Aerosol composition and sources have been extensively studied in
developed regions in China. However, aerosol chemistry in coastal regions of
eastern China with high industrial emissions remains poorly characterized.
Here we present a comprehensive characterization of aerosol composition and
sources near two large steel plants in a coastal city in Shandong in fall
and spring using a PM2.5 time-of-flight aerosol chemical speciation
monitor. The average (±1σ) mass concentration of PM2.5
in spring 2019 (54±44µg m-3) was approximately twice that
(26±23µg m-3) in fall 2018. Aerosol composition was
substantially different between the two seasons. While organics accounted
for ∼30 % of the total PM2.5 mass in both seasons,
sulfate showed a considerable decrease from 28 % in September to 16 % in
March, which was associated with a large increase in nitrate contribution
from 17 % to 32 %. Positive matrix factorization analysis showed that
secondary organic aerosol (SOA) dominated the total OA in both seasons, accounting on average for 92 % and 86 %, respectively, while the
contribution of traffic-related hydrocarbon-like OA was comparable
(8 %–9 %). During this study, we observed significant impacts of steel plant
emissions on aerosol chemistry nearby. The results showed that aerosol
particles emitted from the steel plants were overwhelmingly dominated by
ammonium sulfate and/or ammonium bisulfate with the peak concentration reaching as
high as 224 µg m-3. Further analysis showed similar mass ratios for
NOx/CO (0.014) and NOx/SO2 (1.24) from the two different
steel plants, which were largely different from those during periods in the
absence of industrial plumes. Bivariate polar plot analysis also supported
the dominant source region of ammonium sulfate, CO, and SO2 from the
southwest steel plants. Our results might have significant implications for
better quantification of industrial emissions using ammonium sulfate and the
ratios of gaseous species as tracers in industrial regions and nearby in the
future.
Introduction
Atmospheric fine particles (PM2.5, particles with an aerodynamic
diameter less than 2.5 µm) have great impacts on visibility
(Reddy and Venkataraman, 2000; Jinhuan and Liquan,
2000), climate forcing (Lohmann and Feichter, 2005; Carslaw
et al., 2010), and human health (Laden et al., 2000; Pope III et al.,
2002). Among the sources of aerosol particles, industrial emissions have
become one of the most important sources in rapidly developing countries,
e.g., China (Cao et al., 2011; Huang et al., 2014). However,
previous studies on chemical composition and sources were mostly conducted
in densely populated urban environments and remote or rural areas (Allan et
al., 2010; Aiken et al., 2009; Sun et al., 2012), while industrial plumes that
can have a large impact on urban air quality and residents nearby are much
less characterized. Aerosol particles in regions under the influences of
industrial emissions contain much higher concentrations of toxic substances
than those observed at urban and rural areas (Osornio-Vargas et al.,
2003; Lage et al., 2014), leading to much higher health risks for people
living nearby. Therefore, it is critically important to characterize the
chemical composition and sources of fine particles in the vicinity of
industrial plants and to have a better understanding of their formation
mechanisms, evolution processes, and potential health effects
(Davidson et al., 2005).
Industrial activities in steelmaking such as coke production and
ore sintering emit a large amount of gaseous species, e.g., SO2,
NOx, CO (Brock et al., 2003; Weitkamp et al., 2005), and
particulate matter (Almeida et al., 2015; Taiwo et al., 2014). Heavy
metals, including Fe, Mn, Pb, etc. (Wong et al., 2006; Yang et al., 2018);
specific polycyclic aromatic hydrocarbons (PAHs)
(Athanasios et al., 2011; Leoni et al., 2016); secondary
inorganic species (Setyan and Jing, 2017; Wu et al.,
2018); and OC/EC (organic carbon to elemental carbon ratio)
(Chow et al., 2011) have been used as tracers to identify
various industrial processes and to estimate their influences on air
quality. During the last decade, real-time measurement instruments, e.g.,
the aerosol time-of-flight mass spectrometer (ATOFMS) and the Aerodyne aerosol mass
spectrometer (AMS), have been used to measure the rapid changes in particle
mixing states and chemical composition of industrial particles (Dall'Osto
et al., 2008; Setyan et al., 2019). Dall'Osto et al. (2008)
found several unique types of particles such as Fe-rich, Pb-rich, Zn-rich,
and Ni-rich particles from steelmaking progresses, and Setyan et al. (2019) found a
large increase in nonrefractory chloride from the Fe–Mn plant and the
adjacent steelworks based on the measurements of a high-resolution
time-of-flight AMS. Although the real-time measurements of fine particles
are important for understanding the rapid evolutionary processes of aerosol
composition, size distributions, and mixing states from industrial plumes,
few studies have been conducted in highly industrialized areas in China.
