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Aerosol and Air Quality Research, 16: 2129–2144, 2016 Copyright
© Taiwan Association for Aerosol Research ISSN: 1680-8584 print /
2071-1409 online doi: 10.4209/aaqr.2015.12.0668 Process
Contributions to Secondary Inorganic Aerosols during Typical
Pollution Episodes over the Pearl River Delta Region, China
Zhijiong Huang1, Jiamin Ou1, Junyu Zheng1*, Zibing Yuan1, Shasha
Yin1, Duohong Chen2, Haobo Tan3 1 School of Environmental and
Energy, South China University of Technology, University Town,
Guangzhou 510006, China 2 Guangdong Environmental Monitoring
Center, State Environmental Protection Key Laboratory of Regional
Air Quality Monitoring, Guangzhou, China 3 Key Laboratory of
Regional Numerical Weather Prediction, Institute of Tropical and
Marine Meteorology, China Meteorological Administration, Guangzhou,
China ABSTRACT
The Integrated Process Rate (IPR) analysis embedded in CAMx
model was used to quantify contributions from different atmospheric
processes to the formations and accumulations of ambient PM2.5 and
the secondary inorganic aerosol (SIA) during two typical
particulate pollution episodes in different seasons in the Pearl
River Delta (PRD) region. Process analysis results indicated that
primary fine particle emissions were the major sources of high
ambient PM2.5 in urban areas with intensive anthropogenic
activities. Aerosol process and advection transport were another
two major processes contributing to the increasing PM2.5 and SIA
over the PRD region. Regarding formation of SIA species, elevations
of nitrate and ammonium at Guangzhou (urban), Heshan (rural) and
Panyu (suburban) sites were largely associated with aerosol
process, while those at Huizhou (urban) site were dominated by
advection process, but elevated sulfate concentrations at these
four sites were all dominated by advection process. The difference
can be attributed to spatial variations of SO2, NOx and NH3
emissions, site locations and meteorological conditions. Advection,
aerosol chemistry, deposition and vertical diffusion were important
pathways to remove SIA at these four sites. Within the hours with
most growing PM2.5 concentrations, aerosol process was the most
important contributor to the formation of new SIA throughout the
entire planetary boundary layer. Keywords: Secondary inorganic
aerosol; PM2.5; PRD; CAMx; Process analysis. INTRODUCTION
With the rapid industrialization and urbanization in the past
decades, haze has become a nationwide pollution problem in China
due to its significant effects on visibility, human health and
climate change (IPCC, 2001; Menon et al., 2002; Nel, 2005). In
response to the severe pollution, central government of China
issued a new ambient air quality standard (GB 3095-2012) by taking
PM2.5 into account and announced the aim to reduce concentrations
of PM2.5 by up to 25% by 2017 with respect to 2012 levels in
Beijing-Tianjin-Hebei (BTH) area, Yangtze River Delta (YRD) and
Pearl River Delta (PRD) region.
The PRD region is one of the most densely urbanized and
developed regions in China. With only 0.4% of China’s *
Corresponding author. Tel.: 86-20-39380021; Fax: 86-20-39380021
E-mail address: [email protected]
land area and 3.2% of the population, it contributes 9.3% of
national gross domestic product and 25.2% of total trade in 2013.
Accompanied by the increased economic activities and intensive
industrialization, PRD has severe air pollution problems in the
last decade. The aerosol optical depth (AOD) retrieved from MODIS
satellite data showed a hotspot in this area (So et al., 2005; Wu
et al., 2005; Lin et al., 2015), with higher records during autumn,
winter and spring (GDEMC and HKEPD, 2013). Thanks to the continuous
emission control, the number of haze days in PRD declined in recent
years, from over 100 in 2007 to 65 in 2013
(http://www.grmc.gov.cn). However, annual average PM2.5 levels in
PRD (42 µg m–3 in 2014) still exceeded the national standard (35 µg
m–3) (GDEMC and HKEPD, 2014; GB 3095-2012). To further alleviate
PM2.5 pollution in the PRD, more efficient and evidence-based
control strategies should be formulated and adopted on the basis of
in-depth understanding of interactions among meteorology, primary
emissions, physical and chemical processes and PM2.5 formations in
this region.
