The Sichuan Basin (SCB) is one of the regions suffering from severe air
pollution in China, but fewer studies have been conducted for this region
than for the more developed regions in eastern and northern China. In this study,
a source-oriented version of the Community Multiscale Air Quality (CMAQ)
model was used to quantify contributions from nine regions to PM2.5
(i.e., particulate matter, PM, with an aerodynamic diameter less than 2.5 µm) and its components in the 18 cities within the SCB in the winter
(December 2014 to February 2015) and summer (June to August 2015). In the
winter, citywide average PM2.5 concentrations are 45–126 µg m-3, with 21 %–51 % and
39 %–66 % being
due to local and nonlocal emissions, respectively. In the summer,
15 %–45 % and 25 %–52 % of citywide average
PM2.5 (14–31 µg m-3) are due to local and
nonlocal emissions, respectively. Compared to primary PM (PPM), the
inter-region transport of secondary inorganic aerosols (SIA), including
ammonia, nitrate, and sulfate ions (NH4+, NO3-, and
SO42-, respectively), and their gas-phase precursors are greater.
The region to the east of SCB (R7, including central and eastern China
and others) is the largest contributor outside the SCB, and it can
contribute approximately 80 % of PM2.5 in the eastern, northeastern,
and southeastern rims of the SCB but only 10 % in other SCB regions in
both seasons. Under favorable transport conditions, regional transport of
air pollutants from R7 could account for up to 35–100 µg m-3 of PM2.5 in each of the SCB cities in the winter. This
study demonstrates that it is important to have joint emission control
efforts among cities within the SCB and regions to the east in order to
reduce PM2.5 concentrations and prevent high PM2.5 days for the
entire basin.
Introduction
Particulate matter (PM) is one of the major air pollutants in China,
including primary and secondary components. Primary PM (PPM) is directly
released from emission sources, while secondary PM is formed from their
precursors, such as sulfur dioxide (SO2), nitrogen oxide (NOx),
and ammonia (NH3). All of them are released from local sources or
transported over a long distance (Ying et al., 2014; Zhao et al., 2018). The
relative contributions of secondary components to total PM2.5 (PM with
an aerodynamic diameter less than 2.5 µm) usually increase as
PM2.5 concentration elevates in megacities (Huang et al., 2014; Qiao et
al., 2018).
Air pollution in major economic centers in China, including the North China
Plain (NCP), Yangtze River Delta (YRD), and Pearl River Delta (PRD), has
been extensively studied in recent years. Regional transport of air
pollutants has been identified as an important source of PM in the three
regions, particularly the precursors of secondary PM (Zhang et al., 2013;
X. Zhao et al., 2013; Ying et al., 2014; Jiang et al., 2015; Li et al., 2015;
Wang et al., 2015; Zheng et al., 2015; Tang et al., 2016; Yang et al.,
2018). Several urbanized areas in western China have also been suffering
from air pollution due to rapid industrial and urban development, but fewer
studies have been conducted compared to the NCP, YRD, and PRD. One such
area is the Sichuan Basin (SCB), which covers an area about 0.22 million km2 and is home to more than 100 million residents in 18 cities, among
which Chengdu and Chongqing are the largest two cities in western China
(National Bureau of Statistics of China (NBSC), 2015; Table S1 in the Supplement). The SCB is
topographically isolated, with mountains or plateaus on all sides. They are
the Qinghai–Tibetan Plateau (QTP), Yunnan–Guizhou Plateau (YGP), Wu
Mountains (WUM), and Daba Mountains (DBM) to the west, south, east, and
north of the SCB, respectively. As a result of the basin topography,
emissions released from the SCB tend to accumulate in the basin, causing
severe air pollution (Ning et al., 2018a; Zhao et al., 2018). In addition,
east and central China, which are to the east of the SCB, have considerable
contributions to PM2.5 for the SCB. For example, Ying et al. (2014) predicted
that central and east China had a combined contribution of 29.6 % to the
total mass of NO3- and SO42- for Chongqing in January
2009. Due to high emissions within the basin and deep basin landforms, annual
average concentrations of PM2.5 in the SCB were similar to that of NCP
and Central China (Fig. S1 in the Supplement). Annual PM2.5 measured in Chengdu and
Chongqing in 2015 were 64 and 57 µg m-3, respectively, about
6 times the World Health Organization (WHO) guideline (10 µg m-3)
(WHO, 2006; NBSC, 2015).
To design effective PM2.5 control strategies for the SCB, it is
necessary to quantify the source contributions and inter- and intra-region
transport of PM2.5 and its precursors within the SCB and its
surrounding regions. There are many types of source apportionment methods,
such as receptor-based models, air parcel trajectory models, remote-sensing,
and chemical transport models (CTMs). Receptor-based models, such as the
Positive Matrix Factorization model (PMF) (Paatero and Tapper, 1994; Qiu et al.,
2019), the Chemical Mass Balance model (CMB) (Watson et al., 1990), and a local
contribution model proposed by Zhao et al. (2019), are semiquantitative
and cannot quantitatively determine the source contributions from an exact
emission sector or a specific location. They also require a large number of
monitoring data and can only resolve source contributions at the monitoring
sites (Hopke, 2016). Remote-sensing and air parcel trajectory models, such
as the Potential Source Contribution Function (PSCF) and the Hybrid Single
Particle Lagrangian Integrated Trajectory Model (HYSPLIT) can only reflect
the atmospheric dynamics, so they are not quantitative for source
apportionment of secondary species (Begum et al., 2005; Uno et al., 2009;
Stein et al., 2015; Liu et al., 2018; Wu et al., 2018). Compared to above
methods, CTMs are more quantitative, as they can track the source
contributions to both primary and secondary air pollutants from a specific
region or sectorial source for studies at the local, regional, or global
scale (Bove et al., 2014; Kim et al., 2015; Lelieveld et al., 2015;
Itahashi et al., 2017; Shi et al., 2017). In CTMs, source contributions are
quantified through two methods, namely, sensitivity analysis and
tagged-tracer methods (Burr and Zhang, 2011). Sensitivity analysis such as
the brute-force method is more suitable to estimate air quality changes due
to emission perturbations, as emissions from certain sources would be
eliminated or reduced in each simulation of sensitivity analysis (Burr and
Zhang, 2011; Han et al., 2018; Huang et al., 2018). In the tagged-tracer
method, source-tagged species are used to track air pollutants from specific
emission regions and sectors, and they go through all of the nonlinear
chemical and physical processes in the model, and thus this method is considered
to provide more realistic evaluations of the contributions of different
source sectors or source regions to the current level of air pollution under
the current emission intensity (Wang et al., 2009; Burr and Zhang, 2011;
Chen et al., 2017).
