Relationships between oceanic emissions and air chemistry
are intricate and still not fully understood. For regional air chemistry, a
better understanding of marine halogen emission on the hydroxyl (OH) radical is
crucial. The OH radical is a key species in atmospheric chemistry because it
can oxidize almost all trace species in the atmosphere. In the marine
atmosphere, OH levels could be significantly affected by the halogen species
emitted from the ocean. However, due to the complicated interactions of
halogens with OH through different pathways, it is not well understood how
halogens influence OH and even what the sign of the net effect is.
Therefore, in this study, we aim to quantify the impact of marine-emitted
halogens (including Cl, Br, and I) through different pathways on OH in the
high OH season by using the WRF-CMAQ model with process analysis and
state-of-the-art halogen chemistry in East Asia and near the western
Pacific. Results show a very complicated response of the OH production rate
(POH) to marine halogen emissions. The monthly POH is
generally decreased over the ocean by up to a maximum of about 10 %–15 % in
the Philippine Sea, but it is increased in many nearshore areas by up to about
7 %–9 % in the Bohai Sea. In the coastal areas of southern China, the
monthly POH could also decrease 3 %–5 %, but hourly values can decrease
over 30 % in the daytime. Analysis of the individual reactions using the
integrated reaction rate shows that the net change in POH is controlled
by the competition of three main pathways (OH from O3 photolysis, OH
from HO2 conversion, and OH from HOX, X=Cl, Br, I) through different
halogen species. Sea spray aerosol (SSA) and inorganic iodine gases are the
major species influencing the strengths of these three pathways and
therefore have the most significant impacts on POH. Both of these two
types of species decrease POH through physical processes, while
generally increasing POH through chemical processes. In the ocean
atmosphere, inorganic iodine gases determine the basic pattern of ΔPOH through complicated iodine chemistry, which generally positively
influences POH near O3 sources while negatively influencing it when O3
experiences longer transport over the ocean. Over the continent, SSA is the
controlling species, and the SSA extinction effect leads to the negative
ΔPOH in southern China. Our results show that marine-emitted
halogen species have notable impacts over the ocean and potential impacts on
coastal atmospheric oxidation by species (SSA, inorganic iodine, and
halocarbons), processes (chemistry, radiation, and deposition), and main
pathways. The notable impacts of the marine-emitted halogen species on the
atmospheric oxidation capacity have further implications for the lifetime of
long-lived species such as CH4 in the long term and the quantity of air
pollutants such as O3 in the episodic events in East Asia and in
other circumstances (e.g., different domains, regions, and emission rates).
Introduction
The hydroxyl radical is the most important daytime oxidant in the
troposphere. It can oxidize almost all directly emitted gases such as CO,
CH4, and other volatile organic compounds (VOCs), while producing some
secondary species such as O3 and secondary aerosols at the same time.
The primary source of OH in the troposphere is O3 through the reaction
of water vapor with O(1D), which is produced from the photolysis of
O3. In urban areas, the photolysis of nitrous acid (HONO) is also a significant source
of OH and may be more important than the photolysis of O3, especially
in spring, autumn, and winter because of the very large seasonal variations
of O3 photolysis and humidity (e.g., Tan et al., 2019; Whalley et
al., 2021; Liu et al., 2019). When there is abundant NO, as is typical in the
polluted continental atmosphere, peroxy radicals (RO2 and HO2)
will be formed by the oxidation of hydrocarbons by OH and will form OH
again in the reaction with NO. This HOx (= OH + HO2) cycling maintains
a high OH concentration that cannot be achieved by primary sources alone.
The main sinks of OH are CO and VOCs. Due to the complexity of the HOx
chemistry, the sources and sinks of OH are not fully understood. For
example, recent studies showed that when the NOx concentration is very low (e.g.,
NO concentration less than several hundred parts per trillion by volume) there may be missing
sources of OH (Tan et al., 2019; Rohrer et al., 2014; Lelieveld et al.,
2008; Hofzumahaus et al., 2009; Lu et al., 2019a; Fuchs et al., 2013; Stone et
al., 2012; Fittschen et al., 2019; Whalley et al., 2021). In addition, HOx
chemistry can interact with other oxidizers in the atmosphere in specific
circumstances.
In the marine atmosphere, the abundant marine-emitted halogen species have
significant impacts on OH. Marine-emitted halogen could make the
tropospheric HOx–NOx–O3–VOC chemistry more complex. One relevant
reaction is that XO (X=Cl, Br, and I) shifts the HOx balance towards OH
(Saiz-Lopez and von Glasow, 2012). As a consequence, previous
box-model studies usually showed positive impacts of halogen chemistry on OH
(Stone et al., 2018; Whalley et al., 2010). However, there is an opposite
impact of halogens on OH, as usually shown by chemical transport model (CTM)
studies that halogen species will consume O3, which in turn would reduce
the production of OH (e.g., Sherwen et al., 2016; Stone et al., 2018). For
example, Wang et al. (2021) showed
that the net effect of halogen chemistry on global tropospheric HOx is that
both OH and HO2 are reduced by 3 %–4 %. In a box model, when
long-lived species such as O3 are observation-constrained, it cannot
reflect the complete influence of halogens, which probably explains the
different results between box models and CTMs
(Stone et al., 2018). Therefore, special
attention needs to be paid when using box models to quantify the complicated
impacts of halogen species on the HOx–NOx–O3–VOC chemistry.
To present the global impact quantitatively, a more comprehensive
understanding of the changes by species (sea spray aerosol – SSA, inorganic
iodine, and halocarbons) and their associated processes (chemistry,
radiation, and deposition) is needed in order to better explain relevant
observed or modeled phenomena and their driving factors. However, the
pathways and processes by which halogens influence OH have not been well
quantified in previous studies. Recent studies in understanding the impacts
of halogen chemistry on OH usually focused on the two pre-described pathways
(i.e., enhanced HO2 conversion by XO and O3 consumption by X
atoms). Even though we know all the important pathways, due to their
opposite impacts on OH, we need to further understand the controlling
processes of these pathways in order to better explain the trend of
halogen-induced ΔOH in a specific circumstance. Moreover, since
current estimations of marine halogen emissions, including SSA Cl and Br
ions (and their activations), inorganic iodine (I2 and HOI), and very
short-lived halocarbons, have large uncertainties (Carpenter et al.,
2021; Ordóñez et al., 2012; Ziska et al., 2013; Inamdar et al.,
2020; Lennartz et al., 2015; Zhu et al., 2019; Sekiya et al., 2020; Wang et al.,
2021; Grythe et al., 2014), the net effect of different pathways may be
subject to the uncertainties in the emission estimation and variation in
controlling factors of the various pathways. Therefore, in order to better
understand the role of halogen chemistry in tropospheric OH, we explore the
pathways by which halogen species influence OH, how these pathways
interact with each other, and how they are influenced by different
species-related processes in this study based on current knowledge about
halogen chemistry and marine emissions of halogen species. We carried out
model simulations to quantify the contributions of different pathways by
using a regional CTM (Community Multiscale Air Quality Modeling System,
CMAQ) with process analysis (PA, including integrated process rate, IPR, and
integrated reaction rate, IRR) over East Asia and the western Pacific during
summer. The controlling factors of the strengths of the different pathways,
mainly represented by different species-related processes, are analyzed
based on PA and relevant sensitivity simulations, and their interactions are
discussed. With spatial resolution higher than global models, we also
explore the interaction of anthropogenic emissions with marine halogen
emissions when discussing the controlling factors of the strengths of the
different pathways in iodine chemistry. The emission uncertainties are taken
into consideration by running sensitivity simulations using the largest or
the smallest emission rates that have been used or reported in previous
studies. The setup of the models and the estimations of marine emission and
its extreme uncertainties of halogen species are described in Sect. 2.
