Wet scavenging is an efficient pathway for the removal of particulate matter
(PM) from the atmosphere. High levels of PM have been a major cause of air
pollution in Beijing but have decreased sharply under the Air Pollution
Prevention and Control Action Plan launched in 2013. In this study, 4
years of observations of wet deposition have been conducted using a
sequential sampling technique to investigate the detailed variation in
chemical components through each rainfall event. We find that the major
ions, SO42-, Ca2+, NO3-, and NH4+, show
significant decreases over the 2013–2017 period (decreasing by 39 %,
35 %, 12 %, and 25 %, respectively), revealing the impacts of the
Action Plan. An improved method of estimating the below-cloud scavenging
proportion based on sequential sampling is developed and implemented to
estimate the contribution of below-cloud and in-cloud wet deposition over
the four-year period. Overall, below-cloud scavenging plays a dominant
role to the wet deposition of four major ions at the beginning of the Action
Plan. The contribution of below-cloud scavenging for Ca2+,
SO42-, and NH4+ decreases from above 50 % in 2014 to
below 40 % in 2017. This suggests that the Action Plan has mitigated PM
pollution in the surface layer and hence decreased scavenging due to the
washout process. In contrast, we find little change in the annual volume
weighted average concentration for NO3- where the contribution
from below-cloud scavenging remains at ∼ 44 % over the
2015–2017 period. While highlighting the importance of different wet
scavenging processes, this paper presents a unique new perspective on the
effects of the Action Plan and clearly identifies oxidized nitrogen species
as a major target for future air pollution controls.
Introduction
Atmospheric wet deposition is a key removal pathway for air pollutants and
is governed by two main processes: in-cloud and below-cloud scavenging
(Goncalves et al., 2002; Andronache, 2003, 2004a; Henzing et al., 2006;
Sportisse, 2007; Feng, 2009; Wang et al., 2010; Zhang et al., 2013). The
below-cloud scavenging process depends both on the characteristics of the
rain (snow), including the raindrop size distribution and rainfall rate, and
on the chemical nature of the particles and their concentrations in the
atmosphere (Chate et al., 2003). Previously, below-cloud scavenging was
thought to be less important than in-cloud processes and was simplified or
even ignored in many global and regional chemical transport models (CTMs)
(Barth et al., 2000; Tang et al., 2005; ENVIRON.Inc, 2005; Textor et al.,
2006; Bae et al., 2010). However, more recent extensive research on wet
scavenging has found that precipitation, even light rain, can remove
50 %–80 % of the number or mass concentration of below-cloud aerosols, and
this is supported by both field measurements and semi-empirical
parameterizations of below-cloud scavenging in models (Andronache,
2004b; Zhang et al., 2004; Wang et al., 2014). Xu et al. (2017, 2019) studied
the below-cloud scavenging mechanism based on the simultaneous measurement
of aerosol components in rainfall and in the air in Beijing. They found that
below-cloud scavenging coefficients for PM2.5 widely used in CTMs
(∼ 10-5–10-6) were 1–2 orders of magnitude lower
than estimates from observations (at the range of 10-4–10-5 for
SO42-, NO3-, and NH4+.). This
implies that the simulated below-cloud scavenging of aerosols might be
significantly underestimated. This could be one reason for the
underestimation of SO42- and NO3- wet deposition in
regional models of Asia reported in phase II and III of the Model
Inter-Comparison Study for Asia (MICS-Asia) (Wang et al., 2008; Itahashi et
al., 2020; Ge et al., 2020) and in global model assessments by the Task
Force on Hemispheric Transport of Atmospheric Pollutants (TF-HTAP) (Vet et
al., 2014), in addition to the other sources of model uncertainties (Chen et
al., 2019; Tan et al., 2020; Kong et al., 2020), such as emissions, chemical
transformation, and changes in other ambient compounds of sulfur and
nitrogen. Bae et al. (2012) added a new below-cloud scavenging
parameterization scheme in the CMAQ model and improved the simulation of
aerosol wet deposition fluxes in East Asia by as much as a factor of 2
compared with observations. The below-cloud scavenging process is critical
not only for wet deposition but also for the concentration of aerosols in
the air, and it should be represented appropriately in CTM simulations.
It is important to recognize the contribution of below-cloud scavenging to
total wet deposition. However, many studies have found that it is difficult
to separate the two wet scavenging processes based on measurement methods
alone (Huang et al., 1995; Wang and Wang, 1996; Goncalves et al.,
2002; Bertrand et al., 2008; Xu et al., 2017). A commonly used approach to
separating below-cloud scavenging from total wet deposition is through
sequential sampling (Aikawa et al., 2014; Ge et al., 2016; Aikawa and Hiraki,
2009; Wang et al., 2009; Quyang et al., 2015; Xu et al., 2017). In this way,
precipitation composition during different stages of a rainfall event can be
investigated separately in the lab after sampling. The chemical components
in later increments of rainfall are thought to be less influenced by the
below-cloud scavenging process than by the in-cloud scavenging process
(Aikawa et al., 2014; Aikawa and Hiraki, 2009). Xu et al. (2017) applied this approach to summer
rainfall in Beijing in 2014 and found that more than 50 % of deposited
sulfate, nitrate, and ammonium ions were from below-cloud scavenging. In this
study, an innovated method based on exponential curve to chemical ions in
rainfall by sequential sampling is developed and implemented to estimate the
ratio of below-cloud to in-cloud wet deposition in Beijing over the
four-year period between 2014 and 2017. Together with PM2.5
concentration measurements, the below-cloud scavenging effects of the
decreasing air pollutants at near-surface due to the Air Pollution
Prevention and Control Action Plan (Action Plan) launched in 2013 (State
Council of the People's Republic of China, 2019) is also investigated to
explore the implications of the Action Plain to the precipitation chemistry.
