This study uses a chemistry–climate model CHASER (MIROC)
to explore the roles of heterogeneous reactions (HRs) in global tropospheric
chemistry. Three distinct HRs of N2O5, HO2, and RO2 are
considered for surfaces of aerosols and cloud particles. The model
simulation is verified with EANET and EMEP stationary observations; R/V Mirai ship-based data; ATom1 aircraft measurements; satellite observations
by OMI, ISCCP, and CALIPSO-GOCCP; and reanalysis data JRA55. The
heterogeneous chemistry facilitates improvement of model performance with
respect to observations for NO2, OH, CO, and O3, especially in the
lower troposphere. The calculated effects of heterogeneous reactions cause
marked changes in global abundances of O3 (-2.96 %), NOx
(-2.19 %), CO (+3.28 %), and global mean CH4 lifetime
(+5.91 %). These global effects were contributed mostly by
N2O5 uptake onto aerosols in the middle troposphere. At the
surface, HO2 uptake gives the largest contributions, with a
particularly significant effect in the North Pacific region (-24 %
O3, +68 % NOx, +8 % CO, and -70 % OH), mainly
attributable to its uptake onto clouds. The RO2 reaction has a small
contribution, but its global mean negative effects on O3 and CO are not
negligible. In general, the uptakes onto ice crystals and cloud droplets
that occur mainly by HO2 and RO2 radicals cause smaller global
effects than the aerosol-uptake effects by N2O5 radicals
(+1.34 % CH4 lifetime, +1.71 % NOx, -0.56 % O3,
+0.63 % CO abundances). Nonlinear responses of tropospheric O3,
NOx, and OH to the N2O5 and HO2 uptakes are found in the
same modeling framework of this study (R>0.93). Although all HRs
showed negative tendencies for OH and O3 levels, the effects of
HR(HO2) on the tropospheric abundance of O3 showed a small
increment with an increasing loss rate. However, this positive tendency
turns to reduction at higher rates (>5 times). Our results
demonstrate that the HRs affect not only polluted areas but also remote
areas such as the mid-latitude sea boundary layer and upper troposphere.
Furthermore, HR(HO2) can bring challenges to pollution reduction
efforts because it causes opposite effects between NOx (increase) and
surface O3 (decrease).
Introduction
Heterogeneous reactions (HRs) on the surfaces of atmospheric aerosols and
cloud droplets are regarded as playing crucial roles in atmospheric
chemistry. They affect ozone (O3) concentrations in various pathways
via the cycle of odd hydrogen (HOx) and nitrogen oxides (NOx)
(Jacob, 2000). Tropospheric ozone, an important greenhouse gas, causes
damage to human health, crops, and ecosystem productivity (Monks et al.,
2015). Although tropospheric O3 was recognized as a critical oxidant
species, its global distribution has not been adequately captured to date
because of the limited number of observations. Whereas many sites in the
heavily polluted regions of eastern Asia show ozone increases since 2000
(Liu and Wang, 2020), many sites in other regions show decreases (Gaudel et
al., 2018). Moreover, O3 responds to changes of multiple pollutants
such as NOx and volatile organic compounds (VOCs) in different ways, which challenge the local
pollutant control policy. For instance, since the Chinese government
released the Air Pollution Prevention and Control Action Plan in 2010 (Zheng
et al., 2018), the targets of SO2, NOx, and particulate matter
(PM) decreased drastically, but urban ozone pollution has been worsening
(Liu and Wang, 2020). Indeed, the O3 responses are controlled by
several mechanisms, including heterogeneous effects of HO2 and
N2O5 onto aerosols (Kanaya et al., 2009; Li et al., 2019; Liu and
Wang, 2020; Taketani et al., 2012).
Stationary observations and laboratory experiments are important for
enhancing the understanding of the tropospheric chemistry of O3 and
other essential components (NOx, HOx). However, direct observation
of vertical O3 distribution, including upper tropospheric O3, was
not available before 1970. It has been deployed only at limited sites across the
globe. Global atmospheric modeling is a useful method to reanalyze or
forecast the past and future changes in O3 and their effects on human
health and the climate. To serve this task, atmospheric models use both
laboratory and observational data to help achieve accurate simulations of
O3 and its precursors (HOx, NOx, hydrocarbons). To date, many
modeling studies have suggested that heterogeneous chemistry be included in
a standard model for tropospheric chemistry (Jacob, 2000; Macintyre and
Evans, 2010, 2011; de Reus et al., 2005).
One fundamentally important HR in the troposphere is the uptake of
N2O5 onto aqueous aerosols, known as a removal pathway for
NOx at night (Platt et al., 1984). Actually, NOx plays crucially
important roles in the troposphere because it controls the cycle of HOx
and the production rate of tropospheric O3 (Logan et al., 1981;
Riemer et al., 2003). The morning photochemistry can be affected by NO3
and N2O5, which are important nocturnal oxidants. Since the early
1980s, the role of urban NOx chemistry in Los Angeles pollution
(National Research Council, 1991) has been acknowledged, but the
proclamation of nighttime radicals remained sparse. It was only recognized
in the past decade that N2O5 radical chemistry could have a much
more perceptible effect stemming from reasons including a refined
understanding of heterogeneous processes occurring at night (Brown and
Stutz, 2012). The HR of N2O5 was revealed under different
meteorological conditions in the US, Europe, and China (photosmog, high
relative humidity (RH), or seasonal variation) for particles of various
types: ice, aqueous aerosols with organic-coating, urban aerosols, dust, and
soot (Apodaca et al., 2008; Lowe et al., 2015; Qu et al., 2019; Riemer et
al., 2003, 2009; Wang et al., 2018, 2017; Xia et al., 2019).
The uptake of N2O5 can markedly enhance nitrate concentration in
nocturnal chemistry or PM2.5 explosive growth events in summer,
decrease NOx, and either increase or decrease O3 concentrations in
different NOx conditions (Dentener and Crutzen, 1993; Qu et al., 2019;
Riemer et al., 2003; Wang et al., 2017). Even during daytime, N2O5
in the marine boundary layers can enhance the NOx to HNO3
conversion, and chemical destruction of O3 (Osthoff et al., 2006).
A 10–20 ppbv reduction of O3 because of N2O5 uptake in the
polluted regions of China has also been reported (Li et al., 2018). At mid-
to high latitudes, N2O5 uptakes on sulfate aerosols could engender
80 % and 10 % NOx reduction, respectively, in winter and summer,
leading to an approximate 10 % reduction of O3 in both seasons (Li et
al., 2018).
Another vital process taking place on particles is the HRs of peroxy
radicals (HO2 and RO2). Peroxy radicals are the primary chain
carriers driving O3 production in the troposphere. Moreover, it can
drive the hydrocarbon and NOx concentrations, which are important for
nocturnal radical chemistry (Geyer et al., 2003; Richard, 2000; Salisbury et
al., 2001). In the past, the HR(HO2) effects have been well considered
in the laboratory (Macintyre and Evans, 2011) and field observations (Kanaya
et al., 2001, 2002a, b, 2003, 2007; Taketani et al., 2012), but many
technical problems (e.g., detecting HO2) have created difficulties that
challenge its reported importance in the troposphere, as asserted from
recent studies (Liao and Seinfeld, 2005; Martin et al., 2003; Tie et al.,
2001). More recently, global modeling reports have described that the
inclusion of HO2 uptake can affect atmospheric constituents strongly by
the increment in tropospheric abundances for carbon monoxide (CO) and other
trace gases because of reduced oxidation capacity (Lin et al., 2012;
Macintyre and Evans, 2011). The HOx loss on aerosols can reduce O3
concentrations by up to 33 % in remote areas and up to 10 % in a smog
episode (Saathoff et al., 2001; Taketani et al., 2012). The HOx loss on
sea salt, sulfate, and organic carbon in various environments can decrease
HO2 levels by 6 %–13 %, 10 %–40 %, and 40 %–70 %, respectively
(Martin et al., 2003; Taketani et al., 2008, 2009; Tie et al., 2001). For
RO2 with a typical representative of CH3CO.O2 (peroxyacetyl
radical, PA), it plays a big role in the long-range transport of pollution
(VOC, NOx) (Richard, 2000; Villalta et al., 1996). It can bring
NOx from polluted domains as peroxyacyl nitrates (PAN) to remote regions in the ocean and
higher altitudes (Qin et al., 2018; Richard, 2000). The concentrations of
HO2 and RO2 at nighttime in the marine boundary layer were
measured and confirmed (Geyer et al., 2003; Salisbury et al., 2001).
Moreover, some evidence suggests uptake of HO2 and PA on clouds,
aqueous aerosols, and other surfaces in high-humidity conditions, although
the mechanism is uncertain (Geyer et al., 2003; Jacob, 2000; Kanaya et al.,
2002b; Liao and Seinfeld, 2005; Lin et al., 2012; Richard, 2000; Salisbury
et al., 2001). The predominance of peroxy uptake to clouds results from the
ubiquitous existence and larger SAD maxima of cloud droplets in the
atmosphere. Indeed, aqueous-phase chemistry might represent an important
sink for O3 (Lelieveld and Crutzen, 1990). In addition, PA loss on aqueous
particles can mediate the loss of PAN (CH3CO.O2NO) in fog
(Villalta et al., 1996). Some modeling studies indicate that HOx loss
(including HO2 loss) on aqueous aerosols reduces OH by 2 % ,
increases CO by 7 % and increases O3 by 0.5 % in the annual mean global burden (Huijnen et al., 2014). However, in a coastal
environment in the Northern Hemisphere it increases OH by 15 % and reduces
HO2 by 30 % (Sommariva et al., 2006; Thornton et al., 2008).
Although the contributions of each uptake category to tropospheric chemistry
differ and must be considered both separately and as a whole, few studies
have provided a global overview of heterogeneous chemistry the
comprehensively examines the uptakes of N2O5, HO2, and
RO2 on widely various particles. For instance, uptakes of both
N2O5 and HO2 tend to reduce O3 in particular
environments (Li et al., 2018; Saathoff et al., 2001; Taketani et al.,
2012), but the HO2 loss on clouds can increase the tropospheric O3
burden (Huijnen et al., 2014). The latter trend is not widely suggested yet
because the cloud chemistry is still neglected in many O3 models
(Stadtler et al., 2018; Thornton et al., 2008). The predominant effects of
HO2 uptake on aerosols compared to the effect by N2O5 were
reported during the summer smog condition (Saathoff et al., 2001) but with
lack of confirmation on a global scale. Moreover, the heterogeneous effects
of RO2 have been investigated only insufficiently (Jacob, 2000). In
this study, we examine these uncertainties using the global model CHASER to
perceive the respective and total effects of the HRs of N2O5,
HO2, and RO2 on the tropospheric chemistry. For the interface of
HRs in the atmosphere, we tentatively consider surfaces of cloud particles
and those of aerosols and discuss details of its effects in this study. In
the following text, the research method, including model description and
configuration, is described in Sect. 2. In Sect. 3.1, our model is
verified with available observations including ground stations and
ship, aircraft, and satellite measurements, particularly addressing the roles
of the HRs. The global effects of N2O5, HO2, and RO2
uptake are discussed in Sect. 3.2 to elucidate cloud particles and aerosol
effects. Section 3.3 will discuss sensitivities of tropospheric chemistry to
the magnitudes of HRs. Section 4 presents a summary and concluding remarks.
MethodGlobal chemistry model
The global chemistry model used for this study is CHASER (MIROC-ESM) (Sudo
et al., 2002; Sudo and Akimoto, 2007; Watanabe et al., 2011), which considers detailed
photochemistry in the troposphere and stratosphere. The chemistry component
of the model, based on CHASER-V4.0, calculates the concentrations of 92
chemical species and 262 chemical reactions (58 photolytic, 183 kinetic, and
21 heterogeneous reactions including reactions on polar stratospheric clouds); more details on
CHASER can be found in an earlier report of the literature (Morgenstern et
al., 2017). Its tropospheric chemistry considers the fundamental chemical
cycle of Ox–NOx–HOx–CH4–CO, along with oxidation of
non-methane volatile organic compounds (NMVOCs). Its stratospheric chemistry
simulates chlorine and bromine-containing compounds, CFCs, HFCs, carbonyl sulfide (OCS),
NO2, and the formation of polar stratospheric clouds (PSCs) and
heterogeneous reactions on PSC surfaces. In the framework of MIROC-Chem,
CHASER is coupled with the MIROC-AGCM atmospheric general circulation model
(version 4; Watanabe et al., 2011). The meteorological fields simulated by
MIROC-AGCM were nudged toward the 6-hourly NCEP FNL data (https://rda.ucar.edu/datasets/ds083.2/, last access: 30 October 2018). For this study,
the spatial resolution of the model was set as T42 (about 2.8∘× 2.8∘ grid spacing) in horizontal and L36 (surface to
approx. 50 km) in vertical. Anthropogenic emissions for O3 and aerosol
precursors like NOx, CO, VOCs, and SO2 are specified using the
HTAP-II inventory (Janssens-Maenhout et al., 2015), with biomass burning
emissions derived from the MACC reanalysis system (Inness et al., 2013).
