Introduction
Non-methane volatile organic compounds (NMVOCs) play important roles in the
tropospheric chemistry, especially in ozone production (Atkinson, 2000;
Seinfeld and Pandis, 2006). Aromatic hydrocarbons such as benzene (C6H6),
toluene (C7H8), and xylenes (C8H10)
make up a large fraction of NMVOCs (Ran et al., 2009; Guo et al., 2006; You
et al., 2008) in the atmosphere of urban and semi-urban areas. They are
important precursors of secondary organic aerosol (SOA), peroxyacetyl
nitrate (PAN), and ozone (Kansal, 2009; Tan et al., 2012; Porter et al.,
2017). In addition, many aromatic compounds can cause detrimental effects on
human health and plants (Manuela et al., 2012; Sarigiannis and Gotti, 2008;
Michalowicz and Duda, 2007).
Aromatics are released to the atmosphere by biomass burning as well as
fossil fuel evaporation and burning (Cabrera-Perez et al., 2016; Na et al.,
2004). The dominant oxidation pathway for aromatics is via reaction with the
hydroxyl radical (OH, the dominant atmospheric oxidant), followed by
reaction with the nitrate radical (NO3) (Cabrera-Perez et al., 2016; and
references therein). The corresponding aromatic oxidation products could be
involved in many atmospheric chemical processes, which can affect OH
recycling and the atmospheric oxidation capacity (Atkinson and Arey, 2003;
Calvert et al., 2002; Bejan et al., 2006; Chen et al., 2011). A realistic
model description of aromatic compounds is necessary to improve our
understanding of their effects on the chemistry in the atmosphere. However,
up to now, few regional- or global-scale chemical transport models (CTMs)
include detailed aromatic chemistry (Lewis et al., 2013; Cabrera-Perez et al., 2016).
Despite the potentially important influence of aromatic compounds on global
atmospheric chemistry, their effect on global tropospheric ozone formation
in polluted urban areas is less analyzed with the model simulation. The main
source and sink processes of tropospheric ozone are photochemical production
and loss, respectively (Seinfeld and Pandis, 2006; Monks et al., 2015; Yan et
al., 2016). Observation-based approaches alone cannot provide a full picture
of ozone–source attribution for the different NMVOCs. Such ozone–source
relationships are needed to improve policy-making strategies to address
hemispheric ozone pollution (Chandra et al., 2006). Numerical
chemistry–transport models allow us to explore the importance of impacts
from aromatics and to attribute observed changes in ozone concentrations to
particular sources (Stevenson et al., 2006, 2013; Zhang et
al., 2014). Current global CTMs reproduce much of the observed regional and
seasonal variability in tropospheric ozone concentrations. However, some
systematic biases can occur, most commonly an overestimation over the
Northern Hemisphere (Fiore et al., 2009; Reidmiller et al., 2009; Yan et
al., 2016, 2018a, b; Ni et al., 2018) due to incomplete representation of
physical and chemical processes, and biases in emissions and transport,
including the parameterization of small-scale processes and their feedbacks
to global-scale chemistry (Chen et al., 2009; Krol et al., 2005; Yan et al.,
2014, 2016).
Another motivation for the modeling comes from recent updates in halogen
(bromine–chlorine) chemistry, which when implemented in the Goddard Earth Observing System with chemistry (GEOS-Chem), a global
chemical transport model being used extensively for tropospheric chemistry
and transport studies (Zhang and Wang, 2016; Yan et al., 2014; Shen et al.,
2015; Lin et al., 2016), decrease the global burden of ozone significantly
(by 14 %; 2–10 ppb in the troposphere) (Schmidt et al., 2016). This ozone
burden decline is driven by decreased chemical ozone production due to
halogen-driven nitrogen oxides (NOx=NO+NO2) loss, and the
ozone decline lowers global mean tropospheric OH concentrations by 11 %.
Thus, GEOS-Chem starts to exhibit low ozone biases compared to ozonesonde
observations (Schmidt et al., 2016), particularly in the Southern
Hemisphere, implying that some mechanisms (e.g., due to aromatics) are
currently missing from the model.
A simplified aromatic oxidation mechanism has previously been employed in
GEOS-Chem (e.g., Fischer et al., 2014; Hu et al., 2015), which is still used
in the latest version (v12.0.0). In that simplified treatment, oxidation of
benzene (B), toluene (T), and xylene (X) by OH (Atkinson, 2000) is
assumed to produce first-generation oxidation products (xRO2, x = B,
T, or X). And these products further react with hydrogen peroxide (HO2)
or nitric oxide (NO) to produce LxRO2y (y = H or N), passive tracers
which are excluded from tropospheric chemistry. Thus, in the presence of
NOx, the overall reaction is aromatic + OH + NO = inert tracer.
While such a simplified treatment can suffice for budget analyses of the
aromatic species themselves, it does not capture ozone production from
aromatic oxidation products.
In this work, we update the aromatics chemistry in GEOS-Chem based on the
State-wide Air Pollution Research Center version 11 (SAPRC-11) mechanism,
and use the updated model to analyze the global- and
regional-scale chemical effects of the most abundant aromatics in the gas
phase (benzene, toluene, xylenes) in the troposphere. Specifically, we focus
on the impact on ozone formation (due to aromatics oxidation), as this is of
great interest for urban areas and can be helpful for developing air
pollution control strategies. Further targets are the changes to the
NOx spatial distribution and OH recycling. Model results for aromatics
and ozone mixing ratios are evaluated by comparison with observations from
surface and aircraft campaigns in order to constrain model accuracy.
Finally, we discuss the global effects of aromatics on tropospheric
chemistry including ozone, NOx, and HOx
(HOx = OH +HO2).
The rest of the paper is organized as follows. Section 2 describes the
GEOS-Chem model setups, including the updates in aromatics chemical
mechanism. A description of the observational datasets for aromatics and
ozone is given in Sect. 3. Section 4 presents the model evaluation for
aromatics based on the previously mentioned set of aircraft and surface
observations, and evaluates modeled surface ozone with measurements from
three networks. An analysis of the tropospheric impacts on ozone, NOx,
and OH, examining the difference between models results with simplified (as
in the standard model setup) and with SAPRC-11 aromatic chemistry, is
presented in Sect. 5. Section 6 concludes the present study.
Model description and setup
We use the GEOS-Chem CTM (version 9-02, available at http://geos-chem.org/; last access: 28 December 2018)
to interpret the importance of aromatics in tropospheric chemistry and ozone
production. GEOS-Chem is a global 3-D chemical transport model for a wide
range of atmospheric composition problems. It is driven by meteorological
data provided by the Goddard Earth Observing System (GEOS) of the NASA
Global Modeling Assimilation Office (GMAO). A detailed description of the
GEOS-Chem model is available at http://acmg.seas.harvard.edu/geos/geos_chem_narrative.html (last access: 28 December 2018).
