Through online observation and offline chemistry analysis of samples at
suburban, urban and industrial sites (NJU, PAES and NUIST, respectively) in
Nanjing, a typical polluted city in the Yangtze River Delta, we optimized the
aerosol light scattering estimation method, identified its influencing
factors and quantified the contributions of emission sources to aerosol
scattering. The daily average concentration of PM2.5 during the
sampling period (November 2015–March 2017) was 163.1±13.6µgm-3 for the heavily polluted period, 3.8 and 1.6 times those for the
clean (47.9±15.8µgm-3) and lightly polluted (102.1±16.4µgm-3) periods, respectively. The largest increase in
PM concentration and its major chemical components was found at the size
range of 0.56–1.0 µm for the heavily polluted period, and the
contributions of nitrate and sulfate were the greatest in the 0.56–1.0 µm fraction (19.4 %–39.7 % and 18.1 %–34.7 %, respectively) for all the three
periods. The results indicated that the large growth of nitrate and sulfate
was one of the major reasons for the polluted periods. Based on
measurements at the three sites, the US Interagency Monitoring of Protected
Visual Environments (IMPROVE) algorithm was optimized to evaluate aerosol
scattering in eastern China. The light absorption capacity of organic carbon
(OC) was estimated to account for over half of the methanol-soluble organic
carbon (MSOC) at NJU and PAES, whereas the fraction was lower at NUIST.
Based on the Mie theory, we found that the high relative humidity (RH) could
largely enhance the light scattering effect of accumulation particles, but
it had few effects on the mixing state of particles. The scattering
coefficients of particles within the 0.56–1.0 µm range contributed the
most to the total scattering (28 %–69 %). The mass scattering efficiency
(MSE) of sulfate and nitrate increased with the elevated pollution level,
whereas a low MSE of organic matter (OM) was found for the heavily polluted
period, probably because a proportion of OM had only a light absorption
property. A coupled model of positive matrix factorization (PMF) and the Mie
theory was developed and applied for the source apportionment of aerosol
light scattering. Coal burning, industry and vehicles were identified as the
major sources of the reduced visibility in Nanjing, with an estimated
collective contribution at 64 %–70 %. The comparison between the clean and
polluted period suggested that the increased primary particle emissions from
vehicles and industry were the major causes of the visibility degradation in
urban and industrial regions, respectively. In addition, secondary aerosols
were a great contributor to the reduced visibility.
Introduction
Atmospheric aerosols play a great role in visibility degradation, radiative
balance variation and climate (Liu et al., 2017; Malm and Hand, 2007; Zhang
et al., 2017), resulting largely from their light extinction (Seinfeld and
Pandis, 2006). Understanding the contributions of individual chemical
species to aerosol light extinction is important for policy making to
alleviate the reduced visibility in cities with aerosol pollution. Studies
have estimated that the aerosol single-scattering albedo (the fraction of
the light scattering coefficient to the total extinction) ranges from 0.81 to 0.93
in urban China (Andreae et al., 2008; Cao et al., 2012; Xu et al., 2002, 2012), implying that the deteriorated visibility primarily results
from the scattering effect of aerosols.
Aerosol light scattering is greatly affected by its chemical composition and
hygroscopic growth (Liu et al., 2008; J. C. Tao et al., 2014). Based on
estimation of the mass scattering efficiency (MSE) of different chemical
components, previous studies found that nitrate, sulfate, sea salt and
organic matter (OM) are the dominant contributors to aerosol scattering.
Developed based on the long-term observations in national parks, the US
IMPROVE (Interagency Monitoring of Protected Visual Environments)
algorithm has been applied to calculate the light extinction of chemical
species in aerosols (Watson, 2002). Two versions of the IMPROVE
algorithm (IMPROVE1999 and IMPROVE2007 hereinafter) were deduced
successively (Lowenthal and Naresh, 2003; Pitchford et al., 2007), and both
assumed that OM has no light absorption capacity and only a light scattering
capacity. As part of OM, however, brown carbon (BrC) has been highlighted in
recent studies for its light absorption in the near-ultraviolet region (Alexander et
al., 2008; Bond and Bergstrom, 2006; Ramanathan et al., 2007; Zhang et al., 2017),
and consideration of the light absorption effect of OM in the optimization
process of the IMPROVE formula could improve the understanding of aerosol
optical capacity by chemical species (Yan et al., 2014). In addition,
hygroscopic growth is a key factor influencing aerosol light scattering
(Schwartz, 1996). Previous studies have shown that the light scattering of
sulfate and nitrate in PM2.5 could be largely enhanced at high relative-humidity (RH) conditions (Titos et al., 2016). Aerosol hygroscopicity is
expected to depend largely on the particle size and the abundance of
water-soluble chemical components (Swietlicki et al., 2008; Tang, 1996).
Through the theoretical calculation, Liu et al. (2014) found that smaller
particles were in the highly hygroscopic mode, whereas larger particles were in
the nearly hydrophobic mode.
Recently, many studies have been conducted on the relationships between
visibility and aerosol light scattering in China (Cheng et al., 2015; J. Tao et
al., 2014a, b; Xue et al., 2010; Zhang et al., 2015). They found the
abundance of hygroscopic ammonium nitrate (NH4NO3) and ammonium
sulfate ((NH4)2SO4) in PM2.5, and their characteristics
were an important reason for visibility reduction. However, few studies have
analyzed the size distribution of aerosol light scattering or quantified the
contributions of different emission source categories to the aerosol light
scattering, particularly at the varied air pollution levels. The roles of
particles of different sizes and origins on visibility degradation remained
unclear. To fill this knowledge gap, this study conducted campaigns at three
multifunctional sites in Nanjing, a megacity located in eastern China.
Nanjing suffered relatively heavy aerosol pollution in the Yangtze River
Delta (YRD) attributed to the massive emissions of anthropogenic air
pollutants (Zhao et al., 2015). The mixed sources of primary aerosols (e.g.,
coal burning) and secondary aerosol precursors (e.g., vehicles and the
petrochemical industry) make Nanjing a typical city to study the multiple
influential factors of aerosol light scattering (Chen et al., 2019).
Combining online and offline techniques at different functional regions, the
IMPROVE algorithm was optimized by taking the light absorption OM into account.
The influences of aerosol size distributions and pollution levels on the
aerosol scattering effect were quantitatively evaluated based on
comprehensive analysis of the chemical compositions of particles by size and
location. To explore the reasons for the visibility reduction in different
functional regions, a new coupled PMF–Mie (positive matrix factorization) model was developed, and the source
apportionments of aerosol light scattering were determined for the clean and
polluted periods.