In the model, the aerosol concentrations for black carbon (BC) / organic carbon (OC), sea salt, and soil dust
are handled by the SPRINTAR module, which is also based on the CCSR/NIES
AGCM (Takemura et al., 2000). The bulk thermodynamics for aerosols are
applied, including SO42- chemistry (SO2 oxidation with
OH, O3/H2O2, which is cloud-pH dependent)
SO42-–NO3-–NH4+ and
SO42-–dust interaction.
Heterogeneous reactions in the chemistry–climate model (CHASER)
The CHASER-V4 model considers HRs in both the troposphere and stratosphere.
In this work, we particularly examine HRs in the troposphere. In the current
version of CHASER, tropospheric HRs are considered for N2O5,
HO2, and RO2, using uptake coefficients for the distinct surfaces
of aerosols (sulfate, sea salt, dust, and organic carbons) and cloud
particles (liquid/ice) as listed in Table 2.
Although some other views incorporate the catalysis of transition metal ions
(TMIs) Cu(I)/Cu(II) and Fe(II)/Fe(III) for the HO2 conversion on aqueous
aerosols (Li et al., 2018; Mao et al., 2013; Taketani et al., 2012), this
mechanism remains uncertain (Jacob, 2000). The TMI mechanism might lead to
either H2O2 (Jacob, 2000) or H2O products (Mao et al., 2013).
However, this may not cause any significant difference, since recycling
HO2 from H2O2 is ineffective (Li et al., 2018). For this
study, the uptake of HO2 is affirmed with H2O2 as the product
(Loukhovitskaya et al., 2009; Taketani et al., 2009), as it is generally used in many
atmospheric models such that this is not counted as a terminal sink for
HO2 (Jacob, 2000; Lelieveld and Crutzen, 1990; Morita et al., 2004;
Thornton et al., 2008). The RO2 uptakes are assumed with inert
products, as suggested by Jacob (2000). The heterogeneous pseudo-first-order
loss rate β for the species i is given using the theory of Schwartz
(Dentener and Crutzen, 1993; Jacob, 2000; Schwartz, 1986), in which it is
simply treated with the mass transfer limitations operating two conductances
representing free molecular and continuum regimes for tropospheric clouds and
aerosols, in addition to using reactive uptake coefficient (γ)
instead of the mass accommodation coefficient as follows:
βi=∑j4νiγij+RjDij-1Aj,
where νi stands for the mean molecular speed (cm s-1) of
species i, Dij is the gaseous mass transfer (diffusion) coefficient
(cm2 s-1) of species i for particle type j, and Aj expresses
the surface area density (cm2 cm-3) for particle type j. In the
model, the particle size and effective radius Rj for aerosols are
calculated as a function of RH (Takemura et al., 2000). The aerosol
concentrations are based on SPRINTAR for BC/OC, sea salt, and dust (Takemura
et al., 2000). The surface area density (SAD) for aerosols (Aj) is
estimated using lognormal distributions of particle size (SFj) with
mode radii variable with the RH (Sudo et al., 2002) as follows
Aj,ae=CN⋅4πRj2⋅SFj,
where CN represents number density (cm-3) and Rj signifies the
effective radii (cm) of particle type j. To calculate SAD for cloud
particles, the liquid water content (LWC) and ice water content (IWC) in the
AGCM are converted using the cloud droplet distribution of Battan and Reitan
(1957) and the relation between IWC and the surface area density for ice
clouds (Lawrence and Crutzen, 1998; McFarquhar and Heymsfield, 1996).
Ac=10-4⋅IWC0.9Aj,ice=3⋅Ac,
In these equations, Ac represents the cross-section area for ice
crystals (cm2 cm-3). For liquid clouds, the following holds:
Aj,liq=LWC×10-6⋅3Rj.
The uptake coefficient parameter (γ) is defined as the net
probability that a molecule X undergoing a gas-kinetic collision with a
surface is actually taken up onto the surface. Although several recent model
studies that consider dependency of γ on RH and/or T, the majority of
the earlier studies use constant γ values that only vary with
aerosol particle compositions (Chen et al., 2018; Evans and Jacob, 2005;
Macintyre and Evans, 2010, 2011). For one study, γHO2 for the
uptake onto aqueous aerosols is considered with pH dependence (Thornton et
al., 2008). However, another study demonstrated that the uptake is large,
irrespective of the solubility in cloud water or pH (Morita et al., 2004).
Therefore, we instead choose γHO2 as fixed values depending on
the type of particle. Indeed, from Eq. (1) it is apparent that uptake
coefficients should be unimportant for uptake onto large particles such as
cloud droplets. In this study, γ for cloud particles of liquid and
ice phases are given based on suggestions from earlier reports (Dentener and
Crutzen, 1993; Jacob, 2000). One study (Dentener and Crutzen, 1993) used a
constant γN2O5 of 0.1 for uptake on sea salt, sulfate, and
cloud particles. They also revealed that a smaller γN2O5 of
0.01, which had been reported as laboratory measurements, is insensitive
to effects on tropospheric oxidant components. Results of another study
(Jacob, 2000) indicated constants γN2O5=0.1 and γHO2=0.2 for uptakes on both liquid clouds and aerosols, the
latter aiming to involve HO2 scavenging by clouds without accounting
for details of aqueous-phase chemistry. For ice crystals, Jacob (2000) suggested
γHO2=0.025 based on a report by Cooper and Abbatt (1996).
Jacob (2000) recommended using γRO2=0.1 for hydroxy-RO2
group produced by oxidation of unsaturated hydrocarbons and γRO2=4×10-3 for PA. The γ values for
aerosols are assumed to be fundamentally the same as those for liquid cloud
particles in this study. It is noteworthy that the γ values for
cloud particles are given tentatively in this study and are adjusted based
on evaluation of the resulting species concentrations of O3, NOy,
and OH with the observations.
Experiment setup
In this study, simulations of two types were conducted to isolate the
distinct effects of each HR for the surface types considered in the model
(Tables 3 and S1). Whereas a control simulation standard (STD) run considers
all HRs, cases with no HRs (noHR) cases intentionally ignore one or all of the HRs to calculate
effects of individual HRs. The sensitivity runs turned off the separate
HRs onto clouds (liquid and ice), and aerosols were also added to exploit the
separate aerosol-heterogeneous and cloud-heterogeneous effects, as suggested
in many earlier studies (Apodaca et al., 2008; Jacob, 2000; Lelieveld and
Crutzen, 1990, 1991; Morita et al., 2004). All simulations were run in the
2009–2017 timeframe, with 2009 being treated as a spin-up year. The HR
effects are determined as the differences between noHR cases and an STD
simulation as in Eq. (5):
Impact(i)j=(STDi-noHR(j)i)noHR(j)i⋅100(%),
where STDi stands for the concentration of investigated atmospheric
component i in the STD run and noHR(j)i denotes the
concentration of component i in the sensitivity run in which the HRs of/onto
j was ignored (j could be N2O5, HO2, RO2, clouds,
aerosols).
An additional sensitivity test was run to examine the sensitivity of the
troposphere's responses with the amplified HRs magnitudes
(Table S1). These
simulations only apply for HR(N2O5) and HR(HO2) to verify
some uncertainties that have been argued among earlier studies (Chen et al.,
2018; Evans and Jacob, 2005; Macintyre and Evans, 2010, 2011).
Computation packages in the chemistry–climate model CHASER.
Base modelMIROC4.5 AGCMSpatial resolutionHorizontal, T42 (2.8∘× 2.8∘); vertical, 36 layers (surfaces approx. 50 km)Meteorology (u, v, T)Nudged to the NCEP2 FNL reanalysisEmission (anthropogenic, natural)Industry traffic, vegetation, ocean, Biomass burning specified by MACC reanalysisAerosolBC/OC, sea-salt, and dust BC aging with SOx/secondary organic aerosol (SOA) productionChemical process94 chemical species, 263 chemical reactions (gas phase, liquid phase, non-uniform) Ox–NOx–HOx–CH4–CO chemistry with VOCs SO2, dimethyl sulfide (DMS) oxidation (sulfate aerosol simulation) SO4–NO3–NH4 system and nitrate formation Formation of SOA BC aging (+) Heterogeneous reactions: 8 reactions of N2O5, HO2, RO2; constant uptake coefficients (γ) on types of aerosols (ice, liquid, sulfate, sea salt, dust, OC)
References and the details of these adjustments are given in the main text. The
RO2 uptakes are assumed with inert products, as suggested by Jacob
(2000). ISO2 is denoted for peroxy radicals from C5H8+ OH, and
MACRO2 stands for peroxy radicals from methacrolein
(CH2= C(CH3)CHO).
Main sensitivity simulations for HRs in this work.
No.Simulation IDHR: N2O5HR: HO2HR: RO2HRs on cloudsHRs on aerosols1STD×××2noHR3noHR_n2o5××4noHR_ho2××5noHR_ro2××6noHR(Cld)×7noHR(Ae)×Observation data for model evaluation
Model simulations with and without HRs are evaluated distinctively with
stationary, ship-based, aircraft-based, and satellite-based measurements.
The observational information, locations of the surface sites, and
ship or aircraft tracks for the observations used for this study are summarized
in Table 4 and Fig. 1.
EANET is well known as the Acid Deposition Monitoring Network in eastern
Asia. The monthly data from 45 stations over 13 countries during 2010–2016
were used to verify surface concentrations of aerosols (sulfate, nitrate)
and trace gases (HNO3, NOx, O3) in eastern Asia. We also used
data of the European Monitoring and Evaluation Programme (EMEP), which
compiles observations over 245 European stations.
Additionally, we exploited ship-based observational data from R/V Mirai
cruise (http://www.godac.jamstec.go.jp/darwin/e, last access: 30 June 2020) undertaken by the Japan
Agency for Marine-Earth Science and Technology (JAMSTEC). This study used
data for surface CO and O3 concentrations in summer 2015–2017 along
the Japan–Alaska and Japan–Indonesia–Australia routes (Kanaya et al., 2019).
The model data were compiled in hourly time steps and were interpolated
corresponding with the Mirai time step and coordinates. For verification of
the vertical tropospheric profiles, we used Atmospheric Tomography (ATom1)
aircraft measurements (https://espo.nasa.gov/atom/content/ATom, last access: 30 June 2020) for
NO2, OH, CO, and O3.
The simulated tropospheric ozone was also evaluated using the tropospheric
column O3 (TCO) derived from the OMI satellite data
(https://daac.gsfc.nasa.gov/, last access: 25 February 2020). For distribution of the cloud fraction,
satellite data from International Satellite Cloud Climatology Project
(ISCCP, https://isccp.giss.nasa.gov/, last access: 12 June 2020), GCM-Oriented CALIPSO Cloud Products
(CALIPSO-GOCCP,
https://eosweb.larc.nasa.gov/project/calipso/calipso_table, last access: 12 June 2020),
and Japanese 55-year reanalysis (JRA-55 – 10.5065/D6HH6H41, Japan Meteorological Agency/Japan, 2013)
were used.
Locations of EANET stations (a), EMEP stations (b), Mirai cruises (c), and ATom1 flights (d).
The source for panel (a) is https://monitoring.eanet.asia/document/overview.pdf (last access: 12 June 2020).
Model bias and normalized root-mean-squared error (NRMSE) for each species
were calculated as shown below, where n is the number of available data
(number of stations × time step).
