This study compares the performance of 12 regional
chemical transport models (CTMs) from the third phase of the Model
Inter-Comparison Study for Asia (MICS-Asia III) on simulating the
particulate matter (PM) over East Asia (EA) in 2010. The participating
models include the Weather Research and Forecasting model coupled with Community Multiscale Air Quality (WRF-CMAQ; v4.7.1 and v5.0.2), the Regional Atmospheric Modeling System coupled with CMAQ (RAMS-CMAQ; v4.7.1 and v5.0.2), the Weather Research and Forecasting model coupled with chemistry (WRF-Chem; v3.6.1 and v3.7.1), Goddard Earth Observing System coupled with chemistry (GEOS-Chem), a non-hydrostatic model coupled with chemistry (NHM-Chem), the Nested Air Quality Prediction Modeling System (NAQPMS) and the NASA-Unified WRF (NU-WRF). This study investigates three model processes as the possible reasons for different model performances on PM.
(1) Models perform very differently in the gas–particle conversion of
sulfur (S) and oxidized nitrogen (N). The model differences in sulfur
oxidation ratio (50 %) are of the same magnitude as that in SO42-
concentrations. The gas–particle conversion is one of the main reasons for
different model performances on fine mode PM. (2) Models without dust
emission modules can perform well on PM10 at non-dust-affected sites
but largely underestimate (up to 50 %) the PM10 concentrations at
dust sites. The implementation of dust emission modules in the models has
largely improved the model accuracies at dust sites (reduce model bias to
-20 %). However, both the magnitude and distribution of dust pollution
are not fully captured. (3) The amounts of modeled depositions vary among
models by 75 %, 39 %, 21 % and 38 % for S wet, S dry, N wet and N
dry depositions, respectively. Large inter-model differences are found in
the washout ratios of wet deposition (at most 170 % in India) and dry
deposition velocities (generally 0.3–2 cm s-1 differences over inland
regions).
Introduction
Atmospheric pollution due to particulate matter (PM) has raised worldwide
attention for its relationship with environmental and public health issues
(Fuzzi et al., 2015; Nel, 2005). Fine particles (PM2.5) are
associated with cardiovascular- and respiratory-related cancers and premature
death (Hoek and Raaschou-Nielsen, 2014; Knol et al., 2009). Outdoor
PM2.5 pollution is estimated to cause 2.1–5.2 million premature deaths
worldwide annually (Lelieveld et al., 2015; Rao et al., 2012; Silva et al.,
2013). It accounted for 8 % of global mortality in 2015 and ranks
fifth in the list of global mortality risk (Cohen et al., 2017). East Asia (EA)
has been suffering from severe PM pollution due to anthropogenic emissions
and natural dust emissions (Akimoto, 2003). China and India are the top
two countries suffering from outdoor air pollution, which altogether accounted
for 20 % of global mortality caused by PM2.5 exposure in 2010
(Lelieveld et al., 2015). The mixing of dust with
anthropogenic pollutants can even increase the effects of pollution
(Li et al., 2012). However, the impact evaluation of PM pollution
is of high uncertainty due to the unclearness of the toxicity of PM components
(Lippmann, 2014) and difficulty in the measurement and prediction of
PM concentrations.
For a better understanding of PM pollution, a modeling approach has been
adopted to study the spatial distributions of PM with the aid of
measurements. The multi-model ensemble approach, which interprets modeling
results with combined information from several models, has been proven to
increase the reliability of model accuracy (Tebaldi and Knutti, 2007).
This method has been widely used for studies in Europe (Bessagnet et al.,
2016; Vivanco et al., 2017) and at global scales (Lamarque et al.,
2013; Galmarini et al., 2017) on air quality issues. The Model
Inter-Comparison Study for Asia (MICS-Asia) aims at understanding the air
quality issues in EA. The first phase of MICS-Asia (MICS-Asia I) was
carried out in the 1990s with eight regional chemical transport models
(CTMs). The study focused on air pollution issues related to sulfur
(S, including SO2, SO42- and wet SO42- deposition).
The second phase of MICS-Asia (MICS-Asia II) was launched in the early 2000s
with nine CTMs (Carmichael and Ueda, 2008). The study
covered the chemistry and transport of S, nitrogen (N), PM and acid
deposition. Multi-model results on SO42-, NO3- and
NH4+ (SNA) were evaluated with measurements from 14 sites of
Acid Deposition Monitoring Network in East Asia (EANET) and the Fukue site
in Japan. However, a non-exhaustive evaluation on PM10 concentrations
in China with scarce datasets left an unclear view of the models' ability in
this area, a region recognized as one of the most heavily polluted in EA.
Meanwhile, model results were found with high inconsistencies on simulating
both gas and aerosol phases of S and N (Hayami et al., 2008). Further efforts
are needed to investigate the reasons for model differences to improve model
accuracies.
This study compares the performance of 12 regional models which participated
in the third phase of MICS-Asia (MICS-Asia III) on simulating PM over EA.
The comparison among models aims at identifying the reasons for different
model performances. The models involved in this study include the Weather
Research and Forecasting Model (WRF) coupled with Community Multiscale Air
Quality (CMAQ) modeling (version 4.7.1 and v5.0.2), the Regional Atmospheric Modeling System coupled with CMAQ (RAMS-CMAQ), WRF model coupled with
chemistry (WRF-Chem) (v3.6.1 and v3.7.1), Goddard Earth Observing System
coupled with chemistry (GEOS-Chem), a non-hydrostatic model coupled with
chemistry (NHM-Chem), the Nested Air Quality Prediction Modeling System (NAQPMS)
and the NASA-Unified WRF (NU-WRF). The models' performance on simulating PM has
been reported in a companion paper (Chen et al., 2019). The main findings
are described in Sect. 3.1. Section 3.2–3.4 examine the influences of three
model processes on model performance: (1) formation of fine particles
(PMF) – model differences in the gas–particle conversion; (2) formation of
coarse particles (PMC) – model improvements by implementing dust emission modules for simulating PM and the remaining problems; and (3) removal
processes of particles from the atmosphere – uncertainties lie in the
efficiencies of wet and dry depositions. Section 4 concludes the findings of
this study and provides suggestions for further study.
Multi-model performance on simulating annual average concentrations of
PM10 at the dust and non-dust sites (unit: µgm-3).
Dust siteM1M2M4M5M6M7M8M10M11M12M14MMMMean obs120.7 Mean MMM77.282.281.651.765.647.544.3102.573.577.392.169.2Sa0.40.40.40.30.30.20.20.10.20.20.30.3MB-43.5-38.5-39.2-69.0-55.1-73.2-76.4-18.2-47.2-43.4-28.6-51.5R0.40.40.40.40.40.20.20.10.20.20.20.3Fb66.769.269.238.556.435.933.384.659.066.766.766.7NMB (%)-36.1-31.9-32.4-57.2-45.7-60.6-63.3-15.1-39.1-36.0-23.7-42.6NMEc (%)38.335.436.457.246.260.663.332.842.340.536.142.7MFBd (%)-49.4-44.6-44.6-83.4-64.1-92.9-98.8-19.3-51.8-46.8-31.7-56.9MFEe (%)51.848.348.783.464.792.998.836.155.351.744.556.9Number of sites39 Non-dust siteM1M2M4M5M6M7M8M10M11M12M14MMMMean obs77.2 Mean MMM58.258.566.545.255.244.839.090.064.466.389.557.8S1.01.11.20.81.00.70.61.01.00.91.10.9MB-19.0-18.7-10.8-32.1-22.1-32.5-38.312.7-12.9-10.912.2-19.4R0.70.80.70.80.80.60.60.70.70.70.60.8F82.581.084.166.782.552.446.085.790.593.784.182.5NMB (%)-24.6-24.2-14.0-41.5-28.6-42.0-49.516.5-16.6-14.115.8-25.1NME (%)30.730.727.341.531.443.950.725.726.326.130.828.0MFB (%)-36.8-37.5-25.1-59.2-41.8-62.0-75.013.1-24.9-20.38.3-34.6MFE (%)42.042.835.359.244.464.076.123.433.531.329.137.5Number of sites63
aS stands for linear fit slope. bF stands for fraction (of model results) within ±50 % of observation. c NME stands for normalized mean error. d MFB stands for mean fractional bias. e MFE stands for mean fractional gross error.
