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Analysis of transport path and source distribution of winter air pollution in Shenyang

  • Yunfeng Ma , Qiyao Liu EMAIL logo , Yushan Bian , Lei Feng , Di Zhao , Shuai Wang , Huijie Zhao , Kunyu Gao and Zhengqing Xu
Published/Copyright: September 30, 2021
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Open Geosciences
From the journal Open Geosciences Volume 13 Issue 1

Abstract

Air pollution is one of the most serious environmental problems faced by mankind. It is regional and highly complex, and it is more prominent in China. With the development of air quality management in China, the research on cross-regional transmission of air pollutants is particularly important. This paper reports on pollution characteristics, transport path, and distribution of pollution sources of major contaminants in Shenyang. For this purpose, pollution-monitoring data were gathered from November 2017 to March 2018. Data were analyzed using the HYSPLIT back trajectory model, the potential source contribution function (PSCF), and the concentration weighted trajectory (CWT) model. Results indicated that PM2.5 was the main pollutant in Shenyang during the study period. Air pollution was mainly affected by coal combustion, traffic emissions, and long-distance transmission. Among the 11 monitoring points, the pollution of Shenliaoxilu was relatively serious. Mongolia, eastern Inner Mongolia, northwestern Jilin, and most of Liaoning were the main potential sources of PM2.5 in Shenyang during the winter.

Graphical abstract

Keywords: pollution sources; pollution characteristics; HYSPLIT; TrajStat; PSCF; CWT

1 Introduction

Air pollution has become a global challenge; it has harmful effects on health, ecology, and people’s lives [1]. By analyzing the results of concentration weighted trajectory (CWT) and potential source contribution function (PSCF), Dimitriou (2015) determined the potential sources of PM in Valencia, Spain [2]. Gibergans-Baguena et al. (2020) proposed a new air quality index (AQI) based on component data analysis in Barcelona [3]. In Republic of North Macedonia, Angelevska et al. (2021) designed a guide to urban air quality, which is based on road traffic management measures [1].

In recent years, the industrialization in China has significantly grown. As a result, haze pollution has also increased becoming a serious problem, for example, causing a decrease in visibility which has caught the attention of the ordinary people, experts, and scholars. Chinese State Council issued strict new air-pollution laws in 2014 [4]. In 2017, 70.7% of Chinese 338 prefectures and above cities failed to meet the National Ambient Air Quality Standards of China (NAAQS) [5]. In January 2018, the monthly average maximum concentration of PM2.5, PM10, SO2, and NO2 in 74 major cities in China were 141, 191, 69, and 76 μg/m3, respectively [6]. Besides, per capital PM2.5 in Chinese major cities was 61 μg/m3, which was three times that for the global average value [7]. Based on the ground monitoring data of PM2.5 concentration in 366 cities in China in 2016, Chen et al. established a more sustainable cross-regional joint management strategy [8]. The air pollution during winter season in northern China becomes particularly severe. In this area, the air pollution is mainly caused by the bad weather conditions, high coal consumption, and urban traffic emissions [9,10,11].

Shenyang is located in the northeast of China and central Liaoning Province. It presents a typical semi-humid continental climate. Its economic activity is dominated by industry, and coal has been used as the chief energy source, and the annual consumption is 35 million tons [12]. As the only megacity in the Northeast, Shenyang displays an urbanization rate of 81% and air pollution is becoming worse with time [13]. From 2010 to 2012, Shenyang presented average monthly visibilities of 19.0 ± 4.3 and 17.1 ± 4.3 km for the months of March and September, respectively. These values were relatively high. Corresponding data for January were about 11.0 ± 4.7 km, which was relatively low [14]. In November 2015, serious air pollution appeared in northeastern China, where the maximum hourly PM2.5 concentration in Shenyang exceeded 1,000 μg/m3 [15]. Miao et al. (2018) studied the influence of weather patterns on aerosol transport and the planetary boundary layer in Shenyang during December 1–3, 2016. It was found that the atmospheric aerosol pollution in Shenyang had relation to local atmospheric emissions and atmospheric aerosol transboundary migration. According to the data, the aerosol released from Beijing-Tianjin-Hebei region may be brought to Shenyang under the influence of westerly wind and southwest wind [16]. Authors concluded that there is an imperative requirement for controlling air pollution in this region. Meanwhile, since the diffusion and transport of air pollutants can pose a great challenge to pollution control, the identification and quantification of pollution sources are key to controlling pollutant concentrations [17]. Pollutants transported across regions contribute significantly to changes in air quality. Even if there are studies on the characteristics of air pollutants in Shenyang, most of the existing studies focus on the analysis of short-term pollution events [18,19,20].