6bias=∑1nModel-observationn7NRMSE=∑1n(Model-observation)2nObservation‾
Datasets used for verification in this study.
Verified speciesRegionsDataTime seriesTime stepSulfate, nitrate, NOx, O3, HNO3Eastern AsiaEANET2010–2016Daily to 2-weeklySulfate, nitrate, NOx, O3, COEuropeEMEP2010–2016HourlyCO, O3Surface of the Pacific Ocean (Australia–Indonesia–Japan–Alaska)MiraiAug, Sep 201530 minJan, Aug, Sep 2016Jul, Aug, Sep 2017NO2, OH, CO, O3Various altitudes above the Pacific and Atlantic oceansATom1Aug 201630 minTCO60∘ S–60∘ N (satellite)OMI2010–2016DailyCloud fractionGlobal (satellite)ISCCP2000–2009MonthlyGlobal (satellite)CALIPSO-GOCCP2007–2017MonthlyGlobal (reanalysis)JRA552000–20156-hourlyResults and discussionModel verificationsCloud verification
For this study, we tentatively consider HRs on the cloud particle surface.
Given the great uncertainties related to the reaction coefficient (γ) (Macintyre and Evans, 2010, 2011), the cloud distributions must be
examined adequately in the model to the greatest extent possible. The
model-calculated cloud distributions were verified using satellite
observation data ISCCP D2, CALIPSO-GOCCP, and reanalysis data JRA55.
For the entire troposphere, the calculated cloud fraction was generally
underestimated against the satellite observations and reanalysis data
(Fig. 2, the first row). In the North Pacific region
in JJA (Fig. 2, the second row), when the cloud
fraction peaked in the region, the model was able to reproduce the satellite
observations (ISCCP and CALIPSO). However, for the lower troposphere over
the region, the cloud fraction calculated using CHASER in JJA appears to be
overestimated (Fig. 2, the fourth row), suggesting
that the resulting HR effects would also be exaggerated to some extent.
Comparisons for cloud fraction in the whole troposphere (first and
second rows) and lower troposphere (third and fourth rows). ANN denotes
annual mean, and JJA denotes June + July + August
mean. The first column is for ISCCP (2000–2009), the second column is for CALIPSO/GOCCP
(2007–2017), and third and fourth columns are for JRA55 and CHASER
(2000–2015), respectively. Color bars are the same for all panels. In ISCCP and
CALIPSO data, the pressure boundary layer of the low troposphere is
>680 hPa. In JRA55, the low troposphere was defined as 850–1100 hPa of pressure.
Verification with stationary observations
Verifications with EANET and EMEP stationary observations were conducted to
assess the model performance on land domains of eastern Asia and Europe,
particularly addressing the roles of the heterogeneous reactions considered
for this study.
The mass concentrations of particulate matter (PM2.5); sulfate
(SO42-); nitrate (NO3-); and gaseous HNO3,
NOx, O3, and CO (CO only for EMEP) of 2010–2016 were evaluated
(see Figs. S1 to S8 for monthly concentrations and
Fig. S9 for correlations). In general, the model can
moderately reproduce the PM2.5, SO42-, and
NO3- aerosol concentrations at these locations (R=0.3–0.7,
Table 5), although PM2.5 was underestimated,
sulfate was overestimated slightly. Nitrate was underestimated for EANET and
overestimated for EMEP. It is noteworthy that the model performance for EMEP
stations was better than that for EANET. The PM2.5 concentration was
better estimated with the inclusion of N2O5 and HO2 uptakes
(bias reduction in Table 5).
Figure 3a–g present the median values of NOx,
O3, and CO for grouped stations in the Chinese and South Korean regions (stations in China:
Jinyunshan; stations in South Korea: Kanghwa, Imsil, Cheju), remote stations with
low NOx levels of EANET, and all EMEP stations.
Figure 3h–n show changes in NOx, O3, and CO
for these stations. The model's positive bias for NO3- at Kanghwa
as a remote area is different from the model underestimates at other EANET
stations (e.g., Bangkok, Hanoi, and Hongwen in Fig. S1).
These high negative biases for NO3- can be associated with
undervaluation for NOx and can thereby lessen the effects of
N2O5 uptake.
Nitric acid in both regions was overestimated. The correlations, biases, and
normalized root-mean-square error (NRMSE) of the model for SO42-,
NO3-, and HNO3 are in the ranges reported in a multi-model
study by Bian et al. (2017) (Table 6).
The NOx concentration for eastern Asia and Europe was underestimated,
with significant bias for polluted Asian locations (Bangkok, Metro Manila,
Nai Muaeng, Samutprakarn, Si Phum, Ulaanbaatar, not shown). In Fig. 3a and c,
simulated NOx levels still underestimated the observed values for
Chinese and European regions, at which the observed NOx could reach 16 and 7 ppb, respectively. For the low-NOx EANET region, excluding the
abovementioned sites (Fig. 3b), simulated NOx levels turned to
overestimate the observed levels in January, February, September, and
October. The increasing effects of NOx attributable to heterogeneous
reactions, although minor, mitigated these underestimations (Fig. 3a–c).
Although NOx was partly reduced via uptake of N2O5, the
NOx level was mostly increased because of HO2 and RO2 uptakes
(Fig. 3h–j).
In this comparison, the low correlations of the model with EANET and EMEP
sites for HNO3 and NOx are still a problem. The high biases for
nitrogen species could be ascribed to the low horizontal resolution in this
study (∼2.8∘). Higher resolutions could improve
the model reproduction for surface NOx as previously investigated by
Sekiya et al. (2018). Moreover, the low reproducibility of the model for
NOx is probably caused by lacking mechanisms that reduce HNO3 and
enhance NOx in our model. One possible mechanism is the heterogeneous
reaction of HNO3 on soot surfaces (Reaction R8) (Akimoto et al., 2019, and
references therein): HNO3+ soot → NO + NO2 (Reaction R8).
The additional Reaction (R4) followed by NO2 uptakes onto soot (Jacob, 2000),
NO2+ particles → 0.5 HONO + 0.5 HNO3 (Reaction R9), can be
expected to increase NO and decrease O3 via the consequent titration
reaction. These changes could reduce the model overestimates for HNO3
and O3 and the model underestimates for NOx with EANET and EMEP
stations. Further tests for this issue shall be discussed in a future report.
CO for EMEP was partly underestimated by the model, especially during
January–March (Fig. 3g). This underestimate was
mitigated by increasing effects because of HRs of N2O5 and
HO2. The uptakes of RO2, in contrast, minorly reduced CO levels
(Fig. 3n) so that the model bias was worsened slightly. For O3,
whereas the model tends to overestimate this tracer for both regions
(Fig. 3d–f), O3 reduction effects of all HRs
(Fig. 3k–m) alleviated the model overestimates from April to December,
although advanced reduction is still needed. In January–March, the model
tended to underestimate O3 levels (Fig. 3d–f),
which was exaggerated by reduction effects for O3. In general, the STD
simulation with coupled HRs partly improved the agreement related to the
particulate and gaseous species, showing less bias than that of simulations
without HRs (Table 5).
Monthly averaged concentrations at EANET and EMEP from 2010–2016 (a–g) and corresponding HR effects (h–n) for NOx: (a, h) Jinyunshan
(China), (b, i) other low-NOx EANET stations, and (c, j) EMEP stations; for
O3: (d, k) Korean stations, (e, l) other EANET stations, and (f, m) EMEP
stations; for CO: (g, n) EMEP stations. In (a)–(g), grey lines are observations
(OBS), red lines are the STD simulation, and black lines are the noHR simulation. All lines
showed the median of monthly mean concentrations for each group of stations,
except (a). In (b)–(g), vertical thin lines with markers show the 25th–75th percentiles of monthly mean concentration at the particular group
of stations. In (h)–(n), blue fields are changes caused by HR(N2O5), green
fields are changes in HR(HO2), and yellow fields are changes caused by HR(RO2).
Model correlations and biases with EANET/EMEP observations:
three-sigma-rule outlier detection is applied for each station before
calculating all data. For NOx, all data were filtered once more using
the two-sigma-rule. Bias of the sensitivity run is shown in bold if
it is higher than the bias of the STD run. R has
no unit; the units in brackets are for biases.
Comparisons between EANET and EMEP observations with atmospheric
models. Outlier detection follows that in Table 5. The model result is shown in bold if it is better than or
agreed with Bian's report. R has no unit;
units in brackets are for biases and NRMSE.
The model simulations were also verified with O3 and CO observations
from the Research Vessel (R/V) Mirai for the Pacific Ocean region. This
study specifically examines data from the four cruises of R/V Mirai for the
Japan–Alaska region (40–75∘ N, 140∘ E–150∘ W) in summer, designated as MR15-03 leg 1 and leg 2 (28 August–21 October 2015, labeled as Track 1 in this study), MR16-06 (22 August–3 October 2016
as Track 4), MR1704 leg 1 (11 July–2 August 2017 as Track 5), and MR1705C (24 August–29 September 2017 as Track 6). Two other cruises during DJF for the
Indonesia–Australia region (5–25∘ S,
105–115∘ E) and Indonesia–Japan region (10–35∘ N,
129–140∘ E) are also explored in this study,
respectively designated as MR15-05 leg 1 (23 December 2015–10 January 2016 as Track 2)
and MR15-05 leg 2 (17–24 January 2016 as Track 3). All measuring data for CO
and O3 from the six cruises are respectively plotted in
Fig. 4a and b as grey dots. The ship-based data used in
this study was partly reported (T1–4) in the work of Kanaya et al. (2019),
including the extraordinary peak of CO on 26 September 2016 exceeded 500 ppbv (off the scale in Fig. 4a-T4) associated with
heavy fires in Russia (Kanaya et al., 2019). Data for the North Pacific
region (40–60∘ N) are addressed in light-blue shades
in Fig. 4 (T1, T4–6) for analysis in Sect. 3.2.
Table 7 shows correlation coefficients (plotted in
Fig. S10), indicating that the CHASER simulations
for CO and O3 are in good agreement with Mirai observations (R= approx. 0.6). However, the model still shows some discrepancies for both CO
and O3 concentrations. In general, the estimated CO and O3 are
both reduced for T1 and T4–6 as compared to observations, whereas they are superior for
the data located in 20∘ S–20∘ N during T2–3.
Overestimations for CO and O3 occurring in the region with considerably
low levels of these species might be attributed to the lack of halogen
chemistry in the model, as also discussed for the nearby region in a past
report (Kanaya et al., 2019). Underestimates for O3 levels up to 70 ppbv in the higher latitudes (Fig. 4b: T1, T4–6) are ascribable to the
insufficient downward mixing process of stratosphere O3 in the model
(Kanaya et al., 2019). Except for the CO's peak on 26 September 2016
mentioned above, the mild reductions for CO (<30 ppbv) in the model
during T1 and T4–6 as compared to the observations could be attributed to the
insufficient emission from territories, international shipping, and aviation,
as we used the HTAP-II emission inventory. These reductions for CO in Kanaya's
work are minor, e.g., for T1 (MR15-03 leg 1 and 2 in their work), due to
reanalysis data with finer horizontal resolution (1.1∘) utilized for the
CO emission rate (Kanaya et al., 2019).
The negative biases in the noHR simulations for CO are lower in the STD run
for all cruises, as they are for the North Pacific region (second versus
third/fourth/fifth data rows for CO, Table 7). The
CO-increasing effects by N2O5 and HO2 uptakes (Fig. 4c) are
consistent with the comparison for EMEP. This is also true for CO-reduction effects because
of HR(RO2). Whereas the effects by N2O5 and HO2 reduce
the model bias, the CO-reducing effects by HR(RO2) exaggerated the CO
bias (second versus sixth data rows for CO in Table 7), which is already apparent for comparison with EMEP (last column,
Table 5).