MethodologyFramework of MICS-Asia
MICS-Asia is a model intercomparison study with contributions from
international modeling groups to simulate the air quality and deposition
over EA. MICS-Asia I focused on air quality issues related to S. The
multi-model performances on simulating SO2 and SO42- concentrations and SO42- wet deposition were evaluated with
observations from 18 stations (Carmichael et al., 2002). A
source–receptor relationship of S deposition was developed based on the
sensitivity simulations for seven prescribed receptor regions: Komae, Oki,
Fukue, Yangyang, Beijing, Nanjing and Taichung (Carmichael et al., 2002).
MICS-Asia II was initiated in 2003. Nine regional models simulated the air
quality for 4 months (March, July and December of 2001 and March of
2002) to study the chemistry and transport of air pollutants and acid
deposition (Carmichael and Ueda, 2008). All modeling
groups were enforced to use the same emission: the Transport and Chemical
Evolution over the Pacific (TRACE-P) emission of 2000 and common initial condition (IC) and boundary condition (BC) to facilitate a comparison between the physical and chemical mechanisms of the models.
The modeling species expanded to S, N, O3, PM and acid deposition.
Model evaluations and major findings can be found in the literature
(Carmichael et al., 2008; Fu et al., 2008; Han et al., 2008; Hayami et al.,
2008).
MICS-Asia III was launched in 2010. The simulation time covers the whole year
of 2010. All modeling groups are required to use the prescribed
anthropogenic emissions and natural inputs (including biogenic emissions,
biomass burning emissions and volcanic SO2 emissions. Dust and sea-salt
emissions are produced by the corresponding modules in different models)
(Li et al., 2017). Three purposes are
set for this project. Topic I aims at evaluating the strengths and weaknesses of
current multi-scale air quality models in simulating air qualities over EA
and providing suggestions to reduce uncertainty for future simulations. Topic
II intends to develop a reliable anthropogenic emission inventory for EA. The purpose of Topic
III is to investigate the interaction of aerosol–weather–climate by using
online coupled air quality models. This study focuses on Topic I.
Model configurations
The model setup can be found in Table 1 of Chen et al. (2019). Fourteen
modeling groups (M1-M14) participated, but M3 and M9 are not included in
this study due to uncompleted model submissions. The M14 model has a smaller
simulation domain than the others; therefore, it is not included in the
multi-model mean (MMM) results. The gas and aerosol modules and dust schemes
employed by the participating models were introduced in detail in Sect. 2.1
of Chen et al. (2019). The following are the descriptions on the model setup for
wet and dry depositions.
Wet deposition removes gases and aerosols from the atmosphere by rain
droplets, involving both in-cloud scavenging (rainout) and below-cloud
scavenging (washout). The gases in the atmosphere are dissolved in the
raindrop and then removed from the atmosphere. For the nonreactive gases,
the removal rate depends on the solubility of gases and is a function of
Henry's law. Particles take part in the cloud condensation nuclei in the
presence of supersaturated water vapor and then grow into cloud droplets.
In this study, only M2, M4, M6, M11 and M12 submitted the main
components of S and N depositions. All these models use the same wet
deposition scheme based on Henry's law. The efficiency of wet deposition is
assessed by the so-called “washout ratio”, calculated as the ratio of
particle concentrations in deposition to particle concentrations in surface
air as shown in Eq. (1).
λwet=CdepoCsurface_air×100%,
where λwet (%) is the washout ratio for wet deposition,
Cdepo (µgm-3) is the concentration of particles in
deposition and Csurface_air (µgm-3) is the
concentration of particles at near-surface atmosphere.
Dry deposition is mainly driven by turbulent and molecular diffusion
processes. All models except M12 use the same dry deposition scheme from
Wesely (1989). The dry deposition flux is proportional to the
concentration of pollutants at height. The dry deposition velocity is
calculated with Eq. (2).
2Vd=-Fc/Ca,3Vd=1.0rsurf+ra+rbc,
where Fc (mg m-2 yr-1) is the dry deposition flux, Vd (cm s-1) is the deposition velocity and Ca (µgm-3) is the
concentration of species at height. The negative mark indicates the
direction of the dry deposition velocity. The Vd is determined by the
resistance of the air layer (r). The total r is composed of three factors (Eq. 3): the aerodynamic resistance (ra), boundary layer resistance
(rbc) and canopy resistance (rsurf).
M12 uses the general approach from Wesely (1989) and updates from
Zhang et al. (2003). Zhang et al. (2003) update the value of
non-stomatal resistance (rns), which is a component of rsurf related
to the soil uptake and cuticle uptake of dry deposition. A model evaluation
shows the updates can improve the model prediction of dry deposition
velocities of SO2 (Zhang et al., 2003).
Observational data
To make the discussion clear, we define the regions used in the following
analysis here: northern EA (Russia and Mongolia), central EA (China),
eastern EA (Japan and the Korean Peninsula) and southern EA (Cambodia, Lao PDR, Myanmar,
Thailand, Vietnam, Indonesia, Malaysia and the Philippines). The following
monitoring datasets are used in the analysis in Sect. 3.2–3.4. The air
pollution index (API) provides monthly average PM10 data from
86 sites (A1–A86 in Fig. 1) (http://www.mee.gov.cn/, last access: 17 June 2020). This
dataset has been widely used to study PM pollution (Qu et al.,
2010; Chen et al., 2008; Deng et al., 2011), as well as for model evaluation
(Wang et al., 2012; Xing et al., 2015) in China. It was replaced by the air
quality index (AQI) in 2013. The API data cover eastern China well
with intensively located sites, but sites in western China are very limited.
EANET (E1-E54) provides monthly average concentrations of PM10 and SNA, as well as
S and N depositions, from 54 sites (http://www.eanet.asia/, last access: 28 May 2018). For PM10, this dataset has a very limited
number of sites in China. The sites are generally located along the east
coast of China and could not cover the areas with high PM10
pollution well, such as the Hebei–Beijing–Tianjin (HBT) region (Fig. 1). The
data completeness in northern EA is not as satisfying as the other regions.
Only three sites located in Rishiri (E15), Ochiishi (E16) and Oki (E21) in
Japan have PM2.5 observations during our study period. R1-R35 (green)
are 35 reference (Ref) sites provided by the Institute of
Atmospheric Physics Chinese Academy of Science (IAP-CAS). The
sites are concentrated in three regions: HBT region, Pearl River Delta (PRD)
and Taiwan.
The geographical locations of observation networks of API (red
color, A1–A86), EANET (blue color, E1–E54; only sites with available
observations during simulation time are shown) and Ref (green color, R1–R35)
sites. Gray shaded regions have been reported to be affected by dust storms.
Result and discussionBrief results of model performance evaluation
All models have submitted the monthly average concentrations of PM10,
PM2.5 and SNA at the surface layer except PM10 from M13 and
NO3- and NH4+ from M10. An evaluation of the models' performance
on aerosols can be found in our companion paper (Chen et al., 2019).
The following are the main findings. The differences between MMM and
observational and satellite data for the surface concentrations of PM10,
PM2.5, SO42-, NO3- and NH4+ and the column
integrated aerosol optical depth (AOD) were -32.6 %, 4.4 %, -19.1 %,
4.9 %, 14.0 % and 18.7 %, respectively (calculated with normalized
mean biases, NMBs). PM10 concentrations were generally underestimated
over the simulation domain. PM2.5 concentrations were also
underestimated over eastern EA but were simulated well in central EA.
