ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-11563-2018Exploration of PM2.5 sources on the regional scale in the Pearl River
Delta based on ME-2 modelingSource apportionment of PM2.5 in PRDHuangXiao-FengZouBei-BingHeLing-Yanhely@pku.edu.cnHuMinPrévôtAndré S. H.ZhangYuan-HangKey Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy,
Peking University Shenzhen Graduate School, Shenzhen, 518055, ChinaState Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences
and Engineering, Peking University, Beijing, 100871, ChinaPaul Scherrer Institute (PSI), 5232 Villigen-PSI, SwitzerlandLing-Yan He (hely@pku.edu.cn)16August20181816115631158011March201820March201823July201824July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/11563/2018/acp-18-11563-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/11563/2018/acp-18-11563-2018.pdf
The Pearl River Delta (PRD) of China, which has a population of more than 58
million people, is one of the largest agglomerations of cities in the world
and had severe PM2.5 pollution at the beginning of this century. Due to
the implementation of strong pollution control in recent decades, PM2.5
in the PRD has continuously decreased to relatively lower levels in China. To
comprehensively understand the current PM2.5 sources in the PRD to
support future air pollution control strategies in similar regions, we
performed regional-scale PM2.5 field observations coupled with a
state-of-the-art source apportionment model at six sites in four seasons in
2015. The regional annual average PM2.5 concentration based on the
4-month sampling was determined to be
37 µgm-3, which is still more than 3 times the WHO
standard, with organic matter (36.9 %) and SO42- (23.6 %)
as the most abundant species. A novel multilinear engine (ME-2) model was
first applied to a comprehensive PM2.5 chemical dataset to perform
source apportionment with predetermined constraints, producing more
environmentally meaningful results compared to those obtained using
traditional positive matrix factorization (PMF) modeling. The regional annual
average PM2.5 source structure in the PRD was retrieved to be secondary
sulfate (21 %), vehicle emissions (14 %), industrial emissions
(13 %), secondary nitrate (11 %), biomass burning (11 %),
secondary organic aerosol (SOA, 7 %), coal burning (6 %), fugitive
dust (5 %), ship emissions (3 %) and aged sea salt (2 %).
Analyzing the spatial distribution of PM2.5 sources under different
weather conditions clearly identified the central PRD area as the key
emission area for SO2, NOx, coal burning, biomass
burning, industrial emissions and vehicle emissions. It was further estimated
that under the polluted northerly air flow in winter, local emissions in the
central PRD area accounted for approximately 45 % of the total
PM2.5, with secondary nitrate and biomass burning being most abundant;
in contrast, the regional transport from outside the PRD accounted for more
than half of PM2.5, with secondary sulfate representing the most
abundant transported species.
Introduction
With China's rapid economic growth and urbanization, air
pollution has become a serious problem in recent decades. Due to its smaller
size, fine particulate matter (PM2.5) can carry toxic chemicals into
human lungs and bronchi, causing respiratory diseases and cardiovascular
diseases that can harm human health (Sarnat et al., 2008; Burnett et
al., 2014). In particular, long-term exposure to high concentrations of fine
particulate matter can also lead to premature death (Lelieveld et al., 2015).
The Chinese government has attached great importance to improving air quality
and issued the “Air Pollution Prevention and Control Action Plan” in
September 2013, clearly requiring the concentration levels of fine
particulate matter in a few key regions, including the Pearl River Delta
(PRD), to drop by 2017 from 15 % to 25 % of their values in 2012. The
PRD is one of the fastest-growing regions in China and the
largest urban agglomeration in the world; it includes the cities of
Guangzhou, Shenzhen, Zhuhai, Dongguan, Foshan, Huizhou, Zhongshan, Zhaoqing,
and Jiangmen and contains more than 58 million people. The PM2.5
concentration in this region reached a high level of
58 µgm-3 in 2007 (Nanfang Daily, 2016); however, the air
quality has significantly improved due to the implementation of strict air
pollution control measures, which were implemented earlier here than in other regions
in China. The annual average concentration of PM2.5 in the PRD dropped
to 34 µgm-3 in 2015 (Ministry of Environmental Protection,
2016).
In recent years, the receptor model method (commonly, positive matrix
factorization, PMF) in the PRD was applied to perform the source apportionment of
PM2.5, which was carried out in several major cities, including
Guangzhou (Gao et al., 2013; Liu et al., 2014; Wang et al., 2016), Shenzhen
(X. F. Huang et al., 2014), Dongguan (Wang et al., 2015; Zou et al., 2017)
and Foshan (Tan et al., 2016). However, the above source apportionment
studies only focused on part of PM2.5 (e.g., organic matter) or a single
city in the PRD (e.g., Shenzhen and Dongguan), lacking the extensive
representation of the PRD region in terms of simultaneous sampling in
multiple cities. Since the lifetime of PM2.5 in the surface layer of the
atmosphere is days to weeks and the cities in the PRD are closely linked, the
transport of PM2.5 between cities is specifically noteworthy
(Hagler et al., 2006). Conversely, although the PMF model has been successfully applied to source
apportionment in the PRD, the apportionment with PMF has high rotational
ambiguity and can output non-meaningful or mixed factors. Under such
conditions, the multilinear engine (ME-2) model can guide the rotation toward
a more objective optimal solution by utilizing a priori information (i.e.,
predetermined factor profiles). In recent years, ME-2, initiated and
controlled via the Source Finder (SoFi) written by the Paul Scherrer
Institute, was successfully developed to apportion the sources of organic
aerosols (Canonaco et al., 2013). The novel ME-2 model has become a widely
used and successful source analysis technique (e.g., Crippa et al., 2014;
Fröhlich et al., 2015; Visser et al., 2015; Elser et al., 2016;
Reyes-Villegas et al., 2016). The key challenges in running ME-2 are the
construction of the appropriate constraint source profiles and the
determination of factor numbers, and PMF could serve as the first step when
using ME-2 for the determination of the a priori information needed.
Accurately understanding the regional characteristics of PM2.5 sources
in the PRD can certainly guide the regional joint prevention and control of
PM2.5 in this region and provide useful references for future air
pollution control strategies in China. Thus, in this study, the PM2.5
mass and chemical compositions were measured during four seasons in 2015 at
six sites in the PRD, which basically represent the pollution level of the
PRD on a regional scale rather than on a city scale. For the first time, the
novel ME-2 model via the SoFi was applied to a comprehensive chemical
dataset (including elemental carbon (EC), organic mass (OM), inorganic ions and metal elements) to identify
the sources of bulk PM2.5 on the regional scale of the PRD; then, the
spatial locations of the sources were systematically explored using the
analysis of weather conditions.