Here, the model is run at a horizontal resolution of
2.5∘ long. × 2∘ lat. with a vertical grid
containing 47 layers (including 10 layers of ∼130 m thickness,
each below 850 hPa), as driven by the GEOS-5 assimilated meteorological
fields. The chemistry time step is 0.5 h, while the transport time step is
15 min in the model. A non-local scheme implemented by Lin and McElroy (2010)
is used for vertical mixing in the planetary boundary layer. Model
convection adopts the relaxed Arakawa–Schubert scheme (Rienecker et al.,
2008). Stratospheric ozone production employs the Linoz scheme (McLinden et
al., 2000). Dry deposition for aromatic compounds is implemented following
the scheme by Hu et al. (2015), which uses a standard resistance-in-series
model (Wesely, 1989) and Henry's law constants for benzene (0.18 M atm-1),
toluene (0.16 M atm-1), and xylenes (0.15 M atm-1) (Sander, 1999).
Emissions
For anthropogenic NMVOC emissions, including aromatic compounds (benzene,
toluene, and xylenes), here, we use emission inventory from the RETRO
(REanalysis of the TROpospheric chemical composition) (Schultz et al.,
2007). The global anthropogenic RETRO (version 2; available at
ftp://ftp.retro.enes.org/) inventory includes monthly emissions for
24 distinct chemical species during 1960–2000 with a resolution of
0.5∘ long. × 0.5∘ lat. (Schultz et al., 2007).
It is implemented in GEOS-Chem by regridding to the model resolution
(2.5∘ long. × 2.0∘ lat.). Emission factors in
RETRO are calculated on account of economic and technological
considerations. In order to estimate the time dependence of anthropogenic
emissions, RETRO also incorporates behavioral aspects (Schultz et al., 2007).
The implementation of the monthly RETRO emission inventory in GEOS-Chem is
described by Hu et al. (2015), which linked the RETRO species into the
corresponding model tracers. Here, the model speciation of xylenes includes
m-xylene, p-xylene, o-xylene, and ethylbenzene (Hu et al., 2015). The most
recent RETRO data (for 2000) are used for the GEOS-Chem model simulation, and
the calculated annual global anthropogenic NMVOCs are ∼71 Tg C.
On a carbon basis, the global aromatics (benzene plus toluene plus
xylenes) source accounts for ∼23 % (16 Tg C) of the total
anthropogenic NMVOCs. Figure 1 shows the spatial distribution of
anthropogenic emissions for benzene, toluene, and xylenes, respectively.
Anthropogenic benzene emissions in Asia (mainly over eastern China and
India) are larger than those from other source regions (e.g., over
Europe and the eastern US).
Spatial distribution of anthropogenic emissions from RETRO for
benzene (a), toluene (b), and xylenes (c), respectively.
Global NOx anthropogenic emissions are taken from the EDGAR
(Emissions Database for Global Atmospheric Research) v4.2 inventory. The global
inventory has been replaced by regional inventories in China (MEIC, base
year 2008), Asia (excluding China; INTEX-B, 2006), the US (NEI05, 2005),
Mexico (BRAVO, 1999), Canada (CAC, 2005), and Europe (EMEP, 2005). Details
on these inventories and on the model NOx anthropogenic emissions are
shown in Yan et al. (2016).
Biomass burning emissions of aromatics and other chemical species (e.g.,
NOx) in GEOS-Chem are calculated based on the monthly Global Fire
Emission Database version 3 (GFED3) inventory (van der Werf et al.,
2010). Natural emissions of NOx (by lightning and soil) and
of biogenic NMVOCs are calculated online by parameterizations driven by
model meteorology. Lightning NOx emissions are parameterized based on
cloud-top heights (Price and Rind, 1992) and are further constrained by the
lightning flash counts detected from satellite instruments (Murray et al.,
2012). Soil NOx emissions are described in Hudman et al. (2012).
Biogenic emissions of NMVOCs are calculated by MEGAN (Model of Emissions of
Gases and Aerosols from Nature) v2.1 with the hybrid algorithm (Guenther et al., 2012).
Updated aromatic chemistry
In the GEOS-Chem model setup, the current standard chemical mechanism with
simplified aromatic oxidation chemistry is based on Mao et al. (2013), which
is still the case for the latest version (v12.0.0). As mentioned in the
introduction, this simplified mechanism acts as strong sinks of both
HOx and NOx, because no HOx is regenerated in this reaction,
and NO is consumed without regenerating NO2. However, it is reasonably
well established that aromatics tend to be radical sources, forming highly
reactive products that photolyze to form new radicals, and regenerating
radicals in their initial reactions (Carter, 2010a, b; Carter and Heo,
2013). A revised mechanism that takes the general features of aromatics
mechanisms into account would be much more reactive, given the reactivity of
the aromatic products.
This work uses a more detailed and comprehensive aromatics oxidation
mechanism: the SAPRC-11
aromatics chemical mechanism. SAPRC-11 is an updated version of
the SAPRC-07 mechanism (Carter and Heo, 2013) to give better simulations of
recent environmental chamber experiments. The SAPRC-07 mechanism
underpredicted NO oxidation and O3 formation rates observed in recent
aromatic–NOx environmental chamber experiments. The new
aromatics mechanism, designated SAPRC-11, is able to reproduce the ozone formation
from aromatic oxidation that is observed in almost all environmental chamber
experiments, except for higher (>100 ppb) NOx (Carter and
Heo, 2013). Table S1 in the Supplement lists new model species in addition to those in the
standard GEOS-Chem model setup. Table S2 lists the new reactions and rate
constants. In this mechanism, the tropospheric consumption process of
aromatics is mainly the reaction with OH.
As discussed by Carter (2010a, b), aromatic oxidation has two possible OH
reaction pathways: OH radical addition and H-atom abstraction (Atkinson,
2000). In SAPRC-11, taking toluene as an example in Table S2, the reactions
following abstraction lead to three different formation products: an
aromatic aldehyde (represented as the BALD species in the model), a ketone (PROD2),
and an aldehyde (RCHO). The largest yield of toluene oxidation is the
reaction after OH addition of aromatic rings. The OH–aromatic adduct is
the reaction with O2 to form an OH–aromatic–O2 adduct or HO2 and
a phenolic compound (further consumed by reactions with OH and NO3
radicals). The OH–aromatic–O2 adduct further undergos two competing
unimolecular reactions to ultimately form OH, HO2, an
α-dicarbonyl – such as glyoxal (GLY), methylglyoxal (MGLY), or biacetyl (BACL) – a
monounsaturated dicarbonyl co-product (AFG1, AFG2, the photoreactive products) and a
di-unsaturated dicarbonyl product (AFG3, the non-photoreactive products)
(Calvert et al., 2002).