The models failed to reproduce the high peaks of SO42- concentrations
in central EA probably due to missing SO42- formation mechanisms
(such as heterogeneous SO42- chemistry), which have been reported
as an important formation pathway of SO42- in China.
NO3- concentrations were overpredicted by most models over the
simulation domain and were associated with the underestimation of
SO42-. The M7 and M8 models produced significantly lower
NO3- concentrations than observations and other models due to the
underestimation of NH3 concentrations, which might be caused by low NH3
emissions, and missing N2O5 heterogeneous reactions, which serve
as an important formation pathway of NO3- (Chen et al., 2019).
The spatial distributions of AOD were generally simulated well, but several
models were found to underestimate the AOD values around the Himalaya
mountains, Taklamakan Desert and Gobi Desert.
This study compares the models' performance with a global-scale model study.
The Task Force on Hemispheric Transport of Air Pollution (TF HTAP) is an
intercomparison study of global and regional models to assess the impact of the
hemispheric transport of air pollutants on regional atmosphere. The second
phase of HTAP (HTAP-II) involved more than 20 global models to simulate
the air quality in 2010 (Galmarini et al., 2017). Most models utilize
coarse-resolution grids at about 2–3∘. HTAP-II
and MICS-Asia III share some common points like using the same emission
inventory in East Asia (Li et al.,
2017) and using the same observational dataset to evaluate PM10 (more
than 100 EANET and API sites) and PM2.5 (two EANET sites) (Dong et al.,
2018). The mean bias (MB) of PM10 over EA is -30.7 and
-18.6µgm-3 for HTAP-II and this study, respectively (values for
sites used by both studies). The MB of PM2.5 is -1.6 and -4.3µgm-3 for HTAP-II and this study, respectively.
Both studies find an underestimation of PM10 concentrations, while
PM2.5 concentrations are produced well. The models of MICS-Asia III perform
slightly better than those of HTAP-II with lower model bias in PM10,
probably taking advantage of finer resolutions of model grids.
The so-called “diagnostic evaluation” approach is adopted to check the
model bias oriented by individual processes (Dennis et al., 2010).
Although all modeling groups are required to use the prescribed emission
inventory, a mismatch was found during the temporal and vertical treatments
of emission files by different modeling groups which has caused differences
in the model inputs (Itahashi et al., 2020). To avoid the possible impacts
on the inter-model comparison, we compare the indicators (i.e., sulfur oxidation
ratio, SOR) instead of direct model outputs (i.e., SO42-
concentrations) to focus on the differences caused by model mechanisms. The
following three processes are examined.
Formation of PMF. Section 3.2 investigates the differences in the gas–particle conversion of S and N among different models.
Formation of PMC. Section 3.3 assesses the models' ability to reproduce the
spatial and temporal distributions of PM in regions affected by dust storms.
A comparison is conducted between models with and without dust emission modules.
Removal of particles from the atmosphere. Section 3.4 compares the models'
performance in simulating the amounts of deposition and the efficiencies of
wet and dry depositions.
Gas–particle conversion
The following two indicators are calculated to illustrate the gas–particle conversions of S and N.
4SOR=n-SO42-n-SO42-+n-SO2,5C(NO2)=n-NO3-n-NO3-+n-NO2,
where n-SO42-, n-SO2, n-NO3- and n-NO2 (mol m-3) are the
mole concentrations of SO42- particles, SO2 gas,
NO3- particles and NO2 gas. The C(NO2) (%) indicator only
has NO3- and NO2 in the denominator due to the limitations of
observational data, but it still can portray the conversion of N between the gas
phase and the particle phase.
Figures 2 and 3 show the distributions of the SOR and C(NO2) values of the models. The
SOR values are lowest around the HBT region in northeastern China (10 %–40 %)
and highest in southwestern China (60 %–80 %) (Fig. 2). The CMAQ models
(including WRF-CMAQ and RAMS-CMAQ) produce similar SOR patterns, except that
the CMAQv5.0.2 models (M1 and M2) predict a 10 % higher SOR in the HBT region
than the CMAQv4.7.1 models (M4, M5 and M6). CMAQv502 updated the production
of SO42- in the aqueous reaction of the older version
(Appel et al., 2013; Fountoukis and Nenes, 2007). The
explicit treatment of Fe and Mn allows a more consistent treatment of aqueous
reactions from SO2 to SO42-. For the Chem models (including
WRF-Chem, GEOS-Chem and NHM-Chem), the two WRF-Chem models (M7 and M8)
produce similar SOR magnitudes and distributions in all regions except
southwestern China (around Tibet in Fig. 1) and the open oceans, while the
NHM-Chem (M12) and GEOS-Chem (M13) models produce slightly higher SOR values
over the whole simulation domain. The differences between the CMAQ and the
Chem models are significant over the inland regions of northern and
eastern China, Japan, and southern EA. The CMAQ models generally predict
5 %–20 % higher SOR values than the Chem models. Similarly, the CMAQ models
generally give 20 % higher C(NO2) values than the WRF-Chem models
especially in eastern EA (Fig. 3). The C(NO2) of M8 is extremely low due to
unreasonably low NO3- concentrations.
SOR values at the surface layer for models (unit: %). The SOR is calculated by
SO42-/(SO2+SO42-)×100 %. The SO2
and SO42- concentrations are converted from (ppb) and (µgm-3) to (mol (S) m-3) before calculating the SOR. Values are calculated by
annual average data.
Same as Fig. 2 but for C(NO2) (unit: %). C(NO2) is calculated by
NO3-/(NO2+NO3-)×100 %. The
C(NO2) of M8 is extremely low due to the unreasonably low NO3-
concentration, which is considered an outlier in this study. Values are
calculated by annual average data.
Figure 4 shows the gas–particle conversions of S and N by models and
observations at the EANET sites. The red bars represent concentrations of
gases, and the black bars represent concentrations of aerosols. The values
with the blue color above the bars are observed and modeled SORs and
C(NO2) values. Results for individual sites are available in the Supplement
Fig. S1. According to Fig. 4a, the total amount of S (SO2
gas+SO42- particle) is about 0.15 µmol(S)m-3.
Most models have biases on this value especially the moderate
underestimation by M7, M8 and M13. On the other hand, the SOR value (0.25) is
well simulated by M1 (0.26), M2 (0.20), M10 (0.29) and M13 (0.26). Other
models generally underpredict the SOR value except M12 (0.33) and M14 (0.57).
The WRF-CMAQv5.0.2 models (M1 and M2) produce higher SORs than the WRF-CMAQv4.7.1 models (M4, M5 and M6), probably attributed to the updates in the formation
pathway of SO42-.
Gas–particle conversions of S and N from observations and models at
EANET sites. The unit is micromoles of sulfur or nitrogen per cubic meter (µmol (S or N) m-3). The red bars and
black bars represent the concentrations of gases and aerosols. The
blue-colored values above the bars are observed and modeled SORs and C(NO2). Values
are calculated with annual average concentrations. The concentrations of
gases and aerosols are all converted to (µmol (S or N) m-3)
before calculation. The blue-colored numbers to the top right (e.g., E22) are site
numbers. The locations of the sites are illustrated in Fig. 1. Results for
individual sites are shown in Supplement Fig. S1.
Figure 4b–e show the results in different regions. In northern EA, the
total amount of S is underestimated by all models except M13 and M14.