Results and discussion are in Sect. 3. Section 4 gives conclusions.
MethodsModel setup
We use the CMAQ model, driven by meteorological fields from the Weather Research
and Forecasting Model (WRF), to explore the impact of halogens on OH. For
WRF (version 3.9.1), the domain has a horizontal resolution of 27 km and the
number of grids is 283 × 184. The vertical coordinates contain 39σ levels up to 50 hPa. The initial and boundary conditions are generated
from the NCEP GDAS/FNL 0.25∘ analysis data. Analysis and
observation nudging are applied. The data used for observation nudging are
obtained from NCEP datasets ds461.0 (for the surface) and ds361.1 (for the upper layer). For major
physical parameterizations, the Rapid Radiative Transfer Model (RRTM)
longwave radiation scheme, the Dudhia shortwave radiation scheme, the WRF
single-moment three-class microphysics scheme, the Noah Land Surface Model, and
the Grell–Freitas ensemble cumulus scheme are applied.
CMAQ (version 5.3.2)
(Appel et al., 2021)
has the same horizontal resolution as WRF, but with a slightly smaller
domain. The vertical layers are the lowest 20 layers plus 6 of the
remaining 19 layers of WRF. The chemical mechanism adopted here is CB6r3m
released in CMAQv5.3, which is updated by adding halogen chemistry to CB6r3
mechanism based on the work of Sarwar and co-workers (Sarwar et al., 2012, 2014, 2015, 2019). Details
of the gaseous reactions and heterogeneous reactions can be found in the
recent work of Sarwar et al. (2019) and
Sarwar et al. (2012) (see also Table S1). A Rosenbrock (ROS3)
solver is used to solve the chemical reactions, and the absolute and relative
error tolerances are set to 10-9 ppm and 10-3, respectively. The
initial and boundary conditions for CMAQ are extracted from a seasonal
average hemispheric CMAQ output file that is obtained from the CMAS data
warehouse (https://github.com/USEPA/CMAQ/blob/master/DOCS/Users_Guide/Tutorials/CMAQ_UG_tutorial_HCMAQ_IC_BC.md, last
access: 6 October 2021). This hemispheric CMAQ used the same chemical
mechanism as ours. The anthropogenic emissions are from MEIC (http://www.meicmodel.org/, last access: 5 May 2022), while the emissions in the Guangdong province are replaced
by local emissions that are based on a local emission inventory (Yin et
al., 2015; Zheng et al., 2009) and processed by the Sparse Matrix Operator
Kernel Emissions (SMOKE) processor. No halogen species are contained in the
anthropogenic emissions. The terrestrial biogenic emissions are processed by
MEGAN2.1 (Guenther et al., 2012). Other routine
configuration setups of the model can be found in Fan et al. (2021). Because the OH concentration is highest in summer, the simulations of
this study are for the month of July 2019, including an additional 10 d in
June for spin-up following Li et al. (2020).
Marine emissions of halogen species
There are three main types of halogen species emitted from the ocean: SSA
(Cl and Br), inorganic iodine (I2 and HOI), and halocarbons including
CHBr3, CH2Br2, CH2BrCl, CHBr2Cl, CHBrCl2,
CH3I, CH2ICl, CH2IBr, and CH2I2 (e.g., Sarwar et
al., 2019; Carpenter et al., 2013; Ordóñez et al., 2012; Wang et al.,
2019). The latest release version of CMAQ (v5.3) contains these emissions
online.
The SSA emission in the current CMAQ is updated by Gantt et al. (2015) on top of the work of Kelly et al. (2010). The
source function is based on the widely used source function developed by
Gong (2003), which is an update of Monahan et al. (1986). Two main
changes were implemented by Gantt et al. (2015). One is to
add a sea surface temperature (SST) correction function to the source function because SST has large
impacts on SSA flux (e.g., Barthel et al., 2019; Liu et al., 2021). The
other is to change the shape factor of the source function (which determines
the shape of the flux distribution) to emit more submicron SSA (see Fig. S1
of Gantt et al., 2015). The SST correction function is
based on the work of Ovadnevaite et al. (2014) and is linear.
This is different from another widely used observation-based SST correction
function developed by Jaeglé et al. (2011), which is a
three-order function of SST, but at high temperature (∼ 30 ∘C) their values are close (see Eq. 2 of Gantt
et al., 2015, and Eq. 4 of Jaeglé et al., 2011). In
addition to these two main changes, surf-enhanced emissions are also reduced
by narrowing the surf zone, which was previously defined as 50 m to the coast
and is now reduced to 25 m as in the study of Gantt et al. (2015).
Inorganic iodine and halocarbons, as well as Br in SSA, are implemented as
by Sarwar and co-workers (Sarwar et al., 2019).
Inorganic iodine emissions are based on the work of
Carpenter et al. (2013), which parameterized the
emission of I2 and HOI as functions of O3 concentration, aqueous
iodine concentration, and surface wind speed (see Eqs. 19 and 20 in the SI
of Carpenter et al., 2013). Halocarbon emissions are
calculated based on the work of Ordóñez et al. (2012), which directly related flux of halocarbons to chlorophyll a (chl a)
concentration.
Current estimations of marine halogen emissions have large uncertainties.
There are many different source functions of SSA, and the difference of the
SSA flux calculated based on these source functions is very large
(Grythe et al., 2014). The parameterizations of aqueous iodine
also have different versions and differ largely (MacDonald et al.,
2014; Sherwen et al., 2019; Chance et al., 2014). The halocarbon emissions are
entirely empirical and have few physical bases. Therefore, it is necessary
to consider the influence of the uncertainty in the emissions in final
results. We design two simulation groups with different emission rates, one
high and one low. The high and low emission rates are taken from previously
used estimations, similar to the work of Sekiya et al. (2020). The low emission rate of SSA is calculated using the source function
in Gong (2003) directly, while the high emission rate uses the source
function modified by Gantt et al. (2015) because adding an
SST correction function is somewhat more important than using different
source functions (Barthel et al., 2019), and the source
function of Gong (2003) (or its modifications) is the most widely used
one. The parameterizations of I2 and HOI emissions are less variable
and only that by Carpenter et al. (2013) is widely
used. However, there are two widely used parameterizations of aqueous iodine
with large differences. Therefore, the low emission rate of I2 and HOI
is calculated using a low concentration of aqueous iodine, taken from
MacDonald et al. (2014), while the high emission rate
uses a high concentration, taken from Chance et al. (2014). The
calculation of halocarbon emissions, which is based on the estimation of
Ordóñez et al. (2012), is constrained by the global
annual flux (Sarwar et al., 2015); therefore, we increase
or decrease halocarbon emissions based on the ratios of global annual
halocarbon fluxes reported by the WMO (Engel et al., 2019) to that in
Ordóñez et al. (2012). The scale factors are
shown in Table S2. The chl a data are obtained from the merged products of
the GlobColour dataset (http://globcolour.info, last access: 6
October 2021) that is developed, validated, and distributed by ACRI-ST,
France.
The emissions of inorganic iodine are accompanied by the consumption of
O3 at the ocean surface. An enhanced O3 dry deposition by oceanic
iodine is usually added (Luhar et al., 2018; Fairall et al., 2007; Luhar et
al., 2017). In CMAQ, this O3 deposition to the ocean is based on the work
of Chang et al. (2004) and uses the oceanic iodine
concentration parametrization by MacDonald et al. (2014)
(Sarwar et al., 2015). We use the aqueous iodine
parameterizations consistent with that in the calculation of inorganic
iodine emissions above.