Formed from the phenolic products, the SAPRC-11 mechanism includes species
of cresols (CRES), phenol (PHEN), xylenols and alkyl phenols (XYNL), and
catechols (CATL). Due to their different SOA and ozone formation potentials (Carter et al.,
2012), these phenolic species are represented separately. Relatively high
yields of catechol (CATL) have been observed in the reactions of OH radicals
with phenolic compounds. Furthermore, their subsequent reactions are
believed to be important for SOA and ozone formation (Carter et al., 2012).
Simulation setups
In order to investigate the global chemical effects of the most commonly
emitted aromatics in the troposphere, two simulations were performed: one
with the ozone related aromatic chemistry updates from SAPRC-11 (the SAPRC
case) and the other with simplified aromatic chemistry as in the standard
setup (the Base case). Both simulations (Base and SAPRC) at 2.5∘ long. × 2∘ lat. are conducted from July 2004 to
December 2005, allowing for a 6-month spin-up for our focused analysis over
the year 2005 for comparison to the available observations (Sect. 3).
Initial conditions of chemicals are regridded from a simulation at
5∘ long. × 4∘ lat. started from 2004
with another spin-up run from January to June 2004. For comparison with
aromatics observations over the US in 2010–2011 (Sect. 3), we extend the
simulations from July 2009 to December 2011 with July–December 2009 as the
spin-up period.
Aromatics and ozone observations
We use a set of measurements from surface and aircraft campaigns to evaluate
the model-simulated aromatics and ozone.
Aromatic aircraft observations
For aromatics, we use airborne observations from CALNEX (California;
May/June 2010) aircraft study. A proton transfer reaction quadrupole mass
spectrometer (PTR-MS) was used to measure mixing ratios of aromatics (and an
array of other primary and secondary pollutants) during CALNEX. Measurements
are gathered mostly on a 1 s timescale (approximately 100 m spatial
resolution), which permits sampling of the source regions and tracking
subsequent transport and transformation throughout California and
the surrounding regions. Further details of the CALNEX campaign, including the
flight track, time frame, location, and instrument, are shown in Hu et al. (2015)
and https://www.esrl.noaa.gov/csd/projects/calnex (last access: 28 December 2018). For
comparison to the model results, we averaged the high temporal–spatial
resolution observations to the model resolution.
Summary of the statistical comparison between observed and simulated
concentrations (ppt for aromatics, ppb for ozone). MMOD and MOBS represent the
mean values for the SAPRC simulation and the observation, respectively. MRB is
the relative bias of model results defined as (MMOD – MOBS)/MOBS. SMOD and
SOBS are their standard deviations. TCOR and SCOR are the temporal and spatial
correlations between model results and measurements.
Species
Network
No.
Time
MMOD
MOBS
SMOD
SOBS
TCOR
SCOR
of
resolution
(MRB)
sites
Benzene
CARIBIC
1241
Instantaneous
12.3 (-23 %)
16.0
4.2
15.8
–
0.31
EEA
22
Annual mean
131.6 (-32 %)
194.0
32.1
118.4
–
0.49
EMEP
14
Monthly
106.5 (-36 %)
166.4
38.7
71.7
0.77
0.44
CALNEX
7708
Instantaneous
66.1 (15 %)
57.7
78.3
57.7
–
0.51
KCMP
1
Hourly
99.9 (9 %)
91.5
92.6
56.7
0.65
–
Toluene
CARIBIC
789
Instantaneous
1.5 (-58 %)
3.6
0.7
7.5
–
0.36
EEA
6
Annual mean
180.9 (-25 %)
240.3
66.8
59.4
-
0.41
EMEP
12
Monthly
113.2 (-15 %)
133.1
47.3
66.2
0.81
0.47
CALNEX
7708
Instantaneous
80.6 (10 %)
73.2
179.7
131.9
–
0.46
KCMP
1
Hourly
121.2 (114 %)
56.7
191.4
54.7
0.51
–
Xylenes
EMEP
8
Monthly
78.4 (85 %)
42.3
34.5
41.9
0.78
0.48
C8 aromatics
CALNEX
7708
Instantaneous
28.8 (-41 %)
48.6
112.2
97.2
–
0.39
KCMP
1
Hourly
88.9 (-2 %)
90.3
119.2
79.5
0.46
–
Ozone
WDCGG
64
Monthly
28.6 (-16 %)
34.1
12.8
14.2
0.68
0.54
EMEP
130
Monthly
27.7 (-9 %)
30.6
13.2
10.3
0.76
0.52
We also employ vertical profiles obtained in 2005 from the CARIBIC (Civil
Aircraft for Regular Investigation of the atmosphere Based on an Instrument
Container) project, which conducts atmospheric measurements aboard a
commercial aircraft (Lufthansa A340-600) (Brenninkmeijer et al., 2007; Baker
et al., 2010). CARIBIC flights fly away from Frankfurt, Germany, on the way
to North America, South America, India, and east Asia. Measurements are
available in the upper troposphere (50 % on average) and lower
stratosphere (50 %) (UTLS) at altitudes between 10 and 12 km. To evaluate our
results, measurements are averaged to the model output resolution.
Vertically, results from GEOS-Chem model simulations at the 250 hPa level
are used to compare with observations between 200 and 300 hPa. Then, the annual
means of observations and model data sampled along the flight tracks are
used in the comparison.
Aromatic surface measurements
To evaluate the ground-level mixing ratios of benzene, toluene, and xylenes
as well as their seasonal cycles, surface observations of aromatics are
collected from two networks (EMEP, data available at
http://www.nilu.no/projects/ccc/emepdata.html; last access: 28 December 2018, and the European
Environmental Agency – EEA, data available at
http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8; last access: 28 December 2018,
both for the year 2005) over Europe and the KCMP tall tower dataset (data
available at https://atmoschem.umn.edu/data; last access: 28 December 2018,
for the year 2011) over the US.
EMEP, which aims to investigate the long-range transport of air pollution
and the flux through geographic boundaries (Torseth et al., 2012), locates
measurement sites in locations where there are minimal local impacts; thus,
consequently, the observations could represent the feature of large regions.
EMEP has a daily resolution with a total of 14 stations located in Europe
for benzene, 12 stations for toluene, and 8 stations for xylenes (Table 1).
Here, we use the monthly values calculated from the database to evaluate
monthly model results. Note that measurement speciation of xylenes
(o-xylene, m-xylene, and p-xylene) in EMEP network does not exactly
correspond with the model speciation of xylenes (m-xylene, p-xylene,
o-xylene and ethylbenzene) (Hu et al., 2015). The speciation assumption
probably can partly account for the xylene model–measurement discrepancy
seen in Sect. 4.