For O3 level, the model underestimates (Table 7) are in the opposite direction to the O3 overestimates for EANET and
EMEP stations (Table 5). The lower panels presented
in Fig. 4b show marked O3 reduction with all
HRs (gaps between red and black lines), mostly contributed from the HO2
uptake onto cloud particles (Fig. 4d: green and hatched fields). This
marked reduction of the O3 level is evident at some points during the
cruises, especially in the North Pacific region (the shaded areas) and
for T5. Unlike comparisons for land domain data
(Table 5), O3 reduction because of HRs worsens
the model underestimates during the Mirai cruises. It is noteworthy that one
cannot necessarily confirm whether the STD run simulates these
species better than the noHR does because tropospheric CO and O3 levels are
controlled by a complicated chemical mechanism and an interplay of emission,
transport, deposition, and local mixing in the boundary layers. As discussed
later in Sect. 3.2, the surface aerosols concentration in the western Pacific
Ocean is mostly dominated by liquid clouds (exceeding 50 000 µm2cm-3 during JJA) and sulfate aerosols (approximately 75 µm2cm-3 in JJA). The model improvements in reproducing CO by adding
N2O5 and HO2 uptake indicate that the appropriate mechanisms
of these processes onto cloud droplets and sulfate aerosols are
well established in the model. For HR(RO2), which induces the smallest
and opposite effects on CO compared with the effects of N2O5 and
HO2 uptakes, it can be stated in general for the total HR effects that
including all three HRs partially improves the model during Mirai cruises.
Model correlations and biases for Mirai. No outlier
filtration is applied. The bias of the sensitivity run is shown in bold if
it is higher than the bias of STD run. R has no
unit; the units in brackets are for biases.
Observed and simulated concentrations for CO (a) and O3(b) and
their changes caused by each HR (c, d) during Mirai cruises. The left axis
shows concentrations. Dashed lines show latitudes of cruises scaled with the
right axis. The horizontal axis shows cruise travel times. Light-blue areas
show data in the North Pacific region (140–240∘ E,
40–60∘ N). In (a) and (b), grey dots are for observations, red lines are
for STD simulations, and black lines are for noHR simulations. In (c) and (d), blue
fields are for changes in HR(N2O5), green fields for changes in HR(HO2),
yellow fields for changes in HR(RO2), and hatched fields for changes in HRs (cloud). Note that
in (d) all three fields are stacked but in (c) only blue and green fields
are stacked for better illustration of negative and positive changes.
Verification using aircraft measurements
To verify the model performance in the free troposphere, we used ATom1
aircraft measurements in August 2016 (for NO2, OH, CO, and O3).
Table 8 lists the model's correlation coefficients
and biases of each sensitivity run against ATom1.
Figure 5 shows observed and simulated concentrations
of CO and O3 during flight no. 2 in the North Pacific (NP) region.
Figure 6 exhibits vertical biases of the model and
computed HR effects caused by each HR for all ATom1 flights and NP region.
The spatial and temporal concentrations are available in
Fig. S11. Correlations are shown in
Fig. S12.
In general, the model simulations for NO2, OH, CO, and O3
adequately agree with aircraft measurements with R>0.5
(Fig. S12). However, NO2 and CO still tend to
be underestimated by the model, which is consistent with comparisons for
EANET/EMEP and Mirai observations. In Fig. 5, the
CO-increasing effects, mostly due to the uptake of N2O5 and HO2
(Fig. 6o), mitigated the negative bias of the
model. This CO bias reduction was visible for all flight altitudes, the
lower troposphere, and the North Pacific region (Table 8; Fig. 6d and h). Both N2O5 and HO2
uptakes show improvements for CO reproduction of the model. However,
RO2 uptake seems to worsen the model's CO bias due to its reducing
effect for CO (Fig. 6o and s), which is consistent with
the Mirai comparison.
For the O3 level, the model generally overestimates O3 when
calculating for all altitudes or lower troposphere, which is similar to the
EANET/EMEP observations. In the North Pacific region with P>600 hPa (40–60∘ N, 198–210∘ W), the model bias for O3
in STD run turns to underestimate (second data row and second column from the
right, Table 8), which might be similar with Mirai
data verification for the western North Pacific (143∘ E–193∘ W). However, the underlayers (>700 hPa) again show
overestimation (second data row – last column,
Table 8). As Mirai and ATom1 data show, the
underestimates for O3 exist at the marine boundary layer in the western
North Pacific and extend to the upper troposphere (<700 hPa) of the
east side, which might be ascribed to the insufficient downward mixing process of
stratosphere O3 in the model as discussed previously.
The HR effects on O3 are generally negative effects
(Fig. 6m), although they are small and barely
recognizable in Fig. 5, which mitigates the model
bias in the noHR run. This model improvement is consistent for all flight
altitudes, the low troposphere, and the North Pacific region (second versus
third data rows in Table 8). Both HR(N2O5)
and HR(RO2) typically contribute to this improvement (Fig. 6m and q). In contrast, HR(HO2) seems
to only reduce the model bias in a thin layer: from the ground up to 800 hPa for
all flights and 700 hPa for the North Pacific region
(Fig. 6m and q). At the bottommost layers in this
region, the model's overestimates for O3 are reduced by the negative
effects of HO2 uptake (Fig. 6f and q). The
extension of model bias because of HO2 uptake above 800 hPa is
attributable to its increasing effect on O3 level
(Fig. 6m). We recognize that this O3 increase
effect above 800 hPa is opposite to the effects for EANET/EMEP and Mirai
comparisons, which is discussed in Sect. 3.2 for HO2 uptake effects.
The vertical means of model biases and changes for all four species
(NO2, OH, CO, O3) are presented in Fig. 6.
In general, the STD run reduces model biases for all four species, with
better performance for broader regions (all flights) than for the smaller
region (North Pacific). In the North Pacific region, the negative bias for
O3 is observed only for the 500–900 hPa layers
(Fig. 6f). The model bias is apparently extended in
this region. However, the inclusion of HR(HO2) leading to O3
increment (Fig. 6f) reduces O3 bias in this
region, which might indicate that the O3 increase effect by
HR(HO2) is verified, particularly in 500–900 hPa layers during ATom1.
We also verify the total uptake of N2O5, HO2, and RO2
onto ice and liquid clouds using data obtained from ATom1 flights within the
free troposphere. As Table 8 shows, the inclusion of
HRs onto clouds reduces the model biases for CO and O3 in all
calculations. In Fig. 6l–s, because the HRs(cloud)
effects occupy the major part of total HRs effects for NO2, O3, and
OH, especially for NP region and low troposphere (>800 hPa),
cloud uptakes could also contribute to the overall reduction in model bias
against ATom1. For O3, HRs(cloud) mostly induce negative effects
(Fig. 6m and q). At the layer of 600–800 hPa in the NP
region, this O3 reduction due to HR(RO2) onto clouds
(Fig. 6q: yellow and hatched patterns) might
account for the model worsening in O3 levels as described above. This
result might prove that cloud overestimation for the North Pacific, as
revealed at the beginning of this section, affects the model bias in this
region.
Model correlations and biases with ATom1:
three-sigma-rule was applied for CO and O3.
NP denotes North Pacific region (140–240∘ E,
40–60∘ N). The bias of the sensitivity run is presented in bold when it is higher
than the bias of STD run.
R has no unit; the units in brackets are for biases.
Observations and simulations for CO and O3 during ATom1
flight 2 (198–210∘ E, 20–62∘ N). Blue shaded areas
show data for P>600 hPa.
Vertical bias against ATom1 (a–h) and vertical HR effects (l–s).
Data for each pressure level P are calculated within the range of P±50 hPa, with the applied three-sigma-rule for outlier detection. All rows show calculations for all flight domains the
and the North Pacific region. The horizontal axis shows model bias and absolute
changes with units written in each panel. The vertical axis shows pressure
(hPa). The red numbers in (a)–(h) represent relative reductions (%)
of the STD run's bias compared to that of the noHR run.
Verification with OMI satellite observation for TCO
We also tested STD and noHR simulations using the tropospheric column ozone
(TCO) derived from the OMI satellite instrument (Figs. S13 and 7). In a large area of the Northern
Hemisphere, the inclusion of HRs (STD run) generally improved the
consistency with the OMI TCO, as seen in Fig. 7b,
d, f with less overestimation than Fig. 7a, c, e,
particularly enhancing the winter minima (first and second panels in
Fig. S13). This improvement in DJF is attributed
mostly to the reductive effects of HR(N2O5) and HR(RO2) in
the lower (800 hPa) and middle troposphere (500 hPa), respectively (see
Fig. 10 for vertical profiles of HR(N2O5)
on O3 and Fig. 13 for vertical profiles of
HR(RO2)). In the North Pacific, HRs appeared to exaggerate O3
underestimates, especially for latitudes higher than 40∘ N
(Fig. 7) during the first half of the year (third
panel, Fig. S13). However, such a discrepancy, which
was also observed from comparison for R/V Mirai observations
(Fig. 4), might result from other factors such as
deposition or vertical mixing rather than by HRs.
Differences in tropospheric column ozone (TCO) in CHASER's
noHR (a, c, e) and STD (b, d, f) runs from OMI: ANN is annual; DJF is December,
January, February; and JJA is June, July, and August.
HR effects
This section presents a discussion of the global effects of HRs calculated
using CHASER with their spatial distributions in the troposphere using
standard (STD) and sensitivity simulations (noHR_n2o5,
noHR_ho2, noHRs_ro2, noHR) for the
meteorological year of 2011. Aside from the main simulations described in
Table 3, additional runs that separately turned off
the uptakes onto clouds or aerosols for each HR are also conducted to
exploit the contributions of effects to the troposphere.
Distribution of clouds and aerosols surface area density (SAD)
To obtain the parameters for uptake to clouds and aerosols, SAD estimations
are used together with cloud fraction and aerosols concentration.
Hereinafter, we discuss SAD distributions for total aerosol, ice clouds, and
cloud droplets, which are estimated for the model using Eqs. (2), (3), and
(4), respectively.
In Fig. 8, total surface area concentrations of
liquid clouds and aerosols are both much lower aloft than at the surface (as
counted on the dry and wet depositions). The liquid cloud SAD results are
2 orders of magnitude larger than ice cloud SAD and total aerosol SAD. The
ice cloud SAD, distributed at the middle and upper troposphere, is enhanced
for N/S middle latitudes in wintertime. Liquid cloud SAD concentrates mainly
at the surface with distributions extending to 500 hPa and maximized at
approx. 800 hPa over the mid-latitude storm tracks and in tropical
convective systems, especially at 60∘ N in JJA. Total aerosol SAD
was derived mainly from pollution sources at 40∘ N during both
seasons, with higher concentrations apparent for DJF and a greater spatial
spread observed for JJA. Sulfate aerosols are becoming the dominant source
of aerosol surface area in the model above 600 hPa (approx. 20 µm2cm-3) in addition to organic carbons and soil dust (both are approx.
10 µm2cm-3 in JJA) for the Northern Hemisphere.
In Fig. S14, showing the SAD distribution at the
surface, the SAD for liquid clouds is dominant in JJA, reaching approx.
50 000 µm2cm-3 for mid-latitude and high-latitude ocean
regions. Liquid clouds make the greatest contribution to the SAD at the surface.
Our model performance for aerosol SAD shows agreement with that presented
in an earlier report (Thornton et al., 2008). Sulfate aerosols are prevalent
in the northern mid-latitudes near industrial bases; maximize at the surface
in DJF for the Chinese region (exceeding 1000 µm2cm-3), NE
U.S. (approx. 500 µm2cm-3), and western Europe; and transport to
the North Pacific region in JJA (approx. 250 µm2cm-3). Soil
dust aerosol SAD dominate in the regions of the Sahara and Gobi deserts,
reaching annual average values exceeding 100 µm2cm-3.
Organic carbon (OC) is a dominant source of aerosol SAD over biomass burning
regions in China (up to 1000 µm2cm-3 in DJF), South Africa
(up to 800 µm2cm-3 in JJA), western Europe, and South America.
The black carbon (BC) surface area can reach values exceeding 600 µm2cm-3 in DJF for the region of China or other significant
industrial areas (India, which reaches 75 µm2cm-3, NE U.S., and Europe)
or over tropical forests, primarily in Africa. Sea salt aerosols are most
important in high-latitude oceans during winter. However, the maximum
contributions only reach 2 µm2cm-3 in our model, which is
greatly underestimated compared to Thornton's work (75 µm2cm-3)
(Thornton et al., 2008). In brief, SAD for aerosols of all types contributes
the most during DJF, whereas during JJA, the SAD for liquid clouds and
sulfate aerosols are dominant, particularly for the northern high-latitude
and mid-latitude oceans. The total aerosol SAD in this region is approx.
