Aerosol acidity plays a key role in secondary aerosol formation. The
high-temporal-resolution PM2.5 pH and size-resolved aerosol pH in
Beijing were calculated with ISORROPIA II. In 2016–2017, the mean PM2.5
pH (at relative humidity (RH) > 30 %) over four seasons was
4.5±0.7 (winter) > 4.4±1.2 (spring) > 4.3±0.8 (autumn) > 3.8±1.2 (summer), showing
moderate acidity. In coarse-mode aerosols, Ca2+ played an important
role in aerosol pH. Under heavily polluted conditions, more secondary ions
accumulated in the coarse mode, leading to the acidity of the coarse-mode
aerosols shifting from neutral to weakly acidic. Sensitivity tests also
demonstrated the significant contribution of crustal ions to PM2.5 pH.
In the North China Plain (NCP), the common driving factors affecting
PM2.5 pH variation in all four seasons were SO42-, TNH3
(total ammonium (gas + aerosol)), and temperature, while unique factors
were Ca2+ in spring and RH in summer. The decreasing SO42-
and increasing NO3- mass fractions in PM2.5 as well as
excessive NH3 in the atmosphere in the NCP in recent years are the
reasons why aerosol acidity in China is lower than that in Europe and the
United States. The nonlinear relationship between PM2.5 pH and
TNH3 indicated that although NH3 in the NCP was abundant, the
PM2.5 pH was still acidic because of the thermodynamic equilibrium
between NH4+ and NH3. To reduce nitrate by controlling
ammonia, the amount of ammonia must be greatly reduced below excessive
quantities.
Introduction
Aerosol acidity has a significant effect on secondary aerosol formation
through the gas–aerosol partitioning of semi-volatile and volatile species
(Eddingsaas et al., 2010; Surratt et al., 2010; Pathak et al., 2011; Guo et
al., 2016). Studies have shown that aerosol acidity can promote the
generation of secondary organic aerosols by affecting aerosol acid-catalyzed
reactions (Rengarajan et al., 2011). Moreover, metals can become soluble by
acid dissociation under low aerosol pH (Shi et al., 2011; Meskhidze et al.,
2003; Fang et al., 2017) or by forming ligands with organic species, such as
oxalate, at higher pH (Schwertmann et al., 1991). The investigation of
aerosol acidity is conducive to better understanding the important role of
aerosols in acid deposition and atmospheric chemical reactions.
Aerosol acidity is frequently estimated by the charge balance of measurable
cations and anions. Nevertheless, not all ions (even trace ones) are well
constrained in the observations and the dissociation state of multivalent
ions is unclear. Ion balance and other similar proxies fail to represent
the in situ aerosol pH because such metrics cannot accurately predict the
H+ concentration in the aerosol liquid phase (Guo et al., 2015;
Hennigan et al., 2015). To better understand the in situ aerosol pH, the
aerosol liquid water content (ALWC) and hydrogen ion concentration per
volume air (Hair+) should be determined (Guo et al., 2015).
Most inorganic ions and some organic acids in aerosols are water soluble
(Peng, 2001; Wang et al., 2017). Since the deliquescence relative humidity
(DRH) and the efflorescence relative humidity (ERH) of mixed salts are lower
than that of any single component, ambient aerosols are generally in the
form of droplets containing liquid water (Seinfeld and Pandis, 2016). ALWC
can be derived from hygroscopic growth factors or calculated by
thermodynamic models, and good consistencies in ALWC have been found among
these methods (Engelhart et al., 2011; Bian et al., 2014; Guo et al., 2015).
However, Hair+ can only be obtained by thermodynamic models,
which offer a more precise approach to determine aerosol pH (Nowak et al.,
2006; Fountoukis et al., 2009; Weber et al., 2016; Fang et al., 2017). Among
these thermodynamic models, ISORROPIA II is widely used owing to its
rigorous calculation, performance, and computational speed (Guo et al.,
2015; Fang et al., 2017; Liu et al., 2017; Galon-Negru et al., 2018).
The North China Plain (NCP) is the region with the most severe aerosol
pollution in China. Nitrate and sulfate are the major contributors to haze,
and their secondary formation processes are determined in large part by
aerosol pH (Zou et al., 2018; Huang et al., 2017; Gao et al., 2018).
Therefore, understanding the aerosol pH level in this region is extremely
important and has recently become a trending topic. Fine aerosol pH reported
in the NCP (Liu et al., 2017; Song et al., 2018; Shi et al., 2017, 2019) was higher than that found in the United States or Europe, where
aerosols are often highly acidic with a pH lower than 3.0 (Guo et al., 2015,
2016; Bougiatioti et al., 2016; Weber et al., 2016; Young et al., 2013). The
differences in aerosol pH in the NCP arise from (1) different methods or
different model settings, (2) variations in PM2.5 chemical composition
in the NCP in recent years, (3) the levels of gas precursors of the main
water-soluble ions (NH3, HNO3, and HCl), and (4) differences in
ambient temperature and RH. Studies demonstrated that pH diurnal variations
are largely driven by meteorological conditions (Guo et al., 2015, 2016;
Bougiatioti et al., 2016). In the NCP, a comprehensive understanding of the
impacts of these factors on aerosol pH is still poor.