However, the SOR value (0.12) is reproduced well by most models (0.08–0.20)
except M12 (0.25) and M10 (0.32). There is only one site available for
central EA. Most models (except M12 and M13) have largely underestimated the
SOR value, while M14 has largely overestimated it. For eastern EA, the total
amount of S is captured well by all models except M11, M12 and M14. The
SOR value (0.55) is generally underestimated by all models except M10 (0.55)
and M14 (0.71). For southern EA, the total amount of S is generally
overestimated by all models except M13, while the SOR value is underestimated
by all models except M13 and M14. Overall, the models have both positive and
negative biases in simulating the total amounts of S, but they generally
underestimate the SOR values in all regions. Furthermore, the modeled SOR values
vary largely among models (ranging from 0.12 to 0.57), resulting in a large
inter-model difference (1 standard deviation %, SD %, =50 %, SD % is calculated as 100%×1 SD / MMM). This variation is of the same
magnitude as the variation of SO42- concentrations (1 SD % =50 % in Supplement Fig. S2). The results suggest that differences in
gas–particle conversion among models could account largely for the models'
inconsistency in simulating the SO42- concentrations.
Figure 4f–h compare the gas–particle conversion of N with the
C(NO2) indicator. Only one site in China and one site in Japan have both
NO2 and NO3- observations. At the Hongwen sites in China, all
models except M5 underestimate the sum of NO2 and NO3-, but
the modeled C(NO2) values are close to the observation (0.18) except M5
(0.07), M8 (0.00) and M12 (0.40). Similar to the results of S conversion,
the newer version of the WRF-CMAQ model generally produces higher
C(NO2) than the older version, but the differences between the two in
C(NO2) are smaller than those for SOR. At the Banryu site in Japan, the sum of
NO2 and NO3- is simulated well by all models except M8. The
C(NO2) (0.19) value is also simulated well by all models except M8 (0.00),
M12 (0.53) and M14 (0.77). Overall, the model accuracy of C(NO2) is slightly
higher than that of SOR according to the comparison with observed values.
The models also have higher consistencies with C(NO2) than SOR. However, further
validation is required due to the limited number of observations for the
conversion of N.
Besides the inter-model differences in the pathways of SO42- and
NO3- formation, the interaction between aerosols and atmospheric
oxidants can also affect the formation of aerosols (Liao et al., 2003).
Aerosols affect the tropospheric oxidant (i.e., HOx) budget by altering
the photolysis rates and uptake of reactive gases (Tie et al., 2005; Li et
al., 2018). In turn, the abundance of HOx affects the gas–aerosol
conversion of S and N. In addition, the conversion between sulfuric acid and
SO42- depends on the abundance of neutralizers such as Na+
and NH4+.
Implementation of dust emission modules in the models
The PMC concentrations at the surface layer are calculated by subtracting
PM2.5 from PM10. Figure 5 shows the spatial distribution of annual
average PMC in the models. Most models show very low (<2µgm-3) concentrations of PMC around the Taklamakan Desert and the Gobi
Desert in northern China except M10, M11 and M14. These three models use
dust emission modules in simulations (Chen et al., 2019). M12 also includes
dust emissions, but its PM10 concentrations over northern China are
much lower than the three models. The predicted PMC concentrations of the
three models differ largely. The domain-average concentrations of PMC are
21, 7 and 12 µgm-3 for M10, M11 and M14, respectively. The
distributions of PMC also differ largely over northwestern China, where the
impacts of dust are most significant. The differences among the models
mainly come from the different parameterizations such as source functions,
dust-lifting mechanisms and the size distributions of particles (Chen et al.,
2019). Different PMC concentrations are also found over oceans, mainly
attributed to the sea-salt emissions in this study. The sea-salt emissions
are parameterized in the models with various formulas (Chen et al., 2019). In
this study, the WRF-Chem models (M7 and M8) turned off the sea-salt
emissions; thus, their PMC concentrations over the oceans and seas are not
defined. The two WRF-CMAQ models use the in-line sea-salt emission module of
Gong (2003) with updates by Kelly et al. (2010). They
predict consistent distributions of PMC over oceans. M10 and M11 use the
same module as the CMAQ models (Gong, 2003) but produce higher PMC over
oceans. M12 adopts the method of breaking waves over the seashore by
Clarke et al. (2006) and produces the highest PMC over oceans
among all models.
Annual average PMC concentrations at the surface layer of individual
models (µgm-3). The value is calculated by subtracting
PM2.5 from PM10. The values in the bottom left are domain average
(Avg) and maximum (Max) values.
The implementation of dust emissions is expected to improve the models'
performance, but how significant could the improvement be? Can models
predict the PM concentrations reasonably at regions affected by dust with
current dust emission modules? To answer these questions, all sites are
grouped as dust and non-dust sites according to their locations. The sites
located in regions that have been reported to receive severe impacts and the
rapid deposition of dust are marked as dust sites (Shao and Dong, 2006)
(gray-colored shaded areas in Fig. 1). Figure 6a–b and Table 1 compare the
models' performance at the dust and non-dust sites. For the non-dust sites
(Fig. 6b), most models have captured well the magnitudes of PM10 at
the “API non-coastal, non-dust” sites (MB =-8 % and NMB =-8 %).
The sites marked “API coastal”, which are located close to the
coastal regions, are all slightly underestimated by about 25 µgm-3 (30 %). Similarly, the PRD and Taiwan sites, which are also
located near the coastal regions, are all underestimated by about 20 µgm-3 (37 %). Bias in sea-salt emissions is the possible reason.
Sea-salt emissions are reported to contribute 20 %–40 % of SNA and
PM10 over coastal regions (Liu et al., 2015). Including the sea-salt
emissions in model simulations can improve the model accuracy with an 8 %–20 %
increase in PM10, SNA, Na+ and Cl- (Kelly et al., 2010; Im,
2013). The influence of sea-salt emissions is not the focus of this study,
but further study is strongly recommended.
Multi-model performances on simulating (a–b) annual average PM10
concentrations at the dust sites and non-dust sites and (c–d) monthly
average PM10 concentrations at the dust sites and non-dust sites. The x axis for (a–b) indicates site numbers. The locations of the sites are
illustrated in Fig. 1. The yellow bars are observations, the blue lines
are the MMM and different markers represent individual model results. The R values (e) between model results and observations at the dust and non-dust sites.
For the dust sites (Fig. 6a), most models have generally underestimated
the PM10 concentrations by 10–40 µgm-3 (15 %–50 %). The three models with dust modules perform better than the others at the dust
sites, especially site A2, A30, A68, A69, R5 and R18. However, they miss the
high PM10 concentrations at sites like R1–R3 and R11 and overestimate
the PM10 concentrations at sites such as A60 and A80. This indicates
that the dust emission modules involved in this study can not fully capture
the magnitudes and distributions of dust pollution over EA. In addition, the
modeled PMC differs a lot with different dust emission modules (Fig. 5).
The M10 model produces very high PMC over the whole of eastern China, while the M11
model only predicts high PMC around the HBT region. Overall, the models'
performance with PM over dust regions can be improved largely by including
dust emission modules. However, the concentrations and distributions are
not yet captured well, and large inconsistencies are found among different
dust emission modules.
Figure 6c–d compare the modeled monthly trends of PM10 with
observations at the dust and non-dust sites, and Fig. 6e shows the
correlation (R) values between models and observations. For the non-dust
sites (Fig. 6d), the trends are caught well by most models. The R values
are close to 0.70 for all models except M7 (0.62), M8 (0.58) and M14 (0.63).
The WRF-Chem models (M7 and M8) simulate too low PM10 concentrations in
winter. The M14 model overestimates the PM10 concentrations from March to
May. Most models have much lower R values at the dust sites than the
non-dust sites (Fig. 6e) due to the underestimation of the PM10
concentrations during winter. For instance, R values of M10 drop from 0.7 at
the non-dust sites to 0.11 at the dust sites. Spring (March, April and May)
has the largest model biases at the dust sites, which is coincident with the
dust storm season in Asia (Arimoto et al., 2006). The M10 and M14 models
perform well in most months at both the dust and non-dust sites, taking
advantage of their dust emission modules, but their R values at the dust
sites are still low. Future study is strongly suggested for a better
understanding of the seasonal variations of dust pollution.
