Nitrous acid (HONO) is an important atmospheric gas given
its contribution to the cycles of NOx and HOx, but its role in
global atmospheric photochemistry is not fully understood. This study
implemented three pathways of HONO formation in the chemistry–climate model
CHASER (MIROC-ESM) to explore three physical phenomena: gas-phase kinetic
reactions (GRs), direct emission (EM), and heterogeneous reactions on
cloud and aerosol particles (HRs). We evaluated the simulations by the
atmospheric aircraft-based measurements from EMeRGe-Asia-2018 (Effect of
Megacities on the Transport and Transformation of Pollutants on the Regional
to Global Scales), ATom-1 (atmospheric tomography), observations from the
ship R/V Mirai, EANET (Acid Deposition Monitoring Network in eastern Asia)/EMEP
(European Monitoring and Evaluation Programme) ground-based stationary
observations, and the OMI (Ozone Monitoring Instrument). We showed that the
inclusion of the HONO chemistry in the modelling process reduced the model
bias against the measurements for PM2.5, NO3-/HNO3,
NO2, OH, HO2, O3, and CO, especially in the lower troposphere
and the North Pacific (NP) region.
We found that the retrieved global abundance of tropospheric HONO was 1.4 TgN. Of the three source pathways, HRs and EM contributed 63 % and 26 %
to the net HONO production, respectively. We also observed that reactions
on the aerosol surfaces contributed larger amounts of HONO (51 %) than
those on the cloud surfaces (12 %). The model exhibited significant
negative biases for daytime HONO in the Asian off-the-coast region, compared
with the airborne measurements by EMeRGe-Asia-2018, indicating the existence
of unknown daytime HONO sources. Strengthening of aerosol uptake of NO2
near the surface and in the middle troposphere, cloud uptake, and direct HONO
emission were all potential yet-unknown HONO sources. The most promising
daytime source for HONO found in this study was the combination of enhanced
aerosol uptake of NO2 and surface-catalysed HNO3 photolysis
(maxST+JANO3-B case), which could also remedy the model bias for NO2
and O3 during EMeRGe. We also found that the simulated HONO abundance
and its impact on NOx–O3 chemistry were sensitive to the yield of
the heterogeneous conversion of NO2 to HONO (vs. HNO3).
Inclusion of HONO reduced global tropospheric NOx (NO + NO2)
levels by 20.4 %, thereby weakening the tropospheric oxidizing capacity
(OH, O3) occurring for NOx-deficit environments (remote regions
and upper altitudes), which in turn increased CH4 lifetime (13 %)
and tropospheric CO abundance (8 %). The calculated reduction effect on
the global ozone level reduced the model overestimates for tropospheric column
ozone against OMI spaceborne observations for a large portion of the North Hemisphere. HRs
on the surfaces of cloud particles, which have been neglected in previous
modelling studies, were the main drivers of these impacts. This effect was
particularly salient for the substantial reductions of levels of OH
(40 %–67 %) and O3 (30 %–45 %) in the NP region during summer, given
the significant reduction of the NOx level (50 %–95 %). In contrast, HRs
on aerosol surfaces in China (Beijing) enhanced OH and O3 winter mean
levels by 600 %–1700 % and 10 %–33 %, respectively, with regards to their
minima in winter. Furthermore, sensitivity simulations revealed that the
heterogeneous formation of HONO from NO2 and heterogenous photolysis of
HNO3 coincided in the real atmosphere. Nevertheless, the global effects
calculated in the combined case (enhancing aerosol uptakes of NO2 and
implementing heterogeneous photolysis of HNO3), which most captured the
measured daytime HONO level, still reduced the global tropospheric oxidizing
capacity. Overall, our findings suggest that a global model that does not
consider HONO heterogeneous mechanisms (especially photochemical
heterogeneous formations) may erroneously predict the effect of HONO in
remote areas and polluted regions.
Introduction
Nitrous acid (HONO) is an important atmospheric gas as it participates in
the cycles of nitrogen oxides (NOx= NO + NO2) and radical
chemistry (OH, HO2, and RO2) (Kanaya et al., 2007; Ren et al.,
2013; Whalley et al., 2018). Researchers have suggested to include the HONO chemistry in atmospheric chemistry models for more accurate simulations of
oxidative substances (Jacob, 2000; Li et al., 2011). Despite the empirical
evidence indicating that the HONO concentrations in urban environments
can reach 14 ppbv at night and can reach several hundred pptv throughout the
day (Appel et al., 1990; Febo et al., 1996; Kanaya et al., 2007; Lee et al.,
2016; Tan et al., 2017; Whalley et al., 2018), the HONO formation mechanism
remains unclear. More specifically, the mechanisms of the HONO daytime
sources have recently attracted considerable attention of researchers
(Kleffmann et al., 2003; Li et al., 2014; VandenBoer et al., 2013; Xue et
al., 2022a, b; Ye et al., 2018).
The only homogeneous reaction known to produce HONO in the troposphere is
the direct combination of OH and NO (Reaction R2). Note that the major loss of HONO
occurs via photolysis (Reaction R1) in the atmosphere at 300–405 nm:
R1HONO+hν→OH+NO300nm<λ<405nm,R2NO+OH+M→HONO+M,R3HONO+OH→NO2+H2O.
Moreover, the photolysis of HONO (Reaction R1) has attracted considerable attention
in the literature as a critical source of OH radicals in the polluted urban
atmosphere (e.g. Calvert et al., 1994; Harris et al., 1982; Jenkin et al.,
1988; Platt and Perner, 1980). The OH level at sunrise can be increased by a
factor of 5 due to the photolysis of HONO, with the regional daily maximum
O3 level increasing by 8 % (Jenkin et al., 1988). Besides the direct
loss via photolysis, the reaction of HONO with OH (Reaction R3) may also contribute
to the daytime loss of HONO (Burkholder et al., 1992).
Notably, some nighttime measurements hinted on the heterogeneous sources of
HONO from aerosol surfaces. For instance, Harrison and Kitto (1994) have
provided evidence about the HONO source from high-concentration episodes of
>10 ppbv NO2 for grassland in eastern England (Harrison and
Kitto, 1994). Two reactions have been widely suggested to produce HONO on
aerosol surfaces: 2NO2+ H2O → HONO + HNO3 and NO
+ NO2+ H2O → 2HONO. The first process has been proven to
be first order with NO2 and H2O in reaction chamber studies
(Sakamaki et al., 1983, Jenkin et al., 1988). The second process was
evaluated by using laboratory surfaces (Sakamaki et al., 1983, Jenkin et
al., 1988) and by using field observations in the presence of high O3
and when NO2 was the dominant form of NOx (Kessler and Platt,
1984). As a result, the second process was proposed as a peculiarly
important source of HONO in the urban atmosphere (Ammann et al., 1998;
Gerecke et al., 1998). In the past two decades, researchers have
investigated the heterogeneous NO2 reactivity on vegetated, aqueous,
sea salt, carbonaceous, and soot surfaces (Acker et al., 2001, 2006; Arens
et al., 2001; Kleffmann and Wiesen, 2005; Kleffmann et al.,1998; Lammel and
Cape, 1996; Lee et al., 2016; Notholt et al., 1992; Reisinger, 2000; Rubio
et al., 2002; Stutz et al., 2002). In our model, these two processes are
simplified as NO2→ 0.5 HONO + 0.5 HNO3 (Reaction R4) and NO2→ HONO (Reaction R5).
Also, some modelling studies have reported overestimations of HONO over
remote areas, indicating the HONO release from or deposition in snow (Chu et
al., 2000; Fenter and Rossi, 1996; Kerbrat et al., 2010), partitioning to
cloud water (Bongartz et al. 1994; Cape et al., 1992; Harrison and Collins,
1998; Mertes and Wahner, 1995), and deliquescent aerosol surfaces (Harrison
and Collins, 1998). The loss process occurs via the reaction HONO +
H2O → NO-+ H3O+, simplified in our model as HONO
→ NO (Reaction R6) for surfaces of liquid and aqueous sulfate aerosols.
The natural sources of HONO include plant foliar cuticles or soil biological
crust (Hayashi and Noguchi, 2006; Oswald et al., 2013; Porada et al., 2019;
Su et al., 2011), with an estimated global total emission of 0.69 Tg yr-1
of HONO–N (Porada et al., 2019). Given the widespread occurrence of
nitrite-fertilized soil in natural environment, highly acidic soils are
arguably the strong sources of HONO and OH (Su et al., 2011). This
potentially important source has been likely overseen by many previous
modelling studies at both global and regional scales. Soil emissions could
sustain the daytime HONO budget at relatively low aerosol concentrations (Lu
et al., 2018). Anthropogenic activities can also directly emit HONO through
incomplete combustion, as vehicles, for instance, can yield
concentrations as high as 7 ppb (Kirchstetter et al., 1996; Kurtenbach et al., 2001).
In regional air quality models, HONO sources from vehicles and vessels are
often given at 0.8 %–2.3 % of the NOx emissions level, given the
differences between gasoline and diesel vehicle types (e.g. Aumont et al.,
2003; Kurtenbach et al., 2001; Li et al., 2011; Zhang et al., 2016).
Many field observational studies reported unknown HONO sources during the
day, and various mechanisms have been proposed as efficient daytime HONO
formation mechanisms. The photolysis of particle-phase NO3-<300 nm has been previously suggested as a
supplemental NOx source (Romer et al., 2018) and can be the efficient
HONO production mechanism during the daytime in an aqueous environment with
low pH and the presence of OH scavengers (Benedict et al., 2017a, b; Scharko et al., 2014; Ye et al., 2018). Another study
addressed the altitudes below 300 m, where HONO deposited onto the ground
surface at night and further proposed to be a significant reservoir for HONO
during the day (VandenBoer et al., 2013). Such a parameter for ground
surfaces in a global model is somewhat uncertain. Moreover, the HONO source
from ground surfaces may only affect the lower boundary layer while
insignificantly contributing to the tropospheric HONO budget (Ye et al.,
2018; Zhang et al., 2009). Furthermore, the particle-phase NO3-
photolysis can occur on both ground and aerosol surfaces (HNO3+
hν→ HONO) with a 2-orders-of-magnitude-faster rate than its rate
in the gas phase (Lee et al., 2016; Lu et al., 2018). Photolysis of
ortho-nitrophenols, photoexcited NO2 gas reaction (HO2×H2O + NO2→ HONO), and photosensitized heterogeneous conversion
of NO2 on ground surfaces are all potential daytime HONO sources (Jorba et al.,
2012; Lee et al., 2016; Li et al., 2014), yet the mechanisms are
complicated, and their efficiency is merely evaluated for ground-based
observation.
Many scholars have scrupulously addressed the effects of HONO in polluted
regions as well. For instance, HONO-induced enhancements in winter daytime
HOx (up to >200 % for OH) and O3 (6 %–12 %) over
urban sites in China have been reported (Li et al., 2011; Lu et al., 2018;
Zhang et al., 2016). A box modelling study analysed the detailed budget of
HONO in London and found that HONO chemistry increased OH by 20 % during
the day (Lee et al., 2016). A global modelling study found increments for OH
and O3 across the globe and throughout the troposphere, with a maximum
of 30 ppb O3 in eastern Asia and slight NO2 increment, although
the results were evaluated with only ground-based data (Jorba et al., 2012).
However, enhanced O3 levels in response to additional OH production
from the HONO photolysis only occur in high-NOx regions, although they
can be decreased in some areas under low-NOx conditions (Jorba et al.,
2012). At the same time, another 3D modelling study used a constant occurrence ratio for HONO/NOx of 0.02 globally and reported similar patterns for O3 changes regarding HONO chemistry (Elshorbany et al., 2012). The NOx reduction effects that follow the NO2 conversion are suggested
to be more critical over the oceans than over continental regions, with up
to 20 % NOx reduction and 5 %–20 % HNO3 enhancement over
ocean regions of the lower troposphere (Martin et al., 2003).
As H2O is required for the uptake of NO2 on surfaces, wet surfaces
have been broadly recommended as favoured surfaces for NO2 uptake.
Therefore, cloud droplets can be an important surface for heterogeneous
reactions of NO2 because they are ubiquitous in the troposphere.
Heterogeneous reactions by clouds can have a similar impact to aerosol
particles on tropospheric O3 and OH levels (Holmes et al., 2019).
However, this aspect has been overlooked many times in previous studies,
leading to potentially underestimating (or even dismissing) the potential
effects over remote environments.
This study introduced HONO photochemical processes into the global
atmospheric chemistry model CHASER V4.0, which did not consider HONO
chemistry before. The standard model configuration used basic mechanisms of
HONO chemistry, while various sensitivity cases implementing other potential
HONO sources were also conducted to force simulation into an agreement with
the observed HONO values. The main idea for the HONO inclusion was to
elaborate the model simulation for tropospheric oxidative substances while
focusing on aerosol and cloud processes. The model included the detailed
online calculation of O3–HOx–NOx–CH4–CO coupling and
oxidation of non-methane hydrocarbons (NMHCs) (Sudo et al., 2002) and
heterogeneous processes for N2O5, HO2, and RO2 radicals
(Ha et al., 2021; Sekiya and Sudo, 2014; Sekiya et al., 2018; Sudo and
Akimoto, 2007). In Sect. 2, we describe the approach, including the model
description and configuration. In Sect. 3.1, simulated daytime HONO was
verified with aircraft measurements for an Asian off-the-coast region. In addition, our model was evaluated by the available observations for atmospheric species, including aircraft, ship, and ground
station observations, particularly addressing the roles of the HRs. Section 3.2 presents
the model results for HONO distributions, verification for global effects on
tropospheric column ozone (TCO) with the Ozone Monitoring Instrument (OMI) spaceborne observations, global HONO impacts including different effects from
each pathway, and a discussion on the uncertainty of the calculated effects.