Additionally, most studies on aerosol pH focus on PM1 or PM2.5.
Knowledge regarding size-resolved aerosol pH is still rare (Fang et al.,
2017; Craig et al., 2018). Aerosol chemical compositions are different among
multiple size ranges. Among inorganic ions, SO42-, NO3-,
Cl-, K+, and NH4+ are mainly concentrated in the fine
mode except on dusty days (Meier et al., 2009; Pan et al., 2009; Tian et
al., 2014), whereas Mg2+ and Ca2+ are abundant in the coarse mode
(Zhao et al., 2017). Aerosol pH can be expected to be diverse among
different particle sizes; pH levels at different sizes may be associated
with different formation pathways of secondary aerosols.
To better understand the driving factors of aerosol acidity, in this work,
the thermodynamic model ISORROPIA II was utilized to predict aerosol pH in
Beijing based on a long-term online high-temporal-resolution dataset and a
size-resolved offline dataset. The hourly measured PM2.5 inorganic ions
and precursor gases in four seasons from 2016 to 2017 were used to analyze
the seasonal and diurnal variations in aerosol acidity; samples collected by
multistage cascade impactors (MOUDI-120) were used to estimate the pH
variations among 10 different size ranges. Additionally, a sensitivity
analysis was conducted to identify the key factors affecting aerosol pH and
gas–particle partitioning. The main purposes of this work are to (1) obtain
the PM2.5 pH level based on an online measurement, contributing towards
a global pH dataset; (2) investigate the size-resolved aerosol pH, providing
useful information for understanding the formation processes of secondary
aerosols; and (3) explore the main factors affecting aerosol pH and
gas–particle partitioning, which can help explain the possible reasons for
pH divergence in different works and provide a basis for controlling
secondary aerosol generation.
Data collection and methodsSite
The measurements were performed at the Institute of Urban Meteorology in the
Haidian district of Beijing (39∘56′ N, 116∘17′ E). The
site is located next to a high-density residential area, without significant
nearby air pollution emissions. Therefore, the observation data represent
the air quality levels of the urban area of Beijing.
Online data collection
Water-soluble ions (SO42-, NO3-, Cl-, NH4+, Na+, K+,
Mg2+, and Ca2+) in PM2.5 and gaseous
precursors (HCl, HNO3, HNO2, SO2, and NH3) in ambient
air were measured by an online analyzer (MARGA) with hourly temporal
resolution during spring (April and May 2016), winter (February 2017),
summer (July and August 2017), and autumn (September and October 2017). More
details about MARGA can be found in Rumsey et al. (2014) and Chen et al. (2017). The PM2.5 and PM10 mass concentrations (TEOM 1405-DF),
hourly ambient temperature, and RH were also synchronously obtained. The
hourly concentrations of PM2.5, PM10, and major secondary ions
(SO42-, NO3-, and NH4+) in PM2.5, as well as
meteorological parameters during the observations, are shown in Fig. 1. In
the spring, two dust events occurred (21 April and 6 May). In the following
pH analysis based on MARGA data, it was assumed that the particles were
internally mixed; hence, these two dust events were excluded from this
analysis.
Time series of relative humidity (RH) and temperature (T) (a, e,
i, m); PM2.5, PM10, and NH3 (b, f, j, n); dominant
water-soluble ions: NO3-, SO42, and NH4+ (c, g, k, o); and
PM2.5 pH colored by PM2.5 concentration (d, h, l, p) over four
seasons.
Size-resolved chemical composition
A micro-orifice uniform deposit impactor (MOUDI-120) was used to collect
size-resolved aerosol samples with calibrated 50 % cut sizes of 0.056,
0.10, 0.18, 0.32, 0.56, 1.0, 1.8, 3.1, 6.2, 9.9, and 18 µm.
Size-resolved sampling was conducted 12–18 July 2013, 13–19 January 2014,
3–5 July 2014, 9–20 October 2014, and 26–28 January 2015. A total of 15,
14, and 18 sets of samples were obtained in summer, autumn, and
winter, respectively. Except for two sets of samples, all the samples were
collected in daytime (from 08:00 to 19:00 LST) and nighttime (from 20:00 to 07:00 LST
the next day). A total of 1 h of preparation time was allowed for filter changing
and washing the nozzle plate with ethanol. The water-soluble ions in the
samples were analyzed by using ion chromatography (DIONEX ICS-1000).
Detailed information about the features of MOUDI-120 and the procedures of
sampling, pre-treatment, and laboratory chemical analysis (including quality
assurance and quality control) were described in our previous papers (Zhao
et al., 2017; Su et al., 2018).