MethodologySite description
The campaigns were conducted at three sites in Nanjing, i.e., NJU, PAES and
NUIST, representative for the suburban, urban and industrial region,
respectively (see the site locations in Fig. S1 in the Supplement). NJU
(32.07∘ N, 118.57∘ E) was on the roof (25 m above the
ground) of the School of the Environment building on the Nanjing University
campus in eastern suburban Nanjing (Chen et al., 2017, 2019). PAES
(32.03∘ N, 118.44∘ E) was on the roof (30 m above the
ground) of the Jiangsu Provincial Academy of Environmental Science building
in western urban Nanjing. The site was surrounded by heavy traffic and
commercial and residential buildings (Li et al., 2015). NUIST
(32.21∘ N, 118.72∘ E) was on the roof of the School of
the Environment building in the Nanjing University of Information Science
and Technology campus. There was an industrial-pollution site influenced by
the nearby power, iron and steel, and petrochemical industry plants (Wang
et al., 2016a).
Aerosol sampling and chemical analysis
Precombusted (at 500 ∘C for ∼5h) quartz filters
(90 mm in diameter; Whatman International Ltd., UK) were applied for
PM2.5 sampling. The filter samples were weighed before and after
sampling under constant temperature (23±2∘C) and RH
(40±3 %) for 24 h conditioning. All the PM2.5 samples were
collected using a TH-150C sampler (Wuhan Tianhong Ltd., China) at a flow
rate of 100 Lmin-1. From November 2015 to March 2017, 282 daily PM2.5
samples at the three sites (174 for NJU, 45 for PAES and 63 for NUIST) were
collected.
Three sets of 10-stage Micro-Orifice Uniform Deposit Impactors (MOUDI,
model 110, MSP Corp., USA) were adopted to collect size-segregated
particles. The 50 % cutoff points of the MOUDI 110 were 18, 10, 5.6, 3.2,
1.8, 1.0, 0.56, 0.32, 0.18 and 0.056 µm. Loaded with Teflon and quartz
filters (47 mm in diameter; Whatman International Ltd., UK), MOUDI was
operated at a flow rate of 30 Lmin-1. To obtain sufficient particles at each
stage for the chemical analysis, every sampling lasted continuously for 24 h
from 09:00. All the MOUDI samplers were cleaned using an ultrasonic bath
for 30 min before each sampling. The flow rate was calibrated before each
sampling and was monitored with the flow meter during the whole sampling
process. Those quality control measures assured that the MOUDI samplers were
not blocked during the sampling period and the particles collected in the
filter were evenly distributed, even for the heavily polluted period with
the PM1.8 concentration measured at over 120 µgm-3 (see the
samples in Fig. S2 in the Supplement). Attributed to weather condition and
aerosol sampler maintenance, the sampling periods for the three sites were
different. A total of 75 sets of particle samples were obtained from December 2015 to February 2017 at NJU; 25 sets were obtained from August 2016 to
January 2017 at PAES; and 31 sets were obtained from July 2016 to February 2017 at NUIST. Simultaneous samplings were conducted at the three sites from
1 week to 10 days in each season from summer 2016 to winter 2016–2017.
For the remaining time, two MOUDI samplers were applied to collect Teflon
and quartz filter samples simultaneously at one of the three sites. Besides,
blank filters were applied to correct the possible bias in the analysis of
aerosol chemical species. In total 19 sets of size-segregated blank filters
(10, 4 and 5 for NJU, PAES and NUIST, respectively) and 35 daily blank
PM2.5 filters (25, 6 and 9 for NJU, PAES and NUIST, respectively) were
obtained at the three sites. All the blank filters were put in the samplers
without inlet air flow for 24 h when the field campaigns finished. We took
NJU as an example to check the consistency between the two types of
samplers. As shown in Fig. S3 in the Supplement, excellent agreement was
found between the mass concentrations of PM1.8 collected with quartz
fiber in the MOUDI impactor and PM2.5 collected with the TH-150 sampler.
Three anions (SO42-, NO3- and Cl-) and five cations
(Na+, NH4+, K+, Mg2+ and Ca2+) in particles
were measured in the extracted solution of the filter samples with ion
chromatography (DX-120, Dionex Ltd., USA). A CS12A column (Dionex Ltd.) with
20 mM MSA (methanesulfonic acid) eluent and an AS11-HC column (Dionex Corp.) with 8 mM KOH eluent were
used to measured cations and anions, respectively (Chen et al., 2019).
Elemental carbon (EC) and organic carbon (OC) were measured with an OC–EC
aerosol analyzer (Sunset Inc., USA) following the thermal–optical
transmittance (TOT) protocol. In addition to thermal EC and OC, the
instrument also provides the optical EC and OC by measuring the light
attenuation (ATN). As the ATN is determined not only by EC but also by BrC,
the optical method may overestimate EC and underestimate OC (Cui et al.,
2016; Massabò et al., 2016). The optical EC and OC were thus not adopted
in this work. More details on the analyzer operation were described in our
previous studies (Chen et al., 2017, 2019). We used the methanol-soluble
organic carbon (MSOC) as BrC surrogate. It was believed to be more suitable
than water-soluble organic carbon (WSOC), as a large fraction of BrC
absorption comes from OC, insoluble in water (Cheng et al., 2016, 2017; Huang
et al., 2018; Lei et al., 2018). The analytical procedure was described in
detail in Chen et al. (2019). As shown in Fig. S4 in the Supplement, MSOC
was measured to account for 88 % of the total OC mass for all the samples
in this work, similar to the fraction of 85 % by Cheng et al. (2016).
Elements of size-resolved particles collected in the Teflon filters (As, Al,
Ba, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Ti, V and Zn) were measured with an
inductively coupled plasma-mass spectrometer (ICP-MS, PerkinElmer ELAN 9000,
USA) in order to provide further information on the aerosol sources. More
detailed information on the instrument was provided by Khan et al. (2016),
including the precision, calibration, detection limit and analytical
procedures.
Measurements of real-time aerosol scattering coefficients
The aerosol scattering coefficients were measured using two different types
of integrating nephelometers. Two Aurora model 1000G nephelometers (Ecotech
Pty Ltd, Australia) were operated at NJU and PAES at the wavelength of 520 nm and one model 3563 nephelometer (TSI Inc., USA) was operated at NUIST at
the wavelength of 550 nm. To obtain the dry aerosol scattering coefficient,
the three nephelometers controlled the RH of the inflow air under 50 % by
the heated inlet to mitigate the impact of water vapor on the scattering
coefficient. The nephelometers at NJU and PAES were operated at a flow rate
of 5 Lmin-1, and that at NUIST was operated at 20 Lmin-1. Routine maintenance including
zero calibration and span check was conducted following the instrument
manual.
To explore the RH impact on aerosol light scattering, an online monitoring
instrument, cavity-attenuated phase shift albedo monitor (CAPS; Shoreline
Science Research Inc., Japan), was used to measure the ambient scattering
coefficient at NJU in real ambient conditions. The instrument operates at
the wavelength of 530 nm (Onasch et al., 2015; Petzold et al., 2013), and
more details on its operation during the campaigns were provided by Chen et
al. (2019).