In this work, hourly PM2.5, PM10, SO2, NO2, O3, and CO concentrations were obtained from the national air quality-monitoring network in 11 different stations in Shenyang. This information was combined with meteorological data so as to analyze the spatial and temporal distribution characteristics of air pollutants in Shenyang. The back trajectory model, PSCF, and CWT were also used to study the transport path and source distribution of the pollutants. Shenyang contaminants concentrations were compared with those found in other major cities in China. The results of the study are conducive to comprehend the main characteristics of winter air pollution in Shenyang. In addition, it is possible to identify potential sources of environmental pollutants. This will assist decision-makers in designing effective strategies for air pollution control. The results can provide guidance for the future control strategy and policy-making in Northeast China.

The rest of the paper is organized as follows: Section 2 introduces the source of data and research methods. Next, in Section 3 we report the experiments in detail and discuss the experimental results. Finally, a brief conclusion is drawn in Section 4.

2 Materials and methods

2.1 Data collection

For this research, 11 monitoring points of the National Air Quality-Monitoring Network were selected. The monitoring data from the eleven monitoring points were obtained from the Ministry of Ecology and Environment of China (http://www.mee.gov.cn/). The monitoring methods for PM2.5 and PM10 are β-ray absorption method (dynamic heating DHS) and light scattering method (optical turbidity); Pulse ultraviolet fluorescence method is used for SO2 monitoring; NO2 monitoring uses chemiluminescence method; CO monitoring method is gas filter correlation infrared Absorptiometry; O3 is monitored by ultraviolet photometry. During the monitoring, calibration using NO, CO, and SO2 standard gases produced by the National Institute of Metrology, and the Thermo 49 i-PS UV photometer O3 calibrator was used to calibrate the O3 analyzer. Each instrument needs to be calibrated once every 7 days and the sampling pipeline is cleaned at least once a month, so as to ensure the accuracy and effectiveness of the monitoring data. Six conventional pollutants at hourly concentrations at the 11 stations in Shenyang during the central heating period (November 1st, 2017 to March 31st, 2018) were analyzed. The 11 air quality-monitoring points were: Donglinglu (DLL; 41.841°N, 123.590°E), Hunnandonglu (HNDL; 41.741°N, 123.505°E), Jingshenjie (JSJ; 41.923°N, 123.378°E), Lingdongjie (LDJ; 41.847°N, 123.426°E), Senlinlu (SLL; 41.934°N, 123.684°E), Shenliaoxilu (SLXL; 41.735°N, 123.244°E), Taiyuanjie (TYJ; 41.797°N, 123.402°E), Wenhualu (WHL; 41.765°N, 123.411°E), Xiaoheyan (XHY; 41.787°N, 123.468°E), Xinxiujie (XXJ; 41.699°N, 123.423°E), and Yunonglu (YNL; 41.909°N, 123.595°E), respectively. The locations of Shenyang in China and 11 monitoring points are shown in Figure 1. SLL is located in Qipanshan, and it was used as the background level since it does not contain a pollution source. Other selected points were close to different emission sources, including transportation and industry.

Figure 1 Location (shadow) and monitoring points (solid circles) in Shenyang, China, used in the present research.
Figure 1

Location (shadow) and monitoring points (solid circles) in Shenyang, China, used in the present research.

In this study, weather data (wind speed, wind direction, and temperature) were collected every 3 h from the National Climate Data Center in Shenyang. Since data were scarce, the information obtained in one station was analyzed to represent the general weather. Even when no exact information for every site of analysis existed, this approach may provide a reference to understand the correlation between weather and atmospheric pollution in Shenyang. In the study period, the average temperature was −5.17oC. The dominant wind is from the north and the mean wind speed is 2.18 m/s. A probability of wind speed less than 2 m/s exceeds 50 units. At this speed, pollutants’ concentration showed a peak value; in addition, the lowest visibility was observed.

In the present research, the 8-hourly, daily mean, and monthly average concentrations of six conventional pollutants were used as the average values of the data recorded by the hour. Twenty-four hour pollutant concentration was calculated only when effective data for more than 20 h were available on a given day. The average 8 h O3 concentration was calculated using valid data from at least 6 h. In addition, monthly average was calculated using the average of the hourly data for the whole month. Unless otherwise stated, the pollutant concentration reported for each monitoring point in Shenyang represents the data average for that site.

2.2 Model

For purpose of determining the potential impact of cross-region wind transport on air pollutants’ concentrations in Shenyang, HYSPLIT4 (the Hybrid Single-Particle Lagrangian Integrated Trajectory model version 4 developed by NOAA) was used to calculate the back trajectories of the air mass during this survey [21,22]. The HYSPLIT model is a complete system that calculates simple air mass trajectories through complex diffusion and deposition simulations, and it is able to calculate the source and transport of air blocks to a particular research location. Model calculation uses meteorological data with one degree and one degree spatial resolution of GDAS [22]. A 72 h backward trajectory was calculated from November 1st, 2017 to March 31st, 2018. For this, a height of 500 m for two sites (SLL and SLXL) and four different times (local time 00:00, 06:00, 12:00, and 18:00 h) were considered every day. The calculated trajectory was processed using a cluster analysis method.