Aerosol pH prediction
Aerosol pH can be predicted by thermodynamic models such as AIM and
ISORROPIA (Clegg et al., 1998; Nenes et al., 1998). AIM is considered an
accurate benchmark model, while ISORROPIA has been optimized for use in
chemical transport models. Currently, ISORROPIA II, with the addition of K+,
Mg2+, and Ca2+ (Fountoukis and Nenes, 2007),
can calculate the equilibrium Hair+ and ALWC with reasonable
accuracy by using the water-soluble ion mass concentration, temperature (T),
and RH as input. Hair+ and ALWC were then used to predict aerosol
pH by Eq. (1).
pH=-log10Haq+≅-log101000Hair+ALWCi,
where Haq+ (mol L-1) is the hydronium ion concentration in
the ambient particle liquid water. Haq+ can also be calculated
as Hair+ (µg m-3) divided by the concentration of ALWC
associated with inorganic species, ALWCi (µg m-3). Both the
inorganic species and part of the organic species in particles are
hygroscopic. However, pH prediction is not highly sensitive to water uptake
by organic species (ALWCo) (Guo et al., 2015, 2016). In recent years, the
fraction of organic matter in PM2.5 in the NCP was
20 %–25 %, which is much lower than that in the United
States (Guo et al., 2015). In contrast, approximately 50 % of PM2.5
in the NCP is inorganic ions (Huang et al., 2017; Zhang et al., 2018, 2019). The results obtained by Liu et al. (2017) in Beijing showed
that the mass fraction of organic-matter-induced particle water accounted
for only 5 % of total ALWC, indicating a negligible contribution to
aerosol pH. Hence, aerosol pH can be fairly well predicted by ISORROPIA II
with only measurements of inorganic species in most cases. However,
potential errors can be incurred by ignoring ALWCo in regions where
hygroscopic organic species have a relatively high contribution to fine
particles.
In ISORROPIA II, forward and reverse modes are provided to predict ALWC and
Hair+. In forward mode, T, RH, and the total (i.e., gas + aerosol)
concentrations of NH3, H2SO4, HCl, and HNO3 need to be
input. In reverse mode, equilibrium partitioning is calculated given only
the concentrations of aerosol components, RH, and T as input. In this work,
the online ion chromatography system MARGA was used to measure both
inorganic ions in PM2.5 and gaseous precursors. Moreover, the forward
mode has been reported to be less sensitive to measurement error than the
reverse mode (Hennigan et al., 2015; Song et al., 2018). Hence, ISORROPIA II
was run in forward mode for aerosols in the metastable conditions in this
study.
When using ISORROPIA II to calculate the PM2.5 acidity, all particles
were assumed to be internally mixed, and the bulk properties were used
without considering the variability in chemical composition at a given
particle size. In the ambient atmosphere, the aerosol chemical composition
is complicated; hence, the deliquescence relative humidity (DRH) of aerosols
is generally low (Seinfeld and Pandis, 2016). Once the particles are
deliquescent, crystallization only occurs at a very low RH, which is called the
hysteresis phenomenon. The efflorescence RH (ERH) of a salt cannot be
calculated from thermodynamic principles; rather, it must be measured in the
laboratory. For a particle consisting of approximately 1:1(NH4)2SO4:NH4NO3, the ERH is around 20 %,
while for a 1:2 molar ratio it decreases to around 10 % (Shaw and Rood,
1990). Recently, NO3- has dominated the particles in the NCP (Zhao et
al., 2013, 2017; Huang et al., 2017; Ma et al., 2017); therefore, we assumed
that the particles are in a liquid state (metastable condition). Assumptions
that particles are metastable were adopted by numerous studies in the NCP
(Liu et al., 2017; Guo et al., 2017; Shi et al., 2017, 2019).
Figures 2 and S1–S4 in the Supplement show comparisons between the predicted and
measured NH3, HNO3, HCl, NH4+, NO3-, Cl-,
ε(NH4+) (NH4+/ (NH3+NH4+),
mol / mol), ε(NO3-)
(NO3-/ (HNO3+NO3-), mol / mol)), and ε(Cl-) (Cl-/ (HCl+Cl-), mol / mol) based on real-time ion
chromatography data; all results are colored with the corresponding RH. The
predicted and measured NH3, NH4+, NO3-, and
Cl- values are in good agreement: the R2 values of linear
regressions are all higher than 0.94, and the slopes are approximately 1.
Moreover, the agreement between the predicted and measured ε(NH4+) is better than that of ε(NO3-) and
ε(Cl-). The slope of the linear regression between the
predicted and measured ε(NH4+) was 0.93, 0.91, 0.95,
and 0.96 and R2 was 0.87, 0.93, 0.89, and 0.97 in spring, winter,
summer, and autumn, respectively. However, the measured and predicted
partitioning of HNO3 and HCl show significant discrepancies (R2
values of 0.28 and 0.18, respectively), which may be attributed to the much
lower gas concentrations than particle concentrations, as well as the
HNO3 and HCl measurement uncertainties from MARGA (Rumsey et al.,
2014). Clearly, more scatter points deviate from the 1:1 line when
ISORROPIA II is operated at RH ≤ 30 %, which is highly evident in
winter and spring. It should be noted that when RH is low, ALWC becomes very
small, and PM2.5 pH is subject to considerably more uncertainty. Guo et al. (2016) suggest that the lower RH limit is about 40 %. In this work, due to
the overall good agreement between predictions and measurements when RH was
higher than 30 %, we only determined the PM2.5 pH for data with RH
higher than 30 %.
Comparisons of predicted and measured NH3, HNO3, HCl,
NH4+, NO3-, Cl-, ε(NH4+),
ε(NO3-), and ε(Cl-) colored by RH.
In this figure, data from all four seasons were combined; comparisons of
individual seasons are shown in Figs. S1–S4 in the Supplement.
