ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-14311-2019Sulfate formation during heavy winter haze events and the potential
contribution from heterogeneous SO2+NO2 reactions in the Yangtze
River Delta region, ChinaHeterogeneous sulfate formation during heavy winter haze eventsHuangLingAnJingyuKooBonyounghttps://orcid.org/0000-0003-0526-8113YarwoodGreghttps://orcid.org/0000-0002-4201-3649YanRushaWangYangjunHuangChenghuangc@saes.sh.cnLiLilily@shu.edu.cnhttps://orcid.org/0000-0001-5575-0894School of Environmental and Chemical Engineering, Shanghai University,
Shanghai, 200444, ChinaState Environmental Protection Key Laboratory of the Cause and
Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental
Sciences, Shanghai, 200233, ChinaBay Area Air Quality Management District, San Francisco, CA 94105, USARamboll, Novato, CA 95995, USA
Rapid sulfate formation is recognized as a key
characteristic of severe winter haze in China. However, air quality models
tend to underestimate sulfate formation during heavy haze periods, and
heterogeneous formation pathways have been proposed as promising mechanisms
to reduce gaps between observation and model simulation. In this study, we
implemented a reactive SO2 uptake mechanism through the SO2+NO2 heterogeneous reactions in the Comprehensive Air Quality Model with
Extensions (CAMx) to improve simulation of sulfate formation in the Yangtze
River Delta (YRD) region. Parameterization of the SO2+NO2
heterogeneous reactions is based on observations in Beijing and considered
both the impact of relative humidity and aerosol pH on sulfate formation.
Ammonia is reported to be critical for the formation of secondary inorganic
aerosols. Estimation of ammonia emissions is usually associated with large
uncertainties and models tend to underestimate ammonia concentrations
substantially. Sensitivity tests were conducted to evaluate the influence of
the SO2+NO2 heterogeneous reactions as well as ammonia
emissions on modeled sulfate concentrations during a period with several
heavy haze episodes in the YRD region. Base case model results show large
underestimation of sulfate concentrations by 36 % under polluted
conditions in the YRD region. Adding the SO2+NO2 heterogeneous
reactions or doubling ammonia emissions alone leads to slight model
improvement (∼6 %) on simulated sulfate concentrations in
the YRD region. However, model performance significantly improved when both
the SO2+NO2 heterogeneous reactions and doubled ammonia
emissions were included in the simulation: predicted sulfate concentrations
during polluted periods increased from 23.1 µg m-3 in the base
scenario to 29.1 µg m-3 (representing an increase of 26 %).
Aerosol pH is crucial for the SO2+NO2 heterogeneous reactions,
and our calculated aerosol pH is always acidic and increased by 0.7 with
doubled ammonia emissions. Modeling results also show that this reactive
SO2 uptake mechanism enhanced sulfate simulations by 1 to 5 µg m-3 for the majority of the eastern and central parts of China, with more
than 20 µg m-3 increase in sulfate concentrations over the
northeastern plain. These findings suggest that the SO2+NO2
heterogeneous reactions could be potentially important for sulfate formation
in the YRD region as well as other parts of China. Further studies are
needed to constrain the uncertainties associated with the parameterization
of the SO2+NO2 heterogeneous reactions based on local data
as well as to evaluate this mechanism in other regions. In addition, ammonia
emissions were found to be a key driving variable of the spatial patterns of
sulfate enhancement due to the new pathway. Substantial efforts are needed
to improve the accuracy of the ammonia emission inventory.
Introduction
Rapid sulfate (SO42-) formation has been reported to be a key
characteristic of severe winter haze in China. However, most air quality
models tend to underestimate sulfate formation during severe winter haze
episodes in China, because standard SO2 oxidation pathways, including
gas-phase chemistry (i.e., oxidized by hydroxyl radical OH) and aqueous-phase
chemistry (i.e., oxidized by ozone (O3) and hydrogen peroxide
(H2O2)), are suppressed by weak photochemical activity and low
ozone concentrations (Quan et al., 2014). Meanwhile, analysis of severe
haze events in China shows enhanced secondary inorganic aerosols, especially
sulfate concentrations. For example, Quan et al. (2014) found that observed
sulfate accounted for 13 % of PM2.5 (particulate matter with
dynamic equivalent diameter less than 2.5 µm) on normal clean days
and increased to 25 % on haze days during the infamous January 2013
Beijing haze period. For the same haze episode, Cheng et al. (2016) used
concentration ratios of sulfate to sulfur dioxide
([SO42-] / [SO2]) to diagnose sulfate production rate; this
ratio increased with PM2.5 levels and was 6 times higher under the most
polluted conditions compared to normal conditions. Most current air quality models
(e.g., CMAQ, GEOS-Chem, WRF-Chem, CAMx), which only include the traditional
gaseous- or aqueous-phase mechanisms for sulfate formation, do not show very
good model performances for sulfate concentrations against observations
during haze periods in China (Wang et al., 2014; B. Zheng et al., 2015; Gao et
al., 2016a, b; Li et al., 2015). The underprediction of sulfate
concentrations could be related to uncertainties in the emission inventory,
bias of simulated meteorological fields, and/or some missing sulfate
formation mechanisms that are not included in the current models.
Heterogeneous sulfate production chemistry has been proposed by several
studies to explain the high concentrations and rapid formation of sulfate
during haze episodes in China (e.g., He et al., 2014; Wang et al., 2014, 2016; B. Zheng et al., 2015; Cheng et al., 2016; Guo et al., 2017). He
et al. (2014) suggested a synergistic effect between NO2 and SO2
on the surface of mineral dust (i.e., mineral oxides) as an important source
of sulfate in China and emphasized the essential role of O2 involved in
this process. More generally, heterogeneous loss of SO2 on aerosol
surfaces (not limited to mineral dust) or deliquescent aerosols is discussed
by many studies, although the exact underlying mechanism is still unknown
(e.g., Wang et al., 2014; G. Zheng et al., 2015). For these kinds of heterogeneous
reactions, the sulfate production rate has been parameterized as a pseudo
1st-order reaction with respect to the gaseous SO2 concentration with
the SO2 reactive uptake coefficient (γ) on aerosol surfaces
being the key parameter. This uptake coefficient, representing the
probability that a SO2 gas molecule colliding with an aerosol surface
results in sulfate formation, is reported to be heavily dependent on
relative humidity (RH) (B. Zheng et al., 2015; Wang et al., 2016).