Experimental methodologySampling and chemical analysis
The PRD is located in south central Guangdong Province. Based on the layout
of the cities in the PRD, six sampling sites were selected to represent
urban, suburban, and background sites. Detailed descriptions of these
sampling sites are listed in Table 1, and their locations are shown on the
regional map in Fig. 1.
Description of the sampling sites in the PRD.
SiteSite codeCoordinatesSite descriptionDoumenDMLat: 22.23∘ NSuburbanContains industrial areasLong: 113.30∘ EQi'ao IslandQALat: 22.43∘ NBackgroundAn area for ecotourismLong: 113.63∘ EHeshanHSLat: 22.73∘ NSuburbanContains industrial areas and farmlandsLong: 112.93∘ EModieshaMDSLat: 23.11∘ NUrbanContains dense urban trafficLong: 113.33∘ EUniversity townUTLat: 22.59∘ NUrbanContains urban trafficLong: 113.98∘ EDapengDPLat: 22.63∘ NBackgroundAn area for ecotourismLong: 114.41∘ E
Spatial distribution of the sampling sites in the PRD.
Samples were collected every other day during a 1-month-long period for
each season in 2015, and Table 2 contains the detailed sampling information
for reference. Each sampling period lasted for 24 h at each site. The sampling
sites of University Town (UT) and Dapeng (DP) used Thermo 2300 PM2.5
samplers (Thermo Fisher Scientific Inc., Waltham, Massachusetts, USA, with a
flow rate of 16.7 L min-1 for two channels and a flow rate of
10.0 L min-1 for the other two channels), while those in Modiesha
(MDS), Heshan (HS), Qi'ao Island (QA) and Doumen (DM) used TH-16A PM2.5
samplers (Tianhong Corp., Wuhan, China, with a flow rate of
16.7 L min-1 for four channels). Prior to the sampling campaigns, the
six instruments sampled in parallel three times, and each time lasted for
12 h. The standard deviation of the PM2.5 mass concentrations obtained
by the six samplers in each parallel sampling was within 5 %. The all
sample boxes were then sealed with Parafilm, stored in an ice-packed cooler
during transportation, and stored under freezing temperatures before
analysis. A total of 362 valid samples (15–16 samples at each site for each
season) were collected in this study. In addition, to track the possible
contamination caused by the sampling treatment, a field blank sample was
collected at each site for each season. The PM2.5 mass can be obtained
based on the difference in the weight of the Teflon filter before and after
sampling in a clean room at conditions of 20 ∘C and 50 % relative
humidity, according to the Quality Assurance and Quality Control procedures
of the National Environmental Protection Standard (NEPS; MEE, 2013b). The
Teflon filters were analyzed for their major ion contents (SO42-,
NO3-, NH4+ and Cl-) via an ion chromatography
system (ICS-2500, Dionex; Sunnyvale, California, USA), following the
guidelines of NEPS (MEE, 2016a, b). The metal element contents (23 species)
were analyzed via an inductively coupled plasma mass spectrometer (ICP-MS,
auroraM90; Bruker, Germany), also following the guidelines of NEPS (MEE,
2013a). The Quartz filters were analyzed for organic carbon (OC) and
EC contents using an OC–EC analyzer (2001A, Desert
Research Institute, Reno, Nevada, USA), following the IMPROVE protocol (Chow
et al., 1993). The overall OM was estimated as
1.8× OC. In a previous aerosol mass spectrometer (AMS) measurement
for
PM1, the OM / OC ratio was measured to be 1.6 for an urban atmosphere
(He et al., 2011) and 1.8 for a rural atmosphere (Huang et al., 2011). We
adopted a uniform OM / OC ratio of 1.8 in this study because it is
assumed that the mass difference between PM1 and PM2.5 may mostly
contain aged regional aerosol with higher OM / OC.
Meteorological conditions and weather classification
The meteorological conditions during the observation period, shown in
Table 2, indicated that the PRD region experienced a hot and humid summer and
a cool and dry winter, while spring and fall were two transition seasons.
Furthermore, the back trajectories of the air masses obtained using the NOAA
HYSPLIT model (Fig. S1 in the Supplement) revealed that the air masses
originated from the northern inland in winter, from the northern inland and
the South China Sea in spring, from the South China Sea in summer, and from
the northeast coast and the northern inland in fall.
General meteorological conditions during the observation period in
the PRD.
Sampling days categorized as southerly flow and northerly flow
days.
SoutherlyWind speedPM2.5NortherlyWind speedPM2.5flow(m s-1)(µgm-3)flow(m s-1)(µgm-3)1 Jul 20152.61618 Jan 20152.3783 Jul 20153.61720 Jan 20151.58215 Jul 20151.9173 Feb 201527523 Jul 20152.6127 Feb 20151.710125 Jul 20152139 Feb 20152.27529 Jul 20151.312
Changes in meteorological conditions with the seasons have significant
influences on the air quality in the PRD (Hagler et al., 2006). The same type
of weather is often repeated. Physick and Goudey (2001) classified the weather over the region
surrounding Hong Kong into seven categories based on surface pressure
patterns, i.e., as northerly (winter monsoon), northeasterly (winter
monsoon), easterly or southeasterly, trough, southerly or southwesterly
(summer monsoon), and cyclonic 1 and cyclonic 2 weather types. The PRD region,
including Hong Kong, has nearly similar weather patterns and similar
meteorological conditions. In this study, the daily weather types during the
observation period (excluding rainy days) were also classified into seven
categories based on surface pressure patterns. However, according to the
surface horizontal wind vectors, the PRD was mostly impacted by two types of
airflow, i.e., southerly flow and northerly flow. Southerly flow, including
the southeasterly and southerly or southwesterly (summer monsoon) weather
types, was relatively clean and originated from the ocean (e.g., Figs. S2
and S4). Northerly flow, including the northerly (winter monsoon) and
northeasterly (winter monsoon) weather types, was relatively polluted and
originated from the north mainland (e.g., Figs. S3 and S5). Southerly flow and
northerly flow appeared with the highest frequency in the PRD (i.e., above
80 %), followed by cyclone (10 %), easterly (2 %) and trough
(2 %). In this study, southerly flow days (PM2.5≤17µgm-3; see Table 3) were selected to better reflect the
local source regions in the PRD, and northerly flow days
(PM2.5≥75µgm-3; see Table 3) were selected to
better understand the pollution accumulation process and regional transport
characteristics of pollutants in the PRD. The sampling days for southerly
flow and northerly flow are listed in Table 3.