75 µm2cm-3, which is consistent with the
estimation by Thornton et al. (2008).
Zonal, seasonal mean (upper and middle), and annual mean (lower)
distribution of cloud droplet surface aerosol density (SAD), cloud ice SAD,
total aerosol SAD, sulfate aerosol SAD, and organic carbon SAD (from left to
right).
Effects of N2O5 heterogeneous reaction (HR(N2O5))
The inclusion of HR(N2O5) in the model increases global methane
lifetime by +4.48 % and changes NOx, O3, and CO abundances
by -5.51 %, -2.12 %, and +3.42 %, respectively
(Table 9).
Effects by N2O5 uptake onto both clouds and aerosols in
zonal mean (a–h) and at the surface (i–p). Note that the color scale for (a)–(h) is different from that for (i)–(p).
Averaged effects of N2O5(a–c) and HO2 uptakes (d–f) for each air pressure range. Calculations are for global data (a, d),
Northern Hemisphere data (b, e), and North Pacific region data (c, f). Dashed lines show
effects of the FCTHR_10 run (Sect. 3.3). The legend is in
panel (f) for December–January–February (DJF), June–July–August (JJA), and annual (ANN) data.
In Fig. 9a–h, the changes in OH, NOx, O3,
and CO are most significant in the middle troposphere (400–600 hPa). These
changes are attributed mostly to uptakes of N2O5 onto aerosols,
rather than onto clouds. Marked negative effects on NOx concentration
are apparent for DJF in the middle troposphere (600–700 hPa) of the
60∘ N and the Arctic region (>-20 % at 700 hPa)
(Fig. 9c). The effects are probably associated with
high concentrations of sulfate aerosols, organic carbons, or soil dusts in
the middle troposphere (see the paragraph above) and are also related to a
long chemical lifetime of NOy in the middle and upper troposphere in
winter. When it comes to JJA, these negative effects become significant at
higher altitudes around 30∘ N/S (>-10 % at 400 hPa).
At the surface (Fig. 9i–p), HR(N2O5)
causes negative effects on NOx, O3, and OH concentrations (up to about
-24 %, -5 % and -8 % respectively) and positive effects on CO
concentration (up to about +4 %), which are also mainly attributable to the
N2O5 uptake on aerosols.
In Fig. 10, the latitude–longitude (lat–long) means
of HR(N2O5) effects are calculated for each pressure range
(pressure ranges are defined as in Fig. 6). The
global NOx decrease is up to -9 % at 300–400 hPa. This decrease
causes correspondent reductions in O3 and OH, which are calculated as
about -3 % and -7 % at 400–600 hPa, respectively, for lat–long mean
O3 and OH. About 4 % lat–long mean CO increment throughout the entire
troposphere responds to decreased OH.
The small effects of HR(N2O5) on O3 in the lower troposphere
are consistent with findings from an earlier study (Riemer et al., 2003).
Reductions in O3 and NOx concentrations also agree well with the
collective knowledge summarized in work reported by Brown and Stutz (2012).
Despite a considerable HR(N2O5) effect calculated in the middle
troposphere, its effect in the whole troposphere is apparently not as great
as reported to date. Another study assessed HR(N2O5) effects on
annual burdens of NOx, O3, and OH as -11 %,
-5 %, and -7 %, respectively, when using a similar γN2O5 value (0.1)
(Macintyre and Evans, 2010). Although the effects of magnitude estimated in
our work (Table 9) are almost half those of this
earlier study (probably because of differences in NOx emissions,
estimation of SAD, and chemical mechanism), the effect tendencies are
similar. A strong increase of ozone attributed to N2O5 uptake
under high-NOx conditions calculated using box models was reported from
an earlier study (Riemer et al., 2003), but this is only slightly apparent
in our global model. Our results revealed that the HR(N2O5) effect
might help clean up NOx pollutant. However, it increases the
concentration of other pollutants (such as CO) because of the effects of
reducing oxidizing agents in the atmosphere.
Effects of HO2 heterogeneous reaction (HR(HO2))
Regarding the effects of HR(HO2), the tropospheric methane lifetime
increases by approx. 1.51 %. Abundances in NOx, O3, and CO
change, respectively, by +3.26 %, +0.05 %, and +1.95 %
(Table 9). In the entire troposphere, the influences
of HR(HO2) are not as large as that of HR(N2O5).
As Fig. 11a–f shows, the zonal-mean effects of
HR(HO2) on NOx, OH, and O3 are more widespread in DJF but
are more concentrated at the surface in JJA because of the high level of
HO2. The most substantial effects by HR(HO2) are calculated in JJA
at the surface of North Pacific (140–240∘ E, 40–60∘ N)
by as much as about +69 % (NOx), +7 % (CO), -70 % (OH), and
-21 % (O3), which are more significant than those of
HR(N2O5) at the surface. In the lower troposphere, HR(HO2)
suppresses the NO oxidation (Reaction R10) and thus preserves a high NO/NOx ratio
and generally restricts OH and O3 formations.
HO2+NO→OH+NO2
These effects are primarily attributable to HR(HO2) in clouds rather
than to aerosols (which is opposite to N2O5 uptake). These OH and
O3 reduction effects go along with past studies in which approx. 50 %
OH and approx. 10 % O3 of reductions are calculated for the low
troposphere of northern mid-latitude region ascribed to aqueous-phase
HOx sink in clouds (Lelieveld and Crutzen, 1990, 1991). The efficient
scavenge of HO2 radical by cloud droplets might associate with
acid–base dissociation HO2/O2- and electron transfer of
O2- to HO2 to produce H2O2 (Jacob, 2000).
Furthermore, cloud droplets SAD in our model are 2 orders of magnitude
higher than total aerosol SAD (Fig. 8), which also
contributes to the preference of the aqueous-phase HO2 sink. Our large
calculated effects for the North Pacific region are new findings from other
models, which have considered only aqueous aerosols (Stadtler et al., 2018;
Thornton et al., 2008) because cloud particles are dominant at remote
marine areas in addition to sulfate and aqueous sea salt particles
(discussed at the beginning of Sect. 3.2). The HO2 uptake onto aerosols
is minor; it is observed only in DJF in the Arctic region and polluted areas
(China and US), with apparent changes of up to +17 % for NOx, -40 % for
OH, and -14 % for O3 at the local surface (Fig. 11i, k, m). The aerosol negative effect of HR(HO2) on surface O3
concentration is significant in the Chinese area, which might be in line
with other studies of the Chinese O3 trend (Kanaya et al., 2009; Li et
al., 2019; Liu and Wang, 2020; Taketani et al., 2012), which suggests that
the observed recent O3 increases can be attributed in part to reduced
HO2 uptake under aerosol (PM) decreases brought about by the new
Chinese air pollution policy.
Effects of HR(HO2) on both clouds and aerosols in zonal
mean (a–h) and at the surface layer (i–p). Note that the color scale for (a–h) is different from that for (i–p).
In Fig. 10, vertical profiles show that the lat–long averaged effect of HR(HO2) on OH is -9 % in the lower troposphere. As
a result, the lat–long mean CO level increases by 2 % at the surface.
Additionally, the daytime NOx oxidation by OH is suppressed. Also,
NOx might be preserved in clouds (Dentener, 1993), which increases the
lat–long averaged NOx level by +6 % at 900 hPa. The lat–long mean
O3 is reduced by -1 % at the surface, but it is increased at higher
altitudes (about +0.2 % at 300 hPa). The reduction of O3 is associated
with HO2 depletion in clouds and aqueous aerosols as described above,
coupled with the NOx preservation in clouds, which enhances the
NO/NOx ratio. The preserved NOx in clouds might remain available
for O3 production after the cloud evaporates (Dentener, 1993), along
with the low SAD for both liquid clouds and aerosols at higher altitudes
(Fig. 8), thereby increasing O3 in places other
than the aqueous phase. The O3 increment might be trivial in DJF but
enhanced in JJA. As a result, the Northern Hemisphere mean O3 in JJA
exhibits only positive effects. In contrast, for the North Pacific region in
JJA, due to its large cloud fraction, an O3 reduction effect is
apparent in this region. The effects in JJA for this region show changes of
-25 % OH, +35 % NOx, -12 % O3, and +5 % CO at 900–100 hPa as the most remarkable HR(HO2) effects, as described above. In
general, the regional mean effects of HR(HO2) in the North Pacific
region are enhanced in JJA, but the mean global effects of HR(HO2) are
slightly favored in DJF because of the additional effects of aerosols during
this season.
Macintyre and Evans (2011) also found a similar contrast between the
behaviors of HR(N2O5) and HR(HO2): the uptake of
N2O5 produces both regional and global effects on O3, whereas
the uptake of HO2 affects O3 at regional scales more strongly than
on a global scale (Macintyre and Evans, 2011). Such features are generally
consistent with our results.
Effects of RO2 heterogeneous reactions (HR(RO2))
Effects of HR(RO2) increase the global mean methane lifetime by
+0.15 % and change tropospheric abundances of NOx (+0.52 %),
O3 (-0.93 %), and CO (-1.78 %) (Table 9).
In Fig. 12k–l, significant latitudinal contrasts
exist in the NOx changes: large NOx increases at high latitudes
and decreases at lower latitudes. These NOx changes probably reflect
the reduced formation of PANs, which decreases NOx transport from source
regions to remote areas and from the surface to the upper troposphere
(Villalta et al., 1996). The model calculated especially large NOx
increases (>50 %) for high latitudes around the Arctic Ocean in
JJA, indicating a reduction in the formation of PANs (NO2+RO2→ PANs), which is linked tightly to the enhanced biogenic emissions of
VOCs such as isoprene and terpenes in summer. The PAN-reducing effect of
HR(RO2), associated with the suppression of NO oxidation, as seen in
the case of HR(HO2), causes a double increase for NOx at
the surface as compared to HR(HO2) (144 % in Fig. 12l versus 66 % in
Fig. 11l). The increases in NOx in DJF at high
latitudes offshore of southern oceans could also reflect reduced transport
of NOx under reduced PAN formation caused by HR(RO2) since these
coastal areas are located downwind of the major biogenic volatile organic compounds (BVOC) sources of South
America, South Africa, and Australia. Moreover, the areas with pronounced
NOx increases in Fig. 12k–l are all associated with high-cloud SAD, as
seen in Fig. S14 (left panels).
Effects of HR(RO2) on both clouds and aerosols in the
zonal mean (a–h) and at the surface layer (i–p). Note that the color scale
for (a)–(h) is different from that for (i)–(p).
Averaged effects of HR(RO2) (a–c) and all HRs (d–f) on OH
(first row), NOx (second row), O3 (third row), and CO (fourth row)
for each air pressure range. Calculations are for global (a,d), Northern
Hemisphere (b, e), and North Pacific region (c, f). Legend is on the first
panel for December–January–February (DJF), June–July–August (JJA), and annual (ANN).
Annual zonal mean (a) and surface (b) total HR effects.
In the troposphere, the NO oxidation carried out by RO2 via Reaction (R11)
produces another HOx molecule (Reaction R12). Hence, the uptakes of RO2
onto particles are also expected to confine the OH and O3 formations,
similar to what the uptake of HO2 does.
R11RO2+NO→RO+NO2R12RO+O2→R′O+HO2
However, the different catalytic role of NOx in the oxidizing
mechanisms between polluted and remote regions results in different
tendencies for OH and O3 (Jacob, 1999). Accordingly, O3 and OH
molecules are produced through the oxidation of hydrocarbons in presence of
high NOx, whereas HOx molecules are consumed and no O3 is
produced in the same process that occurred devoid of NOx. For that
reason, less RO2 participating in the hydrocarbon oxidation only
reduces OH and O3 levels in polluted regions, e.g., the Chinese region in
DJF (Fig. 12i and m), while enhancing OH level and
leaving no significant effect on O3 at remote regions, e.g., the NP region
(Fig. 12i, j, m, n). The corresponding changes in
OH concentration at the surface are in the range of about -5 % to
+20 % in JJA (Fig. 12j). O3 levels are
slightly reduced throughout the troposphere, at most about -5 % at the
surface of the Arctic region above Canada, due to HR(RO2), with only
about a 3 % increase, corresponding with the NOx increases at the
tropical coasts (Fig. 12n). For CO, different from the
increasing impacts by HR(N2O5) and HR(HO2) (+3.42 % and
+1.95 %, respectively), a reduction effect occurs through the free
troposphere (-1.78 %). CO decrease might be due to reduced secondary CO
production from RO2 oxidation (RO2→HCHO/RCHO or ROOH → CO) such as isoprene (Bates and Jacob, 2019) when functionalized RO2
species uptake onto aerosols and clouds particles instead.