Rizhao, located to the west of Yellow Sea, is one of the most important steel
production bases in Shandong, China. The steel production in 2018 was
approximately 26×106 t, which is ∼40 % of the total
production in Shandong. In addition to emissions from the steel plants, the
air pollution in Rizhao is subject to multiple influences from local
emissions, regional transport from Linyi to the west and Weifang to the
northwest, and sea–land breezes. According to the Environment Statement of
Shandong province, the air quality in Rizhao has improved significantly with
the annual average PM2.5 decreasing from ∼70µg m-3 in 2013 to ∼50µg m-3 in 2017.
However, the PM2.5 still exceeds the National Ambient Air Quality
Standard (35 µg m-3 as an annual average) by more than 40 %,
and severe haze episodes were also frequently observed in winter. Although
aerosol composition, sources, and formation mechanisms have been widely
characterized in Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Pearl River
Delta during the last decade (Li et al., 2017), aerosol
chemistry in the coastal city of Rizhao, and chemical characteristics of
steel plant emissions, are rarely characterized. Therefore, it is of
importance to investigate the composition, sources, and variations in aerosol
particles near industrial areas and to better understand their impacts on
urban air quality and human health.
In this work, we conducted two campaigns in the vicinity of steel plants in
Rizhao in September 2018 and March 2019 using an Aerodyne time-of-flight
aerosol chemical speciation monitor (ToF-ACSM) that was equipped with a
PM2.5 aerodynamic lens and a capture vaporizer. Aerosol particle
composition, diurnal variations, and potential sources are characterized,
and the sources of organic aerosol are analyzed with positive matrix
factorization. In particular, the chemical characteristics of pollutants
from the emissions of steel plants, and their impacts on air quality, are
elucidated.
Experimental methodsSampling site and measurements
All measurements were carried out at a site near two steel plants in the
coastal city of Rizhao, Shandong (35∘10′59′′ N, 119∘23′57′′ E), from 2 to 29 September 2018 and 2 to 29 March 2019. As shown in
Fig. 1, the sampling site is located approximately 2 km northeast of the
Rizhao steel plant (RSP), ∼1 km southwest of the Shandong
steel plant (SSP), and ∼1 km from the Yellow Sea. As shown in
Fig. 1c and d, the prevailing winds were from the east and southeast
during daytime in both September and March, while they were dominantly from
the north in September and from the west in March during nighttime. As a
result, the sampling site was subject to significant influence from sea–land
breeze. Also note that the two steel plants have differences in technologies
for controlling the emissions of pollutants. The newly built SSP uses more
advanced purification and emission control technologies, e.g., dry
desulfurization technology with the advanced activated coke compared with
wet limestone–gypsum flue-gas desulfurization (FGD) used in RSP. According
to the National Bureau of Statistics of China, the annual steel production
of the RSP was ∼18×106 t and that of the SSP was
∼8×106 t.