The PSCF and CWT methods were used to determine the possible source areas and their contribution to the mass concentration of air pollutants. Analysis was on the basis of the backward trajectory model. In addition, TrajStat was also applied [23]. The probability map of the area around the study location during the research is often generated using the PSCF method [20,24]. This is a method that locates the potential source affecting the air quality of the receptor site on the basis of the residence time and air mass distribution in a particular area [25]. When the concentration is higher than the standard value determined by the user, the obtained PSCF value can be regarded as a conditional probability associated with the trajectory of the air mass having the PSCF value reaching the research site through the mesh unit. A greater PSCF value indicates a greater probability. In our current research, the PM2.5 limit was set to 75 μg/m3 and the PM10 limit was set to 100 μg/m3, according to the NAAQS GradeⅡlimited value. The CWT method can assess the contribution of possible pollution sources to the contaminant concentration at a certain study site with result of the air mass trajectory weighted concentration [26,27,28]. A higher CWT value indicates a higher potential contribution to a heavy contamination of the monitor site. In this work, PSCF and CWT methods took into account the concentrations of PM2.5 and PM10 in SLL and SLXL, respectively. The specific research methods are shown in Figure 2.

Figure 2 The flowchart of main research methods.
Figure 2

The flowchart of main research methods.

3 Results and discussion

3.1 Time distribution of pollutants

The time series of CO, O3, SO2, and NO2 particulate matter with aerodynamic diameters of <2.5 μm (PM2.5) and <10 μm (PM10) concentrations at the eleven monitoring stations were selected for this study. As shown in Figure 3, it was found that for the periods December 29–31, 2017, February 25–28, 2018, and March 23–26, 2018, concentrations of CO, O3, SO2, NO2, PM2.5, and PM10 significantly increased. This resulted in three significant pollution events. These three periods displayed average hourly AQI greater than 150 at every monitoring station to the exclusion of SLL, which indicated severe atmospheric pollution. The mean data corresponding to the three high pollution incidents indicated that average daily concentrations (and IAQI) of CO, O3, SO2, NO2, PM2.5, and PM10 in the city were 1.797 mg/m3(45), 60(30), 48(48), 57(71), 119(156), and 162(106) μg/m3, respectively. In addition, the hourly peak concentrations were 3.845 mg/m3(96), 214(149), 105(78), 105(113), 228(311), and 302(176) μg/m3, respectively. Among the eleven monitoring points, SLXL displayed the peaked PM2.5 and PM10 concentrations, with average daily peaks of 174 and 228 μg/m3, respectively. This may be because Tiexi District is developed in industry with many factories. This is in line with the findings of Tian et al. [29]. These values were twice the NAAQS Grade Ⅱ limited value. Even at the background point (labeled as SLL), the 24 h PM2.5 concentrations exceeded 75 μg/m3, and the peak concentration reached 129 μg/m3, indicating the possibility of regional pollution and pollutant transport. Figure 3 shows that these peaks occurred at a low wind speed (<1 m/s) and southwest winds. At the same time, strong winds (>5 m/s) from the southwest were associated with a cleaner air. The southeast wind or the east wind may transport polluted air from other parts of the Liaoning Province. In general, low speed winds from the south and east may be related to the accumulation of pollutants, which may be emitted locally or transported from other regions.

Figure 3 Concentrations of CO, O3, SO2, NO2, PM2.5, and PM10, T and WS (wind speed) during the research period.
Figure 3

Concentrations of CO, O3, SO2, NO2, PM2.5, and PM10, T and WS (wind speed) during the research period.

To further determine the characteristics of PM pollution, the PM2.5/PM10 was calculated (Figure 3). As a part of PM10, PM2.5 plays an extremely significant role in the ratio analysis of 0–10 μm particle size distribution. Therefore, the clear ratio relationship between PM2.5 and PM10 can be used as a reference for pollution source analysis and prevention. The ensemble average PM2.5/PM10 value for Shenyang was 0.73, which was slightly higher than the mean level for other cities across the country [30]. As shown in Figure 3, the peak pollutant concentration mostly corresponds to a relatively greater PM2.5/PM10 value. At the severe contamination days, this ratio was mostly >0.7. According to relevant literature reports, PM2.5 has a greater impact on air pollution and extinction than PM10 [31,32]. However, in order to further study the interaction between PM and air pollution, it is essential to conduct much deeper research on the chemical composition and atmospheric visibility of PM.