Transport of PM2.5 and its precursors has been studied for Chengdu,
Chongqing, and the entire SCB region (Zhu et al., 2018). Based on in situ
observations and the HYSPLIT model, studies have found that dust storms from
northwestern China and biomass burning activities would cause high PM days
in the two cities, particularly in spring (Zhao et al., 2010; Tao et al.,
2013; Chen and Xie, 2014; Chen et al., 2015). Using the PSCF model, a study
reported that the main potential sources of PM2.5 for Chengdu were from
southeastern cities and the western margin of the SCB, in addition to local
emissions from December 2013 to February 2014 (Liao et al., 2017). Based on
the HYSPLIT and PSCF analyses, air pollution was determined mainly from the
south during persistent extreme haze days in Chengdu from 6 to
16 January 2015 (Li et al., 2017). However, the aforementioned
studies based on the HYSPLIT and PSCF models are not quantitative in terms
of emission contributions. Their accuracy is also limited by the
meteorological inputs that drive these models, which are often in very coarse
resolutions (0.5 to 1.0∘) that are not enough to accurately predict
air pollutant transport within the SCB, as meteorological conditions and
pollutant concentrations may vary greatly within short distances (Shi et al.,
2018). Also, previous country-level modeling studies did not have sufficient
spatial resolution to properly quantify the transport among cities within
the SCB.
Since both inter-regional transport within the cities in the basin and from
outside the region can greatly affect PM2.5 concentrations in the 18 SCB cities, systematically quantifying contributions from different regions
to PM2.5 for all the 18 cities in the SCB is urgently needed as
emission controls are further tightened to improve air quality in this
region. In this study, an improved source-oriented community multiscale air
quality (CMAQ) model was used to quantify the contributions from nine
regions (five within the SCB and four outside) to PM2.5 and its
components for the 18 SCB cities. The assumption is that the transport of
air pollutants is evident among the SCB cities and some cities on the rim
of the SCB are greatly affected by emissions outside SCB. Therefore, the
objectives of this study are to quantitatively determine (1) the
inter-region transport of air pollutants emitted in the SCB and its
contributions to PM2.5 in the 18 SCB cities and (2) the contributions
of emissions outside the basin to PM2.5 in the SCB. In this study, the
percentage contributions and maximum mass contributions from each region to
PM2.5 in each city are both presented to better understand the extent
of air pollutant transport. We modeled PM2.5 and its source
contributions for only two seasons, as PM2.5 concentrations in the SCB
are highest and lowest in winter and summer, respectively (Ning et al., 2018a).
Methods and materialsModel description
The source-oriented CMAQ model used in this study is based on the CMAQ model
version 5.0.1. The gas-phase and aerosol mechanisms are extended from the
standard SAPRC-99 photochemical mechanism and aerosol module version 6
(AERO6). This version of the source-oriented CMAQ is capable of
simultaneously tracking both primary particulate matter (PPM) and secondary
inorganic aerosols (SIA, including NH4+, NO3-, and
SO42-) from multiple source sectors and regions. It unifies the
two previous models individually developed for PPM (Hu et al., 2015) and SIA
(Ying et al., 2014; Shi et al., 2017) into a single consistent model
framework. For SIA, multiple source-tagged reactive species are introduced
in both gas and particle phases to represent the same species originated
from different source sectors or regions. The corresponding photochemical
mechanisms, aerosol and cloud modules are expanded so that SIA and its
precursors from different regions can be tracked separately throughout the
model calculations. For example, NO2_S1 and
NH3_S2 can be used to represent NO2 from region 1
and NH3 from region 2, respectively. After the photochemical mechanism
is expanded, the source-tagged species are allowed to go through all
processed to form (NH4_S2) (NO3_S1)
based on additional reactions of NO2+OH→HNO3 and
NH3+HNO3→NH4NO3. Thus, the contributions of
region 1 to NO3- and region 2 to NH4+ are quantified.
For PPM, source-tagged nonreactive tracers are added to track the total
amount of PPM emitted from different source sectors and regions. SOA is
included in the current model but its source contributions are not resolved.
Model application
The source-oriented CMAQ model was applied to quantify nine source region
contributions to PM2.5 and its components (PPM and SIA) for the 18 cities in the winter (from December 2014 to February 2015) and summer
(June to August 2015) using nested domain settings. The locations of the domains, the nine
source regions, and the 18 cities of the SCB are shown in Fig. 1. The
horizontal resolutions of the parent and nested domains are 36 and 12 km,
respectively. There are 18 vertical layers with an overall height of 20 km
and the layer closest to the land surface is up to 35 m. The geographical
regions of emissions are classified into nine source regions. As Chengdu and
Chongqing are the two largest cities in western China and within the SCB, we
classified Chengdu, eastern Chongqing, and western Chongqing into three
individual regions (R4, R5, and R1, respectively). Western Chongqing is well-urbanized and eastern Chongqing is mostly rural area. The five cities in
the northeastern SCB (Bazhong, Dazhou, Guangyuan, Guang'an, and Nanchong)
are grouped into R2, as they have relatively low anthropogenic emission
densities compared to most of the other SCB cities and they are located in
the upwind areas within the SCB (Qiao et al., 2019). The remaining SCB cities are
grouped into R3. Sichuan Province, excluding those cities within the SCB, is
R8, most of which is remote rural areas. R6 includes three provinces to the
south of the SCB and R7 has the Chinese provinces to the east and northeast
of the SCB. R9 includes the other jurisdictions to the west of the SCB,
including Xinjiang, Qinghai, Gansu, Tibet, and other countries.
(a) Locations of the 12 and 36 km domains (grey rectangles) and
the locations of provincial capitals and municipalities (orange circles),
(b) terrain within and surrounding the 18 cities of the SCB (black line),
and (c) locations of region categories 1–9 and the
prefecture-level cities (yellow circles). Regions 1–5 are the
cities within the SCB. Regions 1, 4, and 5 are western Chongqing, Chengdu,
and eastern Chongqing, respectively. The city center of Chongqing is located
in western Chongqing. Region 8 is the area of Sichuan Province, excluding
those cities in the SCB. QTP, Qinghai–Tibetan Plateau; YGP, Yunnan–Guizhou
Plateau; DBM, Daba Mountains; WUM, Wu Mountains.
Meteorological inputs were generated using the Weather Research and
Forecasting (WRF) model version 3.9 based on the 6-hourly FNL (Final)
Operational Global Analysis data from the National Center for Atmospheric
Research (NCAR) with a spatial resolution of 1.0∘×1.0∘ (https://rda.ucar.edu/datasets/ds083.2/,
last access: 27 April 2019). The anthropogenic emission
inventory used was the Emission Database for Global Atmospheric Research
(EDGAR) version 4.3.1 for the year of 2012 (Crippa et al., 2018). The
inventory was directly used for the model year of 2014–2015, as no reliable
sources for emission changes in the SCB are available. The monthly EDGAR
inventories have a spatial resolution of 0.1∘×0.1∘ (∼10 km × 10 km) and were re-projected
to the model domains using the Spatial Allocator (https://www.cmascenter.org/sa-tools/, last access: 27 April 2019). Temporal profiles specific to
sources were used to allocate the monthly emission rates to hourly values
for CMAQ modeling (Wang et al.,
2010). The EDGAR inventory includes carbon monoxide (CO), NOx,
SO2, NH3, non-methane volatile organic compounds (NMVOCs),
PM2.5, PM10 (PM with an aerodynamic diameter less than 10 µm), elemental carbon (EC), and organic carbon (OC) from various sources.