The effects of HR(RO2) are primarily attributed to the heterogeneous
reaction on clouds rather than on aerosols in terms of changes in NOx
and CH4 lifetime, although this cloud effect is far smaller than the
cloud effect on the HO2 uptake. The areas with pronounced NOx
increases in Fig. 12b are all associated with high-cloud SAD, as seen in
Fig. S14 (left panels). Although it is proper to
expect the high solubility of RO2 (e.g., CH3O2) from its
peroxy substituent (Betterton, 1992; Shepson et al., 1996), it is much less
soluble than HO2 because of its lower polarity and thus lower
Henry law constant (Jacob, 2000). Consequently, the possible accumulation of
CH3O2 in the cloud is attributable to suppression of its
gas-phase sink with HO2 (Jacob, 1986).
Figure 13a–c show lat–long means of HR(RO2)
effects calculated for the respective pressure ranges: the lat–long values are
constrained for the entire globe, the Northern Hemisphere, and North Pacific
region. For the entire globe, the contrast effects of HR(RO2) between
the lower and higher troposphere on NOx and OH are shown clearly
(+3.50 % NOx and +0.55 % OH at 900 hPa, but -2.50 % NOx
and -0.75 % OH at 400–500 hPa annually). As a result, the annual mean
O3 and CO levels decreased throughout the troposphere, reaching their
lowest at -1.60 % O3 and -1.50 % CO at the surface. In JJA, the
global effects by HR(RO2) are more concentrated in the lower
troposphere, especially in the North Pacific (+3 % OH, +10 %
NOx, -3 % O3, -2 % CO at 900–1000 hPa). In DJF, the
HR(RO2) effects are observed mostly in the middle and higher
troposphere, especially when considering the Northern Hemisphere (-1.25 %
OH, -4 % NOx, -2 % O3 at 500–800 hPa).
Tropospheric abundances changes by HR(N2O5),
HR(HO2), HR(RO2), and all HRs for clouds and aerosols.
As discussed above, different heterogeneous reactions affect tropospheric
chemistry differently. However, their effects can either augment or negate
others in performing for the atmospheric chemistry. HR(N2O5) is
the greatest contributor to reduction of tropospheric OH, O3, and
NOx abundances and is more active in the middle troposphere.
HR(HO2) reduces OH but increases the abundances of O3 and
NOx globally, whereas it exposes a negative effect on O3 level at
the surface of the North Pacific region. HR(RO2) similarly has a
smaller distribution to the total heterogeneous effects, but its global mean
negative effects for O3 are not negligible. The uptake of
N2O5 mainly takes place for aerosols, whereas the uptakes of
HO2 and RO2 occur more for liquid and ice clouds. Overall, the
total effects of all HRs for the whole troposphere are +5.91 % for
global mean CH4 lifetime, -2.19 % for NOx (tropospheric
abundance), -2.96 % for O3, and +3.28 % for CO
(Table 9). At the surface, the annual effects ranged
from about -53 % to +2 % for OH, -13 % to +51 % for NOx, -13 % to -2 %
for O3, and -0.3 % to +6 % for CO (Fig. 14).
As Fig. 13d–f show for the vertical profiles of HR
effects, the change of OH largely concentrated in the lower troposphere
(-10 % OH at 900 hPa, calculated for the entire globe) is associated with
the HO2 uptake. By contrast, the NOx change is more intensive at
higher altitudes (-9 % NOx at 400 hPa, calculated for the entire
globe), associated with N2O5 and RO2 uptake. The global mean
HR effects on O3 and CO are vertically even, with the highest effects
reaching -4 % O3 and +4 % CO at the surface. Globally, HR effects
on atmospheric oxidants (OH and O3) are enhanced in DJF because of the
higher pollution in the Northern Hemisphere. However, the largest HR effects
are apparent for JJA at the surface of the North Pacific (-25 % OH,
+38 % NOx, -14 % O3, and +6 % CO as calculated for the
950–1000 hPa layer). These effects are mostly ascribed to HO2 uptake
onto clouds. This finding is also apparent from Fig. S15b: these effects reach -66 % for OH, +206 % for NOx, -23 %
for O3, and +4.4 % for CO at the surface. They were able to extend
up to 400 hPa in the atmosphere. These substantial effects are readily
apparent for the large reduction of O3 level during Mirai observation
(hatched field in Fig. 4d, T5). However, the major contribution of HR(HO2) to these effects is
only partially verified by the ATom1 measurements in this study (Fig.6q, hatched bars at 500–700 hPa).
Because of model overestimates of cloud fraction in JJA for the North
Pacific region, these effects of HR(HO2) should have existed at a
smaller magnitude. For HR effects in the middle to upper troposphere, the
N2O5 uptake on aerosols is dominant in these layers and intense in
both DJF and JJA.
Sensitivities of tropospheric chemistry respond to heterogeneous
reactions
From the discussion presented above, marked effects of HRs on global
tropospheric chemistry are apparent. Here we examine how the tropospheric
chemistry responds to the magnitude of HRs' loss rates. To do this, we
introduced a factor F for application to the first-order loss rate shown in
Eq. (1) for artificially manipulating the HR magnitude.
βi=∑j4νiγij+RjDij-1Aj×F
For this sensitivity test, we only specifically examine HR(HO2) and
HR(N2O5) and consider factors of 0–10 for the STD run
(Table S1). This
test might help to show the effective oxidation sensitivity of the
troposphere because future pollution and climate change might enhance the
activities of these HRs.
Trend lines for the sensitivity of HR(N2O5) (a, b) and HR(HO2) effects (c, d), with uptake rates shown. Panels (a) and (c) show the CH4 lifetime (blue) and tropospheric abundance of CO
(red). Panels (b) and (d) show tropospheric abundances of NOx (blue) and
O3 (red).
For both effects, we performed nonlinear function fitting with their uptake
loss rates, which yielded correlation coefficients higher than 0.93
(Fig. 15). Although both HRs showed negative
tendencies for OH and O3 levels, the effect of HR(HO2) on the
tropospheric abundance of O3 showed only a small increment with an
increasing loss rate (maxima at around F=3) and turned to reduction at
higher rates (F>5). As discussed alongside HR(HO2) effects,
the O3 level is expected to be primarily reduced only in JJA at the
surface of the North Pacific region. At the same time, O3 will be
increased gradually elsewhere because of the persistent NOx increment.
This behavior produces a positive global mean effect.
Figure 10 (dashed lines) shows that manipulation of
the HR(HO2) loss rate that is 10 factors higher will effectively increase the
negative HR(HO2) effects on O3 in DJF (dashed blue versus solid
blue lines, third row, fourth column), which results in a higher
tendency of negative values for global mean effects. This sensitivity in DJF
might be attributable to the HO2 uptake to aerosols rather than to
clouds during this polluted period, which is apparent through comparison of
Figs. 11 and S16 for
notable events. In DJF, by amplifying the HO2 uptake loss
rate by a factor of 10, the effects for the polluted Chinese area (because of HO2 uptake
onto aerosols) significantly magnify from -18 % (Fig. 11m) to -47 % (third row, first
column in Fig. S16b). In contrast, effects
at the surface O3 level in JJA for the North Pacific region (because of
HO2 uptake onto clouds) only enhance from -21 % (Fig. 11n) to -29 % (third row,
second column in Fig. S16b).
When amplifying HR(N2O5) by a factor of 10, the sensitivities of
global effects show no seasonal variation. The HR(N2O5) effects
are more sensitive in DJF for the North Pacific region, which link to the
higher concentration of aerosol in this season. Otherwise, the
HR(N2O5) effects for the generic Northern Hemisphere tend to be
more sensitive in JJA as a result of pollutant transportation to the higher
troposphere.
Consequently, we suggest that the sensitivity of tropospheric chemistry to
HR(N2O5) and HR(HO2) might be attributable to loss activities
of aerosols rather than to clouds. The sharply curved effect on O3
because of amplification of HR(HO2) makes sense in plans for ozone
pollution control when increased pollution or climate change factors cause
the rate of HRs to increase by 3–5 times or more in the future.
Conclusion
The “CHASER” chemistry–climate model was used to investigate global effects
of N2O5, HO2, and RO2 uptake. Verification of the model
with observations from inland and ocean domains showed adequate agreement
for PM2.5, SO42-, and NO3- particles and gaseous
HNO3, NOx, OH, CO, and O3 concentrations. R, bias, and NRMSE
values for SO42-, NO3-, and HNO3 at EANET and EMEP
stations are comparable with other models. Inclusion of HRs reduced model
bias for OH, NO2, CO, and O3, especially in the low troposphere.
However, verification with satellite and reanalysis data showed
deterioration by HRs for TCO and an overestimate for cloud fraction in the
North Pacific region.
The total effects of HRs are important for the tropospheric chemistry that
might change +5.91 % CH4 lifetime and -2.19 % NOx, -2.96 %
O3, and +3.28 % CO abundances. Global effects are -9 % NOx
at 400 hPa, -10 % OH at 900 hPa, and -4 % O3 and +4 % CO at the
surface. Global HR effects tend to be enhanced in DJF because of greater
amounts of pollution in the Northern Hemisphere.
Total HR effects are contributed mainly by HR(N2O5) onto aerosols
in the middle troposphere. At the surface, HR(HO2) is more active and
leaves a remarkable disturbance in JJA at the North Pacific region with
changes of -70 % for OH, -24 % for O3, +68 % for NOx, and
+8 % for CO. These effects were attributed to the uptake of HO2 on
cloud particles, which were partially verified with ATom1 observations.
HR(RO2), which also favors cloud particles, minorly contributes to the
tropospheric chemistry, but it has enormous impacts on PAN and NOx
transportation (+144 % NOx for the North Pacific and Atlantic regions
in JJA) and the negative changes in CO (-1.78 %), as compared to positive
effects by HR(N2O5) and HR(HO2), that can not be neglected.
However, the effect magnitude requires further investigation because of
model overestimates for cloud fractions in this region.
The sensitivity of tropospheric chemistry to HR magnitude was
determined as a nonlinear function. The increasing effect on the global
O3 abundance from HR(HO2) will sharply change to a decreasing effect
when the uptake rate is amplified by more than 3 times. This turning is
ascribed to the uptake onto aerosols in DJF. In general, uptake to aerosols
is more responsive to the heterogeneous loss rate than uptake to clouds.
Overall, the N2O5 and HO2 uptakes will sweep away atmospheric
oxidants, thereby enhancing concentrations of pollutants. Our results reveal
that although HRs are reported to be associated with polluted regions, the
global effects of HRs reach further remote regions such as the marine
boundary layer at mid-latitudes and the upper troposphere. For
ground-based studies of polluted regions such as China, it should be
considered that HR(HO2) and HR(RO2) were able to contribute
to the NOx increment in DJF and JJA, respectively. Moreover, the
HR(HO2) effect might hinder efforts at reducing environmental pollution
in urban areas because it increases NOx but decreases O3 at the
surface. Therefore, if this reaction is minimized because of a decrease in
particulate matter, then the surface ozone level might increase.
Code availability
The source code for CHASER V4.0 and input data to reproduce results in this
work can be obtained from the repository at
10.5281/zenodo.4153452 (Ha et al., 2020).
Data availability
The underlying data from R/V Mirai cruises for the period 2015–2017 are available from http://www.godac.jamstec.go.jp/darwin/e (last access: 30 June 2020). Due to a recent data security incident, the data owner (JAMSTEC) is suspending public access to this dataset. For any inquiries, please send email to yugo@jamstec.go.jp.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-14-3813-2021-supplement.
Author contributions
PTMH performed all simulations (except simulations for the
cloud fraction validation), interpreted the results, and wrote the
manuscript. KS developed the model code, conceived of the presented
idea, and supervised the findings of this work and the manuscript preparation.