To investigate the contribution from different species and pathways, we carried out more than eight simulation runs in
total other than the control run
(BASE) in this study. The description of all the simulations and their
differences can be found in Table 1 (see also Tables S3), and the cross-reference between cases and figures in this study is shown in Table S4.
Case design in this study.
Simulation caseSpecies or reactionsaEmission rate and reference(s)BASENo halogen emissions in the domain0BASE_phyAs BASE but excludingb reactionN2O5(g) + Cl(s), corresponding toSSA_phy below0All_highSSAHigh, from Gantt et al. (2015), ≈ Gong (2003) with SST correction fromOvadnevaite et al. (2014)I2 and HOIHigh, Carpenter et al. (2013) parameterization and Chance et al. (2014) aqueous iodineHalocarbonsHigh, Ordóñez et al. (2012) parameterization and enhancement based on Engel et al. (2019)All_lowSSALow, Gong (2003)I2 and HOILow, Carpenter et al. (2013) parameterization and MacDonald et al. (2014) aqueous iodineHalocarbonsLow, Ordóñez et al. (2012) parameterization and diminution based on Engel et al. (2019)SSA (SSA_Cl + Br)Only SSAAs in All_highSSA_ClAs SSA but excluding BrAs in All_highSSA_phyAs SSA_Cl but excludingb the activationreaction N2O5(g) + Cl(s)As in All_highSSA_chemClSSA_Cl - SSA_phy–SSA_chemBrSSA - SSA_Cl–InorgIOnly I2 and HOIAs in All_highInorgI_chemAs InorgI but excluding enhanced O3dry depositionChang et al. (2004) and Sarwar et al. (2015)O3depoInorgI - InorgI_chem–HaloCOnly halocarbonsAs in All_high
a All cases implement full halogen chemistry as in
Sarwar et al. (2019) and Sarwar et
al. (2012) unless otherwise stated.
b The reaction is unchanged but the uptake coefficient of
N2O5(g) and the yield of ClNO2 are set to 0.
Results and discussionPerformance of the model
To evaluate the performance of our models, O3, the key species for OH
primary production, is compared between simulated and observed data over
land (in China) and an island (Yonaguni) just east of Taiwan. The metrics
for evaluation include the average observation (Obs_mean) and
simulation (Sim_mean) values, root mean square error (RMSE),
normalized mean bias (NMB), normalized mean error (NME), correlation
coefficient (r), and index of agreement (IOA). The benchmarks are taken from
the study of Emery et al. (2017). The statistical metrics of
all stations are calculated, and the average values are presented in Table 2.
We evaluate stations in three major polluted areas near the seas in
mainland China, namely the North China Plain (NCP), the Yangtze River Delta
(YRD), and the Pearl River Delta (PRD) (Fig. S1a). For O3 over the
ocean, which is more relevant to this study, we obtain the measurements at
the Yonaguni island (24.467∘ N, 123.011∘ E) to validate
our simulation (data accessible at https://ebas.nilu.no/, last
access: 11 January 2022) (Torseth et al., 2012). It
can be seen that the O3 concentrations are also reasonably simulated
(Fig. S1b). In addition, adding the halogen emissions (especially with low
emission rates) can noticeably lower the bias for the high ozone
concentration (i.e., days before 22 July) and improve the correlation
between observation and simulations, which indicate the potential to improve
the capability of ozone forecast at coastal stations by adding
marine-halogen emissions in regional CTMs. Except NMB in the
YRD, all these values meet the benchmarks (Emery et al.,
2017), which shows that the model performance is comparable to those
applications in different regions in China (J. Gao et al., 2020; Li et al.,
2022; Gao et al., 2022; Yao et al., 2020) and sufficient for our application.
Model performance metrics for 1 h O3 in mainland China and at
Yonaguni island. The benchmarks are taken from Emery et al. (2017).
Note: there is a threshold value of 40 ppbv for observations in mainland
China as recommended by Emery et al. (2017). For data at
Yonaguni no threshold is applied because there is no significant diurnal
cycle of O3 concentration. Numbers in bold face indicate that the values do not meet the benchmarks.
Figure S2 indicates a pretty good performance of the aerosol optical depth
(AOD) stimulation, which is important for the extinction effect of SSA as
discussed in Sect. 3.4.2.
For the relevant halogen species, although the in situ observational data
over the marine area are limited, the model skills of marine halogens could
generally be evaluated by the levels of BrO and IO due to their importance
in halogen chemistry and the availability of ship- and aircraft-based
data as well as satellite remote sensing data (Li et al., 2020; Stone et al.,
2018; Saiz-Lopez and von Glasow, 2012). Observations of BrO and IO
are very rare around the world, especially in East Asian seas. The available
measurements of mean concentrations of BrO in the western Pacific show 1.0, 1.7,
and <0.5 pptv in three flights (Koenig et al., 2017) and 0.69 pptv (Le Breton et al., 2017) during two related
campaigns (CONTRAST and CAST). These values are generally smaller than
measurements in the Atlantic Ocean
(e.g., Read et al., 2008). In
addition, according to the global model results
(Zhu et al., 2019) and satellite remote
sensing (e.g., http://www.doas-bremen.de/bro_from_gome.htm, last access: 4 June 2021), surface BrO
concentrations have large annual variations in the western Pacific, with the
largest values in January and the smallest values in July. On a cruise in
October from Japan to Australia, Großmann et al. (2013) measured IO, showing that the daytime average of the IO concentration
ranges from ∼ 0.5 to ∼ 1.5 pptv, with a typical
daytime value of ∼ 1 pptv. Previous model results showed that
surface IO in the western Pacific has a significant seasonal variation and
peaks in summer (Huang et al., 2020); the difference between
July and October is about 0.2–0.4 pptv according to their Fig. 3m and p.
Therefore, it is expected that our simulation values (in July) will be
slightly larger than the values reported by Großmann
et al. (2013). Moreover, since modeled IO also decreases with height in the
lower troposphere (see Fig. 2 of Huang et al., 2020), the
surface IO is also expected to be slightly larger than the boundary layer
average of IO.
Daytime (local time 08:00–16:00) average of (a) surface BrO, (b)
difference between planetary boundary layer (PBL) and surface BrO for a high
emission rate, and (c) surface BrO for a low emission rate. Panels (d)–(f) are for
IO. The black lines indicate roughly the trajectories of flights or cruises
in previous studies reporting relevant measurements (see Table 3).
Comparison of BrO and IO in the Philippine Sea in our simulations
(surface layer) with observations and simulations reported in other studies.
Mean/max PlatformSimulation, lowSimulation, highObservation or model emission rateemission rateBrO (pptv)0.2/0.9 Jul0.25/1.2 Jul∼1/2.9a,1 Jan and Feb Flights around Guam(line in Fig. 1a–c) ≲ 500 m0.69/1.712 Jan ∼1/>23 Jan∼0.3/>0.63 JulGEOS-Chemsurface layerIO (pptv)1.0/1.8 Jul1.4/2.5 Jul∼ 1/∼ 1.54 Oct Cruise from Japan toAustralia (line inFig. 1d–f) PBLAverage Jul > Oct CMAQby ∼ 0.2–0.45surface layer
a Only data at altitudes below 500 m.
1 Koenig et al. (2017), 2 Le Breton et
al. (2017), 3 Zhu et al. (2019), 4 Großmann et al. (2013), 5 Huang et
al. (2020).