Data and methodsMeasurement site and sampling methodology
The measurement site is located on the roof of a two-floor building at the
Institute of Atmospheric Physics tower site (IAP-tower; 39∘58′28′′ N, 116∘22′1′′ E) in northern Beijing. It is a typical urban site between the
3rd and 4th ring roads and lying close to the Badaling expressway
(Xu et al., 2017, 2019; Sun et al., 2015). Four years of inter-annual
observations of each rainfall event were conducted at this site. Sequential
sampling of each rainfall event is employed to catch the evolution of
precipitation composition during each event. To investigate the detailed
variation in the concentration of different chemical components in
precipitation, especially the sharp changes occurring during the onset of
rainfall, high resolution sampling of rainfall at 1 mm sequential increments
was performed using an automatic wet–dry sampler. The rainwater collector
uses a circular polyethylene board with a 30 cm diameter and collects up to
eight fractions. About 70 mL of rainwater is collected for each of the first
seven fractions and the rest of the rainfall is collected in the eighth
fraction. For example, if there is 12 mm rainfall volume in a precipitation
event, 1 mm sequential rainfall is collected in each of the first seven
fractions with the rest of 5 mm in the eighth fraction. Rainfall events
where eight fractions are collected and identified as full events, and those
with fewer than eight fractions are characterized as incomplete events.
Manual sampling methods were used to collect more than eight fractions
during heavy rainfall, and these are characterized as extended events.
From 2014–2017, a total of 104 precipitation events, which is almost 690
precipitation samples, were collected. Of the total number of precipitation
events, 33 events (32 %) were discarded from the sequential sampling
analysis due to low rainfall amounts (< 8 mm), which cannot satisfy
the rules of full events. Altogether, 69 full events including six extended
events were recorded over the 2014–2017 period in Beijing as 15, 16, 20, and
18 events at each year, respectively. The rainfall volume of the eighth
fraction of these 69 full events varied from 1 to 55.9 mm.
After collection, all samples are refrigerated at 0–4 ∘C
and analysed at the Key Laboratory for Atmospheric Chemistry, Chinese
Academy of Meteorological Sciences (CAMS), within one month, following the
procedure used for the Acid Rain Monitoring Network run by the China
Meteorological Administration (CMA-ARMN) (Tang et al., 2007, 2010). Nine ions
that include four anions (SO24-, NO3-, Cl- and
F-) and five cations (NH4+, Na+, K+, Ca2+ and
Mg2+) are detected using ion chromatography (IC, Dionex 600, USA).
Their relative standard deviations in reproducibility tests are less than
5 %. Quality assurance is carried out using routine standard procedure of
blind sample inter-comparison organized by CMA (Tang et al., 2010). Quality
control is conducted by assessment of the anion–cation balance and by
comparison of the calculated and measured conductivity. A more detailed
description of the procedure can be found in Ge at al. (2016) and Xu et al. (2017).
Aerosol measurements
Aerosol mass concentration is recorded in routine measurements for the
observation network of the China National Environmental Monitoring Center
(CNEMC). PM2.5 concentrations are used from the Olympic Park station, a
monitoring station located 3 km to the northeast of the IAP-tower sampling
site. In addition, an ambient ion monitor-ion chromatograph (AIM-IC)
developed by URG Corp., Chapel Hill, NC, and Dionex Inc., Sunnyvale, CA, is
used to measure PM2.5 composition at the sampling site between 2014 and
2017. This instrument includes a sample collection unit (URG 9000-D) for the
collection of water-soluble gases and particles in aqueous solution and a
sample analysis unit (two ion chromatographs, Dionex ICS-2000 and ICS-5000)
for analysis of both anions and cations. The limit of detection of AIM-IC is
0.08 mg m-3 for NH4+ and 0.1 mg m-3 for the other ions. Aerosol
mass concentrations and composition are both measured at 1 h time
resolution. Detailed descriptions of the AIM-IC instrumentation can be found
in Malaguti et al. (2015) and Markovic et al. (2012). The average
concentration of aerosols in the 6 h before each rainfall event is
calculated to reflect the air pollution conditions before the event. For
comparisons, the yearly average concentration of aerosols has been
calculated to represent the normal conditions.
Estimation of below-cloud scavenging
Previous studies have shown that the concentrations of chemical ions in
precipitation decreases through the progression of a rainfall event and
eventually stabilizes at low levels (Aikawa and Hiraki, 2009; Aikawa et al., 2014; Ge et al.,
2016; Xu et al., 2017). The in-cloud and below-cloud scavenging contributions
to total wet deposition are estimated based on the assumption that the
concentrations in later increments can be attributed to scavenging by
rainout only. According to (Seinfeld and Pandis, 2006), species can be
incorporated into cloud and raindrops inside the raining cloud and this
process determines the initial concentration of raindrops before they start
falling below the cloud base. In this stage, despite the efficient
process of the nucleation scavenging in cloud, the total mass of aerosol in
cloud is almost stable due to the slow process of interstitial aerosol
collection by cloud droplets, which is the determination process to aerosol
mass. That is to say, the initial concentration of raindrops in cloud is
well mixed and can be considered in a stable state during the whole
rainfall event. That is why many observations in different regions (Aikawa et al.,
2009, 2014; Wang et al., 2009; Quyang et al., 2015; Xu et al., 2017)
reported that the chemical components in a rainfall event show a decayed
variation with the increase of precipitation amount and eventually tends to
a stable and low-concentration level. The assumption in this study as well
as the previous studies is based on this fact. It does not mean the
below-cloud and in-cloud scavenging occur in sequence. But, instead, the two
processes have been mixed in all stages of the rainfall event with the
below-cloud scavenging contributing more in beginning fraction and the
in-cloud scavenging contributing more in the later fraction due to the
depletion of the air pollutants below cloud by washout.
