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Spatial characteristics and influence of topography and synoptic 1
systems on PM2.5 in the eastern monsoon region of China 2
Shengli Zhua, b, #, Zhaowen Wanga#, Kai Quc, Jun Xud, Ji Zhanga, e, Haiyi Yang b, 3
Wenxin Wanga, Xiao Sui a, Min Weia, Houfeng Liu a, * 4
5
a College of Geography and Environment, Shandong Normal University, Ji’nan, Shandong, 6
250014, China 7
b University of Chinese Academy of Sciences, Beijing 100049, China 8
cShandong Province Environment Information and Monitoring Center, Ji’nan, Shandong,9
250101, China 10
d State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research 11
Academy of Environmental Sciences, Beijing, 100012, China 12
eEnvironment Research Institute, Shandong University, Qingdao, Shandong, 266237, China13
14
# These authors contributed equally to this work 15
*Corresponding Author: Houfeng Liu (110027@sdnu.edu.cn)16
17
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Abstract 18
Based on the PM2.5 concentration in the autumn and winter of 2015-2019, the 19
characteristics of urban air pollution in the eastern monsoon region of China were 20
discussed. The spatial distribution and interregional influence of fine particle 21
pollution under different synoptic weather and topography in the eastern monsoon 22
region of China were illustrated. According to synoptic systems, regional PM2.5
23
pollution episodes were classified into three categories, including Uniform Pressure 24
field (UP, 60.00%), Pre-High Pressure (PreHP, 30.91%) and Inverted-Trough (IT, 25
9.09%). The K-Means algorithm combined with the HYSPLIT backward trajectory 26
clustering analysis indicated four clusters under UP controlled, and under weak 27
pressure field was responsible for the elevation of PM2.5 concentration, where the 28
Beijing-Tianjin-Hebei and its surrounding areas were the most polluted region. For 29
PreHP, four clusters eased after cold front. For IT, three clusters were ascertained, 30
and the severe PM2.5 pollution area was in the central and southern of the North 31
China Plain. This study provided a scientific basis for the joint prevention of PM2.5 32
pollution based on topographic and meteorological characteristics in Eastern China. 33
Keywords: Eastern monsoon region; PM2.5; synoptic systems; topographic effect34
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1 Introduction 35
Since 2013, China has released the Air Pollution Prevention and Control Action Plan 36
(The State Council of the People's Republic of China, 2013) and implemented strict air 37
pollution control measures. Although the clean air policy has effectively alleviated air 38
pollution (Ministry of Ecology and Environment of the People's Republic of China, 2020), 39
PM2.5 pollution in eastern China remains severe in winter (Zhang et al., 2018b). According 40
to the Asian Development Bank report, only about 1% of the 500 largest cities in China 41
were up to the air quality standard recommended by the World Health Organization (10 μg 42
m-3 for annual mean and 25 μg m-3 for 24-hour mean) (Zhang and Cao, 2015; Ji et al., 2019). 43
Serious air pollution has pose deleterious affects on environmental ecosystem security and 44
human health (Xie et al., 2016; Song et al., 2017a; Wang et al., 2017; Wu et al., 2018; Xie et 45
al., 2019; Xue et al., 2019; Zhao et al., 2019; Askariyeh et al., 2020; Maji, 2020), especially 46
in most densely populated and developed urban agglomerations, such as the 47
Beijing-Tianjin-Hebei (BTH) and the Yangtze River Delta (YRD)(Yan et al., 2018; Zhang et 48
al., 2019b; Xu et al., 2020). 49
Fine particulate matter (PM2.5) is still a primary air pollutant in China, especially in 50
autumn and winter (Zou et al., 2017; Zhao et al., 2018; An et al., 2019; Meng et al., 2019; 51
Yue et al., 2020). Due to the lack of ground-level PM2.5 monitoring data before 2013, most 52
studies discussed PM2.5 pollution from remotely sensed data (Zhao et al., 2019) or 53
short-term observations (less than a year at a national scale). Studies on PM2.5 regional 54
transport focused on machine learning model (Chang et al., 2020), numerical modeling 55
(Zhang et al., 2021) and Hybrid Single-Particle Lagrangian Integrated Trajectory model 56
(HYSPLIT) (Tiwari et al., 2012). Trajectory clustering analysis, potential source 57
contribution function analysis (PSCF), and concentration weighted trajectory analysis 58
(CWT)(Zhang et al., 2019a) are commonly used to identify pollutant transport and 59
dispersion characteristics and potential source areas, and have been widely used in the study 60
of transport and dispersion of air pollutants. However, most studies on PM2.5 are limited to 61
single-city or lack detailed investigation on spatial variations (Cai et al., 2017). Meanwhile, 62
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the influence of different meteorological conditions on PM2.5 pollution cannot be ignored 63
(Liu et al., 2019), which can affect the transport and dispersion conditions by changing the 64