EEA provides observations from a large number of sites over urban, suburban,
and background regions (EEA, 2014). However, here, we use only rural
background sites to do model comparison, as in Cabrera-Perez et al. (2016),
because the model horizontal scale cannot simulate direct traffic or
industrial influence. This leads to 22 stations available for benzene and
6 stations for toluene. Further details of the sites and location information
of EEA (and EMEP) used here are described in Cabrera-Perez et al. (2016). For
comparison, annual means for individual sites have been used.
The KCMP tall tower measurements (at 44.69∘ N, 93.07∘ W;
Minnesota, US) have been widely used for studies of surface fluxes of
tropospheric trace species and land–atmosphere interactions (Kim et al.,
2013; Hu et al., 2015; Chen et al., 2018). A suite of NMVOCs including
aromatics were observed at the KCMP tower during 2009–2012 with a
high-sensitivity PTR-MS, sampling from a height of 185 m a.g.l. (above ground level).
We averaged the hourly observations of benzene, toluene, and C8
(xylenes plus ethylbenzene, here consistent with the model speciation) aromatics to
monthly values and then used them for our model evaluation. Monthly mean
simulations at the 990 hPa level (∼190 m) are used for comparison.
Ozone observations
Ozone observations are taken from the database of the World Data Centre for
Greenhouse Gases (WDCGG, data available at
http://ds.data.jma.go.jp/gmd/wdcgg/cgi-bin/wdcgg/catalogue.cgi; last access: 28 December 2018), and the
Chemical Coordination Centre of EMEP (EMEP CCC). These networks contain
hourly ozone measurements over a total of 194 background sites in remote
environments. We use monthly averaged observations of surface ozone in 2005
to examine the simulated surface ozone from the GEOS-Chem model. Simulated
ozone from the lowest layer (centered at ∼65 m) is sampled
from the grid cells corresponding to the ground sites.
Evaluation of simulated aromatics and ozone
In this section, the SAPRC model simulation results of aromatics (benzene,
toluene, xylenes, and C8 aromatics) and ozone from GEOS-Chem are
evaluated with observations. Table 1 summarizes the statistical comparison
between measured and simulated concentrations over the monitoring stations
described in Sect. 3. For the statistical calculations, GEOS-Chem simulation
results have been sampled along the geographical locations of the
measurements. Table 1 includes the number of locations and time resolutions.
The number of sites in EEA for xylenes is only two; thus, we do not include
their comparison results in Table 1 due to the lack of representativeness.
Surface-level aromatics
For the aromatics near the surface mixing ratios over Europe, observed mean
benzene (194.0 ppt for EEA and 166.4 ppt for EMEP) and toluene (240.3 ppt
for EEA and 133.1 ppt for EMEP) mixing ratios are higher than observed mean
xylene concentrations (42.3 ppt for EMEP). In general, the model
underestimates EEA and EMEP observations of benzene (by 34 % on average)
and toluene (by 20 % on average). For benzene, the model results
systematically underestimate the annual means (36 %) compared to the EMEP
database, consistent with the model underestimate of the EEA dataset
(32 %). The model underestimate for toluene compared to the EMEP dataset
(15 %) is smaller than that relative to the EEA measurements (25 %). The
simulation overestimates the xylene measurements in EMEP by a factor of 1.9,
in part because the model results include ethylbenzene but the observations
do not (see Sect. 3.2). The fact that the anthropogenic RETRO emissions (for
the year 2000) do not correspond to the year of measurement (2005) may
contribute to the above model–measurement discrepancies. Anthropogenic
aromatics emissions are reported to have significant changes in emissions
and their distributions over the decade by EDGAR v4.3.2 (Crippa et al., 2018;
http://eccad.aeris-data.fr/ B/#DatasetPlace:EDGARv4.3.2$DOI; last access: 28 December 2018). It shows
that the total aromatics emissions from anthropogenic sources are enhanced by
5 % (2005) and 14 % (2011) compared to the year 2000. The model bias
would be partly benefit from this emission increase with enhanced modeled
mixing ratios of benzene and toluene.
The modeled spatial variability of aromatics (with standard deviations of
32.1–66.8 ppt) is 18 %–73 % lower than that of the EMEP and EEA
observations (41.9–118.4 ppt), probably due to the coarse model resolution.
The spatial variability in benzene (46 %–73 % lower) is the most strongly
underestimated among the three aromatic species. Unlike benzene, simulated
concentrations of toluene show a larger standard deviation (66.8 ppt) than
the EEA measurements (59.4 ppt), indicating larger simulated spatial
variability. Simulation results are thus poorly spatially correlated with
observations (R=0.41–0.49). However, the temporal variability of
aromatics is well captured by GEOS-Chem with the correlations above 0.7 for
most stations.
Figure 2 shows a comparison of model results with observations at six
stations for benzene, toluene, and xylenes, respectively, following
Cabrera-Perez et al. (2016). The sites are chosen as the first six stations
with largest amount of data. Model results reproduce the annual cycle at the
majority of sites. Aromatics are better simulated in summer than in winter.
This feature has been previously found for the climate–chemistry ECHAM/MESSy Atmospheric Chemistry (EMAC)
model for aromatics (Cabrera-Perez et al., 2016) and simpler NMVOCs (Pozzer et
al., 2007). In addition, the measurements show larger standard deviations
than the GEOS-Chem simulations, with the ratios between the observed and the
simulated standard deviations being 2–11.
Monthly average EMEP observations (in black) of benzene (a),
toluene (b), and xylenes (c) at six different locations for
the year 2005, as well as the model results in the SAPRC simulation (in red),
both in ppt. Error bars show the standard deviations.
Over the US, annual mean observed concentrations at the KCMP tall tower are
91.5 ppt for benzene, 56.7 ppt for toluene, and 90.3 ppt for C8
aromatics (Table 1). The model biases for benzene (8.4 ppt; 9.2 %) and
C8 aromatics (-1.4 ppt; -1.6 %) are much lower than that for
toluene (64.5 ppt; 114 %). Figure 3 further shows the observed and
simulated monthly averaged concentrations of benzene, toluene, and C8
aromatics. The SAPRC simulation reproduces their seasonal cycles, with
higher concentrations in winter and lower mixing ratios in summer,
consistent with Hu et al. (2015). The model–observation correlations are 0.89,
0.78, and 0.65 for monthly benzene, toluene, and C8 aromatics,
respectively. The large overestimation of modeled toluene is mainly due to
simulated high mixing ratios during the cold season (Fig. 3, October to March).
