Despite the high abundance of secondary aerosols in the
atmosphere, their formation mechanisms remain poorly understood. In this
study, the Master
Chemical Mechanism (MCM) and the Chemical Aqueous-Phase Radical Mechanism (CAPRAM) are used to investigate the multiphase formation
and processing of secondary aerosol constituents during the advection of air
masses towards the measurement site of Mt. Tai in northern China. Trajectories
with and without chemical–cloud interaction are modeled. Modeled radical and
non-radical concentrations demonstrate that the summit of Mt. Tai, with an
altitude of
Secondary aerosols are more abundant than primary aerosols (Volkamer et al.,
2006). Their constituents are formed on a regional scale and transported
over long distances and thus have a direct impact on the air quality of a
wider area (Kim et al., 2007; Matsui et al., 2009; DeCarlo et al., 2010).
Secondary aerosols are usually divided into two classes: secondary inorganic
aerosol (SIA) and secondary organic aerosol (SOA). A number of studies have
been conducted that aimed to investigate their formation mechanisms (Yao et
al., 2002; Duan et al., 2006; Wang et al., 2006; Guo et al., 2010; Zhao et
al., 2013). The SIA components, including sulfate, nitrate and ammonium, are
important contributors to fine particulate matter (PM
Dicarboxylic acids and related compounds (oxo-carboxylic acids and
The present study focuses on the multiphase formation mechanism of key secondary aerosol constituents measured in June 2014 at Mt. Tai, which is the highest mountain on the North China Plain (NCP). Mt. Tai is located in Shandong province on the NCP and between the Bohai Rim (BHR) and the Yangtze River Delta (YRD) regions. Together, the BHR and YRD regions had a population of more than 450 million in 2018 (China Statistical Yearbook in 2019). In summer, clouds frequently occur over the summit of Mt. Tai. Despite a small amount of emissions from temples and small restaurants at Mt. Tai's peak, the sampling site on top of Air Force Hotel, Houshiwu, was typically not influenced much by tourists and temples (Sun et al., 2016). The special altitude and geographical location of Mt. Tai provide a suitable site to measure regional secondary aerosol constituents and to investigate their formation pathways along the advection to the measurement site.
The detailed objectives of the present study are as follows: (i) characterization of modeled radical and non-radical oxidant concentrations;
(ii) assessment of modeled concentrations and formation processes of key
secondary inorganic compounds; (iii) study of modeled concentrations of
DCRCs and a comparison with field observations to assess the model
predictions; (iv) investigation of source and sink pathways of selected
DCRCs; (v) examination of the impact of emission data on modeled secondary
aerosol concentrations; (vi) identification of the key precursors of
selected DCRCs; and (vii) the impact of higher glyoxal (Gly) partitioning
constants on the modeled concentrations of Gly, glyoxylic acid (
Detailed descriptions about the sampling site, the sampling instruments and
the analysis methods can be found in a previous publication (Zhu et al.,
2018). Campaign observation data, meteorological conditions and
corresponding findings are also given there. The sampling period was from
4 June to 4 July 2014. The meteorological data during the campaign were as
follows: temperatures ranged from 10 to 25
In this study, we applied the air parcel model SPACCIM (SPectral Aerosol Cloud Chemistry Interaction Model, Wolke et al., 2005) to simulate multiphase chemistry along main trajectories during a simulated campaign period. SPACCIM combines a multiphase chemical model with a cloud microphysical model, simulating aqueous-phase chemistry in deliquesced particles and cloud droplets. The cloud microphysical model applied in SPACCIM is based on the work of Simmel and Wurzler (2006) and Simmel et al. (2005). Droplet formation, evolution and evaporation are realized by a one-dimensional sectional microphysics considering deliquesced particles and cloud droplets. In the present study, the moving bin version of SPACCIM has been applied. In the model, the growth and shrinking of aerosol particles by water vapor diffusion and nucleation and the growth and evaporation of cloud droplets is considered. The dynamic growth rate in the condensation and evaporation process and the droplet activation is based on the Köhler theory. Due to the emphasis on complex multiphase chemistry, other microphysical processes, such as impaction of aerosol particles and collision and coalescence of droplets and thus precipitation, were not considered in the present study. Moreover, the air parcel model SPACCIM is not able to reflect the complexity of tropospheric mixing processes. Nevertheless, the complex model enables detailed investigations of the multiphase chemical processing of gases, deliquescent particles and cloud droplets. More detailed descriptions of SPACCIM can be found in Wolke et al. (2005), Sehili et al. (2005) and Tilgner et al. (2013). However, SPACCIM cannot assess the complexity of (i) the tropospheric mixing processes along the transport, (ii) the occurring aerosol particle microphysical processes (e.g., nucleation, aggregation, etc.) or (iii) the effects of nonideal solutions on the occurring multiphase chemistry. These limitations have to be kept in mind when studying deliquesced particles and comparing predicted and observed concentrations at Mt. Tai. The potential limitations of an ideal solution assumption compared to a nonideal treatment are discussed in a recent paper by Rusumdar et al. (2020).