Parameterized reactive uptake of SO2 has been implemented in several
current air quality models, including GEOS-Chem, WRF-Chem, CMAQ, and CAMx,
and generally improved model performance of sulfate concentrations during
haze episodes in China (e.g., Wang et al., 2014; B. Zheng et al., 2015; Gao et
al., 2016b). Two more recent papers, Wang et al. (2016) and Cheng et al. (2016), further suggested that reaction between NO2 and SO2 in
aerosol water may contribute substantially to sulfate formation during haze
events in China. Both studies emphasized the importance of higher aerosol pH
(5.4–6.2 reported by Cheng et al., 2016, and 6.0–7.6 by Wang et al., 2016) sustained by abundant gas-phase ammonia (NH3) during haze
periods being an essential precondition for this mechanism. However, the
near-neutralized aerosol pH that facilities SO2 oxidation by NO2
is questioned by Guo et al. (2017), who concluded from a thermodynamic
analysis that aerosol pH was always acidic (4.5–5) regardless of the
ambient NH3 concentrations and that the NO2-mediated oxidation of
SO2 was unlikely to be important in China or any other region of the
world. Guo et al. (2017) pointed out that within low-pH ranges (up to 4.5),
SO2 oxidation catalyzed by transition metals (i.e., Fe(III) and Mn(II))
might become a dominant sulfate formation pathway in aerosol water, and
they suggested it as an alternative to SO2+NO2 reactive uptake,
being a potential sulfate contributor under haze conditions. A similar
conclusion is also made from the most recent work by Shao et al. (2019), who
implemented four heterogeneous sulfate formation mechanisms in GEOS-Chem and
assessed model performance using sulfate oxygen isotope data in Beijing. They found that SO2 oxidation catalyzed by transition metal ion (TMI) to
be the dominant sulfate formation mechanism. On the contrary, another
slightly earlier study by Ye et al. (2018) concluded that SO2 oxidation by
H2O2 was the dominant pathway based on observations of atmospheric
H2O2 concentrations in Beijing. Song et al. (2019) suggested the
heterogeneous hydroxymethanesulfonate (HMS) chemistry to be a potentially
important contributor to heavy haze pollution in northern China. Hung et al. (2018) reported on the interfacial SO2 oxidation on the surface of aqueous
micro-droplets as a potential pathway to explain fast conversion of SO2
to sulfate.
To investigate whether the SO2+NO2 reactions in aerosol water
could help better predict the enhanced sulfate formation during haze periods
in the Yangtze River Delta (YRD) region, we implemented a parameterized
SO2+NO2 reactive uptake mechanism in the Comprehensive Air
Quality Model with Extensions (CAMx), which is a widely used air quality
model in China (e.g., Wang et al., 2009; Huang et al., 2012; Li et al., 2013,
2015; Jia et al., 2017). Our parameterization specifically
incorporated RH and aerosol pH dependencies derived from measurement data
during the 2015 Beijing haze event (Wang et al., 2016). Although the RH
dependency of the SO2 uptake rate has already been implemented in
previous studies (e.g., B. Zheng et al., 2015; Wang et al., 2014), the effect of
aerosol pH has not been explicitly included in most of the previous
modeling studies, except for the most recent study by Shao et al. (2019), who
also considered aerosol pH in their model parameterization. While most of
the previous studies were trying to improve model predictions in the
northern part of China, especially the Beijing–Tianjin–Hebei region (e.g.,
Gao et al., 2016b; B. Zheng et al., 2015), this work is one of the few studies
that focus on the Yangtze River Delta region, which has also suffered from
severe haze problems in recent years due to urban expansion and
industrialization (e.g., Li et al., 2011; M. Wang et al., 2015; Xu et al.,
2016; Ming et al., 2017). In addition to the SO2+NO2
heterogeneous reactions, we also investigated model sensitivities to ammonia
emissions, which have been reported to be crucial for the formation of
secondary inorganic aerosols and because large uncertainties exist with current
ammonia emission inventory (Huang et al., 2011; Fu et al., 2013).
MethodologyCurrent sulfate formation pathways in CAMx
In this study, CAMx version 6.40 (Ramboll Environ, 2016) was used as the
base model to simulate sulfate formation. Table 1 lists the sulfate
formation pathways that are currently considered implemented in standard
CAMx source code. In addition to the traditional SO2 oxidation by OH in
the gas phase and O3, H2O2, and O2 (catalyzed by
Fe(III)/Mn(II)) in cloud droplets, sulfate formation through reactions with
methyl hydroperoxide (MHP) and other organic hydroperoxides, as well as
peracetic acid (PAA) and other organic peracids in the aqueous phase, is also
included. For the heterogeneous formation pathway, the SO2+NO2
reaction is currently considered and implemented as a pseudo gas-phase
reaction with the rate parameterization based on results from B. Zheng et al. (2015), where the key parameter (i.e., gamma) is bounded between lower and
upper limits and changes linearly in response to RH. This relatively simple
parameterization of the SO2+NO2 heterogeneous reaction has been
included in many previous studies, e.g., Wang et al. (2014), B. Zheng et al. (2015).
Sulfate formation pathways currently implemented in CAMx version
6.40.
No.OxidantsRate expressionReferenceGaseous phase 1OHk1[OH][SO2(g)].Seinfeld and Pandis (2006)k1=k0[M]1+k0[M]k∞0.6G, whereG=1+logk0Mk∞2-1,k0=4.50×10-31(T/300)-3.9,k∞=1.30×1016(T/300)-0.7.Aqueous phase 2O3(k2[SO2⋅H2O]+k3[HSO3-]+k4[SO32-])[O3(aq)].Jacobson (1997)k2=2.4×104 M-1 s-1,k3=3.7×105 M-1 s-1,k4=1.5×109 M-1 s-1.3H2O2k5[H+][HSO3-][H2O2(aq)]/(1+13×[H+]).Jacobson (1997)k5=7.45×107×exp(-15.96×(298/T-1)) M-1 s-1.4Fe(III)/Mn(II)When aerosol pH ≤ 4.2:Martin and Good (1991)750[Mn(II)][S(IV)] + 2600[Fe(III)][S(IV)] -k6 [Mn(II)][Fe(III)][S(IV)][H+]0.67.When aerosol pH > 4.2:750[Mn(II)][S(IV)] + 2600[Fe(III)][S(IV)] -k7[Mn(II)][Fe(III)][S(IV)][H+]-0.74.k6=2.51×1013 M-2 s-1,k7=3.72×107 M-2 s-1.5Methyl hydroperoxidek8[H+][HSO3-][CH3OOH(aq)].Jacobson (1997)(CH3OOH) and otherk8=1.90×107×exp(-12.75×(298/T-1)) M-2 s-1.organic hydroperoxides6Peracetic acidk9[H+][HSO3-][CH3C(O)OOH(aq)].Jacobson (1997)(CH3C(O)OOH) and otherk9=3.60×107×exp(-13.42×(298/T-1)) M-2 s-1.organic peracidsAerosol aqueous phase (implemented as pseudo gas phase) 7NO2k10[SO2(g)].B. Zheng et al. (2015)k10=dp2D+4vγ-1Sp, whereγlow=2×10-5,γhigh=5×10-5.SO2+NO2 mechanism in CAMx
In this study, we implemented the SO2+NO2 reactive uptake
mechanism in CAMx version 6.40 (Ramboll Environ, 2016) as a pseudo gas-phase
reaction:
SO2+NO2⟶khetSO42-
Since the vapor pressure of sulfuric acid is very low, we assumed all
sulfuric acid partitions to the aerosol phase. The rate constant khet
is related to the reactive uptake coefficient γ for SO2 as
follows:
dSO42-dt=khetNO2gSO2g=14γC‾S[SO2g],
where C‾ is the mean molecular speed (m s-1), and S is the aerosol
surface area concentration (m2 m-3). Based on the observations
during the Chinese haze events (Wang et al., 2016), this uptake coefficient
γ depends on aerosol pH, RH, and NO2 concentration. Therefore,
we assumed a functional form of γ as the product of each of these
dependencies:
γ=4k0df[NO2g],
where k0 (ppm-1) is the RH-dependent parameter; NO2(g)