Figure 1 shows the daytime (local time 08:00–16:00) average BrO and IO
simulated in our studies. Due to the lack of observation data in the coastal
seas for comparison, we only discuss the results in the Philippine Sea
(i.e., the open ocean east to the line connecting the Philippines, Taiwan,
and Japan). In this sea, the concentrations of BrO and IO are generally
lower than nearshore areas. The maximum mean values of the daytime BrO and
IO are 1.2 (0.9) and 2.5 (1.8) for high (low) emissions; for the average
over all these grids, the daytime BrO is about 0.25 (0.2) pptv, while IO
is about 1.4 (1.0) pptv for high (low) emission rates. For the boundary layer
average, the values for IO are lower than surface values by ∼ 0.08 pptv for the grid average and by ∼ 0.4 pptv for the grid maximum
in the Philippine Sea in the All_high case (Fig. 1d) (due to the
storage limitation, we did not output upper-layer results in
All_low case). Different from IO, BrO does not decrease with
height in the lower ∼ 500 m in the study of Huang
et al. (2020), and our simulations also show that the boundary layer average of
BrO is slightly larger than surface values by ∼ 0.05 pptv
(Fig. 1b).
Table 3 lists the comparison of the available measurements and global model
results in the area and our model results. It can be seen that our model
results generally agree well with measurements and other model results. It
should be emphasized that the comparison is only indirect and there is a
lack of data for even indirect comparison in the nearshore areas where the
IO concentration is the largest. Since the inorganic iodine emission is
closely related to O3 concentration, which is high in the nearshore
areas due to the outflow from the continent, the higher concentration of IO
is reasonable, and in other regions, observations also support a very high
concentration of IO in nearshore areas (Saiz-Lopez and von Glasow,
2012); nevertheless, relevant observations are expected for a better
validation.
The changes in OH production rate (P<inline-formula><mml:math id="M167" display="inline"><mml:msub><mml:mi/><mml:mrow class="chem"><mml:mi mathvariant="normal">OH</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>) and concentration
Figure 2 illustrates the halogen-induced changes in POH and OH
concentration in All_high (all halogen species high emission
rates) and All_low (all halogen species low emission rates)
cases. ΔPOH and ΔOH, with both high and low emission
rates, have similar spatial distributions but with different magnitudes.
The most significant changes in POH and OH appear in the marine
atmosphere (Fig. 2). The impacts are very complicated, with negative ΔPOH and ΔOH in the middle area of the ocean and positive values in
the northern and southern parts of the ocean in the domain, but the area with
negative impacts is larger than that with positive impacts. The decreases in
OH can reach ∼ 13 % and ∼ 8 % (ΔPOH∼ 15 % and ∼ 10 %) in the
Philippine Sea, and the increase can reach ∼ 11 % and
∼ 9 % (ΔPOH∼ 9 % and
∼ 7 %) in the Bohai Sea, with high and low emission rates,
respectively. This is in line with previous studies that generally showed
a decrease in globally averaged OH but a certain increase in some regions due to
halogen chemistry (e.g., Sherwen et al., 2016; Stone et al., 2018). More
specifically, in the East Asian seas, the studies of
Stone et al. (2018),
Wang et al. (2019) and
Sherwen et al. (2016) generally showed a
slight decrease (≲ 5 %) in annually averaged surface
OH, while Stone et al. (2018) also showed a
slight increase in some regions. For studies in July, the study of
Li et al. (2019) showed a decrease in monthly averaged
surface OH in the Atlantic Ocean near Europe but an increase in the
Mediterranean Sea and the Baltic Sea. The decrease in the Atlantic Ocean can
reach ∼ 20 % in the middle latitudes. In the Indian Ocean,
Mahajan et al. (2021) showed a slight decrease (< 5 %) in monthly averaged surface OH near the Indian subcontinent
increasing (< 10 %) near the Equator, and the area with decreased
OH is larger than that with increased OH in their model domain. In the
coastal areas the absolute changes in POH and OH can be comparable to
or even larger than that over the ocean, but the relative values are
relatively small due to the large absolute value over land (Fig. 2b, d, f,
h). The largest decreases in monthly POH and OH can reach
∼ 3 %–5 % and ∼ 4 %–6 %, respectively (Fig. 2b, f).
Generally speaking, our results are comparable to previous studies, showing
overall negative halogen-induced ΔOH but with a complicated spatial
distribution of negative and positive ΔOH (and ΔPOH),
especially in nearshore areas. Previous studies have qualitatively and
partially explained the reasons why halogens have such a complicated impact
on OH, as the two pathways by which halogens influence OH (i.e., enhanced
HO2 conversion by XO and O3 consumption by X atoms) have opposite
impacts on OH (e.g., Stone et al., 2018).
However, the complicated spatial distribution of negative and positive
ΔOH indicates a complicated interaction of the pathways.
Furthermore, it is unclear whether there are other important pathways by
which halogens influence OH. Therefore, in order to better understand the
impacts of halogens on OH, more specifically to understand why halogens
increase OH in certain regions (especially in nearshore areas) but decrease it in
other regions, we need to find out all possible important pathways and to
further analyze the controlling factors of the strengths of the pathways.
In the following, we will further analyze the causes of such a complicated
distribution. Since the spatial distributions of relative ΔPOH
and ΔOH are very similar despite the small difference in magnitudes,
the OH chemistry is generally discussed in terms of POH in the
literature (e.g., Tan et al., 2019; Hofzumahaus et al., 2009; Whalley et
al., 2021), and we can directly separate different pathways which
influence POH, we will focus on POH in the following. In addition,
because the patterns of ΔPOH and ΔOH (Fig. 2), as well
as the IRR results (Figs. 4 and S3), are quite similar in the
All_high and All_low cases (Fig. 2), we will
mainly focus on cases with high emission rates.
Change or relative change compared to the BASE case in monthly averaged
surface-layer POH and OH in All_high case (first row)
and All_low case (second row). The subscript r denotes
“relative”. Note the different scales in the All_high and
All_low cases; the latter is exactly half of the former.
Quantification of different pathway contributions
As mentioned above, there is complexity in the cause of the ΔPOH. In this section, IRR is used to unravel important chemical
reactions in changing POH. The main sources of OH in the CB6 mechanism
of the CMAQ model include primary sources and secondary sources. Primary sources
include the photolysis of O3 (through the reaction
O(1D)+ H2O, which will not always be explicitly stated in the
following), HONO, and H2O2, as well as ozonolysis of some alkenes. The
secondary source is mainly the reactions HO2+Y (Y=NO, O3,
etc.). With halogen chemistry, an additional source, HOX photolysis, needs
to be considered as HOI can be directly emitted and can be very rapidly
cycled. The changes in POH due to the change in all these sources
(denoted as ΔPOH_XX in the following, where XX
is clear from the context) based on IRR analysis are quantified. According
to the IRR results, we only focus on the main three changes (photolysis of
O3 and HOX, as well as the reaction HO2+Y) (Fig. 3a–c). Since
the changes in other sources are ignorable (Fig. 3d), we do not show them
individually. We denote the halogen-induced change in these sources as
pathways by which halogens influence OH, and therefore there are three main
pathways through which marine-emitted halogens influence POH, i.e.,
POH_O1D, POH_HO2, and
POH_HOX.
In line with previous studies, the results show that the change in O3 and
the addition of HOX are the two most important pathways by which halogens
influence POH (Stone et al., 2018).