wind field, resulting in different PM2.5 pollution concentration (Wang et al., 2018). 65
Moreover, topography have different effects on weather systems in different regions and 66
seasons(Lai and Lin, 2020), topography can act as a mechanical barrier or a deflector for the 67
movement of weather systems, while its thermodynamic effects can enhance or weaken 68
high and low pressure systems. A geographical unit enclosed by mountains tends to be 69
affected by the same emission sources and weather systems. However, the studies on PM2.5 70
regional transmission at a large scale were limited, and the division of PM2.5 pollution areas 71
(Yao et al., 2020) and joint prevention and control during severe pollution periods and areas 72
were scarce. In addition, studies on temporal PM2.5 pollution have focused on seasonal 73
variation, and interannual variation has been neglected. To acquire comprehensive and 74
thorough characteristics of PM2.5 pollution in a large area and longtime series, large areas 75
and long-time scales studies are needed, as comparisons between different cities and 76
seasons. 77
As the most important air pollutant in autumn and winter in China, determining the 78
distribution of PM2.5 concentration and the main influencing factors in heavily PM2.5 79
polluted areas are essential. The Hu Huanyong line is a geographic boundary of population 80
(Lu et al., 2016) and a comprehensive ecological boundary, which can be used as a 81
geographical boundary for the PM2.5 pollution in China. In autumn and winter, the east area 82
of the Hu Huanyong line was the most polluted area in China (Zhang and Pan, 2020). The 83
PM2.5 in the eastern monsoon region is jointly influenced by anthropogenic emissions and 84
seasonal variations of meteorological conditions. In winter, due to the heating demand, the 85
emissions of fossil fuel combustion such as coal increase, while the solar radiation 86
decreases, the atmospheric vertical motion energy reduces, and the mixing layer height 87
decreases accordingly. The atmospheric vertical mixing weakens, and the frequency and 88
intensity of winter inversion layer increase, which are unfavorable for the diffusion and 89
removal of pollutants. Therefore, the PM2.5 concentration in the eastern monsoon region is 90
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higher in winter and lower in summer, showing significant seasonal variations (Zhang and 91
Cao, 2015; Song et al., 2017b; Wang et al., 2017). Previous studies have shown that urban 92
area, urban population, share of secondary industry, and population density showed positive 93
correlation with PM2.5 concentration, and a large population is exposed to high PM2.5 94
concentration above the WHO standard (Wang et al., 2017). Therefore, it is urgent to 95
establish the joint prevention and control of air pollution in the eastern monsoon region of 96
China, east of the Hu Huanyong line. 97
Due to the high proportion of secondary industries, the coal-based energy structure, the 98
rapid development of transportation, and the rapid urbanization in eastern China, a large 99
population inhabits in the eastern region. Affected by the complex spatial distribution of 100
anthropogenic emissions and the different climatic and meteorological conditions, it is 101
difficult to take effective measures to control air pollution and regional transport in eastern 102
China. It is essential to determine the spatial distribution of PM2.5 concentration and the 103
heavily polluted areas, to further strengthen the joint prevention and control of PM2.5 104
pollution. The strong correlation between PM2.5 concentration in different cities were 105
related to the similar regional scale meteorological fields (Zhang et al., 2018b). Therefore, 106
the local air prevention and control measures cannot achieve the expected effect. Taking 107
joint prevention and control measures for the similar pollution unit is an effective measure 108
to reduce air pollution.The scientific implementation of joint prevention and control relies 109
on the regional pollution transmission of fine particulate matter impact range and pollution 110
area division. In the present study, the K-Means clustering and HYSPLIT trajectory 111
clustering analysis were used to investigate the spatial distribution and clustering 112
characteristics of PM2.5 pollution under different synoptic weather in the eastern monsoon 113
region east of Hu Huanyong Line in China. Based on the daily average PM2.5 concentration 114
in autumn and winter, including 259 cities and a total of 30 months from 2015 to 2019, the 115
possibility of inter-regional pollutant transport in eastern China were clarified, which 116
provide a reference for the joint prevention and control of regional haze under different 117
synoptic weather. 118
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2 Data and Methods 119
2.1 Study Area 120
The eastern monsoon region of China (EMRC) located east of the Hu Huanyong Line 121
(t), covered a large area east of the Daxinganling-Yinshan-Heilan 122