Tropospheric aromatics
Table 1 shows that, in the UTLS, both CARIBIC-observed (16 ppt) and
GEOS-Chem-modeled (12.3 ppt) benzene mixing ratios are higher than toluene
concentrations (3.6 ppt for CARIBIC and 1.5 ppt for GEOS-Chem). For benzene,
the model underestimations appear to be smaller in the free troposphere (with
an underestimation by 23 %) than at the surface (36 % for EMEP and 32 %
for EEA). In contrast to benzene, annual mean concentrations of toluene are
underestimated by 58 % in the UTLS. The geographical variability of
benzene is larger than that for toluene (with standard deviation of 4.2 ppt
versus 0.7 ppt in model and 15.8 ppt versus 7.5 ppt in observation), probably
because of the shorter lifetime of benzene (between several hours and several days;
http://www.nzdl.org/gsdlmod?a=p&p=home&l=en&w=utf-8; last access: 28 December 2018), in
combination with the lower concentrations in the UTLS for toluene. The model
results show smaller spatial variability than the observations. This
underestimation for spatial variability in the free troposphere (over
70 %) is higher than that at the surface (not shown).
Monthly average KCMP tall tower observations (in black) of benzene,
toluene, and C8 (xylenes plus ethylbenzene) aromatics in the year 2011
and the model results in the SAPRC simulation (in red). Error bars show the
standard deviations.
The black lines in Fig. 4 show the tropospheric aromatics profiles during
the CALNEX campaign. The measured values peak at an altitude of 0.6–0.8 km,
with concentrations decreasing at higher altitudes. Although the
concentrations in the lower troposphere for benzene (40–100 ppt below 2 km)
are lower than mixing ratios for toluene (70–160 ppt below 2 km) and
C8 aromatics (50–120 ppt below 2 km), the benzene mixing ratios
(>30 ppt) in the free troposphere are much higher than those of
toluene and C8 aromatics (<10 ppt). The different profile
shapes in the lower troposphere for benzene, toluene, and C8 aromatics
are mainly due to their different emissions and lifetime. The SAPRC
simulation (red lines in Fig. 4) captures the general vertical variations of
CALNEX benzene and toluene, with statistically significant model–observation
correlations of 0.74 and 0.65 for benzene and toluene, respectively. The
model generally overestimates the measured C8 aromatics below 0.5 km,
albeit with an underestimation above 0.5 km, with lower model–observation
correlation of 0.37. This overestimation below 0.5 km is also seen for
benzene and toluene. The modeled overly rapid aromatics drop-off with
altitude probably implies the modeled aromatics lifetime is short.
Surface ozone
Table 1 shows an average ozone mixing ratio of 34.1 ppb in 2005 over the
regional background WDCGG sites. The annual mean ozone mixing ratios are
lower over Europe (from the EMEP dataset), about 30.6 ppb. The SAPRC
simulation tends to underestimate the mixing ratios over the sites of Europe
and background regions with biases of -2.9 and -5.5 ppb,
respectively. Figure 5 shows the spatial distribution of the annual mean
model biases with respect to the measurements. Unlike the modeled surface
aromatics, the simulated ozone spatial variability can be either slightly
lower or higher than the observed variability, depending on the compared
database: the standard deviation is 12.8 ppb (simulated) versus 14.2 ppb
(observed) for WDCGG sites, 13.2 ppb versus 10.3 ppb for EMEP sites. The
temporal variability (temporal correlations of 0.68–0.72) is better
captured by the model than the spatial variability (spatial correlations of 0.52–0.54).
Global effects of aromatic chemistry
This section compares the Base and SAPRC simulations to assess to which
extent the updated mechanism for aromatics affect the global simulation of
ozone, HOx, and individual nitrogen species. Our focus here is on the
large-scale impacts.
Annual and seasonal mean changes (%) in modeled surface as well as
tropospheric concentrations from the Base to the SAPRC simulation. Also shown
are the numbers for the Northern Hemisphere (NH) and Southern Hemisphere (SH).
Species
Annual
MAM
JJA
SON
DJF
Surface
Trop
Surface
Trop
Surface
Trop
Surface
Trop
Surface
Trop
(NH,
(NH,
(NH,
(NH,
(NH,
(NH,
(NH,
(NH,
(NH,
(NH,
SH)
SH)
SH)
SH)
SH)
SH)
SH)
SH)
SH)
SH)
NO
-0.2 %
0.6 %
-0.4 %
0.7 %
-1.3 %
-0.1 %
-1.5 %
-0.5 %
0.8 %
1.6 %
(-0.2 %,
(0.8 %,
(-0.3 %,
(0.9 %,
(-1.3 %,
(-0.1 %,
(-1.5 %,
(-0.5 %,
(0.9 %,
(2.0 %,
-1.4 %)
-0.2 %)
-1.7 %)
-0.3 %)
-1.2 %)
-0.1 %)
-1.3 %)
-0.3 %)
-1.6 %)
-0.3 %)
O3
0.9 %
0.4 %
1.1 %
0.5 %
0.6 %
0.3 %
0.8 %
0.4 %
1.0 %
0.4 %
(1.2 %,
(0.6 %,
(1.6 %,
(0.8 %,
(0.9 %,
(0.5 %,
(1.1 %,
(0.6 %,
(1.3 %,
(0.6 %,
0.3 %)
-0.1 %)
0.3 %)
-0.1 %)
0.2 %)
-0.1 %)
0.4 %)
-0.1 %)
0.3 %)
-0.1 %)
CO
0.8 %
1.0 %
0.5 %
0.7 %
1.1 %
1.2 %
1.1 %
1.3 %
0.5 %
0.7 %
(0.5 %,
(0.7 %,
(0.2 %,
(0.4 %,
(0.8 %,
(1.0 %,
(0.9 %,
(1.1 %,
(0.3 %,
(0.5 %,
1.3 %)
1.4 %)
1.1 %)
1.3 %)
1.4 %)
1.5 %)
1.5 %)
1.6 %)
1.0 %)