ΔPOH caused by the change in O3 and HOX photolysis
(denoted as ΔPOH_O1D and ΔPOH_HOX, respectively) is very large in the northern part
of the ocean in the domain, especially in the Bohai Sea and the Yellow Sea,
which is probably a result of the higher concentration of related species
such as O3 that is commonly reported at high concentrations in the
midlatitude in summer (e.g., M. Gao et al., 2020; Lu et al., 2019b; Hu et
al., 2017). ΔPOH_HOX (Fig. 3f) can reach
4 × 106 cm-3 s-1 (∼ 0.6 ppbv h-1)
for the whole-day average and 1 × 107 cm-3 s-1
(∼ 1.5 ppbv h-1) for the daytime average. Our results
show that HOX is an important source of OH over the ocean (may be compared
to urban-area HONO), but it was generally ignored in the previous HOx budget
studies (e.g., Tan et al., 2019; Hofzumahaus et al., 2009; Whalley et al.,
2021). Therefore, our results indicate the necessity to measure HOX in HOx
budget studies under the potential influence of the marine atmosphere.
Decomposition of Fig. 2a for different pathways: change in POH
(All_high-BASE case) caused by the changes in (a) O(1D)
(ΔPOH_O1D), (b) HO2+Y (ΔPOH_HO2, Y= NO, O3, etc.), and (c) HOX+ hν (ΔPOH_HOX), as well as (d) other pathways (Fig. 2a
minus the sum of a and c). The red and blue color scales are the same in (a)–(d).
In addition to ΔPOH_O1D and ΔPOH_HOX, ΔPOH_HO2 is also
very important to ΔPOH as shown in Fig. 3b. If we considered
only ΔPOH_O1D and ΔPOH_HOX, only a relatively small area close to Taiwan would show
negative ΔPOH, and the general impacts of halogens on OH would
be positive (compare Figs. 2a and S4a).
As mentioned above, the production rate of OH from HOX is very large.
However, this large production rate is canceled by the large decrease in
ΔPOH_O1D and ΔPOH_HO2 (Fig. 3a, e), resulting in the relatively small net ΔPOH
compared to ΔPOH_O1D and ΔPOH_HOX (and even ΔPOH_HO2 for many regions) over the ocean, but this is still significant along the coastal
areas (Fig. 3). It can be seen that the cancel-out effect of the three
pathways with different signs results in the complicated spatial
distribution of ΔPOH, making ΔPOH positive in the
areas with larger ΔPOH_HOX and negative
otherwise. From these three pathways themselves, however, it is difficult to
explain under what conditions ΔPOH_HOX will be
stronger than the other two pathways, and it is therefore difficult to explain why
ΔPOH is generally positive in the nearshore areas but
negative in the open ocean. Then we need to further analyze the details of
the processes influencing the strengths of these three pathways.
Factors influencing the strengths of the three pathwaysOverview of the contributions from different marine-emitted species
There are several factors that can change the strengths of the three main
sources of OH (Fig. S5). Some of these factors are independent, and some are
interrelated. The independent factors include a decrease in O3 photolysis
rate (J(O1D)) and O3 concentration by SSA-induced light extinction
and enhancement of O3 deposition by oceanic iodine. The interrelated
factors are generally closely related to halogen chemistry, the most
important reactions of which are the four below (R1–R4; Saiz-Lopez and von Glasow, 2012; Simpson et
al., 2015).
R1X+O3→XO+O2R2XO+HO2→HOX+O2R3HOX+hν→X+OHR4Cl+VOCs⟶O2RO2+HCl
For convenience, we also list the two reactions producing OH that are
relevant to Reactions (R1)–(R3) and have been mentioned above.
R5aO3+hν→O(1D)+O2R5bO(1D)+H2O→2OHR6aHO2+NO→OH+NO2R6bHO2+O3→OH+2O2
Since these factors just mentioned above are generally species-related, we
separately modeled the impacts of different halogen species in addition to
the case with all emissions (All_high) (Table 1). The results
are shown in Fig. 4. It can be seen that the most significant contributor
to the three pathways is inorganic iodine (Fig. 4e–g). However, the three
pathways cancel each other out to a large extent, and the resultant ΔPOH is relatively small. Nevertheless, the impact of inorganic iodine
is more pronounced than that of all species together (Figs. 2a and 4e). The
contribution of SSA to ΔPOH is notable and comparable to that of
inorganic iodine in most regions. There is a positive contribution of SSA to
ΔPOH in the Bohai Sea and surroundings, while in other regions
the contribution is negative. The negative contribution again neutralizes
the positive contribution of inorganic iodine, resulting in the more
negative ΔPOH in the All_high case (Fig. 2a) than in the
InorgI case (Fig. 4e). The contribution of halocarbons is relatively small
and restricted to a small area near the China coastline. In addition, the
interactions between these three types of emitted species (Fig. 4m) have very
similar impacts with halocarbons (Fig. 4i) but with opposing sign. Since we
only focus on major contributions of different halogen species to ΔPOH, we will not go into the details about the rest of the
interactions of the three types of halogen emissions, and therefore we also
do not discuss the influences of halocarbons in the following as they
roughly cancel out the effects of the interactions. It should be noted,
however, that this does not imply that the interactions are caused by
halocarbons.
Decomposition of Fig. 3 for different halogen species. Panels (a)–(d),
(e)–(h), and (i)–(l) are results for the SSA, InorgI, and HaloC cases,
respectively. Panels (m)–(p) are the residue between All_high and
SSA + InorgI + HaloC, representing the interactions of different halogen
species. The red and blue color scales are the same in (a)–(p).
Physical and chemical contributions of SSA emission
Regarding SSA, whose components are mainly Cl- ions and inert
non-volatile cations (NVCs, including Na+, K+, Ca2+, and
Mg2+), with minor contributions from sulfate and Br- ions, these
emissions (see Fig. S6c,d for the emission rates) could influence OH through
both actinic flux and chemical effects of Cl and Br. The SSA impact on OH
(Fig. 4a–d SSA) is further decomposed into the impact of the extinction
effect of SSA, Cl chemistry, and Br chemistry (Fig. 5). Figure 5 indicates
that the most important factor that determines the negative impact of SSA on
POH (Fig. 4a) is its extinction effect (Fig. 5a–d). The decrease in
actinic flux caused by the extinction effect of SSA can decrease the
photolysis rate constant of O3 (J(O1D)) and the O3
concentration (probably through influencing J(NO2), see
e.g., J. Gao et al., 2020) at the same time (Fig. S6a, b),
which will in turn decrease the OH production from O3 photolysis (Reaction R5).
The negative impacts of Br chemistry are very small compared to this
extinction effect. In addition to the overall large impacts, the importance
of the SSA extinction effect is also embodied in its impact on the
continental atmosphere. As shown in Fig. 4a, e, and i the ΔPOH over
land induced by SSA is the most significant among the three halogen
emissions, and here we know that the relatively large decrease in POH
in southern China is caused by the extinction effect of SSA. The decrease in
monthly POH caused by SSA can reach ∼ 3 % (Fig. 6a)
(hourly ΔrPOH up to 30 % in the daytime). Therefore,
even without halogen chemistry, adding SSA emissions in CTM studies may be
important for atmospheric chemistry.
Another important factor that influences POH is the Cl chemistry (Figs. 5e–h and 6b). Similar to previous studies, Cl chemistry has positive
impacts on ΔPOH because Cl can oxidize VOCs, which can
come from both anthropogenic and oceanic sources (Yu and Li, 2021),
efficiently producing RO2 radicals (Reaction R4) as shown in Fig. 5e (Li et
al., 2020; Wang et al., 2020; Simpson et al., 2015). As such, the change in
POH by Cl chemistry (Fig. 5e) is mostly through the change in OH
production from HO2 (Fig. 5g). The impacts of Cl chemistry are most
significant in the Bohai Sea and surroundings. As shown in Fig. S7, in these
areas, the concentration of ClNO2 (the key species for the activation
of SSA Cl) is higher than other regions (Fig. S7a) and the Cl reactivity
(kCl, =∑kCl+VOC× [VOC]) is very high, resulting
in the larger impact of the Cl chemistry. ClNO2 is a product of
N2O5 with particulate Cl, and N2O5 is a product of
NO2 and the NO3 radical (e.g., Yu et al., 2020). Therefore, the
larger impacts of Cl chemistry in the Bohai Sea and surroundings probably
reflects the influence of higher NOx in the area. The impact of Br chemistry
on POH is quite small in general (Fig. 5i–l), and we will not discuss
it further (see more discussion about Br chemistry in Sect. 3.5).
