Introduction
Aerosols, as solid or liquid particles suspended in the air, help regulate
Earth's climate mainly by directly scattering or absorbing incoming
radiation, or indirectly changing cloud optical and microphysical properties
(IPCC, 2013). Many studies suggest that aerosols have a direct impact on
human health (Araujo et al., 2008; Anenberg et al., 2010; Liao et al., 2015;
Li et al., 2017). For example, exposure to fine airborne particulates is
linked to increased respiratory and cardiovascular diseases (Hu et al.,
2015). Atmospheric aerosols can also reduce visibility. Poor visibility is
not only detrimental to human health but also hazardous to all means of
transportation (Zhang et al., 2010, 2018).
Poor visibility is caused by the presence of atmospheric aerosols whose
loading depends on both emission and meteorology. The increase in
anthropogenic emissions directly affects the formation of haze, such as
biomass burning, and factory and vehicle emissions (Watson, 2002; Sun et
al., 2006; Q. Liu et al., 2016; Qu et al., 2018). During some major events
like the 2008 Summer Olympic Games, drastic measures were taken to reduce
emissions, which led to a significant improvement in air quality (Huang et
al., 2014; Shi et al., 2016; Y. Wang et al., 2017). This attests to the
major role of emissions in air quality. Surface solar radiation and weather
such as wind conditions also affect aerosol pollution (Yang et al., 2015).
It has been widely known that aerosols interact with the planetary boundary
layer (PBL; Quan et al., 2013; Li et al., 2017; Qu et al., 2018; Su et al.,
2018). More aerosols reduce surface solar radiation, resulting in a more
stable PBL which enhances the accumulation of pollutants within the PBL.
Numerous studies have highlighted that the diurnal evolution of the PBL is
crucial to the formation of air pollution episodes (Tie et al., 2015; Amil
et al., 2016; Kusumaningtyas and Aldrian, 2016; Li et al., 2017; Qu et al.,
2018). Besides feedbacks, the stability of the PBL affects the dispersion of
pollutants.
Aerosol hygroscopicity also significantly affects visibility due to the
swelling of aerosols (Jeong et al., 2007; Wang et al., 2014). A number of
studies have shown that aerosol hygroscopic growth can accelerate the
formation and evolution of haze pollution in the North China Plain (NCP;
e.g., Quan et al., 2011; Liu et al., 2013; Wang et al., 2014; Yang et al.,
2015). There are many ways to measure aerosol hygroscopicity. A widely used
parameter, the aerosol particle size hygroscopic growth factor (GF) is
defined as the ratio of the wet particle diameter (Dp,wet) at a high
relative humidity (RH) to the corresponding dry diameter (Dp,dry). The
GF at a certain particle size can be detected by a hygroscopic tandem
differential mobility analyzer (H-TDMA; e.g., Liu et al., 1978; Swietlicki
et al., 2008; Y. Wang et al., 2017). In general, the H-TDMA system mainly
consists of two differential mobility analyzer (DMA) systems and one
condensation particle counter (CPC). The DMA is first used to select
particles at a specific size, and the second DMA and the CPC are used to
measure the size distribution of humidified particles. Another instrument
known as the differential aerosol sizing and hygroscopicity spectrometer
probe (DASH-SP) can also measure the GF at different RHs (Sorooshian et al.,
2008). The DASH-SP couples one DMA and an optical particle size spectrometer
(OPSS). The dry size-dependent particles are selected by the DMA, then
exposed to different RH environments, and finally measured in the OPSS
(Sorooshian et al., 2008; Rosati et al., 2015).