The applied multiphase chemistry mechanism is comprised of the Master
Chemical Mechanism (MCM3.2 scheme with 13 927 reactions,
Zhu et al. (2018) have shown that during the sampling period (4 June–4 July 2014) air masses arriving at Mt. Tai mainly came from the north (named cluster 2) and the south (named cluster 4) (Fig. S1 in the Supplement). The two clusters accounted for 79 % of the total trajectories. Moreover, the sum of DCRC concentrations in clusters 2 and 4 amounted to 73 % of total DCRC concentration during the sampling period. Therefore, in this study, we selected clusters 2 and 4 to simulate and investigate the formation processes and the fate of DCRCs. Additionally, Zhu et al. (2018) have clearly shown that biomass burning was only an important source during the first half of the sampling period (4–19 June). The aim of the study was to investigate the secondary formation of aerosol constituents along the trajectories towards Mt. Tai. However, biomass burning can be an important primary source of compounds that are often of secondary origin. Therefore, in this study, we focused on the period that was less impacted by biomass burning. In addition, both clusters 2 and 4 exhibited a rather stable transport above the mixing layer to the Mt. Tai site.
A total simulation time of 96 h is chosen (4 d), representing a typical
aerosol lifespan (Willams et al., 2002). The first 24 h are considered a
model initialization day. Thus, only the model results from 24 to 96 h are
presented in this study. With the help of measured RH at Mt. Tai and
meteorological values of clusters 2 and 4 that are obtained by HYSPLIT4.9
(Draxler and Rolph, 2003) and MODIS satellite pictures (Li et al., 2005), we
have ascertained that clouds most likely occurred at the Mt. Tai top and
advected to Mt. Tai at the altitude of the trajectories (Zhu et al., 2018).
Radiosonde data (
We have also carried out sensitivity runs in this study, investigating the
following three aspects: (i) the impact of considered emission data on
modeled secondary aerosol concentrations; (ii) the identification of key
precursors of C
Acronyms of the performed model simulations.
Zhu et al. (2018) have reported that the pollutant concentrations during the
campaign at Mt. Tai were largely controlled by long-range transport. The
formation processes of secondary aerosols during long-range transport
strongly depend on the emission of precursors. Therefore, emission data
passed over in clusters 2 and 4 are implemented in the model. Biogenic
emission data (isoprene,
Due to the key role of radical and non-radical oxidation in the formation
processes of secondary aerosol constituents, their concentration variations
and corresponding reasons are investigated. Several publications have
already focused on the oxidant budget in China. Kanaya et al. (2009) modeled
gas-phase concentrations of OH,
Time series of the modeled gas-phase (
Figure 1 shows the modeled gas- and aqueous-phase concentrations of
important radical oxidants in the C2w and C2wo cases. The gas- and
aqueous-phase OH,
Due to photochemistry, the gas-phase OH and
The
Unfortunately, we did not perform measurements of key radicals during the
campaign. However, the simulated maxima of the gas-phase concentrations of
OH (C2w:
Similar to the gas phase, aqueous-phase concentrations of OH and
Average aqueous-phase concentrations (mol L
The
Time series of modeled gas-phase (
Figure 2 depicts the modeled gas- and aqueous-phase concentrations of
Figure 2 shows that, due to active photochemistry, gas-phase concentrations
of
In the C2wo case, measured gas-phase
The aqueous-phase
Time series of the modeled aerosol mass concentrations (
In Fig. 3, modeled concentrations of the most important SIA constituents are
plotted, including (i) sulfate (sum of all sulfur compounds with oxidation
state
Conducted field observations, together with estimated sulfur oxidation rates using a tracer method in previous studies at Mt. Tai, have suggested that sulfate formation is closely related to cloud chemistry (Zhou et al., 2009; Shen et al., 2012; Guo et al., 2012). However, these studies are not able to comprehensively quantify the impact of cloud chemistry on sulfate concentration and have not performed detailed investigations on chemical formation pathways of sulfate during the transport to Mt. Tai. In this study, we primarily present modeled concentrations of sulfate and discuss the differences between the different day vs. night and cloud vs. non-cloud cases using a multiphase chemistry model. Moreover, findings of sulfate source and sink chemical reactions are presented for the different model cases.