Mountains-Wuqinling-Nenqing Tanggula Mountains-Transverse Ranges (Huang, 1959). 123
Most terrain pattern of the EMRC is plain, mainly distributed on the third step of China, 124
bordering the Eurasian continent to the north and facing the vast Pacific Ocean. The east 125
area of the Hu Huanyong line accounts for 36% of land area and nearly 96% of the total 126
population(Hu, 1990) , and is of great importance to economic development (Ge and Feng, 127
2010). In winter, under the control of Siberian High, strong cold air activity is frequent with 128
less precipitation. In summer, the Pacific warm air prevails with abundant precipitation (Liu 129
et al., 2009). The average annual temperature varies from above 20oC in southern China to 130
about 0oC in northeast China, and the average annual precipitation ranges from 200 mm to 131
2200 mm. Rapid urbanization and local pollutant emission were observed in the EMRC 132
over the last two decades (Zhang et al., 2018a), and most pollutants originated from 133
secondary aerosol emission, industrial emission, and vehicle emission (Li et al., 2018; Chen 134
et al., 2019; Yang et al., 2019). Excessive emissions and limited environmental capacity 135
made the east of the Hu Huanyong Line the most polluted area in China. 136
137
Figure 1 Eastern monsoon region of China (ERMC) including main cities in the east of Hu 138
Huanyong Line (DEM: Digital Elevation Model) 139
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2.2 Data collection 140
Daily average PM2.5 concentration, meteorological and digital elevation data in autumn 141
and winter (October 1-March 31) of 2015-2019 in the eastern monsoon region of China 142
were obtained. The PM2.5 concentration was obtained from the National Real-Time Urban 143
Air Quality Dissemination Platform (http://113.108.142.147:20035/emcpublish), complied 144
with the China Ambient Air Quality Standard (CAAQS, GB3095-2012) (Ministry of 145
Environmental Protection of the People’s Republic of China, 2012) and the Chinese 146
Technical Regulation for Ambient Air Quality Assessment (HJ663-2013) (Ministry of 147
Environmental Protection of the People’s Republic of China, 2013). 148
The meteorological data required for the HYSPLIT model was from the Global Data 149
Assimilation System (GDAS, ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1) provided 150
online by the National Center for Environmental Prediction (NCEP), with the spatial 151
resolution of 1.0°×1.0°, and the temporal resolution was 1h. The synoptic weather chart was 152
provided by the National Meteorological Center (NMC, http://www.nmc.cn/) of China. The
153
digital elevation data were obtained through the NASA/NIMA Shuttle Radar Topography 154
Mission (SRTM) with a resolution of 90 m. 155
The original PM2.5 data from 2015-2019 were preprocessed, and the missing data were 156
complemented by the linear interpolation method. In the present study, heavy pollution was 157
defined as the daily PM2.5 mass concentration >75 μg m-3 for three consecutive days and a 158
peak value >200 μg m-3 (Wei et al., 2020). To classify the main synoptic weather system in 159
the EMRC area, the 6-hour weather charts of the regional heavy pollution process were 160
analyzed based on the pressure field and wind circulation characteristics (Peng et al., 2015; 161
Zhang et al., 2018c; Dai et al., 2020). Finally, the heavy pollution process in the EMRC 162
were classified into Uniform Pressure field (UP), Pre-High Pressure (PreHP), and 163
Inverted-Trough (IT) (Fig.2). 164
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165
Figure 2. Representative 6-hour weather charts for three PM2.5 heavy pollution process synoptic 166
systems. (a) Uniform Pressure field (UP, 2018/11/28 06:00:00); (b) Pre-High Pressure (Pre-HP, 167
2019/01/04 00:00:00); (c) Inverted-Trough (IT, 2018/1/15 18:00:00) 168
2.3 Methods 169
2.3.1 Empirical Bayesian Kriging 170
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The Empirical Bayesian Kriging (EBK) is a geostatistical technique that permits 171
accurate interpolation of spatially intensive data (Roberts et al., 2014; Zhang et al., 2016). 172
The EBK is more accurate in interpolating PM2.5 than the Ordinary Kriging. The monthly 173
average PM2.5 concentration in autumn and winter of 2015-2019 was estimated using the 174
EBK method. Spatial interpolation was conducted in ArcGIS 10.7(Krivoruchko, 2012) 175
using empirical Bayesian kriging (EBK) for 259 cities in the study area to calrify the spatial 176
and temporal distribution patterns of PM2.5 concentration. 177
2.3.2 K-Means Clustering 178
The K-Means clustering algorithm has been widely used in the spatial analysis of air 179
pollutants (Liu et al., 2020), which is a data mining technique in machine learning that seeks 180
to partition M points in N dimensions into k clusters. The algorithm allows data to be 181
grouped into clusters so that the objects within a cluster are similar, but objects in the other 182