This assumption relies on the efficient scavenging of air pollutants below
cloud through the evolution of precipitation. However, the concentration of
chemical ions in precipitation may also be affected by many other factors in
addition to below-cloud air pollutant concentrations and in-cloud scavenging
processes. For example, the precipitation intensity may affect the
scavenging efficiency of air pollutants below cloud and hence influence wet
deposition (Andronache, 2004b; Wang et al., 2014; Xu et al., 2017, 2019). Yuan
et al. (2014) reported that in central North China high intensity rainfall
events of short duration (lasting less than 6 h) are dominant rather than
long-duration rainfall that is more common in the Yangtze River Valley.
Therefore, the time window for the definition of in-cloud stage is very
important for estimating the below cloud and in-cloud contributions.
Previous studies have estimated the concentrations of chemical ions scavenged
in-cloud based on the judgement that 5 mm of accumulated precipitation is
sufficient to identify the contribution of the in-cloud scavenging process
(Wang et al., 2009; Aikawa and Hiraki, 2009; Xu et al., 2017). Based on this
approach, the concentrations of NO3- and SO42- in cloud
in Japan were found to be 0.70 and 1.30 mg L-1, respectively (Aikawa and
Hiraki, 2009). In Beijing, high concentrations of NH4+,
SO42-, and NO3- in 2007 were found at
2.1–5.5, 3.1–14.9, and 1.5–5.9 mg L-1, respectively (Wang et al., 2009; Xu et al., 2017).
In this study, a new method based on fitting a curve to the chemical ion
concentrations with successive rainfall increments has been developed to
estimate the contribution of the in-cloud process. As shown in Fig. 1, an exponential curve is fitted to the median, 25th, and
75th percentiles of the chemical ion concentrations in each fraction
through the rainfall increments. Note that the fitted exponential curve is
applied to the combination of all 69 full events to estimate the yearly
median concentration of chemical ions in-cloud and to compare with the
results from the previously reported method (i.e. median concentration after 5 mm increments). In addition, the exponential approach to each unique event was
also employed. Ideally, the concentrations of chemical ions stabilize at
higher rainfall increments and this represents the concentrations in cloud.
However, the decrease during each rainfall event is distinctly different,
and this regression method is not fully applicable to all rainfall events in
practice. Therefore, the exponential regression method is used to estimate
the in-cloud concentrations under most circumstances, but where the
decreasing trend with the increment of rainfall is not significant, the
average value of rainfall increments 6–8 of the event is used. The below-cloud contributions to wet deposition of each species are then calculated
using the following equations (Eqs. 1–2):
1Wetdepbelow-cloud=∑i=1n(Ci-C¯)×Pi2Contributionbelow-cloud=Wetdepbelow-cloud∑i=1nCi×Pi,
where Ci and C¯ represent the concentration
of each chemical ion in fraction i in cloud, Pi
represents the volume of rainfall, and n represent the total fractions in
a rainfall event (equal to 8 in this study).
Concentrations of SO42-(a), NO3-(b), NH4+(c), and Ca2+(d) in each 1 mm fraction of rainfall
(i.e. F1#, F2#, …) over different rainfall events in the
observation periods. The red line shows an exponential fitting using the
50th percentile of the data and the red shading indicates the range
between the 25th and 75th percentiles.
Results and discussionInter-annual variations in chemical components
The Action Plan launched in 2013 is called “Ten rules” to improve the air
quality in China. It includes the comprehensive control of industrial emissions,
non-point emissions, fugitive dust, and vehicles emissions. It is also aimed at adjusting
and optimizing the industrial structure and promoting economic transformation
and upgrading, such as increasing the supply of clean energy. These actions
are ensured to work by both of legislation and market mechanisms. According
to the Beijing Environmental Statement published by the Beijing Municipal Environmental Protection Bureau
from 2013 to 2017, many measures have been implemented to meet the Action
Plan, including replacement of residential coal with electricity and natural
gas, upgrading the emission standards of gasoline, diesel vehicles, and power
plants, and closing high-emission enterprises. Significant declines in
atmospheric PM2.5 concentration have been observed nationwide between
2013 and 2017 during the Action Plan (Zhang et al., 2019). However, few
studies have investigated the benefits of the Action Plan for wet
deposition. A significant increase of NO3- in precipitation of
7.6 % was observed at a regional background station in North China between
2003 and 2014 (Pu et al., 2017). A decrease in the ratio of
SO42-/NO3-, mostly due to the decreasing of
SO42- and increasing of NO3-, suggests the transformation
of a sulfuric acid type to a mixed type of sulfuric and nitric acid in North
China. However, the updated record especially after the Action Plan is
important to assess the mitigation of the air pollutants not only in the
atmosphere but also in rainfall. A nationwide investigation of the wet
deposition of inorganic ions in 320 cities across China was recently made
based on observations between 2011 and 2016 from the National Acid
Deposition Monitoring Network (NADMN), which was established by the China
Meteorological Administration (Li et al., 2019). Briefly, both
SO42- and NO3- across China experienced significant
changes before and after 2014, with increases from 2011 to 2014 and then
decreases from 2014 to 2016.