Data analysisEstimation of the scattering coefficient of aerosol chemical species
with different methods
The details of IMPROVE1999 and IMPROVE2007 are summarized in the Supplement
Sect. S1. Neglecting the light-absorbing effect of BrC, the two algorithms
could overestimate the scattering coefficient of OM (Yan et al., 2014). The
major difference between the two versions is that the IMPROVE2007 algorithm
considers the variety of mass scattering efficiencies due to particle size
for (NH4)2SO4, NH4NO3 and OM. With consideration of
the BrC presence, we conducted the multiple linear regression between the
measured light scattering components and aerosol scattering coefficient with
SPSS 16.0 (Cheng et al., 2011; Tian et al., 2016) to obtain the mass
scattering efficiency (MSE). The contributions of sea salt and soil dust
were excluded by subtracting their light scattering coefficients from the
measured PM2.5 one. The PM2.5 scattering coefficient can then be
estimated statistically based on the concentrations of individual chemical
species as in Eq. (1):
bsca=a×fS(RH)[Small(NH4)2SO4]+b×fL(RH)[Large(NH4)2SO4]+c×fS(RH)[SmallNH4NO3]+d×fL(RH)[LargeNH4NO3]+e×[SmallOM]-m×[SmallMSOC]+f×[LargeOM]-n×[LargeMSOC],
where bsca is the measured PM2.5 scattering
coefficient excluding the contribution of sea salt and soil dust; a, c and e
are the MSEs of (NH4)2SO4, NH4NO3 and OM (except for
light-absorbing BrC) in the small-size mode, respectively; b, d and f are the
MSEs of (NH4)2SO4, NH4NO3 and OM (except for light-absorbing
BrC) in the large-size mode, respectively (definitions of small- and large-size modes for various aerosol components can be found in Pitchford et
al., 2007); and m and n indicate the mass fractions of light-absorbing BrC to
total MSOC in small and large modes, respectively (definitions of small- and
large-size modes of MSOC are the same as other species); f(RH) (including
fL(RH) and fS(RH)) of sulfate and nitrate indicate the scattering
hygroscopic growth factor under a given relative humidity (RH), as obtained from
Pitchford et al. (2007).
In addition to PM2.5, the scattering coefficient for particles at a
given size (bsca(RH)) is calculated with the Mie theory (Bohren and Huffman, 1998; Cheng et al., 2015):
bsca(RH)=∫π[Dp×g(RH)2]2×Qsca[m(RH),Dp,λ]×N(Dp)×g(RH)dDp,
where m(RH) is the aerosol refractive index; g(RH) is the hygroscopic growth
factor; Qsca is the scattering efficiency for a single spherical
particle with diameter Dp and can be calculated with the Mie theory by
inputting Dp, m(RH) and the incident wavelength (λ); and N(Dp) is
the number concentration of particle with diameter Dp. In general, three
typical models are proposed to represent the particle mixing state including
internal, external and core–shell mixture (Jacobson, 2001; Seinfeld and
Pandis, 2006). The methods of calculating the parameters including m(RH) and
N(Dp) are different for the three mixed states, and the details can be
found in Ding et al. (2015).
Source apportionment of aerosol scattering coefficients with a coupled
model of PMF and the Mie theory
Positive matrix factorization (PMF) is an effective technology for source
apportionment of atmospheric aerosols (Kim and Hopke, 2004). PMF does not
require the source profile (i.e., the aerosol chemistry speciation by source
category) as a model input and thus excludes the uncertainty of source
profiles which were commonly developed based on studies or measurements
across the country in China. In this study, PMF 5.0 software was applied for
the source apportionment of accumulation mode particles. In total, 245, 145
and 163 aerosol samples were analyzed at NJU, PAES and NUIST, respectively.
It is currently difficult to resolve the sources of secondary organic
aerosol (SOA) with PMF. In this study, a simplified method was applied to
differentiate the sources of primary and secondary aerosols. Organic carbon
is split into primary and secondary organic carbon (POC and SOC), and the
SOC concentration was calculated with the EC-tracer method (Chen et al.,
2017). The source contributions of primary particles were obtained using the
PMF model, and those of secondary inorganic aerosol (SIA) and SOA were
further determined based on estimates of the nitrogen oxide (NOx),
sulfur dioxide (SO2) and volatile organic compound (VOC) emissions in
a local inventory (Huang, 2018; Lang et al., 2017; Wang et al., 2015). The
chemical components applied in the PMF model included inorganic ions,
carbonaceous components and metallic elements. We followed the method
described in the PMF manual and Tian et al. (2016) to calculate the chemical
component uncertainties in the measurement dataset. Criteria including the
optimum number of factors and the minimization of an objective function Q
were determined based on the principles described in previous studies (Moon
et al., 2008; Tian et al., 2016; Watson et al., 2015) and applied in the
model to obtain the best PMF solution.
A coupled model combining PMF and the Mie theory was developed to evaluate
the sources of aerosol light scattering. The procedure of the method was as
follows: (1) the EPA (Environmental Protection Agency) PMF model was applied to quantify the contributions of
different sources to the mass concentrations of chemical species in
size-segregated particles; (2) the contribution (%) of the ith
chemical component to the aerosol scattering coefficient at size
Dp was estimated based on the Mie theory; (3) the percentage
contribution (%) of the ith component in the jth source category
to the total scattering at size Dp was calculated as the product
of the percentage contribution (%) of the ith chemical species to the
total scattering as was that of the jth source category to the mass
concentration of the ith species in the particles at size
Dp, as indicated in Eq. (3); and (4) the percentage contribution (%)
of the jth source to the total scattering at size Dp was estimated
by summing ηijDp, as shown in Eq. (4).
3ηijDp=aijDp⋅biDp∑i=1mbiDp×100%4ηjDp=∑i=1IηijDp,
where i and j stand for the number of aerosol chemical components and potential
sources, respectively; ηijDp (%) is the contribution (%) of the
ith scattering component in the jth source to the total particle
scattering at size Dp; ηj (%) is the contribution (%)
of the jth source to the total scattering at size Dp; aijDp is
the relative contribution (%) of the jth source to the ith
chemical component in particles with size Dp from PMF modeling; and biDp
is the contribution of the ith chemical component to the total
scattering from Mie modeling.
The concentrations of particulate matter and its chemical
components (µgm-3), light scattering coefficients (Mm-1), and
selected meteorological parameters including wind speed (WS; ms-1) and
relative humidity (RH; %) at all the three sites for different pollution
levels from November 2015 to January 2017.
CategoryClean periodLightly polluted periodHeavily polluted periodAQI65.8±15.7110.6±21.3209.4±30.1PM1080.4±26.3143.1±28.6244.2±21.2PM2.547.9±15.8102.1±16.4163.1±13.6OC8.6±3.214.2±3.227.6±5.0EC1.9±0.93.0±1.25.3±0.1SO42-6.9±3.913.5±5.633.8±9.2NO3-10.5±5.422.7±8.747.9±17.7Cl-1.8±1.52.2±1.34.8±1.4Ca2+1.2±0.81.3±1.60.8±0.1Na+0.8±0.20.9±0.31.0±0.1Mg2+0.1±0.10.2±0.10.1±0.0NH4+5.1±1.99.2±2.216.9±2.5K+0.9±0.21.3±0.32.1±0.7CO0.8±0.21.3±0.31.6±0.1NO257.4±18.071.6±20.091.2±32.8SO217.7±6.521.1±6.029.5±12.5WS1.6±0.31.4±0.51.0±0.3RH56.1±13.562.7±10.868.9±4.9bsp251.4±170.8558.3±236.41286.2±293.3Results and discussionMass concentrations and size distributions of PM compositions
Based on the national definition of the ambient air quality index (AQI) (MEP,
2012), we divided the whole sampling period into three categories, i.e., the
clean period with an AQI value less than 100, the lightly polluted period with an AQI value
between 100 and 200, and the heavily polluted period with an AQI value above 200.