An Aerodyne ToF-ACSM, which is equipped with a PM2.5 aerodynamic lens
and a capture vaporizer (CV), was deployed for real-time measurements of
non-refractory aerosol species in fine particles (NR-PM2.5), including
organics (Org), sulfate (SO4), nitrate (NO3), ammonium (NH4),
and chloride (Chl) at a time resolution of 2 min. Briefly, ambient aerosol
particles were first drawn into the sampling line at a flow rate of
∼3 L min-1 after passing through a PM2.5 cyclone
(URG-2000-30EH). Aerosol particles were then dried with a Nafion dryer and
sampled into the ToF-ACSM at a flow rate of ∼0.1 L min-1. After
focusing into a narrow particle beam and flying through the vacuum chamber,
non-refractory aerosol species were flash vaporized at ∼540∘C and then ionized immediately by the 70 eV electron impact. The
ions were analyzed using the time-of-flight mass analyzer and detected by an
SGE dynode detector (Fröhlich et al.,
2013). Similar to quadrupole-ACSM (Ng et
al., 2011b), the mass concentrations of aerosol species were derived from
the differences between sampling mode and filter mode. The ionization
efficiency (IE) and relative ionization efficiencies (RIEs) were calibrated
using pure NH4NO3 and (NH4)2SO4 before and after
the campaigns. The RIE of sulfate was fairly robust (1.1), while
that of ammonium changed from 3.1 in September 2018 to 3.8 in March 2019.
The default RIE values of 1.1, 1.4, and 1.3 were used for nitrate, organics,
and chloride, respectively.
In addition to NR-PM2.5 aerosol species, a seven-wavelength Aethalometer
(model AE33, Magee Scientific) was used to measure black carbon (BC) at a
time resolution of 1 min. The gaseous species including SO2 (model
43i), CO (model 48i), and NOx (model 42i) were measured by various gas
analyzers from Thermo Fisher Scientific, and the meteorological parameters
including temperature (T), relative humidity (RH), wind speed (WS), and wind
direction (WD) were measured by a WS500-UMB smart weather sensor (Lufft).
Data analysis
The mass concentration and chemical composition were analyzed using the
standard ToF-ACSM data analysis software (Igor-based Tofware_2_5_13_ACSM;
https://www.tofwerk.com/tofware, last access: 20 July 2019). A collection efficiency (CE) of ∼1
was used for mass quantifications as indicated by previous studies that AMS
or ACSM with CV has a fairly robust CE of ∼1 (Hu et al.,
2016a, 2018a, b). Our results showed that NR-PM2.5+ BC
was well correlated with the total PM2.5 mass in both spring and fall
(r2=0.54 and 0.86), and the regression slopes of 0.89 and 0.78
suggested that the mass quantification of the ToF-ACSM was reasonable,
considering that mineral dust was not measured in this study.
The bilinear model positive matrix factorization (PMF) (Paatero and
Tapper, 2010) has been widely used to deconvolve organic aerosol (OA) into
different factors (Lanz et al., 2007; Ulbrich et al., 2009; Sun et
al., 2011). In this study, PMF analysis was performed for the organic mass
spectra from ToF-ACSM measurements to determine the potential OA from
different sources and processes. The detailed procedures for pretreatment of
the data and error matrices were presented in
Zhang et al. (2011). Considering the
limited sensitivity of the ToF-ACSM, m/z's larger than 120 and 180 were excluded
in PMF analysis in September 2018 and March 2019, respectively, due to their
low signal-to-noise ratios and low contributions to the total OA mass.
After careful evaluations of mass spectral profiles, diurnal variations, and
correlations with external tracers, three OA factors including a
hydrocarbon-like OA (HOA), a less oxidized oxygenated OA (LO-OOA), and a more
oxidized OOA (MO-OOA) were determined in September 2018, and four factors,
i.e., HOA, LO-OOA, MO-OOA, and a coal combustion OA (CCOA), were identified in
March 2019. Compared with previous OA factors identified from PMF-AMS
analysis (Ng et al., 2011a), the mass spectra of both
primary and secondary OA factors showed much higher f44 (fraction of
m/z 44 in OA) in this study due to increased thermal decomposition on the
surface of CV (Hu et al., 2018a, b). Hu et al. (2018a)
also found that the previous AMS tracer m/z's are still present and usable in
mass spectra although the fragmentation in CV tends to shift towards small
m/z's due to additional thermal decomposition. Comparisons of PMF results further
showed consistent time series of OA factors between CV and standard
vaporizer (SV), yet the CV may introduce higher uncertainty in separating
different types of OOA (Hu et al., 2018b).