is the NO2 gas concentration and df is the pH-dependent distribution
factor of SO2, i.e., the ratio of SO2 concentration in the
aqueous phase to the gaseous phase and is calculated as Eq. (4) in the
model:
df=[S(IV)(aq)][SO2g]=HeffRTwL,
where Heff is the effective Henry's law constant of SO2 (M atm-1), R is the universal gas constant (L atm mol-1 K-1), T
is air temperature (K), and wL is the aerosol water content (µg m-3). We used the data in Tables S2 and S5 by Wang et al. (2016) to back
calculate the RH dependency of k0 under clean (observed sulfate
concentration less than 10 µg m-3), transition (sulfate between
10 and 20 µg m-3), and polluted (sulfate more than 20 µg m-3) conditions during Beijing 2015 episodes. The reactive uptake
coefficient γ derived by Wang et al. (2016) contains an assumption
that all sulfate formation was due to the SO2+NO2
heterogeneous reactions due to low photochemical reactivity and low Fe/Mn
concentrations during the measurement period. Thus we started with an upper
bound estimate of the SO2+NO2 heterogeneous reactions. Aerosol
pH was calculated using the ISORROPIA (Fountoukis and Nenes, 2007) thermodynamic equilibrium model
implemented in CAMx assuming a metastable aerosol liquid phase, which is an
appropriate assumption for most ambient conditions including the Chinese
haze events (Guo et al., 2017). Wang et al. (2016) only reported NOx (not
NO2) concentrations in Beijing during the 2015 haze event. We simply
assumed a NO2/NOx ratio of 0.5. Inserting NO2 concentrations,
γ values from Wang et al. (2016), and calculated aerosol pH from
ISORROPIA into Eq. (3), we obtained the expression of k0 depending upon
RH as follows (parameters for k0 calculation is shown in Table S1):
k0=RH<21%199.2521%≤RH<41%(284.22-199.25)×(RH-21%)/(41%-21%)+199.2541%≤RH<56%(322.16-284.22)×(RH-41%)/(56%-41%)+284.22RH≥56%332.16
Due to a lack of observation data at high RH values, as a conservative
assumption we set a constant k0 value when RH increases from 56 % and
up. This would lead to underestimated sulfate formation due to the SO2+NO2 heterogeneous reactions at high RH values, which is a favorable
condition for the heterogeneous sulfate production. In addition, the
differences in aerosol hygroscopicity in Beijing vs. Shanghai could add more
uncertainties in the dependency of k0 on RH. Reported values of
hygroscopicity parameter κ were 0.25–0.31 for Shanghai (Ye et
al., 2011, 2013), which are higher than values reported for Beijing
(0.14–0.24; Massling et al., 2009). Our conservative
assumption that the heterogeneous reaction rate does not increase with RH
above 56 % most likely tends to offset the upper bound estimate of the
γ values derived by Wang et al. (2016). The rate constant khet
of SO2+NO2 is formulated as
khet=k0dfC‾S.SO2 lifetime (h) associated with the SO2+NO2
reactive uptake mechanism is calculated as
SO2lifetime=1khet[NO2g].
Figure 1 shows the SO2 lifetime as a function of aerosol pH for clean,
transition, and polluted conditions, with other variables kept constant. The
SO2 lifetime shortens as aerosol pH becomes more neutralized,
indicating faster conversion of SO2 to sulfate by SO2+NO2
reactive uptake on aerosol. For pH from 2 to 7, one unit increase in
aerosol pH shortens SO2 lifetime by about 1 order of magnitude. The
blue, orange, and red symbols in Fig. 1 correspond to the clean,
transition, and polluted conditions during Beijing 2015 based on data in
Table S1. As shown in Fig. 1, the aerosol pH values calculated by
ISORROPIA are 5.5 (for clean conditions) and 4.1–4.2 (for transition and
polluted conditions), all lower than the value (7.6) reported by Wang et al. (2016). As noted by Guo et al. (2017), it is important to make a
consistent assumption for aerosol state (i.e., metastable) in deriving and
implementing the parameterization for reactive uptake. The most recent paper
by Song et al. (2018) identified coding errors with the ISORROPIA model,
which resulted in unrealistic pH values of 7.7 using the standard ISORROPIA
model with the stable state assumption in previous studies. Nevertheless,
our results are not compromised by this coding error, because the metastable
assumption was chosen in our ISORROPIA calculation.
SO2 lifetime (h-1) due to SO2+NO2
reactive uptake mechanism as a function of aerosol pH under clean,
transition, and polluted conditions. Values of relative humidity,
temperature, and NO2(g) concentrations are based on values in Table S1.
Model configuration
Two versions of the Comprehensive Air Quality Model with Extensions (CAMx),
modified based on the original version 6.40 (Ramboll Environ, 2016), were
used in this study: one with the SO2+NO2 heterogeneous
reactions (described in Sect. 2.1) and one without (forcing khet
equals zero). The modeling domain consists of three nested grids
(Fig. 2): the outer 36 km domain (D01) covers
most of China, Japan, Korean Peninsula, parts of India, and southeast Asia; the 12 km
domain (D02) covers eastern China; and the inner 4 km domain (D03) covers
Shanghai, Jiangsu province, Zhejiang province, Anhui province, and parts of
the surrounding provinces, together referred to as the Yangtze River Delta (YRD)
region. Meteorological fields were based on simulation results from the
Weather Research and Forecasting (WRF) model (version 3.7) driven by the
National Centers for Environmental Prediction (NCEP) National Center for
Atmospheric Research (NCAR) Operational Global Analysis data
(http://dss.ucar.edu/datasets/ds083.2/, last access: 23 November 2019). Details of the WRF configurations
can be found in previous studies (Liu et al., 2018). Boundary conditions for
D01 were generated from the Model for OZone And Related chemical Tracers
(MOZART) global chemistry model (Emmons et al., 2010). The Carbon Bond 6
(CB6) mechanism (Yarwood et al., 2010) was used for the gas-phase chemistry
and the static two-mode coarse/fine (CF) scheme was used to represent
particle size distribution. The Zhang dry deposition (Zhang et al., 2003) and
default wet deposition scheme was used to for removal processes.
Anthropogenic emissions for areas outside the YRD region were from the
Multi-resolution Emission Inventory for China (MEIC, http://www.meicmodel.org/, last access: 23 November 2019). For emissions within the YRD region, a
YRD-specific emission inventory (Huang et al., 2011; Li et al., 2011) was
updated to the year 2014 and utilized in this study. This YRD-specific emission
inventory includes emissions from sources of combustion, such as industry, transport
and residential sectors. Primary sulfate emissions over the 4 km domain are estimated
to be 994 t d-1 for December 2013 (accounting for 14.8 % of
primary PM2.5) with dense emissions from Shanghai and southern Jiangsu
province (see Fig. S1 for spatial distribution). At the Shanghai Academy of Environmental Sciences (SAES) site, primary
sulfate emissions were estimated to be 757 kg per month (only accounting for
1.0 % of primary PM2.5). Biogenic emissions were simulated using the
Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1;
Guenther et al., 2012) based on the WRF simulation results. The modeling
episode is December 2013, during which several heavy haze events with hourly
PM2.5 concentration higher than 500 µg m-3 were observed in
the YRD region.
CAMx model domains.