Such interactions can be simulated by a three-dimensional
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2130
(3-D) air quality model (AQM) which has been widely used in the
PRD region (e.g., Cheng et al., 2007; Feng et al., 2007; Wang et
al., 2009; Kwok et al., 2010; Zhang et al., 2011). Process analysis
(PA) is an embedded tool that analyzes contributions from different
physical and chemical processes within the model, thereby can
provide comprehensive insight to the formation of pollution
(Jeffries and Tonnesen, 1994). In China, attempts have been made to
study the formation and fate of particulate matter in recent years
using PA. For example, Liu et al. (2010) used the PA tool embedded
in Community Multiscale Air Quality (CMAQ) model to identify the
most influential processes and chemical reactions of ozone and PM10
in China in different seasons. Li et al. (2014) investigated
governing chemical and physical processes contributing to the
change of PM2.5 during a haze episode in the YRD region. Fan et al.
(2014) applied PA tool to discern the different formation of PM10
in various cities in the PRD region during October 10–12, 2004.
However, previous studies generally focused on PM10 or a single
haze period, while governing processes in the formation of haze
events characterized by high concentrations of PM2.5 and its
temporal and spatial variations across the PRD region remained
largely unknown.
In this study, the Comprehensive Air Quality Model with
Extensions (CAMx) with the embedded PA was applied to two typical
haze periods in the PRD. Four locations with distinct backgrounds
or emission characteristics in the PRD region were selected for
analysis. The objective was to identify governing chemical and
physical processes that contribute to elevated PM2.5 concentrations
and their variations over the PRD region. Considering the large
proportion of secondary inorganic aerosol (SIA) during haze periods
(Lai et al., 2007; Yang et al., 2011; Fu et al., 2014; Huang et
al., 2014), this study mainly focused on SIA. DATA AND METHODOLOGY
Modeling System Set up
The Weather Research and Forecasting (WRF) model version 3.2
(Skamarock et al., 2005), the Sparse Matrix Operator Kernel
Emissions processor for the PRD region (SMOKE-PRD) (Wang et al.,
2011) and the CAMx model version 5.4 (Environ, 2011) were
configured as the regional air quality modeling system in this
study. The lambert-conformal projection was used as the basic
projection scheme. Three levels of nested domains were used in CAMx
model (Fig. S1) and WRF model with grid resolutions of 27 km, 9 km,
and 3 km, respectively. The outermost domain (D1) coveres most
parts of East Asia, Southeast Asia and the northern part of Western
Pacific. The middle domain (D2) coveres Guangdong province and its
surroundings, and the innermost domain (D3) coveres the whole PRD
region. CAMx has 18 vertical layers from the surface to 50 hPa,
while WRF has 26 vertical layers with 18 layers under 1000 meters
height. Heights of the lower 12 vertical layers that were used in
the IPR analysis in the CAMx model are 36, 80, 131, 200, 298, 437,
635, 916, 1098, 1316, 1576 and 1887 m, respectively. More
information of model configurations in WRF and CAMx are listed in
Table S1 of the supporting
information (SI). Since Asymmetric Convective Model (ACM) module
is not supported in the process analysis, K-theory was used instead
to calculate the vertical diffusion. Clean air profiles available
in CAMx were used as boundary conditions for D1. For the nested
domain, boundary conditions were generated from parent domains.
The Integrated Processes Rate (IPR) analysis, one component of
PA, identifies detailed physical and chemical process rates for
selected grid cells and pollutants. Processes considered in this
study were: vertical advection (VADV), vertical diffusion (VDIF),
horizontal advection (HADV), horizontal diffusion (HDIF),
deposition (TDEP), aqueous aerosol chemistry (AQUE), aerosol
chemistry (AERO) and emission (EMIS). In IPR analysis, AERO refers
to the effect of inorganic aerosol chemistry and organic aerosol
chemistry in the aerosol module. TDEP refers to the effect of dry
deposition only occurring in the surface layer and wet deposition.
It should be noted that, unlike the upper layer, vertical transport
in the surface layer only interacts with the second layer via VADV
and VDIF.
Emission Inventories and Processing
The 2010 Guangdong emission inventory developed by Pan et al.
(2014) was adopted for Guangdong area in D2 and D3. This emission
inventory, containing NOx, SO2, CO, VOC, NH3, PM10, PM2.5, BC and
OC pollutants, covered all major emission sources in Guangdong
province, including mobile sources, combustion sources, dust
sources, VOCs solvent usage, agricultural sources, industrial
processes, biomass burning, and other sources. For Hong Kong area
in D2 and D3, annual emissions provided by the Hong Kong
Environment Protection Department (HKEPD, http://www.
epd.gov.hk/epd/english/top.html) was used. The MEGAN (Guenther et
al., 2012) model was used to create hourly biogenic VOC emissions
in all domains. For anthropogenic emissions, the SMOKE-PRD modeling
system was used to process the bulk annual emission amount into
hourly and gridded model-ready emission data for all domains, with
updated temporal and spatial profiles (Yin et al., 2015).