In order to quantify the influence of the Action Plan on wet deposition in
Beijing, 4 years of observations of each rainfall event are considered in
this study. Figure 2 shows the volume weighted average (VWA) of inter-annual
mean concentrations of SO42-, NO3-, NH4+, and
Ca2+ observed in Beijing from 2014 to 2017 along with those reported
before 2010 from previous studies (Yang et al., 2012; Pan et al., 2012, 2013)
(more detail is provided in Table S1 in the Supplement). A
continuous decrease in VWA concentrations between 1995 and 2017 is found for
SO42-, with decreases of 3.1 % yr-1 in the earlier stage
(1995–2010) and decreases of 9.8 % yr-1 in the later stage
(2014–2017). This is consistent with the annual changes in its emission and
concentration as shown in Fig. 3, in which the emission and the
concentration data are collected from the annual Environmental Bulletin in Beijing from 1994 to
2017. It is clearly shown that the concentration of SO2 experienced a
sustainably decreasing trend due to significant reduction of its emission
from 1996 to 2017, with the decrease rate being 4.5 % yr-1 and 13.9 % yr-1 in emission and 2.8 % yr-1 and 14.0 % yr-1 in
concentration during the earlier stage and later stage (of the Action Plan
period), respectively. The significant declines in VWA concentration of
Ca2+ is found in precipitation with decrease rates of 36.1 % yr-1
in 1995–2010 and 8.8 % yr-1 in 2014–2017. The emission and
concentration data of Ca2+ are absent in this study. Instead, the
difference between the PM10 and PM2.5 (PM10–PM2.5) concentration
from 2013–2017 have been calculated to represent the coarse particles,
which mainly contain Ca2+. The results show that the
concentration decreased from 31.2 µgm-3 in 2013–2014 to 24.0 µgm-3 over 2015–2017. This indicates the improvement in coarse
particles, which are derived from crustal emissions, that has been made
through the Action Plan. As mentioned above, the Action Plan
including emission reduction is not only from the energy consumption of industry
but also the fugitive dust in cities, which should result in the decline of Ca2+. For NO3- and NH4+, increases are found during
the earlier stage (∼ 60 %) and decreases in the later stage
(12 % for NO3- and 25 % for NH4+). As for NOx
emissions, data have been collected in recent years. Although a clear
reduction is found in the annual changes in emissions from 2010, the ambient
concentration of NO2 do not show a significant decreasing trend
(∼ 3.6 % yr-1) compared with SO2 (14 % yr-1). However, before the Action Plan, the decreasing ratio in
concentration was only 1.8 % yr-1, which is slower than the Action
Plan period. Despite the increases of VWA NO3- in precipitation
during the earlier stage, the small decreases in the later stage could also be attributable to the Action Plan.
Time series of annual volume weighted average (VWA)
concentration and wet deposition of the four major components NH4+(a), Ca2+(b), SO42-(c), and NO3-(d) in
precipitation in Beijing.
For a better understanding of the impacts of acidification on ecosystems,
wet deposition fluxes of the four major ions in precipitation are also
plotted in Fig. 2. Similar variations are found as those presented in the VWA
of the four major ions. Observations of sulfur and nitrogen wet deposition (Pan et al.,
2012, 2013) from 2007–2010 show the value of 21.5 kg S ha-1 yr-1 and 27.9 kg N ha-1 yr-1 (19.7 and 8.2 kg N ha-1 yr-1 through NO3- and NH4+) in Beijing,
respectively. Compared with these results, significant decreases (11.4 kg S ha-1 yr-1 and 23.6 kg N ha-1 yr-1) were observed in
the 4 years of measurements from 2014–2017 in this study. All four
components in the later stage show significant decreases, suggesting that
the Action Plan, which was implemented over this period, has a substantial
impact. While Ca2+ and SO42- played a prominent role in
precipitation during the earlier stage before 2010, NH4+ and
NO3- became the primary components in the later stage after 2010.
It should be noted that NH4+ has a double role in environmental
pollution because it mitigates acid rain through neutralization, but also
acidifies the soil by nitrification. Hence, while sulfur in precipitation
has been further reduced under the Action Plan, additional attention is
needed for nitrogen to prevent deterioration of the environment by acid rain
resulting from nitrate and ammonium.
Annual changes in emission and concentration of SO2
and NOx in Beijing; data are collected from the annual Environmental Bulletin in Beijing from 1994 to 2017.
Relationship between concentration in precipitation and the
atmosphere
Wet deposition of a substance involves its removal from the associated air
mass. The scavenging ratio H can be estimated by comparing the monthly
average concentration in precipitation with that in the air (Okita et al.,
1996; Kasper-Giebl et al., 1999; Hicks, 2005; Yamagata et al., 2009). Xu et al. (2017) first calculated the rainfall event H based on the hourly
concentration of aerosol components measured with an Aerodyne Aerosol
Chemical Speciation Monitor (ACSM) and AIM-IC in 2014. In this study, 4 years of aerosol component observations were undertaken by AIM-IC.