Results and discussionMass concentrations, chemical composition, and diurnal variations
Figure 2 shows the time series of aerosol species, gaseous species, and
meteorological parameters during the two seasons. The average (±σ) RH and T was 62 % (±15 %) and 23 ∘C (±3∘C) in September 2018, which were overall higher than those
(60±4 % and 10±4∘C, respectively) in March 2019,
while the average WS was comparable between the two seasons (2.8±1.1 m s-1 vs. 3.2±1.9 m s-1). One of the major differences in meteorological
conditions is wind direction. While the prevailing winds were both from the
east and southeast during daytime, they were mainly from the north in
September and the west in March at night. We also noticed a considerable
frequency of southwesterly winds in spring of 2019, suggesting a high
potential impact of RSP on the sampling site.
Time series of meteorological variables including T, RH, WD, and WS;
mass concentrations of gaseous species including SO2, NO2, and CO;
and chemical species in PM2.5 in (a) September 2018 and (b) March 2019,
respectively. The pie charts show the average chemical composition for the
entire campaign.
The average PM2.5 (NR-PM2.5+ BC) concentration was
∼26±23µg m-3 for the entire study in
September 2018, which was approximately half of that (54±44µg m-3) in March 2019. This result suggested that air quality in March
was much worse than that in September, and the PM loading was even more than
50% higher than the National Ambient Air Quality Standard (35 µg m-3 as an annual average). According to the measurements at the
Environmental Monitoring Station of Rizhao, the average PM2.5 mass
concentration in September showed a continuous decrease from 50 µg m-3 in 2013 to 22 µg m-3 in 2018, suggesting a significant
improvement in air quality in September. However, the PM2.5 mass
concentration remained at relatively high levels ranging from 54 to 78 µg m-3 in March during the years of 2013–2018, indicating a great
challenge in reducing PM in this season.
Figure 2 shows the average aerosol composition during fall and spring. While
organics consisted of approximately one-third of the total PM2.5 mass in
both spring and fall, large differences in secondary inorganic species were
observed. For example, sulfate was the second largest component in
PM2.5 in fall, accounting for 28 %, and its contribution decreased to
16 % in spring. In contrast, nitrate showed largely elevated contributions
from 17 % to 32 %. These results indicate that the formation mechanisms
and sources of secondary inorganic species could be different during the two
seasons. BC contributed similar fractions to PM2.5 in both seasons,
which are 7 %–8 %, and chloride was generally small at less than 2 %.
We noticed that the average composition in spring was similar to that
observed at a receptor site (Changdao island) in the north coast of Shandong
province in spring 2011 (Hu et al., 2013).
These results might suggest that aerosol composition did not change
significantly, although the PM levels decreased substantially during the last
decade. We further compared aerosol composition during and after the heating
season in March, and observed remarkably similar mass concentrations and
aerosol compositions during the two periods. One of the major reasons was
that the residents near the steel company were all relocated to other
places. As a result, the emissions from residential coal combustion were
small. Further support is that the coal combustion OA only accounted for
∼5 % to the total OA mass as discussed in Sect. 3.2.
Although the mass concentrations and aerosol compositions were substantially
different, the diurnal patterns were overall similar for all aerosol species
during the two seasons, indicating that the factors driving the diurnal
variations were similar. As shown in Fig. 3, higher mass concentrations at
nighttime and in the early morning than in the daytime were observed for most aerosol species. Such diurnal patterns can be explained by the rising
boundary layer height during daytime and the prevailing winds from the east
and southeast that brought relatively clean air from the ocean. In contrast,
sulfate presented similar and relatively flat diurnal patterns in both fall
and spring, consistent with the fact that sulfate was mainly formed over a
regional scale. Despite the rising boundary layer height, the sulfate
concentrations remained relatively high during daytime. One reason was
likely due to the sea–land breeze that brought the potentially high sulfate
concentrations over the Yellow Sea (Li et al., 2018) to the sampling
site. Organics in fall also showed a relatively flat diurnal pattern due to
the dominance of MO-OOA that was highly correlated with sulfate.