Specifically, the most up-to-date local chemical profiles of VOC
sources in the PRD region were also adopted in this study (Zheng et
al., 2013).
The Multi-resolution Emission Inventory for China (MEIC,
http://www.meicmodel.org) was applied for D1 and non-Guangdong
regions in D2 and D3. For the region in D1 but outside China, the
Regional Emission inventory in Asia (REAS,
http://web.nies.go.jp/REAS/) emission inventory (Kurokawa et al.,
2013) was adopted. MEIC and REAS are in the form of monthly
emission inventory with horizontal resolutions of 0.25 degree. A
re-gridded program was developed to process the MEIC and REAS data
into model-ready emission data for the target domain by using
temporal allocation profiles in SMOKE-PRD and national spatial
allocation profiles.
Observation Data and Evaluation Protocol
To evaluate the performance of CAMx, measurements of PM2.5,
PM10, NOx, NO2 and SO2 concentrations at 12 monitoring stations of
the Pearl River Delta Regional Air
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2131
Quality Monitoring Network (RAQM), hourly concentrations of
sulfate, nitrate and ammonium in April 2013 from Panyu station
(Zhao et al., 2014), and 24-h average concentrations of sulfate,
nitrate and ammonium in November 2013 in Nansha, Nanhai, Guangzhou
and Dongguan were adopted. Locations of monitoring stations were
shown in Fig. 1 and details of their surrounding environments were
given in Table S2 of SI. The evaluation protocol proposed by the
U.S. Environmental Protection Agency (EPA) was followed for
validation, with indicators including the mean bias (MB), mean
error (ME), normalized mean bias (NMB), normalized
mean error (NME), root mean square error (RMSE), fraction bias
(FB), fraction error (FE), and correlation coefficient (R) (Boylan
and Russell, 2006).
Selection of Analysis Periods and Locations
Two typical pollution episodes with different weather conditions
during 13–15 April and 19–21 November 2013 were selected as study
periods. As shown in Fig. 2, the central PRD region, including
Guangzhou, Foshan, and Dongguan, were severely polluted on 15
April. The average PM2.5 concentration over the PRD region was 82.3
µg m–3
Fig. 1. Locations of IPR sites, SIA observation stations and
PRDRAQM (Guangzhou, Panyu, Heshan and Huizhou site was selected as
IPR sites).
(a) (b)
Fig. 2. Regional air quality index distributions over the PRD
region on April 15, 2013 (a) and November 20, 2013 (b).
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2132
during 13–15 April, and the highest PM2.5 concentration of 226.0
µg m–3 was recorded at Dongguan at 07:00 a.m. on 15 April. Another
pollution episode on 19–21 November was selected, during which the
average region-wide PM2.5 concentration was 80.3 µg m–3 with the
highest PM2.5 concentration of 139.0 µg m–3 at Zhongshan.
For IPR analysis, four sites in the PRD region, i.e., Heshan,
Panyu, Guangzhou and Huizhou site, were selected. Heshan is a rural
site located in the downwind area of PRD, Panyu is a suburban site,
Guangzhou is an urban site with intensive anthropogenic emissions,
while Huizhou is located in the eastern PRD often but shows
different PM2.5 formation (Fan et al., 2014). These four sites with
different environmental background can basically capture the
formation patterns of PM2.5 across the PRD region. RESULTS
AND DISCUSSION Model Performance
Table 1 summarizes overall performance statistics for the hourly
concentrations of NO2, NOx, SO2, PM2.5 and PM10 in April and
November. In general, the model underestimated NO2 and NOx in both
months and SO2 in April, but it performed relatively better for NO2
and NOx than SO2 with lower NMEs and higher R. Biases of simulated
NO2, NOx and SO2 were attributed to the uncertainties inherent in
emission inventories and inaccuracies of simulated meteorological
fields (Liu et al., 2010).