Emission sources are grouped into six categories: energy, industries,
residential activities, on-road transportation, off-road transportation, and
agriculture. In addition to these six anthropogenic sources, contributions
of biogenic sources were also determined using emissions generated by the
Emissions of Gases and Aerosols from Nature (MEGAN) model version 2.1
(Guenther et al., 2012). The emissions from open burning were estimated
based on the Fire Inventory from the National Center for Atmospheric
Research (NCAR FINN) (Wiedinmyer et al., 2011). Contributions of windblown
dust and sea salt emissions were determined based on in-line generated
emissions during CMAQ simulations. It should be noted that the uncertainties
in emission inventories potentially lead to uncertainties in the
contributions.
The initial and boundary conditions (ICs and BCs, respectively) for the
36 km domain were based on CMAQ default profiles, and those for the 12 km
domain were generated using the CMAQ outputs from the 36 km simulations.
More details about the setup and configuration of the WRF/CMAQ modeling
system can be found for China in a previous publication (Kang et al., 2016).
Results and discussionModel performance
The model performance on meteorological parameters and 24 h PM2.5 in
the 12 km domain for the two seasons has been evaluated in a companion paper
(Qiao et al., 2019) and is briefly summarized here (Fig. S2). As the
predictions on wind speed (WS) and wind direction (WD) are important in
modeling air pollutant transport (Zhao et al., 2009), the WRF model
performance for WS, WD, ambient air temperature (T), and relative humidity
(RH) were evaluated by using hourly observations at China's national
meteorological stations and the observation data that were downloaded from
the National Climate Data Center (NCDC;
ftp://ftp.ncdc.noaa.gov/pub/data/noaa/, last access: 20 June 2018). The
mean biases (MBs) of predicted RH (-10.8 % to -1.1 %) and T (-0.9 to
-0.1∘C) in each month are comparable to other studies in China
(Wang et al., 2010, 2014; B. Zhao et al., 2013). The MB of WD in
each month (-5 to 6∘) meet the benchmark of < ±10∘ suggested by Emery et al. (2001). Although the MB of
WS in each month (0.5 to 1.1 m s-1) does not meet the benchmark of
< ±0.5 m s-1, the gross errors (GEs: 1.4–1.9 m s-1)
are within the benchmark of 2.0 m s-1. For 24 h PM2.5
concentrations, the statistical metrics of model performance are generally
within the criteria recommended by Emery et al. (2017) for regulatory
applications, with only a few cities exceeding the normalized mean bias
(NMB) criteria of < ±30 % in the winter (Chongqing
42 %, Guangyuan 41 %, Mianyang 37 %, Meishan 31 %, Ziyang 48 %) and in the
summer (Dazhou -39 %) (Fig. S2). The 24 h PM2.5 predictions meet
the goals of normalized mean error (NME ±35 %), fractional bias
(FB ±30 %), and fractional error (FE ±50 %) in all the
cities in both seasons, except for the NME of Ziyang (58 %) in the winter.
The predictions of major PM2.5 components (including OC, EC,
NH4+, NO3-, and SO42-) in Chengdu and
Chongqing are comparable with observations, and both predictions and
observations suggest that OC and SIA are the largest contributors to
PM2.5 in summer and winter, with combined contributions of about 70 %
(Qiao et al., 2019).
Predicted source-region contributions to PM2.5 in the 18 SCB
city centers in the winter. The data in bold font show the contributions due to local emissions, emissions within SCB, or emissions outside SCB.
Region IDCityPM2.5Contributions from each region, SOA, and othersa ( %) (µg m-3)R1R2R3R4R5WithinR6R7R8R9OutsideSOAOthersNon-SCBSCBlocalbR1Chongqingc19167.74.53.70.41.878.12.711.10.10.914.83.93.325.2R2Bazhong652.042.62.81.22.951.51.731.90.12.736.46.95.145.3Dazhou892.447.31.70.57.559.41.5270.11.630.26.04.342.3Guang'an1098.950.42.00.55.467.22.319.60.11.423.45.83.540.2Guangyuan602.345.55.21.81.756.51.325.50.14.231.16.55.842.1Nanchong1206.156.53.20.63.169.51.818.10.11.321.35.73.434.3R3Deyang1432.74.658.014.50.880.60.89.40.11.611.94.23.434.5Leshan1252.93.158.715.10.680.41.28.20.11.210.75.73.032.4Luzhou14913.94.853.91.81.275.63.811.20.11.016.15.42.937.8Meishan1532.53.140.236.30.682.70.97.70.11.19.84.62.952.3Mianyang1142.76.860.34.81.175.70.912.40.12.215.64.93.831.0Neijiang14011.37.651.92.81.374.92.412.70.11.116.35.63.139.3Suining10011.733.514.41.13.263.92.621.40.11.725.86.83.675.3Ya'an793.23.645.920.10.773.51.411.00.32.315.07.63.842.6Yibin1346.84.160.15.50.977.42.89.70.11.113.76.03.031.0Zigong1458.96.057.13.21.176.32.411.40.11.115.05.53.334.2Ziyang1315.87.354.07.11.275.41.712.60.11.415.85.63.237.2R4Chengdu1442.23.520.455.20.681.90.88.20.11.410.54.23.537.2
a Others includes initial and boundary conditions, windblown dust, and sea
salt.
b Nonlocal = within SCB + outside SCB – local.
c The city center of Chongqing.
Seasonal average contributionsSource contributions at the city centers
In each city, there are 4 to 17 national air quality stations (NAQs), and
almost all the NAQs are located in the urban areas, where population
densities are higher. Thus, coordinates of the NAQs in the urban area of a
given city were averaged to define the city center in order to understand
PM2.5 concentrations and its sources for the most-populated region of
each city. The predicted PM2.5 concentrations and source-region
contributions at the 18 SCB city centers are presented in Table 1 for winter
and Table S2 for summer. In all the city centers, the predicted PM2.5
concentrations are much higher in the winter (60–191 µg m-3) than in the summer (14–64 µg m-3). The
city centers are considerably affected by both local and regional emissions
in both seasons. Emissions within the SCB are the major contributor to
PM2.5 in Chengdu and Chongqing in both seasons (∼80 %)
and emissions outside the SCB contribute approximately 7 %–15 %. Among the regions within the SCB, local emissions (i.e., emissions
from the region where the city center is located) are the largest
contributor to PM2.5 in Chongqing and Chengdu in both seasons (about
70 % and 58 %, respectively). However, emissions from R3 (i.e., the 11 cities in the northwestern, western, and southwestern SCB) also have
considerable contribution in Chengdu (∼20 % and 14 % in
the winter and summer, respectively). For the R3 cities, the contributions
of emissions within the SCB (64 %–83 %) are also larger than
that from outside the SCB (8 %–26 %) in both seasons. Local
emissions are the largest contributor for R3 cities (40 %–60 %), except that Suining has only ∼13 % due to its
local region. The low local contribution in Suining might be because it is
less economically developed compared to other cities, except for Bazhong,
Guangyuan, and Ya'an, as suggested by the 2015 gross domestic product
(GDP; Table S1). For the five cities in the northern SCB (R2), emissions
within the SCB account for 40 %–70 % in both seasons,
including 37 %–57 % from local emissions. Emissions outside
the SCB also have large contributions to the R2 cities (21 %–36 % and 17 %–28 % in the winter and summer, respectively),
as R2 is located in one of the regions where winds from R7 intrude the basin
(Fig. 2a). In the winter, contributions from SOA and others (including IC,
BC, windblown dust, and sea salt) are less than 8 % each. In the summer,
SOA and others each contribute 9 %–28 % and less than 10 %,
respectively, but the SOA contributions larger than 15 % are found only in
the city centers where summer PM2.5 concentrations are less than 30 µg m-3. In summary, local emissions are the largest contributor
for all the city centers in both seasons, except for Suining. The nonlocal
contributions for the city centers are in the range of 25 %–52 % in the winter (except for 75 % in Suining) and of
14 %–40 % in the summer (except for 61 % in Suining) and emissions outside
the SCB account for 7 %–36 % in both seasons.