The constraints of factor species for ME-2 modeling.
FactorsOMECCl-NO3-SO42-NH4+CaTiVNiZnCdPbNaMgAlKFeSecondary sulfate–000––000000000000Secondary nitrate–00–0–000000000000Sea salt00–––0–000000––0–0Fugitive dust000000––00000–––––Input data matrices for source apportionment modeling
PMF is a multivariate factor analysis tool widely used for aerosol source
apportionment. The PMF algorithm groups the measured matrix X
(Eq. 1) into two nonnegative
constant matrices G (factor time series) and F (factor
profiles), and E denotes the model residuals (Paatero and Tapper,
1994). The entries in G and F are fitted using a
least-squares algorithm that iteratively minimizes the object function Q in
Eq. (2), where eij are the elements of the residual matrix E,
and uij are the errors/uncertainties of the measured species xij.
X=G×F+EQ=∑i=1n∑j=1meijuij2
The multilinear engine (ME-2) was later developed by Paatero (1999) based on
the PMF algorithm. In contrast to an unconstrained PMF analysis, ME-2 can
utilize the constraints (i.e., predetermined factor profiles) provided by the
user to enhance the control of rotation for a more objective solution. One or
more factor profiles can be expediently input into ME-2, and the output
profiles are allowed to vary from the input profiles to some extent. When
using ME-2 modeling, the mixed factors can usually be better resolved.
In this study, both PMF and ME-2 models were run for the datasets observed in
the PRD. We first need to determine the species input into the models.
Species that may lead to high species residuals or lower R2 values between
measured and model-predicted or non-meaningful factors, such
as those that fulfilled the following criteria, were not included: (1) species that were below
detection in more than 40 % of samples, (2) species that yielded
R2 values of less than 0.4 in interspecies correlation analysis, and
(3) species that had little implication for pollution sources and lower
concentrations. Therefore, 18 species were input into the models; these
species accounted for 99.6 % of the total measured species and included
OM, EC, SO42-, NO3-, NH4+, Cl-, K, Ca,
Na, Mg, Al, Zn, Fe, Cd, V, Ni, Ti and Pb.
The application of PMF or ME-2 also depends on the estimated realistic
uncertainty (uij) of the individual data point of an input matrix, which
determines the Q value in Eq. (2). Therefore, the estimation of uncertainty
is an important component of the application of these models. There are many
sources of uncertainty, including sampling, handling, transport, storage,
preparation and testing (Leiva et al., 2012). In this study, the sources of
uncertainty that contributed little to the total uncertainty could be
neglected, such as replacing filters, sample transport and sample storage
under strict quality assurance and quality control. Therefore, we first
considered the uncertainties introduced by sampling and analysis processes,
such as sampling volume, repeatability analysis and ion extraction. The
species uncertainties uij are estimated using Eq. (3), where
u‾c is the error fraction of the species, which is
estimated using the relative combined error formula Eq. (4) (BIPM et
al., 2008).
uij=u‾c×xij,u‾c=u‾f2+u‾r2+u‾e2,
where u‾f is the relative error of the sampling volume,
u‾r is the relative error of the repeatability analysis
of the standard species, and u‾e is the relative error
of the ion extraction of multiple samples. When the concentration of the
species is below the detection limit (DL), the concentration values were
replaced by 1/2 of the DL, and the corresponding uncertainties were set at
5/6 of the DL. Missing values were replaced by the geometric mean of the
species with corresponding uncertainties of 4 times their geometric mean
(Polissar et al., 1998). The uncertainties of SO42-, NH4+
and all metal elements, which have scaled residuals larger than ±3 due
to the small analytical uncertainties, need to be increased to reduce their
weights in the solution (Norris and Duvall, 2014). In addition, the uncertainties
of EC caused by pyrolyzed carbon (PC) and the uncertainties of OM, NO3-
and Cl- due to semi-volatility under high ambient temperatures should
also be taken into account (Cao et al., 2018). In this study, more reasonable
source profiles can be obtained when further increasing the estimated
uncertainties (u‾c) of all species by a factor of 2.
Constraint setup in ME-2 modeling
In this study, the U.S. EPA PMF v5.0 was applied with the concentration matrix
and uncertainties matrix described above to identify the PM2.5 sources.
After examining a range of factor numbers from 3 to 12, the nine-factor
solution output by the PMF base run (Qtrue / Qexp=2.5)
was found to be the optimal solution, with the scaled residuals approximately
symmetrically distributed between -3 and +3 (Fig. S6) and the most
interpretable factor profiles (Fig. S7). The model-input total mass of the 18
species and the model-reconstructed total mass of all the factors showed a
high correlation (R2=0.97, slope =1.01) (Fig. S8). The factor of
biomass burning was not extracted in the eight-factor solution, while the
factor of fugitive dust was separated into two non-meaningful factors when
more factors were set to run PMF. For the nine-factor solution of secondary
sulfate-rich aerosol, secondary nitrate-rich aerosol, aged sea salt, fugitive dust, biomass
burning, vehicle emissions, coal burning, industrial emissions and ship
emissions, the source judgment based on tracers for each factor was identical
to that of the ME-2 results detailed in Sect. 3.2. However, in Fig. S7, some
factors seemed to be mixed by some unexpected components and were thus
overestimated. For example, the secondary sulfate-rich and secondary
nitrate-rich factors of PMF had certain species from primary particulates,
such as EC, Zn, Al, K and Fe, among which EC had obvious percentage explained
variation (EV) values, i.e., the percent of a species apportioned to the
factor, of 18.7 % and 9.7 %; the EV value of OM in the
sea salt factor (which was theoretically negligible) had a high value of
6.4 %, and OM accounted for 37 % of the total mass of this factor;
the EV value of SO42- in the fugitive dust factor (which was
theoretically negligible) had a high value of 8.6 %, and the
SO42- concentration accounted for 26 % of the total mass of
this factor.
The comparison of the major chemical compositions of PM2.5 in
typical cities (unit: µgm-3).