For PM2.5, simulated concentrations showed a high consistency
with observations, with FEs of 40.8% and 44.9% and FBs of –2.4% and
–4.7% in April and November, respectively, indicating that PM2.5
was slightly underestimated in these two months. According to the
recommended values of FB (≤ ±30%) and FE (≤ 50%) by Boylan and
Russell (2006), simulated PM2.5 showed a satisfactory performance.
In comparison, PM10 was underestimated in April but overestimated
in November. Given the fact that dust fugitive was a major
contributor to PM10 emissions in Guangdong province, the negative
bias in April and positive bias in November might be partially
explained by uncertainties of temporal allocations of dust sources
in the SMOKE-PRD system.
Fig. 3 shows that the model well reproduced temporal variations
of PM2.5 and SIA at Panyu site in April, with R of 0.70, 0.65, 0.65
and 0.58 for PM2.5, nitrate, sulfate, and ammonium, respectively.
Changes of SIA species during the pollution episode on 14–15 April
were captured by CAMx model. Nitrate and ammonium were generally
overestimated in April, especially during 9–14 April. By contrast,
sulfate was underestimated in general, particularly during 16–24
April. Table 2 summarizes comparisons between observed and
simulated SIA at Nansha, Nanhai, Guangzhou and Dongguan in
November. The simulated and observed SIA were at similar levels,
with the mean bias of –22.8% to 44.2% for ammonium, –29.0% to 31.2%
for nitrate and –20.1% to 31.0% for sulfate. The relatively small
biases of simulated SIA at these four stations indicated that the
spatial distribution of SIA was well reproduced in the PRD region,
which was reliable for following IPR analysis.
Tabl
e 1.
Mod
el p
erfo
rman
ce o
f CA
Mx
in A
pril
and
Nov
embe
r.
Met
rics
O3
NO
2 N
Ox
SO2
PM10
PM
2.5
Apr
. N
ov.
Apr
. N
ov.
Apr
. N
ov.
Apr
. N
ov.
Apr
. N
ov.
Apr
. N
ov.
FB
29.0
%
13.9
%
–13.
5%
–18.
7%
–16.
4%
–27.
1%
–15.
1%
0.9%
–2
3.1%
6.
3%
–2.4
%
–4.7
%
FE
65.3
%
64.6
%
49.1
%
48.1
%
53.3
%
53.8
%
73.0
%
60.1
%
46.7
%
51.2
%
40.8
%
44.9
%
NM
B
26.0
%
4.1%
–8
.2%
–1
1.7%
–9
.8%
–1
9.4%
–1
2.7%
15
.5%
–2
2.5%
18
.8%
–3
.5%
–1
.8%
N
ME
54.1
%
47.3
%
40.9
%
41.6
%
49.5
%
48.7
%
74.5
%
71.5
%
41.5
%
56.1
%
37.9
%
41.8
%
RM
SE
0.02
0.
02
0.01
0.
02
0.03
0.
03
0.01
0.
01
45.1
69
.36
25.2
1 31
.35
R
0.69
0.
64
0.63
0.
64
0.50
0.
51
0.20
0.
32
0.53
0.
49
0.64
0.
54
Bia
s Fac
tor
2.71
3.
1 1.
11
0.99
1.
08
0.92
1.
56
1.55
0.
93
1.36
1.
15
1.13
N
umbe
r of P
oint
s 13
268
1291
6 13
418
1308
6 11
809
1206
0 13
463
1300
0 13
004
1276
9 12
424
1220
1
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2016 2133
Fig. 3. Times series of simulated SIA and observed SIA at Panyu
site.