RM carried out the simulations and plots for the validation of cloud
fraction. YK and FT provided the R/V Mirai ship data and contributed to the discussion of the work's findings.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are
grateful to the NASA scientists and staff for providing the ATom data
(https://espo.nasa.gov/atom/content/ATom, last access: 30 June 2020). The simulations were completed
using the supercomputer (NEC SX-Ace and SX-Aurora TSUBASA) at NIES, Japan.
The surface observational data for the model validation were taken from the
monitoring networks EANET (https://www.eanet.asia/, last access: 25 February 2020) and EMEP
(https://www.emep.int/, last access: 25 February 2020).
We also would like to thank the two anonymous reviewers for their helpful
suggestions and advice on the earlier draft of the manuscript.
Financial support
This research was supported by the Global Environment Research Fund (grant nos. S-12 and S-20) of the Ministry of the Environment (MOE), Japan, and by JSPS KAKENHI (grant nos. JP20H04320, JP19H05669, and JP19H04235).
Review statement
This paper was edited by Gerd A. Folberth and reviewed by two anonymous referees.
ReferencesAkimoto, H., Nagashima, T., Li, J., Fu, J. S., Ji, D., Tan, J., and Wang, Z.: Comparison of surface ozone simulation among selected regional models in MICS-Asia III – effects of chemistry and vertical transport for the causes of difference, Atmos. Chem. Phys., 19, 603–615, 10.5194/acp-19-603-2019, 2019.Apodaca, R. L., Huff, D. M., and Simpson, W. R.: The role of ice in N2O5 heterogeneous hydrolysis at high latitudes, Atmos. Chem. Phys., 8, 7451–7463, 10.5194/acp-8-7451-2008, 2008.Bates, K. H. and Jacob, D. J.: A new model mechanism for atmospheric oxidation of isoprene: global effects on oxidants, nitrogen oxides, organic products, and secondary organic aerosol, Atmos. Chem. Phys., 19, 9613–9640, 10.5194/acp-19-9613-2019, 2019.
Battan, L. J. and Reitan, C. H.: Droplet size measurements in convective
clouds, in Artificial simulation of Rain, Pergamon Press, New York, 184–191, 1957.
Betterton, E. A.: Henry's Law constants of soluble and moderately soluble
organic gases: effects on aqueous-phase chemistry, in: Gaseous pollutants:
Characterization and cycling, Wiley, 24, 1–50, 1992.Bian, H., Chin, M., Hauglustaine, D. A., Schulz, M., Myhre, G., Bauer, S. E., Lund, M. T., Karydis, V. A., Kucsera, T. L., Pan, X., Pozzer, A., Skeie, R. B., Steenrod, S. D., Sudo, K., Tsigaridis, K., Tsimpidi, A. P., and Tsyro, S. G.: Investigation of global particulate nitrate from the AeroCom phase III experiment, Atmos. Chem. Phys., 17, 12911–12940, 10.5194/acp-17-12911-2017, 2017.
Brown, S. S. and Stutz, J.: Nighttime radical observations and chemistry,
Chem. Soc. Rev., 41, 6405–6447, 2012.Chen, Y., Wolke, R., Ran, L., Birmili, W., Spindler, G., Schröder, W., Su, H., Cheng, Y., Tegen, I., and Wiedensohler, A.: A parameterization of the heterogeneous hydrolysis of N2O5 for mass-based aerosol models: improvement of particulate nitrate prediction, Atmos. Chem. Phys., 18, 673–689, 10.5194/acp-18-673-2018, 2018.Cooper, P. L. and Abbatt, J. P. D.: Heterogeneous interactions of OH and
HO2 radicals with surfaces characteristic of atmospheric particulate
matter, J. Phys. Chem., 100, 2249–2254, 1996.
Dentener, F. J.: Heterogeneous chemistry in the troposphere, PhD Thesis,
U. of Utrecht, the Netherlands, 1993.Dentener, F. J. and Crutzen, P. J.: Reaction of N2O5 on
tropospheric aerosols: Impact on the global distributions of NOx,
O3, and OH, J. Geophys. Res.-Atmos., 98, 7149–7163, 1993.de Reus, M., Fischer, H., Sander, R., Gros, V., Kormann, R., Salisbury, G., Van Dingenen, R., Williams, J., Zöllner, M., and Lelieveld, J.: Observations and model calculations of trace gas scavenging in a dense Saharan dust plume during MINATROC, Atmos. Chem. Phys., 5, 1787–1803, 10.5194/acp-5-1787-2005, 2005.Evans, M. J. and Jacob, D. J.: Impact of new laboratory studies of
N2O5 hydrolysis on global model budgets of tropospheric nitrogen
oxides, ozone, and OH, Geophys. Res. Lett., 32, 1–4, 2005.
Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows,
J. P., Clerbaux, C., Coheur, P.-F., Cuesta, J., Cuevas, E., Doniki, S., Dufour, G., Ebojie, F., Foret, G., Garcia, O., Granados-Muñoz, M. J., Hannigan, J. W., Hase, F., Hassler, B., Huang, G., Hurtmans, D., Jaffe, D., Jones, N., Kalabokas, P., Kerridge, B., Kulawik, S., Latter, B., Leblanc, T., Le Flochmoën, E., Lin, W., Liu, J., Liu, X., Mahieu, E., McClure-Begley, A., Neu, J. L., Osman, M., Palm, M., Petetin, H., Petropavlovskikh, I., Querel, R., Rahpoe, N., Rozanov, A., Schultz, M. G., Schwab, J., Siddans, R., Smale, D., Steinbacher, M., Tanimoto, H., Tarasick, D. W., Thouret, V., Thompson, A. M., Trickl, T., Weatherhead, E., Wespes, C., Worden, H. M., Vigouroux, C., Xu, X., Zeng, G., and Ziemke, J.:
Tropospheric Ozone Assessment Report: Present-day distribution and trends of
tropospheric ozone relevant to climate and global atmospheric chemistry
model evaluation, Elem. Sci. Anth., 6, 1–58, 2018.
Geyer, A., Bächmann, K., Hofzumahaus, A., Holland, F., Konrad, S.,
Klüpfel, T., Pätz, H.-W., Perner, D., Mihelcic, D., Schäfer,
H.-J., Volz-Thomas, A., and Platt, U.: Nighttime formation of peroxy and hydroxyl radicals during the
BERLIOZ campaign: Observations and modeling studies, J. Geophys. Res.-Atmos., 108, 1–16, 2003.Ha, T. M. P., Taketani, F., Kanaya, Y., Matsuda, R., and Sudo, K.: Effects of
heterogeneous reactions on global tropospheric chemistry (Version
CHASER-V4.0) [Code], Zenodo, 10.5281/zenodo.4153452, 2020.Huijnen, V., Williams, J. E., and Flemming, J.: Modeling global impacts of heterogeneous loss of HO2 on cloud droplets, ice particles and aerosols, Atmos. Chem. Phys. Discuss., 14, 8575–8632, 10.5194/acpd-14-8575-2014, 2014.Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H., Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George, M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L., Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M., Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O., Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC team: The MACC reanalysis: an 8 yr data set of atmospheric composition, Atmos. Chem. Phys., 13, 4073–4109, 10.5194/acp-13-4073-2013, 2013.
Jacob, D. J.: Chemistry of OH in remote clouds and its role in the
production of formic acid and peroxymonosulfate, J. Geophys. Res., 91, 9807–9826, 1986.
Jacob, D. J.: Introduction to Atmospheric Chemistry, Princeton University
Press, USA, 1999.
Jacob, D. J.: Heterogeneous chemistry and tropospheric ozone, Atmos. Environ., 34, 2131–2159, 2000.Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, 10.5194/acp-15-11411-2015, 2015.Japan Meteorological Agency/Japan: JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 10.5065/D6HH6H41 (last access: 12 June 2020), 2013, updated monthly.Kanaya, Y., Sadanaga, Y., Hirokawa, J. U. N., Kajii, Y., and Akimoto, H.:
Development of a Ground-Based LIF Instrument for Measuring HOx
Radicals: Instrumentation and Calibrations, J. Atmos. Chem., 38, 73–110,
2001.Kanaya, Y., Nakamura, K., Kato, S., Matsumoto, J., Tanimoto, H., and Akimoto,
H.: Nighttime variations in HO2 radical mixing ratios at Rishiri Island
observed with elevated monoterpene mixing ratios, Atmos. Environ., 36,
4929–4940, 2002a.Kanaya, Y., Yokouchi, Y., Matsumoto, J., Nakamura, K., Kato, S., Tanimoto,
H., Furutani, H., Toyota, K., and Akimoto, H.: Implications of iodine chemistry
for daytime HO2 levels at Rishiri Island, Geophys. Res. Lett., 29, 1–4,
2002b.
Kanaya, Y., Kajii, Y., and Akimoto, H.: Solar actinic flux and photolysis
frequency determinations byradiometers and a radiative transfer model at
Rishiri Island: Comparisons, cloud effects, and detection of an aerosol
plumefrom Russian forest fires, Atmos. Environ., 37, 2463–2475, 2003.Kanaya, Y., Cao, R., Kato, S., Miyakawa, Y., Kajii, Y., Tanimoto, H.,
Yokouchi, Y., Mochida, M., Kawamura, K., and Akimoto, H.: Chemistry of OH
and HO2 radicals observed at Rishiri Island, Japan, in September 2003:
Missing daytime sink of HO2 and positive nighttime correlations with
monoterpenes, J. Geophys. Res., 112, D11308, 10.1029/2006JD007987, 2007.Kanaya, Y., Pochanart, P., Liu, Y., Li, J., Tanimoto, H., Kato, S., Suthawaree, J., Inomata, S., Taketani, F., Okuzawa, K., Kawamura, K., Akimoto, H., and Wang, Z. F.: Rates and regimes of photochemical ozone production over Central East China in June 2006: a box model analysis using comprehensive measurements of ozone precursors, Atmos. Chem. Phys., 9, 7711–7723, 10.5194/acp-9-7711-2009, 2009.Kanaya, Y., Miyazaki, K., Taketani, F., Miyakawa, T., Takashima, H., Komazaki, Y., Pan, X., Kato, S., Sudo, K., Sekiya, T., Inoue, J., Sato, K., and Oshima, K.: Ozone and carbon monoxide observations over open oceans on R/V Mirai from 67∘ S to 75∘ N during 2012 to 2017: testing global chemical reanalysis in terms of Arctic processes, low ozone levels at low latitudes, and pollution transport, Atmos. Chem. Phys., 19, 7233–7254, 10.5194/acp-19-7233-2019, 2019.
Lawrence, M. G. and Crutzen, P. J.: The impact of cloud particle
gravitational settling on soluble trace gas distributions, Tellus, Ser. B
Chem. Phys. Meteorol., 50B, 263–289, 1998.
Lelieveld, J. and Crutzen, P. J.: Influences of cloud photochemical
processes on tropospheric ozone, Nature, 343, 227–233, 1990.
Lelieveld, J. and Crutzen, P. J.: The role of clouds in tropospheric
photochemistry, J. Atmos. Chem., 12, 229–267, 1991.
Li, J., Chen, X., Wang, Z., Du, H., Yang, W., Sun, Y., Hu, B., Li, J., Wang,
W., Wang, T., Fu, P., and Huang, H.: Radiative and heterogeneous chemical effects of
aerosols on ozone and inorganic aerosols over East Asia, Sci. Total
Environ., 622–623, 1327–1342, 2018.
Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., and Bates, K. H.:
Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China,
P. Natl. Acad. Sci. USA, 116, 422–427, 2019.Liao, H. and Seinfeld, J. H.: Global impacts of gas-phase chemistry-aerosol
interactions on direct radiative forcing by anthropogenic aerosols and
ozone, J. Geophys. Res.-Atmos., 110, D18208, 10.1029/2005JD005907, 2005.Lin, J.-T., Liu, Z., Zhang, Q., Liu, H., Mao, J., and Zhuang, G.: Modeling uncertainties for tropospheric nitrogen dioxide columns affecting satellite-based inverse modeling of nitrogen oxides emissions, Atmos. Chem. Phys., 12, 12255–12275, 10.5194/acp-12-12255-2012, 2012.Liu, Y. and Wang, T.: Worsening urban ozone pollution in China from 2013 to 2017 – Part 2: The effects of emission changes and implications for multi-pollutant control, Atmos. Chem. Phys., 20, 6323–6337, 10.5194/acp-20-6323-2020, 2020.