Average diurnal variations in mass concentrations of chemical
species in PM2.5 in September 2018 (red) and March 2019 (blue),
respectively. The shaded areas and error bars indicate 25th and
75th percentiles. Also, the diurnal cycles of aerosol species, by
excluding the periods with the influences from steel plants in March, are
shown as blue open circles.
OA composition and sources
The mass spectra, time series, and diurnal variations of OA factors in fall
and spring are shown in Figs. 4 and 5. The mass spectra of HOA in both
seasons resembled those of primary emissions reported previously
(Canagaratna et al., 2004; Lanz et al., 2007; Mohr et al., 2009), showing
the typical characteristics of hydrocarbon ion series of
CnH2n-1+ and CnH2n+1+. However, the HOA
spectra showed generally higher f44 compared with previous studies
(14 % in spring and 6 % in autumn) due to the increased thermal
decomposition in CV. Indeed, Hu et al. (2018b) also
observed much higher f44 in the HOA spectrum from CV than that from SV. HOA
was well correlated with NOx (r2= 0.52–0.65) and BC (r2= 0.66–0.75) in both fall and spring, supporting that HOA was mainly
from traffic emissions. This is further supported by the pronounced diurnal
cycles of HOA showing higher mass concentrations during morning and evening
rush hours. The average mass concentration of HOA was 0.7
and 1.3 µg m-3 in September and March, respectively. Although the
mass concentration had a difference by a factor of 2 during the two seasons,
the HOA contribution to total OA was comparable (8 % vs. 9 %), which is
also close to that ∼10 % reported in Beijing (Zhang et
al., 2014; Hu et al., 2016b; Sun et al., 2016).
(a, b) Time series and (c) mass spectral profiles of OA factors in
September 2018 and March 2019. The time series of external tracer species
are also shown for comparisons. The two pie charts present the average OA
composition in September and March, respectively. Note that the periods with
the influences from steel plants were excluded when performing correlation
analysis between MO-OOA and SO4 in panels (a) and (b).
Diurnal variations in mass concentration of OA factors in (a) September 2018 and (b) March 2019. The error bars indicate 25th and
75th percentiles.
A coal combustion OA factor was identified in March 2019 (Fig. 4b and c).
The CCOA spectrum showed some similarities to that of HOA at small m/z's, yet it
was featured by pronounced PAH-related m/z's such as m/z 91, 115, 152, 165, 178,
etc. (Dzepina et al., 2007). Despite the high thermal
decomposition in CV, the PAH signatures can be well retained as those in
previous studies (Sun et al., 2016; Hu et al., 2013, 2016b). In
fact, CCOA was highly correlated with these PAH-related m/z's. For instance,
CCOA was tightly correlated with m/z 115 (mainly C9H7+, r2=0.76). Compared with HOA, CCOA presented a higher mass fraction of
larger m/z's (>120), indicating that coal combustion can be an
important source of high-molecular-weight organic matter during the heating
period. CCOA on average accounted for 5 % of the total OA mass in March
with an average concentration of 0.8 µg m-3. Compared with
previously reported CCOA at urban sites, e.g., Beijing (∼20 %) and the receptor site Changdao in Shandong (∼9 %)
(Sun et al., 2016; Hu et al., 2013), CCOA in this study seemed not to be an
important contributor to OA, although it presented a pronounced diurnal
pattern with higher concentration and fraction at night. This is consistent
with the fact that local residents were all relocated to other places, and
the residential coal combustion emissions could not be significant.