Table 2. Comparison between simulated SIA and observed SIA at
four off-line aerosol sampling stations. (unit: µg m–3)
Sites Month Date Ammonium Nitrate Sulfate Obs Sim Percentage Obs
Sim Percentage Obs Sim Percentage
Dongguan 11 20 6.3 5.5 –13.3% 9.0 6.6 –26.6% 11.9 9.5 –20.1% 11
26 4.0 3.8 –7.0% 4.7 3.3 –29.0% 7.7 7.6 –1.7%
Guangzhou 11 20 5.6 7.4 32.2% 6.9 8.5 24.3% 12.8 13.4 4.6% 11 26
3.5 5.1 44.2% 5.2 4.8 –6.4% 7.8 9.9 27.1%
Nanhai 11 20 8.6 9.1 5.8% 11.6 14.1 22.1% 16.3 13.4 –18.1% 11 26
4.0 6.2 55.4% 6.6 8.6 31.2% 7.5 9.9 31.0%
Nansha 11 19 4.0 3.1 –22.8% 5.7 4.2 –26.2% 10.4 8.6 –17.4% 11 26
4.3 3.0 –31.6% 5.8 4.9 –15.7% 8.2 9.6 17.2% Results also indicated
that the model overestimated nitrate and ammonium and
underestimated sulfate when SIA was relatively high. Positive
biases of nitrate and ammonium were also found in other simulation
studies (Fountoukis et al., 2011; Walker et al., 2012; Wang et al.,
2013; Barbaro et al., 2015) which might be caused by the
uncertainties in NOx emissions, •OH concentration, oxidation rate
of NO2 by •OH, removal rate of HNO3 and under-predicted planet
boundary layer (PBL) (Heald et al., 2012; Pirovano et al., 2012;
Walker et al., 2012). Process Contributions to Ground-Level PM2.5
and SIA in the PRD Region
The IPR results of hourly contributions from atmospheric
processes to the formations of PM2.5 and its major compositions
including ammonium (PNH4), nitrate (PNO3)
and sulfate (PSO4) at Guangzhou, Heshan, Panyu, and Huizhou site
during 13–15 April and 19–21 November are shown in Figs. 4–7.
Concentrations of PM2.5 and SIA contributed by atmospheric
processes are shown in Table S2 of SI. To investigate the
relationships between different processes at these four sites,
correlation coefficients between EMIS/HADV and other processes were
calculated, as shown in Table S3 of SI.
● Guangzhou Site
For urban Guangzhou site, EMIS was found to be one of the
dominant contributors to PM2.5 formation during the two episodes.
Contributions from EMIS showed obvious diurnal patterns with higher
concentrations in the daytime and lower concentrations in the
nighttime. Located in the most densely populated areas, Guangzhou
site had significant
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2134
emissions from various anthropogenic sources, including fugitive
dust, mobile and combustion sources. HADV was another major process
contributing to the PM2.5 formation,
while VADV and VDIF were the two major atmospheric processes
responsible for the loss of PM2.5. Throughout the study periods in
April and November, VADV and HADV
(a) (b) Fig. 4. Process contributions to PM2.5 and SIA at the
surface layer at Guangzhou site during April 13–15 (a) and November
19–21 (b) (LST).
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2135
(a) (b) Fig. 5. Process contributions to PM2.5 and SIA at
the surface layer at Heshan during April 13–15 (a) and November
19–21 (b) (LST). showed highly negative correlation (Table S2).
Diurnal profile of VDIF was highly consistent with that of EMIS,
with a correlation coefficient of –0.95. It was suggested that
intensive EMIS was the main driving force for VIDF that diffused
PM2.5 to the upper layers. Compared with VDIF, the effect of HDIF
at Guangzhou site was negligible. In both
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2016 2136
months, parts of the ground-level PM2.5 was explained by AERO,
with average rates of 27.1 ± 12.1 µg m–3 h–1 and 18.2 ± 7.5 µg m–3
h–1 in April and November, respectively,
indicating that chemical production of PM2.5 in Guangzhou cannot
be neglected.
Hourly contributions from atmospheric processes to
(a) (b) Fig. 6. Process contributions to PM2.5 and SIA at
the surface layer at Panyu during April 13–15 (a) and November
19–21 (b) (LST).
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2016 2137
(a) (b) Fig. 7. Process contributions to PM2.5 and SIA at
the surface layer at Huizhou during April 13–15 (a) and November
19–21 (b) (LST). PNH4, PNO3, and PSO4 were also identified (Fig.
4). It was found that contribution patterns of HADV and VADV to SIA
were similar with those to PM2.5. However, differences
were noticeable concerning the contribution patterns of AERO to
SIA. AERO played an important positive role in the formation of
PNH4 and PNO3 but was peripheral to the
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2138
evolution of PSO4. Instead, PSO4 at Guangzhou site was largely
influenced by HADV and VADV. This is because NOx emissions, which
were generally emitted from mobile and combustion sources, were
intensive in the downtown area of Guangzhou, while SO2 emissions
were relatively lower. Similar results were also found by Xue et
al. (2014) using observation-based model. Moreover, the
contribution of AERO was more significant in April, due to higher
NH3 emissions (Zheng et al., 2012) and humidity in spring.
● Heshan Site
Located in the rural southwestern PRD, Heshan saw significant
contributions from advection transport (HADV and VADV). During the
pollution episode on 15 April, ground-level PM2.5 was mainly
enhanced by VADV from the upper air under the surface divergence.