Modeled multiphase (gas
Figure 3 shows that sulfate concentrations mainly increase under in-cloud
conditions throughout the whole simulation due to active in-cloud chemical
sulfur oxidation pathways. Although in-cloud residence time is slightly
higher during the night, sulfate concentrations increase more in the daytime
clouds (35 %) than the nighttime clouds (15 %) because of the
increased aqueous reaction of
In the C2wo case, sulfate concentrations gradually increase over time (Fig. 3). The highest increase occurs during the day as a consequence of the
gas-phase
Studies at Mt. Tai focused on nitrate suggested that photochemical formation
of
As can be seen in Fig. 3, nitrate concentrations are increased throughout
the simulation. Under in-cloud conditions, nitrate concentrations are increased
by about 10 % and 24 % during the day and the night, respectively. The
concentration time profiles in the C2w and C2wo cases show only small
differences, indicating that most of the nitrate formation occurs during
non-cloud periods. Therefore, the end concentrations of C2w and C2wo do not
differ significantly. An analysis of chemical sink and source in the C2w
case (Fig. 4) has revealed that nitrate is mainly produced by aqueous-phase
A comparison of daytime and nighttime fluxes in the C2wo case has revealed
that 31 % and 69 % of nitrate formation fluxes occur during the day and at
night, respectively. In the C2wo case, nighttime nitrate is mainly produced
by aqueous-phase
The modeled nitrate concentrations are 69.5 and 65.3
Measured ammonium concentrations at Mt. Tai can be strongly impacted by acidification and cloud chemistry (Guo et al., 2012; J. Li et al., 2017). Still, a detailed analysis of the occurring processes is missing. Therefore, we provide a detailed insight into the ammonium concentration variation trends and the impact of acidification and cloud processing along the simulated trajectories towards Mt. Tai.
Similar to sulfate and nitrate, ammonium concentrations also gradually
increased throughout the simulation due to the included emissions rates and
the followed uptake of gaseous
The differences between the modeled and measured concentrations of sulfate, nitrate and ammonium can be attributed to several issues such as (i) the indefiniteness of the input emission data, (ii) the initial concentrations, (iii) the missing entrainment–detrainment processes and (iv) the performed heating of the inlet during the sampling of wet aerosol (see Sect. 3.3.2 for further details).
In recent years, a number of field observations on DCRCs have been conducted in the NCP. For example, He et al. (2013), Ho et al. (2015), Zhao et al. (2018) and Yu et al. (2019) observed DCRCs in Beijing, and Wang et al. (2009), Kawamura et al. (2013), Meng et al. (2018) and Zhao et al. (2019) measured DCRCs at Mt. Tai. Our field observation about DCRCs at Mt. Tai has been reported in Zhu et al. (2018). However, these studies are focused on DCRC concentrations, molecular compositions, temporal variations, size distributions, source implications and stable carbon isotopic composition. They have not investigated the chemical formation of DCRC concentrations along the trajectory or the impact of in-cloud and non-cloudy conditions on DCRC concentrations. To our knowledge, a multiphase chemical model study investigating the DCRCs concentration variations and their chemical processing along the trajectory to Mt. Tai considering day vs. night and cloud vs. non-cloud cases has not been yet reported.