(a) Spatial distributions of predicted PM2.5 concentrations
(µg m-3) and (b–l) source-region contributions to PM2.5
(%) in the winter. Others includes IC, BC, windblown dust, and sea salt.
Black arrows in (a) are wind vectors.
Spatial variations and citywide area-weighted averages
The spatial variations in source-region contributions to PM2.5 in the
winter and summer are presented in Figs. 2 and 3, respectively. In both
seasons, local emissions are generally the largest contributor in each city,
except that R7 has contributions similar to or larger than those of local
emissions for most regions in eastern Chongqing (R5) and R2. Specifically,
the contributions from R7 to PM2.5 in R2 and R5 are approximately
20 %–80 % in the winter and 20 %–60 % in the
summer. R7 also has contributions larger than 20 % for a few areas in R3.
The regions of R6, R8, and R9 outside the SCB each has contributions of
< 5 % across the basin, except for some very limited areas in the
western and southern rims of the SCB. The contributions from R6, R8, and R9
are low because these areas are less urbanized and industrialized. In
addition, the mountains to the west and south of the SCB also prevent the
transport of air pollutants from these regions into the SCB (Figs. 1c, 2a,
and 3a). In summary, R7 is the sole non-SCB region that can have
> 20 % contributions to PM2.5 in the SCB, and its impact
decreases from the northeast, east, and southeast of others in the basin.
Predicted citywide area-weighted average PM2.5 concentrations
and source-region contributions in the 18 SCB cities in the winter and
summer.
* Nonlocal =100 %-local-SOA-others; Others includes initial and
boundary conditions, windblown dust, and sea salt.
As shown in Figs. 2 and 3, PM2.5 concentrations and their source
contributions from a given region may vary greatly within a city in both
seasons. For example, about 20 %–80 % of PM2.5 across
Chengdu (R4) and western Chongqing (R1) are due to local emissions in each
season, and higher PM2.5 concentrations are generally related to higher
local contributions. For the downwind regions of Chengdu and western
Chongqing, they receive considerable contributions from the two megacities.
For example, over half of Meishan and Ya'an, which are downwind of
Chengdu, have 20 %–40 % and 20 %–60 % of
PM2.5 concentrations due to Chengdu in the winter, respectively. In the
two seasons, western Chongqing contributes about 10 %–40 %
of PM2.5 concentrations in its neighboring cities, except that most
of eastern Chongqing (R5) is not affected by emissions from western
Chongqing, as R5 is upwind of western Chongqing (Fig. 2a). Because of the
large spatial variations of PM2.5 and its source contributions in the
basin, we further calculated their citywide area-weighted averages (Table 2). In the winter, the citywide average PM2.5 concentrations in Chengdu
and urban Chongqing are 99 and 110 µg m-3, with only 38 % and
47 % due to local emissions, respectively. Nonlocal emissions also have
high contributions in other SCB cities, with citywide averages of
39 %–66 % and 25 %–52 % in the winter and
summer, respectively. The above suggests the importance of regional emission
control for reducing PM2.5 concentrations for the entire basin.
(a) Spatial distributions of predicted PM2.5 concentrations
(µg m-3) and (b–l) the source-region contributions to PM2.5
(%) in the summer. Others includes IC, BC, windblown dust, and sea salt.
Differences in PPM and SIA
The transport distances of PPM, NH4+, NO3-, and
SO42- might be different because of the differences in chemical
and physical processes that affect their concentrations in the atmosphere
(Ying et al., 2014; Hu et al., 2015). This leads to significant differences
in their regional distributions and thus requires different control
strategies. From the source-region contributions to PPM and SIA for each
city center shown in Fig. 4, it is obvious that the regional transport of
SIA is more significant than that of PPM. In the city centers of Chengdu and
Chongqing, 55 %–65 % of PPM and 25 %–45 % of
SIA are due to local emissions in the two seasons. In the city centers of
R2, PPM is also more from local emissions (65 %–80 %) than
SIA is (25 %–45 %) in both seasons. Similarly, local
emissions have larger contributions to PPM (50 %–85 %) than
to SIA (34 %–50 %) in all the city centers of R3 except for
Suining, which is not significantly affected by local emissions. The spatial
distributions of source-region contributions to PPM and SIA also indicate
more significant transport of SIA (Figs. S3–6) than PPM. For example, R3
contributes to > 20 % of SIA across all of Chengdu, but only
half the areas of Chengdu are about equally affected (> 20 %) by R3
for PPM. From the north to south in R2, the contributions from R7 to PPM
decrease from ∼55 % to ∼10 %, while the
contributions of R7 to SIA decrease from ∼75 % to
∼20 %. The contributions to NH4+, NO3-,
and SO42- in each city center from local emissions and emissions
within and outside SCB are further analyzed (Tables S3 and S4). In each city
center, concentrations of SO42- (3.8–12.6 and
12–41 µg m-3) are much higher than that of
NH4+ (1.4–4.0 and 6.0–17.0 µg m-3) and NO3- (0.3–2.4 and 6–20 µg m-3)
in the summer and winter, respectively. Also, the
transport of SO42- and its precursor is greater than the other
two ions, as the percentage contributions from emissions outside the SCB to
SO42- is higher than that to NH4+ and NO3-
in each city center. In both seasons, emissions outside SCB contribute
< 25 % of NH4+ in the city centers, except for Chongqing
(26 %) in the summer and Bazhong (36 %) and Guangyuan (33 %) in the
winter. As for NO3-, emissions outside SCB also contribute
< 25 % in the city centers in both seasons, except for Bazhong
(49 %), Dazhou (34 %), and Guangyuan (25 %) in the summer and all the
cities of R2 (27 %–57 %) in the winter. In the two seasons,
emissions outside SCB account for 22 %–33 % of
SO42- in Chengdu and Chongqing, while they contribute
52 %–70 % of SO42- for the R2 cities. For the R3
cities, emissions outside SCB account for 25 %–53 % of
SO42- in the city centers in both seasons, except for Meishan
(21 %) in the winter. All the above suggest that it would be more
efficient to control the SIA (particularly SO42-) and its
precursors than PPM in order to reduce the transport of air pollutants
within and into the basin.