Two secondary organic aerosol (SOA) factors with different oxidation levels were determined in both
September 2018 and March 2019. The total SOA (LO-OOA + MO-OOA) correlated
well with secondary inorganic components (SIA, i.e., sulfate + nitrate),
and the ratios of SOA to SIA were 0.37 and 0.41 in September and March,
respectively, which were close to those reported in Beijing (0.36–0.42)
(Huang et al., 2010; Sun et al., 2010). The mass spectra of the two SOA
factors were both characterized by a prominent m/z 44 (mainly CO2+)
peak, and the f44 in MO-OOA was higher than that in LO-OOA
(∼36 % vs. ∼25 %). Comparatively,
f29 and f43 were notably higher in LO-OOA than MO-OOA, suggesting
that MO-OOA was more oxidized than LO-OOA. Indeed, the MO-OOA tracked better
with sulfate (r2= 0.45–0.71), while the LO-OOA correlated better
with nitrate (r2= 0.56–0.60). The diurnal patterns of MO-OOA and
LO-OOA were also different. LO-OOA showed similar and pronounced diurnal
variations in both September and March with much higher mass concentration
at nighttime than daytime. Such diurnal patterns were very similar to
nitrate, suggesting the similar semivolatile properties of LO-OOA and nitrate
(Docherty et al., 2011). In
contrast, MO-OOA presented relatively flat diurnal cycles that were
remarkably similar to those of sulfate, supporting the fact that MO-OOA was highly
aged and formed over a regional scale. SOA together accounted for 92 % and
86 % of the total OA mass in September and March, respectively. These
results highlight an overwhelming dominance of SOA in OA during both seasons
even though our sampling site is located near the steel plants. However, we also
found a change in SOA composition from March to September. In particular,
MO-OOA showed an ∼10 % higher contribution to OA in September
than March (60 % vs. 51 %), while the fraction of LO-OOA was comparable
(32 % vs. 34 %).
Potential sources of aerosol species
Figure 6 shows the bivariate polar plots of aerosol components and gaseous
species in September and March. In general, high concentrations near the
center area is associated with local sources, while that far away from the
center area is indicative of regional transport (Carslaw and Ropkins,
2012). It can be seen that a high concentration of PM2.5 in September was
mainly located in the region to the west (Fig. 6a), suggesting that regional
transport played the most important role for air pollution in Rizhao.
Comparatively, two high potential source regions for PM2.5 were
observed in March including local emissions and transport from the southwest
region where RSP is located. The source regions were also substantially
different for different aerosol species in September and March. As indicated
in Fig. 6a, high mass concentrations of sulfate, ammonium, and chloride were
mainly located in the southwest region in both September and March,
suggesting the large impacts of the emissions from the RSP. High
concentration of chloride likely existed mainly in the form of ammonium
chloride, considering that sea salt particles cannot be detected efficiently
at a vaporizer temperature of ∼540∘C. We also
observed high potential source regions to the west of the sampling site for
sulfate, ammonium, and chloride in September. This result indicated that
regional transport from the west, e.g., Linyi, can also be a significant
contribution to air pollution in Rizhao. Comparatively, organics and nitrate
showed very different sources between September and March. While organics
and nitrate were dominantly from regional transport from the west in
September, they were mainly from local emissions and production in March. BC
showed similar source regions in the two seasons, which were from both local
emissions and regional transport.
Bivariate polar plots of (a) PM2.5 and aerosol species; (b)SO2, CO, NO2, and OA factors in September 2018 (third row) and March 2019 (fourth row), respectively.
HOA and LO-OOA showed similar potential source regions to those of organics
and nitrate, i.e., mainly transported from the west in September 2018 with
an increased contribution from local emissions in March 2019. In addition, a
potential source region located to the northwest was also observed for
LO-OOA in March. Similar to HOA and LO-OOA, the major source region of CCOA
was located to the west with an additional source region to the southwest.
The high potential source regions of MO-OOA were more complicated and have
many differences between the two seasons. As shown in Fig. 6b, regional
transport played a significant role for the high mass concentrations of
MO-OOA in both September and March. While MO-OOA was mainly transported to
the sampling site from the west in fall, the transport from the southwest
and northwest also contributed substantially to the high mass loading of
MO-OOA in spring, elucidating the diverse sources of MO-OOA during this
season. Overall, the regional transport from the west was the most
significant and common source for all OA factors in September and March,
although that from the northwest and southwest also played a role in spring.
The large differences in sources between sulfate, ammonium, and OA factors in
spring also demonstrated that the industrial plumes with high concentrations
of ammonium sulfate were not associated with correspondingly high OA.