Note that the AQI is a unitless index calculated based on the daily
concentrations of regulated air pollutants including NO2, SO2, CO,
O3, PM2.5 and PM10 (MEP, 2012). As summarized in Table 1, the
average daily PM2.5 mass concentrations at the three conditions were
calculated at 47.9±15.8, 102.1±16.4 and 163.1±13.6µgm-3, respectively. The mass concentration of secondary inorganic
ions (SO42-, NO3- and NH4+) for the heavily
polluted period was 4.4 and 2.2 times those for the clean and lightly
polluted periods, respectively. The corresponding values for the
carbonaceous aerosols (the sum of OC and EC) were 3.1 and 1.9 times,
respectively, and the OC–EC ratios increased from 4.5 for the clean
period to 5.2 for the heavy period. In addition to the particulate
components, gaseous pollutants such as NO2 and SO2 were also
significantly elevated from the clean to the heavy periods. These results
imply that secondary aerosol formation was an important source of enhanced
PM2.5 for the heavily polluted period.
Figure S5 in the Supplement compares the size distributions of mass
concentrations for particles and selected chemical components under three
pollution levels. Bimodal size distributions were found for PM and OC mass
concentrations, with the two peaks at the ranges of 0.56–1.0 µm and
3.2–5.6 µm, respectively. This bimodal pattern could partly result from
the coexistence of primary and secondary sources of OC. POC with larger
sizes may contribute largely to the peak in the coarser particles. In
contrast, due to chemistry reactions of biogenic and anthropogenic VOCs, SOC
was expected to be abundant in the accumulation mode (0.18–1.8 µm) (Cao
et al., 2007). The size distributions of NO3- and SO42-
followed a unimodal distribution with the mass concentrations peak at the
range of 0.56–1.0 µm, as most of the inorganic aerosols were generated
through secondary formation. The mass concentrations of PM, NO3-,
SO42- and OC for all sizes were enhanced from the clean to the
polluted periods, and the biggest differences were found in the size bin of
0.56–1.0 µm. As shown in Fig. S5a, the concentrations of
PM0.56-1.0 for the heavily and lightly polluted periods were 7.0 and
2.7 times greater than that for the clean period, respectively. Moreover,
PM0.56-1.0 contributed 31 %, 23 % and 15 % to the total mass
concentrations of particles for the heavily and lightly polluted and clean
periods, respectively, implying that the enhanced concentration of
PM0.56-1.0 was an important reason for the aggravated pollution. As
shown in Fig. S5b–d, the sum of NO3-, SO42- and OC
for the heavily polluted period was 2.9 and 10.7 times greater than those
for the lightly polluted and clean periods, respectively. From clean to
heavily polluted periods, the collective mass fraction of the three
components to PM0.56-1.0 increased from 42 % to 64 %. The results
indicated that the increased NO3-, SO42- and OC at the
size bin of 0.56–1.0 µm could be an indicator for the serious air
pollution events. As indicated in Table 1, moreover, SNA (sulfate, nitrate and ammonium) was elevated more
than OC in the heavily polluted period compared to the clean days. During
the heavily polluted episodes in winter, enhancement of SO42- and NO3- could be more significant than OM because the high
relative humidity and precursor emissions promoted the generation of SNA
(Tian et al., 2014). During clean periods (commonly in summer),
NH4NO3 would dissociate to NH3 and HNO3 at high
temperatures, while the SOC concentration might increase due to the high
levels of O3 and solar radiation. A similar result was found in Beijing,
where the mass fraction of SNA was observed to increase from 19 % on
non-haze days to 31 % on haze days, while that of OM decreased from 38 %
to 31 % (Tian et al., 2016).
To explore the mass fractions of major chemical species in the particles,
the PM mass was reconstructed as (NH4)2SO4 (1.38×SO42-), NH4NO3 (1.29×NO3-), OM
(1.55×OC), fine soil (FS) and EC (Cheng et al., 2015; Pitchford et
al., 2007). As shown in Fig. S6 in the Supplement, strong correlations
were found between the reconstructed PM mass concentrations and the
measurements for PM1.8 (R2=0.91) and PM10 (R2=0.89)
at the three sites. The slope of PM1.8 (0.85) was greater than that of
PM10 (0.72), indicating a smaller unidentified fraction in
PM1.8. The larger unidentified mass in the reconstructed PM10 was
probably due to underestimation in the crustal components (Hueglin et al.,
2005).
The mass concentrations and fractions of the main chemical
components of particles with different sizes in Nanjing on clean,
lightly polluted and heavily polluted days during the sampling period. Dp refers to the particle diameter.
Figure 1 presents the mass concentrations and fractions of the reconstructed
aerosol chemical species by particle size under the three pollution levels.
NH4NO3, (NH4)2SO4 and OM were the dominant
components in particles. From the clean to heavily polluted periods, their
mass fractions to PM1.8 increased from 16.9 % to 35.3 %, from 14.9 % to
28.6 % and from 16.7 % to 22.2 %, respectively (Fig. 1b, d and
f). The mass fraction of OM in PM1.8 was 5.4 % and 7.4 %
larger than NH4NO3 and (NH4)2SO4 for the clean
period, while they were 13.3 % and 6.6 % smaller than those for the heavily
polluted period, respectively. The results further confirmed that
substantial growth in the mass of NH4NO3 and
(NH4)2SO4 was an important reason for the aerosol pollution.
The formation of sulfate, nitrate and ammonium (SNA) is mainly affected by
the emissions of precursors and the atmospheric oxidation capacity. Due to
the great use of fossil fuel consumption, the emissions of precursors
SO2 and NOx per unit area in eastern China were estimated to be 2.3
and 3.4 times larger than the national average, respectively (Cheng et al.,
2012; Shi et al., 2014). Under high RH, moreover, the SNA formation could
significantly be elevated through gas-to-particle heterogeneous reactions
for the heavily polluted period (Seinfeld and Pandis, 2006). The sulfate mass
concentration, for example, increased from 6.4 µgm-3 for the clean
period to 53.3 µgm-3 for the heavily polluted period. Among all
the size bins, NH4NO3 and (NH4)2SO4 were estimated
to contribute the most to the mass concentrations for 0.56–1.0 µm
particles, with their mass fraction ranging from 19.4 % to 39.7 % and from 18.1 % to 34.7 %,
respectively, across different pollution levels. In comparison, the largest
contributions of OM appeared in the 0.056–0.18 µm fraction and were
31.2 %, 29.0 % and 52.3 % for the clean, lightly polluted and heavily
polluted periods, respectively. As the largest PM fraction was found in the
0.56–1.0 µm size bin for the heavily polluted period, the elevated
concentrations of NH4NO3 and (NH4)2SO4 in
PM0.56-1.0 were the major causes of the increased aerosol pollution.