Predicted maximum daily contribution from a given region (MDCs) in
the SCB city center and the corresponding PM2.5 concentrations in the
city center on the same day. Only winter data are included in this table.
The units are µg m-3. The numbers in the bold present the
contributions due to local emissions or that from R7.
a Others includes initial and boundary conditions, windblown dust,
and sea salt.
b Includes the city center of Chongqing.
Predicted source-region contributions to SIA (A) and PPM (P)
(bars, left y axis) and the predicted proportions of SIA and PPM in
PM2.5 (circles, right y axis) for the 18 city centers of the SCB in the
summer (S) and winter (W). Others includes IC, BC, windblown dust, and sea
salt.
Maximum daily contributions from a given region
The maximum daily contribution from a given region to PM2.5 (MDC, µg m-3) in each city center is shown in Table 3 for winter and Table S5
for summer. The largest MDC for each city center (79–291 and
13–147 µg m-3 in the winter and summer,
respectively) are found to be associated with local emissions, except for
Guangyuan and Suining. In Guangyuan and Suining, the largest MDCs in the
winter are from R7 (62 µg m-3) and R2 (110 µg m-3), both
are slightly higher than that from the local region of 60 and 105 µg m-3, respectively. Table 3 also shows that the interbasin transport of
air pollutants can have large contributions (> 50 µg m-3) on high PM2.5 days (> 150 µg m-3). For
example, R7 contributes 99 µg m-3 to total PM2.5 (200 µg m-3) in Chongqing on a winter day. In Nanchong, the MDC due to
western Chongqing (R1) is 58 µg m-3, when daily PM2.5 is 180 µg m-3 on that day.
In Chengdu, R3 and R7 can contribute up to 86
and 63 µg m-3 on the days with daily PM2.5 of 267 and 151 µg m-3, respectively. In Deyang and Meishan, the MDCs from Chengdu
are 147 and 138 µg m-3 on the days that have daily PM2.5 of
288 and 235 µg m-3, respectively. Table S4 shows that air
pollutant regional transport is also significant on certain days in the
summer. For example, the highest summer MDC from R7 among the 18 central
cities is found for Bazhong (36 µg m-3), when daily PM2.5 is
63 µg m-3. Chengdu contributes about 44, 16, 55, 13, 7, and 21 µg m-3to Deyang, Leshan, Meishan, Ya'an, and Mianyang on the
summer days, when daily PM2.5 are 89, 56, 100, 85, 22, and 54 µg m-3, respectively. All the above suggest that joint effects should be
made by neighboring cities and the provinces to the east of the SCB in order
to prevent high PM2.5 episodes for the SCB.
Impacts of topography on PM2.5 concentrations
While air pollutant emissions are the root of air pollution, topography and
meteorological conditions play a very important role in determining the fate
of pollutants including dispersion, accumulation, and transformation (Arya,
1999; Zhang et al., 2015; He et al., 2017). It has been widely noticed that
heavy air pollution often occurs in well-urbanized and/or industrialized
cities associated with mountains and basins, such as Beijing, Chengdu,
Xi'an, and Lanzhou in China (Chambers et al., 2015; Bei et al., 2017, 2018; Ning et al., 2018a, b);
Mexico City, Salt Lake City, and Los Angeles
in the North America (Langford et al., 2010; Whiteman et al., 2014;
Calderón-Garcidueñas et al., 2015); and megacities in the
Mediterranean Basin of the Europe (Kanakidou et al., 2011). The SCB is
surrounded by the QTP to the west, YGP to the south, WUM to the east, and
DBM to the northeast. Mainly affected by the high elevations of the QTP and
YGP, near-surface winds mainly intrude the basin from the north, east, and
southeast in the summer and winter, as shown in Figs. 2a and 3a.
Consequently, R7 is the largest contributor outside the basin, contributing
20 %–60 % of PM2.5 in the eastern, northeastern, and
southeastern parts of the SCB (Figs. 2h and 3h), where PM2.5
concentrations are relatively low in the SCB (< 75 and 25 µg m-3 in the winter and summer, respectively). The contributions from R6
(including YGP) and R8 (including QTP) are < 10 % along the
western and southern rims of the SCB. Within the basin, near-surface winds
travel anticlockwise and form a cyclone near Yibin, Zigong, Neijiang,
and Luzhou in the south (Figs. 1b, 2a, and 3a) (Lin, 2015), causing
air pollutants transported to be accumulated within the cyclone. PM2.5
concentrations in the cyclone-affected region (mostly 100–150 and
30–50 µg m-3 in the winter and summer,
respectively) are generally lower than those of Chengdu and Chongqing but are
higher than those of most of the other regions. In Yibin, Zigong, Neijiang, and
Luzhou at least 39 %–53 % and 25 %–44 % of
citywide average PM2.5 concentrations are not due to their own
emissions in the winter and summer, respectively (Tables 2 and S2). R7 only
contributes about 10 % to PM2.5 in the cyclone-affected region. In
order to reduce seasonal and annual concentrations of PM2.5 within the
SCB, the emissions and intercity transport of air pollutants within the
basin should be prioritized for being controlled.
Conclusions
In this study, a source-oriented CMAQ model was applied to quantify
contributions of nine regions to PM2.5 for the 18 cities in the SCB.
The simulations were carried out for winter (December 2014 to February 2015)
and summer (June to August 2015). Predicted citywide area-weighted average
PM2.5 concentrations are much higher in the winter (60–191 µg m-3) than in the summer (14–64 µg m-3).
In the winter, the citywide average PM2.5 concentrations in
Chengdu and western Chongqing are 99 and 110 µg m-3, with 44 %
and 52 % due to nonlocal emissions, respectively. Nonlocal emissions
also have high contributions in other SCB cities, with citywide averages of
39 %–66 % and 25 %–52 % in the winter and
summer, respectively. Among the four regions outside the SCB, only the one
to the northeast, east, and southeast of the SCB (R7) has large
contributions to PM2.5 concentrations for the SCB in both seasons
(10 %–80 %), and the contributions decrease from the rims of
the northeastern, eastern, and southeastern SCB to other regions. However,
the MDCs from R7 are large (35–99 µg m-3) for all
the city centers in the winter. On high PM2.5 days in the winter,
emissions outside SCB can contribute up to 99 µg m-3 in the city
center, suggesting the importance of regional emission control in not just
reducing averaged PM2.5 but also preventing severe PM pollution events.
The transport of SIA and its precursors is greater than that of PPM, suggested by the fact that local
emissions have higher contributions to PPM (> 55 %) than to SIA
(< 45 %) in the city centers in both seasons. Among the three ions
of SIA, the transport of SO42- and its gas-phase precursor
(SO2) is the greatest in general, as > 50 % of it in all
the city centers is associated with nonlocal emissions in both seasons,
except that the contributions are 37 %–44 % in Chongqing and
Chengdu in the summer and Chongqing in the winter. In conclusion, in order
to reduce PM2.5 concentrations and prevent high PM2.5 days for the
entire SCB, local emissions and the transport of air pollutants within and
across SCB should be controlled simultaneously.