Running ISORROPIA II in the forward mode with only aerosol component
concentrations as input may result in a bias in predicted pH due to
repartitioning of ammonia in the model, leading to a lower predicted pH when
gas-phase data are not available (Hennigan et al., 2015). In this work, no
synchronous gas phase was available during the MOUDI sampling periods; the
gas-phase measurements that were taken by the MARGA in 2017 were therefore
applied. Even if the periods were not perfectly aligned, the order of
magnitude of NH3, HNO3, and HCl during a certain period did not
change drastically. Guo et al. (2017) found that even if there was some
error in NH3, pH was less sensitive to it; a change with a factor of 10
in NH3 was required to change pH by 1 unit. Averaged values of
NH3, HNO3, and HCl measured by MARGA matched to PM2.5 mass
concentration levels during the MOUDI sampling periods. Together with ion
concentrations of samples collected by MOUDI, the average RH and T
during each sampling period were used to determine the aerosol pH for
different size ranges. Similar to calculating the PM2.5 pH, it was
assumed that all the particles in each size bin were internally mixed and
had the same pH.
Comparisons of the measured and predicted NO3-, NH4+, and
Cl- for MOUDI samples are shown in Fig. 3. The measured and predicted
NO3-, NH4+, and Cl- agreed very well in fine-mode
particles; the slopes are approximately 1. In the coarse mode, the predicted
NH4+ was lower than the measured NH4+ due to the impact
of crustal ions.
Comparisons of predicted and measured NH4+,
NO3-, and Cl- colored by particle size. In this figure, all
MOUDI data were combined.
Sensitivity of PM2.5 pH to SO42-, TNO3,
TNH3, Ca2+, RH, and T
To explore the major influencing factors on aerosol pH, sensitivity tests
were performed. In the sensitivity analysis, SO42-, TNO3
(total nitrate (gas + aerosol) expressed as equivalent HNO3), TNH3
(total ammonium (gas + aerosol) expressed as equivalent NH3),
Ca2+, RH, and T were selected as the variables since SO42-
and NO3- are major anions in aerosols, NH4+ and
Ca2+ are major cations in aerosols, and Ca2+ is generally
considered representative of crustal ions. To assess how a variable affects
PM2.5 pH, the real-time measured values of this variable and the
average values of other species (K, Na, Mg, and total chloride
(gas + aerosol) were also included) in each season were input into
ISORROPIA II. The magnitude of the relative standard deviation (RSD) of the
calculated aerosol pH can reflect the impact of variable variations on
aerosol acidity. The higher the RSD is, the greater the impact, and vice
versa. The average value and variation range for each variable in the four
seasons are listed in Table S1 in the Supplement.
The sensitivity analysis in this work was only aimed at PM2.5 (i.e., fine
particles) since the MARGA system equipped with a PM2.5 inlet had a
high temporal resolution (1 h). In addition, the dataset had a wide range,
covering different levels of haze events. The sensitivity analysis in this
work only reflected the characteristics during the observation periods, and
further work is needed to determine whether the sensitivity analysis is
valid in other environments.
Results and discussionOverall summary of PM2.5 pH over four seasons
The average mass concentrations of PM2.5 and major inorganic ions in
the four seasons are shown in Table 1. Among all the ions measured,
NO3-, SO42-, and NH4+ were the three most
dominant species, accounting for 83 %–87 % of the total
ion content. The average concentrations of primary inorganic ions (Cl-, Na+,
K+, Mg2+, and Ca2+) were higher in spring
than in other seasons. PM2.5 in Beijing showed moderate acidity, with
PM2.5 pH values of 4.4±1.2, 4.5±0.7, 3.8±1.2, and
4.3±0.8 for spring, winter, summer, and autumn observations,
respectively (data at RH ≤ 30 % were excluded). The overall winter
PM2.5 pH was comparable to the result (4.2) found in Beijing by Liu et al. (2017) and that (4.5) found by Guo et al. (2017), but lower than that
(4.9, winter and spring) in Tianjin (Shi et al., 2017), another megacity
approximately 120 km away from Beijing. The PM2.5 pH in summer was
lowest among all four seasons. The seasonal variation in PM2.5 pH in
this work was similar to the results in Tan et al. (2018), except for
spring, and followed the trend of winter (4.11±1.37) > autumn (3.13±1.20) > spring (2.12±0.72) > summer (1.82±0.53).
Average mass concentrations of NO3-, SO42, NH4+,
and PM2.5, as well as ALWC, Hair+, and PM2.5 pH, under
clean, polluted, and heavily polluted conditions over four seasons.
To further investigate the PM2.5 pH level under different pollution
conditions over four seasons, the PM2.5 concentrations were classified
into three groups: 0–75, 75–150, and > 150 µg m-3, representing
clean, polluted, and heavily polluted conditions, respectively. The
relationship between PM2.5 concentration and pH is shown in Fig. S5.
The PM2.5 pH under clean conditions spanned 2–7, while
that under polluted and heavily polluted conditions was mostly concentrated
from 3 to 5. Table 1 shows that as the air quality deteriorated,
the aerosol component concentration as well as ALWC and Hair+
all increased in each season. The average PM2.5 pH under clean
conditions was the highest (Table 1), followed by polluted and heavily
polluted conditions in spring, summer, and autumn. In winter, however, the
average pH under polluted conditions (4.8±1.0) was the highest.