Four simulations with identical model configurations and input data including
meteorology, initial and boundary conditions, and emission inventory (except
ammonia emissions) were conducted using the two abovementioned different CAMx
versions:
noHet (base case). Simulation based on CAMx version without the SO2+NO2 heterogeneous reactions (this is also our base case). Note
that this CAMx version differs from the distributed CAMx v6.40 in that we
removed the original heterogeneous sulfate formation reaction with NO2
which only included a simple parameterization based on RH (ref. reaction
no. 7 in Table 1) in the distributed version. This is done on purpose to
quantify the influence of the newly parameterized SO2+NO2
heterogeneous reactions on sulfate formation.
Het. Simulation based on CAMx with the SO2+NO2 heterogeneous
reactions. Other model configurations were identical to scenario noHet.
noHet_2NH3. CAMx version and model configurations were
the same as scenario noHet except ammonia emissions were doubled for the 4 km
domain.
Het_2NH3. CAMx version and model configurations were
the same as scenario Het but ammonia emissions were doubled for the 4 km domain.
We first ran CAMx for 36 and 12 km domains with a two-way nested mode; for the 4 km
domain, we used boundary conditions extracted from the 12 km model outputs
and conducted the four abovementioned scenarios. Fourteen vertical layers were used
extending from the surface to 100 mbar. In addition to default CAMx outputs,
we modified the source code to generate additional diagnostic variables
(e.g., aerosol pH, RH, and khet) to evaluate the SO2+NO2
heterogeneous reactions.
Observations
Hourly observations of ozone, SO2, NO2, PM2.5 and its
components including sulfate, nitrate, ammonium, organic carbon (OC), and
elemental carbon (EC) are available between 1 and 29 December 2013
at a monitoring site located at the center of the urban area of Shanghai
(referred to as the SAES site; 31.1695∘ N, 121.4305∘ E;
Fig. 3). Hourly PM2.5 observations are also
available at another 23 monitoring sites across the YRD region
(Fig. 3; see locations in Table S2). During this
period, the YRD region experienced relatively clean days as well as several heavy
haze episodes with peak PM2.5 exceeding 600 µg m-3 during the
most heavily polluted period of 5 to 7 December. At the SAES
site, maximum hourly PM2.5 concentration reached 540.3 µg m-3 on 6 December with a monthly average of 118.7 µg m-3. We followed the method in Wang et al. (2016) to divide the period
into clean (observed sulfate < 10 µg m-3), transition
(10–20 µg m-3), and polluted (> 20 µg m-3) periods based on observed hourly sulfate concentration at the SAES
site. Compared with clean period, all PM species increased by more than 3
times (sulfate, nitrate, and ammonium (SNA) increase by more than 5 times)
during the polluted period as indicated by the enhancement ratio (calculated as
the ratio of average concentrations during the polluted period divided by
those during the clean period). In terms of fraction of PM2.5, SNA
increased from 44 % during clean period to 69 % during polluted period
while carbonaceous aerosols (OC and EC) decreased from 32 % to 24 %.
This is consistent with the commonly observed characteristics of winter haze
periods in China reported by many previous studies (e.g., Wang et al., 2014;
B. Zheng et al., 2015; Cheng et al., 2016), i.e., SNA is playing a more
important role during the heavy haze periods. Average sulfate concentration
of clean, transition, and polluted periods was 6.7, 14.2, and 36.1 µg m-3, respectively, accounting for 17 %–23 % of PM2.5 (Fig. S2).
Locations of observation sites for WRF (two meteorology sites) and CAMx
model performance evaluation (SAES site and Fudan University (FDU) site within Shanghai;
another 23 air quality (AQ) sites distributed over Jiangsu, Zhejiang, and Anhui provinces
with locations shown in Table S2).
Observations of ambient ammonia concentrations are also available at the
SAES site; however, the quality of measurements is questionable. Therefore,
we used ammonia observations from a similar urban site nearby (referred to as
the FDU site, ∼15 km north of the SAES site; 31.3005∘ N,
120.9778∘ E; Fig. 3) for analysis
in this study. Observations at the FDU site have been discussed by S. Wang
et al. (2015) and demonstrated data reliability. Diurnal NH3
concentrations at the FDU site during our modeling period showed a weak
bimodal pattern with an average of 7.3 ppb (ranging 1.6–25 ppb) during this
period (Fig. S3). This two-peak diurnal variation is caused by vehicle
emissions and evolution of the boundary layer (S. Wang et al., 2015). In
summary, observations for gases species (except NH3) and PM species at
the SAES site and NH3 at the FDU site were used for model validation in
this study.
Statistical metrics for model validation
For WRF and CAMx model performance evaluation, mean bias (MB), normalized
mean bias (NMB), and index of agreement (IOA) were used in this study.
Calculations of these selected metrics are shown below:
8MB=1N∑(Pj-Oj),9NMB=∑(Pj-Oj)∑Oj×100,10IOA=1-∑(Pj-Oj)2∑Pj-O‾+Oj-O‾2,
where Pj and Oj are predicted and observed hourly concentrations
or values, respectively; N is the number of paired model and observation
data; O‾ is the average concentration or value of observations; and
IOA ranges from 0 to 1 with 1 indicating perfect agreement between model and
observation.
Results and discussionsModel evaluationWRF results evaluation
Model performance of WRF results is generally acceptable in this study.
Table S3 summarizes the meteorological performance statistics of WRF during
December 2013 at Pudong and Hongqiao airport stations in Shanghai (Fig. 3). Temperature and relative humidity were well reproduced with NMB and normalized mean error (NME)
within 37 % and 41 %, respectively; IOA values are above 0.8. Wind speed
is overestimated with a MB of 1.5 m s-1 at Pudong and 0.5 m s-1 at
Hongqiao station; NMB of predicted wind direction at the two stations is
-36 % and -27 %, respectively. Comparisons of hourly observed and
simulated relative humidity, wind speed, and temperature at these two
stations suggest reasonable model results in terms of temporal variations
(Fig. S4). Overall, the WRF simulated results are acceptable to be used in
subsequent CAMx simulations.
CAMx base scenario (noHet) evaluation
Figure 4 depicts the time series of simulated and
observed concentrations for sulfate and PM2.5 during 1 to 29 December 2013 at the SAES site (see Fig. S5 in the Supplement for other
species). Overall, the model is successful in capturing the temporal
variations of ozone and PM species with IOA values above 0.5 (Table S4).
Nevertheless, the model tends to systematically underestimate all gaseous and PM
species with NMB values ranging from -5 % for NO2 to -68 % for
NH3. This could be partially explained by the higher simulated wind
speeds compared with observed values, especially at Pudong station where the
observed average wind speed during the modeling period was 4.5 m s-1,
while simulated wind speed was 6.0 m s-1, representing an
overprediction by 33 %. For sulfate, the model captured the day-to-day
sulfate variations reasonably well with an overall MB of -2.8µg m-3 and IOA of 0.80. For clean and transition periods, the model showed
slight overprediction with MB of 1.1 and 0.5 µg m-3 (Table S5).
However, during the polluted period when observed sulfate concentrations are
higher than 20 µg m-3, the model significantly underestimated
sulfate formation with a MB of -13.0µg m-3 (NMB of -36 %).
Observed maximum sulfate concentration reached 93.4 µg m-3 but
the model only predicted 52.2 µg m-3. Nitrate and ammonium
concentrations were also underestimated by 20 % on average and
exacerbated to more than 40 % during polluted periods. For carbonaceous
aerosols, elemental carbon (EC) was underestimated by 32 % while organic
carbon (OC) exhibited even more underestimation of almost 50 %.