Finally, Sect. 4 effectively represents the summary and concluding
remarks.
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, oceanBiomass burning specified by MACC reanalysisAerosolBC/OC, sea salt, and dustBC ageing with SOx/SOA productionChemical process94 chemical species, 263 chemical reactions (gas phase, liquid phase, non-uniform)Ox–NOx–HOx–CH4–CO chemistry with VOCsSO2, DMS oxidation (sulfate aerosol simulation)SO4–NO3–NH4 system and nitrate formationFormation of SOABC ageing(+) Heterogeneous reactions: eight reactions of N2O5, HO2, RO2; constant uptake coefficients (γ) on types of aerosols (ice, liquid, sulfate, sea salt, dust, OC)Method and configurationsGlobal chemistry model
This study applied the global chemistry model CHASER (MIROC-ESM) (Sudo et
al., 2002, Suda and Akimoto, 2007; Watanabe et al., 2011), which considered the detailed
photochemistry in the troposphere and stratosphere. The chemistry component
of the model, based on CHASER V4.0, retrieved the concentrations of 94 total
species and 258 chemical reactions (57 photolytic, 180 kinetic, and 21 heterogeneous reactions on tropospheric aerosol and cloud surfaces and polar
stratospheric clouds) (Table 1), excluding the new
HONO chemistry implemented in this study. We used the HTAP-II (Hemispheric
Transport of Air Pollution) emission inventory for 2008
(https://edgar.jrc.ec.europa.eu/dataset_htap_v2, last access: 16 November 2021) for O3 and aerosol precursors
(NOx, CO, VOCs, SO2), with biomass burning emissions derived from
the MACC (Monitoring Atmospheric Composition and Climate) reanalysis system
(https://gmao.gsfc.nasa.gov/reanalysis/MERRA/ceop.php, last access: 16 November 2021). The details about
CHASER could be found in the earlier studies (Ha et al., 2021; Morgenstern
et al., 2017; Sekiya et al., 2018). In this study, the newly added HONO
system included three pathways of HONO formation and interactions: (1)
gas-phase formation via the NO + OH Reaction (R2), the photolysis of HONO
Reaction (R1), and Reaction (R3) of HONO with OH, hereafter denoted as GRs; (2)
HONO direct emissions estimated from anthropogenic- and soil-NOx
emissions (hereafter denoted as EM); and (3) the HONO conversion from
NO2 (Reactions R4, R5) and its loss on liquid/ice surfaces and aqueous aerosols
(Reaction R6), hereafter denoted as HRs.
The investigation on heterogeneous photolysis of HNO3 (HNO3+
hν→ HONO), which was suggested as an efficient HONO source at
daytime (Lee et al., 2016; Zhou et al., 2011), is presented in chap. 3 as
sensitivity cases in the effort of making the simulation for daytime HONO
compatible with measurement. This photolysis was simply accessed in the
model via its rate using a multiplication factor to the gas-phase HNO3
photolysis (HNO3+ hν→ OH + NO2) (see Sect. 3.1.2).
Another proposed daytime HONO source from the light-dependent gas-phase
reaction of HO2 and NO2 (HO2×H2O + NO2→ HONO) (Li et al., 2014) was not investigated in this study. However,
a simple gas-phase reaction of HO2 and NO2 (HO2+ NO2→ HONO + O2) (Burkholder et al., 2015) was introduced, but it
did not successfully preserve the total reactive nitrogen chemistry
(NOy); hence, it was omitted in this study.
Experimental setup
The Global Emissions Initiative (GEIA) inventory
(http://www.geiacenter.org/, last access: 16 June 2021) was applied to quantify the soil NOx
emissions (6 TgN yr-1) and anthropogenic NOx emissions (45 TgN yr-1). Since this broadly applied inventory was not currently available
for HONO, this study tentatively imposed the HONO direct emissions based on
the above NOx emission inventory through a constant factor of 0.1
(10 % of NOx emissions). This assumption (soils + combustion) led
to a global HONO soil-emission estimate of about 0.6 TgN yr-1,
equivalent to the estimate from Porada et al. (2019), and it suggested that
the anthropogenic emission for HONO is 4.5 TgN yr-1. For HONO from
exhaust sources, this factor (10 %) was considerably higher than the
previously reported estimate of 0.7 %, derived for combustion (Xue et al.,
2022b), or 0.8 %–2.3 % for on-road vehicles (Aumont et al., 2003; Kurtenbach
et al., 2001; Li et al., 2011) and 3 %–6 % for commercial aircraft (Lee
et al., 2011). However, this factor of HONO emission (10 % NOx
emission) intended to show the apparent potential impacts of direct HONO
sources on the tropospheric chemistry.
The photolysis reaction HONO + hν→ OH + NO (300<λ<405 nm) (Reaction R1) was employed with the wavelength-dependent
cross sections following the recent study of Burkholder et al. (2015).
The kinetic homogeneous reactions NO + OH + M → HONO + M (Reaction R2)
and HONO + OH → NO2+ H2O (Reaction R3) were applied with the low-
and high-pressure-limit rate constants, which were temperature dependent, as
suggested in the aforementioned study.
In CHASER, the heterogeneous chemistry of interest was simplified as a
first-order chemical loss in the aerosol phase for a species transferred
from the gas phase. The rate of this pseudo-loss was combined, and the
first-order-loss rate for heterogeneous processes was calculated by using
the Schwartz theory (Jacob, 2000; Schwartz, 1986), being simply treated with
the mass transfer limitations in addition to the reactive uptake coefficient
(γ) (Ha et al., 2021). Note that only surface reactions were
considered in CHASER, and there was no bulk particle reaction for the HR
scheme.
The uptake coefficient parameter (γ) is defined as the net
probability that a molecule X undergoing a gas-kinetic collision with a
surface is taken up onto the surface. An average uptake coefficient for
NO2 (Reaction R4) of 10-4 (10-6–10-3) for the conversion of
aqueous aerosols and clouds has been previously suggested (Jacob, 2000;
Kleffmann et al., 1998; Li et al., 2018; Lu et al., 2018). The NO2
uptake by organic carbon aerosols has been reported to have similar
coefficient values (Salgado-Muñoz and Rossi, 2002). The uptake
coefficient for fresh black carbon is highly efficient and equals
3×10-3 (Ammann et al., 1998; Li et al., 2018). The parameters
for the uptake coefficients of Reaction (R4) applied in the CHASER model are shown in
Table 2.
As previous studies have noted, the fast initial uptake of NO2 is
observed on soot with an uptake coefficient in the range of
10-1–10-4 (Ammann et al., 1998). However, it rapidly
decreased to ∼ 10-7 over 5 min (Kleffmann et al., 1999)
and to <4×10-8 for 5 d aged surfaces (Saathoff et al.,
2001). In organic soot, γ is in the range of 10-4–10-6
(Al-Abadleh et al., 2000; Arens et al., 2001;
Salgado-Muñoz et al., 2002). In CHASER, the NO2 conversion on
organic carbon and soot (Reaction R5) was tentatively applied with uptake
coefficients of 10-4 and 3×10-4, respectively, which
also falls within the previously suggested range (10-6–10-3)
considering the higher efficiency for soot (Table 2).
Uptake coefficients for heterogeneous formation and loss of HONO.
Also, previous laboratory experiments have introduced a wide range for the
uptake coefficient of HONO by Reaction (R6), that is, 3.7×10-3 at 178 K to 1.3×10-5 at 200 K for the ice surface (Fenter and Rossi,
1996; Chu et al., 2000) and 4×10-3–4×10-2 at
278 K (Mertes and Wahner, 1995) or 0.03–0.15 at 297 K (Bongartz et al. 1994)
for liquid water surfaces. In the aerosol flow reactor experiment on
deliquescent sodium chloride and ammonium sulfate droplets at 279 K, the
HONO reactive uptake coefficient of 0.0028 for 85 % relative humidity has
been previously obtained (Harrison and Collins, 1998). In CHASER, the
aforementioned reference values for HONO uptake on ice, liquid clouds, and
aqueous sulfate were simply averaged to be used as a heterogeneous loss of
HONO (Reaction R6) in the atmosphere (Table 2: last row).
In this study, two main simulations, OLD and STD, and three sensitivity
simulations (Table 3, no. 2–4) were conducted to
isolate the distinct impacts of each pathway of the HONO chemistry for
different surface types considered in the model
(Table 3). The OLD simulation was run with the base
model configuration without any HONO species and HONO-related processes. The
heterogeneous scheme in the OLD simulation contained eight reactions on
N2O5 (N2O5→ 2HNO3), HO2 (HO2→
0.5 H2O2+ 0.5 O2), and RO2 (RO2→ inert
products) (Ha et al., 2021). The control case (STD) considered all three
types of HONO sources: direct emissions (EMs), gas-phase reactions (GRs), and
heterogeneous reactions (HRs). To quantify the effects of each mechanism
using Eq. (1), two sensitivity cases (GR, GR+HR) intentionally implemented
GRs (Reactions R1, R2, R3) into the OLD case and HRs (Reactions R1, R2, R3, R4, R5, R6) into the
GR case, respectively. GR+HR(cld) was another sensitive case like
GR+HR, with HRs on aerosols excluded to investigate the different effects
of clouds and aerosols. Equation (1) determines the effects of each mechanism on
atmospheric species i (i= OH, O3, NOx, CO) by concentration
differences of i in two relevant cases being compared to that in the OLD
case.
Ei=Case1i-Case2iOLDi×100(%),
where Case1i and Case2i are the concentrations of i in two separate
cases: GR and OLD cases for the pure effects by the gaseous mechanism,
GR+HR and GR cases for the effects of heterogeneous mechanisms, STD and
GR+HR cases for the HONO emissions effects, and GR+HR(cld) and GR cases
for the effects of heterogeneous reactions that exclusively occur on ice and
cloud particles.
Lists of the datasets used in this study for verification. Related
simulations with their original model time step are interpolated to the
comparing time step.
Verified speciesRegionsDataset nameTimeMeasuring stepModel stepInterpolating stepPM2.5, SO42-, NO3-,NOx, O3, HNO3East AsiaEANET (station)2010–2016Daily to2-weeklyDailyMonthlyPM2.5, SO42-, NO3-,NOx, O3, COEuropeEMEP (station)2010–2016HourlyDailyMonthlyCO, O3Australia, Indonesia, Japan, and AlaskaMirai (vessel)8,9/2015 1,8,9/2016 7,8,9/201730 min1 h30 minNO2, OH, CO, O3Pacific, Atlantic Ocean, Greenland, and North AmericaATom-1 (aircraft)8/201630 min1 h30 minTropospheric column ozone (TCO)60∘ S–60∘ NOMI (instrument)2010–2016DailyDailyMonthlyHONOJeju (South Korea), Taiwan, and the PhilippinesEMeRGe (aircraft)17/3/2018–4/4/201815–30 sHourly14–30 s
Location of measurements. (a) EANET stations for NOx and O3 and (b) for PM2.5, SO42, NO3-, and
HNO3, (c) EMEP stations, and (d) ATom-1 cruising altitudes are plotted.
In panels (a) and (b), each number describes the station name (see Table S1 in the Supplement). In panel (d),
the numbers show the flight tracks.
Observation data for model evaluation
We evaluated the OLD, STD, and sensitivity simulations with aircraft,
ship-based, ground-based, and satellite measurements. The observational
information and locations of the ship/aircraft tracks and surface sites for
the observations used in this study are summarized in
Table 4, Figs. 1 and 6, and Fig. S6 in the Supplement.
Daytime HONO concentrations were analysed by using the DLR HALO aircraft
(Deutsches Zentrum für Luft- und Raumfahrt High Altitude and LOng Range Research Aircraft) measurements made during the EMeRGe-Asia
(Effect of Megacities on the Transport and Transformation of Pollutants on
the Regional to Global Scales) campaign in March and April 2018, over an
off-the-coast region between South Korea (including Jeju Island as part of the
domain), Taiwan, and the Philippines
(http://www.iup.uni-bremen.de/emerge/home/, last access: 16 November 2021). The measuring time falls in the
range of 00:00 to 09:00 UTC, around 08:00 to 17:00 in local time (UTC+8).
The payload during the EMeRGe-Asia mission could be retrieved from the similar
mission EMeRGe-Europe (Andrés Hernández et al., 2021). Verification
with EMeRGe data helps explore the daytime HONO chemistry mechanisms in the
free troposphere.