During the episode in November when Heshan was the downwind of the
central PRD under the prevailing northeasterly wind, high ambient
PM2.5 was mainly contributed by HADV. As a typical rural site,
anthropogenic emissions at Heshan site were relatively small,
indicating that ambient PM2.5 and its precursors were largely
transported from upwind areas.
AERO also made contributions to the change of PM2.5
concentrations in Heshan site by enhancing the ground-level SIA at
night but depleting SIA in the daytime. Examining hourly
contributions of SIA, we can find that PNO3 dominated the dynamic
aerosol process, while AERO of PSO4 provided a negligible
contribution. The depletion of SIA in the day time was resulted
from vaporizations of PNO3 and PNH4 due to the higher temperature
(Griffith et al., 2015). Additionally, contributions of AERO to
PNO3 and PNH4 were more significant during pollution episodes,
indicating the important role of chemical production in high PM2.5
concentrations at Heshan site.
● Panyu Site
Panyu is a typical suburban site and advection process was
revealed as the most significant contributor to the change of PM2.5
concentrations. In April and November, high PM2.5 concentrations
mainly resulted from VADV which brought PM2.5 from upper layers
under the prevailing northeasterly winds (62 degree on average for
April and 52 degree on average for November). In addition, VDIF was
an important process to remove the ground-level PM2.5, particularly
during severely polluted periods. EMIS also
made part of contributions to the ground-level PM2.5. Due to the
relatively high NOx emissions and abundant SIA
precursor transported from upwind areas, contributions of AERO
to PNO3 and PNH4 at Panyu site were obvious, especially during the
time when ambient PM2.5 was high. Contribution patterns of AERO at
Panyu site were similar with those at Heshan site, as shown in
Figs. 5 and 6. Owing to relatively large local SO2 emissions (Fig.
S3), AERO of PSO4 was larger than that at Heshan site. The rate
even reached about 8.00 µg m–3 h–1 during the peak period.
Additionally, advection transport was another major contributor to
the change of SIA at Panyu, indicating the importance of transport
in the formation of ambient SIA in suburban areas of the PRD
region.
● Huizhou Site
EMIS, HADV, VADV, and VDIF were all significant contributors to
the PM2.5 formation at Huizhou site. This site is located in the
downtown area of Huizhou city where there were moderate PM2.5
emissions from fugitive dust, mobile and combustion sources.
Compared with other sites, loss of surface PM2.5 by DIFF was
noticeable at this site.
AERO, HADV, and VADV either increase or remove SIA at Huizhou
site depending on the variation of weather conditions. Contribution
patterns of PNO3 showed that AERO dominated the change of nitrate,
especially in April. For instance, AERO constituted a major
positive contribution to the change of nitrate during the two peaks
in April, while it also depleted most nitrate at noontime on 14
April. Contribution patterns of AERO to PNH4 in both periods were
consistent with those of PNO3, though the rates were smaller. On
the contrary, PSO4 formation was mainly caused by atmospheric
advection transport during the two periods, which were consistent
with the previous study (Qin et al. 2015).
Spatial Differences in Process Contributions to SIA
As discussed above, AERO, HADV, and VADV were the major
processes responsible for the change of SIA in the PRD region. To
characterize their spatial variations across the PRD region,
correlations between process contributions and changes of SIA and
spatial distributions of AERO were analyzed in Table 3 and Fig. 8,
respectively. Since HADV and VADV represent the divergence and
convergence of atmosphere, respectively, they showed highly
negative
Table 3. Correlation coefficients between processes and changes
of SIA concentrations.
Guangzhou Heshan Panyu Huizhou
Pa Nb P N P N P N
SIA TADVc 0.32 0.41 0.48 0.18 0.31 0.34 0.68 0.53
AERO 0.44 –0.32 0.52 0.3 0.51 –0.01 0.3 0.03
PNO3 TADV 0.08 0.32 0.36 0.27 0.32 0.13 0.50 0.40 AERO 0.53
–0.31 0.52 0.20 0.41 0.04 0.42 0.04
PNH4 TADV 0.36 0.39 0.42 0.24 0.32 0.35 0.73 0.56 AERO 0.41
–0.32 0.53 0.23 0.46 0.01 0.34 0.04
PSO4 TADV 0.83 0.80 0.68 0.65 0.63 0.86 0.84 0.67 AERO 0.38
–0.25 –0.05 –0.31 0.10 –0.13 –0.09 0.22 a P: Positive change; b N:
Negative change; c TADV = HADV + VADV.