Multi-model performances on simulating wet deposition (unit: mg (S or N) m-2 yr-1).
Wet SO42- deposition Wet NO3- deposition M2M4M6M11M12MMMM2M4M6M11M12MMMMean obs931.3931.3931.3931.3931.3931.3460.9460.9460.9460.9460.9460.9Mean MMM633.7724.27751313.2826.2854.5187.5266.7279.5597.8308.3328S0.30.30.30.30.20.30.10.10.10.20.10.1MB-297.7-207.1-156.3381.9-105.1-76.9-273.4-194.2-181.4137-152.6-132.9R0.50.40.40.20.30.40.10.20.20.20.10.2F61.261.261.224.540.85138.84946.944.938.846.9NMB-32-22.2-16.841-11.3-8.3-59.3-42.1-39.429.7-33.1-28.8NME49.350.251.5117.362.853.666.260.960.678.468.858.2MFB-37.4-23.4-15.84.6-11.4-4.6-75.8-49.8-42.125.8-40.9-27.6MFE57.855.953.793.866.757.684.971.269.36174.662.3Number of sites494949494949494949494949Wet NH4+ deposition M2M4M6M11M12MMMMean obs558.4558.4558.4558.4558.4558.4Mean MMM459.9349.4497.4505478337.6S0.30.10.30.30.20.2MB-98.5-208.9-61-53.4-80.4-220.7R0.30.20.30.40.40.3F40.844.944.95146.938.8NMB-17.6-37.4-10.9-9.6-14.4-39.5NME64.865.564.958.25763.6MFB-21.2-42.4-14.4-18-12.6-41.9MFE70.777.969.165.962.976.1Number of sites494949494949Wet and dry depositions
Figure 7 and Table 2 show the models' performance on simulating wet deposition. For wet
SO42- deposition, despite the fact that the two sites with the highest
deposition (E2 and E3) in China are underestimated, the other sites are
generally well simulated by MMM with a low MB of -8 %. The individual
model bias varies from -22 % to 41 %. The CMAQ models (M2, M4 and M6)
all underestimate the wet SO42- deposition. There are large
differences between CMAQv4.7.1 and CMAQv5.0.2 in Japan, where the CMAQv4.7.1
models (M4 and M6) slightly overestimate the wet SO42- deposition
at E19 and E23, while the CMAQv5.0.2 model (M2) slightly underestimates the
value at these sites. The M11 model produces a considerably higher wet
deposition of SO42- and NO3- than the other models in
eastern EA. The possible reasons are discussed later. The MMM underestimates
the NO3- wet deposition by 29 % due to the large underprediction
in southern EA. Southern EA has several sites with very high depositions,
such as the E29 site in Malaysia and the E35 and E36 sites in the Philippines, but all models fail to
catch those high peaks. The individual model bias varies from -59 % to
30 % among the models. M2, M4, M6 and M12 perform similarly with a high
underestimation ranging from 39 % to 59 %. M11 is the only model
that succeeds in capturing the high wet NO3- deposition at E2 and E3
in China, but it overestimates most sites in China, Japan and the Korean Peninsula. In the case of wet
NH4+ deposition, the MMM generally underestimates the amount at
all sites with a bias of -40 % especially at E2–E4 in China, E45 in Thailand, and
E35 and E36 in the Philippines. The individual model bias varies from -10 % to -37 %.
The M2, M4 and M6 models perform similarly, while the M11 and M12 models predict
higher depositions at all sites. Overall, large inter-model disagreements
are found in eastern EA for wet depositions of SO42- and
NO3- and in southern EA for the wet NH4+ deposition.
The observations of dry depositions are composed by the observed concentration of
air pollutants and the simulated deposition velocity. Since the EANET network
only provides the former, a complete evaluation of the dry deposition is
not available in this study (complete observation for dry deposition with velocity is
available after 2013).
Modeled annual-accumulated wet depositions of SO42-,
NO3- and NH4+ compared with observations from the EANET
network. The units are (mg (S or N) m-2 yr-1). Abbreviation for
regions: RU – Russia, MN – Mongolia, CH – China, JP – Japan, KR – the Korean Peninsula, KH – Cambodia,
MN – Myanmar, TH – Thailand, VN – Vietnam, ID – Indonesia, MY – Malaysia and
PH – the Philippines.
Table 3 lists the domain-total, annual-accumulated amounts of S and N
depositions by the models. The total wet S deposition (DSwet) includes wet
depositions of SO2, H2SO4 and SO42-. The total dry
S deposition (DSdry) includes dry depositions of SO2,
H2SO4 and SO42-. The total wet N deposition (DNwet)
includes wet depositions of NO3-, NH4+, HNO3 and
NH3. The total dry N deposition (DNdry) includes dry depositions
of NO, NO2, NO3-, NH4+, HNO3 and NH3.
DSwet values range from 10.5 to 31.3 Tg (S) yr-1 among models
(1 SD % =75 %). The estimation by the M11 model is 2-fold higher than the
other four models. The inter-model difference is significant even among the
same type of models with different versions. The CMAQv4.7.1 models (M4 and
M6) produce 12.5 Tg (S) yr-1 (M4) and 13.8 Tg (S) yr-1 (M6) of
DSwet, while the prediction by the CMAQv5.0.2 model (M2) is 25 % lower.
Despite the large discrepancies in the total amount, all five models agree
that over 95 % of DSwet is wet SO42- deposition. The
total amounts of DSdry range from 4.3 to 10.6 Tg (S) yr-1 among
models (1 SD % =39 %). M11 predicts higher DSdry than the other models,
and the CMAQv5.0.2 model (M2) predicts 45 % lower DSdry than the two
CMAQv4.7.1 models (M4 and M6). Similar to DSwet, all models have strong
agreements on the proportions of the components. DNwet ranges from 12.2
to 20.0 Tg (N) yr-1 among models (1 SD % =21 %). The CMAQ models
(M2, M4 and M6) simulate close results (12–15 Tg (N) yr-1), while M11
(20.0 Tg (N) yr-1) and M12 (16.5 Tg (N) yr-1) simulate slightly
higher amounts. As for the proportion of components, the M2, M4, M6 and M12
models predict high proportions of wet NO3- and wet NH4+
depositions (particle phase), while the M11 model produces higher percentages of
wet HNO3 and wet NH3 depositions (gas phase). DNdry ranges
from 3.9 to 14.1 Tg (N) yr-1 (1 SD % =38 %). M12 gives a
considerably lower amount than the other models. The models are quite consistent
on the proportions of components. The amount of wet deposition is determined
by the Csurface_air and λwet (mentioned
in Sect. 2.2). In this study, Csurface_air may be
partially influenced by different model inputs, caused by a mismatch occurring in the
vertical and temporal allocation of emission inputs and the employment of
different mechanisms to produce dust and sea-salt emissions. Thus, we used
λwet, instead of direct model outputs of wet deposition, as an
indicator to reveal the inter-model differences of wet deposition in the
following analysis. For the same reason, we used Vd as an indicator for the
inter-model comparison of dry deposition.
Domain-total annual-accumulated S and N depositions of models (Tg (S
or N) yr-1).
Empty values mean no model submissions or the values are 0.