On clean days, some higher PM2.5 pH values (> 6) appeared
and were generally accompanied by a higher mass fraction of crustal ions
(Mg2+ and Ca2+). In contrast, lower
PM2.5 pH (< 3) was often accompanied by a higher mass fraction
of SO42- and lower mass fraction of crustal ions; such conditions
were most obvious in summer (Fig. 4). Under polluted and heavily polluted
conditions, the mass fractions of major chemical components were similar,
and the difference in PM2.5 pH between these two conditions was also
small. All of these results indicated that the aerosol chemical composition
should be an essential factor that drives aerosol acidity. The impact of
aerosol composition on PM2.5 pH is discussed in Sect. 3.3.
Time series of mass fractions of NO3-, SO42-,
NH4+, Cl-, Mg2+, and Ca2+ with
respect to the total ion content, as well as PM2.5 pH in all four
seasons (PM2.5 pH values at RH ≤ 30 % were excluded).
In spring, summer, and autumn, the pH of PM2.5 from the northern
direction was generally higher than that from the southwest direction, and
the higher pH in summer also occurred with strong southwest winds (wind
speed > 3 m s-1) (Fig. 5). Generally, northern winds occur
with cold-front systems, which can sweep away air pollutants but raise dust
in which the crustal ion species (Ca2+, Mg2+) are
higher. In winter, the PM2.5 pH was distributed relatively evenly in
all wind directions, but we surprisingly found that the pH in northerly
winds on clean days could be as low as 3–4, which was
consistent with the high mass fraction of SO42-.
Wind-dependence map of PM2.5 pH over four seasons. In each
picture, the shaded contour indicates the mean value of PM2.5 pH for
varying wind speeds (radial direction) and wind directions (transverse
direction) (data at RH ≤ 30 % were excluded).
Diurnal variation in ALWC, Hair+, and PM2.5 pH
Obvious diurnal variation was observed based on the long-term online
dataset, as shown in Fig. 6. To understand the factors that can drive
changes in PM2.5 pH, the diurnal variations in NO3-,
SO42-, ALWC, and Hair+ were investigated and are
exhibited in Fig. 6. Generally, ALWC was higher during nighttime than
daytime and reached a peak near 04:00–06:00 (local time).
After sunrise, the increasing temperature resulted in a rapid drop in RH,
leading to a clear loss of particle water, and ALWC reached the lowest level
in the afternoon. Hair+ was highest in the afternoon, followed by
nighttime, and Hair+ was relatively low in the morning. The low
ALWC and high Hair+ values in the afternoon resulted in the
minimum PM2.5 pH. The average nighttime pH was 0.3–0.4
units higher than that during daytime. From the above discussion, we found
that both Hair+ and ALWC had significant diurnal variations, which
means that in addition to chemical composition, the PM2.5 pH diurnal variation
was also affected by meteorological conditions. This trend is slightly
different from in the United States: Guo et al. (2015) found that the
ALWC diurnal variation was significant and the diurnal pattern in pH was
mainly driven by the dilution of aerosol water.
The correlation between NO3- concentration and PM2.5 pH was
weakly positive at low ALWC, and PM2.5 pH was almost independent of the
NO3- mass concentration at higher ALWC values (Fig. S6). In
contrast, at a low ALWC level, increasing SO42- decreased the pH;
at a high ALWC level, a negative correlation still existed between
SO42- mass concentration and PM2.5 pH. SO42- had a
greater effect than NO3- on PM2.5 pH.
Diurnal patterns of mass concentrations of NO3- and
SO42- in PM2.5, predicted aerosol liquid water content
(ALWC), Hair+, and PM2.5 pH over four seasons. Mean and
median values are shown, together with 25 % and 75 % quantiles. Data at
RH ≤ 30 % were excluded, and the shaded area represents the time period
when most RH values were lower than 30 %.
Factors affecting PM2.5 pH
In this work, the effects of SO42-, TNO3, TNH3,
Ca2+, RH, and T on PM2.5 pH were determined through a four-season
sensitivity analysis. The common important driving factors affecting
PM2.5 pH variations in all four seasons were SO42-,
TNH3, and T (Table 2), while the unique influencing factors were
Ca2+ in spring and RH in summer. For ALWC, the most important factor
was RH, followed by SO42- or NO3-. Figures 7 and S7–S14 show how these factors affect the PM2.5 pH, ALWC, and
Hair+ over all four seasons.
Sensitivity of PM2.5 pH to SO42, TNH3,
TNO3, Ca2+, RH, and T. A larger magnitude of the relative standard
deviation (RSD) represents a larger impact derived from variations in
variables.
H2SO4 can be completely dissolved in ALWC and in the form of
sulfate. As shown in Table 3, HNO3 also had a high conversion rate to
nitrate when RH > 30 %. Under ammonia-rich conditions (defined
and explained in Fig. S15), sulfate and nitrate mostly exist in the aerosol
phase with ammonium. The thermodynamic equilibrium between NH4+
and NH3 makes aerosol acidic (Weber et al., 2016). In the sensitivity
tests, we found that elevated SO42- was crucial in the increase in
Hair+ (Table S2, Figs. S7, S9, S12) and ALWC (Table S2, Figs. S8, S10, S13), and had a key role in aerosol acidity (Figs. 7, S11, S14).