Underestimation of OC is usually associated with underestimation of
secondary organic aerosols (SOAs). Discussion of OC underprediction is
beyond the scope of the current work and will be addressed in future studies.
Results of the four CAMx simulations in this study showed negligible changes
in predicted EC/OC concentrations and thus are excluded in the following
discussions.
Simulated and observed PM2.5(a) and sulfate (b)
concentrations (µg m-3) at the SAES site during 1 to 29 December 2013.
Spatial distribution of observed and simulated monthly average
PM2.5 concentrations (µg m-3) over the YRD region for
the base case scenario (a) and Het_2NH3 scenario (b). Locations of the monitoring sites are listed in Table S2.
Figure 5 depicts the averaged PM2.5 during the
modeling episode over the YRD region with observations at 24 monitoring
sites. Observed PM2.5 concentrations generally showed a decreasing
trend from north to south of the YRD region, which was well captured by the
model. For sites located in southern Jiangsu and southern Zhejiang provinces,
the model showed favorable agreement with the observations. Underestimations
existed for sites located in the northern part of Jiangsu and Zhejiang
provinces. MB across all 24 monitoring sites ranged from as low as -90.4µg m-3 (site in north Jiangsu province) to a slight overestimation
of 11.4 µg m-3 (site in south Zhejiang province); corresponding
NMB ranged from -46 % to 16 % (Table S2).
Simulated sulfate concentrations at the SAES site
Four scenarios – noHet, Het, noHet_2NH3 and
Het_2NH3 – were conducted to evaluate the impact of the
SO2+NO2 heterogeneous reactions and ammonia emissions on
sulfate simulation. We first analyzed the modeled sulfate results at the
SAES site; then we discussed the spatial patterns over the YRD region.
Similar discussions of nitrate, ammonium, and PM2.5 are included in the
Supplement. Table 2 shows the average sulfate concentration
for different scenarios by clean, transition, and polluted periods;
corresponding scatter plots are shown in Fig. 6. A complete summary of
statistical metrics for each scenario or period is presented in Table S5.
Observed and simulated sulfate concentrations (µg m-3) for
different scenarios by clean, transition, and polluted periods at the SAES site
during 1 to 29 December 2013.
Scatter plots of hourly sulfate concentrations for different
scenarios at the SAES site during 1 to 29 December 2013. Solid lines indicate
1:1 lines and dashed lines are 1:2 and 2:1 lines.
Impact of SO2+NO2 heterogeneous
reactions (noHet vs. Het)
As shown in Fig. 6, simulated sulfate
concentrations compared well with observations under clean and transition
conditions in the noHet scenario with overprediction by 16 % and 4 %,
respectively. By contrast, large underprediction of sulfate concentration
existed during polluted periods (MB of -13.0µg m-3, NMB of -36 %). Adding the SO2+NO2 heterogeneous reactions showed small
enhancement on sulfate formation, reducing the overall NMB from -16 % to
-12 %. If only polluted periods are considered, simulated sulfate
concentrations increased from 23.1 to 24.6 µg m-3 with the
heterogeneous reactions, corresponding to an increase of 6.5 %. Thus even
with the SO2+NO2 heterogeneous reactions, model was still
underpredicting sulfate concentrations on heavy haze days with a NMB of -32 %. This is because aerosol pH was always acidic (pH < 3; this
will be discussed in the following section) and the SO2+NO2
heterogeneous reactions were not being appreciable within this pH range
(Fig. 1). Model performances for clean and
transition periods were slightly compromised with the SO2+NO2
heterogeneous reactions since the base scenario was already overestimating
sulfate concentrations.
Impact of NH3 emissions
(noHet vs. noHet_2NH3)
Being the dominant base gas in the atmosphere, ammonia plays an essential
role in the formation of secondary inorganic aerosols, and estimation of
ammonia emissions is usually associated with large uncertainties (e.g., Huang
et al., 2011; Fu et al., 2013). With the base case ammonia emissions,
NH3 concentration was underpredicted by 3.0 ppb (NMB of -60 %).
With doubled ammonia emissions, ammonia concentration was overpredicted by
1.7 ppb with NMB of 34 %, but the MB values of the total ammonia (NH3+ ammonium) concentrations were reduced from -6.9µg m-3 (NMB of
-36 %) in the base case scenario to -1.9µg m-3 (NMB of
-10 %). NMB of sulfate concentrations during polluted period is -32 %,
which is similar to the enhancement caused by that of the SO2+NO2 heterogeneous reactions. Clearly, doubling ammonia emissions is not
enough to close the gap between observed and simulated sulfate
concentrations during heavy haze periods. We performed additional
sensitivity tests with even higher ammonia emissions and found that 10 times
ammonia emissions would be needed to achieve an average sulfate
concentration (33.2 µg m-3) that is comparable with observation
(36.1 µg m-3) under polluted conditions (with no heterogeneous
reactions). However, in that case, model performance of ammonia is
significantly compromised with overprediction by 32.3 ppb. These results
indicate that the uncertainties associated with the ammonia emissions are
not enough to fully explain the underprediction of sulfate formation during
heavy haze periods in the YRD region.
Impact of both (noHet vs. Het_2NH3)
A fourth scenario (Het_2NH3) with the SO2+NO2 heterogeneous reactions as well as doubled ammonia emissions gave
the best model performance of sulfate concentrations with an overall MB of
-0.2µg m-3 (NMB of -1 %, Fig. 6). During polluted periods,
average sulfate concentration was predicted to be 29.1 µg m-3
(representing an increase of 26 % from the base case) and NMB was reduced
from -36 % in the base scenario to -19 % in the Het_2NH3 scenario. Maximum sulfate concentration simulated under scenario
Het_2NH3 was 97.2 µg m-3, which compared
well with the observed maximum of 93.4 µg m-3 at the SAES site.
With doubled ammonia emissions, the heterogeneous reactions were playing an
increasingly important role in sulfate formation by boosting average sulfate
concentrations from 24.5 (noHet_2NH3) to 29.1 µg m-3 (Het_2NH3) under polluted conditions,
representing an increase of 19 %. This is because with more ammonia
available, aerosol pH was elevated by ∼0.7, pushing it closer
to the actual pH (as discussed more in Sect. 3.3); the rate of heterogeneous reactions is positively correlated with aerosol pH (Fig. 1),
therefore leading to the best model performances from the Het_2NH3 scenario. These results indicate that the SO2+NO2
heterogeneous reactions and sufficient ammonia emissions are both
needed to greatly improve model simulation of sulfate formation under
polluted conditions. However, it is important to mention that model performance under
clean and transition periods was compromised most under scenario
Het_2NH3.
