al., 2019b), indicating
the potential roles of vapor wall loss for the biomass burning sector.
Figure 6 shows the modeled OA, SOA, and POA in winter
(December–January–February). VBS_WLS leads to the highest
domain average OA (2.3
Differences in modeled OA, SOA, and POA in winter (DJF, December–January–February) by VBS schemes with (VBS_WLS) and without (VBS_noWLS) vapor wall corrections.
The effects of different VBS schemes on OA are much smaller in summer (Fig. S6). Despite a slight increase from the VBS_BASE (1.2
Modeled fractions of annual mean SOA to total OA (fSOA) using different OA schemes. Modeled results for VBS_3POA are very similar to VBS_noWLS and are therefore not shown here.
The effects of the updated VBS schemes on the fraction of annual average SOA
in total OA (fSOA
Comparison between modeled and measured fSOA from the literature over the year (see data and sources in Table S2). The shading indicates the confidence intervals of the regression lines.
The modeled fSOA values were compared with measurements from previous studies in Europe (Crippa et al., 2014; Jiang et al., 2019b). The measured fSOA from the literature covered 18 sites and different seasons between 2008 and 2011 (Table S2). SOAP tends to underestimate the fSOA, while VBS_BASE significantly overpredicts the fSOA (Fig. 9). Both WLS and noWLS tend to underestimate the high fSOA and overestimate the low fSOA. VBS_WLS has 5 % higher fSOA than VBS_noWLS and shows the highest agreement on the range of fSOA with the measurements and the average fSOA values (measured: 69.6 %; VBS_WLS: 69.1 %). The largest improvements occur in winter when the vapor-wall-loss-corrected yields of biomass burning emissions largely increase the SOA production.
In this study, we optimized the SOA yields for a VBS-based box model using 14 chamber experiments with biomass burning and implemented the fitted VBS parameters (SOA yields, IVOC emissions from biomass burning, and enthalpy of vaporization) in the regional air quality model CAMx v6.5. The influence of the vapor wall loss correction on the model performance was investigated by comparing modeled primary and secondary OA with traditional and modified OA schemes, including the two-product approach (SOAP), the standard VBS (VBS_BASE), VBS with 3 times the POA to compensate for the missing SVOCs (VBS_3POA), VBS with vapor wall loss correction (VBS_WLS), and an additional reference scenario with the same parameterizations as in VBS_WLS except for using the default SOA yields from biomass burning IVOCs (VBS_noWLS).
The vapor wall loss correction increases the mass distributed in the
low-volatility bins (
The optimized parameterization with vapor wall loss correction in this study is expected to provide some insight to improve SOA underestimation in CTMs. Despite the overall improvement of model performance for predicting SOA, the VBS_WLS was found to increase the mean bias at specific sites compared to noWLS. To achieve a more robust parameterization and to further improve the model performance, complementary studies on SVOC and IVOC emissions, as well as on the formation and removal mechanisms of SOA based on extensive laboratory studies and field observations with higher spatial and temporal coverage, are still needed.
The source code of the standard CAMx model is available at the RAMBOLL
website (
The supplement related to this article is available online at:
JJ and IEH conceived the study. JJ carried out the model simulation and data analysis. GS and AB conducted the chamber measurements. NM, FC, JEP, OF, and SG provided the measurement data. SA, ASHP, and UB supervised the entire work development. The paper was prepared by JJ. All authors discussed and contributed to the final paper.
The authors declare that they have no conflict of interest.
We are grateful to the European Centre for Medium-Range Weather Forecasts (ECMWF) for the meteorological data, the National Aeronautics and Space Administration (NASA) and its data-contributing agencies (NCAR, UCAR) for the TOMS and MODIS data, the global air quality model data, and the TUV model. We thank RAMBOLL for support with CAMx. Simulations with WRF and CAMx models were performed at the Swiss National Supercomputing Centre (CSCS). We thank the Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS) and the Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL (COLOSSAL) cost action (CA16109) for support and harmonization within OA measurements and data treatments.
This research has been supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (grant no. 200021_169787) and the European Union's Horizon 2020 research and innovation program through the EUROCHAMP-2020 Infrastructure Activity (grant no. 730997).
This paper was edited by Christoph Knote and reviewed by two anonymous referees.