Measurements made in the 6 h before each rainfall event are averaged to
represent the precondition of wet deposition precursors in the atmosphere.
Figure 4 shows the relationship between the major chemical ions in
precipitation and in the air. The VWA concentration of SO42-, NO3-, and NH4+ (hereafter SNA) as well as Ca2+ in
each rainfall event was calculated and compared with that in the first
1 mm rainfall fraction, F1#. As shown in Fig. 4, positive correlations
are found between the concentrations of ions in precipitation and in air,
with Pearson correlation coefficients (R) generally higher than 0.7
(p< 0.01). The concentration in the first fraction represents
a high proportion of below-cloud scavenging due to the washout of air
pollutants below cloud by the first rainfall, while the VWA represents a
greater contribution from in-cloud removal (Aikawa and Hiraki, 2009; Wang et
al., 2009; Xu et al., 2017). Thus, it is reasonable that the correlations are
stronger for the first fraction than for the VWA (see Table 1). This
indicates that the concentration of chemical ions in precipitation at the
start of rainfall is more greatly influenced by the air pollutants below the
cloud. As rainfall continues and below-cloud concentrations are reduced,
there is an increased contribution from in-cloud scavenging, which is less
influenced by aerosols in the surface layer. This is confirmed by the
substantial difference in the two R coefficients for the cation Ca2+ (0.85 for the first fraction, 0.47 for the VWA), which often
exists in coarse particles below cloud. For the fine particle
SO42-, which is present both in and below cloud (Xu et al., 2017),
the difference in the two R coefficients is small. The R coefficients for
NO3- and NH4+ show less difference than Ca2+ but
more difference than SO42-. This may relate to their complicate
sources from the ambient precursors. For example, the NO3- in
precipitation is both from the fine and coarse particles (i.e. particulate
NO3-) as well as the gaseous HNO3, while the NH4+
in precipitation is mainly from the fine particles in addition to NH3.
Correlation of the concentrations of major ions in air in the 6 h before rainfall with those in precipitation. Pearson correlation
coefficients are presented for monthly volume weighted average (VWA)
concentrations and for the first fraction (F1#) in each event.
Note: * and ** represent
significant correlations at p< 0.01 and p< 0.05, respectively.
Relationships between the concentration of NO3-(a), SO42-(b), NH4+(c), and Ca2+(d) in
precipitation and in air in the 6 h before each precipitation event. The red
squares and blue triangles represent the relationships between the
concentration of ions in air with that in F1# and VWA, respectively.
The slope of the linear fits in Fig. 4 can be used to calculate the
scavenging ratio W, which is the ratio of the ions concentration in
precipitation (mg L-1) and in air (µgm-3). The W ratio is
0.25 × 106, 0.16 × 106, and 0.15 × 106
for SO42-, NO3-, and NH4+,
respectively. This is similar to that reported for rainfall events in 2014
in Beijing (0.26 × 106, 0.35 × 106, and
0.14 × 106 for SNA; Xu et al. (2017)) and consistent with
those estimated in the eastern United States (0.11–0.38 × 106,
0.38–0.97 × 106, and 0.2–0.75 × 106 for SNA;
(Hicks, 2005)). Compared with SO42- and NH4+, the
scavenging ratio for NO3- shows larger differences between this
study and previous studies, corresponding to larger uncertainties to the R
between the concentrations of ions in precipitation and in air for VWA in
Fig. 4a (lower significance p< 0.05). It should be noted that the
W calculated in this study is based on the fine particles in air, which
may not represent the accurate reflection of the wet scavenging efficiency
of SNA. These uncertainties have been evaluated. For sulfur, gaseous SO2 was
considered to testify its role in the relationships. Figure S1 in the Supplement shows the
relationships between the concentration of SO42- in precipitation
and in air (SO42- in precipitation vs. SO42- and
SO42- in precipitation vs. SO2+SO42-). The
correlation coefficients R increased if the role of gaseous SO2 was
considered (the R of SO42- in precipitation vs. SO42- is 0.7
and the R of SO42- in precipitation vs. SO2+SO42- is
0.75). However, the scavenging ratio W was not changed, with the
difference being lower that 1 %. For nitrogen, the contribution of gaseous HNO3 to
total inorganic nitrate is less than 2 % in the North China Plain according to Zhai et al. (2021), which can be ignored in this study. According to more than one year of
measurements in Beijing (Tian et al., 2016), SO42-, NO3-,
and NH4+ in coarse particles account for 18 %, 27 %, and
10 %, respectively. The lower R coefficient for NO3- than for
SO42- and NH4+ in Fig. 4 is attributed to the absence
of considering NO3- in coarse particles. In addition, due to high
concentration of NH3 at ground surface over NCP (Pan et al., 2018), the
NH4+ in precipitation from gaseous NH3 cannot be ignored
(Kasper-Giebl et al., 1999). The ratio of
NH4+/(2SO42-+NO3-) in precipitation and in
PM2.5 was calculated. A lower ratio in precipitation than in
PM2.5 was found, with 0.95–1.01 in precipitation and 1.35 in air. This is due to the impacts of rich gaseous NH3 at ground surface going into the
precipitation by reacting with gaseous HNO3 and forming
NH4NO3 after (NH4)2SO4. Thus, the contribution of
coarse particles and gases to the relationships between sulfur and nitrogen compounds in
precipitation and the atmosphere is not as important as the fine particles,
except for NO3- in coarse particles and gaseous NH3, which
should be considered in the future.