Nevertheless, the results from Br chemistry emphasize the importance of
pathway POH_HO2 in interpreting the roles of halogens in
influencing HOx cycling: when considering the influence of halogens on HOx, it
is believed that XO will shift the HOx balance to OH in general (e.g., Li
et al., 2020; Stone et al., 2018; Saiz-Lopez and von Glasow, 2012). But these
results are derived without considering the indirect influence of halogens
on Reaction (R6) (i.e., pathway 2, POH_HO2, in our study). In our
study, when the inhibition of halogen chemistry on the HO2 conversion
to OH through HO2+Y is considered (Reaction R6), only IO can uniformly enhance
the HO2 conversion and BrO cannot in some areas (compare Fig. 5k and
l, or see Fig. S4b), which is probably because of the lower reactivity of BrO
with HO2 (see also discussion in Sect. 3.4.2 of
Whalley et al., 2010).
In regards to the three main pathways discussed in Sect. 3.3, the physical
contribution of SSA is achieved not only through the decrease in the
photolysis of O3 (J(O1D) and the O3 concentration) (Fig. 5b),
but also through the decrease in HO2 conversion to OH (Fig. 5c), which
is probably feedback induced by the decrease in O3 photolysis because
HO2 production is less influenced by photolysis change. In contrast,
the chemical contribution of SSA Cl is achieved through the increase in
RO2 that can rapidly react with NO to form HO2 (Seinfeld and
Pandis, 2016), and therefore the second pathway, OH from HO2, is more
prominent (Fig. 5g), while the increase in O3 concentration is of minor
importance (Fig. 5f).
Decomposition of Fig. 4a–d (SSA): the changes in POH,
POH_O1D, ΔPOH_HO2, and
ΔPOH_HOX caused by (a)–(d) the extinction effect
of SSA, denoted as SSA_phy, (e)–(h) Cl chemistry (only the
activation of Cl through ClNO2), and (i)–(l) Br chemistry.
Relative change in POH compared to BASE caused by (a) SSA
extinction effect, (b) SSA Cl chemistry, (c) enhanced O3 deposition by
aqueous iodine, and (d) atmospheric inorganic iodine chemistry.
Physical and chemical contributions of inorganic iodine species and
the interactions between O<inline-formula><mml:math id="M416" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> and iodine chemistry
Regarding the contributions from inorganic iodine species to ΔPOH, the indirect effects from the enhanced O3 deposition by
iodine ion in the ocean (e.g., Pound et al., 2020) and
atmospheric iodine chemistry (including the direct emission of HOI) should
be considered. The change in POH caused by the enhanced O3
deposition is shown in Fig. 7a. This O3-deposition-induced decrease in
POH is most significant in the Bohai and Yellow Sea, where it can reach
∼ 0.4 × 106 cm-3 s-1, corresponding to
∼ 6 % (hourly ΔrPOH can reach > 30 % in the daytime) in the Yellow Sea relative to POH in the BASE
case (Fig. 6c). The larger absolute decrease in POH (Fig. 7a) is
probably caused by the higher deposition of O3 in the Bohai and Yellow
Sea because of the higher O3 concentration there as mentioned above
(see also Fig. 8). For the relative change, the decrease in POH is most
significant in the Sea of Japan, where the relative decrease can reach more
than 10 % (hourly ΔrPOH can reach > 45 % in
the daytime) (Fig. 6c). By decomposing to different pathways, the decrease
in POH induced by O3 deposition is caused by the decrease in
O3 (POH_O1D, Fig. 7b) and by the decrease in HOX
photolysis (POH_HOX, Fig. 7d), which probably results
from the slower cycling of HOI through Reactions (R1)–(R3) due to the lower
O3 concentration.
Decomposition of Fig. 4e–h (InorgI): the changes in net OH
production rate caused by (a)–(d) the enhanced O3 deposition by oceanic
iodine ions and (e)–(h) atmospheric iodine chemistry (including direct HOI
emission). The red and blue color scales are the same in (a)–(h).
Regarding the contributions from the atmospheric iodine chemistry to ΔPOH, it has a generally positive impact on POH except in a limited
area (Figs. 6d and 7e). More specifically, the decrease in ΔPOH is relatively significant in the Philippine Sea (up to ∼ 8 % of the BASE case). The positive ΔPOH in the Bohai and Yellow
Sea can reach more than 10 % of BASE case, and the relative increase can
reach more than 15 % in the Sea of Japan. The changes in POH caused
by the atmospheric iodine chemistry from different pathways are shown in
Fig. 7e–h. It can be seen in Fig. 7 that the contributions of the three pathways
are all significantly influenced by iodine chemistry. Different from Cl
chemistry, iodine chemistry can both increase POH via Reaction (R3) (Fig. 7h) and
decrease POH via Reactions (R1)–(R2) (Fig. 7f–g).
Monthly average of parameters related to inorganic
emission in the All_high case. (a) HOI emission rate, (b) I2
emission rate, (c) HOI concentration, (d) O3 deposition velocity over
the ocean, (e) seawater iodine ion concentration, and (f) O3
concentration. Note the different scales of (a) and (b). Panel (d) is comparable
to the values in the study of Pound et al. (2020), with an annual
average O3 deposition velocity of about 0.03 cm s-1, and in summer the
deposition velocity is close to the annual average in the western Pacific; see
their Figs. 2b and 3c.
The negative change in POH caused by atmospheric iodine chemistry in
the Philippines Sea shown in Fig. 6d is interesting, and to our knowledge
there is no study previously published detailing the cause of both
the positive and negative impacts of iodine chemistry
(e.g., Stone et al., 2018). To investigate
the factors driving the spatial variability of iodine-induced OH production,
the monthly averaged values of the parameters related to
inorganic emission in the All_high case are plotted in Fig. 8. As
expected, the pattern of ΔPOH does not coincide with the
patterns of seawater iodine concentration (Fig. 8e) and the ozone deposition
velocity (Fig. 8d), which decrease with latitude due to the increase in
temperature. The location of the area with negative ΔPOH
roughly coincides with the area with lower emission rates of HOI and I2
(Fig. 8a, b) and concentrations of HOI (Fig. 8c) and O3
(Fig. 8f). The positive change in POH could be directly attributable
to the effect of iodine chemical cycling, but what are the causes of the
negative change in POH? To figure out the causes, we conduct
sensitivity simulations to analyze the contribution of I2 and HOI emissions
as well as the O3 concentration to the negative ΔPOH.