Figure S7 in the Supplement compares the size distributions of PM mass
concentrations and selected chemical species at the three sites. As
mentioned above, a bimodal distribution with two peaks at 0.56–1.0 µm
and 3.2–5.6 µm was observed for PM and OC at all the three sites,
which has attributed to the coexistence of primary and secondary sources. Different
from PAES and NUIST, NO3- had an obvious small coarse-mode peak at
NJU. Previous studies suggested that the chemistry of coarse-mode
NO3- can vary in different locations, and the components include
NH4NO3, NaNO3 and Ca(NO3)2 (Pakkanen et al., 1996).
As NJU was close to the G25 highway, the reaction of HNO3 with crustal
particles could be an important process for coarse-mode NO3-
formation. The highest mean concentrations of NO3- and
SO42- at the 0.56–1.0 µm size among the three sites were
observed at NJU, followed by NUIST and PAES. As NO3- and
SO42- were the major components of the aerosol light scattering,
the variety of their mass concentrations at 0.56–1.0 µm could be a
crucial reason for the visibility difference among the three sites. A
greater difference was found for the size distribution of OC among the three
sites, and the highest concentration at the 0.56–1.8 µm size was
observed at NUIST. Our previous work found that NUIST was greatly influenced
by VOC emissions of surrounding industrial plants (Chen et al., 2019).
Given its capability of light scattering and absorption, the abundant OC in
the area could play an important role in visibility.
Evaluation and optimization of the IMPROVE algorithm
Figure S8 in the Supplement presents the linear regressions between the
measured daily aerosol scattering coefficients with nephelometers
(bsp-m) and those calculated with IMPROVE algorithms (bsp-1999 and bsp-2007) based on the measured concentrations of particle
components at the three sites. At each site, strong correlations were found
between the observation and IMPROVE estimation (R2≥0.94),
indicating consistency between the different techniques. As shown in Fig. S8a, the calculated aerosol scattering coefficients bsp-1999 were
30 %, 16 % and 19 % smaller than the measured values at NJU, PAES and
NUIST, respectively. Similar results were found for other megacities in
eastern China. Based on the online analytical methods, for example, Cheng et
al. (2015) estimated that the scattering coefficients predicted by the
IMPROVE1999 algorithm were 34 % smaller than the measurement for a heavily polluted period in Shanghai. A greater underestimation of the scattering
coefficient existed at NJU compared to the other two sites, partly due to the
relatively abundance of sulfate and nitrate in particles at NJU. The sum of
SO42- and NO3- accounted for 35.3±13.2 % of
the total mass concentrations of PM2.5 at NJU, larger than the
fraction at PAES (27.6±12.9 %) and NUIST (24.1±11.6 %)
(note the SO42- and NO3- concentrations at the 0.56–1.0 µm size were the largest at NJU as well, as shown in Fig. S7). J. Tao et al. (2014a) and Cheng et al. (2015) suggested that the relatively small MSE of
sulfate and nitrate aerosols in the IMPROVE1999 algorithm might result in
underestimation of the scattering coefficient in China, as sulfate and
nitrate were the main light scattering components in PM2.5.
As shown in Fig. S8b, bsp-2007 was only 4 % smaller than the
measurement at NJU and 4 % and 18 % larger at PAES and NUIST,
respectively. Overall, the performance of the IMPROVE2007 algorithm was
better than that of the IMPROVE1999 algorithm, although deviation still existed due to
the uncertainty in MSEs for chemical species and the presence of light-absorbing organic matter such as BrC. A relatively large deviation between
bsp-m and bsp-2007 was found at NUIST compared to NJU and PAES.
Chen et al. (2019) and Shao et al. (2016) found a higher annual average
concentration of non-methane hydrocarbon at NUIST (34.4 ppbv) than NJU (22.0 ppbv) or PAES (27.1 ppbv). The more VOCs in the atmosphere were expected to
increase the SOC formation and to result in a big deviation of bsp-2007,
as the OM with a light absorption capability was not considered in
IMPROVE2007.
Linear regressions between the measured light scattering
coefficients and those estimated with the optimized IMPROVE algorithm at
NJU, PAES, NUIST and all three sites.
Using the optimized IMPROVE algorithm as described in Sect. 2.4.1, the
aerosol scattering coefficients were recalculated and compared against the
observation at the three sites, as illustrated in Fig. 2. Good
correlations were found between the observed and calculated scattering
coefficients at all the sites (R2≥0.96), and the regression
slopes were estimated to be much closer to 1 than those between observations
and predictions with the IMPROVE1999 or IMPROVE2007 algorithms (Fig. S8).
In addition, the MSEs calculated based on the Mie theory were applied to
evaluate the results of the IMPROVE algorithms. As presented in Fig. S9 in
the Supplement, the MSEs of (NH4)2SO4 and NH4NO3
calculated with the optimized IMPROVE algorithm were closer to the MSE
simulated by the Mie theory than those with the IMPROVE2007 algorithm. The
results indicated the optimized algorithm had a better performance and could
reduce the bias from the US IMPROVE algorithm.
The mass scattering efficiencies (MSEs; m2g-1) of chemical
species in the optimized and the existing algorithms from the Interagency
Monitoring of Protected Visual Environments (IMPROVE). The sample numbers
and the mass fractions of light absorption BrC to MSOC for small- and large-size modes (i.e., m and n in Eq. 1) are provided for the optimized algorithm.
ModesNJUPAESNUISTAll the three sitesIMPROVE2007IMPROVE1999MSE of (NH4)2SO4Small2.322.022.432.292.2–Large4.714.924.864.824.8–Overall3.913.884.033.94–3MSE for NH4NO3Small2.672.482.562.622.4–Large5.375.315.265.355.1–Overall4.414.134.234.31–3MSE of OMSmall4.44.564.224.462.8–Large6.236.366.456.416.1–Overall5.265.035.355.25–4m–0.660.710.390.67––n–0.290.270.330.31––Sample number–1744563282––
As summarized in Table 2, the MSEs estimated with the optimized IMPROVE
algorithm were 2.29, 4.82, 2.62, 5.35, 4.46 and 6.41 m2g-1 for small
sulfate, large sulfate, small nitrate, large nitrate, and small and large OM,
respectively. In comparison, the MSEs for the small- and large-size modes
using the IMPROVE2007 were 2.2 and 4.8 m2g-1 for
(NH4)2SO4, respectively, and 2.4 and 5.1 m2g-1 for
NH4NO3, respectively. The slightly larger MSEs from the optimized
IMPROVE algorithm for (NH4)2SO4 and NH4NO3 implied underestimation of the scattering coefficients of inorganic
components when applying the previous algorithm. There were clear
differences in the MSEs of OM (especially for fine OM) between the two
algorithms, resulting from consideration of the light-absorbed OM in the
optimized algorithm. Indicated by the m values in Table 2, the
light-absorbed OC accounted for 66 % and 71 % of the fine-MSOC mass at
NJU and PAES, respectively, indicating that most of the fine MSOC had only a
light absorption capacity. Unlike NJU and PAES, less than half of the fine
MSOC (39 %) had a light absorption capacity at NUIST, likely resulting from
the varied sources of OM at the three sites. As described in our previous
study (Chen et al., 2019), a substantial amount of OC was from the secondary formation
in the industrial polluted region, and its light absorption capacity was weaker
than that from the primary emissions.