1.2 %)
HNO3
1.1 %
0.3 %
1.2 %
0.4 %
0.7 %
-0.1 %
1.0 %
0.2 %
1.4 %
0.6 %
(1.3 %,
(0.7 %,
(1.3 %,
(0.7 %,
(0.9 %,
(0.2 %,
(1.4 %,
(0.7 %,
(1.6 %,
(1.1 %,
-0.6 %)
-0.9 %)
-0.4 %)
-0.9 %)
-0.6 %)
-1.0 %)
-0.7 %)
-1.0 %)
-0.7 %)
-0.8 %)
NO2
1.0 %
2.1 %
0.8 %
1.8 %
-0.2 %
0.6 %
0.5 %
1.3 %
2.0 %
3.6 %
(1.0 %,
(2.4 %,
(0.8 %,
(2.0 %,
(-0.3 %,
(0.6 %,
(0.6 %,
(1.5 %,
(2.1 %,
(4.0 %,
0.2 %)
0.7 %)
0.3 %)
0.8 %)
0.1 %)
0.8 %)
0.2 %)
0.5 %)
0.2 %)
0.5 %)
NO3
-0.9 %
-4.1 %
-1.5 %
-5.6 %
-0.9 %
-3.7 %
-0.5 %
-3.4 %
-0.8 %
-4.1 %
(-0.6 %,
(-4.5 %,
(-1.3 %,
(-7.0 %,
(-0.5 %,
(-4.3 %,
(-0.1 %,
(-3.4 %,
(-0.5 %,
(-4.2 %,
-2.7 %)
-3.5 %)
-2.7 %)
-3.0 %)
-2.5 %)
-3.0 %)
-2.6 %)
-3.6 %)
-3.6 %)
-4.5 %)
BENZ
-0.5 %
-0.4 %
-0.9 %
-1.0 %
0.1 %
0.7 %
-0.1 %
0.2 %
-0.6 %
-0.6 %
(-0.6 %,
(-0.6 %,
(-1.0 %,
(-1.1 %,
(-0.1 %,
(0.5 %,
(-0.2 %,
(-0.1 %,
(-0.6 %,
(-0.7 %,
0.6 %)
1.4 %)
0.7 %)
1.7 %)
0.5 %)
1.0 %)
0.8 %)
1.6 %)
0.9 %)
2.0 %)
TOLU
-1.2 %
-1.9 %
-1.5 %
-2.8 %
-0.8 %
-0.9 %
-1.0 %
-1.5 %
-1.3 %
-1.9 %
(-1.3 %,
(-2.0 %,
(-1.6 %,
(-3.0 %,
(-1.0 %,
(-1.2 %,
(-1.1 %,
(-1.6 %,
(-1.3 %,
(-2.0 %,
0.1 %)
0.4 %)
0.3 %)
0.8 %)
-0.2 %)
-0.1 %)
0.2 %)
0.6 %)
0.4 %)
1.3 %)
XYLE
-1.4 %
-2.3 %
-1.2 %
-2.1 %
-1.2 %
-1.5 %
-1.6 %
-2.3 %
-1.5 %
-2.4 %
(-1.5 %,
(-2.3 %,
(-1.2 %,
(-2.2 %,
(-1.3 %,
(-1.6 %,
(-1.7 %,
(-2.4 %,
(-1.5 %,
(-2.4 %,
-0.3 %)
-0.2 %)
-0.2 %)
0.3 %)
-0.6 %)
-0.9 %)
-0.1 %)
0.2 %)
-0.1 %)
0.5 %)
OH
1.1 %
0.2 %
1.4 %
0.4 %
1.2 %
0.3 %
0.9 %
0.1 %
1.0 %
0.1 %
(1.6 %,
(0.6 %,
(1.9 %,
(0.8 %,
(1.3 %,
(0.5 %,
(1.5 %,
(0.4 %,
(2.1 %,
(0.9 %,
0.3 %)
-0.3 %)
0.3 %)
-0.4 %)
0.5 %)
-0.2 %)
0.3 %)
-0.4 %)
0.2 %)
-0.3 %)
HO2
3.0 %
1.3 %
2.9 %
1.4 %
3.3 %
1.3 %
3.1 %
1.3 %
2.8 %
1.2 %
(3.2 %,
(1.4 %,
(2.8 %,
(1.5 %,
(3.2 %,
(1.2 %,
(3.4 %,
(1.5 %,
(3.7 %,
(1.9 %,
2.8 %)
1.2 %)
3.1 %)
1.2 %)
3.6 %)
1.6 %)
2.8 %)
1.2 %)
2.2 %)
0.9 %)
OH/HO2
-1.4 %
-0.9 %
-1.2 %
-0.8 %
-1.6 %
-1.0 %
-1.4 %
-1.0 %
-1.1 %
-0.8 %
(-1.0 %,
(-0.7 %,
(-1.1 %,
(-0.5 %,
(-1.1 %,
(-0.7 %,
(-0.9 %,
(-0.8 %,
(-0.5 %,
(-0.6 %,
-1.7 %)
-1.3 %)
-1.9 %)
-1.4 %)
-2.0 %)
-1.6 %)
-1.9 %)
-1.4 %)
-2.1 %)
-1.3 %)
NOy species
Figure 6 and Table 2 show the changes from Base to SAPRC in annual average
surface NO mixing ratios. A decrease in NO is apparent over NOx source
regions, e.g., by approximately 0.15 ppb (∼20 %) over much
of the US, Europe and China (Fig. 6). In contrast, surface NO increases at
locations downwind from NOx source regions (up to ∼0.1 ppb
or 20 %), including the oceanic area off the eastern US coast, the
marine area adjacent to Japan, and the Mediterranean area. The change is
negligible (by -0.2 %) for the annual global mean surface NO (Table 2).
Seasonally, the decrease in spring, summer, and fall is compensated partly by
the increase in winter (Table 2). This winter increase versus decline in
other seasons is probably attributed to the weakened photochemical reactions
involving NOx in winter.
Measured (black) and simulated (red for the SAPRC case) vertical
profiles of aromatics in May/June 2010 for the CALNEX campaigns. Model results
are sampled at times and locations coincident to the measurements. Horizontal
lines indicate the standard deviations.
The zonal average results in Fig. 7 show a clear decline in NO in the
planetary boundary layer, in contrast to significant increases in the free
troposphere, from Base to SAPRC. The free tropospheric NO increases are
about the same from 30∘ S to 90∘ N, with an annual average
enhancement up to 5 % (Fig. 7), and are particularly large in winter (up
to 10 %, not shown). For the whole troposphere, the average NO increases
by 0.6 % from Base to SAPRC (Table 2).
Annual mean model biases for surface ozone in the SAPRC simulation,
with respect to measurements from WDCGG (a) and EMEP (b) networks.
Figure 6 shows that simulated surface NO2 mixing ratios in the SAPRC
scenario are enhanced over most locations across the globe, in comparison
with the Base simulation. Over the source regions, the changes are mixed,
with increases in some highly NOx polluted regions (by up to 10 %)
and decreases in other polluted regions. On a global mean basis, NO2 is
increased (by 2.1 % in the free troposphere and 1.0 % at the surface;
Table 2), due mainly to the recycling of NOx from PAN associated with
the aromatics, and the reactions of oxidation products from aromatics with
NO or NO3 (primarily) to form NO2 and HO2. Combing the
changes in NO and NO2 means that the total NOx mixing ratios
decrease in source regions but increase in the remote free troposphere (Figs. 8 and 9).
The NO3 mixing ratios decrease at the global scale (-4.1 % on
average in the troposphere; Fig. 7 and Table 2) in the SAPRC simulation,
except for an enhancement in surface NO3 over the northern polar
regions and most polluted areas like the eastern US, Europe and eastern
China (Fig. 6). The NO3 global decreases are mainly due to the
consumption of NO3 by reaction with the aromatic oxidation products.