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2016 2139
(a) PNH4-Apr. (b) PNH4-Nov.
(c) PNO3-Apr. (d) PNO3-Nov.
(e) PSO4-Apr. (f) PSO4-Nov.
Fig. 8. Spatial distributions of the average inorganic aerosol
process in layer 1–12 over the PRD region during April 13–15 (a, c,
e) and November 19–21 (b, d, f) (LST).
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2140
correlation, which might obscure the relationship between
advection transportation and changes of SIA (Table S5). In order to
eliminate this impact, total advection process (TADV, the sum of
HADV and VADV) was used in the following analysis.
It was revealed that the accumulation of SIA was influenced by
TADV in different degrees. TADV showed high contributions to the
elevated SIA concentrations at Guangzhou, Heshan, Panyu, and
Huizhou site in both months, indicating that advection transport
had significant impacts on the accumulations of SIA over the entire
PRD region. As Guangzhou site is located in the downtown area with
high anthropogenic emissions while Huizhou site is located in the
upwind area of the PRD region, TADV at Guangzhou site might be
partly originated from the surrounding areas while TADV at Huizhou
might be greatly influenced by super-regional transport. TADV was
also responsible for the dispersion of SIA in the PRD region.
AERO were also found highly correlated to the elevation of
ambient SIA, with correlation coefficients from 0.31 to 0.68,
indicating high contributions from the aerosol process over the PRD
region. The correlations were even higher than those of TADV at
Guangzhou, Heshan, and Panyu, which means that SIA growths at these
sites were more dominated by AERO. In terms of SIA species, the
increasing PNO3 and PNH4 at Guangzhou, Heshan, and Panyu site were
dominated by AERO. In urban areas with high NOx emissions, such as
Guangzhou site, abundant NOx emissions were conducive to AERO of
PNO3 and PNH4. In suburban and rural areas characterized by low NOx
emissions (As shown in Fig. S3), such as Panyu and Heshan site, the
dominated AERO was largely due to the transport of precursors of
SIA (HNO3) from upwind areas. On the contrary, the increasing PNO3
and PNH4 at Huizhou site were mainly influenced by TADV, which was
highly related to the super-regional transport. In term of PSO4,
TADV generally dominated at all four sites. Nonetheless, AERO of
PSO4 at Panyu site was the largest among these four sites since it
is close to an industrial area (Fig. S3).
Examination of spatial distributions of AERO species revealed
similar results. As shown in Fig. 8, during the two episodes,
contributions of AERO to PSO4 both peaked within narrow areas where
SO2 emissions are intensive, such as ports, power plants and major
industries. For other areas with lower SO2 emissions, PSO4 growth
was highly associated with advection transport. AERO of PNO3 and
PNH4 were both concentrated in the central PRD region that covered
Guangzhou, Heshan, and Panyu sites. Those areas with high AERO of
PNO3 and PNH4 were somewhat overlapped with areas with high
emissions of NOx and NH3. In addition, their spatial distributions
were also affected by the transport of ambient NH3, NOx, and HNO3.
For instance, AERO of PNO3 and PNH4 were also significant in
downwind areas in November due to the influence of regional
transport.
Atmospheric Process Contributions to SIA at Different Layers
As SIA was the dominated components to ambient PM2.5
in the PRD region, further investigation on process
contributions to SIA in different vertical layers is important to
understand the PM2.5 formation in this region. Therefore,
vertical-dependent process contributions to SIA at the hour with
the largest increasing rate of PM2.5 concentrations during the
pollution episode were examined (9:00 on 15 April and 19:00 on 20
November at Guangzhou, 6:00 on 15 April and 21:00 on 20 November at
Heshan, 3:00 on 15 April and 20:00 on 20 November at Panyu, and
3:00 on 15 April and 21:00 on 20 November at Huizhou).
As shown in Fig. 9, positive contributions of AERO to SIA at
Guangzhou site were observed throughout the PBL and the rates
ranged from 6% to 35% of net SIA changes in April and from 12% to
14% in November. The maximum AERO occurred at the surface layer and
declined along the upper layers. Similar declining patterns of AERO
throughout the PBL can also be found at other sites. This was
attributed to the fact that precursors emissions (NOx and NH3) were
mainly emitted in the ground level over the PRD region.