In this study, the optimized IMPROVE algorithm for PM2.5 did not
include the contribution of sea salt or soil dust. As illustrated in Fig. S10 in the Supplement, sea salt and soil dust accounted collectively for
less than 10 % of the total PM2.5 scattering coefficient, suggesting
that the two species should have limited impact on the IMPROVE algorithm
optimization. In order to be concise in the optimized formula and to ensure
the stability of the multiple linear regression, therefore, only
(NH4)2SO4, NH3NO3 and OM were used as independent
variables. Through field measurement and data reconstruction in different
cities, previous studies explored the concentrations of PM2.5 and its
chemical components for various cities in China (Chen et al., 2019; Feng et
al., 2012; Lai et al., 2016; Tao et al., 2013, Yang et al., 2011; Zhao et
al., 2013). The major components of light scattering in aerosols, SNA, were
found to typically account for half of the PM2.5 mass concentrations
in eastern Chinese cities like Nanjing, Shanghai and Jinan (Yang et al.,
2011). Given the similar SNA levels and strong regional transport of
pollution among those cities, the optimized IMPROVE algorithm applied in
Nanjing in this work is believed to be more suitable than the previous
algorithms for eastern China. Moreover, for other regions with rapidly
developing economies and fast industrialization in China including the
Beijing–Tianjin–Hebei or Pearl River Delta regions, the current work
provides methodology and data support for the studies of aerosol light
scattering in cities with relatively serious aerosol pollution. Given the
fast changes in emission control and aerosol pollution in those regions,
more campaigns on aerosol optical and chemical properties are recommended to
further evaluate and improve the applicability of the optimized IMPROVE
algorithm.
The comparison of measured and estimated dry scattering
coefficients based on the assumptions of external, internal and core–shell
mixture at NJU (a), PAES (b) and NUIST (c).
Effects of mixing state and relative humidity on aerosol light
scattering
Figure 3 presents the scattering coefficients measured by nephelometer and
those simulated by the Mie theory at the three sites under dry conditions (RH<40 %). The simulated scattering coefficients based on the
assumption of an external mixing state were larger than those based on
core–shell and internal mixing states at all the three sites. Compared with
the internal and core–shell states, the simulated scattering coefficients in
the external mixing state were closer to the measurements at NJU and PAES
(Fig. 3a, b), indicating the reasonable assumption of external
mixtures as the main mixing state of particles. Similarly, Ma et al. (2012)
also suggested that the external mixture was an important particle mixing
state in northern China based on a stochastic particle-resolved aerosol box
model. Assuming the aerosol components were externally mixed, Cheng et al. (2015) estimated the MSEs of aerosol species in Shanghai and found better
agreement between the optimized scattering coefficients and the
measurements. At NUIST, the measured scattering coefficients were closer to
the simulated values in internal and core–shell states, likely due to the
high aging level of SOA at the industrial site (Fig. 3c). Due to the
strong atmospheric oxidation and thereby the abundance of SOA coatings at
NUIST, our previous study suggested that the aerosol aging process could
result in the growth of internally mixed BC (black carbon; Chen et al., 2019). Based on
the observation of O3 and percentage of internally mixed BC, Lan et al. (2013) suggested that photochemical production of secondary aerosol
components was the main reason for the switching from an external mixing
state to an internal mixing state for BC.
In an actual environment, ambient aerosols are typically hygroscopic under
the conditions of high RH, and it is an important reason for visibility
degradation. Table S1 in the Supplement summarizes the growth factors (GFs)
of particle size measured in Nanjing at different RH levels in previous
studies. To evaluate the rationality of those GF values, we followed the
method by J. Tao et al. (2014a) and calculated the scattering hygroscopic
growth factor (f(RH)) at NJU based on the measured ambient scattering
coefficients by CAPS and the dry scattering coefficients by nephelometer, as
shown in Fig. S11 in the Supplement. The correlation between f(RH) and RH
was fitted through the power regression. Figure S12 in the Supplement
presents a good agreement between the scattering coefficients estimated by
f(RH) and those obtained by the Mie theory (R2=0.95). The results
indicate the accuracy of the GF values applied on different particle sizes
and RH levels. The estimated and measured scattering coefficients at NJU
under ambient conditions are shown in Fig. S13 in the Supplement. Different
from the estimation under the dry conditions, the lowest value was found for
the external mixing state among the three mixing modes. In the external mixing state, only sulfate and nitrate particles had hygroscopicity under
wet conditions, whereas each particle had the capability of hygroscopic
growth in the internal mixing and core–shell states, resulting in a
significant increment in the scattering coefficient. Similarly, comparing
the measured scattering coefficients under the dry and ambient conditions
(Figs. 3 and S13), the simulated values based on an external mixing
state were closer to the measurements than the other two modes, implying
that RH had a limited effect on the particle mixing state.
The size distribution of hygroscopic scattering growth of
particles under varied relative-humidity levels at the three sites.
To explore the impact of RH on the light scattering of particles with
different sizes, the size distribution of f(RH) was estimated and shown in
Fig. 4. Large differences were found between f(RH) when the RH was above
and below 75 %, and high RH enhanced the capacity of scattering
hygroscopicity growth of small-size particles. Approximately 140 nm
particles had strong hygroscopicity when the RH was below 75 %, whereas a
high f(RH) (1.41±0.03) was observed for the accumulation mode
particles from 100 to 400 nm when the RH was above 75 %. Similar results
were reported for Beijing: larger hygroscopic GF was measured for
accumulation mode particles (100–300 nm) with a hygroscopicity tandem
differential mobility analyzer (H-TDMA), consistent with the elevated
abundance of the light scattering compositions such as sulfate and nitrate
(Meier et al., 2009).
The size distribution of scattering coefficients of aerosol
particles (a), (NH4)2SO4(b), NH4NO3(c) and OM (d) under different pollution levels. The contributions of particles with
different sizes to the total scattering coefficient are indicated in the panels
as well.