Time series of the modeled aerosol mass concentrations of selected
DCRCs (
Figure 5 shows the modeled aerosol mass concentrations of Gly,
In the C2w case, Gly and MGly concentration patterns show a substantial uptake into cloud droplets. Gly concentrations decreased during the daytime and nighttime cloud droplet periods due to in-cloud oxidation processes. On the other hand, MGly concentrations display a decrease in the daytime cloud droplets but an increase under nighttime in-cloud conditions. This might have been caused by the fact that the aqueous oxidation fluxes under nighttime in-cloud conditions are lower than the ones under daytime. This might have been caused by the fact that the aqueous oxidation fluxes under nighttime in-cloud conditions are lower than the ones under daytime conditions because of the much lower OH radical concentrations under nighttime in-cloud conditions (Fig. S3). In the C2wo case, Gly and MGly concentrations are very low due to the low partitioning towards aqueous particles that has been predicted by the model. The effect of a potentially higher partitioning constant of Gly (Volkamer et al., 2009; Ip et al., 2009) is investigated in Sect. 3.5.3. It is worth noting that Gly or MGly have similar concentrations at the end of the simulation with or without cloud chemistry.
In the C2w case, aqueous-phase concentrations of
In the C2w case, modeled aqueous-phase concentrations of C
In the C2w case, Pyr concentrations are raised during the daytime and
nighttime in-cloud conditions as well as in the late mornings of the non-cloud
periods. Pyr concentrations are decreased in the early morning, afternoon
and nighttime non-cloudy conditions (caused by the efficient degradation from
the reaction with aqueous-phase
The aqueous-phase C
The ratios of the average concentration of modeled and measured DCRCs can be
found in Table 3. The results show that model predictions are higher than
the measured concentrations of C
SPACCIM overestimates the measured
Ratios of the concentrations of the modeled and measured DCRC compounds in the different model trajectories at Mt. Tai.
The overestimation and underestimation of the measured concentrations of inorganic
and organic aerosol constituents could have the following reasons.
Apart from MGly, the concentration ratios of the modeled and measured
species ranged from 0.1 to 8.3. Interestingly, the ratio of C
Although field observations have speculated about several potential formation pathways of some DCRCs species by correlations or ratios analyses (Hegde and Kawamura, 2012; Kawamura et al., 2013; Zhao et al., 2019), the detailed pathways of DCRCs need to be studied.
Multiphase model simulations are a suitable tool to investigate DCRC
formation processes. In recent years, DCRC formation processes have been
examined by several model studies. For example, Tilgner and Herrmann (2010)
have modeled gas- and aqueous-phase processing of C
Due to the similar concentration levels and corresponding variation trends
of
Modeled multiphase (gas phase
In Fig. 6, the multiphase source and sink fluxes of
Under daytime and nighttime in-cloud conditions, the major formation pathways
of
Under daytime clouds,
Concentration variations in modeled sulfate, nitrate, ammonium,
Gly,
Figure 6 also depicts the source and sink fluxes of C
The most important source of C
The most important sink of C
The modeled source and sink fluxes of Pyr in the C2w case on the third model day can be found in Fig. 6. A net formation flux is modeled mainly under in-cloud conditions, especially during the day, along with a net degradation during non-cloud periods. About 72 % of the net Pyr flux occurs in clouds, whereas 28 % is formed under non-cloudy conditions. However, 100 % of the multiphase Pyr net sink fluxes are related to non-cloud oxidation.