However, only the PM2.5 pH in winter and autumn decreased significantly
with elevated TNO3 (Figs. 7, S14). In spring and summer, PM2.5 pH
changed little with elevated TNO3. When the TNO3 concentration was
low, PM2.5 pH even increased with elevated TNO3 (Figs. 7, S11).
The effect of TNO3 on Hair+ and ALWC is similar to that of
SO42-; that is, the elevated TNO3 will also result in the
increase in Hair+ and ALWC. The difference is that SO42-
can lead to a much higher concentration of Hair+ than TNO3 due
to its low volatility (Figs. S7, S9, S12). Thus, the sensitivity of
PM2.5 pH to TNO3 is less than that to SO42-. Moreover,
in spring and summer, more excessive NH3 could continuously react with
the increasing TNO3 (Table S1), leading to the minimal changes in
PM2.5 pH with elevated TNO3. Differently, TNH3 mass
concentration was lower in winter and TNO3 was higher in autumn (Table S1), which made TNH3 not excessive enough and resulted in the
decreased PM2.5 pH with elevated TNO3.
In the process of increasing NH3 concentration in the ammonia–nitric
acid–sulfuric acid–water system, NH3 first reacts with sulfuric acid
and consumes a large amount of H+, and then reacts with HNO3 to
produce ammonium nitrate (Seinfeld and Pandis, 2016). After most nitric acid
is converted to ammonium nitrate, it is difficult to dissolve more ammonia
into aerosol droplets. The sensitivity tests described this mechanism well.
Changes in TNH3 in the lower concentration range had a significant
impact on Hair+ and PM2.5 pH, and variations in TNH3 at
higher concentrations could only generate limited pH changes (Figs. 7, S11,
S14). The nonlinear relationship between PM2.5 pH and TNH3
indicates that although NH3 in the NCP was abundant, the PM2.5 pH
was far from neutral.
Sensitivity tests of PM2.5 pH to SO42-, TNO3,
TNH3, Ca2+, and meteorological parameters (RH and T) in summer (S)
and winter (W).
In this work, PM2.5 pH was lowest in summer but highest in winter,
which was consistent with the SO42- mass fraction with respect to
the total ion content. The SO42- mass fraction was highest in
summer among the four seasons, with a value of 32.4 % ± 11.1 %, but
lowest in winter, with a value of 20.9 % ± 4.4 %. In recent years,
the SO42 mass fraction in PM2.5 in Beijing has
decreased significantly due to the strict emission control measures for
SO2; in most cases, NO3- dominates the inorganic ions (Zhao et al.,
2013, 2017; Huang et al., 2017; Ma et al., 2017), which could reduce aerosol
acidity. A study in the Pearl River Delta of China showed that the in situ
acidity of PM2.5 significantly decreased from 2007 to 2012; the variation
in acidity was mainly caused by the decrease in sulfate (Fu et al., 2015).
The excessive NH3 in the atmosphere and the high NO3- mass
fraction in PM2.5 is the reason why the aerosol acidity in China is
lower than that in Europe and the United States (Guo et al., 2017).
Ca2+ is an important crustal ion; in the output of ISORROPIA II, Ca
exists mainly as CaSO4 (slightly soluble). Elevated Ca2+ concentrations can increase PM2.5 pH by decreasing Hair+
and ALWC (Figs. 7 and S7–S14). As discussed in Sect. 3.1, on clean
days, PM2.5 pH reached 6–7 when the mass fraction of
Ca2+ was high; hence, the role of crustal ions in PM2.5 pH cannot
be ignored in areas or seasons (such as spring) in which mineral dust is an
important particle source. Due to the strict control measures for road dust,
construction sites, and other bare ground, the crustal ions in PM2.5
decreased significantly in the NCP, especially on polluted days.
In addition to the particle chemical composition, meteorological conditions
also have important impacts on aerosol acidity. RH had different impacts on
PM2.5 pH in different seasons (Figs. 7, S11, S14). In winter, elevated
RH could reduce PM2.5 pH. However, an opposite tendency was observed in
summer. In spring and autumn, RH had little impact on PM2.5 pH.
Elevated RH can enhance water uptake and promote gas-to-particle conversion,
resulting in increased Hair+ and ALWC synchronously for all
four seasons. Therefore, the effect of RH on PM2.5 pH depends on the
differences in the degree of RH's effect on Hair+ and ALWC. Temperature can alter the PM2.5 pH by affecting gas–particle
partitioning. At higher ambient temperatures, ε(NH4+), ε(NO3-), and ε(Cl-) all showed a decreased tendency (Figs. 8, S16). The
volatilization of ammonium nitrate and ammonium chloride can result in a net
increase in particle H+ and lower pH (Guo et al., 2018). Moreover, a
higher ambient temperature tends to lower ALWC, which can further decrease
PM2.5 pH.
Sensitivity tests of ε(NH4+) and ε(NO3-) to TNO3, TNH3, RH, and T colored by PM2.5
pH in summer (S) and winter (W).