Logan, J. A., Prather, M. J., Wofsy, S. C., and McElroy, M. B.: Tropospheric
chemistry: a global perspective, J. Geophys. Res., 86, 7210–7254, 1981.Loukhovitskaya, E., Bedjanian, Y., Morozov, I., and Le Bras, G.: Laboratory
study of the interaction of HO2 radicals with the NaCl, NaBr,
MgCl2⋅ 6H2O and sea salt surfaces, Phys. Chem. Chem.
Phys., 11, 7896–7905, 2009.Lowe, D., Archer-Nicholls, S., Morgan, W., Allan, J., Utembe, S., Ouyang, B., Aruffo, E., Le Breton, M., Zaveri, R. A., Di Carlo, P., Percival, C., Coe, H., Jones, R., and McFiggans, G.: WRF-Chem model predictions of the regional impacts of N2O5 heterogeneous processes on night-time chemistry over north-western Europe, Atmos. Chem. Phys., 15, 1385–1409, 10.5194/acp-15-1385-2015, 2015.Macintyre, H. L. and Evans, M. J.: Sensitivity of a global model to the uptake of N2O5 by tropospheric aerosol, Atmos. Chem. Phys., 10, 7409–7414, 10.5194/acp-10-7409-2010, 2010.Macintyre, H. L. and Evans, M. J.: Parameterisation and impact of aerosol uptake of HO2 on a global tropospheric model, Atmos. Chem. Phys., 11, 10965–10974, 10.5194/acp-11-10965-2011, 2011.Mao, J., Fan, S., Jacob, D. J., and Travis, K. R.: Radical loss in the atmosphere from Cu-Fe redox coupling in aerosols, Atmos. Chem. Phys., 13, 509–519, 10.5194/acp-13-509-2013, 2013.Martin, R. V., Jacob, D. J., Yantosca, R. M., Chin, M., and Ginoux, P.:
Global and regional decreases in tropospheric oxidants from photochemical
effects of aerosols, J. Geophys. Res.-Atmos., 108, 4097, 10.1029/2002JD002622, 2003.
McFarquhar, G. M. and Heymsfield, A. J.: Microphysical characteristics of
three anvils sampled during the Central Equatorial Pacific Experiment, J.
Atmos. Sci., 53, 2401–2423, 1996.Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent, R., Fowler, D., Granier, C., Law, K. S., Mills, G. E., Stevenson, D. S., Tarasova, O., Thouret, V., von Schneidemesser, E., Sommariva, R., Wild, O., and Williams, M. L.: Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer, Atmos. Chem. Phys., 15, 8889–8973, 10.5194/acp-15-8889-2015, 2015.Morgenstern, O., Hegglin, M. I., Rozanov, E., O'Connor, F. M., Abraham, N. L., Akiyoshi, H., Archibald, A. T., Bekki, S., Butchart, N., Chipperfield, M. P., Deushi, M., Dhomse, S. S., Garcia, R. R., Hardiman, S. C., Horowitz, L. W., Jöckel, P., Josse, B., Kinnison, D., Lin, M., Mancini, E., Manyin, M. E., Marchand, M., Marécal, V., Michou, M., Oman, L. D., Pitari, G., Plummer, D. A., Revell, L. E., Saint-Martin, D., Schofield, R., Stenke, A., Stone, K., Sudo, K., Tanaka, T. Y., Tilmes, S., Yamashita, Y., Yoshida, K., and Zeng, G.: Review of the global models used within phase 1 of the Chemistry–Climate Model Initiative (CCMI), Geosci. Model Dev., 10, 639–671, 10.5194/gmd-10-639-2017, 2017.Morita, A., Kanaya, Y., and Francisco, J. S.: Uptake of the HO2 radical
by water: Molecular dynamcs calculations and their implications for
atmospheric modeling, J. Geophys. Res.-Atmos., 109, D09201, 10.1029/2003JD004240, 2004.
National Research Council: Rethinking the Ozone Problem in Urban and
Regional Air Pollution, The National Academies Press, Washington, D.C., 1991.Osthoff, H. D., Sommariva, R., Baynard, T., Pettersson, A., Williams, E. J.,
Lerner, B. M., Roberts, J. M., Stark, H., Goldan, P. D., Kuster, W. C., Bates, T. S., Coffman, D., Ravishankara, A. R., and Brown, S. S.: Observation of daytime N2O5 in the marine boundary layer
during New England Air Quality Study – Intercontinental Transport and
Chemical Transformation 2004, J. Geophys. Res.-Atmos., 111, D23S14, 10.1029/2006JD007593, 2006.
Platt, U. F., Winer, A. M., Biermann, H. W., Atkinson, R., and Pitts, J. N.:
Measurement of Nitrate Radical Concentrations in Continental Air, Environ.
Sci. Technol., 18, 365–369, 1984.
Qin, M., Zhongming, C., Hengqing, S., Huan, L., Huihui, W., and Yin, W.: Impacts
of heterogeneous reactions to atmospheric peroxides: Observations and budget
analysis study, Atmos. Environ., 183, 144–153, 2018.Qu, Y., Chen, Y., Liu, X., Zhang, J., Guo, Y., and An, J.: Seasonal effects of
additional HONO sources and the heterogeneous reactions of N2O5 on
nitrate in the North China Plain, Sci. Total Environ., 690, 97–107, 2019.
Richard, W. P.: Chemistry of Atmosphere, Oxford University Press, USA, 2000.Riemer, N., Vogel, H., Vogel, B., Schell, B., Ackermann, I., Kessler, C.,
and Hass, H.: Impact of the heterogeneous hydrolysis of N2O5 on
chemistry and nitrate aerosol formation in the lower troposphere under
photosmog conditions, J. Geophys. Res., 108, 4144, 10.1029/2002JD002436, 2003.Riemer, N., Vogel, H., Vogel, B., Anttila, T., Kiendler-Scharr, A., and
Mentel, T. F.: Relative importance of organic coatings for the heterogeneous
hydrolysis of N2O5 during summer in Europe, J. Geophys. Res., 114,
D17307, 10.1029/2008JD011369, 2009.Saathoff, H., Naumann, K.-H., Riemer, N., Kamm, S., Mohler, O., Schurath,
U., Vogel, H., and Vogel, B.: The loss of NO2, HNO3,
NO3/N2O5, and HO2/HOONO2 on soot aerosol: A chamber
and modeling study, Geophys. Res. Lett., 28, 1957–1960, 2001.
Salisbury, G., Rickard, A. R., Monks, P. S., Allan, B. J., Bauguitte, S.,
Penkett, S. A., Carslaw, N., Lewis, A. C., Creasey, D. J., Heard, D. E., Jacobs, P. J., and Lee, J. D.: Production of peroxy radicals at night via reactions of ozone and the
nitrate radical in the marine boundary layer, J. Geophys. Res.-Atmos., 106, 12669–12687, 2001.
Schwartz, S. E.: Mass-Transport Considerations Pertinent to Aqueous Phase
Reactions of Gases in Liquid-Water Clouds, in: Chemistry of Multiphase
Atmospheric Systems, Springer, Berlin Heidelberg, 415–471, 1986.Sekiya, T., Miyazaki, K., Ogochi, K., Sudo, K., and Takigawa, M.: Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0, Geosci. Model Dev., 11, 959–988, 10.5194/gmd-11-959-2018, 2018.Shepson, P. B., Mackay, E., and Muthuramu, K.: Henry's law constants and
removal processes for several atmospheric β-hydroxy alkyl nitrates,
Environ. Sci. Technol., 30, 3618–3623, 1996.Sommariva, R., Bloss, W. J., Brough, N., Carslaw, N., Flynn, M., Haggerstone, A.-L., Heard, D. E., Hopkins, J. R., Lee, J. D., Lewis, A. C., McFiggans, G., Monks, P. S., Penkett, S. A., Pilling, M. J., Plane, J. M. C., Read, K. A., Saiz-Lopez, A., Rickard, A. R., and Williams, P. I.: OH and HO2 chemistry during NAMBLEX: roles of oxygenates, halogen oxides and heterogeneous uptake, Atmos. Chem. Phys., 6, 1135–1153, 10.5194/acp-6-1135-2006, 2006.Stadtler, S., Simpson, D., Schröder, S., Taraborrelli, D., Bott, A., and Schultz, M.: Ozone impacts of gas–aerosol uptake in global chemistry transport models, Atmos. Chem. Phys., 18, 3147–3171, 10.5194/acp-18-3147-2018, 2018.Sudo, K. and Akimoto, H.: Global source attribution of tropospheric ozone:
Long-range transport from various source regions, J. Geophys. Res.-Atmos., 112, D12302, 10.1029/2006JD007992,
2007.
Sudo, K., Takahashi, M., Kurokawa, J. I., and Akimoto, H.: CHASER: A global
chemical model of the troposphere 1. Model description, J. Geophys. Res.-Atmos., 107, ACH 7-1–ACH 7-20, 2002.
Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., and
Nakajima, T.: Global three-dimensional simulation of aerosol optical
thickness distribution of various origins, J. Geophys. Res., 105,
17853–17873, 2000.Taketani, F., Kanaya, Y., and Akimoto, H.: Kinetics of heterogeneous
reactions of HO2 radical at ambient concentration levels with
(NH4)2SO4 and NaCl aerosol particles, J. Phys. Chem. A, 112,
2370–2377, 2008.Taketani, F., Kanaya, Y., and Akimoto, H.: Heterogeneous loss of HO2 by
KCl, synthetic sea salt, and natural seawater aerosol particles, Atmos.
Environ., 43, 1660–1665, 2009.Taketani, F., Kanaya, Y., Pochanart, P., Liu, Y., Li, J., Okuzawa, K., Kawamura, K., Wang, Z., and Akimoto, H.: Measurement of overall uptake coefficients for HO2 radicals by aerosol particles sampled from ambient air at Mts. Tai and Mang (China), Atmos. Chem. Phys., 12, 11907–11916, 10.5194/acp-12-11907-2012, 2012.Thornton, J. A., Jaeglé, L., and McNeill, V. F.: Assessing known
pathways for HO2 loss in aqueous atmospheric aerosols: Regional and
global impacts on tropospheric oxidants. J. Geophys. Res.-Atmos., 113, D05303, 10.1029/2007JD009236, 2008.
Tie, X., Brasseur, G., Emmons, L., Horowitz, L., and Kinnison, D.: Effects
of aerosols on tropospheric oxidants: A global model study, J. Geophys. Res.-Atmos., 106, 22931–22964, 2001.
Villalta, P. W., Lovejoy, E. R., and Hanson, D. R.: Reaction probability of
peroxyacetyl radical on aqueous surfaces, Geophys. Res. Lett., 23,
1765–1768, 1996.Wang, H., Lu, K., Guo, S., Wu, Z., Shang, D., Tan, Z., Wang, Y., Le Breton, M., Lou, S., Tang, M., Wu, Y., Zhu, W., Zheng, J., Zeng, L., Hallquist, M., Hu, M., and Zhang, Y.: Efficient N2O5 uptake and NO3 oxidation in the outflow of urban Beijing, Atmos. Chem. Phys., 18, 9705–9721, 10.5194/acp-18-9705-2018, 2018.Wang, Z., Wang, W., Tham, Y. J., Li, Q., Wang, H., Wen, L., Wang, X., and Wang, T.: Fast heterogeneous N2O5 uptake and ClNO2 production in power plant and industrial plumes observed in the nocturnal residual layer over the North China Plain, Atmos. Chem. Phys., 17, 12361–12378, 10.5194/acp-17-12361-2017, 2017.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, 10.5194/gmd-4-845-2011, 2011.Xia, M., Wang, W., Wang, Z., Gao, J., Li, H., Liang, Y., Yu, C., Zhang, Y.,
Wang, P., Zhang, Y., Bi, F., Cheng, X., and Wang, T.: Heterogeneous Uptake of N2O5 in Sand
Dust and Urban Aerosols Observed during the Dry Season in Beijing,
Atmosphere, 10, 204, 10.3390/atmos10040204, 2019.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, 10.5194/acp-18-14095-2018, 2018.