CitiesPeriodsPM2.5OCECSO42-NO3-NH4+ReferencesZhuhai (DM)Jan 2015–Nov 2015356.42.38.14.43.6This studyZhuhai (QA)377.22.29.93.54.4Jiangmen (HS)479.02.89.85.65.0Guangzhou (MDS)419.32.79.23.74.6Shenzhen (UT)377.83.08.02.63.7Shenzhen (DP)286.21.88.01.13.3Hong Kong (urban)Oct 2002–Jun 200334.36.61.99.31.02.5Hagler et al. (2006)Shenzhen (urban)47.111.13.910.02.33.2Guangzhou (urban)70.617.64.414.74.04.5BeijingJun 2014–Apr 201599.515.56.214.317.911.5Huang et al. (2017)ShanghaiSep 2013–Aug 201494.69.891.6314.518.08.13Ming et al. (2017)Chengdu, SichuanOct 2014–Jul 201567.010.93.611.29.17.2Wang et al. (2018)Paris, FranceSep 2009–Sep 201014.83.01.42.02.91.4Bressi et al. (2013)London, UKDec 2003–Apr 200531.05.61.62.83.52.1Rodríguez et al. (2007)Los Angeles, US2002–201317.12.21.32.74.90.1Hasheminassab et al. (2014)Santiago, ChileMar 2013–Oct 20134012.14.31.97.13.3Villalobos et al. (2015)Chuncheon, KoreaJan 2013–Dec 201434.69.01.63.92.82.0Cho et al. (2016)
Chemical compositions of 4-month average PM2.5 in the PRD
region.
SoFi is a user-friendly interface developed by PSI for initiating and
controlling ME-2 (Canonaco et al., 2013), and it can conveniently constrain
multiple factor profiles. Although the U.S. EPA PMF v5.0 can also use some a priori
information (such as the ratio of elements in factor) to control the rotation
after the base run, it is not able to use multiple constrained factor
profiles to control the rotation (Norris and Duvall, 2014). Therefore, SoFi is a
more convenient and powerful tool to establish various constrained factors
for source apportionment modeling. Using the same species concentration
matrix and uncertainties matrix, we ran the ME-2 model via SoFi for 9–12
factors with the four factors constrained as described above, as shown in
Table 4. The following considerations were used. Secondary sulfate and
secondary nitrate factors should theoretically not contain species from
primary particulates, but they may contain secondary organic matter related
to the secondary conversion process of SO2 and NOx
(He et al., 2011; Z. B. Yuan et al., 2006; X. F. Huang et al., 2014).
Therefore, the contributions of the species from primary particulates were
constrained to zero in the input secondary aerosol factors, while others were
not constrained. In addition, the factors of sea salt and fugitive dust in
primary aerosols could be understood based on the abundance of species in
seawater and the upper crust (Mason, 1982; Taylor and Mclennan, 1995). As
seen in Table S1 in the Supplement, the abundances of Cl-,
Na+, SO42-, Mg2+, Ca2+ and K+
in sea salt were relatively high, as were the abundances of Al, Fe, Ca, Na,
K, Mg and Ti in fugitive dust. Therefore, these high-abundance species were
not constrained in the sea salt and fugitive dust factors, while the other
species (with abundances of less than 0.1 % in the particulates) were
constrained to zero (Table 4). In addition, HNO3 might react with sea
salt to displace Cl- (Huang et al., 2006); thus, NO3- was
also not constrained in the sea salt factor.
Results and discussionSpatiotemporal variations in PM2.5 in the PRD
The 4-month average PM2.5 concentration for all six sites in the PRD was
37 µgm-3, which was slightly higher than the Grade II
national standards for air quality (with an annual mean of
35 µgm-3). The chemical compositions of PM2.5 in the
PRD are shown in Fig. 2. OM had the highest contribution of 36.9 %,
suggesting severe organic pollution in the PRD. Other important components
included SO42- (23.6 %), NH4+ (10.9 %),
NO3- (9.3 %), EC (6.6 %) and Cl- (0.9 %). The
major metallic components included K (1.5 %), Na (1.1 %), Fe
(0.7 %), Al (0.6 %), and Ca (0.6 %), and trace elements accounted
for 1.0 %. Figure 3a shows the spatial distribution of the PM2.5 and
chemical components among the six sites. The PM2.5 pollution level in the
PRD was distinctly higher in the northwestern hinterland (HS and MDS) and
lower in the southern coastal areas (DM and DP). The DP background site had
little local emissions and was hardly influenced by the emissions from the PRD
under both southerly flow and northerly flow. Thus, DP air pollution
reflects the large-scale regional air pollution. The average PM2.5
concentration at DP was as high as 28 µgm-3, indicating that
the PRD had a large amount of air pollution transported from outside this
region. At the background DP site, the fractions of Cl- and
NO3- in PM2.5 were the lowest of the six sites, i.e., 0.3 %
and 3.9 %, respectively, suggesting that they had dominantly local
sources in the PRD. The highest concentration level of PM2.5 was
observed at HS (suburban), which was influenced by the pollution transport of
Foshan (industrial city) and Guangzhou (metropolis) under the northeastern
wind, which is the most frequent wind in the PRD. The back trajectories of
the air masses (Fig. S1) show that the northern monsoon prevails in winter
and the southern monsoon prevails in summer in the PRD. Under the winter
monsoon, the air masses mostly came from inland and carried higher
concentrations of air pollutants. However, under the summer monsoon, the air
masses largely originated from the South China Sea and were clean. In
addition, the frequent rainfall and higher planetary boundary layer (PBL) in
summer in the PRD also favored the dispersion and removal of air pollutants
(X. F. Huang et al., 2014). Figure 3b shows that the normalized seasonal
variations in the major components in PM2.5 in the PRD were evidently
higher in winter and lower in summer, which is consistent with the seasonal
variations in the monsoon and other meteorological factors as mentioned above.
The spatial distributions of (a) and seasonal
variations in (b) the PM2.5 chemical compositions in the PRD.
Sizes of the pie charts indicate the concentrations of PM2.5 at the six
sites, with the detailed numbers (unit: µgm-3) in brackets.
Comparison of the results of source apportionment of PM2.5 in
the PRD.