groups are different, and the algorithm can help to reveal hidden information in the large 183
dataset(Franceschi et al., 2018). The selection of the appropriate number of clusters is one 184
of the most influential factors on the results of k-means algorithm. 185
The daily PM2.5 concentration data of the heavy pollution process under three synoptic 186
systems were grouped separately using the K-Means algorithm with the following steps. 187
a. Specify the number of clusters (k), set the cluster centre to arbitrary 188
b. Calculate the distance of each data centre cluster using the equation (1), where 189
is the Euclidean distance from the i-th data point to the k-th cluster center, is the value 190
of the i-th data point on the j-th dimension, is the value of the k-th cluster center on the 191
j-th dimension, and m is the number of dimensions: 192
2
1
m
ik ij kj
j
d cc
=
= −
∑
(1) 193
c. Group data into cluster with the shortest distance using the equation (2): 194
2
11
km
ik ij kj
kj
Min d c c
= =
= −
∑∑
(2) 195
d. Calculate the new cluster center using the equation (3), where is the value of the 196
ik
d
ij
c
kj
c
ij
x
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i-th data point on the j-th dimension, and p is the number of data points in the k-th cluster: 197
1
p
ij
i
kj
x
cp
=
=∑
(3) 198
Repeat steps two through four until no more data move to another cluster and use the 199
spatial distribution map to represent the clustering results. The algorithm searches for a 200
local solution that minimizes the Euclidean distance between the observations and the 201
cluster centers. The maximum number of iterations was set to 100 applying this algorithm, 202
and the selection of the initial center of mass was randomized. In this study, the distance 203
refers to the difference between the PM2.5 concentration of each city and the PM2.5 204
concentration of the cluster center, which is used to measure the similarity of PM2.5 205
pollution levels among cities. This method can effectively divide different pollution regions 206
with similar concentration levels and changes, which are more influenced by common 207
external factors such as terrain and weather systems. Mathematics and experience were 208
combined in selecting the optimal number of clusters due to the significant difference in 209
PM2.5 concentration of cities in the EMRC (Lv et al., 2015). 210
2.3.3 HYSPLIT Model 211
The TrajStat-Trajectory Statistics program in MeteoInfo was used to calculate the 212
backward trajectories of air mass during heavy pollution to determine the source of air mass 213
and the regional transport (Wang et al., 2009). The Hybrid Single-Particle Lagrangian 214
Integrated Trajectory (HYSPLIT) model was used to calculate the trajectories (Wang et al., 215
2016). The Euclidean distance clustering and GIS technology was performed for air mass 216
trajectory visualization and statistical analysis. In the present study, the city clusters in the 217
K-Means clustering results were under the control of a uniform pressure field and were 218
influenced by the same or similar air flow paths. To represent the spatial distribution 219
characteristics of the cities in each cluster, we selected the center city of K-Means clustering 220
of typical pollution process of three synoptic system as the starting point by using the mean 221
centroid method, with the top height of the model set at 10,000 m, and the 36-h backward 222
trajectory of 500 m height was simulated by using the HYSPLIT model. The backward 223
trajectories of air masses in the center city of K-Means clustering qualitatively indicated the 224
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source of atmospheric pollutants as well as the direction path and influence range of 225
long-range transport dispersion. In the clustering analysis, 3168, 1632 and 480 trajectories 226
were used for the UP, the PreHP and the IT, respectively. 227
3 Result 228
3.1 Spatiotemporal variation of monthly average PM2.5 concentration 229
Table 1 presents the descriptive statistics of PM2.5 concentration for 259 cities in the 230
EMRC during autumn and winter from 2015 to 2019. The monthly average concentration 231
varied from 35.2 μg m-3 to 84.9 μg m-3, with the highest standard deviation of 41.6 μg m-3 in 232
December 2015. The most polluted cities were Baoding, Shijiazhuang, and Anyang, which 233
were located in the North China Plain and surrounded by mountains on three sides (Fig. 1). 234
In the early stage of air pollution, the North China Plain was typically affected by 235
low-pressure weather systems, and the near-surface southeast wind transported pollutants to 236
the northwest (An et al., 2019). The Taihang Mountains and the east of Loess Plateau 237
blocked the moving air flow, and therefore air pollutants were accumulated. Affected by the 238
high-pressure system, the northwest air flow form a rolling down circulation on the leeward 239
slope due to the terrain effect, which strengthened the upslope wind and vertical upward air 240
flow over the plain. Pollutants were diffused to the high-altitude areas, and then were 241