ModelWet S deposition Dry S deposition SO2H2SO4SO42-Total wet SSO2H2SO4SO42-Total dry SM10.06–––––––M20.04–10.410.53.40.010.94.3M40.06–12.512.56.60.011.17.6M5––––––––M60.05–13.713.86.30.011.47.7M7––––––––M8––––––––M10––––––––M111.10.329.931.36.92.21.510.6M12––16.316.33.7–0.44.2M136.0–––––––M140.02–6.2–5.4–3.2–ModelWet N deposition Dry N deposition NO3-NH4+HNO3NH3Total wet NNONO2NO3-NH4+HNO3NH3Total dry NM1–––––––––4.36.9–M24.08.3––12.20.030.40.60.62.07.511.0M45.47.4––12.80.030.30.70.52.84.79.0M5––––––0.5–––––M65.69.1––14.60.020.30.80.72.96.511.1M7––––––––––––M8––––––––––––M10––––––––––––M111.52.88.17.620.0––1.32.43.37.114.1M125.411.0––16.50.040.40.40.30.52.23.9M13––4.1––––––4.54.6–M14––––––––––––
Washout ratios (λwet) of (a–e) S deposition and (f–j) N deposition of models. Values are calculated with annual accumulated
depositions. The unit is percent (%).
Dry deposition velocities (Vd) of (a–e) S deposition and (f–j) N deposition of models. Values are calculated with annual accumulated
depositions. The unit is (cm s-1).
Figure 8a–e show λwet of S deposition (λswet) by the models. The CMAQ models (M2, M4 and M6) have similar patterns
for λswet over the inland regions, while the M12 model predicts
30 %–90 % lower ratios in India. The M11 model generally predicts about
20 %–70 % lower λswet than the other four models except India,
where the difference could reach up to 170 %. For λwet of N
deposition (λNwet) (Fig. 8f–j), the CMAQv4.7.1 models (M4
and M6) and M12 perform similarly, but the CMAQv5.0.2 model (M2) predicts
30 % lower λNwet in India, Japan and the Korean Peninsula. M11 generally
predicts lower ratios in India (60 % lower), Indonesia and the Philippines
(120 % lower) than the CMAQ models. Figure 9 shows the spatial
distributions of Vd. For Vd of S deposition (VSd) (Fig. 9a–e),
the CMAQ models (M2, M4 and M6) simulate very similar spatial distributions.
The M11 and M12 models predict a 0.5 cm s-1 lower VSd than the CMAQ
models over the whole inland regions especially in eastern China and the Indian
peninsula. For Vd of N deposition (VNd) (Fig. 9f–j), the CMAQ
models (M2, M4 and M6) predict very similar distributions. M11 and M12
predict about a 0.3 and 1–2 cm s-1 lower VNd than the
CMAQ models over the inland regions. Both λwet and Vd of
M11 are much lower than the other models especially over eastern EA. This is a possible reason for the biased performance of M11 for wet
deposition (Fig. 7). Overall, large inter-model differences are found in
predicting both the amounts of depositions and the efficiencies of
depositions.
Conclusions
Topic I of the MICS-Asia III aims at (i) evaluating the strengths and
weaknesses of current multiscale air quality models in simulating
concentration and deposition fields over East Asia and (ii) providing
suggestions for future model developments. This study compares the
performance of 12 regional models for the prediction of PM
concentrations over EA. The participating models include WRF-CMAQ (v4.7.1
and v5.0.2), RAMS-CMAQ, WRF-Chem (v3.6.1 and v3.7.1), GEOS-Chem, NHM-Chem, NAQPMS and
NU-WRF. Three processes/mechanisms are investigated to identify the causes
of inter-model differences.
For the formation of PMF, SOR and C(NO2) values are used to demonstrate the
inter-model differences in gas–particle conversions. The SOR values are
generally underestimated by most models at the EANET sites. A general
trend is found that the WRF-CMAQv5.0.2 models produce the highest SOR values
among all models, followed by the WRF-CMAQv4.7.1 models (10 % lower in the HBT
region), the WRF-Chem models and other models (5 %–20 % lower over inland
regions). The inter-model variation of SOR (1 SD % =50 %) is of the same
magnitude as that of SO42- concentrations. Similar results are
found in C(NO2), but models have stronger agreements on C(NO2) than SOR. The
different treatments of gas–particle conversions account largely for the
different model performances on simulating PMF.
For the formation of PMC, the models without dust emission modules
generate very low (<2µgm-3) PMC concentrations. They
can capture well the PM10 concentrations at non-dust-affected sites but
underestimate the PM10 concentrations at sites affected by dust storms
by up to 50 %. This underestimation is largely improved by implementing
dust emission modules (bias reduced to around -20 %). However, both the
magnitude and distribution of dust pollution are not fully captured. In
addition, models employing different dust emission modules show strong
disagreements on the distribution of PMC.
For the removal of PM from the atmosphere, the amounts of atmospheric
deposition vary largely among models (1 SD %) by 75 %, 39 %, 21 % and
38 % for DSwet, DSdry, DNwet and DNdry, respectively. The
λwet and Vd indicators are used to exclude the influences
brought by model inputs. For λwet, models agree more on the
DSwet than DNwet. The largest model inconsistencies are found in
India (up to 170 %), Indonesia and the Philippines (up to 120 %). For
Vd, models differ more on DNdry than DSdry. The inter-model differences are widely found over the
inland regions for DSdry (about 0.5 cm s-1) and DNdry (0.3–2 cm s-1).
The following are the main observations of this study.
(1) We compare the modeled conversions of S
and N between gas and particle phases with observations, which makes it possible to both
quantify the inter-model differences and tell which module might be more
reasonable. (2) Several new updates on dust modules have been published in
recent literature, but there is limited study on the intercomparison. This
study provides an opportunity to bring together the new updates on dust
emission modules and review their performance in EA. (3) The study provides a
comprehensive view on the total budget of S and N aerosols by including the
analysis of the removal processes. It turns out that this process brings
significant uncertainties to inter-model differences. It should be noted
that other factors such as vertical diffusion can also contribute to model
differences. Meanwhile, this study focuses on comparing the models' ability
in simulating PM in 2010. The chemical regimes may have changed drastically
due to the rapid changes in emissions and the implementation of control policies in
Asia. Studies on more recent years and heavily polluted episodes are under
preparation.
Data availability
The observation data are introduced with details in Sect. 2.3 with web links of publicly available datasets. The model data are
available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-7393-2020-supplement.
Author contributions
JT and JSF designed the study. JT processed and analyzed the data. JSF,
GRC, SI and ZT contributed to the results and discussions. JSF, ZT, KH, SI,
KY, TN, YM, XW, YL, HJL, JEK, CYL, BG, MK, JZ, MZ, LH and ZW provided
modeling data. All co-authors provided comments on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional assessment of air pollution and climate change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
Acknowledgements
We thank all participating modeling groups of MICS-Asia III. We acknowledge
the support of the Advanced Computing Facility (ACF), Joint Institute for
Computational Sciences of the University of Tennessee. We are also thankful for the
support of the Oak Ridge National Laboratory for computational resources,
which is supported by the Office of Science of the U.S. Department of Energy
(contract DE-AC05-00OR22725).
Review statement
This paper was edited by Alma Hodzic and Barbara Ervens and reviewed by three anonymous referees.
ReferencesAkimoto, H.: Global air quality and pollution, Science, 302, 1716–1719, 10.1126/science.1092666, 2003.Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, 10.5194/gmd-6-883-2013, 2013.Arimoto, R., Kim, Y. J., Kim, Y. P., Quinn, P. K., Bates, T. S., Anderson,
T. L., Gong, S., Uno, I., Chin, M., Huebert, B. J., Clarke, A. D.,
Shinozuka, Y., Weber, R. J., Anderson, J. R., Guazzotti, S. A., Sullivan, R.