Size distribution of aerosol light scattering by pollution level
Figure 5 shows the size distribution of the scattering coefficients for
particles and given chemical components under the three pollution levels,
based on the measurements at all the three sites. Note the result was
obtained with the Mie theory under the assumption of external mixing at ambient
conditions. Although other mixing states might be more important at a specific
site (e.g., internal mixing at NUIST), their influence on scattering
coefficient estimation was modest (Fig. 3). The scattering coefficients of
particles for all size categories were the largest for the heavily polluted
period (Fig. 5a). The accumulation mode particles (0.18–1.8 µm)
accounted for 92.9 %, 92.6 % and 93.4 % of the total scattering
coefficients for the clean, lightly polluted and heavily polluted periods,
respectively. In particular, particles in the size bin of 0.56–1.0 µm
accounted for 57 % and 63 % of the scattering coefficient for the
heavily and lightly polluted periods, respectively, much larger than that
for the clean period, 38 %. From the results of Sect. 3.1, the abundance
of particles of different sizes was considered to be an important factor for
the variety of scattering coefficients across the whole size range.
As the dominant chemical components of aerosol light scattering,
(NH4)2SO4, NH4NO3 and OM collectively contributed
90 %, 76 % and 60 % to the mass concentrations of PM0.56-1.0 for
the heavily polluted, lightly polluted and clean periods, respectively
(Fig. S5b–d). The scattering coefficients of (NH4)2SO4 and NH4NO3 were the largest in the size bin of 0.56–1.0 µm for the three pollution levels, and their contributions increased along
with the aggravation of pollution (Fig. 5b, c). The OM
concentration in the size bin of 0.56–1.0 µm was 2.5 µgm-3
for the clean period, and those for the lightly and heavily polluted periods
were 160 % and 510 % larger, respectively. The scattering coefficient of
OM in the size bin of 0.56–1.0 µm for the heavily polluted period was
15 % less than that for the lightly polluted period, indicating the more
important role of OM in the particle scattering effect for the lightly
polluted period (Fig. 5d). The large OM scattering contribution could
likely be explained by the elevated mass fraction of OM and/or enhancement
of the OM MSE. It could be inferred that the low visibility during heavy
pollution resulted mainly from the enhancement of the scattering effect of
SNA.
The size distribution of mass concentrations of
(NH4)2SO4 and NH4NO3(a) and OM (b) under different
pollution levels and mass scattering efficiencies (MSEs) for PM1.8. The
size of the dot represents the MSEs of PM1.8 (in units of m2g-1). Dp refers to the particle diameter, and dC refers to the differential mass concentration.
The MSEs of given chemical components in PM1.8 are presented by
pollution level in Fig. 6. Increased MSEs for (NH4)2SO4 and NH4NO3 were found along with the elevated PM2.5
pollution (Fig. 6a). The large contributions of inorganic components and
their strong light scattering ability were important reasons for the reduced
visibility during the heavily polluted period. Although the largest OM
concentrations were observed in each size bin for the heavily polluted
period, the smallest MSE of OM in PM1.8 was found for the heavily
polluted period (3.73 m2g-1, Fig. 6b). As discussed in Sect. 3.2,
most of the fine MSOC was expected to have only a light absorption effect,
whereas large MSOC had light scattering capability. With the optimized
IMPROVE algorithm, the mass fraction of light absorption OC to total MSOC
mass was estimated at 66.9±5.8 % for the heavily polluted period,
much larger than those for clean and lightly polluted periods at 44.3±6.5 % and 50.8±5.9 %, respectively, as shown in Fig. S14 in the
Supplement. Therefore, the small MSE of OM for the heavily polluted period
was partly attributed to the abundance of light absorption BrC in
PM2.5.
For the whole research period, the MSEs of (NH4)2SO4,
NH4NO3 and OM in PM1.8 were calculated at 3.95, 4.26 and 4.14 m2g-1 with the Mie theory, while the analogue numbers in PM2.5 were
3.94, 4.31 and 5.25 with the optimized IMPROVE algorithm, respectively
(Table 2). Very good agreement between the two methods was found for SNA,
and a clearer discrepancy existed for OM, indicating a larger uncertainty in
the evaluation of organic aerosol scattering.
Source apportionment of accumulation mode particles at NJU (a),
PAES (b) and NUIST (c) and source apportionment of light scattering for
accumulation mode particles at NJU (d), PAES (e) and NUIST (f). The shadow
represents the contribution of secondary aerosols from each source category.
Source apportionment of aerosol light scattering with the PMF–Mie
coupled model
As illustrated in Fig. 5, the light scattering of the accumulation mode
(0.18–1.8 µm) accounted for the largest proportion of the total light
scattering. To better understand the causes of visibility degradation, the
source apportionment of aerosol light scattering at this size range was
conducted for different pollution levels with the PMF and Mie coupled model,
as described in Sect. 2.4.2. The PMF model was adopted to identify the
potential sources and to estimate their respective contributions to the mass
concentration of accumulation mode particles. To resolve the appropriate
number of factors, different numbers of identifiable sources were tested.
The source profiles and contributions to accumulation mode particles at the
three sites are presented in Fig. S15 in the Supplement and Fig. 7a–c,
respectively. The main sources identified at the three sites include coal
combustion, industrial pollution, vehicles, fugitive dust, biomass burning
and SIA (Fig. S15). Compared to NJU and NUIST, vehicles contributed more to
accumulated particles at the urban site PAES (Fig. 7b). As stated in
Sect. 2.4, we assumed that the contribution of the individual source
category to the secondary particle component was proportional to the
fraction of that source category to the emissions of corresponding
precursors (Lang et al., 2017). Based on the emission inventory of
precursors of SOC (VOCs) and SIA (NOx, SO2 and NH3) in
Nanjing (Huang, 2018), the source apportionment for primary and secondary
particles of the accumulation mode at the three sites were estimated, and the
results are presented in Tables S2–S4 in the Supplement. With the source
apportionment of secondary components, the contributions of coal combustion
and industrial pollution increased 45 %–50 % and 138 %–478 % compared to
those for primary particles across the three sites, respectively. The result
indicates that the gaseous precursors from coal combustion and industrial
pollution greatly elevate the aerosol pollution.
The contributions of different aerosol species to the aerosol light
scattering were estimated using the Mie model, and the results are presented
in Table S5 in the Supplement. OM contributed the most to the total
scattering at the three sites (31 %, 29 % and 33 % for NJU, PAES and
NUIST, respectively). Compared to other Chinese megacities, the
contribution of OM in Nanjing was close to that for inland cities like
Beijing (Tao et al., 2015) and Tianjin (Wang et al., 2016b) but was much
larger than that observed in a coastal megacity, Guangzhou (J. Tao et al.,
2014b).
Combined with the source apportionment from the PMF model, Fig. 7d–f
illustrates the source contribution to aerosol light scattering at the three
sites. Coal combustion, industrial plants and vehicles were the major sources
of the aerosol light scattering in Nanjing, and the three source categories
collectively accounted for 64 %–70 % of the total scattering capacity of
aerosols. Given their relatively intensive activities in urban and
industrial regions, vehicles and industrial plants were identified as the
largest contribution sources at PAES and NUIST, respectively. Indicated by
the dashed lines in Fig. 7d–f, the collective contributions of secondary
aerosol components were estimated to be 26.7 %–35.2 % of the total
scattering at the three sites, suggesting the important role of secondary
aerosol formation in visibility reduction.
Source apportionment of light scattering for accumulation mode
particles for the clean and polluted periods at NJU (a), PAES (b) and NUIST (c). The shadow represents the contribution of secondary aerosols from each
source category.