To verify the vertical profiles of atmospheric species for the oceanic
tropospheric environment, ATom-1 aircraft measurements
(https://espo.nasa.gov/atom/content/ATom, last access: 16 June 2021) for NO2, OH, CO, and O3
during August 2018 were employed. We also utilized the ship-based
observational data from the R/V Mirai cruise (http://www.jamstec.go.jp/e/about/equipment/ships/mirai.html, last access: 16 June 2021) undertaken by Japan Agency for Marine-Earth
Science and Technology (JAMSTEC) for surface CO and O3 in summers
2015–2017 along the Japan–Alaska routes. The monthly data from 45 stations
during 2010–2016 were used to verify aerosol surface concentrations
(sulfate, nitrate) and trace gases (HNO3, NOx, O3) in the
Acid Deposition Monitoring Network in eastern Asia (EANET:
https://www.eanet.asia/, last access: 16 June 2021). We also used the European Monitoring and
Evaluation Programme (EMEP: https://www.emep.int/, last access: 16 June 2021) data, which compiles
observations over 245 European stations. To this end, simulated tropospheric
column ozone was also evaluated by using tropospheric column O3 (TCO)
derived from the OMI (Ozone Monitoring Instrument) spaceborne observations
(https://daac.gsfc.nasa.gov/, last access: 16 June 2021). For these evaluations and verifications, the
model data were compiled in the monthly or hourly time step, interpolated
corresponding to the observed data time step and coordinates.
A model bias for each species was calculated as the difference between the
simulated and observed concentrations, as shown in Eq. (2), where N is the
total number of data points used in the calculation.
bias=∑1Nmodel-observationN
Results and discussionVerification and validation of model simulations for cloud fraction, surface
area density, atmospheric species, and effects on HONO mechanismsCloud fraction and surface area density for cloud and aerosols
In this study, besides NO2 conversion onto clouds and aqueous particles
(Reaction R4), the losses of HONO onto the ice and liquid clouds (Reaction R6) are also
included. Therefore, for accurate simulations of HRs, we need to examine the
cloud distribution. The CHASER model applied the common cloud maximum–random
overlap assumptions (MRAN) in the radiation and cloud microphysics schemes
as other general circulation models to estimate the distribution of the
cloud fraction. The verification by using the satellite observation data
ISCCP D2, CALIPSO-GOCCP, and reanalysis data JRA55 generally revealed good
correlation, whereas notable (10 %–20 %) underestimation for the entire
troposphere was yet salient. During June–July–August (JJA), CHASER's
cloud fraction was likely overestimated for the lower troposphere of the
North Pacific (NP) region (10 %–20 % compared to JRA55 reanalysis data).
This finding indicated that thorough scrutiny of any impacts in this region
is highly required (see the discussion in Sect. 3.2). Note that more
detailed information for cloud verification for CHASER has been provided by
Ha et al. (2021).
The heterogeneous processes by clouds and aerosol particles were
parameterized by using surface area density (SAD) estimations alongside the
cloud fraction and aerosol concentration. During DJF, the simulated total
SAD was attributed to all types of aerosols. However, for JJA, liquid clouds
and sulfate aerosols were the principal SAD sources. This was a peculiarly
visible pattern for the northern polar and mid-latitude maritime regions.
The performance for aerosol SAD in our model was in line with the earlier
report by Thornton et al. (2008), except for sea salt density, which was
very low in our model (up to 2 µm2 cm-3) compared to their
work (up to 75 µm2 cm-3). This disagreement might be ascribed
to the two models' different size distributions for sea salt. The calculated
SAD for the liquid cloud was 2 orders of magnitude higher than SAD for ice
cloud and total aerosols. Liquid cloud SAD maximized at ∼800 hPa in the tropical convective systems and over the mid-latitude storm
tracks, reaching ∼50000µm2 cm-3 at the
surface of the North Pacific region in JJA. Sulfate aerosols dominated above
600 hPa for the Northern Hemisphere (∼ 20 µm2 cm-3) among the total aerosol surface area, followed by organic carbons
and soil dust (∼10µm2 cm-3 in JJA). At the
surface layer, sulfate aerosols were prevalent in DJF for the Chinese region
(>1000µm2 cm-3), the northeastern US
(∼500µm2 cm-3), and North Pacific region in
JJA (∼250µm2 cm-3). SAD for soil dust
dominated in desert regions, with annual average values >100µm2 cm-3. Organic carbon (OC) was dominant in winter over
biomass burning regions such as China (up to 1000µm2 cm-3) and South Africa (up to 800µm2 cm-3). For the
Chinese region, SAD for black carbon (BC) could reach 600µm2 cm-3 in DJF and 75µm2 cm-3 in India. The
total-aerosol SAD for the northern high-latitude and mid-latitude oceans was
∼75µm2 cm-3, consistent with the estimation
by Thornton et al. (2008).
Daytime concentrations of HONO and other atmospheric species
This section evaluated CHASER-based HONO estimates using the HONO
measurements collected during the EMeRGe campaign off the coast of eastern Asia in
spring 2018 (Andrés Hernández et al., 2021). This is the first
global HONO modelling work using EMeRGe as the validation source. The HONO
measurements in the free troposphere could provide essential information on
the underlying gas-phase and heterogeneous HONO formation mechanisms as most
current HONO measurements were conducted in the surface air. The daytime
HONO concentration was retrieved from the aircraft-borne limb measurements
using the HALO mini-DOAS (differential optical absorption spectroscopy)
instrument, in which the absorbed UV light (310–440 nm) by HONO was
detected (Hüneke et al., 2017). The mini-DOAS's measurement method
relies on near-UV/VIS/IR skylight spectroscopy in nadir and limb geometry.
Data evaluation consists of three steps: (1) retrieval of slant column
densities (SCDs) of trace gases by the DOAS method (Platt and Stutz, 2008),
(2) forward radiative transfer modelling for each measurement using McArtim
(Deutschmann et al., 2011), and (3) retrieval of concentration through a new
scaling method for UV/VIS data (Stutz et al., 2017; Hüneke et al., 2017;
Werner et al., 2017; Kluge et al., 2020; Rotermund et al., 2021).
Vertical profiles of (a) HONO, (b) NO2, (c) CO, and (d)
O3 measured in the EMeRGe campaign and calculated in the sensitivity runs.
Diamonds (for OBS, STD, OLD), batches (for maxST, JANO3-B, JANO3-C cases),
and filled circles (for maxST+JANO3-B, maxST+JANO3-C cases) show mean
vertical concentrations, and the corresponding boxes indicate 25th–75th percentile value ranges. In panel (a), whiskers with two caps show minimum and maximum HONO levels; all sensitivity runs are shown except OLD (the case without
HONO chemistry). In all plots, black is for observation (OBS), colours are
for simulations: STD (red), OLD (grey), maxST (magenta), JANO3-B (cyan),
JANO3-C (orange), maxST+JANO3-B (filled cyan), and maxST+JANO3-C (filled
orange).
Additional sensitivity simulations in this work.
No.Simulation IDDescriptionNote1maxSTγoc and γec (Reactions R4, R5) = 0.1See Table 2 for γ values in STD2ratR4NO2→ 0.9HONO + 0.1HNO3 (Reaction R4)Product ratio is 0.5:0.5 in STD3ratR4+CLDratR4 and γliq. (Reaction R4) = 0.01Equals 0.0001 in STD4JANO3-AAdd HNO3+ hν→ HONO (Reaction R7) (z=1, rate=100× rate of HNO3+ hν→ OH + NO2)HONO from HNO3 photolysis (adsorbed on ground surfaces) (Lee et al., 2016)5JANO3-BAdd Reaction (R7) (100<SAD<10000µm2cm-3)HONO from HNO3 photolysis (adsorbed on ground and aerosol surfaces for continental regions excluding cloud surface)6JANO3-CAdd Reaction (R7) (SAD≥10µm2 cm-3)Similar to JANO3-B but using a larger SAD threshold7maxST+ JANO3-Bγoc and γec (Reactions R4, R5) = 0.1 Add Reaction (R7) (100<SAD<10000µm2 cm-3)Combination of maxST and JANO3-B cases8maxST+JANO3-Cγoc and γec (Reactions R4, R5) = 0.1 Add Reaction (R7) (SAD≥10µm2 cm-3)Combination of maxST and JANO3-C cases
Additional sensitivity runs were conducted to explore potential HONO sources
during the daytime (Table 5). The ratR4+CLD case
is run in an attempt to produce more HONO from heterogeneous sources by
altering the HONO:HNO3 yield ratio in Reaction (R4) to 0.9:0.1, and
γliq. increased 100-fold
(10-4→ 10-2). The main idea here is to evaluate whether the
missing HONO source was sensitive to cloud uptake in this region or not. The
maxST case maximized the uptake coefficients (γ values) of NO2
on organic and black carbons to 0.1 (Reactions R4, R5), to estimate the separate role
of soot uptake under daytime conditions (George et al., 2005; Monge et al.,
2010; Ndour et al., 2008), which could achieve an unrealistically high
γ value of 10-1 (Ammann et al., 1998; Kalberer et al.,
1999). In three other runs (JANO3-A, JANO3-B, JANO3-C), the photolysis of
aerosol nitrate/adsorbed HNO3 on the ground and other surfaces
(NO3-/HNO3) was examined, simply as HNO3+
hν→ HONO (Reaction R7). These heterogeneous photolyses of
HNO3 were previously proposed as potential HONO sources during the day (Lee et
al., 2016; Scharko et al., 2014; Zhou et al., 2011). Because aerosol nitrate and aqueous surfaces are ubiquitous in the atmosphere, the photolysis (Reaction R7)
was simply set for the gaseous HNO3 species to occur in particular
model spatial grids exposing ground surfaces and sufficient surface area
density for aerosols and clouds. The photolysis (Reaction R7) was taken at a
rate 2 orders of magnitude faster than the gas-phase photolysis rate of
HNO3 (HNO3+ hν→ OH + NO2) (Zhou et
al., 2011) and presumably yields 100 % HONO to access the maximum effects
by this photolysis (Lee et al., 2016). This setting allows Reaction (R7) not only to
occur at the surfaces of particles but also in the gas and bulk phases.
However, in this test, Reaction (R7) generally refers to surface-catalysed photolysis
or heterogeneous photolysis of HNO3. The JANO3-A case investigated the
photolysis of adsorbed HNO3 on ground surfaces by implementing Reaction (R7) for
the first vertical layer (z=1). The JANO3-B case explored photolysis of nitrate
particles and adsorbed HNO3 gas on both ground surfaces and aerosol surfaces,
applying Reaction (R7) for model grid cells with the SAD of 10-6–10-4 cm2 cm-3 (100 to 10 000 µm2 cm-3) to
use the 10-4 cm2 cm-3 threshold to exclude cloud surfaces (Sect. 3.1.1). The JANO3-C case examined Reaction (R7) for regions present of all particles
with SAD ≥10-7 cm2 cm-3 (10µm2 cm3).
The SAD of 10-6 and 10-7 cm2 cm-3 was supposed to be
the threshold for continental aerosols. The specifications of HONO chemistry in the maxST case and JANO3-B/JANO3-C cases were also combined in two additional cases (maxST+JANO3-B and maxST+JANO3-C, respectively), with the expectation that the opposite effects of the separate cases on NO2–O3–CO chemistry could compensate for each other in the combined cases; this is discussed in the following. Other tests examined the possible HONO sources from aviation
crafts (AIRC), amplified emissions (EM×8), and amplified homogeneous HONO
formation Reaction (R2) (GR×8), descriptions of which were listed in Table S3 in the Supplement.
The model's discrepancies from measurements for HONO (ΔHONO) versus that for NO2 (ΔNO2). Only results from
STD (first column) and helpful sensitivity cases (second, third, and fourth
columns) are plotted. The scale is shared for each row. The altitude range
(0, 1000, 3000, 5000, 6000 m ±500 m) and the sensitivity case names
are shown at the top of each panel. Small points represent discrepancies
distribution (observation – model). Diamonds mark the median point of each
cruise distribution. Edge and fill colours indicate flight cruises (see
legend). Vertical, horizontal, and diagonal lines show ΔNO2=0, ΔHONO= 0, and ΔNO2=ΔHONO,
respectively.
The correlation coefficient (R) and model biases against EMeRGe for HONO are
shown in Table S4. As seen for the STD run, general underestimations of HONO
simulations were identified, in which better correlations were found at
1000–2000 m (R=0.31–0.49). Vertical profiles for HONO and other species
(NO2, O3, CO), retrieved from the EMeRGe flights, were applied for
the measurement-based model evaluation (Fig. 2).
The model discrepancies for the measurement for HONO (ΔHONO)
and NO2 (ΔNO2) in each flight trajectory, i.e. from
Taiwan to South Korea, Japan, and the Philippines, were separated into bins
of altitude ranges 0–1000–3000–5000–6000 m (Fig. 3). The frequency distributions of ΔHONO,ΔNO2,ΔO3, and ΔCO
are shown in Figs. S7 and S8.
Figure 2a shows the vertical average score
(cruising altitudes ±500 m) for the measured (black) and simulated
HONO concentrations in STD (reds) and those results of sensitivity cases.
The measured daytime HONO concentration was close to the boundary layer
(below 1000 m) over Taiwan, averaged at 115 ppt, and peaked at
∼250 ppt. Also, the HONO concentration decreased up to 9000 m (±500 m), with mean values dropping from 70 ppt (2000±500 m) to <20 ppt (5000±500 m) and <10 ppt
above. These measured HONO values for this Asian coastal region were
surprisingly high, which range from 10–115 ppt for 2000±500 m altitudes, compared to Wang's report of <100 ppt (maximum) and
<30 ppt (4 daytime hour means) for 1500–2000 m altitudes
measured by a MAX-DOAS at a station near the HONO source (Wang et al.,
2019). This indicates that the source of HONO during EMeRGe might relate to
mechanisms other than emission sources. In this study, the simulated HONO
concentration in the STD case significantly underestimated the observations.