C., Sodeman, D. A., Prather, K. A., and Sokolik, I. N.: Characterization of
Asian Dust during ACE-Asia, Global Planet. Change, 52, 23–56,
10.1016/j.gloplacha.2006.02.013, 2006.Bessagnet, B., Pirovano, G., Mircea, M., Cuvelier, C., Aulinger, A., Calori, G., Ciarelli, G., Manders, A., Stern, R., Tsyro, S., García Vivanco, M., Thunis, P., Pay, M.-T., Colette, A., Couvidat, F., Meleux, F., Rouïl, L., Ung, A., Aksoyoglu, S., Baldasano, J. M., Bieser, J., Briganti, G., Cappelletti, A., D'Isidoro, M., Finardi, S., Kranenburg, R., Silibello, C., Carnevale, C., Aas, W., Dupont, J.-C., Fagerli, H., Gonzalez, L., Menut, L., Prévôt, A. S. H., Roberts, P., and White, L.: Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology, Atmos. Chem. Phys., 16, 12667–12701, 10.5194/acp-16-12667-2016, 2016.Carmichael, G. R. and Ueda, H.: MICS-Asia II: The model intercomparison
study for Asia phase II, Atmos. Environ., 42, 3465–3467,
10.1016/j.atmosenv.2007.10.003, 2008.Carmichael, G. R., Calori, G., Hayami, H., Uno, I., Cho, S. Y., Engardt, M.,
Kim, S. B., Ichikawa, Y., Ikeda, Y., Woo, J. H., Ueda, H., and Amann, M.:
The MICS-Asia study: model intercomparison of long-range transport and
sulfur deposition in East Asia, Atmos. Environ., 36, 175–199,
10.1016/S1352-2310(01)00448-4, 2002.Carmichael, G. R., Sakurai, T., Streets, D., Hozumi, Y., Ueda, H., Park, S.
U., Fung, C., Han, Z., Kajino, M., Engardt, M., Bennet, C., Hayami, H.,
Sartelet, K., Holloway, T., Wang, Z., Kannari, A., Fu, J., Matsuda, K.,
Thongboonchoo, N., and Amann, M.: MICS-Asia II: The model intercomparison
study for Asia Phase II methodology and overview of findings, Atmos.
Environ., 42, 3468–3490,
10.1016/j.atmosenv.2007.04.007, 2008.Chen, L., Gao, Y., Zhang, M., Fu, J. S., Zhu, J., Liao, H., Li, J., Huang, K., Ge, B., Wang, X., Lam, Y. F., Lin, C.-Y., Itahashi, S., Nagashima, T., Kajino, M., Yamaji, K., Wang, Z., and Kurokawa, J.: MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia, Atmos. Chem. Phys., 19, 11911–11937, 10.5194/acp-19-11911-2019, 2019.Chen, Z. H., Cheng, S. Y., Li, J. B., Guo, X. R., Wang, W. H., and Chen, D.
S.: Relationship between atmospheric pollution processes and synoptic
pressure patterns in northern China, Atmos. Environ., 42, 6078–6087,
10.1016/j.atmosenv.2008.03.043, 2008.Clarke, A. D., Owens, S. R., and Zhou, J. C.: An ultrafine sea-salt flux
from breaking waves: Implications for cloud condensation nuclei in the
remote marine atmosphere, J. Geophys. Res.-Atmos., 111, D06202,
10.1029/2005JD006565, 2006.Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep,
K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V.,
Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y.,
Martin, R., Morawska, L., Pope, C. A., Shin, H., Straif, K., Shaddick, G.,
Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L.,
and Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of
disease attributable to ambient air pollution: an analysis of data from the
Global Burden of Diseases Study 2015, Lancet, 389, 1907–1918,
10.1016/s0140-6736(17)30505-6, 2017.Deng, J. J., Wang, T. J., Jiang, Z. Q., Xie, M., Zhang, R. J., Huang, X. X.,
and Zhu, J. L.: Characterization of visibility and its affecting factors
over Nanjing, China, Atmos. Res., 101, 681–691,
10.1016/j.atmosres.2011.04.016, 2011.Dennis, R., Fox, T., Fuentes, M., Gilliland, A., Hanna, S., Hogrefe, C.,
Irwin, J., Rao, S. T., Scheffe, R., Schere, K., Steyn, D., and Venkatram,
A.: A framework for evaluating regional-scale numerical photochemical
modeling systems, Environ. Fluid Mech., 10, 471–489, 10.1007/s10652-009-9163-2, 2010.Dong, X., Fu, J. S., Zhu, Q., Sun, J., Tan, J., Keating, T., Sekiya, T., Sudo, K., Emmons, L., Tilmes, S., Jonson, J. E., Schulz, M., Bian, H., Chin, M., Davila, Y., Henze, D., Takemura, T., Benedictow, A. M. K., and Huang, K.: Long-range transport impacts on surface aerosol concentrations and the contributions to haze events in China: an HTAP2 multi-model study, Atmos. Chem. Phys., 18, 15581–15600, 10.5194/acp-18-15581-2018, 2018.Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient
thermodynamic equilibrium model for
K+–Ca2+–Mg2+–NH4+–Na+–SO42-–NO3-–Cl-–H2O
aerosols, Atmos. Chem. Phys., 7, 4639–4659,
10.5194/acp-7-4639-2007, 2007.Fu, J. S., Jang, C. J., Streets, D. G., Li, Z., Kwok, R., Park, R., and Han,
Z.: MICS-Asia II: Modeling gaseous pollutants and evaluating an advanced
modeling system over East Asia, Atmos. Environ., 42, 3571–3583,
10.1016/j.atmosenv.2007.07.058, 2008.Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier van der Gon, H., Facchini, M. C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J. G., Spracklen, D. V., Vignati, E., Wild, M., Williams, M., and Gilardoni, S.: Particulate matter, air quality and climate: lessons learned and future needs, Atmos. Chem. Phys., 15, 8217–8299, 10.5194/acp-15-8217-2015, 2015.Galmarini, S., Koffi, B., Solazzo, E., Keating, T., Hogrefe, C., Schulz, M., Benedictow, A., Griesfeller, J. J., Janssens-Maenhout, G., Carmichael, G., Fu, J., and Dentener, F.: Technical note: Coordination and harmonization of the multi-scale, multi-model activities HTAP2, AQMEII3, and MICS-Asia3: simulations, emission inventories, boundary conditions, and model output formats, Atmos. Chem. Phys., 17, 1543–1555, 10.5194/acp-17-1543-2017, 2017.Gong, S. L.: A parameterization of sea-salt aerosol source function for sub-
and super-micron particles, Global Biogeochem. Cy., 17, 1097,
10.1029/2003GB002079, 2003.Han, Z., Sakurai, T., Ueda, H., Carmichael, G. R., Streets, D., Hayami, H.,
Wang, Z., Holloway, T., Engardt, M., Hozumi, Y., Park, S. U., Kajino, M.,
Sartelet, K., Fung, C., Bennet, C., Thongboonchoo, N., Tang, Y., Chang, A.,
Matsuda, K., and Amann, M.: MICS-Asia II: Model intercomparison and
evaluation of ozone and relevant species, Atmos. Environ., 42,
3491–3509, 10.1016/j.atmosenv.2007.07.031, 2008.Hayami, H., Sakurai, T., Han, Z., Ueda, H., Carmichael, G. R., Streets, D.,
Holloway, T., Wang, Z., Thongboonchoo, N., Engardt, M., Bennet, C., Fung,
C., Chang, A., Park, S. U., Kajino, M., Sartelet, K., Matsuda, K., and
Amann, M.: MICS-Asia II: Model intercomparison and evaluation of particulate
sulfate, nitrate and ammonium, Atmos. Environ., 42, 3510–3527,
10.1016/j.atmosenv.2007.08.057, 2008.Hoek, G., and Raaschou-Nielsen, O.: Impact of fine particles in ambient air
on lung cancer, Chinese Journal of Cancer, 33, 197–203,
10.5732/cjc.014.10039, 2014.Im, U.: Impact of sea-salt emissions on the model performance and aerosol
chemical composition and deposition in the East Mediterranean coastal
regions, Atmos. Environ., 75, 329–340,
10.1016/j.atmosenv.2013.04.034, 2013.Itahashi, S., Ge, B., Sato, K., Fu, J. S., Wang, X., Yamaji, K., Nagashima, T., Li, J., Kajino, M., Liao, H., Zhang, M., Wang, Z., Li, M., Kurokawa, J., Carmichael, G. R., and Wang, Z.: MICS-Asia III: overview of model intercomparison and evaluation of acid deposition over Asia, Atmos. Chem. Phys., 20, 2667–2693, 10.5194/acp-20-2667-2020, 2020.Kelly, J. T., Bhave, P. V., Nolte, C. G., Shankar, U., and Foley, K. M.: Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model, Geosci. Model Dev., 3, 257–273, 10.5194/gmd-3-257-2010, 2010.Knol, A. B., de Hartog, J. J., Boogaard, H., Slottje, P., van der Sluijs, J.