Wet deposition can affect much of the atmospheric column through in-cloud
and below-cloud scavenging processes. The vertical column density (VCD) of
SO2 and NO2 from satellite retrieval from the 2000s to 2017 is used here to
compare with the inter-annual variations in wet deposition in Beijing
(Fig. S2 in the Supplement). Consistent variation of the VCD and the yearly VWA
concentration in precipitation is found for sulfur and nitrogen. A continuous decrease is
found in VCD SO2 from 2005 to 2017, matching the trend in
SO42- deposition, while VCD NO2 shows an increase from
2001 to 2011, a decrease after 2011, and little change over the
2014–2017 period. This implies that the Action Plan not only benefits air
pollutants in the surface layer but also those in the total column. Due to
faster decreases in emissions of sulfur than nitrogen (Zheng et al., 2018), the ratio of
S/N in both precipitation (SO42-/NO3-, µeq.L-1) and
air (SO42-/NO3-, µgm-3) are found to decrease,
with the change in ratio in precipitation at 17.5 % yr-1, 11 % yr-1, and 20.0 % yr-1 from 1995–2010, 2014–2017, and 1995–2017,
and in air at 12 % yr-1 from 2014–2017, respectively (see Fig. S3 in the Supplement). This is also consistent with the trend reported for all of China from
2000–2015 by Itahashi et al. (2018). The ratio of S/N in precipitation is a
useful index to investigate the relative contributions of these acidifying
species. In addition, the ratio of NH4+/NO3- is
investigated here and a clear decrease is found from 2014–2017 both in
precipitation and in air. This indicates that NH4+ is decreasing
faster than NO3-. This evidence clearly confirms that nitrate
should be the major target for air pollution controls in the next action
plan.
Proportion of below-cloud scavenging
As described in Sect. 2.3, the in-cloud ion concentration (C¯, in
Eq. 1) can be derived from the exponential fit of the observed rainwater
concentrations. Table 2 lists the asymptote value and the exponential
fitting equation of the evolution of each ion concentration in precipitation
with the increment of rainfall. As shown, the asymptote value (hereafter,
exponential approach) based on the median data for SO42-,
NO3-, and NH4+ was 3.18, 2.32, and 1.39 mg L-1,
respectively. The SO42- and NO3- are within the range of
reported in-cloud concentrations for Beijing (3.33 and 2.75 mg L-1 for
SO42- and NO3-; Xu et al., 2017), while the
NH4+ in this study is lower than previous studies (2.51 mg L-1 in Xu
et al., 2017 and 2.1–4.5 mg L-1 in Wang et al., 2009). In-cloud concentrations
for other ions, i.e. Ca2+, F-, Cl-, Na+, K+, and
Mg2+, are 0.67, 0.04, 0.27, 0.1, 0.06, and 0.08 mg L-1, respectively. For comparison, the average concentration in fractions 6
to 8 (F6#–F8#) in each rainfall event (hereafter,
average approach) is used to estimate the in-cloud concentration for events
where successive rainwater concentrations do not show an obvious decrease or
where other factors such as precipitation intensity are important (see Table 2). Similar results are found for most ions with the exponential and average
approach except for NH4+, F-, K+, and Mg2+, where the maximum difference is less than 20 % (Table 2). Thus, the
replacement of in-cloud concentration by the average value is acceptable for
SO42-, NO3-, Ca2+, Cl-, and Na+ but there is much
uncertainty for the other ions. It is worth noting that for all ions the
average approach gives higher estimates of in-cloud concentrations, and this
can be recognized as an upper limit for in-cloud concentrations. It is also
important to note that the increased concentrations of ions in the latter
fractions were observed in few events in this study. This may due to the
unique meteorological conditions and air pollutants movement during each
precipitation event. Despite the longer precipitation fractions collected in this study, even longer fraction measurements and more detailed analysis on
the uncertainties are needed in the future. The influences of meteorological
conditions (i.e. rainfall type and intensity) are discussed in Sect. 4.
Exponential fitting for the concentrations of major ions in
different fractions of rainfall and the contribution of below-cloud
scavenging to total deposition.
a Fitting for the median of each fraction in different rainfall
events. b Below-cloud portion calculated based on the fitting curve.
c Below-cloud portion calculated based on the average value of
fractions 6 to 8 (F6#–F8#) in rainfall events. d Difference in concentrations between adjacent 1 mm increments after 5 mm
accumulated precipitation.
The model study in Japan showed consistent fractions of in-cloud and
below-cloud scavenging to total wet deposition between simulated and
observed values, except for one site, which is the region of high emission flux
of SO2. In this region, the simulated below-cloud scavenging
contribution was apparently greater than the observed results. Specifically,
the model shows that the SO2 and HNO3 gases dominantly contributed to
the below-cloud scavenging of SO42- and NO3- in the
regions of high emission flux of SO2, while the aerosol removal was
dominated by the in-cloud scavenging process. In their model setup, all of
below-cloud gaseous SO2 was assumed to be dissolved into raindrops and fully oxidized to SO42-. However, as suggested by Seinfeld and
Pandis (2006), the aqueous equilibrium between ambient gas and precipitation
cannot be assumed due to the relatively short residence times of falling
precipitation. Thus, the assumptions used in Kajino and Aikawa (2015) might
overestimate the contribution of gaseous SO2 to below-cloud scavenging.