They reached only 30–70 ppt at 1000 m and nearly zero from 2000 m upward
(Fig. 2a: red versus black triangles for the
simulation and the observations, respectively). These discrepancies indicate
a significant unknown HONO source during the daytime, although the proposed
heterogeneous HONO formation mechanisms were incorporated in our model. This
finding adds another instance of evidence about missing HONO sources in the
polluted boundary layer and free troposphere (e.g. Kleffmann et al., 2003;
Li et al., 2014; VandenBoer et al., 2013; Xue et al., 2022a; Ye et al.,
2018).
In Fig. 3, which shows model discrepancies, the
measured NO2 below 3000 m (±500 m) close to land was well
captured in the model (Fig. 3a, e, i: magenta and
green), with 34 % of the data being quite close for NO2 (±70 ppt) (Fig. S7d). However, the modelling still underestimated the
simulated HONO mixing ratio by up to 250 ppt (Fig. 3a: green). Over the region off the coast of Taiwan bound to Japan, NO2 was
overestimated by up to 600 ppt in the model, corresponding to 20–70 ppt
missing HONO (Fig. 3a, e: small orange area left of
vertical line). The missing HONO can be driven by low HONO emission from
land and low uptake of NO2 on organic carbon and soot, as the amplified
EM×8 and maxST cases could alleviate the model underestimates for HONO
(Fig. 3a vs. b: orange; Fig. 3a vs. c and e vs. f:
green and magenta). The model also underestimated O3 and CO, usually
by 25 ppb O3 (freq. 79 %) and 100 ppb CO (freq. 60 %) (Figs. S10,
S11), which were larger than the model biases against ATom-1 observations
(Sect. 3.1.3; Fig. 5) because of possible inland
influence. A more accurate and detailed emission inventory for substances such
as HONO, NOx, and CO was thus sensible as this region is the outflow of
the Pearl River Delta and Yangtze River Delta regions. Besides the uptake on
organic and black carbon, identified in the maxST simulations, we identified
the NO2 uptake on sulfate nearly as important through a parallel test
(not shown). In particular, the heterogeneous photolysis of HNO3 could not
provide a significant HONO amount near Taiwan, South Korea, and Japan (in
the JANO3-B and JANO3-C cases) and a small HONO amount for the
Philippines bounding route (in JANO3-C case) for the altitudes below 2500 m (Fig. 3d, h: green, orange, blue).
For the middle troposphere (5000–6000±500 m) over the island of Taiwan, too abundant NO2 was predicted by the model during the cruises
bounding to South Korea (up to 40 ppt) and the Philippines (up to 20 ppt)
(Fig. 3m, p: small green and blue areas left of the
vertical line). These overabundances might hint on the deep stratospheric
intrusion in springtime that caused imperfect downward mixing fluxes (Lin et
al., 2012; Stohl et al., 2003; Trickl et al., 2014). This excessive NO2
and the corresponding missing HONO were also sensitive to the AIRC and GR×8
cases (Fig. 3n, q), indicating that
aircraft exhaust of HONO could adjust the HONO:NO2 ratio and more
homogeneous HONO production might contribute more, given the high abundances
of oxidizing substances at these altitudes. The possibility of emission of aviation-induced particles on which NOx to HONO conversion could reach
45 % (Meilinger et al., 2005) could support the need for NO2
reduction and HONO formation for this height across EMeRGe's near-land
domains. Moreover, the surface-catalysed photolysis of HNO3 in the
JANO3-C runs could serve as an efficient source and greatly reduced the
model negative bias for HONO at 6000 m. However, the model overestimated NO2 levels in this case (ΔNO2 negative in Fig. 3s) because HNO3 was more photolysed to HONO and served less as a NOx removal process.
The model underestimation for HONO was also associated with the concurrent
underestimation of NO2, observed more often at the altitudes of
>1000 m. The erroneous NO2 concentrations of ∼1.8 ppb (1000 m) and ∼220 ppt (3000 m) across Taiwan were linked with a lack of HONO of as high as 290 ppt (1000 m) and 140 ppt (3000 m) (Fig. 3e, i). These likely inadequate NO2 abundances could be partially alleviated through the
enhanced HONO:NOx emission ratio and more efficient NOx-recycling
process in the ratR4+CLD cases, respectively
(Fig. 3g, j). Here, the missing HONO was largely
supplemented only at ∼1000 m over the marine environment
(Taiwan–Japan cruise), when we identified more products for HONO on cloud in
the ratR4+CLD case (Fig. 3e, g: red-orange
diamonds). At ∼6000 m, small deficits of 60 ppt NO2
corresponding to ∼10 ppt HONO (Fig. 3p: orange and magenta) might correspond to lightning NOx emissions (Sudo et al., 2002) and stratospheric sources. Some homogeneous
mechanisms at ∼6000 m as in the GR×8 could be effective
(Fig. 3n, o). Moreover, the heterogeneous
photolysis of HNO3 in the JANO3-C case could be an effective HONO
supplement above 5500 m (Fig. 3s), while this
photolysis acted as a NO2 production mechanism at any altitudes.
In general, the upper limit for the aerosol-uptake coefficients (maxST case)
may be applicable for the lowest cruising altitudes, which induced the
increase of modelled HONO levels during both daytime and nighttime (Fig. S10). The photolysis of adsorbed HNO3 on ground surfaces implemented in
the JANO3-A case was impractical to be a source for HONO during EMeRGe, as
this case only provided a mild HONO amount at a thin surface layer
(<500 m; not shown). Fortunately, the surface-catalysed photolysis
of HNO3 in JANO3-B and JANO3-C cases could remedy the model-measurement
discrepancies; i.e. RHONO>0.6 and the model bias for HONO
was reduced from -112 ppt (STD) to -22 ppt (JANO3-B) and -18 ppt (JANO3-C)
for 0–500 m altitudes (Table S4). The HONO source from this photolysis of
HNO3 was sufficient for continental and near-land regions. In
particular, the photolysis of HNO3 adsorbed on particles with smaller
SAD (JANO3-C case) was responsible for the 500–3000 m atmosphere around
Philippines and South Korea and at higher altitudes where robust solar
radiance might enhance the HNO3 photolysis. In the combined cases
(maxST+JANO3-B and maxST+JANO3-C), HONO production was boosted, and the
estimated NO2/O3 concentrations were best captured for 2000–5000 m (±500 m) altitudes (Fig. 2a, b, d:
black diamonds vs. orange circles). Furthermore, the sensitivity cases
including combined cases changed the global tropospheric effects
differently, as discussed in Sect. 3.2.3.
The remaining drawbacks in reproducing HONO and other atmospheric species
(NO2, O3, CO) by model urges further elucidation of efficient HONO
formation mechanisms. To this end, one needs (1) to elaborate the combined
HONO production mechanisms from enhanced NO2 aerosol uptakes and
HNO3 photolysis alongside testification other potential HONO formation
mechanisms and NOx-recycling processes; (2) to simulate the lower and
upper limits for the uptake coefficients of NO2 on aerosols and clouds;
(3) to provide better emission inventories for anthropogenic sources of
pollutants from Southeast Asia and East Asia, lightning-produced NOx
and HOx, and aviation-induced aerosols; and (4) to improve the vertical
mixing and air mass transport from the stratosphere.
NO2, OH, HO2, O3, and CO concentrations within the oceanic
free troposphere
The model performance of the free troposphere was evaluated through the
atmospheric tomography (ATom-1) aviation in August 2016 for NO2, OH, CO,
HO2, and O3. The STD run reconstructed the chemical field observed
in ATom-1 with moderate or strong positive correlations for NO2, OH, CO,
and O3 (RNO2=0.730, RO3=0.751, ROH=0.579,
RCO=0.659; Table S2). For the NP region, the model correlations for
these species were slightly lower (RNO2=0.621, RO3=0.609,
ROH=0.407, RCO=0.596; Table S2). The R values for NO2 and CO
were consistently higher in the STD run than those in the OLD run, while for
OH and O3, the R values are only improved for the NP region (Table S2).
Concentrations and variations by HONO chemistry for NO2,
O3, OH, and CO during ATom-1 flight no. 2 (20–62∘ N, 198–210∘ E). In panels (a)–(b) and panels (e)–(f), concentrations by observation (grey
dots) and simulations in the OLD case (black lines) and in the STD case (red lines)
are plotted. In panels (c)–(d) and panels (g)–(h), changes in concentrations by GRs (blue bars), EM
(red), HRs on clouds (orange), and HRs on aerosols (green) are plotted.
Vertical blue and grey columns reflect the data for the regions with air
pressure P>500 hPa.
Vertical profile of model bias against aerial ATom-1 data (a–h, t–u) and changes by HONO chemistry (l–s, v–w) for NO2, O3, OH, CO,
and HO2 (from left to right columns). Biases in OLD (black lines) and
STD (red lines) runs are calculated for all flights (a–d, t) and NP region
(e–h, u). The red numerical texts are the relative reductions (%) of the
bias in the STD run compared to that in the OLD run. Changes by GRs (blue),
HRs on clouds (orange), HRs on aerosols (green), and EM (red) are calculated
for all flights (l–o, v) and NP region (p–s, w).
Figure 4 shows measured (grey) and simulated (red
and black) NO2, O3, OH, and CO concentrations and the effects of
including HONO in the simulation for the NP region (flight no. 2 on 3 August). Figure 5 displays vertical profiles of
the model biases in STD vs. OLD cases and photochemical effects by each HONO
formation mechanism. Here, the data in all flights or in the NP region were
classified based on the air pressure from 1000–200 hPa (±50 hPa) and
separated into nine bins. In the NP region, the OLD run (black lines) tended
to overestimate NO2, O3, OH, and HO2, but it underestimated CO
at the lower troposphere, whereas the unsteady discrepancies at the upper
layer were visible (Fig. 4a, b, e, f). All five
species tended to be underestimated near the tropopause (300–400 hPa) and
to be overestimated in the lower stratosphere (Fig. 5e–h, u). The HONO inclusion in the STD run (red lines) reduced NO2, OH,
and O3 and increased CO levels, thereby dwindling the model biases for
NO2, OH, and CO in the NP region except near the tropopause
(Fig. 5e, g, h). HO2 was reduced near the
surface layer and increased from the middle troposphere
(Fig. 5v, w), reducing model bias for most parts
of HO2's vertical profile (Fig. 5t, u). The
reduction in the HO2 level near the surface might follow a similar
cloud effect as that for OH, which turned into minor increases in the HO2
level at the middle and high atmosphere, given a lower HO2 level at these
altitudes.
In the NP region, the surface NO2 level was reduced under the effects
of HONO uptake on clouds (Fig. 4c: orange bars
in vertical grey columns and Fig. 5p). Hence,
O3 and OH were correspondingly reduced as their formations are presumably
limited in the absence of sufficient NOx, that is, lacking atomic
oxygen from NO2 photolysis and OH formation via HO2+ NO →
OH + NO2 (Fig. 4d, g: orange bars in grey
columns and Fig. 5q, r). Near the surface,
aerosol HRs only slightly affected atmospheric species, whereas at high
altitudes, the aerosol uptake was more relevant, especially for O3
concentrations (Fig. 4c, d, g, h and
Fig. 5q–s: green bars), due to the contribution
of aerosol direct effects to the O3 level (Xing et al., 2017). The dominant
cloud effects near the surface appeared plausible for an ocean region with
high cloud fractions at the lowest layer (Fig. S2). GRs also affected OH,
O3, HO2, and CO, whereas the effects manifest in the upper
troposphere rather than in the lower troposphere. This was likely the most
influential factor that increases OH, HO2, and O3 levels at these
high altitudes (Fig. 4d, g and
Fig. 5: blue bars). The additional HONO from
direct emissions had minor effects on NO2 and OH but contributed to the
reductions of O3 and CO at high altitudes
(Fig. 4c, d, g and Fig. 5q, s: red bars). At 900 hPa, the HONO emissions significantly reduced
NO2 near the continental areas (Fig. 5l:
red bars) due to its uptake by particles. These effects of the HONO
chemistry in the STD simulation somewhat reduce the model biases for
NO2, O3, OH, HO2, and CO (Fig. 5a–h, t, u: red numerical texts are the percentage reduction in model bias). Note that these biases were very pronounced near the surface
(∼1000 hPa) in the NP region (51.7 % for NO2 and
77.3 % for OH). To capture the patterns identified by observations in the
upper troposphere, except the NP region, more robust increases for NO2,
OH, and HO2 levels were still required (Fig. 5a, c, t). At these altitudes, NOx and HOx sources from lightning
(Brune et al., 2021) or aviation could also be relevant, as discussed in Sect. 3.1.2.
Model comparison with Mirai cruises: no outlier filter is applied. N is
the available data for each calculation. Correlation coefficient (R, no unit)
and biases (ppbv) in the STD run are shown as bold if better than those in the OLD
run.