The aerosol optical hygroscopic enhancement factor (f(RH)) has also been
employed to investigate aerosol hygroscopicity, which is defined as the
ratio of aerosol optical properties (aerosol extinction, scattering, or
backscattering coefficients) between wet and dry conditions (Kotchenruther
et al., 1998). Two tandem nephelometers are used to measure f(RH) (e.g.,
Covert et al., 1972; Feingold and Morley, 2003; Titos et al., 2018). One
nephelometer measures the aerosol optical properties of dry ambient aerosols
at RH < 40 %, and another measures that of wet aerosols at
different RHs adjusted by a humidifier placed between them
(Koloutsou-Vakakis et al., 2001; Titos et al., 2018). MacKinnon (1969) was
the first to find that the lidar backscattering signal is affected by
environmental RHs. Later studies have demonstrated the possibility of using
the lidar to observe aerosol hygroscopic growth (Tardif et al., 2002; Pahlow
et al., 2006; Veselovskii et al., 2009; Di Girolamo et al., 2012;
Fernández et al., 2015; Granados-Muñoz et al., 2015; Lv et al.,
2017; Bedoya-Velásquez et al., 2018). Compared with tandem
nephelometers, lidar technology allows for measurements under unmodified
ambient atmospheric conditions without drying ambient aerosols. Actual
aerosol properties are not as affected when measured this way (Lv et al.,
2017; Bedoya-Velásquez et al., 2018). The lidar also provides an
opportunity to study the vertical characterization of aerosol
hygroscopicity. Many ground-based Raman lidar systems have been operated
around the world for measuring both atmospheric water vapor and aerosol
profiles at higher spatial and temporal resolutions (Leblanc et al., 2012;
Froidevaux et al., 2013; Wang et al., 2015; Bedoya-Velásquez et al.,
2018). These measurements are useful for examining the effects of aerosol
hygroscopic growth on pollution events (e.g., Y.-F. Wang et al., 2012, 2017;
Su et al., 2017). Many studies on aerosol hygroscopic growth are based on
the surface measurements, but few studies have investigated the vertical
characterization of aerosol hygroscopicity.
Xingtai as a city with a high density of heavy industries was ranked as one
of the most polluted cities in the central NCP. A joint field campaign was
carried out in this region in the summer of 2016. Some studies based on this
campaign have been done for understanding the causes and evolution of
pollution events in this region (Y. Wang et al., 2018; Zhang et al.,
2018). These studies have shown that aerosols in Xintai are highly aged and
internally mixed due to strong secondary formation. The goal of this study
is to further investigate how aerosol hygroscopic growth affects haze events
and what are the controlling factors by combining surface and vertical
measurements of aerosol optical, physical, and chemical properties.
The following section describes the instruments and methodology. Section 3
presents the results and discussion. Section 4 provides a brief summary of
the study.
Instruments and methodology
Instruments
A Raman lidar was used (i) to analyze the relationship between atmospheric water
vapor content and PM1 or PM2.5 mass concentrations and (ii) to explore
the atmospheric aerosol hygroscopic growth effect on haze events. The lidar
is an automated system that retrieves atmospheric water vapor mixing ratios
(W) and aerosol optical property profiles (aerosol extinction and
backscattering coefficients; Ångström exponent, AE; and the
depolarization ratio) throughout the day. The system employs a pulsed
neodymium-doped yttrium aluminum garnet laser as a light source and emits
three laser beams simultaneously at 355, 532, and 1064 nm with a time
resolution of 15 min and a range resolution of 7.5 m based on its original
factory settings. The lidar sends 5000 laser beams in the first 4 min and 10 s of the 15 min cycle, then the mean value of the
received 5000 signals are stored as the signal profile to enhance the
signal-to-noise ratio. When a laser beam is sent into the atmosphere, the
received backscattering signal generally includes Mie scattering by
aerosols, Rayleigh scattering by atmospheric molecules, and Raman scattering
caused by the rotation and vibration of the molecules. The size of many
molecules and atoms in the atmosphere is typically much smaller than the
wavelength of the laser, so Rayleigh scattering occurs when they interact
(Strutt, 1871). Mie scattering describes the interaction between large
particles (mainly atmospheric aerosols) and laser beams. As for the optical
receiving unit of this lidar system, optical fiber (OF), dichroic beam
splitter (DBS), and ultra-narrowband filters following an ultraviolet
telescope divide atmospheric Mie-scattering signals and vibrational
Raman-scattering signals from H2O and N2 molecules (at 355, 386, and
407 nm, respectively). Atmospheric Mie-scattering signals at 532 and 1064 nm
are divided by OF, DBS, and ultra-narrowband filters after a visible infrared
telescope. Based on the perpendicular and parallel components at 532 nm
received by the lidar system, the aerosol depolarization ratio, a parameter
that measures the shapes of aerosols, can be calculated. In general, the
more irregular the particle shape, the larger the value of the
depolarization ratio (Chen et al., 2002; Baars et al., 2016). The AE can
also be calculated using lidar signals at 532 and 1064 nm, which is
inversely related to the average size of the aerosols (Ångström,
1964; Tiwari et al., 2016).