Figure 8 illustrates the source apportionment of aerosol light scattering
for the clean and polluted periods at the three sites. Coal combustion
contributed the most to the total scattering for the clean period, and the
contribution declined significantly for the polluted period, from 39 % to
21 %, from 38 % to 19 % and from 35 % to 18 % at NJU, PAES and
NUIST, respectively. The results implied that coal combustion might not be
the most important reason for visibility degradation in polluted periods.
Similarly, the contribution of fugitive dust during the polluted period was
estimated to be smaller than that for the clean condition. In contrast, the
contributions of vehicles and industrial pollution to light scattering
increased from 27 % to 48 %, from 27 % to 47 % and from 31 % to
62 % for the polluted periods compared to the clean period at NJU, PAES and NUIST, respectively. As shown in Fig. 8b and c, particularly, the
contribution of primary emissions from vehicles to aerosol scattering was
estimated to increase from 11.4 % to 21.5 % at PAES, and that from
industrial plants increased from 4.5 % to 13.5 % at NUIST. The primary
aerosol emissions from vehicles and industrial plants were thus identified
as the main cause of visibility reduction in the urban and industrial areas,
respectively. Similarly, Wang et al. (2016b) suggested that vehicles were the
dominant source of aerosol light extinction in Hangzhou, with the
contribution to the total extinction coefficient of PM2.5 reaching
30.2 %. The present study indicated that more effective measures for
reducing the primary particle emissions from vehicles and industrial
production should be conducted to avoid severe haze pollution in urban and
industrial regions.
The source contributions of secondary aerosols to aerosol light
scattering at the three sites for the clean and polluted periods (%).
Air quality levelSourcesNJU PAES NUIST SIASOASIASOASIASOACleanCoal combustion6.60.86.51.17.51.3Industrial plants5.83.64.21.58.26.3Vehicles2.11.06.11.54.21.1Total19.9 20.9 28.6 PollutedCoal combustion12.41.68.82.310.22.2Industrial plants10.25.87.8312.69.9Vehicles5.01.77.92.65.21.6Total36.7 32.4 41.7
In addition, the results suggest that secondary aerosols were another
important contributor to the reduced visibility. From the clean to the
heavily polluted periods, as shown in Fig. 8, the contributions of
secondary aerosols to the total light scattering increased from 19.9 % to
36.7 %, from 20.9 % to 32.4 % and from 28.6 % to 41.7 % at NJU,
PAES and NUIST, respectively. As shown in Table 3, the contributions of SIA
to the total scattering at the three sites were 14.5 %–19.9 %
and 24.5 %–28.0 %, much more than those of SOA at 4.1 %–8.7 % and
7.9 %–13.7 % for the clean and polluted periods, respectively. The
results imply that SIA had a greater impact on visibility degradation.
Although the contribution of coal combustion to the total scattering
declined from clean to polluted periods, the contributions of SIA from coal
combustion for the polluted periods were 88 %, 35 % and 36 % larger
than those for the clean period at NJU, PAES and NUIST, respectively. The
enhancement of SIA from coal combustion was thus an important cause of
polluted days. Moreover, the contribution of SOA to the total scattering
coefficient during the polluted periods was estimated at 13.7 % at NUIST,
larger than the 7.9 % and 9.1 % at PAES and NJU, respectively,
indicating that the contribution of SOA to visibility reduction at
industrial polluted areas should not be ignored. Notably, there is
uncertainty in the methodology of source apportionment of aerosol scattering
coefficients. In particular, the assumption that the secondary components
were proportional to the emissions of their precursors is a simplified
method and probably led to large bias, as the complicated nonlinear
mechanism of secondary aerosol formation is not recognized. To further
reduce the uncertainty and to improve the source apportionment, some
specific tracers of secondary aerosols like semi-volatile and low-volatile
oxygen-containing organic aerosols can be observed with advanced technology
such as aerosol mass spectrometry (AMS), and the observation data can then
be combined with the receptor models to quantify the source contribution of
secondary aerosols. Besides, an air quality model that integrates particle
source apportionment technology (PSAT) is recommended to be applied to
evaluate and confirm the performance of the source apportionment of
secondary aerosols with the receptor model.
Conclusions
A comprehensive investigation of the light scattering properties of
atmospheric aerosols was conducted from November 2015 to March 2017 at three
functional sites in Nanjing. High concentrations of sulfate and nitrate in
PM0.56-1.0 were the major causes of the heavy-particle pollution
events. The varied abundance of secondary inorganic components at the three
sites was an important reason for the visibility differences, and OC played
an important role in the visibility reduction in the industrial area due to
its complicated optical effect. Based on the measured aerosol scattering
coefficients and the mass concentrations of aerosol components, an optimized
algorithm of IMPROVE that considered the light absorption effect of OM was
developed to better represent the aerosol optical property.
Compared with internal and core–shell mixing states, the simulated
scattering coefficients based on an external mixing assumption were closer
to the measurements at NJU and PAES, indicating that externally mixed
particles widely existed at urban and suburban areas. At the industrial site
NUIST, the high aging level of SOA was the main reason for particle
switching from external to internal mixing states. The results for the
scattering coefficients under dry and ambient conditions indicated that RH
had little effect on the particle mixing state but a large impact on the
scattering coefficients. Particles in the size range of 0.56–1.0 µm
contributed the most (38 %–63 %) to the total scattering coefficient
under different pollution levels. As the dominant light scattering species
in aerosols, NH4NO3, (NH4)2SO4 and OM
collectively contributed 90 %, 81 % and 76 % of the mass
concentrations of PM0.56-1.0 for the heavily polluted, lightly polluted
and clean periods, respectively. The low visibility during the heavily polluted period mainly resulted from the enhanced light scattering of SNA.
The abundance of light absorption OC was an important reason for the
relatively low contribution of OM to light scattering in the heavily polluted
period.
Through a coupled model of PMF and the Mie theory, we found coal combustion,
industrial plants and vehicles were the main sources of the visibility
reduction in Nanjing. Vehicles and industrial plants were the main causes
for visibility reduction in urban and industrial areas, respectively. The
increased emissions of SIA precursors from coal combustion were an important
cause of polluted days, and the contribution of SOA to visibility reduction
at industrial-pollution areas should not be ignored. The source
apportionment of aerosol light scattering in this work provides scientific
evidence for the control of haze pollution in different functional areas of
cities in developed eastern China.
Data availability
All data in this study are available from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-10193-2020-supplement.
Author contributions
DC developed the strategy and methodology of the work and wrote a draft of the paper.
YZ improved the methodology and revised the paper. JZ, HY and XY
provided observation data of aerosol scattering coefficient.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was sponsored by the National Natural Science Foundation of China (grant nos. 41922052
and 91644220) and the National Key Research and Development Program of
China (grant no. 2017YFC0210106).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41922052 and 91644220) and the National Key Research and Development Program of China (grant no. 2017YFC0210106).
Review statement
This paper was edited by Yun Qian and reviewed by two anonymous referees.
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