brought back to the plain by the north wind, resulting in pollutants lingering and gathering 242
(Liu et al., 2023), with mean PM2.5 concentrations exceeding 100 μg m-3. The cities such as 243
Lijiang, Puer, and Sanya were observed lower PM2.5 concentrations of less than 20 μg m-3, 244
mostly located in the south and coastal areas of China and the terrain was mainly hilly and 245
relatively open. In winter, these cities were affected by air flows from northwest and 246
northeast (Chen et al., 2020), and were greatly influenced by the marine air mass, which 247
was conducive to the diffusion and dissipation of pollutants. The maximum PM2.5
248
concentration recorded was 727.0 μg m-3 at Xingtai, Hebei province, in November 2016, 249
which was more than tenfold the national air quality standard. The minimum PM2.5 250
concentration recorded was 6.6 μg m-3 at Lijiang, Yunnan province, in February 2019. The 251
PM2.5 pollution was more severe during winter than other months, with mean PM2.5 252
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concentration above the national secondary standard (75 μg m-3). 253
Table 1. The descriptive statistics of PM2.5 concentration for 259 cities in the EMRC during 254
autumn and winter, 2015-2019 (μg m-3) 255
Period
Minimum Maximum
Median Mean Standard
deviation
PM2.5 City name PM2.5 City name
2015-01 14.0 Lijiang 189.7 Baoding 82.2 84.9 31.3
2015-02 12.9 Lijiang 159.1 Baoding 67.7 70.2 23.7
2015-03 15.9 Lijiang 115.0 Jiaozuo 51.2 53.0 19.0
2015-10 16.6 Puer 114.3 Liaocheng 51.7 53.0 18.4
2015-11 13.1 Sanya 147.9 Harbin 50.5 57.2 28.8
2015-12
15.7 Hezhou 214.1 Baoding 76.2 79.1 41.6
2016-01 15.4 Sanya 173.8 Luoyang 70.7 71.6 34.6
2016-02 19.9 Sanya 115.2 Zhoukou 57.2 58.2 19.4
2016-03 16.3 Sanya 108.5 Jincheng 57.9 58.9 18.2
2016-10 13.0 Baoshan 116.1 Shijiazhuang 35.8 38.1 14.1
2016-11 11.6 Sanya 173.1 Linfen 55.1 60.7 27.9
2016-12 12.6 Lijiang 276.3 Shijiazhuang 74.8 84.2 39.3
2017-01 12.9 Lijiang 199.5 Shijiazhuang 79.1 82.2 35.2
2017-02 13.0 Lijiang 146.1 Baoding 62.5 65.0 24.3
2017-03 13.0 Lijiang 85.5 Baoding 49.8 50.7 15.1
2017-10 10.2 Guangyuan 93.7 Harbin 37.4 40.3 14.9
2017-11 15.7 Lijiang 103.4 Xianyang 53.3 53.4 17.4
2017-12
21.9 Heihe 127.0 Xianyang 68.1 70.0 23.1
2018-01 13.5 Sanya 145.9 Xianyang 60.6 66.5 28.2
2018-02 11.0 Lijiang 109.5 Xingtai 58.5 58.1 17.5
2018-03 13.9 Lijiang 105.2 Shijiazhuang 45.5 48.3 16.6
2018-10 9.0 Puer 65.0 Anyang 34.8 35.3 10.9
2018-11 13.3 Puer 129.4 Anyang 44.4 51.2 24.2
2018-12 11.9 Puer 125.7 Luohe 49.6 52.9 23.9
2019-01 12.7 Lijiang 173.5 Linfen 69.8 71.7 33.2
2019-02 6.6 Lijiang 165.6 Anyang 56.3 60.1 32.3
2019-03 8.3 Lijiang 68.4 weifang 43.9 43.4 12.4
2019-10 11.1 Lijiang 62.2 zaozhuang 35.5 35.2 10.7
2019-11 12.0 Lijiang 83.1 Anyang 41.4 42.9 15.5
2019-12
14.7 Sanya 120.5 Anyang 56.4 58.8 20.5
256
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257
Figure 3. Distribution of Monthly Average PM2.5 Concentration in EMRC in Autumn and Winter, 258
2015-2019 259
3.2 Synoptic system analysis of heavy pollution process 260
The EMRC was mainly influenced by the periodic activity of the Siberian High, and 261
the three typical synoptic systems were Uniform Pressure field (UP), Pre-High Pressure 262
(PreHP) and Inverted-Trough (IT) (Fig. 2), which accounted for 60.00%, 30.91% and 263
9.09%, respectively (Table 2). The UP was a weather system that occurred before the 264
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Siberian high pressure moved southward and affected the eastern monsoon region. The UP 265
system was characterized by sparse isobars, weak pressure gradient, low wind speed, and 266
unfavorable conditions for pollutant dispersion, which aggravated the accumulation of 267
particulate matter, and further intensifying the accumulation of PM2.5 and causing the 268
greatest impact and longest duration. The PreHP weather was typically followed by the UP, 269
and PM2.5 pollution in the EMRC was serious, the airflow from the northwest gathered air 270
pollutants in the east with the passing cold front, the clean air will accelerate the diffusion of 271
particulate matter and dilute the pollutant concentration, thus reducing the PM2.5 272
concentration significantly and easing air pollution. The duration of IT was the shortest, and 273
the moderate PM2.5 pollution was generally observed. Fig. 4 shows the classification and 274
integration of the heavy PM2.5 pollution in the EMRC during the autumn and winter of 275
2015-2019 under different synoptic systems. The heavy PM2.5 pollution is mainly 276
concentrated in January or December, with December 2016 having the largest frequency (24 277
days). The PM2.5 heavy pollution days is decreasing from 2015 to 2019, which indicated 278
that air pollution in eastern China had gradually alleviated. 279
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Table 2. The summary of pollution characteristics of different synoptic systems in the EMRC 280
Pattern Frequency Some typical periods characteristics Max.