Figure S6 shows a Q–Q plot of modeled versus observed sulfate concentrations
for the four scenarios. Underestimations of sulfate concentrations become
noticeable around 35 µg m-3 in all scenarios, and between 35 and
55 µg m-3 there appears to be a systematically low bias in
predicted sulfate concentrations that neither doubled ammonia emissions nor
the heterogeneous reactions, or both, could stimulate notable sulfate
formation. Scenario Het_2NH3 gives the best model
performance with an overall MB of -0.2µg m-3 but still
underpredicts sulfate formation under heavy haze periods by -19 %. This
could be related to still biased ammonia emissions, less direct emissions of
sulfate and/or SO2, and/or other missing sulfate formation pathways
that need further investigation. For example, Shao et al. (2019) included
heterogeneous sulfate formation via oxidations by O3, H2O2,
and Fe(III)/Mn(II), in addition to the aqueous-phase reactions, and concluded
that the metal-catalyzed reactions dominated the heterogeneous sulfate
formation. These heterogeneous reactions were not included in the current
study and could lead to some underestimate of sulfate formation. As
mentioned above, the parameterization of the k0 values is relatively
conservative at high-RH conditions, which are favorable for sulfate
formation. In addition, reported aerosol hygroscopicity bias in meteorology
could also play some roles here as we are seeing systematic
underestimation of all gaseous and PM species. Another explanation is that although
the SO2+NO2 heterogeneous reactions implemented in this
study were parameterized based on observations in Beijing, the simulation
is performed over the YRD region. It would be ideal to use local
observations for model parameterization in future studies.
Sulfate formation budget
To gain a closer look at the sulfate formation via different pathways (e.g.,
gas phase vs. aqueous phase vs. heterogeneous phase, Table 1), we
constructed a sulfate formation budget in a similar manner as Shao et al. (2019). Figure 7 shows the relative contribution of primary sulfate
emissions as well as the individual sulfate formation pathways to the total
sulfate concentrations at the SAES site under different conditions. Overall,
primary sulfate emissions and secondary formation accounted for half of the
total sulfate concentrations. Of the secondary sulfate, gas-phase reactions
always dominated secondary sulfate formation, with relatively consistent
contributions around 38 %–39 % under different conditions. As
pollution developed, contributions from secondary formation exceeded that of
primary emissions, accounting for 60 % of total sulfate abundances under
polluted conditions. In contrast to the relatively consistent contribution
from the gas-phase formation, both aqueous and heterogeneous sulfate
formation doubled from clean to polluted periods, and relative contributions
increased from 4.1 % to 9.4 % for the former and from 5.0 % to
12.6 % for the latter.
Relative contribution of different pathways to sulfate
concentrations at the SAES site during clean, transition, and polluted
periods. Primary sulfate emissions were excluded in the bottom row.
If we exclude the contribution of primary sulfate emissions (i.e., bottom row in Fig. 7), the absolute sulfate formation via the gas-phase
reactions more than doubled from clean (1.59 µg m-3) to polluted
(3.61 µg m-3) periods; however, the relative contribution from
gas-phase formation among all formation pathways dropped from 80.9 % to
63.3 % as pollution developed. Sulfate formation from all aqueous-phase
reactions increased from 0.17 µg m-3 under clean conditions to
0.89 µg m-3 under polluted conditions, corresponding to an
increase in relative contribution from 8.6 % to 15.6 %. Under all
conditions, aqueous oxidation due to MHP and PAA is negligible, with less
than 1 % of sulfate contribution. The rest of the three aqueous pathways in turn
dominated aqueous sulfate formation depending on the specific condition. For
instance, under clean conditions, oxidation by O3 was the dominant
aqueous contributor (accounting for 5.4 % of all sulfate formation
pathways) but ignorable (< 1 %) under polluted conditions. While
modeled SO2 concentrations increased from 33.2 to 53.5 µg m-3 as pollution developed, simulated O3 concentrations
dropped by almost half from 8.7 ppb (∼18.7µg m-3)
under clean conditions to 5.2 ppb (∼11µg m-3)
under polluted conditions, leading to reduced sulfate formation from aqueous
oxidation by O3 under more severe haze. Predicted O3
concentrations in this study are much higher than the values
(∼1 ppb) assumed by Cheng et al. (2016) and Wang et al. (2016) but are comparable to values reported by Shao et al. (2019; 9 ppb) for
a haze episode in Beijing.
Sulfate formation associated with H2O2 and Fe(III)/Mn(II) showed
the opposite trend: the H2O2 pathway only contributed 1.4 %
(0.03 µg m-3) of total sulfate formation under clean conditions
and increased to 5.6 % (0.12 µg m-3) under polluted conditions,
representing an increase by a factor of 3. Predicted H2O2
concentration at the SAES site was 0.03 ppb on average and the maximum value
could reach 0.29 ppb. These values are slightly lower than the values
observed in Beijing (average around 0.05 ppb and maximum of 0.90 ppb) by Ye
et al. (2018) but are comparable in terms of the magnitude. However, without
any H2O2 observations in Shanghai, it would be inappropriate to
conclude whether the model is over- or underpredicting H2O2 levels in
Shanghai. Based on our current results, H2O2 oxidation is not the
dominant contributor to sulfate formation during our study period.
Oxidation pathway involving Fe(III)/Mn(II) also contributed more to sulfate
formation as polluted developed. Under polluted conditions, Fe(III)/Mn(II)-catalyzed sulfate oxidation is the dominant aqueous formation pathway,
accounting for 8.4 % (0.48 µg m-3) of secondary sulfate
formation. CAMx estimates the Fe(III) and Mn(II) concentrations by assuming
a constant mass fraction (3.355 % for Fe(III) and 1.15 % for Mn(II))
based on the dust and primary PM2.5 concentrations. Values of 10 %
for Fe (III) and 50 % for Mn (II) were set to represent the soluble
fractions in the cloud water. Values of 10 % for Fe(III) during the day and 90 % for
Fe(III) during the night as well as all Mn(II) were assumed to be in the oxidized
ionic state. Based on these assumptions, modeled soluble concentrations
during December 2013 were 1.51±1.68µM for Fe(III) and 0.51±0.31µM for Mn(II); the ranges of estimated
soluble Fe(III) and Mn(II) were 0.1–10.7 and 0.05–2.47 µM, respectively. These results are somewhat lower than the values reported by Shao et al. (2019) and other studies cited in the paper, but the overall magnitudes are well comparable. We realize that assuming
constant Fe and Mn mass fraction is a simplification, and the latest CAMx version
has the option to treat Fe and Mn as primary species. However, using this
option would put even more burden on the emission inventory to have accurate
source speciation profiles for different source sectors. Nevertheless,
although this Fe(III)/Mn(II)-catalyzed pathway stands out among all aqueous
pathways under polluted conditions, the relative contribution (8.4 %) is
only about one of that from the SO2+NO2 heterogeneous
reactions (21.1 %). As for the SO2+NO2 heterogeneous
reactions, its contribution to sulfate formation doubled from 10.5 % (0.21 µg m-3) under clean conditions to 21.1 % (1.2 µg m-3)
under polluted conditions. Under all conditions, the relative contribution
of the SO2+NO2 heterogeneous reactions exceeds the sum of all
aqueous pathways, indicating the importance of heterogeneous oxidation
pathways compared to aqueous pathways.
Sulfate formation under selected episodes
We further selected four heavy haze episodes (EP1-EP4) with observed sulfate
concentrations continuously exceeding 30 µ g m-3 (as highlighted
in Fig. 4) at the SAES site. These episodes
lasted from 9 h (EP2) to as long as 37 h (EP1) with episode average
sulfate concentrations all above 50 µg m-3 (Fig. S7),
except for EP3 (36.2 µg m-3) (Table S6). Maximum hourly sulfate
concentrations ranged from 48.6 µg m-3 for EP3 to 93.4 µg m-3 for EP2. The averaged molar sulfate and SO2 ratio
([SO42-] / [SO2]) for EP1 and EP2 are higher (0.52 and 0.70,
respectively) than those for EP3 (0.17) and EP4 (0.19). In the base case
scenario, sulfate formation was significantly underestimated for all four
episodes with NMB ranging from -39 % to -72 %. Figure S8 shows the
sulfate formation budget for the four episodes of the base case scenario.