The potential source regions for high concentrations of CO and SO2 were
located in the southwest region in both September and March, which were also
similar to those of sulfate and ammonium. These results illustrated the
similar and significant impacts of the emissions from the RSP on CO an
SO2 as those of ammonium sulfate. We also noticed relatively high
concentrations of CO and SO2 in the northeast region in March,
suggesting the potential impacts of the SSP. However, the impacts appeared
to be much smaller than that of RSP. One reason is that the current steel
production of the SSP was much smaller than that of RSP. Another reason
could be due to the different FGD (Saarnio et al., 2014) and emission
control technologies. Compared with CO and SO2, NO2 was subject to
multiple influences from steel plants, regional transport from the west and
south, and local production.
Evolution of meteorological parameters including T and RH, gaseous
species, mass concentrations, and mass fractions of chemical species in
PM2.5 during six steelworks plume episodes. The pie charts show the
contribution of each species emitted completely from steel plants. The wind
rose plots depict the prevailing wind direction during each plume period.
Correlations between (a)NOx and CO, (b)NOx and
SO2, (c) BC and CO, and (d) BC and NOx during periods with (open
circles) and without (gray triangle) influences of the emissions from the
steel plants. RSP is the Rizhao steel plant and SSP is the Shandong steel plant.
Industrial plumes
Six clear industrial plumes, one in September and five in March, were
observed during this study. Depending on meteorological conditions, the
duration of plumes lasted from ∼2 to 13 h. As shown in
Fig. 7, the steelworks plumes were characterized by dramatic increases in
sulfate and ammonium, while the changes in organics and nitrate were small.
This result was consistent with a study near a steel plant in Wales (UK),
which also showed sharp increases in sulfate and sulfur-containing particles
(Dall'Osto and Harrison, 2006). For example, the sulfate
concentrations varied significantly during the plume period with the highest
concentrations being 108 and 189 µg m-3 in
September and March, respectively. By subtracting the background
concentration that was determined as the average of 1 h data before and
after the plume, we found that the average mass concentration of PM2.5
emitted from steel plants varied from 18 to 55 µg m-3 during the
six plumes, suggesting a large impact of steel plant emissions on the air
quality nearby. Figure 7 shows that aerosol particles from the steel plants
were overwhelmingly dominated by ammonium sulfate, and the contribution of
ammonium bisulfate to the total mass was in the range of ∼ 20 %–40 %. We noticed that the occurrence of six plumes was associated
with the southerly and southwesterly winds (Fig. 7), suggesting that
ammonium sulfate and bisulfate particles were dominantly from RSP, while the
emissions from the SSP appeared to be small. One reason is that more
advanced purification and emission control technologies are used for the
newly built SSP. Unfortunately, whether ammonium sulfate and bisulfate were
directly emitted from the steel plants or from the reactions of SO3
with NH3 during transport from the steel plant to the sampling site
is unknown.
We also observed large increases in gaseous species of CO, SO2, and
NOx from steel plants during the plume periods. As shown in Fig. 8, CO
was highly correlated with NOx (r2=0.83), and NOx was
also moderately correlated with SO2 (r2=0.51) during the
periods of the six plumes, while such correlations were much weaker during
periods in the absence of plume influences (r2= 0.26–0.17). These
results suggest that a large fraction of CO, NOx, and SO2 were
co-emitted from the steel plants, while these gaseous pollutants were
subject to multiple influences from different sources during other periods.
For example, BC, a tracer for incomplete combustion, was highly correlated
with NOx during periods with small influences from steel plants
(r2=0.66), suggesting that BC and NOx were mainly from
traffic emissions, because steel plants unlikely emit a large amount of BC.
As shown in Fig. 8, the average mass ratios of NOx/SO2 and
NOx/CO from the steel plant plumes were significantly lower than those
during periods in the absence of plumes (1.24 vs. 1.55 and 0.014 vs. 0.04,
respectively). Although the SSP emits much less ammonium sulfate particles,
and gaseous CO and NOx, we found that the ratios of NOx/SO2
and NOx/CO were similar between these two steel plants. The previously
reported values of NOx/CO from other emission sources, e.g., biomass
burning (0.056–8.33) and on-road motor vehicles (0.04–0.05), were
significantly larger than the NOx/CO ratio during steel plant plumes in
this study (Schürmann et al., 2007; Fujita et al., 2012; Tiwari et al.,
2015; Santos et al., 2018). Moreover, the NOx/CO ratio during plume-excluded period was close to the values from vehicle emissions (0.04 vs.