Table 5 summarizes some previous studies that used similar filter-sampling
and analytical methods to allow for a better comparison with this study. In
2002–2003, Hagler et al. (2006) also conducted observations and analysis of
PM2.5 in the PRD and Hong Kong region, nearly 12 years before this
study, as shown in Table 5. Compared with Hagler's results, the PM2.5
concentrations in this study decreased by 42 % in Guangzhou (MDS) and
21 % in Shenzhen (UT), especially OC, EC and SO42-, which
decreased significantly by 20 %–47 %, indicating that the measures
taken to desulfurize coal-fired power plants, improve the fuel standards of
motor vehicles, and phase-out older and more polluting vehicles have played
important roles in improving the air quality in the PRD region (People's
Government of Guangdong Province, 2012). Compared with the PM2.5
concentrations reported by other cities in China in recent years, the
PM2.5 concentrations in urban Guangzhou and Shenzhen in this study were
39 %–63 % lower than those in Beijing (Huang et al., 2017) in
northern China, Shanghai (Ming et al., 2017) in eastern China, and Chengdu
(Wang et al., 2018) in western China. However, the PM2.5 concentrations
in urban Guangzhou and Shenzhen observed in this study were clearly higher
than those in famous megacities in developed countries, such as Paris
(Bressi et al., 2013), London (Rodríguez et al., 2007) and Los Angeles
(Hasheminassab et al., 2014), while they were similar to those of Santiago
(Villalobos et al., 2015) and Chuncheon (Cho et al., 2016). It should be
highlighted that the higher concentration of SO42- in the urban
atmosphere of the PRD is one of the major reasons leading to the higher
degree of PM2.5 pollution in the PRD compared to that in developed
cities.
The factor profiles and explained variations in the ME-2 modeling.
Source apportionment of PM2.5 using ME-2
The solutions of 9–12 factors of the ME-2 were modeled with the four factors
constrained in Table 4, using the SoFi tool, an implementation of ME-2
(Canonaco et al., 2013). Again, the nine-factor solution provided the most
reasonable source profiles since uninterpretable factors were produced
(e.g., a high Ti factor) when more factors were set to run ME-2. Based on the
EV and the contributed concentrations of species in each factor shown in
Fig. 4, the sources of PM2.5 can be judged as follows: (1) the first
factor was explained as secondary sulfate rich, which had large EV values of
SO42- and NH4+. (2) The second factor was explained as
secondary nitrate rich, which had significant EV values of NO3- and
NH4+. (3) The third factor was related to sea salt due to the large
EV values and concentrations of Na and Mg. However, the low Cl-
concentration and high SO42- concentration implied that
SO42- replaced Cl- during the sea salt aging process.
Therefore, this factor was identified as aged sea salt (Z. Yuan et
al., 2006). (4) The fourth factor was identified as fugitive dust due to its
significant EV values of Al, Ca, Mg and Fe. In this study, the undetermined
mass of O and Si in this factor was compensated for using the elemental abundance
in dust particles in Table S1 (Taylor and Mclennan, 1995). (5) The fifth
factor was identified as biomass burning due to its significant
characteristic value of K (Yamasoe et al., 2000). (6) The sixth factor had
high concentrations and large EV values of OM and EC, as well as a certain
range of EV values of Fe and Zn, which were related to tires and the brake
wear of motor vehicles (Z. Yuan et al., 2006; He et al., 2011). Therefore,
this factor was identified as vehicle emissions. (7) The seventh factor had a
high EV value of Cl- and certain concentrations of OM, EC,
SO42- and NO3-, implying a combustion source. This factor
was identified as coal burning, which was a major source of Cl- in
the PRD (Wang et al., 2015). (8) The eighth factor had large EV values of Zn,
Cd, and Pb and certain concentrations of OM and EC. Zn, Cd and Pb had high
enrichment factors (Table S2) of 821, 4121 and 663, respectively, and were
thus considered to be related to industrial emissions (Wang et al., 2015).
(9) The last factor had large EV values of V and Ni. V and Ni were
predominantly derived from heavy oil combustion, and they had high enrichment
factors (Table S2) of 64 and 89, respectively. Heavy oil was related to ship
emissions in the PRD (Chow and Watson, 2002; X. F. Huang et al., 2014). Although these nine
factors of the ME-2 modeling generally showed high correlations (R2=0.81–0.97) with the corresponding factors of the PMF modeling in terms of
time series, it is easy to see that the ME-2 modeling provided a better
Qtrue / Qexp ratio (1.2) than that of the PMF modeling
(Qtrue / Qexp=2.5), indicating that the species
residuals were decreased in the ME-2 modeling, and the EV values of tracers
(e.g., SO42-, NO3-, OM, EC, Cl-, V, Ni, Pb and
Cd) were assigned to factors more intensively. Therefore, it is concluded
that the source apportionment results of the ME-2 modeling were more
environmentally meaningful and statistically better than those of the PMF
modeling.
In this study, secondary organic aerosol (SOA) did not appear as a single
factor, even if we run the ME-2 with 10 or more factors. SOA can usually be
described by low-volatility oxygenated organic aerosol (LV-OOA) and
semi-volatile oxygenated organic aerosol (SV-OOA), based on the volatility
and oxidation state of organics (Jimenez et al., 2009). In previous studies
(e.g., He et al., 2011; Lanz et al., 2007; Ulbrich et al., 2009), the time
series of LV-OOA and SV-OOA were highly correlated with those of sulfate and
nitrate, respectively, implying that LV-OOA and sulfate (or SV-OOA and
nitrate) cannot be separated easily in cluster analysis, especially when
there is no effective tracer of SOA. In this study, the high OM concentration
in the secondary sulfate-rich factor was considered to represent LV-OOA,
while the high OM concentration in the secondary nitrate-rich factor was
considered to represent SV-OOA (Z. B. Yuan et al., 2006; He et al., 2011).
Therefore, it should be acknowledged that mixed secondary factors cannot be
solved even using ME-2. However, the contribution time series of LV-OOA (or
SV-OOA) can be extracted based on the contribution time series of the
secondary sulfate-rich factor (or the secondary nitrate-rich factor) and the
mass percentage of OM in this factor, leaving the remaining mass as the
“pure” secondary sulfate (or secondary nitrate). Therefore, a new SOA
factor can be reasonably estimated by LV-OOA + SV-OOA.
The 4-month average contributions of PM2.5 sources in the PRD.
The spatial distributions of (a) and seasonal variations in
(b) PM2.5 sources in the PRD. Sizes of the pie charts
indicate the concentrations of PM2.5 at the six sites, with the detailed
numbers (unit: µgm-3) in brackets.