Co-located radiosondes were launched twice a day, i.e., at ∼ 07:15 and ∼ 19:15 China Standard Time (CST),
during the field campaign.
The GTS1 detector collected profiles of atmospheric RH, temperature, and
pressure at a resolution of 1.0 %, 0.1 ∘C, and 0.1 hPa, respectively.
The radiosonde ascension velocity was typically ∼ 5–6 m s-1.
A co-located Doppler lidar system (TWP3-M) was also in operation at Xingtai.
This system emits electromagnetic beams in different directions to the upper
air, then directly receives the backscattering signals after those beams
interact with atmospheric turbulence. Based on the Doppler effect, this
system can derive time series of horizontal wind velocity and direction at a
time resolution of 5 min and a range resolution of 60 m below 1 km and 120 m above 1 km. The root-mean-square
errors (RMSEs) of the
Doppler-lidar-retrieved wind speed and direction are typically ≤1.5 m s-1
and ≤10∘, respectively. The maximum and minimum detection
distances of this system are 3–5 and 0.1 km, respectively.
A GrayWolf six-channel handheld particle and mass meter (model PC-3016A) was used
to directly monitor the total mass concentrations of PM2.5 and PM1
in the actual atmosphere (Yan et al., 2017). The minimum detection particle
size is 0.3 µm, and the counting efficiency for 0.3 µm
particles is 50 % and for particle sizes greater than 0.45 µm it is 100 %. The
non-refractory PM1 (NR-PM1) chemical components
including organics, sulfate, nitrate, ammonium, and chloride were measured in
situ by an aerodyne quadrupole aerosol chemical speciation monitor (ACSM) at
a time resolution of 5 min. Detailed information about the operation
of the ACSM and its application in this campaign can be found elsewhere
(Zhang et al., 2018). Briefly, aerosols with vacuum aerodynamic diameters of
∼ 40–1000 nm are sampled into the ACSM through a 100 mm
critical orifice mounted at the inlet of an aerodynamic lens. The particles
are then directed onto a resistively heated surface (∼600 ∘C) where NR-PM1 components are flash vaporized and ionized by a
70 eV electron impact. The ions are then analyzed by a commercial quadrupole
mass spectrometer. Mass spectra are the raw data collected by the ACSM, and
standard analysis software offered by Aerodyne Inc. is provided to derive
mass concentrations of each chemical component. In this study, the ACSM was
calibrated with pure ammonium nitrate following the procedure detailed by Ng
et al. (2011) to determine its ionization efficiency. The aerosol aerodynamic
particle size was determined by an aerodynamic lens. The uncertainties of
ACSM-derived quantities are insignificant (Ng et al., 2011).
The aerosol GF probability distribution function (GF-PDF) at RH = 85 %
was measured by an in situ H-TDMA. The H-TDMA system mainly consists of a
Nafion dryer, a bipolar neutralizer, two DMAs, a CPC, and a Nafion
humidifier. The first DMA is used to select monodispersed aerosols with a
set mobility size (40, 80, 110, 150, and 200 nm in this study) after the
sample is dried and neutralized by the Nafion dryer and the bipolar
neutralizer. The selected particles are then humidified when passing through
a Nafion humidifier with controlled RH (85 %). The second DMA and the CPC
are responsible for measuring the number size distribution of the humidified
particles. Finally, the TDMA-fit algorithm is used to retrieve GF-PDF
(Stolzenburg and McMurry, 2008). Uncertainties of these retrieved parameters
are insignificant. More detailed descriptions about the H-TDMA system are
given by Tan et al. (2013) and Y. Wang et al. (2017, 2018). All data are
reported in CST in this study.