However, the NO3 regional increases are probably caused by the enhanced
regional atmospheric oxidation capacity.
Table 2 shows that nitric acid (HNO3) increases in the SAPRC
simulation, both near the surface (by approximately 1.1 %) and in the
troposphere (by 0.3 %). The enhancement in HNO3 appears uniformly
over most continental regions in the Northern Hemisphere (not shown), due to
the promotion of direct formation of HNO3 from aromatics in the SAPRC simulation.
OH and HO2
Compared to the Base simulation, OH increases slightly by 1.1 % at the
surface in the SAPRC simulation; with that, declines over the tropics
(30∘ S–30∘ N) are compensated by enhancements over
other regions (Fig. 10 and Table 2). The largest increases in OH
concentrations are found over source regions dominated by anthropogenic
emissions (i.e., the US, Europe, and Asia) and in subtropical continental
regions with large biogenic aromatic emissions. In these locations, the
peroxy radicals formed by aromatic oxidation react with NO and HO2,
which can have a significant effect on the ambient ozone and NOx mixing
ratios. This in turn influences OH, as the largest photochemical sources of
OH in the model are the photolysis of O3 as well as the reaction of
NO with HO2. Seasonally, a few surface locations see OH concentration
increases of more than 10 % during April–August (not shown), including
parts of the eastern US, central Europe, eastern Asia, and Japan.
(a, c, e) Modeled spatial distributions of annual mean surface
NO (a, b), NO2 (c, d), and NO3 (e, f)
simulated in the Base case for the year 2005. (b, d, f) The respective
changes from Base to SAPRC.
The OH enhancement (0.2 %) is also seen in the free troposphere in the
SAPRC simulation (Fig. 11 and Table 2). OH is increased in the troposphere
of the Northern Hemisphere, in contrast to the decline in the troposphere of
tropics and Southern Hemisphere (Fig. 11). These OH changes correspond to
the hemispherically distinct changes in aromatics (benzene, toluene, and
xylenes), which show a decrease in the Northern Hemisphere, an increase in
the Southern Hemisphere (Figs. 12 and 13), and an increase in global mean (by
1 %) (Table 2). Despite the overall increase in tropospheric OH, CO is
increased by ∼1 % (Table 2) due to additional formation from aromatics oxidation.
Table 2 shows that, from Base to SAPRC, HO2 shows a significant increase
at the global scale: 3.0 % at the surface and 1.3% in the troposphere,
due to regeneration of HOx from aromatics oxidation products.
Correspondingly, the OH / HO2 ratio decreases slightly. These changes
mean that, compared to the simplified aromatic chemistry in the standard
model setup, the SAPRC mechanism is associated with higher OH (i.e., more
chemically reactive troposphere) and even higher HO2.
Ozone
From Base to SAPRC, the global average surface ozone mixing ratio increases
by less than 1 % (Table 2). This small difference is comparable to the
result calculated by Cabrera-Perez et al. (2016) with the EMAC model, which
is based on a reduced version of the aromatic chemistry from the Master
Chemical Mechanism (MCMv3.2). Figure 10 shows that the 1 % increase in
surface ozone occurs generally over the Northern Hemisphere. Similar to the
changes in OH, the most notable ozone increase occurs in
industrially polluted regions. These regions show significant local ozone
photochemical formation in both the Base case and the SAPRC simulation. The
updated aromatic chemistry increases ozone by up to 5 ppb in these regions.
Increases of ozone are much smaller (less than 0.2 ppb) over the tropical
oceans than in the continental areas. In contrast, ozone declines in regions
of South America, central Africa, Australia, and Indonesia over the tropics
(30∘ S–30∘ N). Changes elsewhere in the troposphere
are similar in magnitude, as shown in Fig. 11.
Annual and seasonal mean model ozone biases for the Base and the SAPRC
cases, compared to measurements from WDCGG and EMEP.
Species
Annual
MAM
JJA
SON
DJF
(ppb)
Base
SAPRC
Base
SAPRC
Base
SAPRC
Base
SAPRC
Base
SAPRC
WDCGG
-6.0
-5.4
-9.0
-8.4
-0.4
0.1
-2.5
-2.1
-11.9
-11.5
EMEP
-3.5
-2.8
-5.5
-4.7
4.5
5.2
0.3
0.8
-13.1
-12.8
(a, c, e) Modeled zonal average latitude-altitude distributions
of annual mean NO (a, b) and NO2 (c, d), and
NO3 (e, f) simulated in the Base scenario for the year 2005.
(b, d, f) The respective changes from Base to SAPRC.
Two general factors likely contribute to the ozone change from Base to
SAPRC. In the SAPRC simulation, the addition of aromatic oxidation products
(i.e., peroxy radicals) can contribute directly to ozone formation in
NOx-rich source regions and also in the NOx-sensitive remote
troposphere (i.e., from PAN to NOx and to ozone). The second factor is
a change in the NOx spatial distribution, with an overall enhancement
in average NO2 concentrations. The redistribution is mainly caused by
enhanced transport of NOx to the remote troposphere (see Sect. 5.1).
The enhanced NOx in the remote troposphere enhances the overall ozone
formation because this process is more efficient in the remote regions
(e.g., Liu et al., 1987). The increased ozone, NO2, and NOx
transport all lead to the aforementioned changes. This is described in
detail in Sect. 5.4.
There are notable decreases (more than 5 %; Fig. 11) in simulated ozone
and OH in the free troposphere (above 4 km) over the tropics (30∘ S–30∘ N).
A similar decrease is found in modeled NOx (above
6 km; Fig. 9). These decreases are probably related to the upward transport
of aromatics by tropical convection processes. The aromatics transported to
the upper troposphere may cause net consumption of tropospheric OH and
NOx, which can further reduce ozone production.
Same as Fig. 6 but for NOx (a) and PAN (b).
From Base to SAPRC, the modeled ozone concentrations are close to the WDCGG
and EMEP network measurements (Table 3). For the WDCGG background sites, the
annual and seasonal model biases are ∼10 % smaller in the
SAPRC simulation compared to the Base case. For the EMEP stations, although
the model results are not improved in summer and fall, the annual model bias
is 25 % smaller (-2.8 ppb versus -3.5 ppb) in the SAPRC simulation.
Same as Fig. 7 but for NOx (a) and PAN (b).