Figure 5 shows the 4-month average contributions of the PM2.5 sources in
the PRD in 2015 based on the source apportionment of ME-2. The total
secondary aerosols accounted for 39 % of PM2.5 in the PRD, which
were secondary sulfate (21 %), secondary nitrate (11 %) and SOA
(7 %). However, the identified primary particulates contributed 54 %
of PM2.5, which comprised vehicle emissions (14 %), industrial
emissions (13 %), biomass burning (11 %), coal burning (6 %),
fugitive dust (5 %), ship emissions (3 %) and aged sea salt
(2 %). The unidentified sources, including both the residual from ME-2
and the unmeasured species, accounted for 7 %.
Spatiotemporal variations in sources in the PRD
The spatial distributions of the PM2.5 sources among the six sites are
shown in Fig. 6a. Secondary sulfate represented the largest fraction
(31 %) of PM2.5 at DP, indicating that it was a major air pollutant
in the air mass transported to the PRD. Vehicle emissions also contributed
relatively highly to urban sites (18 % in MDS and 17 % in UT).
Industrial emissions, biomass burning, secondary nitrate and coal burning
contributed larger fractions of PM2.5 at HS, which could be attributed
to both strong local sources (e.g., the surrounding township factories and
farmlands) and regional transport from upwind cities at this site. Fugitive
dust, which is primarily related to construction activities, was relatively
high at DM (9 %). The contributions of ship emissions and aged sea salt
were the highest at QA due to the site being located on Qi'ao Island in the
Pearl River estuary, which records the greatest impact from the sea. SOA
contributed similar amounts (7 %–8 %) at all sites. It should be
noted that, although QA was a background site without local anthropogenic
sources, its PM2.5 level was moderate in the PRD, indicating that QA was
impacted by severe regional transport from the surrounding cities.
Figure 6b shows the seasonal variations in the major sources of PM2.5 in
the PRD. The contributions of most sources were higher in winter and lower in
summer, e.g., secondary sulfate, secondary nitrate, fugitive dust, biomass
burning, vehicle emissions, coal burning, industrial emissions and SOA; these
sources were greatly influenced by the seasonal variations in monsoon,
rainfall and PBL, as discussed in Sect. 3.1. For example, although secondary
sulfate was proven to be a typical regional pollutant in the PRD (X. F. Huang
et al., 2014; Zou et al., 2017), the more polluted continental air mass in
the winter monsoon made its concentrations in winter much higher than in
summer. The semi-volatile secondary ammonium nitrate was also significantly
affected by seasonal ambient temperatures. In contrast, the average
contributions of aged sea salt and ship emissions for the whole region
displayed few seasonal variations, consistent with the fact that the emissions were
from local surrounding sea areas.
Previous studies of the source apportionment of bulk PM2.5 in the PRD
have mainly focused on Guangzhou, Dongguan and Shenzhen, as seen in Table 6.
It can be seen that in those studies, PM2.5 was apportioned to six to
nine
sources and that secondary sulfate was the prominent source, although the
results of different studies exhibited certain differences due to the use of
different models or data inputs. Compared with the study of X. F. Huang et
al. (2014) in Shenzhen in 2009, the contributions of secondary sulfate and
vehicle emissions in Shenzhen in this study were obviously lower due to power
plant desulfurization and motor vehicle oil upgrades in recent years
(People's Government of Shenzhen Municipality, 2013). Compared with previous
studies in Guangzhou, this study attained more PM2.5 sources, which can
more clearly describe the source structure of PM2.5 in this region,
especially industrial emissions (11 %). The PRD region has experienced a
high degree of industrialization; thus, industrial sources should be a major
source, contributing 8.1 % of PM2.5 reported by the Guangzhou
Environmental Protection Bureau (2017), similar to our results. Tao et
al. (2017) apportioned PM2.5 to six sources using PMF in Guangzhou,
including some mixed sources. For example, ship emissions in Tao's study may
not actually represent a primary source due to the significant contribution of
some secondary inorganics and sea salt in the source profile; thus, they
obtained a significantly higher contribution (17 %) than that in our
study. Ship emissions were unidentified in R. Huang's study (2014) in
Guangzhou.
The contributions of PM2.5 sources under southerly flow and
northerly flow conditions in the PRD.
Identification of high-emission areas in the PRD in typical
meteorological conditions
Figure 7 shows the contributions of PM2.5 sources under southerly flow
and northerly flow conditions in the PRD, based on the classification of
weather types in Sect. 2.2. Southerly flow primarily originated from the
South China Sea and carried clean ocean air masses to the PRD with overall
PM2.5 values of 15 µgm-3. As shown in Fig. 7, secondary
sulfate (19 %), vehicle emissions (15 %) and biomass burning
(11 %) had higher contributions under southerly flow. In contrast, in
northerly flow, the level of PM2.5 (82 µgm-3) was 4.5
times higher than that of southerly flow due to the transport of polluted air
masses southward from the northern mainland. Under northerly flow, secondary
sulfate (18 %) and biomass burning (10 %) were still the major
sources, but secondary nitrate became the dominant source of PM2.5,
accounting for 20 % of PM2.5. In addition, industrial emissions also
exhibited a relatively high contribution (14 %).
The spatial distributions of the PM2.5 sources under southerly flow and
northerly flow are shown in Fig. 8. The high-emission areas for different
sources identified by the discussion below are marked on the map in Fig. 9.
The average concentration levels of aged sea salt were similar in the summer
southerly flow and the winter northerly flow, reflecting local release of sea
salt. The spatial distribution of aged sea salt among the different sites was
a complex result of the site locations relative to the sea and meteorological
conditions, e.g., wind and tide. A relatively high level of aged sea salt was
observed at Qi'ao Island (QA), especially in the northerly flow, which
can be attributed to the fact that the QA site was surrounded by the sea and had lower
wind speeds in the northerly flow (in Table 3).
The average contributions of PM2.5 sources at six sites in the
PRD: (a) those in southerly flow and (b) those in northerly
flow.
The influences of ship emissions exhibited large differences among the six
sites, showing significant local characteristics. In addition, the ship
emissions have similar average concentrations in the summer southerly flow
and winter northerly flow, also reflecting the emissions of local ports in
the PRD region. The concentrations of ship emissions were the highest at DP
under southerly flow, mainly due to the impact of vessels in the upwind
Yantian port, while they were the highest at QA under northerly flow,
primarily due to the effects of the upwind Nansha port, as shown in Fig. 9.