Interestingly, for Heshan and Panyu site in April, AERO was
significant in the fourth layer at night, leading to peak SIA
concentrations at the 200 m height at Heshan site and on the 131 m
height at Panyu site. Noteworthy is that this aloft high SIA
concentrations occurred during the nighttime. It can be explained
by “pollutant cloud” phenomenon which appears when long distance
transport of precursors is significant under the backdrop of
shrinking nocturnal boundary layer that isolated the pollutant
aloft from the surface (Pecorari et al., 2013). Similarly, obvious
contributions of AERO were found in layer 10–12 (about 1316 m to
1887 m height) at Heshan, Guangzhou, and Panyu site in November,
which might be due to the super-regional transport of aerosol
precursors under the prevailing northeasterly wind. VADV and HADV
played important but opposite roles in different layers throughout
the PBL due to the atmospheric convergence and divergence. SIA
changes caused by these two processes were somewhat weaker in the
upper layers. CONCLUSIONS
The Integrated Process Rate analysis embedded in CAMx model was
used to investigate contributions from different atmospheric
processes to the formations of PM2.5 and SIA at four typical sites
in the PRD region: Guangzhou (urban site), Heshan (rural site),
Panyu (suburban site) and Huizhou (urban site). Two typical
particulate pollution episodes in spring and winter were selected
for IPR analysis.
EMIS was an important process that contributed to PM2.5
formation in urban areas. Guangzhou site had the largest
contribution rates of EMIS among the four sites, followed by
Huizhou site. The highly positive relationship between EMIS and
PM2.5 formation suggested that local PM2.5 emissions control in
urban areas should be continued or even strengthened. EMIS at other
sites had fewer impacts on PM2.5 formations.
Compared to EMIS, AERO has wider impacts on formations of PM2.5
and SIA across the PRD region. It was positively associated with
the elevation of ambient SIA in
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2016 2141
(9:00 April 15th; 8th layer) (19:00 November 20th; 6th
layer)
(6:00 April 15th; 3rd layer) (21:00 November 20th; 6th
layer)
(3:00 April 15th; 4th layer) (20:00 November 20th; 5th
layer)
(3:00 April 15th; 4th layer) (21:00 November 20th; 2nd
layer)
Fig. 9. Process contributions to SIA at layer 1 to layer 12 at
Guangzhou, Heshan, Panyu and Huizhou site in the hour with most
growing PM2.5 concentrations; Descriptions inside the parentheses
are the local standard time and PBL layer of the analysis hour.
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Huang et al., Aerosol and Air Quality Research, 16: 2129–2144,
2016 2142
urban areas and rural areas. The relationship was intensified at
Guangzhou site due to its intensive NOx emissions. AERO at suburban
Panyu and rural Heshan sites were also outstanding as a result of
rich precursors transported from upwind areas. In terms of SIA
species, contributions of AERO to the elevated PNO3 and PNH4 were
significant in the PRD region, with the exception of Huizhou where
elevated PNO3 and PNH4 were mainly transported by TADV. Spatial
distribution also revealed that AERO of PNO3 and PNH4 were both
concentrated in the central PRD region and downwind areas, while
AERO of PSO4 was merely concentrated in narrow areas with intensive
SO2 emissions. In addition, AERO constituted the major positive
contribution to the change of SIA throughout the PBL in the hour
with most growing PM2.5 concentrations. Within the PBL, AERO
generally peaked on the ground level and decreased in upper
layers.
Advection transport (including HADV and VADV) was another
important process that contributed to domain-wide formations of
PM2.5 and SIA, indicating that regional transport was significant
over the entire PRD region. Particularly, PSO4 elevations at all
four sites were largely influenced by advection transport. For
Huizhou site, super-regional transport might be the major driving
force for the SIA formation.
ACKNOWLEDGMENTS
This work was supported by the National Science Fund for
Distinguished Young Scholars of China (No. 41325020), the National
Natural Science Foundation of China (NSFC) (No. 41175085), Chinese
Academy of Science - The PRD gridded emission inventory study under
the atmospheric haze key project (No. XDB05020303), and the NSFC-GD
Joint Foundation of the Key Projects (No. U1033001). We thank
Atmospheric Research Center, HKUST Fok Ying Tung Graduate School
for providing the offline speciation data. SUPPLEMENTARY
MATERIAL
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Received for review, December 23, 2015 Revised, July 6, 2016
Accepted, July 10, 2016