concentration
Uniform
Pressure
field (UP)
60.00%
2015.12.06 - 2015.12.08;
2016.12.15 - 2016.12.18;
2017.02.12 - 2017.02.15;
2018.11.27 - 2018.12.01;
2019.01.09 - 2019.01.12
Eastern monsoon area under
the control of uniform pressure
field. The isobars are thin,
surface wind speed is small,
atmospheric diffusion ability is
weak. Blocked by Taihang
Mountains, air pollutants
accumulate in the North China
Plain.
727 μg m-3
Pre-High
Pressure
(PreHP)
30.91%
2015.12.09 - 2015.12.10;
2016.11.18 - 2016.11.02;
2017.01.07 - 2017.01.08;
2018.12.02 - 2018.12.03;
2019.01.03 - 2019.01.06
Pre-
High pressure weather
systems weaken the
topographic effect. The cold
high pressure usually extends
to the southeast, the isobars in
the southern region are thin,
pollutants are easy to
accumulate before cold fronts
arrival. Co
ld fronts usually
bring northwest clean air mass
to dissipate the heavy pollution.
644 μg m-3
Inverted-
Trough
(IT)
9.09%
2015.01.25 - 2015.01.26;
2016.11.12 - 2016.11.14;
2017.01.27 - 2017.01.29;
2018.01.15 - 2018.01.16;
2019.12.22 - 2019.12.25
Air mass underground
inverted-
trough control is
governed by low pressure. The
trough opens to the
south or
southwest, the bottom of the
trough extends to the north or
northeast, moving to the
northeast from the southwest of
China. The IT weather system
has weak influence on heavy
pollution.
542 μg m-3
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281
Figure 4. Frequency distribution of heavy pollution days under three weather systems of PM2.5 in 282
autumn and winter from 2015 to 2019 283
3.3 Spatial clustering pattern and backward trajectory under different synoptic 284
systems 285
A significant difference was found between coastal and inland cities under the control 286
of different weather systems in regional PM2.5 heavy pollution. Moreover, topography 287
aggravated the complexity of spatial clustering (Fig. 5). 288
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289
Figure. 5 PM2.5 clustering and box line diagrams of prefecture-level cities in the eastern monsoon 290
region during the study period under the control of different weather systems 291
3.3.1 Uniform Pressure field (UP) 292
Under the uniform pressure, eastern China was divided into four clusters (Fig.6). Cluster 293
Ⅰ was located in northeast, northwest and south China, at the outer edge of the uniform 294
pressure field; Cluster Ⅱ was mainly in the middle and lower reaches of the Yangtze River 295
and the northeast plain, outside the heavily polluted area and near the cleaner Cluster Ⅰ area; 296
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Cluster Ⅲ was mainly in the central part of the north China plain, Henan-Shandong, near 297
the heavily polluted Cluster Ⅳ area; Cluster Ⅳ was mainly in the east of Taihang 298
Mountains, near Beijing-Tianjin-Hebei city cluster and central Shaanxi-Jinan. Due to the 299
influence of meteorological flow field, the PM2.5 concentration significantly varied among 300
clusters. Generally, neighboring cities in the similar topographic or airflow area belonged to 301
the same cluster. Cluster Ⅳ was near the eastern side of Taihang Mountains and had the 302
maximum PM2.5 concentration due to the influence of the converging airflow caused by the 303
topography (Fig.6), with an average concentration of 161.3 μg m-3; followed by Cluster Ⅲ, 304
which was second to Cluster Ⅳ with a concentration of 134.8 μg m-3. The average and 305
median concentration in the peripheral area Cluster Ⅰ were lower than the national 306
secondary standard. In general, under the control of the uniform pressure field, the low wind 307
speed caused by the weak pressure gradients, as well as the occurrence of near-surface 308
inversions due to the warm surface currents that normally accompany them, leaded to 309
disadvantageous conditions for particulate matter diffusion and increased concentration 310
accumulation. Weak wind fields leaded to slow air mass movement, which aggravated the 311
pollution. In addition, particulate matter emission increased in autumn and winter due to 312
winter heating. Once the uniform pressure field weather was encountered, which might lead 313
to a rapid deterioration of air quality and a significant increase in PM2.5 concentration and 314
an outbreak of large-scale air pollution. 315
Backward trajectory of air mass at 500 m height is shown in Fig. 6. Under the uniform 316
pressure field control, Cluster Ⅰ (e.g., Yichun) was affected by the trajectory airflow in six 317
directions, among which the most frequent influence was from the inland airflow in two 318
directions, northwest (32.83%) and northeast (31.06%), with low air mass temperature and 319
relatively less pollution. Cluster Ⅱ (e.g. Suizhou) was driven by the trajectory airflow in 320
three directions, among which the highest frequency is from northeast (50.66%) and 321
southwest (41.45%) inland airflow in two directions, the air mass have slightly lower 322
temperature and less pollution. Cluster III (e.g., Heze) was subject to the influence of 323
trajectory airflow in four directions, primarily by inland airflow in the southeast (37.59%) 324
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and southwest (29.58%). The air mass from southern area was slightly warmer and more 325
polluted and passed the economically developed area and had relatively high-emission 326