P., Lebret, E., Cassee, F. R., Wardekker, A., Ayres, J. G., Borm, P. J.,
Brunekreef, B., Donaldson, K., Forastiere, F., Holgate, S. T., Kreyling, W.
G., Nemery, B., Pekkanen, J., Stone, V., Wichmann, H. E., and Hoek, G.:
Expert elicitation on ultrafine particles: likelihood of health effects and
causal pathways, Part. Fibre Toxicol., 6, 19, 10.1186/1743-8977-6-19,
2009.Lamarque, J.-F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I., Eyring, V., Bergmann, D., Cameron-Smith, P., Collins, W. J., Doherty, R., Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S. J., Horowitz, L. W., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M., Rumbold, S. T., Schulz, M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., Voulgarakis, A., and Zeng, G.: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): overview and description of models, simulations and climate diagnostics, Geosci. Model Dev., 6, 179–206, 10.5194/gmd-6-179-2013, 2013.Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, p. 367, 10.1038/nature15371, 2015.Li, J., Wang, Z., Zhuang, G., Luo, G., Sun, Y., and Wang, Q.: Mixing of Asian mineral dust with anthropogenic pollutants over East Asia: a model case study of a super-duststorm in March 2010, Atmos. Chem. Phys., 12, 7591–7607, 10.5194/acp-12-7591-2012, 2012.Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, 10.5194/acp-17-935-2017, 2017.Li, J., Chen, X. S., Wang, Z. F., Du, H. Y., Yang, W. Y., Sun. Y. L., Hu,
B., Li, J. J., Wang. W., Wang. T., Fu, P. Q., and Huang, H. L.: Radiative and
heterogeneous chemical effects of aerosols on ozone and inorganic aerosols
over East Asia, Sci. Total Environ., 622–623, 1327–1342,
10.1016/j.scitotenv.2017.12.041, 2018.Liao, H., Adams, P. J., Chung, S. H., Seinfeld, J. H., Mickley, L. J.,
and Jacob, D. J.: Interactions between tropospheric chemistry and aerosols
in a unified general circulation model, J. Geophys. Res., 108, 4001,
10.1029/2001JD001260, 2003.Lippmann, M.: Toxicological and epidemiological studies of cardiovascular
effects of ambient air fine particulate matter (PM2.5) and its chemical
components: Coherence and public health implications, Crit. Rev. Toxicol., 44,
299–347, 10.3109/10408444.2013.861796, 2014.Liu, Y. M., Zhang, S. T., Fan, Q., Wu, D., Chan, P. W., Wang, X. M., Fan, S.
J., Feng, Y. R., and Hong, Y. Y.: Accessing the Impact of Sea-Salt Emissions
on Aerosol Chemical Formation and Deposition over Pearl River Delta, China,
Aerosol Air Qual. Res., 15, 2232–2245, 10.4209/aaqr.2015.02.0127, 2015.Nel, A.: Air pollution-related illness: Effects of particles, Science, 308,
804–806, 10.1126/science.1108752, 2005.Qu, W. J., Arimoto, R., Zhang, X. Y., Zhao, C. H., Wang, Y. Q., Sheng, L. F., and Fu, G.: Spatial distribution and interannual variation of surface PM10 concentrations over eighty-six Chinese cities, Atmos. Chem. Phys., 10, 5641–5662, 10.5194/acp-10-5641-2010, 2010.Rao, S., Chirkov, V., Dentener, F., Van Dingenen, R., Pachauri, S., Purohit,
P., Amann, M., Heyes, C., Kinney, P., Kolp, P., Klimont, Z., Riahi, K., and
Schoepp, W.: Environmental Modeling and Methods for Estimation of the Global
Health Impacts of Air Pollution, Environ. Model. Assess., 17,
613–622, 10.1007/s10666-012-9317-3, 2012.Shao, Y. and Dong, C. H.: A review on East Asian dust storm climate, modeling
and monitoring, Global Planet. Change, 52, 1–22, 2006.Silva, R. A., West, J. J., Zhang, Y. Q., Anenberg, S. C., Lamarque, J. F.,
Shindell, D. T., Collins, W. J., Dalsoren, S., Faluvegi, G., Folberth, G.,
Horowitz, L. W., Nagashima, T., Naik, V., Rumbold, S., Skeie, R., Sudo, K.,
Takemura, T., Bergmann, D., Cameron-Smith, P., Cionni, I., Doherty, R. M.,
Eyring, V., Josse, B., MacKenzie, I. A., Plummer, D., Righi, M., Stevenson,
D. S., Strode, S., Szopa, S., and Zeng, G.: Global premature mortality due
to anthropogenic outdoor air pollution and the contribution of past climate
change, Environ. Res. Lett., 8, 3, 10.1088/1748-9326/8/3/034005, 2013.Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in
probabilistic climate projections, Philos. T. R. Soc. A, 365, 2053–2075,
10.1098/rsta.2007.2076, 2007.Tie, X., Madronich, S., Walters, S., Edwards, D. P., Ginoux, P., Mahowald,
N., Zhang, R., Lou, C., and Brasseur, G.: Assessment of the global impact of
aerosols on tropospheric oxidants, J. Geophys. Res., 110, D03204,
10.1029/2004JD005359, 2005.
Vivanco, M. G., Bessagnet, B., Cuvelier, C., Theobald, M. R., Tsyro, S.,
Pirovano, G., Aulinger, A., Bieser, J., Calori, G., Ciarelli, G., Manders,
A., Mircea, M., Aksoyoglu, S., Briganti, G., Cappelletti, A., Colette, A.,
Couvidat, F., D'Insidoro, M., Kranenburg, R., Meleux, F., Menut, L., Pay, M.
T., Rouil, L., Silibello, C., Thunis, P., and Ung, A.: Joint analysis of
deposition fluxes and atmospheric concentrations of inorganic nitrogen and
sulphur compounds predicted by six chemistry transport models in the frame
of the EURODELTAIII project, Atmos. Environ., 151, 152–175,
10.1016/j.atmosenv.2016.11.042, 2017.Wang, K., Zhang, Y., Nenes, A., and Fountoukis, C.: Implementation of dust emission and chemistry into the Community Multiscale Air Quality modeling system and initial application to an Asian dust storm episode, Atmos. Chem. Phys., 12, 10209–10237, 10.5194/acp-12-10209-2012, 2012.Wesely, M. L.: Parameterization of Surface Resistances to Gaseous Dry
Deposition in Regional-Scale Numerical-Models, Atmos. Environ., 23,
1293–1304, 10.1016/0004-6981(89)90153-4, 1989.Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.-M., Wong, D. C., Wei, C., Gilliam, R., and Pouliot, G.: Observations and modeling of air quality trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe, Atmos. Chem. Phys., 15, 2723–2747, 10.5194/acp-15-2723-2015, 2015.Zhang, L., Brook, J. R., and Vet, R.: A revised parameterization for gaseous dry deposition in air-quality models, Atmos. Chem. Phys., 3, 2067–2082, 10.5194/acp-3-2067-2003, 2003.