The Yantian port and Nansha port are among the 10 largest ports in the world
(Hong Kong Marine Department, 2012).
The contributions of fugitive dust also exhibited significant differences
among the six sites, which are consistent with local construction activities.
DM is located in a newly developed zone that has experienced relatively high
levels of fugitive dust during southerly flow and northerly flow due to
active construction activities. Sample records indicate that the high value
of fugitive dust at UT under southerly flow may be related to its surrounding
short-term road construction project, while the high value at QA under
northerly flow may be related to the reconstruction project of the adjacent
Nansha port (Guangzhou Municipal People's Government, 2015).
The schematic diagram of high-emission areas in the PRD (map from
Google Earth). The white shaded area indicates the key emission area for the
multiple sources of SO2, NOx, coal burning, biomass
burning, industrial emissions and vehicle emissions and is explained further
in the text.
Motor vehicles are a common source of air pollution in the highly urbanized
and industrialized PRD region. The average concentration of vehicle emissions
during northerly flow was nearly 3-fold that during southerly flow. Under
southerly flow, MDS, HS and UT, which are located in the hinterland of the
PRD, had much higher levels of vehicle emissions than the other three sites;
in particular, the highest level at the urban MDS site was caused by the high
density of motor vehicles in Guangzhou. Under northerly flow, the highest
concentration of vehicle emissions was still at the urban MDS site, while QA
also recorded a prominent contribution of vehicle emissions, which was
probably closely related to the container trucks in the neighboring Nansha
port. It should be noted that the concentration of vehicle emissions at the
background DP site exceeded half the regional average value, approaching
4 µgm-3, thus indicating that vehicle emissions had a
significant impact on the regional transport of air masses from the north.
During southerly air flow, the background DP and QA sites and the urban UT
site all recorded similar concentrations of secondary sulfate, suggesting
that the secondary sulfate at these sites was dominated by regional transport
from the Southern Ocean with heavy vessel transport and had little to do with
the urban emissions at UT. Kuang et al. (2015) also found that ship emissions
could be a major source of secondary sulfate in the PRD in summer. HS and MDS
had significantly higher concentrations than their upwind site, DM,
suggesting that the area between MDS and HS could be a
high-SO2-emission area, which is consistent with the fact that this
area is an intensive industrial area. During northerly air flow in winter, HS
and DM had lower concentrations than the four upwind sites, i.e., MDS, QA,
UT and especially DP (the background site), indicating that secondary
sulfate could mainly be derived from regional transport from outside the PRD
in this season. Although the industrial area between HS and MDS could emit
significant amounts of SO2, the lower temperatures and dry air in
winter did not appear to favor the quick conversion of SO2 to
secondary sulfate. Since both secondary sulfate and LV-OOA belong to a mixed
factor with fixed proportions, the spatial distribution of secondary sulfate
also reflects the corresponding characteristics of LV-OOA.
The spatial distributions of coal burning were significantly different
among the six sites during periods of both south wind and north wind, thus
showing conspicuous local characteristics. The contribution of coal burning
was higher at MDS under southerly flow and higher at HS under northerly flow.
Most of the coals in the PRD were consumed by thermal power plants, but there
were no coal-fired power plants near the urban MDS and background DP sites.
Therefore, it is speculated that the high-emission areas of coal burning
sources mainly exist in the region between HS and MDS, as shown in Fig. 9.
The distribution of coal-fired power plants in Guangdong (Wang et al., 2017)
reveal that some important coal-fired power plants are distributed in this
region. Additionally, DM also exhibited relatively obvious contributions of
coal burning during southerly flow and northerly flow, which is also
consistent with the distribution of coal-fired power plants in the vicinity.
The average concentration of secondary nitrate during northerly flow in
winter was 40 times greater than that during southerly flow in summer; this
occurred not only because of the unfavorable conditions of atmospheric
diffusion in winter but also due to the high semi-volatility of ammonium
nitrate, which cannot stably exist in fine particles in the PRD during hot
summer weather (Huang et al., 2006). Under southerly flow conditions, the
concentrations of secondary nitrate presented prominent differences among the
six sites, showing local characteristics. Moreover, the relatively low
concentrations at the background DP site during northerly flow also indicated
that secondary nitrate mainly originated from the interior of the PRD. The
spatial distribution characteristics of secondary nitrate were very similar
to those of coal burning, with the highest occurring at MDS under southerly
flow, the highest occurring at HS under northerly flow and significantly high
values occurring at DM under southerly and northerly flow, showing that
the NOx emissions produced by coal burning may be the main
reason for the high nitrate levels in those areas. Since both secondary
nitrate and SV-OOA belong to a mixed factor with fixed proportions, the
spatial distribution of secondary nitrate also reflects the corresponding
characteristics of SV-OOA.
Under southerly flow, the influence of industrial emissions differed vastly
among the six sites, showing obvious local characteristics. Under northerly
flow, the average concentration of industrial emissions reached 14-fold that
of southerly flow, and the high contributions at background DP suggested that
regional transport probably dominated the industrial sources of fine
particulate matter in the PRD in winter. HS had the highest concentration of
industrial emissions during southerly flow and northerly flow conditions,
which is consistent with the dense factories present in the surrounding area
(Hu, 2004; Environmental Protection Agency of Jiangmen City, 2017). In
addition, the contribution of industrial emissions was relatively high at MDS
during southerly flow and relatively high at QA during northerly flow, which
supports the inference that a high-emission region of industrial sources was
located between MDS and QA, as seen in Fig. 9.
The impacts of biomass burning exhibited relatively large differences among
the
six sites during both south and north wind conditions, presenting somewhat
local characteristics. The suburban HS site had relatively high biomass burning
levels during southerly flow and northerly flow, which should be related to
the presence of many farmlands in its vicinity and thus the popular events of
open burning and residential burning of biomass wastes. The concentrations of
biomass burning were relatively high at the urban MDS site during southerly
flow and relatively high at the background QA site during northerly flow,
implying that there was a high-emission area of biomass burning between MDS
and QA, as shown in Fig. 9. Those spatial distribution characteristics of
biomass burning were similar to those of industrial emissions in the PRD,
suggesting that not only the combustion of residential biomass but also the
use of industrial biomass boilers could make important contributions to
PM2.5 in the PRD.