In addition, considering the high concentration of particles (60–90 µgm-3 in mass concentration) below-cloud in Beijing, the gaseous compounds may not be
as important as in the Japan simulation. According to the Environmental Bulletin in Beijing from 1994 to 2017, the annual concentration of SO2 has dramatically decreased from 26.5 µgm-3 in 2013 to 8 µgm-3 in 2017. This relatively low-level concentration of SO2 at the
surface may not contribute a dominant role in wet deposition of
SO42-. Similarly for NO3-, the ratio of gas-phase
HNO3 and the total NO3- in the summer in Beijing is only 0.12
according to the measurement study of Yue et al. (2013). The fraction of total
inorganic nitrate as particulate nitrate instead of gaseous nitric acid over
the NCP increased from 90 % in 2013 to 98 % in 2017 (Zhai et al., 2021),
which means the gaseous nitric acid has been consumed by high levels of
ammonia concentrations. We assumed a 10 % ratio of gases added into the
washout process, which only leads to less than 5 % difference of below-cloud
scavenging contribution to total wet depositions. Regardless,
there might be larger uncertainties for NH3 due to the high concentration of
NH3 at ground surface over NCP (Pan et al., 2018). Kasper-Giebl et al. (1999) reported that 49 %–79 % of NH4+ in precipitation is from
particulate ammonium, which indicates that large uncertainties in the contributions
from gases still exist in the form of NH4+ wet deposition. The
uncertainties are mainly from the indistinct window for the in-cloud
scavenging judgement due to high concentration of gaseous NH3 at ground
surface, which is not easy to be scavenged completely during the short time
fraction measurements. This is also confirmed by the larger difference in
below-cloud contribution to NH4+ wet deposition than other ions
estimated by the exponential approach and the average approach in Table 2.
As mentioned above, more and longer fraction measurements as well as studies on the
influence of NH3 to NH4+ wet deposition are needed in the
future.
Following Eq. (2), the contributions of below-cloud scavenging to the wet
deposition in each rainfall event from 2014–2017 are estimated from the
in-cloud concentration. Figure 5 shows the yearly VWA of SNA and Ca2+
and the in-cloud and below-cloud contributions. The ratio of below-cloud
contribution to the four major components based on the yearly median value
of the in-cloud concentration is also shown in Fig. 5. Benefiting from the
Action Plan, the air quality at the surface layer have been significantly
improved (Zhang et al., 2019), which in turn leads to the decreases in the
below-cloud scavenging. In this study, it also shows that the below-cloud
contributions of SO42-, NO3-, NH4+, and
Ca2+ decrease from > 50 % in 2014 to ∼ 40 % in 2017. In 2017, the contribution of below-cloud scavenging declines
to lower than 40 % for SO42- and NH4+, but remains at
44 % for NO3-. Over the four-year period from 2014–2017, the average
yearly wet deposition for all ions and the below-cloud wet scavenging
contributions are given in Table 2. Similar to the concentrations in
precipitation, the wet deposition of SO42-, NO3-, and
NH4+ decreased from 21.5 kg S ha-1 yr-1, 8.9, and 19.0 kg N ha-1 yr-1 from 2007–2010 (Pan et al., 2012, 2013) and to 11.4 kg S ha-1 yr-1 (3.42 × 103 mg m-2 yr-1), 6.9,
and 16.7 kg N ha-1 yr-1 (3.05 × 103 and 2.15 × 103 mg m-2 yr-1) from 2014–2017, respectively. Below-cloud
scavenging contributed to almost half of total deposition estimated with the
exponential approach (50 %–60 %), which is higher than the average
approach (40 %–50 %).
The annual volume weighted average below-cloud and
in-cloud portion of SO42-(a), Ca2+(b), NO3-(c),
and NH4+(d) from 2014–2017. The ratio of annual median
below-cloud contribution for each component is represented as the black line
in each panel. The #M and #A marks in the below-cloud ratio
represent the estimation based on the median value and average value of
in-cloud concentration in each year, while the first quartile and the third
quartiles are also included in the figure.
Factors influencing below-cloud scavenging
Each precipitation event is unique in terms rainfall intensity, droplet
sizes and distribution, rainfall type (thunderstorms or deep convective
scavenging), air concentrations of chemical components, etc. The unique
characterization of each precipitation event was considered in calculation
of the proportions from in-cloud and below-cloud processes, as the
exponential approach to each unique event was made. The below-cloud
proportions varied from 20 % to 80 % among the 69 rainfall events. The
influence of these factors affecting wet scavenging were investigated
through the correlation analysis between below-cloud proportions with the
rainfall type as well as the rainfall intensity.
Rainfall type
The rainfall over the North China Plain in summertime is usually determined
by the synoptic system such as the upper-level trough or the cold vortex.
The 69 rainfall events have been classified based on the synoptic system
according to records from the Beijing Meteorological Service (http://bj.cma.gov.cn, last access: 17 June 2021) with 33 events associated with upper-level troughs,
23 events associated with a cold vortex, and 13 events associated with other
systems. Figure 6 shows the contributions of below-cloud scavenging for the
two major systems. A high contribution from below-cloud scavenging is found
for rainfall events associated with an upper-level trough with the median
contributions for SO42-, NO3-, NH4+, and
Ca2+ of 56.2 %, 62.1 %, 56.3 %, and 61.9 %, respectively. In the
contrast, the contributions during rainfall events under cold vortex
conditions are significantly lower, with the values of 42.2 %, 44.5 %,
41.7 %, and 53.9 %, respectively. Rainfall events associated with an
upper-level trough are usually accompanied by orographic or frontal
precipitation and are characterized by long and continuous precipitation
(Shou et al., 2000). This suggests that below-cloud scavenging of chemical
components is important for this rainfall type due to air mass transport
from outside Beijing. In contrast, rainfall events associated with a cold
vortex usually originate from strong thermal convection and are
characterized by short heavy rainfall (Zhang et al., 2008; Liu et al.,
2016; Zheng et al., 2020). This is common during the summer months in Beijing
with deep convective clouds (Yu et al., 2011; Gao and He, 2013) and suggests
that there is a large contribution from in-cloud scavenging to the total wet
deposition.