Methodology
Water vapor retrieval
Using the ratio of the Raman signals of H2O (PH) and N2
(PN), W is calculated as follows (Melfi, 1972; Leblanc et al., 2012; Su
et al., 2017):
W(z)=CWΔqPH(z)PN(z),Δq=exp-∫0zαNm+αNpdzexp-∫0zαHm+αHpdz,
where CW is the Raman lidar calibration constant which can be
calculated using co-located radiosonde data (Melfi, 1972; Sherlock et al.,
1999). The parameters αNm and αHm are the
molecular extinction coefficients at 386 and 407 nm, respectively. These can
also be calculated using temperature and pressure profiles from radiosonde
measurements (Bucholtz, 1995). The parameters αNp and αHp are the
aerosol extinction coefficients (AECs) at 386 and 407 nm,
respectively. Here, we use the Fernald method to retrieve AECs (Fernald et
al., 1972; Fernald, 1984), which is an analytic solution to the following
basic lidar equation for Mie scattering:
PS(z)=ECZ-2β1(z)+β2(z)T12(z)T22(z),
where PS(z) is the return signal; E is the energy emitted by the
laser; C is the calibration constant of the lidar system; and β1(z) and
β2(z) are the backscattering cross sections of atmospheric
aerosols and molecules at altitude z, respectively. T1(z) and T2(z) are the transmittances of aerosols and air molecules at height z. Note
that, during the daytime, the height of the retrieved W profile is limited
because the Raman signal is affected by radiation (Tobin et al., 2012).
(a, c) Water vapor mixing ratio
(W) and relative humidity (RH) profiles at 05:15 CST 24 May 2016 retrieved by the Raman lidar (blue line) and the radiosonde
(red dashed line), respectively, and (b, d) the absolute error in
W and RH between the lidar and radiosonde
retrievals (lidar minus radiosonde), respectively.
We can also calculate the vertical distribution of RH based on the vertical
profile of W retrieved from Raman lidar measurements and the temperature and
pressure profiles provided by radiosonde data. The following equations are
used to retrieve the RH profile:
RH(z)=e(z)es(z)×100%,e(z)=W(z)p(z)0.622+W(z),es(z)=6.1078exp17.13T(z)-273.16T(z)-38,
where e(z) and es(z) are the vertical profiles of water vapor
pressure (in hPa) and saturation vapor pressure (in hPa) at a certain
temperature, respectively; W(z) is the W profile obtained from the Raman
lidar; p(z) is the pressure profile (in hPa); and T(z) is the temperature
profile (in K) provided by radiosonde data.
(a, c) Water vapor mixing ratio
(W) and relative humidity (RH) profiles at 20:00 CST 23 May 2016 retrieved by the Raman lidar (blue line) and the radiosonde
(red dashed line), respectively, and (b, d) the absolute error in
W and RH between the lidar and radiosonde
retrievals (lidar minus radiosonde), respectively.
To assess the accuracy of the retrieval algorithm, Raman-lidar- and
radiosonde-derived W and RH profiles at 05:15 CST on 24 May 2016 and their
differences are shown in Fig. 1. The W profiles agree reasonably well with an
absolute error between them less than 0.5 g kg-1. Absolute errors
between Raman-lidar- and radiosonde-derived RH profiles are generally less
than 5 %. The same inversion results for a relatively wet case on 23 May 2016 are given in Fig. 2. In general, large absolute errors tend to occur at
the inflection points. Figures 1 and 2 suggest that the retrieval algorithm
can produce reasonable results.
Selection of aerosol hygroscopic cases and their optical
properties
How aerosol hygroscopic growth cases were chosen is described here. First,
atmospheric mixing conditions were examined using radiosonde-based vertical
potential temperature (θ) and W profiles. Cases with near-constant
values of θ and W in the analyzed layer (variations less than
2 ∘C and 2 g kg-1, respectively) represent good atmospheric
mixing conditions (Granados-Muñoz et al., 2015). Then aerosol
backscattering coefficient profiles at 532 nm were calculated using the
Fernald method (see details in Sect. 2.2.1).