The gas-phase oxidation pathway was the dominant contributor, accounting for
52 % (EP2) to 79 % (EP3) of total secondary sulfate formation, followed
by the SO2+NO2 heterogeneous reactions with contributions of
20 %–39 %. For EP1 and EP2, the Fe/Mn-catalyzed
oxidation pathway contributed ∼10 % of sulfate formation
but were negligible for the other two episodes. It is interesting to note
that for all selected episodes, except EP3, sulfate formation was enhanced in
scenario Het_2NH3 by 10.4 to 14.6 µg m-3,
while EP3 only exhibits minimal increase in modeled sulfate concentrations
by only 0.8 µg m-3. We performed additional sensitivity tests
and found that even with 10 times ammonia emissions, modeled sulfate
concentration during EP3 is enhanced by only 2.3 µg m-3, which
is still much lower compared to the observed values. We suspect that other
factors, e.g., meteorology, might be biased during EP3 and lead to the
underpredicted sulfate concentrations. For instance, we looked at the model
performance of WRF predictions for individual episodes. All four episodes had
some overprediction of wind speeds with NMB values ranging from 4 % for EP2 to as
much as 43 % for EP3. Clearly, the large overprediction of wind speeds
during EP3 contributed partially to the underestimated sulfate
concentrations by the model. Another potential cause for sulfate
underprediction could be a failure to capture episodic primary sulfate
emissions during EP3. When EP3 is excluded, modeled sulfate concentrations
during heavy pollution episodes are greatly enhanced from 33.5 µg m-3 in the base scenario to 46.2 µg m-3 in scenario
Het_2NH3 (increase of 38 %), due to the combined
influences of the SO2+NO2 heterogeneous reactions and doubled
ammonia emissions.
Observed and predicted aerosol pH at the SAES site
Aerosol pH, which is calculated from ISORROPIA either based on observations
or within CAMx, is crucial for the heterogeneous SO2+NO2
reactions to be effective. Observation-based aerosol pH was calculated using
forward metastable mode by ISORROPIA to be consistent with the CAMx ISORROPIA
configuration. Figure 8 shows the distribution of observation-based and
modeled aerosol pH at the SAES site by scenario or period. As indicated by both
observation-based and modeled pH values, aerosols become more acidic as
pollution develops. This is consistent with the higher SO2
concentrations observed under polluted conditions (Fig. S9). For
observation-based values, aerosol pH dropped by 35 % from clean to
polluted conditions, while modeled aerosol pH dropped by 13 %–17 % under different scenarios. As also shown by Fig. 8,
observation-based aerosol pH values are consistently higher than modeled
values for all scenarios. Averaged observed-based pH values during clean,
transition, and polluted periods are 5.5, 4.7, and 3.6, while corresponding
values for the base scenario (noHet) are 2.8, 2.6, and 2.3, each representing an
underestimation by 48 %, 45 %, and 34 %. Maximum aerosol pH reached
5.0, 4.4, and 3.8 under clean, transition, and polluted periods in the base
scenario in contrast to observation-based values of 7.7, 6.5, and 5.3.
Adding the SO2+NO2 heterogeneous reactions causes a small
decrease (0.03–0.07) in predicted aerosol pH. The
discrepancies between observation-based and model-based aerosol pH values
might be due to significant underprediction of NH3 and ammonium
concentrations. Therefore, when NH3 emissions are doubled, modeled
aerosol pH increases by ∼0.7 to 3.0–3.5 and underestimation
of aerosol pH for scenario noHet_2NH3 is reduced to
36 % during clean periods and 15 % during polluted periods. Maximum
aerosol pH during clean, transition, and polluted periods is 5.7, 5.1, and
4.2 under scenario noHet_2NH3. Again, adding the
SO2+NO2 reactions on top of doubled NH3 emissions
slightly decreases the aerosol pH by 0.03–0.12, with stronger reduction
associated with further enhancement of sulfate formation. Both
observation-based and model-based aerosol pH values at the SAES site
indicate that aerosol pH is acidic, which is lower than the more neutralized
values reported in previous studies for the Beijing–Tianjin–Hebei region
(e.g., values of 5.4 to 6.2 reported by Cheng et al. (2016) and values of 6.0 to 7.6 by Wang et al. (2016); the latter were later found to be associated with a coding bug in
ISORROPIA). This difference might be due to lower ammonia levels in Shanghai
compared with Beijing (S. Wang et al., 2015). However, even when ammonia
emissions are increased by 10 times, the maximum modeled aerosol pH value is 4.8
under polluted conditions, which is still lower than the values reported for
north China. Our results indicate that the aerosol pH at the SAES site
tends to be moderately acidic regardless of the ambient ammonia
concentrations. However, the acidity of aerosols in China still remains to
be subject to vigorous debate. For example, Shi et al. (2017) reported a wide range
of pH values between 0.33 and 13.6, depending on the source contributions.
Xie et al. (2019) found that the predicted particulate pH values increased
from moderately acidic to nearly neutral with the increase in nitrate-to-sulfate
molar ratio.
Box and whisker plot of observed and predicted aerosol pH by
scenario and period.
Spatial impact of the SO2+NO2 heterogeneous reactions and
ammonia emissions
Figure 9 shows the spatial distribution of monthly
mean sulfate, SO2, ammonia concentrations, and aerosol pH simulated in
the base case and the differences between base case and three other
sensitivity runs in the YRD region. Similar plots of nitrate, ammonium, and
PM2.5 are shown in Fig. S10. Overall, impacts of the heterogeneous
reactions and ammonia emissions over the YRD region are similar to that
observed at the SAES site. With the SO2+NO2 heterogeneous
reactions only, predicted monthly mean sulfate concentrations show
a ubiquitous increase of 0.1–5 µg m-3 across the domain with
a larger increase observed in the north and northwest directions. Regions with
a relatively higher increase in predicted sulfate concentrations closely track
regions with relatively high aerosol pH and high ammonia concentrations.
Aerosol pH decreases slightly because more SO2 is pulled into the
aerosol phase. For nitrate concentrations (Fig. S10), however, the
heterogeneous reactions lead to an increase in the northwest region but
decrease for the rest of the YRD region, and magnitudes of changes in both
directions are within 1 µg m-3. Predicted ammonium
concentrations show a less than 1 µg m-3 increase over the
majority of the domain. Domain-averaged PM2.5 concentrations increased
by 1.2 µg m-3 with spatial patterns similar to sulfate.
Spatial distribution of simulated monthly sulfate (top row),
NH3 (second row), and SO2 (third row) concentrations (µg m-3) and aerosol pH (bottom row) over the YRD region for the base case
scenario (first column) and the differences (µg m-3 for
concentrations) between the base case and other three scenarios: Het (second
column), noHet_2NH3 (third column), and
Het_2NH3 (fourth column).
Spatial distribution of simulated monthly sulfate concentrations
(µg m-3) over the 36 km domain for the base case scenario (a), Het (b) and the differences between the two scenarios (c) and ammonia concentrations (µg m-3; d).