pollution. Cluster Ⅳ (e.g., Xingtai) was influenced by the trajectory airflow in three 327
orientations, among which the influence frequency was the airflow from the eastern of the 328
province from local area (42.80%), and the air mass had low temperature and serious 329
pollution (Wang et al., 2018). Regional severe pollution episodes mainly occur in the 330
Yellow-Huaihai Plain and Fenwei Basin owing to the influence of topography. Neighboring 331
cities located in the similar topographic or airflow area constantly interact with each other, 332
thus these cities had similar characteristics and were classified into the same clusters. 333
3.3.2 Pre-High Pressure (PreHP) 334
Eastern monsoon region was divided into four clusters under the pre-high pressure 335
weather system (Fig.S1). Cluster I mainly included the northeast and south China, which 336
were at the edge of the cold high-pressure march. Cluster II consisted primarily of the 337
middle and lower reaches of the Yangtze River, which was outside the heavily polluted area 338
and close to the cleaner Cluster I area. Cluster III mainly contained the 339
Beijing-Tianjin-Hebei area in the northern of the North China Plain, which was close to the 340
severe polluted Cluster IV area. Cluster IV was primarily located south of the Taihang 341
Mountains, near the Central Plains urban cluster. However, cluster Ⅳ had the highest PM2.5 342
concentration (Fig.6), with an average concentration of 155.8 μg m-3, which was affected by 343
the topography and heavy air mass from the north. Due to the proximity to the Taihang 344
Mountains, the cluster Ⅳ area were frequently located in front of the cold high pressure. 345
Cluster III and Cluster IV have similar characteristics, with PM2.5 concentration of 143.2 μg 346
m-3 (Fig.S1). 347
The backward trajectory clustering of pre-high pressure is indicated in Fig.S1. Cluster Ⅰ 348
(e.g., Siping) was affected by trajectory airflow in five directions, among which the highest 349
frequency was from inland northwest (30.33%) and southwest (23.04%) airflow, with low 350
temperature and less pollution. Cluster II (e.g., Xinyang) was affected by six directional air 351
currents, of which the most frequent were inland air currents from the southeast (32.11%) 352
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and northwest (21.51%), with low air mass temperature and slightly heavier pollution. 353
Cluster Ⅲ (e.g., Zhengzhou) was impacted by trajectory airflow in four directions, primarily 354
by inland southeast (42.83%) and northwest (27.57%) airflow, with slightly higher air mass 355
temperature and heavy pollution from the southeast, and severe pollution from the 356
northwest due to the air-mass from more polluted Hebei and Shanxi province. Cluster Ⅳ 357
(such as Hengshui) including five directional track airflow were maily affected by airflow 358
from the eastern of the province form local emission (35.54%), and inland northwest 359
airflow (24.57%), air mass with low temperature and severe pollution. As the 360
Mongolia-Siberia cold high pressure was active and southward in autumn and winter, air 361
pollutants were accumulated in North China at first, and with the cold front southward 362
could remove the atmospheric pollutants in North China rapidly. The cold front was the 363
gathering zone of atmospheric pollutants, when the gathering zone was pushed to a certain 364
area, the pollutant concentration raised sharply, and then the air quality in the area before 365
the front deteriorated rapidly (Kang et al., 2019). Affected by the Mongolian-Siberian cold 366
high pressure, the intense weather system weakened the topographic effect and most of the 367
clusters had a relatively similar backward trajectory. When the high pressure kept moving 368
southward, the dominant airflow changed, originating in the Siberian and Mongolian 369
regions, air mass cross the North China Plain, passing through the middle and lower reaches 370
of the Yangtze River, and reaching the southern part of the eastern monsoon region, with 371
most of the clusters mainly were influenced by the north or northwest airflow. 372
3.3.3 Inverted-Trough (IT) 373
Under the inverted-trough weather system, the eastern monsoon region of China was 374
divided into three clusters (Fig.S1). Cluster I included South China and coastal areas, which 375
were far from the low-pressure center controlling the surface inversion trough. Cluster II 376
included the outer part of the North China Plain, the Sichuan Basin and the Northeast Plain, 377
which were in the transition area of heavy pollution. Cluster III was in the south-central part 378
of the North China Plain and the central Shaanxi-Jinan region (Fig.S1). Cluster III was 379
located at the bottom of the inversion trough of the ground, where the isobars were sparse 380
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and blocked by the Taihang Mountains, and air pollutants were accumulated, with an 381
average concentration of 166.9 μg m-3 (Fig.S1). 382
The backward trajectory air mass of representative cities under Inverted-Trough 383
weather systems are analyzed in Fig.S1. Cluster Ⅰ (e.g., Huangshi) was subject to six 384
trajectory airflow, and the most frequently influenced airflow was from trajectory 4 (38.54%) 385
and trajectory 1 (23.96%) in the inland area form southwest. The air mass was warmer and 386