Data availability
Hourly modeling results of all the domains are not
available online yet because we are using the data for two other studies.
We are using hourly modeling data of PM2.5, PM10, SO2, NO2, O3, and CO to
characterize air pollution for the entire basin. The results will be
published in Qiao et al. (2019). We are using hourly source apportionment
data to quantify the risks from different regions and sectors to human health
through PM2.5 pollution. All of the modeling results will be available online
after we publish the paper.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-5791-2019-supplement.
Author contributions
XQ, YT, and HZ designed research. HG, JH, QY, and HZ
contributed to model development and configuration. XQ, HG, PW, WD, and XZ
analyzed the data. XQ prepared the manuscript and all coauthors helped
improve the manuscript.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air
pollution in eastern China”. It does not belong to a conference.
Acknowledgements
Portions of this research were conducted with
high-performance computing resources provided by Louisiana State
University (http://www.hpc.lsu.edu, last access: 28 April 2019). This study is sponsored by the
International Collaboration Project of the Science & Technology
Department of Sichuan Province [2017HH0048], the National Natural Science
Foundation of China [41628102], the Program of Introducing Talents of
Discipline to Universities [B08037], PM2.5 monitoring in the
campuses of Sichuan University [SCU2015CC0001], and the Chinese Scholarship
Council [201706245007].
Review statement
This paper was edited by Yuan Wang and reviewed by three anonymous referees.
References
Arya, S. P.: Air pollution meteorology and dispersion, Oxford University
Press, New York, United States, 1999.
Begum, B. A., Kim, E., Jeong, C.-H., Lee, D. W., and Hopke, P. K.:
Evaluation of the potential source contribution function using the 2002
Quebec forest fire episode, Atmos. Environ., 39, 3719–3724, 2005.
Bei, N. F., Zhao, L. N., Xiao, B., Meng, N., and Feng, T.: Impacts of local
circulations on the wintertime air pollution in the Guanzhong Basin, China,
Sci. Total Environ., 592, 373–390, 2017.
Bei, N. F., Zhao, L. N., Wu, J. R.,Li, X., Feng, T., and Li, G. H.: Impacts
of sea-land and mountain-valley circulations on the air pollution in
Beijing-Tianjin-Hebei (BTH): A case study, Environ. Pollut., 234, 429–438,
2018.Bove, M., Brotto, P., Cassola, F., Cuccia, E., Massabò, D., Mazzino, A.,
Piazzalunga, A., and Prati, P.: An integrated PM2.5 source
apportionment study: positive matrix factorisation vs. the chemical
transport model CAMx, Atmos. Environ., 94, 274–286, 2014.Burr, M. J. and Zhang, Y.: Source apportionment of fine particulate matter
over the Eastern U.S. Part I: source sensitivity simulations using CMAQ with
the Brute Force method, Atmos. Pollut. Res., 2, 300–317,
10.5094/APR.2011.036, 2011.
Calderón-Garcidueñas, L., Kulesza, R. J., Doty, R. L., D'Angiulli,
A., and Torres-Jardón, R.: Megacities air pollution problems: Mexico
City Metropolitan Area critical issues on the central nervous system
pediatric impact, Environ. Res., 137, 157–169, 2015.
Chambers, S. D., Wang, F., Williams, A. G., Deng, X. D., Zhang,H., Lonati,
G., Crawford, J., Griffiths, A. D., Lanniello, A., and Allegrini, I.:
Quantifying the influences of atmospheric stability on air pollution in
Lanzhou, China, using a radon-based stability monitor, Atmos. Environ., 107,
233–243, 2015.Chen, D., Liu, X., Lang, J., Zhou, Y., Wei, L., Wang, X., and Guo, X.:
Estimating the contribution of regional transport to PM2.5 air
pollution in a rural area on the North China Plain, Sci. Total Environ.,
583, 280–291, 10.1016/j.scitotenv.2017.01.066,
2017.Chen, Y. and Xie, S.: Characteristics and formation mechanism of a heavy
air pollution episode caused by biomass burning in Chengdu, Southwest China,
Sci. Total Environ., 473–474, 507–517,
10.1016/j.scitotenv.2013.12.069, 2014.
Chen, Y., Luo, B., and Xie, S.: Characteristics of the long-range transport
dust events in Chengdu, Southwest China, Atmos. Environ., 122, 713–722,
2015.Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van Aardenne, J. A., Monni, S., Doering, U.,
Olivier, J. G. J., Pagliari, V., and Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period 1970–2012 within EDGAR
v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013, 10.5194/essd-10-1987-2018, 2018.
Emery, C., Tai, E., and Yarwood, G.: Enhanced meteorological modeling and
performance evaluation for two Texas ozone episodes, Final report submitted
to Texas natural resources conservation commission, prepared by ENVIRON,
International Corp., Novato, 2001.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar,
N.: Recommendations on statistics and benchmarks to assess photochemical
model performance, J. Air Waste Manage., 67, 582–598, 2017.Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions
of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions,
Geosci. Model Dev., 5, 1471–1492, 10.5194/gmd-5-1471-2012, 2012.Han, X., Zhu, L., Wang, S., Meng, X., Zhang, M., and Hu, J.: Modeling study of impacts on surface ozone of regional transport
and emissions reductions over North China Plain in summer 2015, Atmos. Chem. Phys., 18, 12207–12221, 10.5194/acp-18-12207-2018, 2018.
He, J., Gong, S., Yu, Y., Yu, L. J., Wu, L., Mao, H. J., Song, C. B., Zhao,
S. P., Liu, H. L., Li, X. Y., and Li, R. P.: Air pollution characteristics
and their relation to meteorological conditions during 2014–2015 in major
Chinese cities, Environ. Pollut., 223, 484–496, 2017.
Hopke, P. K.: Review of receptor modeling methods for source apportionment,
J. Air Waste Manage., 66, 237–259, 2016.
Hu, J., Wu, L., Zheng, B., Zhang, Q., He, K., Chang, Q., Li, X., Yang, F.,
Ying, Q., and Zhang, H.: Source contributions and regional transport of
primary particulate matter in China, Environ. Pollut., 207, 31–42, 2015.
Huang, R. J., Zhang, Y. L., Bozzetti, C., Ho, K. F. Cao, J. J., Han, Y. M.,
Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P.,
Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G.,
Pizaazlunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J.,
Zimmermann, R., An, Z. S., Szidat, S., Baltensperger, U., Haddad, I. E.,
and Prévôt, A. H.: High secondary aerosol contribution to particulate
pollution during haze events in China, Nature, 514, 218–222, 2014.Huang, Y., Deng, T., Li, Z., Wang, N., Yin, C., Wang, S., and Fan, S.:
Numerical simulations for the sources apportionment and control strategies
of PM2.5 over Pearl River Delta, China, part I: Inventory and
PM2.5 sources apportionment, Sci. Total Environ., 634, 1631–1644,
10.1016/j.scitotenv.2018.04.208, 2018.