0.04–0.05) (Fujita et al., 2012). The lower
NOx/SO2 ratio was found to be characteristic of emissions from
coal burning, e.g., in power generation plants or steelworks, while
higher values were generally attributed to vehicle emissions
(Parrish et al., 1991). For example, the NOx/SO2
value of 1.04, obtained from the fresh flue-gas plume from a coal-fired
power plant when FGD and fabric filter were used (similar to the flue-gas
cleaning conditions of RSP), is close to the value of 1.24 during steel
plant plumes in this study (Mylläri et al., 2016). In conclusion,
our results highlight that NOx/SO2 and NOx/CO ratios combined
with a significant increase in ammonium sulfate can be used as good
diagnostics for evaluating the impacts of steel plant emissions on air
quality in industrial regions and nearby.
Conclusions
We conducted two campaigns in the vicinity of two steelworks in a coastal
city in Shandong, China, using a PM2.5 ToF-ACSM and various colocated
instruments to investigate aerosol composition and sources in coastal
regions and chemical characteristics of air pollutants from the emissions of
steel plants. Our results showed that the mass concentrations of PM2.5 varied greatly in the two seasons, and aerosol composition was also
substantially different. The average PM2.5 concentration in March 2019
(54±44µg m-3) was approximately twice that in September 2018 (26±23µg m-3) with nitrate being the largest fraction
(32 %) followed by organics (29 %). Comparatively, aerosol composition
in September showed a high contribution of sulfate (28 %) and
correspondingly low nitrate (17 %). PMF analysis showed the dominance of
SOA in both March and September (86 % vs. 92 %). While LO-OOA
contributed similarly to OA (32 % vs. 34 %), MO-OOA in September showed
a higher contribution than that in March (60 % vs. 51 %). Most aerosol
species showed similar diurnal variations in the two seasons with higher
concentrations during nighttime and lower values at daytime, which were
primarily driven by boundary layer dynamics and sea–land breeze. Sulfate and
MO-OOA, however, presented relatively flat diurnal patterns, because they were
formed over a regional scale. Bivariate polar plots reveal the two major
sources of air pollutants in this study, including the regional transport
from the west and the impacts of steel plant emissions from the southwest.
By analyzing six industrial plumes, we found that the emissions of steel
plants were characterized by large increases in sulfate and ammonium, as well as in gaseous species of CO and SO2. In fact, aerosol particles of
the six plumes were overwhelmingly dominated by ammonium sulfate and
ammonium bisulfate. Although the SSP appeared to emit low concentrations of
sulfate and ammonium, we found that the ratios of NOx/CO and
NOx/SO2 were close to those from RSP, and they were both
significantly different from those during periods in the absence of
industrial plumes. Our results highlight that ammonium sulfate, NOx/CO,
and NOx/SO2 can be used to evaluate and quantify the
impacts of steel plant emissions on air quality in industrial regions and
nearby. Also, our results demonstrate a need for the RSP to reduce sulfur
emissions using more effective and advanced emission control technologies in
the future.
Data availability
The data in this study are available from the authors upon request
(sunyele@mail.iap.ac.cn).
Author contributions
YS and ZW designed the research. LL, CX, QW, YC, and DW conducted the
measurements. LL, YH, WZ, and YS analyzed the data. PF, WH, XP, ZW, DRW, and YS
reviewed and commented on the paper. LL and YS wrote the paper.
Competing interests
Douglas R. Worsnop is an employee of Aerodyne Research, Inc. (ARI), which developed the ToF-ACSM utilized in this study.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant nos. 2017YFC0212704 and 2017YFC0209601) and the National Natural Science Foundation of China (grant no. 41711540297).
Review statement
This paper was edited by Alex Lee and reviewed by two anonymous referees.
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