COCO (40–60∘ N)O3O3 (40–60∘ N)N=4030N=1374N=3893N=1418R(STD)0.6900.5860.5680.618R(OLD)0.6960.6010.6280.642Bias (STD)4.087-4.948-8.823-7.823Bias (OLD)-8.136-16.158-3.625-1.472Surface O3 and CO in the marine environment
The simulations was also compared with the research vessel (R/V) Mirai's observation in the western
Pacific Ocean for O3 and CO. The interpolation of model results for six cruises was provided, with four cruises across the Japan–Alaska region (40–75∘ N,
140∘ E–150∘ W) in July, August, and September 2015–2017 (summer), as well as one cruise for the Indonesia–Australia region (5–25∘ S,
105–115∘ E) and one cruise for the Indonesia–Japan region (10–35∘ N,
129–140∘ E) in December 2015 and January 2016 (winter). All the measured and simulated data were provided, whereas the data
for the NP region (40–60∘ N) were analysed
separately, as discussed in Sect. 3.2. More detailed information about the
R/V Mirai can be found in Kanaya et al. (2019). Furthermore, the model evaluation
with Mirai for the OLD run can be found in Ha et al. (2021).
Percentage discrepancies of STD (a) and OLD (b) simulations from
Mirai for O3 and HONO concentration in STD (c). The red numbers in panel (c)
indicate maximum HONO concentrations for each cruise.
Validation with ship-based data. Observed and simulated
concentrations (a, c) and daily mean effects by HONO chemistry (b, d) for
O3 and CO during Mirai cruises. (a, c) Grey dots: observation; black
lines: OLD case; red lines: STD case. (b, d) Blue bars: changes by GRs;
orange: changes by HRs on clouds; green: changes by HRs on aerosols; red: changes by EM. The left
axis exhibits the concentrations and changes (ppbv). The right axis shows
cruising latitudes plotted as dashed lines. The horizontal axis is travel
times (UTC). Vertical light-blue shaded areas are for data in the NP region
(40–60∘ N, 140–240∘ E).
Table 6 shows correlation coefficients, which
indicated that the STD simulation for CO and O3 agreed well with Mirai (R=∼0.6). However, these correlation coefficients were slightly
worsened compared with the OLD case. Although the HONO inclusion mostly
reduced the model bias for CO, especially in the NP region (-16.158 to
-4.948 ppb), the model bias for O3 was increased. The model biases
exhibited a negative trend for both CO and O3 in the OLD case. This
simulation pattern for O3 in the NP region was in line with the OMI
comparison (Sect. 3.1.1). This finding seemingly indicated an insufficient
downward mixing process of O3 in the free troposphere or
inconsistent surface deposition (Ha et al., 2021; Kanaya et al., 2019).
However, the CO underestimations in the NP region might mark the inadequate
CO emission in the HTAP inventory in CHASER (Ha et al., 2021). In
Fig. 6a and c, overestimations of CO and O3
were visible along Japan–Indonesia–Australia (Track-2) during the low
episodes in December/January. Here, the larger model biases might account
for the model's insufficient halogen chemistry (Kanaya et al., 2019; Ha et
al., 2021). Figure 6 shows the model's percentage
discrepancies for O3 from Mirai's data, except those from HONO
concentrations interpolated for these regions. The underestimated
simulations of O3 were enlarged, especially in the Japan–Alaska
region, being driven by the reduction effects in the STD case. In another
way, these effects weakened the O3 overestimates across the land areas,
namely, over the region near Japan and Indonesia–Australia. Moreover, the
higher HONO levels were identified for these offshore data with up to 1.4 ppb abundances (Fig. 6c: red numbers). This high
HONO level might underestimate an accurate level as a stronger reduction for
O3 was still required for the STD run (Fig. 6a: red marks).
The effects of the HONO chemistry along the R/V Mirai tracks exhibited various trends for
each mechanism. Figure 7b and d illustrate O3
and CO changes triggered by the HONO gas reactions (GRs), uptakes (HRs), and
emission (EM). The gaseous reactions (blue bars) had mostly increased CO
levels due to the reduced OH and O3 levels (Fig. S14). The gaseous
mechanisms caused some reductions at the peak CO level because the higher OH
level from HONO photolysis near the land domain or extra OH flux from
the stratosphere near 60∘ N latitude could dilute CO. Furthermore,
the O3 level was slightly increased due to GRs north of 60∘ N, as
GRs was a source for NO2 and thus enhanced O3 formation at these high
latitudes (Fig. S14). The O3 level was often decreased in the NP region
since minor NO2 increased and stronger OH reduction was seen for this
region (Fig. S14). The change tendencies in O3 near land areas were
varied (T2, T3) because the vertical effects to NOx and OH were
stronger during DJF for this region (Fig. S14). HRs, largely consuming
NO2, reduced O3 (as large as 8 ppb) and increased CO
(∼10 ppb) levels (Fig. 7b, d:
orange+green). HRs (particularly HRs on cloud surfaces, shown by orange
bars), exerted the strongest contribution to the calculated changes in
O3 and CO among the three HONO pathways. This predominant cloud effect
was also prominent in the previous comparisons, especially EMEP
(Fig. 10ii, jj: blue), thereby indicating
substantial effects of clouds at the mid-latitudes where the cloud SAD is
higher (Fig. S1). HRs on aerosols (green bars) had minor contributions
during all cruises, despite causing a marked increase in the O3
concentrations off the coast of Japan (track no. 3). It should be noted that this
is not enough to explain the simulation bias with regard to the
measurements. The additional HONO from direct emission (red bars) mainly
increased O3 and reduced CO concentrations, especially near land
(latitude <50∘ N). This finding resonated with the
comparison for continental stations (Fig. 10cc, ff, ii, jj: orange). The overall effects of the HONO chemistry along
Mirai cruises tended to reduce O3 and increase CO levels. For the NP region,
the CO level increased and the OH level reduction also ameliorated the model
performance. The improved model performance is evidenced from the comparison
of the simulation with ATom-1 aircraft data as well
(Fig. 5g, h). Thus, the strengthened
underestimation of O3 concentration in the NP region was not likely driven by HR on cloud particles (Sect. 3.1.1 and Ha et al., 2021). It was
rather related to the inconsistencies in the surface deposition of ozone.
These inconsistencies were supported by empirical evidence as the negative
bias in this comparison turns neutral or positive for the aircraft
measurements in the same region (Fig. 5f: at
1000 hPa).
Overall, the comparisons between the model and ATom-1 Mirai might indicate
that the HRs on cloud surfaces were the main contributing factor to the
marine boundary's photochemistry, whose effects emerged during the ATom-1
flights in the marine atmosphere. GRs and aerosol HRs had a stronger impact
on atmospheric chemistry at higher altitudes than the near-surface layer.
Also, their effects should be enhanced through the additional NOx and
HONO sources to reconcile the model simulations with the observations.
HONO and HONO:NOx ratio concentrations estimated in the model
and reproduced from Xue et al. (2022a) for Mt Tai's foot and summit
stations during July. Shaded area shows minimum–maximum ranges, while colours
show simulations as in legend.
Daytime (05:00–18:00) average O3 concentration during 9–31
July 2018 at the foot and summit of Mt Tai simulated in the model
(coloured) and reproduced from Fig. 7 in Xue et al. (2022a) (black). In
panel (b), JANO3-B/C cases are overlapped with maxST+JANO3-B/C cases,
respectively.
HONO, NOx, O3, and other atmospheric species at ground-based
stations
In this part, HONO concentration and HONO-related species measured for
summer 2018 at the leg (150 m) and the summit (1534 m) of Mt Tai (Shandong
province, China) by Xue et al. (2022a) were reproduced using a data extraction tool
from images (Fig. 5 in Xue et al., 2022a). We compared our model's
additional sensitivity simulations (Table 5) with Xue's measurements
(Fig. 8). The HONO estimated in the STD case for
the foot station was rapidly reduced after 4:00 (from ∼1100 pptv), while the observed HONO level peaks at ∼6:00 and
remains about 0.5 ppbv at noon (Fig. 8a). The
HONO level produced by the sole NO3- photolysis on ground,
aerosols, and cloud particles (JANO3-A/B/C cases – respectively in solid
blue, dashed cyan, and dashed orange lines in Fig. 8a) can append moderately the simulated daytime HONO to reach the
ground-based observatory levels comparing to the STD case. This addition
indicated a partially important role of NO3- photolysis on all
surfaces to HONO sources since early morning. Also, NO3-
photolysis adjusted the ratio of HONO:NOx more analogue to the observed
daytime ratios at the ground (Fig. 8c, d). The
lack of NO2 sources, especially during nighttime (Fig. S15), even in
the OLD case (without HONO chemistry), was one of the reasons for the
remaining unknown HONO sources existing during ∼5:00–11:00. The
combined cases (maxST+JANO3-A/B/C cases) produced too much HONO at the leg
compared to observations (not shown in Fig. 8),
indicating the improper mechanism of enhanced NO2 aerosol uptakes for
ground-based stations. In contrast, the enhanced aerosol uptakes were more
compatible with observation at the summit, where these combined cases
provided the best agreement to observation (Fig. 8a, b: solid cyan and orange lines). However, the best simulation for HONO
at the summit station only reached the lower line of the averaged daytime
HONO level (Fig. 8b). Xue suggested that the
high HONO level at the summit of Mt Tai was dominated by the rapid upward
transport from the ground and the in situ heterogeneous formation on the
mountain surfaces (Xue et al., 2022a), which the mismatching between the
actual locations and the coarse model grid (2.8∘) of our model,
including vertical layer, might not provide. Similar to NO2, the
simulated CO concentrations at Mt Tai were very low (∼200 ppb at the foot station in the STD case versus ∼400–600 ppb
measured CO; not shown), even in the OLD case. In our model, the inadequate
emission inventory of CO and NO2 for the Asian region using
HTAP-II-2008 and the coarse model resolution (2.8∘×2.8∘) were the reasons for the low ground-based emissions and
vertical transport (Ha et al., 2021). Such emission inventory and vertical
transportation improvement could further close the HONO observation gap and
reduce the unrealistically high HONO:NOx ratios. Such a study could
better show the validity of the HONO production mechanisms from enhanced
NO2 aerosol uptake and NO3- photolysis (combined cases) for
the summit station.
The above discussion confirmed that NO3- photolysis (Reaction R7) on the
ground, aerosols, and clouds surfaces (JANO3-A/B/C cases) enhanced daytime
HONO but being ignorant of the NO2 level (Fig. S15) due to an absent
NOx recovery process (Ha et al., 2021). However, HONO sources via Reaction (R7)
in the JANO3-A/B/C cases still increased daytime O3 levels at the foot of
Mt Tai (Fig. 9a) as a result of rapid NOx
cycling. Either the JANO3-A/B/C or the combined maxST+JANO3-B/C case was
closest to O3 observation during cleaner (50–75 ppb O3 on
16–22 July) or dirtier episodes (<50 ppb
O3), suggesting an enhanced role of aerosol uptakes during the polluted
episode (indicated by the corresponding increases for CO; Fig. 7 in Xue et
al., 2022a). At the summit station, NO3- photolysis in the JANO3-C and
maxST+JANO3-C cases (all surfaces including clouds) boosted O3 up to
the observational level, indicating the contribution of cloud surface at the
summit (∼1500 m).
The effects of HONO chemistry in the continental near-surface layer of East
Asia and Europe were also investigated. To this end, we conducted model
comparisons versus EANET and EMEP stationary observations for mass and
gaseous concentrations of PM2.5, SO42-, NO3-,
HNO3, NOx, O3, and CO (CO for EMEP only).
Table 7 shows the correlation coefficients (R) and
model biases for each species in the OLD and STD cases. The OLD simulation
had its fair correlations and RMSEs with observation for SO42-
(R(EANET) = 0.56, R(EMEP) = 0.63), NO3- (R(EANET) = 0.36,
R(EMEP) = 0.71), and HNO3 (R(EANET) = 0.18, R(EMEP) = 0.12), which
were in line with other atmospheric chemistry models' R and RMSE values
against EANET and EMEP (Bian et al., 2017), as also discussed in Ha et al. (2021).
Concentrations and changes by HONO inclusion for EANET and EMEP
stations. (a–j) Observed and simulated concentrations during 2010–2016.
Black lines: observation; red: STD case; blue: OLD case. In panel (b),
concentrations in STD and OLD are increased 10 times for better visualization
(red and blue lines). For each group of stations, dotted lines are all
the station medians from each station's monthly-mean values. Thick solid lines
represent two-quarters averaged from dotted lines. (aa–jj) Calculated
monthly-mean changes by HONO chemistry. Green bars: monthly changes by GRs;
blue: monthly changes by HRs on clouds; grey: monthly changes by HRs on aerosols; orange: monthly changes by EM. Stations
are grouped as high-NOx EANET (first and fourth rows), low-NOx
EANET (second and fifth rows), and all EMEP stations (third and sixth rows).
First column: HNO3; second column: NOx; third column: O3;
fourth column: CO.
Figure 10 compares the measured versus simulated
HNO3, NOx, HONO, O3, and CO concentrations for the EANET and
EMEP stations. The stations were divided into three groups: (1)
high-NOx EANET stations, including Jinyunshan (China), Kanghwa, Imsil,
Jeju (South Korea), Bangkok, Nai Mueang, Samut Prakan, Si Phum (Thailand),
Metro Manila (Philippines), and Ulaanbaatar (Mongolia); (2) other EANET
stations (39 for HNO3, 22 for O3, and 15 for NOx); and (3)
all EMEP stations. The ground-based observations in the period 2010–2016
revealed the slightly decreasing NOx for moderate NOx
concentrations, as well as PM2.5, and aerosols
(Figs. 10e, h and S4e, g, h, i). These
decreasing trends were not captured by our simulations, which used the high-emission scenario for the EDGAR/HTAP-2008 inventory. Note that NOx and
PM2.5 concentrations were generally underestimated in the model (OLD),
especially in high-NOx regions (Figs. 10b, e, h and S4a, d, g) with the model's averaged biases of -0.8 ppb
NOx for EMEP and -4 ppb NOx for EANET
(Table 7). These underestimations were stronger
during winter, particularly for the high-NOx regions. It was possible
that complex domestic sources could lead to diluted emissions for the
simulations' moderate horizontal resolution (∼2.8∘). Higher model resolutions, such as 1.1∘, 0.56∘, or
even higher, could remedy such effects (Sekiya et al., 2018).