less polluted. Cluster Ⅱ (e.g., Jinan) was influenced by six airflow, primarily from the 387
eastern of Shandong province (50.00%), with heavy pollution. Cluster III (e.g., Jiaozuo) 388
was affected by six directional air mass, mostly affected by air-mass from the inland area 389
form west (41.25%) and northwest (22.08%), with low air temperature and severe pollution. 390
Duration of the surface inversion trough was usually short and initiated in the southern area 391
and moved in the north-east, with thin isobars, small pressure gradients, and weak surface 392
wind. The weak prevailing southerly wind was not beneficial for pollutant diffusion, e.g., 393
under the control of the surface inversion trough, atmospheric pollutants near Beijing 394
converged and formed aggregates in front of Taihang Mountains, thereby causing regional 395
severe pollution episodes(Yu et al., 2020). 396
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397
Figure. 6 Backward trajectories of air mass of different spatial patterns under different synoptic 398
systems controlled. ((a) Uniform Pressure field (UP)). 399
4 Conclusion and Implication 400
4.1 Conclusion 401
The autumn and winter PM2.5 concentration in the eastern monsoon region of China 402
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from 2015-2019 were investigated in the present study. The spatial pattern of severe air 403
pollution was closely related to the synoptic system and the topographic effect. The 404
pollution weather types can be classified as uniform pressure field (60.00%), pre-high 405
pressure (30.91%) and inverted-trough (9.09%). K-Means algorithm combined with 406
HYSPLIT backward trajectory clustering was used to analyze the regional classification and 407
regional transport of PM2.5 pollution under the three synoptic systems. The results showed 408
that under the control of a uniform pressure field, the weak wind speed caused by the 409
low-pressure gradient and the complex topography made the prevailing wind direction 410
unstable and the regional differences were significant. The neighboring cities situated in the 411
same topographic area or airflow path often had similar characteristics. Affected by pre-high 412
pressure, the advance of the cold front in front of the intense cold high pressure weakened 413
the influence of the topography. Under the influence of surface inversion trough, the small 414
pressure gradient, low surface wind speed, and the weak southerly wind were available for 415
air pollutants accumulation. Therefore, in regional PM2.5 severe pollution, it was necessary 416
to carry out detailed prevention and control methods according to the synoptic weather 417
systems and the distribution pattern of the corresponding regional influence clusters. 418
4.2 Policy suggestions 419
According to the aforementioned results, the eastern monsoon region in China should 420
establish a regional joint prevention and control mechanism for air pollution, and actively 421
promote regional cooperation for the overall haze control in the eastern monsoon region. 422
The main suggestions were as follows. 423
(1) Under severe pollution, to establish joint prevention and regional control units based 424
on different weather systems with long influence time and develop different pollution 425
prevention and control policies for different areas, implement differentiated emergency 426
management was necessary. In most heavy pollution, the eastern monsoon area was 427
controlled in the Uniform Pressure field, and the east of the Taihang Mountains, Beijing, 428
Tianjin and Hebei urban agglomeration should be used as a focus control unit. From the 429
Central North China Plain to Henan, Shandong should be used as a secondary priority 430
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control unit, other areas with low pollution levels can be used as a general control unit. 431
(2) To improve the monitoring and forecasting and early warning system for weather 432
system with short impact time. The duration of pre-high pressure and inverted-trough 433
control of severe pollution was usually short. Therefore, the corresponding environmental 434
pollution monitoring and early warning platform should be established, implementation of 435
regional environmental information sharing, joint warning and demonstration effect 436
mechanism, accurate forecasting of the pollution process weather situation and the 437
dominant airflow, to further take targeted joint control measures. 438
(3) Strengthen the construction of a joint regional environmental law enforcement and 439
regulatory system. Establish a coordinated regional linkage environmental management 440
mechanism and explore the linked management system of cities outside the 441
Beijing-Tianjin-Hebei corridor. 442
Acknowledgement 443
This work was supported by the National Natural Science Foundation of China 444
(42075183), China Postdoctoral Science Foundation (2019T120606), the Opening Project 445
of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) 446
(FDLAP19008). 447
Declaration of competing interest 448
The author declare that they have no known competing financial interests or personal 449
relationships that could have appeared to influence the work reported in this paper. 450
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