A simultaneous increase in atmospheric RH and the aerosol backscattering
coefficient is also needed, which might indicate aerosol hygroscopic growth
(Bedoya-Velásquez et al., 2018). Based on the above criteria, individual
cases with the same ambient humidity and different pollution conditions were
selected for studying the influence of aerosol hygroscopicity on haze
events. Aerosol hygroscopic properties of the selected cases were
investigated in terms of the hygroscopic enhancement factor for the aerosol
backscattering coefficient defined as follows:
fβRH,λ=βRH,λβ(RHref,λ),
where βRH,λ and βRHref,λ represent aerosol backscattering coefficients at a
certain RH value and at a reference RH value, respectively, at wavelength
λ. In this study, we selected RHref=80%, which is the lowest
RH in the layer.
Finally, a relationship between fβ(RH) and RH was established.
The most commonly used equations are the single-parameter fit equation
(e.g., Hänel, 1980; Kotchenruther and Hobbs, 1998; Gassó et al.,
2000) and the dual-parameter fit equation (e.g., Hänel, 1980; Carrico,
2003; Zieger et al., 2011). The single-parameter fit equation introduced by
Hänel (1976) is
fβRH=1-RH1-RHref-γ,
where γ in an empirical parameter. Larger γ values in this
formulation denote a stronger hygroscopic growth.
The dual-parameter fit equation is (Fernández et al., 2015)
fβRH=a(1-RH)-b.
The single- and dual-parameter fit equations are similar, but with an
additional scale factor parameter, a, in the case of the dual-parameter
fit equation. The parameter b is also an empirical parameter with larger
values of b indicating particles with stronger hygroscopicities. In this
study, both parameterized equations are used to verify the consistency of
the results. The equation that fits the measurement data best is selected.
Calculation of aerosol acidity
Aerosol acidity is associated with aerosol hygroscopic growth (e.g., Sun et
al., 2009; Fu et al., 2015; Zhang et al., 2015; Lv et al., 2017). When
atmospheric aerosols are acidic, they have stronger hygroscopicities than
when in their neutralized forms (Zhang et al., 2015). The swelling of
aerosols due to hygroscopic growth enhances their ability to scatter solar
radiation. We examined acidity by comparing the measured NH4+ mass
concentration with the needed amount to fully neutralize sulfate, nitrate,
and chloride ions (NH4+predicted) detected by the ACSM (Sun et
al., 2009; Zhang et al., 2015; Lv et al., 2017):
NH4+predicted=(2×SO42-/96+NO3-/62+Cl-/35.5)×18,
where SO42-, NO3-, and Cl- represent the mass
concentrations (in µg m-3) of the three species measured by
the ACSM. The molecular weights of SO42-, NO3-, Cl-,
and NH4+ are 96, 62, 35.5, and 18, respectively. Aerosols are
considered “more acidic” if the measured NH4+ mass concentration
is significantly lower than that of NH4+predicted. Aerosols are
considered “bulk neutralized” if the two values are similar (Zhang et al.,
2007, 2015; Sun et al., 2009; Lv et al., 2017).
The acidity of aerosols can be quantified by a parameter called the acid
value (AV) (Zhang et al., 2007):
AV=(2×SO42-/96+NO3-/62+Cl-/35.5)/(NH4+/18).
The chemical formula and numbers after the equal sign have the same meanings
as in Eq. (10). Aerosols are considered bulk neutralized if AV = 1 and
“strongly acidic” if AV > 1.25. When AV = 1.25, 50 % of the
total sulfate ions in the atmosphere consist of NH4HSO4, and the
other 50 % consist of (NH4)2SO4.