First, we find that the emission of HOI as a primary OH source is not the
cause. The PA results show that the emission rate of HOI is much smaller
than that of the HOI cycling rate (Fig.S8a, b). Moreover, the change in
POH_HOX by replacing direct HOI emission with an
equivalent amount of I2 using the emission scaling technique developed
by Murphy et al. (2021) is relatively
ignorable (Fig. S8c). Even without direct HOI emission (i.e., replaced by
I2) there can also be negative ΔPOH in the area (Fig. S8d). Second, we show that the lower emission rate of inorganic iodine
(emitted as HOI or I2) in the Philippine Sea is probably not the cause
of the negative ΔPOH there by two sensitivity runs (addressing
the impacts of the absolute value and the spatial distribution of the
emissions, respectively). When reducing HOI and I2 emission rates by 50 %
uniformly, the area of negative ΔPOH does not increase (Fig. S9a, b), indicating that fewer available iodine atoms do not necessarily
enlarge the area of negative ΔPOH in the Philippines
Sea. Furthermore, when we set HOI and I2 emission rates in the
whole domain to constants (6.86 =5000/272µmole km-2 s-1 for HOI and 1/20 of HOI for I2; see Fig. 8a and b for comparison),
which are between the emission rates in the Philippine Sea and that in the
Bohai Sea, the pattern of ΔPOH is very similar to that in the
InorgI_chem case (Figs. S9e and 7e). This similarity strongly
suggests that the distribution of inorganic iodine emission is also not
important to determine the positive or negative pattern of
iodine-chemistry-induced ΔPOH.
Is it the complex interaction between the marine iodine chemistry and the
continental atmospheric pollution that leads to the special negative ΔPOH in the Philippine Sea? Third, to investigate whether this is
true, we conduct simulations increasing the O3 concentration downwind of
the Philippines (Fig. S10a) by increasing NOx and VOC emission rates in the
Philippines by a factor of 5. The iodine-chemistry-induced change in
POH is negative in most of the Philippines Sea but positive near the
land (Fig. S10c). This characteristic results from the different
distributions of ΔPOH_O1D (Fig. S10d) and
ΔPOH_HOX (Fig. S10e) along the O3 plume:
the former is relatively evenly distributed, while the latter weakens
gradually along the plume. The gradual decrease in ΔPOH_HOX (via Reaction R2) along the plume path is similar to the
O3 distribution (Fig. S10a) because the HOI cycle is essentially local
due to the very high cycling rate, leading to the local impact of
POH_HOX. In contrast, ΔPOH_O1D caused by the O3 consumption via Reaction (R1) does not gradually weaken
along the plume as the O3 concentration and ΔPOH_HOX do, which is because the decrease in POH_O1D (via Reaction R1) is not only controlled by the local O3 consumption but also
the upwind O3 depletion along the plume. To confirm the impact of the
upwind O3 depletion by iodine (Reaction R1) in the Philippine Sea, the ratio
of the total O3 concentration decrease to the local O3 loss rate is shown
in Fig. 9c. Indeed, the ratio has maxima in the Philippine Sea where the
iodine-chemistry-induced ΔPOH is negative (Fig. 7e),
indicating an “excessive” decrease in O3 compared to its local
consumption in the area (Fig. 9), which can only be explained by the gradual
enhancement of the upwind O3 depletion by iodine along the plume paths
(as the iodine atom is the only significant consumer of O3 in the InorgI_chem case compared to the BASE case).
Iodine-chemistry-induced change in (a) daytime (LT
08:00–16:00) O3 concentration and (b) production rate of the effective
iodine atom (iodine atom that can consume O3, including all reactions
that can produce the iodine atom except IO photolysis and
reactions which produce NO2; ≈ O3 loss rate by local
iodine chemistry). (c) The ratio between (a) and (b), only for qualitative
illustration. The large negative values in the Philippine Sea of (c) indicate the large “excessive” decrease in O3 compared to its local
consumption.
As such, we can conclude that whether the iodine-chemistry-induced ΔPOH is negative or positive over the ocean is determined
by the relative abundance of O3 and the transport length of
the O3-abundant air mass before it reaches the target ocean areas as the
accumulation of O3 decrease depends on both speed and time of the
accumulation, and the latter is possibly more important (Fig. S10). In the
Philippine Sea area, O3 concentration is low, and O3 is generally
transported far away from the Philippines and the southeastern boundaries,
indicating the possibility to experience significant consumption by iodine
when the “fresh” O3 arrives there (see Fig. S10c as an example);
therefore, it is possible for the dominant effect to be the upwind
O3 depletion by iodine along the plume paths, which results in an
overall negative iodine-chemistry-induced ΔPOH. On the
other hand, the O3 in the Chinese seas and the Sea of Japan has different
sources and higher concentrations than that of Philippine areas,
possibly resulting in a shorter transport path length before the air mass
arrives at ocean areas, and there is therefore a relatively prominent effect of iodine
cycling and a relatively weak effect of the upwind O3 depletion by
iodine along the plume paths, which results in an overall positive
iodine-chemistry-induced ΔPOH. In short, the negative
ΔPOH in part of the Philippine Sea results from the more
important contribution from the effect of the O3 depletion
(local + upwind) than the influence of the additional local production of OH
from HOI photolysis.
Summary of the influences of the factors
In summary, we can conclude that marine-emitted halogen species can
result in a complicated change in POH in East Asia (Fig. 2), with
negative ΔPOH in most areas of the ocean but positive in the
northern and southern parts, especially in the nearshore areas. IRR analysis
results show that the changes in photolysis of O3 and HOX and the
reaction HO2+Y are the main contributors to ΔPOH (Fig. 3). These three pathways are influenced by different factors related to
different species. For the photolysis of O3, both SSA and iodine can
significantly decrease it, but with different mechanisms. SSA mainly
influences photolysis through a physical factor, by extinction of solar
radiation, which in turn can decrease both J(O1D) and O3
concentration. Inorganic iodine can only decrease O3 concentration
through enhanced O3 deposition and the atmospheric destruction of
O3, but to a much larger extent than that caused by SSA. For the
photolysis of HOX, only the cycling of HOI has a significant contribution.
For the conversion of HO2 to OH, IO will compete with NO and O3 to
consume HO2, resulting a significant decrease in the conversion, while
SSA can also lead to a decrease in the conversion, probably through more
complicated feedbacks.
By influencing the strengths of the three pathways, these factors determine
the pattern of the net ΔPOH. More specifically, the basic
pattern of ΔPOH, with the largest relative decrease in the
Philippine Sea (Fig. 2b), is controlled by the atmospheric iodine chemistry,
which shows a negative ΔPOH value only there (Fig. 6d), while the
other marine areas can be roughly seen as the competition between the
relatively evenly distributed negative ΔPOH and the positive
iodine-chemistry-induced ΔPOH. In the Philippine Sea, the
O3 concentration decrease is more than consumed locally, resulting
in an “excessive” decrease in O3 and therefore negative ΔPOH. The excessive decrease in O3 illustrates the effect of
accumulated upwind O3 depletion by iodine, which results from the
high concentration of O3 and possibly the long transport path length of the
O3-abundant air mass before reaching the location; whether the emission
rates of HOI / I2 in the area are low or high is not important. Other
areas show more influence of local chemical consumption of O3 (Fig. 9c), which is generally accompanied by the more efficient HOI cycling (and
OH production) and therefore positive iodine-chemistry-induced ΔPOH.
In the Bohai Sea, the chemistry of SSA Cl also plays a role in increasing
POH, probably due to the higher concentrations of NOx (for Cl
activation) and VOCs (for RO2 production) there. Similarly, the NCP
also shows a noticeable contribution of SSA Cl chemistry to ΔPOH,
but the impact is overshadowed by the negative contribution of the SSA extinction
effect to ΔPOH (Figs. 4a, 5a,e).