Model comparison of different species with observations at the EMEP
and EANET stations. Three-sigma-rule outlier detection is applied for each
station before calculating correlation coefficients R. NOx data are
filtered once more using the two-sigma rule. R and bias of the STD run are
shown as bold if improved compared to the OLD run. Units for model biases: µm m-3 for PM2.5, SO42-, and NO3-;
ppb for HNO3, NOx, O3, and CO.
HONO chemistry in the STD case increased HNO3, NO3-,
SO42-, and PM2.5 for EANET and EMEP stations compared to the
OLD case (Figs. 10 and S4: red vs. blue
lines). HNO3 and NO3- were increased as the products of
NO2 conversion (Reaction R4); thus, the model underprediction for NO3-
in EANET stations was mitigated (bias OLD → STD: -0.439→-0.223µm m-3). As a result of the increased OH level at the surface of
these ground-based stations, SO42- was also increased (Li et al.,
2015; Lu et al., 2018) (Fig. S4j, k, l), although this effect enlarged the
model overestimation for SO42- species at EANET and EMEP stations
(Table 7). The consequent increase in PM2.5,
though minor, remedied the model underestimate for PM2.5, e.g. model
bias in OLD → STD: -3.044→-2.494µm m-3 (EMEP).
Unfortunately, the model overestimate for HNO3 in the OLD case was
enlarged with the inclusion of HONO.
In the STD case, including HONO photochemistry, the negative biases of
NOx in the model had been adversely enhanced due to the NO2 loss
processes (bias OLD → STD: -3.997→-4.358 ppb for EANET; Table 7). These processes also suppressed the
NOx seasonality observed at most sites (Fig. 10b, h, red lines). The lack of seasonality was driven by the substantial
loss of NO2 on the surfaces of atmospheric particles during winter. For
EANET's low-NOx and EMEP stations, this huge NO2 loss was
attributed to cloud surfaces (Fig. 10ee, hh: blue
bars). However, NO2 uptake by aerosols has a comparable contribution
effect to the cloud effect in high-NOx environments such as Jinyunshan
(Fig. 10bb, grey bars). Namely, nearly half of
the NO2 was converted to HNO3 in Reaction R4; Fig. 10aa, dd, gg) without an efficient recycling process, leading to an overall
removal of NOx. This lack of NOx could be the main driver for the
seasonal NOx deterioration and the exacerbated overestimations of
HNO3 by simulations.
The STD O3 simulation exhibited moderate and strong positive
correlations with EANET and EMEP observations, 0.595 and 0.707, respectively
(Table 7). The model improvements for
SO42-, NO3-, PM2.5, and HNO3 were minor.
However, the model improvement for O3 was considerable, with a bias
reduction of ∼67 % for EMEP and ∼74 % for
EANET (Table 4). In the STD case, too little NOx was left from its
heterogeneous loss, causing a net O3 chemical destruction (because
lacking atomic oxygen from NO2 photolysis), which in turn reduced the
model overestimates for O3 in the OLD case
(Table 7; Fig. 10c, f, i,
red versus blue lines). However, further improvements in the chemical scheme
were necessary to reproduce the O3 measurements better; namely, a
larger O3 reduction for the summer and a reduced effect in simulated
O3 for the winter might alleviate the undesired effects. A delayed
minimum from summer (as observed) to early winter (calculated in OLD and STD
runs) causing opposite seasonality for O3 was prominent for the
low-NOx EANET stations (Fig. 10f). The
effects of HONO chemistry on the mean OH levels were small, although it
showed slight increases for OH's minima (Fig. S4j, k, l). Thus, due to the
apparent O3 reduction for EMEP stations, CO was increased. Despite the
reductions in NOx and O3 levels being exaggerated during winter,
the increment in CO reconciled the model's underestimation of CO high peaks
in spring (Fig. 10j), thereby strongly dwindling
the bias for CO by ∼59 % (Table 4). However, the CO
concentrations during summer should be reduced in the STD case to capture
the measurement. This finding might indicate inadequate HONO emissions for
the EMEP stations (Fig. 10jj: orange), which
otherwise had reducing effects on NOx, O3, and CO levels during
summertime.
The breakdown scrutinies for aerosols and clouds effects for the
ground-based stations (EANET/EMEP) also revealed the vast role of
cloud uptakes in the HONO impacts on NO3- aerosols, NOx,
O3, and CO (Figs. 10 and S16: blue
bars), while the HONO impacts on HNO3, PM2.5, and SO42-
aerosols were governed by aerosol uptakes and HONO emission (grey and yellow
bars).
The existing ill reproduction in NOx's seasonality and overestimations
for HNO3 might be amended by an explicit inventory for direct NOx
emissions and an efficient NOx-recycling process. Such a mechanism via
HNO3 uptakes on soot surfaces (HNO3→ NO2) was also tested
in this study using the uptake coefficient range from 3×10-5-4.6×10-3 (Lary et al., 1997; Akimoto et al., 2019).
Unfortunately, this heterogeneous HNO3 conversion could not solely
serve as a productive NOx-recycling process in the EANET/EMEP stations
(not shown). Among the additional cases described in
Table 5, the alternated HONO:HNO3 (0.9:0.1)
product ratio of Reaction (R4) (ratR4 case) showed a good remedy for NOx at EMEP
stations (Fig. S11g: brown vs. black). For the EANET sites, the
photolysis of adsorbed HNO3 on ground surfaces (JANO3-A case) avoiding
NOx removal via HNO3 could remedy the NOx seasonality issue
for these ground-based stations (Fig. S11a, d: green vs. black).
However, the photolysis of adsorbed HNO3 on ground surfaces and aerosol/cloud
surfaces (JANO3-B and JANO3-C cases) was not an effective NOx-recycling process
for EANET/EMEP measurements, leaving only slight differences in surface
NOx levels compared to the STD case (Fig. S11a, d, g: cyan and orange
vs. reds). However, the heterogeneous photolysis of HNO3 increased
O3 and OH at the high-NOx regions instead of O3 reduction in
the STD case (Fig. S11b, c: cyan and orange vs. blue), which brought
reconciliation to the underestimates for O3 peak in springs (Fig. S11e: dotted cyan vs. dotted black), although the runs with HNO3
photolysis still did not capture the O3 minimum in summer. Only the
ratR4 case could capture the O3 minimum in summer among the sensitivity
cases, which indicated the need for stronger NOx-recycling processes for
these ground-based stations.
Distribution of HONO levels at the surface (a–b) and meridional mean (c–d).
Contribution of HRs and EM to surface HONO concentrations.
Contributions of HRs onto ice and clouds (a, b), HRs onto aerosols (c, d), and
EM (e, f) in DJF (a, c, e) and JJA (b, d, f) are plotted. Each contribution is
determined by the difference of HONO in two simulations: (a, b)
GR+HR(cld) and GR; (c, d) GR+HR and GR+HR(cld); and (e, f) STD and
GR+HR, divided by HONO in the STD case. The maximum and minimum values are
out-scaled and hence displayed at the top of each panel.
Distribution of HONO and global effects of HONO chemistryGlobal HONO distribution and burden
This section sheds light on the global HONO distribution computed for the
STD case. The surface HONO concentration peaked over the geographical
region that includes China, with seasonal mean levelled up to 2.8 ppbv during
summer and 7.8 ppbv during winter (Fig. 11a, b).
The winter peak agreed with observations for a large industrial region in
the Yangtze River Delta of China (Zheng et al., 2020). The high
concentrations of HONO were also identified in other industrial regional
clusters: the northeastern US (seasonal mean up to 0.5–1 ppbv); India (up to
1–3 ppbv); forest regions, especially the extratropical evergreen forest in
Europe (up to 1–3 ppbv); and Africa (up to 0.5–1 ppbv). Over the ocean,
HONO levels remained at 10–30 pptv in the coastal regions and below 10 pptv
far off the coast. The simulated HONO distribution was in line with a
previous study (Elshorbany et al., 2012) despite the peaks over polluted
Chinese areas being markedly higher in our model (10-fold). The
overestimation associated with the soot uptake in our model has been
previously neglected. The highest HONO concentrations (10–30 pptv) in the
free troposphere (at 2500 m) were simulated over Africa's biomass burning
region during wet months (JJA) (Fig. 11c, d),
which could arise due to the NO2 uptake on aerosols, originated from
this wildfire source.
In the model, HRs and EM were the main contributors to HONO at the surface
layer (Fig. 12) by providing efficient HONO
formation and promoting gas Reaction (R2). Of the various surfaces provided
for HRs in our model, liquid/ice cloud particle surfaces were supposed to
catalyse significant photochemical effects in remote regions. This
phenomenon has not been previously addressed in other studies in detail. The
uptake of liquid/ice cloud particles either increased HONO formation via Reaction (R4) for the tropical and southern oceans or reduced it via Reaction (R6) along 60∘ S in DJF and the Arctic in JJA (Fig. 12a, b). Besides cloud particles, HRs on aqueous aerosols also produced
HONO in a continental atmosphere rich in sulfate, dust, and soot particles
(Fig. 12c, d). EM included in the model has
sharply increased the HONO level over deserts (Sahara, Arabian), grasslands
(South Africa, South America), and boreal and agricultural land (western Europe,
Australia). This finding agreed well with another study, based on spaceborne
observations for HONO in wildfire plumes (Theys et al., 2020) and along-ship
tracks in the marine boundary layer (Fig. 12e, f).
Global sources and sinks of tropospheric HONO calculated by CHASER (2011). The bold figures signify the two pathways that contribute the most to tropospheric HONO.
Table 8 summarizes the global sources and sinks of
tropospheric HONO quantified by CHASER. The simulations indicated that GRs
contribute only 11 % of the HONO net production. HRs and EM produced more
significant HONO (63 % and 26 % HONO net production, respectively). The
pyrogenic HONO emission estimated in this study might be underestimated as
the HONO:NOx emission ratio could be enhanced by up to 1 at extratropical
evergreen forests (universally 0.1 in this study (STD)) (Theys et al.,
2020). For large metropolitan areas such as those in China, HRs and EM had
also been reported as the two most significant contributors to HONO
formation, at ∼59 % and 26 %–29 %, respectively (Li et
al., 2011; Zhang et al., 2016). Of the various surfaces provided for HRs,
aerosols represent a more effective HONO formation site (∼51.2 %) compared with ice and clouds, as they are contributing only
11.8 % to HONO production. Moreover, the HONO loss through photolysis Reactions (R1)
and (R3) was equivalent to its uptake onto the particle Reaction (R6). In
equilibrium, the tropospheric abundance of HONO averaged over the globe was
estimated to be 1.4 TgN in our model.
HONO production calculated in sensitivity cases (Sect. 3.1.2) is recorded
in Table S5 (last column), and the spatial distributions are plotted in
Fig. S12. The small supplement by the photolysis of adsorbed HNO3 on
ground surfaces (JANO3-A case) to surface HONO concentration and
tropospheric HONO burden (1.40→1.45 TgN) was consistent with the
discussion for the EMeRGe campaign. In the JANO3-B and JANO3-C cases,
tropospheric HONO burden was increased to 2.02 and 2.93 TgN,
respectively, mainly remaining for the lower troposphere (∼600 hPa) (Fig. S12g, h). Compared to HNO3 photolysis, the enhanced
aerosol uptake (maxST case) produced HONO more extensively over the source
region and in the winter hemisphere where there was no photolysis (Fig. S12a–d). Therefore, the maxST case did not produce enough HONO during
EMeRGe (worse than the JANO3 cases). However, the global HONO burden in the maxST
case was added to 7.79 TgN, which might be because we set the enhanced
aerosol uptake of NO2 for all environments. The combined cases
(maxST+JANO3-B or maxST+JANO3-C), although appropriately approaching
daytime HONO production as well as NO2 and O3 levels during
EMeRGe, incredibly escalated the global HONO burden to 12.64 and 17.13 TgN,
respectively (Table S5), more via the enhanced aerosol-uptake setting that
could reach the upper troposphere (Fig. S12k, l). However, it might be
more realistic if HONO production stayed at the lower troposphere (Eshorbany
et al., 2012). In future work for the combined cases as standard cases, the
amplified NO2 conversion on aerosols should be confined to high SAD
regions (Kalberer et al., 1999; Stadler and Rossi, 2000) or to the daytime only
(Notholt et al., 1992; Stemmler, 2007).
P denotes chemical production, S denotes source (emission + chemical
production), and L denotes loss. The numbers in parentheses represent the
portion of each pathway to the total HONO net production. Bold lines show
the most significant contributing mechanisms to the HONO burden.
TCO percentage differences between model and OMI. (a, c, e)
STD versus OMI and (b, d, f) OLD versus OMI for annual (a, b),
December–January–February (DJF) (c, d), and June–July–August (JJA)
(e, f). (g, h, k) Differences for the maxST+JANO3-B case versus OMI for
annual, DJF, and JJA, respectively (g, h, k).