Size-resolved aerosol pH
Inorganic ions in particles present clear size distributions, and the
size-resolved chemical composition can change at different pollution levels
(Zhao et al., 2017; Ding et al., 2017, 2018), which may result
in variations in aerosol pH. Thus, we further investigated the size-resolved
aerosol pH at different pollution levels. According to the average
PM2.5 concentration during each sampling period, all the samples were
also classified into three groups (clean, polluted, and heavily polluted)
according to the rules described in Sect. 3.1. A severe haze episode
occurred during the autumn sampling period; hence, there were more heavily
polluted samples in autumn than in other seasons. Figure 9 shows the average
size distributions of PM components and pH under clean, polluted, and
heavily polluted conditions in summer, autumn, and winter. NO3-,
SO42, NH4+, Cl-, K+, organic carbon (OC), and elemental carbon (EC) were mainly
concentrated in the size range of 0.32–3.1 µm, while
Mg2+ and Ca2+ were predominantly distributed in the coarse mode
(> 3.1 µm). During haze episodes, the sulfate and nitrate in
the fine mode increased significantly. However, the increases in Mg2+
and Ca2+ in the coarse mode were not as substantial as the increases in
NO3-, SO42, and NH4+, and the low wind speed made it
difficult to raise dust during heavily polluted periods. More detailed
information about the size distributions for all analyzed species during the
three seasons is given in Zhao et al. (2017) and Su et al. (2018).
Size distributions of aerosol pH and all analyzed chemical
components under clean (a, d, g), polluted (b, e, h), and heavily polluted
conditions (c, f, i) in summer, autumn, and winter.
The aerosol pH in both the fine mode and coarse mode was lowest in summer among
the three seasons, followed by autumn and winter. The seasonal variation in
aerosol pH derived from MOUDI data was consistent with that derived from the
real-time PM2.5 dataset. In summer, the predominance of sulfate in the fine
mode and high ambient temperature resulted in a low pH, ranging from 3.2 to
3.9. The fine-mode aerosol pH in autumn and winter was in the range of 3.9–5.2 and 4.7–5.7, respectively. The fine-mode
aerosol pH was overall comparable to the PM2.5 pH. Moreover, in the
fine mode, the difference in aerosol pH among size bins was not significant
because the aerosol is in thermodynamic equilibrium with the gas phase (Fang
et al., 2017). Additionally, the size distributions of aerosol pH in the
daytime and nighttime were explored and are illustrated in Fig. S17. In
summer and autumn, the pH in the daytime was lower than that in the
nighttime, while in winter, the pH was higher in the daytime. During the
winter sampling periods, SO42 mass fraction was obviously
higher in the nighttime and led to abundant Hair+.
The abundance of Ca2+ in the coarse mode led to a predicted aerosol pH
approximately at or higher than 7 in autumn and winter. Even if the
coarse-mode Ca2+ mass concentration in the summer was low, the
coarse-mode aerosol pH was still more than 1 unit higher than the fine-mode
aerosol pH. The difference in aerosol pH (with and without Ca2+)
increased with increasing particle size above 1 µm (Fig. S18).
Moreover, the coarse-mode aerosols during severely hazy days shifted from
neutral to weakly acidic, especially in autumn and winter. As shown in
Fig. 9, the pH in stage 3 (3.1–6.2 µm) declined from 7.4 (clean) to
5.0 (heavily polluted) in winter. The significant decrease in the mass ratio
of Ca2+ in the coarse-mode particles on heavily
polluted days resulted in the loss of acid-buffering capacity. The different
size-resolved aerosol acidity levels may be associated with different
generation pathways of secondary aerosols. According to Cheng et al. (2016)
and Wang et al. (2016), the aqueous oxidation of SO2 by NO2 is key
in sulfate formation under high-RH and neutral conditions. However, it is
speculated that dissolved metals or HONO may be more important for secondary
aerosol formation under acidic conditions.
Factors affecting gas–particle partitioning
Gas–particle partitioning can be directly affected by the concentration
levels of gaseous precursors and meteorological conditions. In this work,
sensitivity tests showed that decreasing TNO3 lowered ε(NH4+) effectively, which helped maintain NH3 in the gas
phase. Elevated TNH3 can increase ε(NO3-) when
TNO3 is fixed, which means that the elevated TNH3 altered the
gas–particle partitioning and shifted more TNO3 into the particle
phase, leading to an increase in nitrate (Figs. 8 and S16). Controlling the
emissions of both NOx (gaseous precursor of NO3-) and
NH3 is an efficient way to reduce NO3-. However, the
relationship between TNH3 and ε(NO3-) in the
sensitivity tests (Figs. 8 and S16) showed that the ε(NO3-) response to TNH3 control was highly nonlinear, which
means that a decrease in nitrate would happen only when TNH3 is greatly
reduced. The same result was also obtained from a study by Guo et al. (2018). The main sources of NH3 emission are agricultural
fertilization, livestock, and other agricultural activities, which are all
associated with people's livelihoods. Therefore, in terms of controlling the
generation of nitrate, a reduction in NOx emissions is more feasible
than a reduction in NH3 emissions.