Limitations
There are several limitations in our investigation. Our results rely heavily on
the current halogen chemistry in CTMs, which is still under development. The
uncertainty in the Cl activation and its oxidations of VOCs may have larger
impacts, but the recent update of N2O5 uptake in China does not
improve Cl chemistry significantly (Dai et al., 2020), and
due to the complexity of VOC reactions, there are very few studies focused
on the updates of Cl–VOC chemistry. More studies on the parameterization of
Cl activation and following Cl–VOC reactions are needed. Recent GEOS-Chem
studies improved the uptake of HOBr substantially, but the two major
revisions have opposite effects on BrO: increasing HOBr uptake by using more
sophisticated parameterizations (Schmidt et al.,
2016) and decreasing Br2 yield by adding competition reactions of HOBr
with S(IV) (Zhu et al., 2019; Chen et al., 2017; Wang et al., 2021). The
uptake of HOBr in our study is simply parameterized with a constant reactive
uptake coefficient of 0.1 (Sarwar et al., 2019),
which may result in a lower debromination rate. However, since Br chemistry
influences OH mainly through the consumption of O3
(Stone et al., 2018), the constraint on
modeled BrO is sufficient for our purpose. As shown in Sect. 3.1, modeled BrO is
comparable to previous studies (Zhu et al.,
2019), indicating the update of HOBr chemistry may not be critical to our
results, but more measurements of BrO with seasonality information are
needed for further evaluation of the impacts of Br chemistry. Furthermore,
the fact that exclusion of SSA debromination through HOBr + Br-
reaction in several previous GEOS-Chem studies (Sherwen et al.,
2016; Stone et al., 2018; Schmidt et al., 2016) does not decrease BrO burden
(Zhu et al., 2019) indicates that there are
more complicated interactions between different reactive bromine species
(Bry) and more careful checks are needed. The largest limitation comes
from the iodine chemistry because it is the main contributor to the change
in POH through different pathways. A recent observation study reported
a much faster uptake of HOI and release of ICl and IBr (Tham et al.,
2021), which may have large impacts on the cycling of HOI. In particular,
since the photolysis of ICl and IBr is faster than that of HOI, the iodine atom
would be more rapidly recycled and O3 would be more efficiently
consumed (Tham et al., 2021) but without producing OH. At the same time,
OH production from HOI photolysis would be slower since HOI is more
efficiently removed from the system. As a result, the impact of iodine
chemistry on OH would be more negative (Kanaya et al., 2002).
Related to the iodine chemistry, the enhancement of O3 deposition to
the ocean is also not satisfactorily parameterized (Loades et al.,
2020; Luhar et al., 2018; Pound et al., 2020). Therefore, incomplete
halogen chemistry may limit the representativeness of our results but
probably result in a larger impact of halogen chemistry on OH.
On the other hand, the uncertainties in the emission estimations cannot be
fully described by using different emission rates, since some discrepancy
could be driven by spatial variability of emissions. For example, a new
dataset of gridded iodide concentration produced using machine-learning
methods based on observations has recently become available
(Sherwen et al., 2019), showing different average concentrations and spatial distributions from the two parameterizations used
in this study (Chance et al., 2014; MacDonald et al., 2014). Future
studies focusing on the impacts of iodine chemistry should include
the new dataset, though the reported iodide values by Sherwen et al. (2019)
lie between those calculated values used in this study. In addition, the
emissions of halocarbons are less understood than SSA and inorganic iodine,
and simply scaling the emission rates using global annual fluxes may not
capture the variations of emission rates in different areas well. In our
domain, because the observations are very sparse, the constraints on the
emission estimations are very weak, and more studies are needed to better
characterize the halocarbon emissions, especially in the tropical western
Pacific, which is potentially important for stratospheric injection.
Conclusions
To examine the impacts of gas and particle exchanges between the ocean and atmosphere
on the regional air oxidation capacity, we explore the impact of
marine-emitted halogen species on atmospheric OH in East Asia in summer. The
net ΔPOH caused by all marine-emitted halogen species has both
positive and negative signs in the marine atmosphere, and the positive
ΔPOH appears mainly at nearshore areas. The monthly POH is
generally decreased over the ocean, with maxima of 10 %–15 % in the
Philippine Sea, but is increased in many nearshore areas, with maxima of
7 %–9 % in the Bohai Sea. In the coastal areas of southern China, the
monthly change in POH can be comparable to or even larger than that
over the ocean, though the relative values are relatively small (up to
3 %–5 %) due to the large absolute value over land. These results indicate a
notable impact of marine-emitted halogens on atmospheric oxidation capacity,
which could have significant implications for the lifetime of long-lived
species such as CH4 (one of the major greenhouse gases) in the long term
and the quantity of air pollutants such as O3 in episodic events.
IRR analysis shows that the net effect of ΔPOH is controlled by
the competition of three main pathways through different halogen species,
while the contributions of other pathways are minor. In addition to the
two well-known pathways involving changes in the photolysis of O3
and HOX, the competition on HO2 of XO with NO and O3 also
significantly changes the OH production rate. These three main pathways are
influenced by different factors that are related to different halogen
species. SSA and inorganic iodine gases have the most significant impacts on
POH. In this study, in addition to the chemical impacts, the physical
impacts of the marine-emitted halogens on OH are also explicitly and
quantitatively examined. More specifically, SSA and inorganic iodine
decrease POH through physical processes, including the extinction effect
of SSA and the enhancement of ozone deposition by oceanic iodine, while
generally increasing POH through chemical processes among which the Cl
(from SSA) and inorganic iodine chemistry are the most important. The
physical impacts are quite comparable to the chemical impacts. On the
continent, SSA is the controlling species and its extinction effect leads to
the negative ΔPOH in southern China. In the ocean
atmosphere, inorganic iodine gases are more important as the complicated
iodine chemistry determines the basic pattern of ΔPOH. It is
the competition between iodine's enhancing effect on the conversion of
HO2 to OH and iodine's decreasing effect on OH production from O3
that determines the sign of iodine-chemistry-induced ΔPOH. The
relative strengths of these two opposing effects are controlled by the
O3 concentration and the transport path length of an O3-abundant air
mass over the ocean, which determine the relative importance of accumulated
upwind O3 depletion by iodine (negative effect) compared to the local
iodine cycle (positive effect).
Although the uncertainties in estimating the emission rates of different
halogen species could influence the magnitude and even the distribution
of the halogen-induced change in POH, the response of the main
contributors of POH to the individual species and pathway as well as their
influencing factors have been quantified, which explains the spatial
variability of halogen-induced ΔPOH over East Asia and can also
be applied in other circumstances (e.g., different domains, regions, and
emission rates).
Data availability
Hourly O3 concentration data in mainland China were obtained from
the national air quality monitoring network (https://air.cnemc.cn:18007/, China National Environmental Monitoring Center, 2022).
NCEP datasets are available at https://rda.ucar.edu (NCAR, 2022). The chl a data can be
downloaded from the merged products of the GlobColour dataset (https://globcolour.info/, ACRI-ST, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-7331-2022-supplement.
Author contributions
YL designed the study and wrote the paper. SF ran the simulations,
conducted analyses, and wrote the paper.
Competing interests
The contact author has declared that neither they nor their co-author has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the editor and three anonymous reviewers for their valuable
comments. We thank the principal investigators of the AERONET sites used in
this study for maintaining their stations. We thank Yousuke Sawa for
maintaining the Yonaguni station and the Center
for Computational Science and Engineering at Southern University of Science
and Technology.
Financial support
This research has been supported by the Guangdong Basic and Applied Basic Research Fund Committee (grant no. 2020B1515130003), the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou, grant no. GML2019ZD0210), the National Natural Science Foundation of China (grant nos. 41961160728, 41575106, and 42105124), the Shenzhen Science and Technology Program (grant no. KQTD20180411143441009), the Key-Area Research and Development Program of Guangdong Province (grant
no. 2020B1111360001), the Shenzhen Key Laboratory Foundation (grant no. ZDSYS20180208184349083), and the Guangdong Province Science and Technology Planning Project of China (grant no. 2017A050506003).
Review statement
This paper was edited by John Orlando and reviewed by Daniel Stone and two anonymous referees.
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