Global effects on tropospheric column ozone
The comparison between the simulation and OMI spaceborne observations for tropospheric
column ozone (TCO) can be examined as a global effect for ozone. In
Fig. 13, the STD run with HONO inclusion improved
the overall tropospheric column ozone (TCO) distribution observed by the
OMI, especially at the mid-latitudes. Figure S3 indicated a TCO reduction
when HONO chemistry was included in the STD case (red lines vs. green lines).
Although HONO photolysis Reaction (R1) was a source of OH, supposedly increasing the
tropospheric oxidizing capacity, the calculation in the STD case showed the OH
and O3 increased only occur at the surface of polluted sites
(Fig. 14a, e). The NO2 conversion to HONO
and HNO3 (Reaction R4) became a NOx's removal pathway at remote regions,
thus restricting the formation of O3 and OH for the larger part of the
troposphere (lacking atomic oxygen from NO2 photolysis).
Figure 13 shows that the O3-reducing effects of
HONO chemistry greatly reduced the model overestimates in the OLD simulation
for the general Northern Hemisphere and polluted regions such as China.
However, in the NP region, the inclusion of HONO only reduced the model
overestimates during the insignificant episodes of TCO (autumn to early
winter) while extending the underestimates for TCO for the rest of the year
(Fig. S3b). These O3 underestimates in the NP region are also
visible for the modelled surface air versus the measurements during the
Mirai cruises (Fig. 6a, b). Notably, these
underestimates for O3 could hold up to 400 hPa, as seen in comparison
with the ATom-1 flights (Fig. 5f: 400–900 hPa).
This phenomenon could be related to the stratospheric downward transport and
insufficient vertical mixing, as discussed in Sects. 3.1.3 and 3.1.4, for
comparisons in the NP region's surface air and free troposphere. Although
the HONO level in STD remained <10 ppt for this area
(Fig. 11), the O3-reducing effects
exacerbated the model discrepancy. The HONO photochemistry was unlikely to
be the primary driver of this phenomenon as the ozone simulation was
improved over the continents when the HONO photochemistry is included.
In the combined sensitivity case maxST+JANO3-B (Sect. 3.1.2), O3 was
further reduced than the STD case. The reduction in TCO might be due to the
enhanced NO2 uptake on aerosols, leading to more substantial O3
formation restriction. The maxST+JANO3-B case showed better harmony with
OMI for the regions of TCO overestimations (Fig. 13g, h, k), especially the annual mean (g panel). However, the underestimates of TCO, including the NP region, were worsened. These results indicated that
the reduction for O3 by HONO chemistry was reasonable, and the combined
cases such as maxST+JANO3-B could be plausible, although the estimated
reduction degree should be reduced by elaborating the maxST's reactive
conditions.
Implication of HONO on the tropospheric photochemistry
In this section, the global impact of HONO photochemistry is elucidated. To
this end, Table 8 summarizes the HONO budget and the
contribution of each pathway to the HONO photochemical cycle. Table 9
describes its consequences for the lifetime of CH4 and the budgets of
NOx, O3, and CO. The gaseous reactions of HONO tended to increase
the abundance of NOx, O3, and CO (+1.01 %, +0.15 %,
+0.44 %, respectively) and CH4 lifetime (+0.36 %) in the
troposphere. Without heterogeneous and direct emissions, the relatively low
HONO formation by gaseous reactions (11 % of the total net HONO
production; Table 8) did not cause any significant
effects on NOx, O3, and CO in the troposphere.
CH4 lifetime and tropospheric abundances for NOx,
O3, and CO and the changes by HONO chemistry.
(a) Simulation IDCH4 lifetime (yr)Abundances of tropospheric NOx (TgN)O3 (TgO3)CO (TgCO)OLD9.090.119408.79327.20GR9.120.120409.38328.65GR+HR10.490.092384.25359.90GR+HR(cld)10.170.102390.46351.53STD10.280.094388.21354.57(b) EffectsChanges (%) GRs+0.36+1.01+0.15+0.44HRs+14.99-23.19-6.15+9.55HR(cld)+11.52-15.28-4.63+6.99HR(ae)+3.47-7.91-1.52+2.56EM-2.30+1.77+0.97-1.63Total+13.05-20.40-5.03+8.36HRs(N2O5, HO2, RO2) (Ha et al., 2021)+5.7-3.87-2.91+3.43
Effects of the HONO photochemistry on the tropospheric oxidants
OH (first row of panels), NOx (second row of panels), O3 (third
row of panels), and (CO last row of panels). Effects at the surface (a–h) and
zonal means (i–p) are shown.
Effects of HONO photochemistry for the surface layer, for OH
(a–b, g–h), NOx(c–d, i–j), and O3(e–f, k–l) over
the northeastern China region in DJF (a–f) and the NP region in JJA (g–l) from
dominant pathways of HONO by heterogeneous reactions of aerosols (a, c, e), heterogeneous reactions ice and clouds (g, i, k), and direct
HONO emission (b, d, f, h, j, l).
Calculated global-mean changes of tropospheric abundances in
additional simulations compared to the OLD case (without HONO chemistry). From
left to right, the order of shown simulations follows the percentage change
magnitudes in CH4 lifetime (largest negative change to largest positive
change; purple bars). Other bars show percentage changes in NOx (red),
O3 (blue), and CO (grey).
Heterogeneous reactions that produce HONO were the most salient contributing
factors to tropospheric chemistry, thereby decreasing the tropospheric
oxidizing capacity and increasing the CH4 lifetime by 15 % and CO
abundance by 10 %. HRs also reduced the NOx level by 23 % and the O3
level by 6 %, respectively (Table 9). The global
HONO distribution from Fig. 14 was mainly caused
by the HR formation of HONO. Here, the reducing effects for NOx levels,
with consequences for OH and O3 level reductions by heterogeneous
reactions, were significant at middle to high latitudes during summer – more
specifically, in DJF along 60∘ S and the Arctic and NP oceans
during JJA, which amounted to about a -100 % reduction in the NOx level
at the surface (-60 % reduction in OH and -40 % reduction in O3)
(Fig. 14a–f: blue areas). These reductions in
NOx, OH, and O3 levels extended up to 400 hPa at high N/S
latitudes (Fig. 14k, l). All these reduction
effects for NOx, OH, and O3 levels were due to the removal of
HONO on ice and cloud particles (Reaction R6) (Fig. 12a,
b: blue fields). On the one hand, it accelerated the conversion of NO2
to HONO and ultimately strengthened its deposition by particulate nitrate (Reaction R4)
(Fig. S13a). On the other hand, HRs occurring on aerosol surfaces led to
increments in OH and O3 near the surface of polluted regions during
winter. These were the main contributors to the regional photochemical
effects over China, western Europe, and eastern US regions in winter (up to
-74 % reduction in NOx, +1500 % increase in OH, and +48 % increase in O3;
Figure 14a, c, e). However, these OH and O3
level increases were only accumulated in the surface layer (only small red
areas at ∼1000 hPa in Fig. 14i, m). Compared to HRs on aerosol, HRs on clouds exhibited twice the effectiveness when reducing the tropospheric NOx level (-15 % versus
-8 %) and caused 3 times the effects on the tropospheric oxidation capacity
(+11.5 % in CH4 lifetime, -4.6 % in O3, +7 % in CO),
compared with HRs on aerosol (+3.5 % CH4 lifetime, -1.5 %
O3, +2.6 % CO) (Table 9).
Given the direct emissions of HONO (∼10 % of NOx
emission inventory), the surface NOx, O3, and OH concentrations
were generally enhanced in the STD case compared to the GR+HR case. They
induced the concentration modification for NOx (+1.77 %), O3
(+0.97 %), and CO (-1.63 %), as well as a significant reduction (-2.3 %) in the
CH4 lifetime (Table 9). Remarkable enhancements
for NOx (up to +198 %), OH (+243 %), and O3 (+24 %)
(Fig. 14b, d, f: red fields) were identified for
the cropland and shrubland/forest regions in Australia, South America, and
South Africa during JJA, as well as the boreal vegetation prevailing at middle to high
latitudes in Europe, North America, and the polluted Chinese region in DJF
(up to +748 % OH) (Fig. 14a: red fields).
NOx and OH were elevated in these mid-latitude regions because of the
enhanced HONO photolysis (Reaction R1) by the additional HONO source. However, OH,
NOx, and O3 levels were reduced near the surface of the Northern
Hemisphere's land during summer (up to -47 % reduction in OH, -82 % reduction in NOx, and
-15 % reduction in O3) (Fig. 14b, d, f). The latter
phenomenon was similar to the heterogeneous cloud effects for the high
latitudes discussed above.
Overall, the inclusion of the three HONO processes (gas phase, aerosol and
cloud uptakes, direct emission) caused changes of -20 % in NOx, -5 % in
O3, and +8 % in CO, as well as a significant increase of +13 % CH4
lifetime in the troposphere (Table 9).
Figure 15 highlights the consequences of HOx,
NOx, and O3 for the Chinese and NP regions. The NOx level
reduction accumulated in the Arctic and Antarctic during summer, especially
over the NP ocean (reducing the NOx level by 60 %–90 %;
Fig. 15i). These reductions in NO2 and HONO
concentrations were due to their uptake onto ice and clouds in these
regions. However, these reducing effects caused further reductions in OH and
O3 levels for a larger part of the troposphere. As NOx was
essential in regulating O3 and OH in the troposphere, a reduction of
the NOx level increased the HO2:OH ratio (due to the HO2+ NO
→ OH + NO2 reaction), which restrained the formation of OH and
ultimately of O3. Moreover, a NO2-deficit environment directly
affected the O3 level as NO2 was a primary source of an oxygen
atom that engages in the formation of O3. Thus, in summer, both OH and
O3 levels were drastically reduced over the NP region (35 %–67 % for
OH, 30 %–43 % for O3; Fig. 15g, k), and the CO
level was increased by 18 % in this region (Fig. 14h).
The significant impacts of HONO photochemistry were especially relevant over
eastern China in winter, which might reduce the NOx level by 48 %–78 %
(Fig. 12c) due to the uptake of NO2 onto aqueous aerosols. At the
surface, the OH level was enormously increased as a result of HONO photolysis
(Reaction R1), heterogeneous NO2 conversions (Reactions R4, R5), and additional direct
emissions (Fig. 15a, b). The corresponding
increase in the O3 level was only identified at the surface of the Beijing
region during winter, with +28.8 % caused by HRs on aerosols
(Fig. 15e). For Beijing with high NOx
emissions, VOC-limited O3 chemistry was likely the driving mechanism
(Liu et al., 2010). The vast increases in OH and O3 levels over Beijing
in winter were basically in line with the present knowledge of HONO
photochemistry (e.g. Lu et al., 2018). Elshorbany et al. (2012) also
reported an increase in OH (2–5×106 molecules cm-3) and
O3 (0.3–0.5 ppbv) concentrations over polluted regions in China during
winter. Compared with Elshorbany's work, the increases in OH and O3
concentrations in our model were higher due to the different HONO mechanisms
applied in the two models, simply an averaged HONO:NOx ratio (0.02) in
their model. In particular, our newly added heterogeneous reactions on cloud
particles (Reaction R4) caused significant reductions in OH, NOx, and O3
levels in the NP region during summer, which their model did not cover. The
overall reductions in tropospheric oxidizing capacity due to HONO
photochemistry were in line with the expected response to heterogeneous
processes (Liao et al., 2003; Martin et al., 2003) and agreed with those
previously reported for other HRs (HO2, N2O5, and RO2; Ha et al., 2021; Table 9). Our findings indicate that a global model without heterogeneous processes for
HONO would neglect the significant changes in OH and O3 concentrations
in remote areas and, thus, will underestimate the potential effects in
polluted regions.
As mentioned above, the relative importance of ice and cloud surfaces to the
oxidant chemistry was negative for Arctic and NP regions. NO2's uptakes
on ice and cloud surfaces (Reaction R4) were the main reason for the reductions in
surface NOx, OH, and O3 concentrations in these regions during JJA
(Fig. S13a). These reductions also occurred for the free troposphere,
which generally improved the model comparison with ATom
(Fig. 5) and partially improved in CO simulation
by cloud effect (Fig. 7). To HONO formation,
enhancement of NO2's uptake coefficient on cloud surface
(γliq. (Reaction R4) = 10-4→10-2), along
with changing the HONO:HNO3 yield ratio from 0.5:0.5 to 0.9:0.1 in Reaction (R4) in
the ratR4+CLD case compared to the STD case, helped preserve more NOx
during EMeRGe's flights. Here, a supplement for HONO production is only seen
in the ratR4+CLD case for the marine environment ∼1000 m near Japan (Fig. 3g: red-bordered orange diamonds). The ratR4+CLD case introducing an approach to recycle NOx
led to lowered global effects of HONO (only -8.57 % NOx globally,
CH4 lifetime=9.6 years; Fig. 16). For
the ground-based station in comparison with Xue's data (Xue et al., 2022a),
the NO3- photolysis in JANO3-C and maxST+JANO3-C cases (all
surfaces including clouds) boosted the O3 level, which improved the
agreement with observed O3 at the Mt Tai summit station, indicating
the contribution of cloud surface to O3 formation at the altitudes
∼1500 m over a mountainous area
(Fig. 9b). The NO3- photolysis on
cloud surface in the JANO3-C case could also be an effective HONO supplement
for the tropospheric part above 2000 m over the Asian coastal region
compared with EMeRGe's data as compared to the JANO3-B case, which excluded
clouds (Fig. 2a). The simulated concentrations
of NO2 and O3 also agreed better with the measured data in this
comparison.