With doubled ammonia emissions, predictions of all three inorganic PM
species are enhanced with most profound impacts observed for nitrate (Figs. 8 and S10). A uniform increase across the YRD region is observed for
predicted sulfate concentrations; for nitrate and ammonium, an increase in
predicted concentrations is more significant towards the south. Domain-averaged sulfate, nitrate, ammonium, and PM2.5 concentrations increase
of 0.5, 6.2, 0.3, and 8.0 µg m-3, respectively. Aerosol pH is
also elevated (on average by 0.3) with more ammonia available. In south
Anhui and south Zhejiang provinces, elevation of aerosol pH exceeds one
unit. Areas with larger pH increase are also areas with relatively lower pH
values in the base scenario, indicating that aerosol pH responds nonlinearly
to changes in ammonia emissions.
When both the heterogeneous reactions and doubled ammonia emissions are
considered, simulated sulfate concentrations are enhanced by 2.7 µg m-3 across the YRD region. Again, areas with relatively larger
enhancement of sulfate concentrations are regions with relatively high
aerosol pH values and not necessarily regions with maximum increase in
aerosol pH. Minimal changes in nitrate and ammonium concentrations are
observed with and without the heterogeneous reactions when ammonia emissions
are doubled. For PM2.5, domain-averaged concentrations increase of 11.6 µg m-3. Simulated PM2.5 concentrations show better
agreement with observations at the 24 monitoring sites
(Fig. 5); averaged NMB is reduced from -21 %
in the base scenario to -11 % in scenario Het_2NH3.
Figure 10 further compares the average simulated sulfate concentrations
between the base case and Het scenarios for the outer 36 km domain during the
modeling period. In the base case simulation, high sulfate concentrations
were noticed at scattered cities over the North China Plain, central China,
and the central part of the Sichuan Basin, corresponding to regions with
elevated SO2 concentrations. Implementing the SO2+NO2
heterogeneous reactions enhanced simulated sulfate concentrations by at
least 1–5 µg m-3 for regions to the east of the
“Hu Line”. In particular, the Northeast China Plain shows the most significant
sulfate enhancement of more than 10 µg m-3; simulated average
sulfate concentrations in the Northeast China Plain increased from less than
20 µg m-3 during the base case scenario to more than 30 µg m-3 in the Het scenario. For other regions, including the
North China Plan and Sichuan Basin that show relatively high sulfate
concentrations in the base case scenario, sulfate concentrations were
increased by 5–10 µg m-3 due to the implementation of the
reactive SO2 uptake mechanism. The spatial pattern of sulfate
enhancement generally follows that of predicted ammonia concentrations, once
again suggesting the important role of ammonia emissions for this mechanism.
Future studies and local sulfate observations are needed to evaluate this
mechanism for other parts of China, especially for the Northeast China Plain.
Conclusions and recommendations
A new parameterization of the SO2+NO2 heterogeneous reactions
based on observations in Beijing to improve model simulation of sulfate
formation under heavy haze conditions in the YRD region was implemented in
this study. Unlike previous studies that only considered the influence of
relative humidity on sulfate formation, we included the impact of
aerosol pH in our parameterization. Four CAMx sensitivity runs were
conducted to evaluate the importance of the SO2+NO2
heterogeneous reactions as well as ammonia emissions on simulated sulfate
concentrations in the YRD region. Base case simulation showed reasonable
model performance of sulfate with an overall MB of -2.7µg m-3
but significantly underpredicted sulfate concentrations by 36 % during
polluted conditions. Implementation of the SO2+NO2
heterogeneous reactions alone showed a slight improvement in sulfate
simulation (increase of 6.5 %) under polluted conditions due to acidic
aerosol pH. Ammonia concentrations were significantly underestimated by the
model. Doubling ammonia emissions alone exhibited a similar impact (sulfate
increase of 5.6 %) with that of the SO2+NO2 heterogeneous
reactions alone. Nevertheless, aerosol pH increased by 0.7 with doubled
ammonia emissions, which enabled the SO2+NO2 heterogeneous
reactions to become effective. Thus, in a fourth scenario where both the
SO2+NO2 heterogeneous reactions and doubled ammonia emissions
were considered, simulated sulfate concentrations during polluted periods
increased from 23.1 µg m-3in the base case to 29.1 µg m-3, representing an increase of 26 %. Results for sulfate
simulations over entire China shows that for some parts of China, especially
the Northeast China Plain, implementing the SO2+NO2
heterogeneous reactions could lead to an as much as 20 µg m-3 increase in sulfate concentrations and the spatial pattern of sulfate
enhancement follows closely to that of ammonia concentrations.
In this study, we considered two questions. First, is the observation-based
estimate from Beijing compatible with data from Shanghai? The answer is yes.
Based on the observed data of our modeling period in Shanghai, heavy winter
haze events share many similarities between Beijing and Shanghai; for
example, increased sulfate fraction under polluted conditions and sulfate to
SO2 ratio increases with RH. If the parameterization based on Beijing
data generates too much sulfate in Shanghai, we could conclude that the
parameterization is inconsistent with the data for Shanghai. However, this
was not the case, even with increased ammonia emissions. So the
parameterization is consistent with the data for Shanghai. The second
question we wanted to answer is the following. When we apply the rate estimate for
Beijing to Shanghai, do we find that NO2+SO2 can be
potentially important in Shanghai? The answer is also yes, depending on the
pH level, which depends on the NH3 emission. However, underprediction
of sulfate concentration still exists (by 20 %) in the YRD region under
polluted conditions even with the SO2+NO2 heterogeneous
reactions and doubled ammonia emissions, which urges further efforts to
better constrain the parameterization of the SO2+NO2
heterogeneous reactions using local data and to improve the accuracy of
the ammonia emission inventory. For instance, we would ideally like to have
observed sulfate and precursor concentrations at a pair of sites oriented
upwind and downwind (so that the differences between the two sites constrain
sulfate production), observed particle characterization (e.g., particle
diameter, number concentrations), and robust NH3 concentrations to
constrain aerosol pH. Follow-up studies can be conducted once these data
become available. The analysis of the modeled sulfate formation budget
shows that transition metal (Fe and Mn) concentrations are influential on
sulfate production and therefore improving the emission inventory for
PM2.5, Fe, and Mn will improve model performance for sulfate and could
also influence model assessments of how sulfate concentrations respond to
emission management strategies that reduce PM2.5, Fe, and Mn
concentrations in the YRD region.
Code and data availability
All data and modified CAMx code are available upon request from the
corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-14311-2019-supplement.
Author contributions
LH, JA, LL, CH, and GY designed the research; BK and LH
modified the code; RY conducted the WRF simulation; JA conducted the CAMx
simulations; LH and JA analyzed the data; LL, GY, CH, and YW
provided important academic guidance; LH and JA wrote the paper with
contributions from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Multiphase chemistry of secondary aerosol formation under severe haze”. It is not associated with a conference.
Acknowledgements
We thank Qi Zhang, Qian Wang, and
Hongli Li from Shanghai University for helping with the data analysis.
Financial support
This research has been supported by the Shanghai Sail Program (grant no. 19YF1415600), the National Natural Science Foundation of China (grant no. 41875161), and the Chinese National Key Technology R&D Program (grant nos. 2014BAC22B03 and 2018YFC0213800).
Review statement
This paper was edited by Hang Su and reviewed by two anonymous referees.
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