As a summary, the central PRD area, i.e., the middle region in between MDS, HS
and QA (the shaded region in Fig. 9), represents the most important pollutant
emission area in the PRD; these emissions include SO2,
NOx, coal burning, biomass burning, industrial emissions and
vehicle emissions, thus leading to high pollution levels in the PRD.
Therefore, this area is a key area for pollution control in the PRD. Primary
fine particulate matter and SO2 from ship emissions had significant
impacts on PM2.5 in the southern coastal area of the PRD during summer
southerly flow, and special attention must be paid to them.
Distinguishing local and regional PM2.5 pollution in the
PRD
The analyses presented in Sect. 3.4 indicate that the secondary sulfates at
the four southern coastal sites (DM, QA, UT and DP) in the PRD were almost
entirely derived from the conversion of SO2 from the emissions of
ships in the Southern Ocean during southerly flow, contributing approximately
20 % of the average PM2.5 (13 µgm-3) at the four
sites. Considering that the ship emissions directly contributed approximately
10 % of the average PM2.5 at the four sites, the total ship
emissions contributed approximately 30 % of PM2.5 in the southern
coastal PRD area and acted as the largest source of PM2.5. Under
northerly flow conditions, the background DP site, which was barely affected
by pollution emissions within the PRD, reflected regional transport from the
north air mass outside the PRD, while the background QA site reflected the
superposition effect of regional background pollution and the input of the
most serious pollution area in the PRD. The consistency of the secondary
sulfate concentrations at the background QA and DP sites was interpreted to
reflect almost the same regional background effect during northerly flow;
thus, the differences in the six anthropogenic sources between the two
background sites, including secondary nitrate (and SV-OOA), biomass burning,
industrial emissions, coal burning, vehicle emissions and ship emissions,
could be used to trace the internal inputs from the most serious pollution
area within the PRD to the downwind area. The internal inputs of six
anthropogenic sources to the corresponding sources of PM2.5 at the
background QA site were 66 %, 67 %, 28 %, 76 %, 59 % and
75 %, and the total internal input of
37.7 µgm-3 accounted for 45 % of PM2.5 at the
background QA site (83 µgm-3), showing that the local
contributions of anthropogenic pollution emissions in the key source area of
the PRD were still crucial in winter but lower than the contribution of the
regional background. Ignoring natural sources, such as aged sea salt and
fugitive dust, under northerly flow, the contributions of other anthropogenic
sources to DP were considered to represent regional background pollution
(47.5 µgm-3), and the differences in their corresponding
source concentrations between QA and DP were expected to represent the local
emissions of source areas in the PRD. Therefore, the source structures in the
regional background air mass and local emissions of heavy pollution sources
area in the PRD are shown in Fig. 10. Secondary sulfate and LV-OOA occupied
the vast majority (45.6 %) of the regional background air mass from the
northern mainland, followed by industrial emissions (17.8 %), secondary
nitrate and SV-OOA (15.5 %). However, the major sources between the
sources output by local emissions from the heavy pollution source area of the
PRD were secondary nitrate and SV-OOA (37.3 %), biomass burning
(20.6 %), vehicle emissions (14.9 %) and coal burning (11.9 %).
Therefore, measures implemented for the effective control of PM2.5 in
the PRD should focus on local controls and regional joint prevention and
control under winter northerly flow conditions.
The PM2.5 source structures in regional background air and
local contributions of the central PRD area under northerly flow.
Conclusions
The PRD is one of the largest agglomerations of cities in the
world, and its air quality has largely improved in the past 10 years. To
reveal the current PM2.5 pollution characteristics on a regional scale
in the PRD, six sampling sites were selected to conduct 4 months (one for
each season) of sampling and chemical analysis in 2015; then, the source
exploration of PM2.5 was performed using a novel method. The conclusions
are described below.
The 4-month average PM2.5 concentration for all six sites in the
PRD was 37 µgm-3, of which OM, SO42-,
NH4+, NO3-, EC, metal elements and Cl- contributed
36.9 %, 23.6 %, 10.9 %, 9.3 %, 6.6 %, 6.5 % and
0.9 %, respectively. The spatiotemporal PM2.5 variations were
generally characterized as being higher in the northern inland region and higher
in winter.
This study revealed that the ME-2 model produced more environmentally
meaningful and statistically robust results of source apportionment than the
traditional PMF model. Secondary sulfate was found to be the dominant source
of PM2.5 in the PRD, at 21 %, followed by vehicle emissions
(14 %), industrial emissions (13 %), secondary nitrate (11 %),
biomass burning (11 %), SOA (7 %), coal burning (6 %), fugitive
dust (5 %), ship emissions (3 %) and aged sea salt (2 %). Only
aged sea salt and ship emissions did not show obvious seasonal variations.
Based on the spatial distribution characteristics of PM2.5 sources
under typical southerly and northerly airflow conditions, the central PRD
area in between MDS, HS and QA is identified as a key area for source emissions,
including SO2, NOx, coal burning, biomass burning,
industrial emissions, and vehicle emissions, and thus deserves more attention
when implementing local pollution control in the PRD. In addition, ship
emissions should be controlled more strictly during summer due to their
contribution of approximately 30 % of PM2.5 in the southern coastal
area of the PRD under southerly air flow.
Under typical northerly winter flow, the contributions of anthropogenic
pollution emissions in the central PRD area contributed
37.7 µgm-3 (45 % of PM2.5) to the regional
background air. Secondary sulfate (36.9 %), industrial emissions
(17.8 %) and secondary nitrate SV-OOA (12.8 %) were the major
PM2.5 sources for the PM2.5 transported in the regional background
air mass, while secondary nitrate (30.9 %), biomass burning (20.6 %),
vehicle emissions (14.9 %) and coal burning (11.9 %) were the major
sources for the PM2.5 produced in the central PRD area. Therefore,
effective control measures of PM2.5 in the PRD in the future should pay
more attention to both local controls and regional joint prevention.
Datasets are available by contacting the corresponding
author, Ling-Yan He (hely@pku.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-11563-2018-supplement.
X-FH, B-BZ, and L-YH analyzed the data and
wrote the paper. L-YH, MH, and Y-HZ designed the study. B-BZ
performed the chemical analysis. ASHP helped with the ME-2
running. All authors reviewed and commented on the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the National Natural Science Foundation of China
(91744202, 41622304) and the Science and Technology Plan of Shenzhen
Municipality (JCYJ20170412150626172, JCYJ20170306164713148).
Edited by: James Allan
Reviewed by: two anonymous referees
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