Under in-cloud conditions, the dominant source for Pyr is hydrolysis of the
aqueous-phase oxidation product of nitro-2-oxopropanoate, with a
contribution of 89 % during the day and 70 % during the night. The
result is different from former model studies, e.g., Ervens et al. (2004),
Lim et al. (2005), Tilgner and Herrmann (2010) and Tilgner et al. (2013),
which modeled the aqueous oxidations of MGly as the major formation pathway
of Pyr. However, these model studies have also modeled different
environmental conditions with much lower anthropogenic pollution, including
lower
The key sinks of Pyr under daytime in-cloud conditions are aqueous-phase
reactions of pyruvate with OH (58 %) and
In Fig. 6, the modeled source and sink fluxes of C
The major modeled sources of C
Differences between the sink fluxes under in-cloud and non-cloudy conditions are
modeled. The C
Due to the similarity between clusters 2 and 4, as mentioned above,
sensitivity tests are only performed under cluster 2 conditions. The present
study investigated the (i) impact of emissions on modeled compound
concentrations; the (ii) key precursors of DCRCs; and the (iii) impact of increased
Gly aerosol partitioning on Gly,
First, sensitivity tests are performed to evaluate the effect of different
emission strengths on the concentrations of key secondary inorganic
compounds and selected DCRCs during the transport. The emission
sensitivities of sulfate, nitrate, ammonium, Gly,
Further sensitivity tests are conducted to identify key primary precursors of DCRCs during atmospheric transport. We have adopted the relative incremental reactivity (RIR) method by Carter and Atkinson (1989) for the sensitivity tests. The positive or negative RIR value reveals that reducing precursor emissions would weaken or aggravate DCRC formation, respectively. The RIR method has already been applied in a former study to investigate the precursors of peroxy acetyl nitrate in urban plume in Beijing (Xue et al., 2014).
The calculated RIRs for C
Correlations between the decreasing ratios of radical oxidants and
C
Concentration variations in modeled Gly,
As can be seen in Fig. 8, C
In the C2wo case, alkenes account for the highest RIR. The RIR of alkenes is
more than 2 times higher than that of the second highest group (aromatic
compounds). Among the alkenes, the dominant compound is isoprene. Contrary
to the C2w case, 1,3-butadiene reveals very low RIR under C2wo conditions. In
the C2wo case, ethene exhibits a positive but low RIR. Among aromatic
compounds, toluene shows the highest RIR. Xylene, ethylbenzene and
isopropylbenzene also present significantly positive RIR values in the
C2wo case. Alkanes again have negative RIRs. As shown in Fig. S10, in the
C2wo case, the reactions of dissolved
For Pyr, in both C2w and C2wo cases, alkenes are the dominant precursor group with the largest RIRs. The major compound is isoprene. The absolute RIR values for other selected species are less than 0.05. These results indicated that Pyr formation during atmospheric transport is highly sensitive to isoprene.
In the C2w case, aromatic compounds are the most significant precursors of
Figure 8 shows that aromatic compounds account for the highest RIR under
C2wo conditions and that toluene is a major contributor. Ethylbenzene and
isopropylbenzene also made significant contributions. The alkene RIRs are
the next highest. Isoprene is the most abundant compound during
Phase partitioning between gas and aqueous phase in a multiphase model can
be affected, e.g., by salting-in and salting-out effects and other reversible
accretion reactions (Herrmann et al., 2015). For example, Ip et al. (2009)
and Kampf et al. (2013) have reported that
The present study focuses on the formation processes of secondary aerosols
constituents along trajectories towards Mt. Tai using the multiphase chemistry
air parcel model SPACCIM. The modeled radical and non-radical concentrations
(e.g., gas-phase OH concentration of
Additionally, the simulations show that increased Gly aerosol
partitioning plays an important role in
The input data used in the paper are given in the Supplement. The output data of all figures given in this study are publicly available at
The supplement related to this article is available online at:
YZ, AT and HH designed the SPACCIM modeling work. YZ, AT and EHH performed the different SPACCIM simulations. YZ, AT, EHH and HH analyzed the SPACCIM simulation results. YZ and LX performed and interpreted the RIR analysis. YZ, AT, KK, LY and WW compared the model results with field data. YZ, AT, EHH, HH and LX wrote the paper and prepared the manuscript material with contributions from all the co-authors.
The authors declare that they have no conflict of interest.
We thank the European Commission for support of the MARSU project (contract no. 69089). The authors acknowledge the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) MEGAN-MACC dataset. The authors also acknowledge the China Scholarship Council for supporting Yanhong Zhu to study in the project at the Atmospheric Chemistry Department (ACD) of the Leibniz Institute for Tropospheric Research (TROPOS), Germany.
This research has been supported by the National Key Research and Development Program of China (grant no. 2016YFC0200500), the National Natural Science Foundation of China (grant nos. 21577079 and 41922051), the Japan Society for the Promotion of Science through Grant-in-Aid (grant no. 24221001), and the European Commission for support of the MARSU project (grant no. 69089).
This paper was edited by Holger Tost and reviewed by two anonymous referees.