Discussion of SAPRC aromatic–ozone chemistry
As discussed in Sect. 5.3, the increased O3 mixing ratios from Base to
SAPRC are due to the direct impact of aromatic oxidation products (i.e.,
peroxy radicals) and to the effect of increased NO2 concentrations. The
simulated odd oxygen family
(Ox=O3+O(1D)+O(3P)+NO2+2×NO3+3×N2O5+HNO3+HNO4+PAN;
Wu et al., 2007; Yan et al., 2016) formation increases by 1 %–10 %, both over the source regions
and in the remote troposphere (Figs. 10 and 11). Although the percentage
changes are similar, the driving factors over the source regions are
different from the drivers in the remote troposphere.
Regions with large aromatics emissions show a significant increase of
oxidation products from Base to SAPRC. The modeled ozone in these regions
increases with increasing NO2 and its oxidation products. NO and
NO3 are often lower in these regions in the SAPRC scenario because of
their reactions with the aromatic–OH oxidation products to form NO2 and
HO2. In remote regions and in the free troposphere, ozone production is
also enhanced by both NO2 and HO2 increases in the SAPRC
simulation, but the increase in ozone formation is mainly attributed to the
increase in NOx mixing ratios.
Same as Fig. 6 but for OH (a, b),
O3 (c, d), and Ox (e, f).
NOx concentrations decrease in source regions and increase in the
remote regions because of more efficient transport of PAN and its analogues
(represented by PBZN here in SAPRC-11). From Base to SAPRC, modeled PAN has been
enhanced on a global scale (Figs. 8 and 9) via reactions of aromatic–OH
oxidation products with NO2 (equation of BR13 in Table S2). In the
SAPRC-11 aromatics chemical scheme, the immediate precursor of PAN
(peroxyacetyl radical) has five dominant photochemical precursors. They are
acetone (CH3COCH3, model species: ACET), methacrolein (MACR),
biacetyl (BACL), methyl glyoxal (MGLY), and other ketones (e.g., PROD2, AFG1). These compounds explain
the increased rate of PAN formation. For example, the SAPRC simulation has
increased the concentration of MGLY by a factor of 2. In addition, production of
organic nitrates – PBZN (reactions of BR30 and BR31 in Table S2) and RNO3 (PO36) – in
the model with SAPRC aromatics chemistry may also explain the increase in
ambient NOx in the remote regions, due to the rerelease of NOx
from organic nitrates (as opposed to removal by deposition). Due to such
rerelease of NOx from PAN-like compounds and also transport of
NOx, NOx increases by up to 5 % at the surface in most remote
regions and by ∼1 % in the troposphere as a whole. This
then leads to increased ozone due to the effectiveness of ozone formation in
the free troposphere.
Same as Fig. 7 but for OH (a, b),
O3 (c, d), and Ox (e, f).
SAPRC is a highly efficient and compact chemical mechanism with the use of
maximum ozone formation as a primary metric in the chamber experiment
benchmark. The mechanism has been primarily used and evaluated in regional
CTMs such as the Community Multi-scale Air Quality model (CMAQ) and Comprehensive Air Quality Model with Extensions (CAMx), at much finer resolution (i.e., a few
kilometers). Our study has significant application to use it in a global
model. Implementing SAPRC-11 aromatic chemistry would add ∼3 % more
computational effort in terms of model simulation times.
Same as Fig. 6 but for benzene (a, b),
toluene (c, d), and xylene (e, f).
SAPRC is based on lumped chemistry, which is partly optimized on empirical
fitting to smog chamber experiments that are representative of 1-day
photochemical smog episodes typical of, for example, Los Angeles and other
US urban centers. However, SAPRC-11 gives better simulations of ozone
formation in almost all conditions, except for higher (>100 ppb)
NOx experiments where O3 formation rates are consistently
overpredicted (Carter and Heo, 2013). This overprediction can be corrected
if the aromatics mechanism is parameterized to include a new NOx
dependence on photoreactive product yields, but that parameterization is not
incorporated in SAPRC-11 because it is inconsistent with available laboratory data.
Same as Fig. 7 but for benzene (a, b),
toluene (c, d), and xylene (e, f).
Other options, such as the condensed MCM mechanism, which are based upon more
fundamental laboratory and theoretical data and used for policy and
scientific modeling of multi-day photochemical ozone formation, are experienced
over Europe by Cabrera-Perez et al. (2016). Our results are consistent with the
simulation of the EMAC model implemented with a reduced version of the MCM
aromatic chemistry. Moreover, aromatic chemistry is still far from being
completely understood. For example, Bloss et al. (2005) show that for alkyl
substituted mono-aromatics, when compared to chamber experiments over a
range of VOC/NOx conditions, the chemistry underpredicts the
reactivity of the system but overpredicts the amount of O3 formation
(model shows more NO-to-NO2 conversion than in the experiments).
Conclusions
A representation of tropospheric reactions for aromatic hydrocarbons in the
SAPRC-11 mechanism has been added to GEOS-Chem to provide a more realistic
representation of their atmospheric chemistry. The GEOS-Chem simulation with
the SAPRC-11 aromatics mechanism has been evaluated against measurements
from aircraft and surface campaigns. The comparison with observations shows
reasonably good agreement for aromatics (benzene, toluene, and xylenes) and
ozone. Model results for aromatics can reproduce the seasonal cycle, with a
general underestimation over Europe for benzene and toluene, and an
overestimation of xylenes; meanwhile, over the US, a positive model bias for benzene
and toluene and a negative bias for C8 aromatics are found. From the
Base to the SAPRC simulation, the model ozone bias is reduced by 10 %
relative to WDCGG observations and by 25 % relative to EMEP observations.
The simplified aromatics chemistry in the Base simulation underpredicts NO
and NO3 oxidation, and it does not represent ozone formed from
aromatic–OH–NOx oxidation. Although the global average changes in
simulated chemical species are relatively small (1 %–4 % from Base to
SAPRC), on a regional scale, the differences can be much larger, especially
over aromatics and NOx source regions. From Base to SAPRC, NO2 is
enhanced by up to 10 % over some highly polluted areas, while reductions
are notable in other polluted areas. Although the simulated surface NO
decreases by approximately 0.15 ppb (∼20 %) or more in the
northern hemispheric source regions, including most of the US, Europe, and
China, increases are found (∼0.1 ppb, up to 20 %) at
locations downwind from these source regions. The total NOx mixing
ratios decrease in source regions but increase in the remote free
troposphere. This is mainly due to the addition of aromatic oxidation
products in the model that lead to PAN, which facilitates the transport of
nitrogen oxides to downwind locations remote from the sources. Finally, the
updated aromatic chemistry in GEOS-Chem increases ozone concentrations,
especially over industrialized regions (up to 5 ppb or more than 10 %).
Ozone changes in the model are partly explained by the direct impact of
increased aromatic oxidation products (i.e., peroxy radical) and partly by
the effect of the altered spatial distribution of NOx. Overall, our
results suggest that a better representation of aromatics chemistry is
important to model the tropospheric oxidation capacity.