Itahashi, S., Hayami, H., Yumimoto, K., and Uno, I.: Chinese province-scale
source apportionments for sulfate aerosol in 2005 evaluated by the tagged
tracer method, Environ. Pollut., 220, 1366–1375, 2017.Jiang, C., Wang, H., Zhao, T., Li, T., and Che, H.: Modeling study of PM2.5 pollutant transport across cities in China's
Jing-Jin-Ji region during a severe haze episode in December 2013, Atmos. Chem. Phys., 15, 5803–5814, 10.5194/acp-15-5803-2015, 2015.
Kanakidou, M., Mihalopoulos, N., Kindap, T., Im, U., Vrekoussis, M.,
Gerasopoulos, E., Dermitzaki, E., Unal, A., Koçak, M., Kostas, M., Melas, D.,
Kouvarakis, G., Youssef, A. F., Richter, A., Hatzianastassiou, N., Hiboll, A.,
Ebojie, F., Wittrock, F., Savigny, C. V., Burrows, J. P., Ladstaetter-Weissenmayer, A., and Moubasher, H.: Megacities as hot spots of air pollution in the
East Mediterranean, Atmos. Environ., 45, 1223–1235, 2011.Kang, Y., Liu, M., Song, Y., Huang, X., Yao, H., Cai, X., Zhang, H., Kang, L., Liu, X., Yan, X., He, H., Zhang, Q., Shao, M., and
Zhu, T.: High-resolution ammonia emissions inventories in China from 1980 to 2012, Atmos. Chem. Phys., 16, 2043–2058,
10.5194/acp-16-2043-2016, 2016.Kim, P. S., Jacob, D. J., Fisher, J. A., Travis, K., Yu, K., Zhu, L., Yantosca, R. M., Sulprizio, M. P., Jimenez, J. L., Campuzano-Jost, P.,
Froyd, K. D., Liao, J., Hair, J. W., Fenn, M. A., Butler, C. F., Wagner, N. L., Gordon, T. D., Welti, A., Wennberg, P. O., Crounse, J. D.,
St. Clair, J. M., Teng, A. P., Millet, D. B., Schwarz, J. P., Markovic, M. Z., and Perring, A. E.: Sources, seasonality, and trends of
southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport
model, Atmos. Chem. Phys., 15, 10411–10433, 10.5194/acp-15-10411-2015, 2015.Langford, A. O., Senff, C. J., Alvarez II, R. J., Banta, R. M., and Hardesty,
R. M.: Long-range transport of ozone from the Los Angeles Basin: A case
study, Geophys. Res. Lett., 37, L06807, 10.1029/2010GL042507, 2010.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371, 2015.Li, L., Tan, Q., Zhang, Y., Feng, M., Qu, Y., An, J., and Liu, X.:
Characteristics and source apportionment of PM2.5 during persistent
extreme haze events in Chengdu, southwest China, Environ. Pollut., 230,
718–729, 2017.Li, P., Yan, R., Yu, S., Wang, S., Liu, W., and Bao, H.: Reinstate regional
transport of PM2.5 as a major cause of severe haze in Beijing, P. Natl. Acad. Sci. USA,
112, E2739–E2740, 2015.
Lin, N.: The research on transport law of atmospheric pollutant and joint
prevention and control of air pollution technology in Sichuan Province,
Southwest Jiaotong University, Chengdu, China, 2015.Liao, T., Wang, S., Ai, J., Gui, K., Duan, B., Zhao, Q., Zhang, X., Jiang,
W., and Sun, Y.: Heavy pollution episodes, transport pathways and potential
sources of PM2.5 during the winter of 2013 in Chengdu (China), Sci.
Total Environ., 584, 1056–1065, 2017.
Liu, S., Hua, S., Wang, K., Qiu, P., Liu, H., Wu, B., Shao, P., Liu, X., Wu,
Y., and Xue, Y.: Spatial-temporal variation characteristics of air pollution
in Henan of China: Localized emission inventory, WRF/Chem simulations and
potential source contribution analysis, Sci. Total Environ., 624, 396–406,
2018.National Bureau of Statistics (NSBC): China Statistics Yearbook, available at:
http://www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm (last access: 28 April 2019), 2015.
Ning, G., Wang, S., Ma, M., Ni, C., Shang, Z., Wang, J., and Li, J.:
Characteristics of air pollution in different zones of Sichuan Basin, China,
Sci. Total Environ., 612, 975–984, 2018a.Ning, G., Wang, S., Yim, S. H. L., Li, J., Hu, Y., Shang, Z., Wang, J., and Wang, J.: Impact of low-pressure systems on winter
heavy air pollution in the northwest Sichuan Basin, China, Atmos. Chem. Phys., 18, 13601–13615, 10.5194/acp-18-13601-2018, 2018b.
Paatero, P. and Tapper, U.: Positive matrix factorization: A non-negative
factor model with optimal utilization of error estimates of data values,
Environmetrics, 5, 111–126, 1994.Qiao, X., Ying, Q., Li, X. H., Zhang, H. L., Hu, J. L., Tang, Y., and Chen
X.: Source apportionment of PM2.5 for 25 Chinese provincial capitals and
municipalities using a source-oriented community multiscale air quality
model, Sci. Total Environ., 612, 462–471, 2018.
Qiao, X., Guo, H., Wang, P. F., Tang, Y., Ying, Q., Zhao, X., Deng, W. Y.,
and Zhang, H. L.: Modeling Fine Particulate Matter and Ozone in the 18
Cities of Sichuan Basin, Southwestern China, in preparation, 2019.
Qiu, Y. M., Xie, Q. R., Wang, J. F., Xu, W. Q., Li, L. J., Wang, Q. Q., Zhao,
J., Chen, Y. T., Chen, Y. F., Wu, Y. Z., Du, W., ZHou, W., Lee, J., Zhao, C. F.,, Ge, X. L., Fu, P. Q., Wang, Z. F., Worsnop, D. R., and Sun, Y. L.:
Vertical characterization and source
apportionment of water-soluble organic aerosol with high-resolution aerosol
mass spectrometry in Beijing, China, ACS Earth Space Chem., 3, 273–284,
2019.Shi, X. Q., Zhao, C. F., Jiang, J. H., Wang, C. Y., Yang, X., and Yung, Y.
L.: Spatial representativeness of PM2.5 concentrations obtained using
observations from network stations, J. Geophys. Res.-Atmos., 123,
3145–3158, 2018.Shi, Z., Li, J., Huang, L., Wang, P., Wu, L., Ying, Q., Zhang, H., Lu, L.,
Liu, X., Liao, H., and Hu, J.: Source apportionment of fine particulate
matter in China in 2013 using a source-oriented chemical transport model,
Sci. Total Environ., 601–602, 1476–1487,
10.1016/j.scitotenv.2017.06.019, 2017.
Stein, A., Draxler, R. R., Rolph, G. D., Stunder, B. J., Cohen, M., and
Ngan, F.: NOAA's HYSPLIT atmospheric transport and dispersion modeling
system, B. Am. Meteorol. Soc., 96, 2059–2077, 2015.Tang, L., Yu, H., Ding, A., Zhang, Y., Qin, W., Wang, Z., Chen, W., Hua, Y.,
and Yang, X.: Regional contribution to PM1 pollution during winter haze
in Yangtze River Delta, China, Sci. Total Environ., 541, 161–166, 2016.Tao, J., Zhang, L., Engling, G., Zhang, R., Yang, Y., Cao, J., Zhu, C.,
Wang, Q., and Luo, L.: Chemical composition of PM2.5 in an urban
environment in Chengdu, China: Importance of springtime dust storms and
biomass burning, Atmos. Res., 122, 270–283, 2013.