RH and temperature can also alter gas–particle partitioning. The equilibrium
constants for solutions of ammonium nitrate or ammonium chloride are
functions of T and RH. The measurement data also showed that lower T and
higher RH contribute to the conversion of more TNH3, TNO3, and TCl
into the particle phase (Table 3). When the RH exceeded 60 %, more than
90 % of TNO3 was in the particle phase for all four seasons. In
summer and autumn, more than half of the TNO3 and TCl was partitioned
into the gaseous phase at lower RH conditions (≤30 %). In winter, low
temperatures favored the existence of NO3- and Cl- in
the aerosol phase, and ε(NO3-) and ε(Cl-) were higher than 75 %, even at low RH. ε(NH4+) was lower than ε(NO3-) and
ε(Cl-). In spring, summer, and autumn, the average
ε(NH4+) was still lower than 0.3 even when the RH was
> 60 %; this trend was associated with excess NH3 in the
NCP. Higher RH and lower temperature are typical meteorological
characteristics of haze events in the NCP (Fig. 1), which are favorable
conditions for the formation of secondary particles.
Average measured ε(NH4+), ε(NO3-), and ε(Cl-) based on the real-time MARGA
dataset and ambient temperature at different ambient RH levels in four
seasons.
Long-term high-temporal-resolution PM2.5 pH and size-resolved aerosol
pH in Beijing were calculated with ISORROPIA II. In 2016–2017 in Beijing,
the mean PM2.5 pH (RH > 30 %) over four seasons was
4.5±0.7 (winter) > 4.4±1.2 (spring) > 4.3±0.8 (autumn) > 3.8±1.2 (summer), showing
moderate acidity. In this work, both Hair+ and ALWC had
significant diurnal variations, indicating that aerosol acidity in the NCP
was driven by both aerosol composition and meteorological conditions. The
average PM2.5 nighttime pH was 0.3–0.4 units higher than
that in the daytime. The PM2.5 pH in northerly wind was generally
higher than that in wind from the southwest. Size-resolved aerosol pH
analysis showed that the coarse-mode aerosol pH was approximately equal to
or even higher than 7 in winter and autumn, which was considerably higher
than the fine-mode aerosol pH. The presence of Ca2+ had a crucial
effect on coarse-mode aerosol pH. Under heavily polluted conditions, the
mass fractions of Ca2+ in coarse particles decreased significantly,
resulting in an evident increase in the coarse-mode aerosol acidity. The
PM2.5 pH sensitivity tests also showed that when evaluating aerosol
acidity, the role of crustal ions cannot be ignored in areas or seasons
(such as spring) where mineral dust is an important particle source. In
northern China, dust can effectively buffer aerosol acidity.
The sensitivity tests in this work showed that the common important driving
factors affecting PM2.5 pH are SO42-, TNH3, and T, while
unique influencing factors were Ca2+ in spring and RH in summer. Owing
to the significantly rich NH3 in the atmosphere, the change in
PM2.5 pH was not significant with the elevated TNO3, especially in
spring and summer. Excess NH3 in the atmosphere and a high
NO3- mass fraction in PM2.5 is the reason why aerosol acidity
in China is lower than that in Europe and the United States. Notably,
TNH3 had a great influence on aerosol acidity at lower concentrations
but had a limited influence on PM2.5 pH when present in excess. The
nonlinear relationship between PM2.5 pH and TNH3 indicated that
although NH3 in the NCP was abundant, the PM2.5 pH was still
acidic due to the thermodynamic equilibrium between aerosol droplet and
precursor gases. Higher ambient temperature could reduce the PM2.5 pH
by increasing ammonium evaporation and decreasing ALWC. RH had different
impacts on PM2.5 pH in different seasons, which depends on the
differences in the degree of RH's effects on Hair+ and ALWC.
In recent years, nitrates have dominated PM2.5 in the NCP, especially
on heavily polluted days. Sensitivity tests showed that decreasing TNO3
and TNH3 could lower ε(NH4+) and ε(NO3-), helping to reduce nitrate production. However, the
ε(NO3-) response to TNH3 control was highly
nonlinear. Given that ammonia was excessive in most cases, a decrease in
nitrate would occur only if TNH3 were greatly reduced. Therefore, in
terms of controlling the generation of nitrate, a reduction in NOx
emissions is more feasible than a reduction in NH3 emissions.
Data availability
All data in this work are available by contacting the corresponding author
P. S. Zhao (pszhao@ium.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-7939-2019-supplement.
Author contributions
PZ designed and led this study. PZ was responsible for all observations
and data collection. JD, PZ, and YZ interpreted the data and discussed
the results. JS and XD analyzed the chemical compositions of size-resolved
aerosol samples. JD and PZ wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the National Natural Science Foundation of China
(41675131), the Beijing Talents Fund (2014000021223ZK49), and the Beijing
Natural Science Foundation (8131003). Special thanks are extended to the Max
Planck Institute for Chemistry and Leibniz Institute for Tropospheric
Research where Pusheng Zhao visited as a guest scientist in 2018.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41675131), the Beijing Talents Fund (grant no. 2014000021223ZK49), and the Beijing Natural Science Foundation (grant no. 8131003).
Review statement
This paper was edited by Athanasios Nenes and reviewed by three anonymous referees.
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