Aerosol-chemical-ion-pairing scheme
The magnitude of f(RH) is correlated with the inorganic mass fraction (Zieger
et al., 2014). However, GFs differ with different inorganic salts. To
examine the mass fractions of neutral inorganic salts, ACSM measurements
were used to calculate their mass concentrations and volume fractions (Gysel
et al., 2007). This approach is based on the ion-pairing scheme introduced
by Reilly and Wood (1969). The ACSM mainly measures the mass concentrations
of SO42-, NO3-, NH4+, Cl-, and organics. The
chlorine ion was not considered here because its concentration is low. The
aerosol-chemical-ion-combination scheme is given by the following equations:
nNH4NO3=nNO3-,nNH4HSO4=min2nSO42--nNH4++nNO3-,nNH4+-nNO3-,nNH42SO4=maxnNH4+-nNO3--nSO42-,0,nH2SO4=max0,nSO42--nNH4++nNO3-,nHNO3=0,
where n donates the mole numbers, and “min” and “max” are minimum and
maximum values (Gysel et al., 2007). The volume fractions of inorganic salts
can be calculated based on the ion combination scheme and the parameters in
Table 1. Furthermore, for a multicomponent particle, the
Zdanovskii–Stokes–Robinson mixing rule (Zdanovskii, 1948; Stokes and
Robinson, 1966) can be applied to calculate the hygroscopicity
parameter κ:
κ=∑iεiκi,
where κi is the hygroscopicity parameter of each individual
component. The parameter εi is the volume fraction of each
component.
Aerosol properties of selected compounds used for the calculation of
the hygroscopicity parameter κ, i.e., the density (ρi) and
(κi) of each compound.
Species
NH4NO3
NH4HSO4
(NH4)2SO4
H2SO4
Densitya
1.725
1.78
1.76
1.83
κb
0.68
0.56
0.52
0.91
a Tang and Munkelwitz (1994); Carrico et al. (2010);
b Fountoukis and Nenes (2007); Carrico et al. (2010); Liu et
al. (2014).
Conclusions
During late May 2016, the water vapor mixing ratio in the 0.3–4 km layer
over Xingtai was generally less than 6 g kg-1 with a strong daily
variability. Overall, the simultaneous temporal changes in the mass
concentrations of PM1 and PM2.5 were strongly associated with that
of the atmospheric water vapor content due to the hygroscopicity of
aerosols. Two cases where this relationship was not seen were identified and
further examined. Case I represents a relatively clean case, and Case II
represents a polluted case. The lidar-estimated aerosol backscattering
coefficient hygroscopic enhancement factor (fβ(RH)) for Case II
is greater than that for Case I. The γ and b values from the
Hänel and Kasten parameterizations, respectively, for Case II were
larger than those for Case I. A key parameter affecting the hygroscopicity
of aerosols, namely, the acid value (AV), was examined by comparing measured
NH4+ and predicted NH4+ based on data obtained by the ACSM.
The AV for Case I (1.35) was less than that for Case II (1.50) and the main
form of inorganics was NH4NO3, NH4HSO4, and
(NH4)2SO4. The aerosol chemical composition determined by
the ACSM showed that the aerosol hygroscopicity parameter κ for Case II (0.610) was greater than that for Case I (0.577) due to the greater mass
fraction of nitrate salt. Based on H-TDMA measurements, model results showed
that the aerosol size hygroscopic growth factor (GF) in each particle size
category (40, 80, 110, 150, and 200 nm) for Case II was greater than that
for Case I.
The fβ(RH), GF, AV, and κ are completely different
quantities for calculating the hygroscopicity of aerosols and are difficult
to compare quantitatively. The lidar-estimated fβ(RH) and ACSM
and H-TDMA measurements show that the hygroscopic growth of aerosols has a
strong influence on the process of air pollution. Under the same atmospheric
relative humidity conditions, the stronger the hygroscopicity of aerosols,
the more likely they are to cause severe air pollution. The mass fraction of the
nitrate ion in aerosols was one of the main factors that determined the
hygroscopic ability of aerosols in the study area (Xingtai). These findings
not only reveal why haze events in Xintai can be severe, but they also
provide scientific evidence that may be used to persuade the local
government to prevent and control environmental contamination in this
heavily polluted part of China.