Uno, I., Eguchi, K., Yumimoto, K., Takemura, T., Shimizu, A., Uematsu, M.,
Liu, Z., Wang, Z., Hara, Y., and Sugimoto, N.: Asian dust transported one
full circuit around the globe, Nat. Geosci., 2, 557–560, 2009.Wang, L., Jang, C., Zhang, Y., Wang, K., Zhang, Q., Streets, D., Fu, J.,
Lei, Y., Schreifels, J., He, K., Hao, J., Lam, Y. F., Lin, J., Meskhidze,
N., Voorhees, S., Evarts, D., and Phillips, S.: Assessment of air quality
benefits from national air pollution control policies in China. Part I:
Background, emission scenarios and evaluation of meteorological predictions,
Atmos. Environ., 44, 3442–3448,
10.1016/j.atmosenv.2010.05.051, 2010.Wang, L. T., Wei, Z., Yang, J., Zhang, Y., Zhang, F. F., Su, J., Meng, C. C., and Zhang, Q.: The 2013 severe haze over southern
Hebei, China: model evaluation, source apportionment, and policy implications, Atmos. Chem. Phys., 14, 3151–3173, 10.5194/acp-14-3151-2014, 2014.
Wang, M., Cao, C., Li, G., and Singh, R.: Analysis of a severe prolonged
regional haze episode in the Yangtze River Delta, China, Atmos. Environ.,
102, 112–121, 2015.Wang, Z. S., Chao-Jung, C., and Tonnesen, G. S.: Development of a tagged
species source apportionment algorithm to characterize three-dimensional
transport and transformation of precursors and secondary pollutants, J.
Geophys. Res.-Atmos., 114,
D21206, 10.1029/2008JD010846, 2009.
Watson, J. G., Robinson, N. F., Chow, J. C., Henry, R. C., Kim, B., Pace,
T., Meyer, E. L., and Nguyen, Q.: The USEPA/DRI chemical mass balance
receptor model, CMB 7.0, Environ. Softw., 5, 38–49, 1990.
Whiteman, C. D., Hoch, S. W., Horel, J. D., and Charland, A.: Relationship
between particulate air pollution and meteorological variables in Utah's
Salt Lake Valley, Atmos. Environ., 94, 742–753, 2014.WHO: WHO air quality guidelines for particulate matter, ozone, nitrogen
dioxide and sulfur dioxide, available at:
https://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf;sequence=1 (last access: 28 April 2019), 2006.Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire
INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4,
625–641, 10.5194/gmd-4-625-2011, 2011.
Wu, Y., Arapi, A., Huang, J., Gross, B., and Moshary, F.: Intra-continental
wildfire smoke transport and impact on local air quality observed by
ground-based and satellite remote sensing in New York City, Atmos. Environ.,
187, 266–281, 2018.
Yang, X., Zhao, C., Zhou, L., Li, Z., Cribb, M., and Yang, S.: Wintertime
cooling and a potential connection with transported aerosols in Hong Kong
during recent decades, Atmos. Res., 211, 52–61, 2018.Ying, Q., Wu, L., and Zhang, H.: Local and inter-regional contributions to
PM2.5 nitrate and sulfate in China, Atmos. Environ., 94, 582–592,
10.1016/j.atmosenv.2014.05.078, 2014.
Zhang, H. L., Wang, Y., Hu, J. L., Ying, Q., and Hu, X. M.: Relationships
between meteorological parameters and criteria air pollutants in three
megacities in China, Environ. Res., 140, 242–254, 2015.Zhang, R., Jing, J., Tao, J., Hsu, S.-C., Wang, G., Cao, J., Lee, C. S. L., Zhu, L., Chen, Z., Zhao, Y., and Shen, Z.:
Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective, Atmos. Chem. Phys.,
13, 7053–7074, 10.5194/acp-13-7053-2013, 2013.Zhao, B., Wang, S., Dong, X., Wang, J., Duan, L., Fu, X., Hao, J., and Fu,
J.: Environmental effects of the recent emission changes in China:
implications for particulate matter pollution and soil acidification,
Environ. Res. Lett., 8, 024031, 10.1088/1748-9326/8/2/024031, 2013.
Zhao, C., Andrews, A. E., Bianco, L., Eluszkiewicz, J., Hirsch, A.,
MacDonald, C., Nehrkorn, T., and Fischer, M. L.: Atmospheric inverse
estimates of methane emissions from Central California, J. Geophys.
Res.-Atmos., 114, 10.1029/2008JD011671, 2009.
Zhao, C. F., Li, Y. N., Zhang, F., Sun, Y. L., and Wang, P. C.: Growth rates
of fine aerosal particles at a site near Beijing in June 2013, Adv. Atmos.
Sci., 35, 209–217, 2018.Zhao, C. F., Wang, Y., Shi, X. Q., Zhang, D. Z., Wang, C. Y., Jiang, J. H.,
Zhang, Q., and Fan, H.: Estimating the contribution of local primary
emissions to particulate pollution using high-density station observations,
J. Geophys. Res.-Atmos., 124, 10.1029/2018JD028888, 2019.Zhao, Q., He, K., Rahn, K. A., Ma, Y., Jia, Y., Yang, F., Duan, F., Lei, Y., G, Chen, Cheng, Y., H, Liu, and Wang, S.: Dust
storms come to Central and Southwestern China, too: implications from a major dust event in Chongqing, Atmos. Chem. Phys., 10, 2615–2630, 10.5194/acp-10-2615-2010, 2010.
Zhao, S., Yu, Y., Yin, D., Qin, D., He, J., and Dong, L.: Spatial patterns
and temporal variations of six criteria air pollutants during 2015 to 2017
in the city clusters of Sichuan Basin, China, Sci. Total Environ., 624,
540–557, 2018.
Zhao, X., Zhao, P., Xu, J., Meng, W., Pu, W., Dong, F., He, D., and Shi, Q.:
Analysis of a winter regional haze event and its formation mechanism in the
North China Plain, Atmos. Chem. Phys., 13, 5685–5696, 2013.Zheng, G. J., Duan, F. K., Su, H., Ma, Y. L., Cheng, Y., Zheng, B., Zhang, Q., Huang, T., Kimoto, T., Chang, D., Pöschl, U., Cheng, Y. F.,
and He, K. B.: Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions,
Atmos. Chem. Phys., 15, 2969–2983, 10.5194/acp-15-2969-2015, 2015.
Zhu, Y., Huang, L., Li, J., Ying, Q., Zhang, H., Liu, X., Liao, H., Li, N.,
Liu, Z., and Mao, Y.: Sources of particulate matter in China: Insights from
source apportionment studies published in 1987–2017, Environ. Int., 115,
343–357, 2018.