Contrary to cloud surfaces, the aerosol effect was only crucial for regional
photochemistry at the surface layer of polluted regions, such as China,
western Europe, and the eastern US in winter time (Fig. 14). As discussed above, aerosol uptakes reduce NOx but increase
regional OH and O3 levels. For the sensitivity of HONO formation to
aerosol effect, the cases JANO3-B and maxST+JANO3-B, which only included
ground and aerosol surfaces for NO3- photolysis, also remedied the
discrepancies for the daytime HONO level across various altitudes during EMeRGe
flights (Fig. 2a). For the comparison with Mt Tai station, enhanced uptakes of NO2 onto aerosol surfaces in the
combined cases (maxST+JANO3-B/C) provided more HONO production at the
summit (Fig. 8b), as well as adjusting O3 levels at both foot and
summit stations during the polluted episode (Fig. 9).
The estimated global effects of HONO chemistry in the STD case was the
abatement of global tropospheric oxidizing power, despite surface OH and
O3 levels being increased at polluted sites. The reduction tendency in
global OH and O3 contrasted with other modelling studies (e.g.
Elshorbany et al., 2012; Jorba et al., 2012; Lee et al., 2016; Zhang et al.,
2021). Some discussions on the tendency of HONO's global effects are
addressed here. The positive or negative impacts on oxidizing species (OH
and O3) were constrained to HONO formation mechanisms rather than
NOx concentration. For high-NOx regions such as EANET stations
with 6-month-averaged NOx higher than 20 ppb, O3 and OH were
reduced due to NOx removal via NO2 uptakes
(Fig. 8b, c), especially at night. If NOx was highly underestimated in the model for these high-NOx regions and an efficient NOx-recycling process was still absent, OH and O3 might be reduced daily. The calculation for daytime only in comparison with
Xue's data showed that O3 could be increased when a complementary HONO
source was provided via NO3- photolysis
(Fig. 9a) and sole enhancement of
the aerosol effect rather than both aerosols and clouds (JANO3-C versus JANO3-B;
Fig. 9b), even though NO2 was still
underestimated with a large extent in the model (Fig. S15). At higher
altitudes over remote regions in the ATom (NO2<10 ppb) and
EMeRGe comparisons, OH and O3 can be increased due to more potent
gas-phase chemistry of HONO in the STD case, including HONO photolysis
(Fig. 4) and particle NO3- photolysis
(Fig. 2).
The amplified aerosol uptake of NO2 (maxST case) further reduced an
unrealistic degree of global NOx abundance (-55.4 %) and tropospheric
oxidizing capacity, leading to 14.5 years for a global CH4 lifetime.
The ratR4+CLD case introducing an approach to recycle NOx led to
lowered global effects of HONO (only 8.57 % of NOx was reduced globally,
CH4 lifetime=9.6 years). The photolysis of adsorbed HNO3 on
ground surfaces (JANO3-A case) still showed reductions in global OH and
O3 abundances (Table S5). The ground-surface HNO3 photolysis in
the JANO3-A case caused only minor changes for a thin surface layer, which is
in line with other studies (Ye et al., 2018; Zhang et al., 2009). In JANO3-B
and JANO3-C cases, a recycling process for NOx via HNO3 photolysis
was expected. However, only the JANO3-C case showed an increment in global
NOx and O3 (+29 % and +16.1 %, respectively), leading to
only 5.4 years for the global CH4 lifetime, which was impractical. This
was because that simplified approach and maximum thresholds for the phase
HNO3 photolysis were used. The combined case maxST+JANO3-B led to
more convincing effects (CH4 lifetime was 10.2 years; Table S5), which
held the same tendencies as those calculated in the STD case. However,
validating this combined case was only conducted for the daytime environment
during EMeRGe (Sect. 3.1.2) and for TCO at northern mid-latitudes with OMI
(Fig. 13).
Figure 16 illustrates the calculated global-mean
changes of tropospheric abundances in additional simulations from those in
the OLD case (without HONO chemistry). The simulations of the largest
negative to largest positive magnitudes of changes (%) in CH4
lifetime (purple bars) are shown from left to right. Other bars show
percentage changes in NOx (red), O3 (blue), and CO (green). In
simulations including ratR4, ratR4+CLD, JANO3-A, maxST+JANO3-B, and
maxST cases, HONO's impacts on tropospheric CH4 lifetime and
abundances of NOx, O3, and CO showed similar tendencies to those
impacts in the STD simulation. These similarities indicated that the
heterogeneous chemistry of HONO has a general tendency to reduce
tropospheric oxidizing capacity (OH and O3) as a result of NOx
removal globally via NO2's uptakes on aerosols and clouds. In these
settings, more substantial reductions in oxidizing species and NOx were
seen in maxST and maxST+JANO3-B cases via enhanced aerosol uptakes of
NO2. The particle-phase NO3- photolysis can solely compensate
for NOx removal processes and act as an efficient NOx-recycling
mechanism (on a global scale), which can be seen in the JANO3-B and JANO3-C
cases. The enhanced aerosol uptake in the maxST setting of the combined
maxST+JANO3-C case neutralized this compensation. However, the
tropospheric oxidizing capacity (OH and O3) was increased in the cases
configured with NO3- photolysis (JANO3-B/C, maxST+JANO3-C
cases), leading to a reduction in global CH4 lifetime. The
quantitative impact was unrealistic in some cases, e.g. 30 % of CH4 lifetime in
the maxST+JANO3-C case or -50 % of global NOx in the maxST+JANO3-B case,
proven as the proper mechanism for a particular environment (along
EMeRGe-Asia-2018 flights) and not globally. Thus, these changes merely provided
the tendencies of impact sensitivity for different pathways of HONO
formation, still with high uncertainty in their magnitudes.
In conclusion, we suggest the global effect tendency was towards
tropospheric oxidizing capacity reduction, although further elaboration for
enhanced aerosol uptakes of NO2 and surface-catalysed photolysis of
HNO3 could drive the effect magnitude. The implication of HONO
chemistry in a bottom-up-approached global model such as CHASER
needs an intense examination of possible HONO sources and profound
evaluations with observed HONO in the troposphere.
Conclusions
The HONO photochemical processes, including (1) the gas-phase reaction
involving HONO, (2) direct HONO emission from combustion and soil crust, and (3) heterogeneous processes involving HONO, were added to the
chemistry–climate model (CHASER), which did not consider HONO chemistry
before. We compared the measurements during the EMeRGe flights off the
coastal region of East Asia and discerned good agreement between the
measured and simulated NO2, O3, and CO profiles. However, the
model does not reflect the influence of the Chinese river delta regions, as
the large reductions in air masses affected by land emissions were
identified. The model also stood out with NO2, OH, HO2, and CO
improvements in the NP region, compared with the observations made during
Mirai and ATom-1, although the simulation underestimations of surface O3 in
this region were associated with the inconsistent surface deposition or
vertical fluxes (from the stratosphere) becoming strong. We found that
the model biases were reduced against the EANET/EMEP stationary observations
for PM2.5, NO3 components, O3, and CO concentrations when the
HONO photochemistry was included.
In the model, the tropospheric abundance for HONO was 1.4 TgN, with
26 % from direct emissions and 63 % from HRs, in which HRs on clouds
caused 11.8 % and HRs on aerosols caused 51.2 %. The HONO concentrations
over the continents ranged from 30 ppt to 7 ppb and were maximized due to
HRs over eastern China during winter. Only 5–10 ppt of HONO could be
transported up to ∼2000 m, indicating that its impacts
remained mainly in the planetary boundary layer. We argue that these
simulated HONOs might underestimate the actual concentrations off the coast of
eastern Asia in spring 2018. The unknown daytime HONO concentrations of up
to 200 ppt measured in the boundary layer and free troposphere during the
EMeRGe campaign were not reproduced by the STD simulation. Fortunately, the
measured HONO was moderately captured by the combined simulation, which
enhanced aerosol uptakes of NO2 and heterogeneous photolysis of
HNO3 (maxST+JANO3-B case). However, the enhancement for NO2
uptakes on aerosols should be confined to particular environments to
eliminate the effect exaggeration. Moreover, a further improvement of the
model performance for the HONO photochemistry requires (1) the revised
model's emission inventory with the emission sources of NOx and CO from
southeastern and eastern Asia, (2) the lighting-related NOx module to be
upgraded, and (3) the vertical mixing and downward fluxes from the
stratosphere to be elaborated.
One or more renoxification mechanisms converting HNO3 into NOx
should be added to the model to overcome the observed and simulated NOx
seasonality mismatches. Shifting the product ratio towards more HONO and
less HNO3 in Reaction (R4) could also provide more HONO and mitigated the
deteriorated representation of NOx seasonality. The sensitivity tests
also suggested that more robust aerosol processing in polluted areas and
less HNO3 product in R4 could further reduce the O3 level in summer,
reducing the bias against measurements. The photolysis of adsorbed HNO3
on ground surfaces (JANO3-A case) could also serve as a recycling process
for NOx at Asian ground-based sites (EANET).
As calculated in the STD case, HONO chemistry reduced the global
tropospheric oxidizing capacity, including OH and O3 levels on the global
scale. It should be underlined that this finding is rather unexpected and
contrasts with the increasing oxidation capacity previously reported for
polluted areas. However, the global reduction effect on O3 reduced the
overestimations of OMI-based TCO by simulations, which notably included the geographical Chinese region. Of the three HONO sources, HRs produced the most prominent effects on the tropospheric photochemistry: reducing OH, NOx, and O3 and increasing CO levels in the troposphere, leading to a
+13.05 % longer CH4 lifetime and -20.4 % less NOx,
-5.03 % less O3, as well as an increased CO (+8.36 %) abundance. In
winter near the surface, gas-phase reactions involving HONO and NO2
conversions on soot induced significant photochemical effects over eastern
Chinese regions, with changes of -60 % in NOx, +1700 % in OH, and +33 % in
O3. During summer, HRs on ice and cloud particles could cause
significant changes of -67 % in OH, -45 % in O3, -75 % in
NOx, and +17 % in CO in the NP region. Albeit the more significant
contribution of aerosols' heterogeneous reactions to the net HONO
production, the heterogeneous processes involving ice and cloud particles
were more significant globally. Our results from sensitivity tests
demonstrated that the tendencies and magnitudes of HONO's global effects
debated along with the effort regarding daytime HONO formation mechanisms.
In capturing HONO measurement during EMeRGe campaign, the combined case
enhancing NO2 aerosol uptake and implementing heterogeneous photolysis
of HNO3 (maxST+JANO3-B) still resulted in the reduction for global
tropospheric oxidizing capacity. In this case, the effect magnitude was
smaller for CH4 lifetime, but those for the NOx–O3–CO
chemistry were stronger compared with the calculation in the STD case.
Overall, our results proved that a global model without heterogeneous HONO
formation, especially photochemical heterogeneous HONO formations, could
bias the overall impacts of HONO on tropospheric photochemistry as it
neglected the photochemical effects of HONO in remote areas and
underestimated them in polluted regions. Our new finding on the tropospheric
oxidizing capacity reduction may affect climate change mechanisms and, as a
result, may influence its mitigation policies.
Code availability
The CHASER V4.0 source code and input data to recreate this work's results
can be acquired from the repository at
10.5281/zenodo.4153452 (Ha et al., 2020).
Data availability
The primary data from R/V Mirai cruises for the period 2015–2017 are available from
http://www.godac.jamstec.go.jp/darwin/e (Japan Agency for Marine-Earth Science and Technology, 2023).
Due to a recent data security incident, the data owner (JAMSTEC) has
suspended public access to this dataset. For any inquiries, please send an
email to yugo@jamstec.go.jp. The data collected by the HALO aircraft during the
EMeRGe campaign are listed on
https://www.iup.uni-bremen.de/emerge/home/halo_payload.html (Institut für Umweltphysik, 2023)
and can be acquired via email to Lola Andrés Hernández (lola@iup.physik.uni-bremen.de).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-16-927-2023-supplement.
Author contributions
PTMH composed all simulations and text. KS has the model code and supervised
the findings of this study. YK and FT provided R/V Mirai ship data. MDAH, BS, and KP
provided EMeRGe-Asia data. All authors have equally contributed to the
discussion provided within the manuscript and post-writing formatting and
revisions.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are grateful to the NASA scientists and staff for providing ATom data
(https://espo.nasa.gov/atom/content/ATom, last access: 30 June 2020) and OMI data
(https://daac.gsfc.nasa.gov/, last access: 30 June 2019). The simulations were completed using a
supercomputer (NEC SX-Ace and SX-Aurora TSUBASA) at NIES Japan. The surface
observational data for model validation were obtained 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 four anonymous reviewers for their constructive comments and helpful suggestions on the earlier draft of the manuscript.
Financial support
This research has been supported by the Japan Society for the Promotion of Science (JSPS KAKENHI grant numbers JP20H04320, JP19H05669, and JP19H04235), the Ministry of the Environment, Government of Japan (grant nos. S-12 and S-20), and the Deutsche Forschungsgemeinschaft (grant nos. HALO-SPP 1294, PF 384/16, PF 384/17, and PF 384/19).
Review statement
This paper was edited by Jason Williams and reviewed by two anonymous referees.
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