Contribution of below-cloud scavenging during rainfall
events associated with different synoptic conditions.
Precipitation intensity and rainfall volume
To illustrate the impacts of rainfall on below-cloud aerosol scavenging, the
relationship between the below-cloud fraction and the rainfall volume and
precipitation intensity are investigated (see Fig. 7). Negative
correlations in the below-cloud fraction are found for both the rainfall volume
and precipitation intensity, although the relationship with the former is
stronger (R: 0.63–0.93 vs. 0.03–0.64). This is
consistent with the results for 2014 in Beijing reported by Xu et al. (2017).
Atmospheric particles are efficiently removed below cloud by washout at the
beginning of precipitation events (almost 70 % of SNA is removed in the
first two to three fractions, as shown in Fig. 1). As the rainfall progresses,
in-cloud scavenging makes an increasingly important contribution as
below-cloud aerosol concentrations fall. Xu et al. (2017) found that heavy
summertime rainfall events with more than 40 mm of rainfall usually occur
over very short periods of time, usually 2–3 h. This heavy rainfall leads to
the scavenging of aerosols in a relatively localized region and prevents the
compensation associated with transport of air pollutants from outside the
region during longer-duration light rainfall events. This contributes to the
decreased contribution of below-cloud scavenging during the high intensity
rainfall events.
Contribution of below-cloud scavenging in events with
different rainfall volume and precipitation intensity.
Conclusions
This paper presents an analysis of below-cloud scavenging from 4 years of
sequential sampling of rainfall events in Beijing from May of 2014 to
November of 2017. The concentration of ions in precipitation varied
dramatically, with yearly volume weighted averaged concentrations of
SO42-, NO3-, NH4+, and Ca2+ decreasing by
39 %, 12 %, 25 %, and 35 % between 2014 and 2017, respectively. Due
to faster decreases in SO42- than NO3-, both in
precipitation and in the air during the observation period, there is a
significant decrease in the S/N ratio in precipitation at 44 % and in air at
48 %. Benefiting from the national Air Pollution Prevention and Control
Action Plan, sulfur has been further reduced, while nitrogen,
especially nitrate, needs further attention in the next action plan to
prevent deterioration of the environment associated with acid rain and
photochemical pollution.
A new method has been developed and employed to estimate the below-cloud
contribution to wet deposition in Beijing. The new approach suggests that
the contribution from below-cloud scavenging is greater than that estimated by
applying simpler approaches used in previous studies. Overall, the
contribution of below-cloud scavenging to the wet deposition of the four
major components is important at 50 %–60 %. The contribution
of below-cloud scavenging shows a decrease over the 2014–2017 period for
Ca2+, SO42-, and NH4+, but little change for
NO3- from 2015–2017. Below-cloud scavenging also has a strong
cleansing effect on air pollution, and the hourly concentration of
PM2.5 is found to decrease sharply as the rainfall events occur, even
when the effects from wind have been accounted for.
Rainfall types also influence the contribution of below-cloud scavenging.
Seventy-five rainfall events during the four-year period were classified
based on the local synoptic conditions. Lower contributions from below-cloud
scavenging (∼ 40 %) are found for the four major ions in
rainfall events associated with a cold vortex, while higher contributions
(∼ 60 %) are associated with an upper-level trough.
Precipitation volume and intensity both show a negative correlation with the
below-cloud fraction. This suggests that atmospheric particles are
efficiently removed via below-cloud scavenging processes at the beginning of
precipitation events. As the event progresses, rainfall in the later
fractions shows a greater contribution from in-cloud scavenging processes as
aerosols in the surface layer have already been removed. To better
understand the mechanism of below-cloud scavenging processes, high-resolution measurements both in precipitation and in the air, especially at
the beginning of rainfall events, are needed in the future.
Data availability
To request the observed data for scientific research purposes, please contact
Baozhu Ge at the Institute of Atmospheric Physics, Chinese Academy of
Sciences, via email (gebz@mail.iap.ac.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-9441-2021-supplement.
Author contributions
BG and ZW designed the whole structure of this work and prepared the
manuscript with contributions from all co-authors. DX, XY, JW, and QT helped
with the data processing. OW, XC, and XP were involved in the scientific
interpretation and discussion.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the CNEMC for providing the data of the six criteria pollutants in
Beijing. We also acknowledge the Beijing Municipal Environmental Monitoring
Center for providing the aerosol components data in Beijing.
Financial support
This work is supported by the National Natural Science Foundation of China (grant nos. 41877313, 91744206, 41620104008), the National Key Research and Development Plan (grant no. 2018YFC0830802), the Priority Research Program of CAS (grant no. XDA19040204) and the UK Natural Environment Research Council (NE/N0006925/1).
Review statement
This paper was edited by Leiming Zhang and reviewed by two anonymous referees.
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