3.2 Concentration patterns

From November 1st, 2017 to March 31st, 2018, the hourly mean concentrations of PM2.5, PM10, SO2, NO2, 8 h O3, and CO in Shenyang were 55, 88, 37, 42, and 42 μg/m3, and 1.114 mg/m3, respectively. The average environmental pollutant concentrations at the eleven points studied in this research are shown in Table 1. The pollution level of SLXL was generally the highest, followed by HNDL. In addition, the air in SLL was the cleanest. The 24 h mean concentrations of PM2.5, PM10, and CO in SLXL were the highest (183 and 243 μg/m3 and 3.167 mg/m3, respectively). At the same time, peak concentrations of SO2 and NO2 appeared in TYJ and LDJ, and O3 concentration peak appeared in JSJ and SLXL. PM2.5 is the major pollutant at each research site, which proves that high levels of PM2.5 were the primary cause of air pollution in Shenyang during the study period.

Table 1

Hourly average concentration of pollutants and AQI at 11 sites during the study period (Units: CO in mg/m3; other pollutants’ concentrations are given in μg/m3)

Site Description AQI PM2.5 PM10 SO2 NO2 O3 CO
DLL Donglinglu 74 54 84 34 35 45 1.130
HNDL Hunnandonglu 84 62 95 36 43 40 1.177
JSJ Jingshenjie 76 56 84 37 43 47 1.111
LDJ Lingdongjie 71 52 91 44 51 43 1.120
SLL Senlinlu 63 45 67 23 27 57 1.011
SLXL Shenliaoxilu 86 64 103 37 40 47 1.117
TYJ Taiyuanjie 79 58 88 53 51 33 1.204
WHL Wenhualu 79 58 95 42 49 35 1.101
XHY Xiaoheyan 75 55 91 43 51 37 1.191
XXJ Xinxiujie 79 58 95 34 46 39 1.125
YNL Yunonglu 69 50 73 30 37 47 0.982

The PM2.5, PM10, and NO2 24 h mean values in Shenyang were compared with the NAAQS GradeⅡlimited values. The concentrations of SO2, O3, CO, and NO2 were all below the NAAQS GradeⅡlimited values, whereas the concentrations of PM2.5 and PM10 clearly exceeded mandatory standards. According to the data, average PM2.5 reached the NAAQS standard during 113 days, which represents a 74.8% of the total number of days (151) when simultaneous measurements at the eleven monitoring points were performed. The fractions of PM2.5 exceeding the standard day values in the eleven monitoring points were between 9.93% (SSL) and 30.46% (HNDL), and the fraction of PM10 exceeding the standard day values was between 2.64% (SLL) and 14.56% (SLXL). PM2.5 concentrations exceeded standard values in a higher ratio as compared to other pollutants. The study found that PM2.5 was the major contamination in Shenyang with poor atmospheric visibility and serious pollution.

Daily variation of the studied pollutants was estimated based on the hourly measured data. We needed to use this information to identify potential sources of emissions. Figure 4 shows the monthly average daily variation of PM2.5, SO2, NO2, O3, and CO in Shenyang. According to Figure 4, the highest concentration of PM2.5 and CO appeared in December and ranked second in February. In addition, the concentrations of SO2 and NO2 were sighted to be higher in December and January. In December, the hourly mean concentrations of PM2.5 and CO were >60 μg/m3 and 1.155 mg/m3, respectively. By relating these data to changes in wind speed (Figure 3), it was observed that at higher frequencies of the low wind speed in December, a higher pollution level was likely to occur. And, due to the low wind speed, pollutants are not easy to spread. Therefore, it can be concluded that wind speed is negatively correlated with PM2.5 concentration.

Figure 4 Diurnal variation of CO, SO2, NO2, O3, and PM2.5 concentrations in Shenyang in different months.
Figure 4

Diurnal variation of CO, SO2, NO2, O3, and PM2.5 concentrations in Shenyang in different months.

SO2 displayed a similar daily variation graph during the five months. The same was observed for NO2, and CO. In every month, NO2 and CO show two peaks at about 9:00 and 20:00 h, which corresponded to peak hours. Thus, it is likely that this pollution proceeds from vehicle emissions [33]. Also, a concentration increase was observed after 21:00 h, which was probably due to an increment in the boundary layer. O3 reached its peak value at 14:00 h, while NO2 concentration decreased at this time, indicating that NO2 is negatively correlated with O3 concentration due to photochemical reaction [34]. This is similar to previous research [29,35]. It was also observed that every month, SO2 concentration reached a peak between 9:00 and 11:00 h. An increasing trend started again after 16:00 h. This was probably owing to changes in meteorological conditions such as differences in boundary layer height. Since the study was carried out in the central heating period in Shenyang, the heating coal has a certain influence on the concentration of SO2.

Differences in daily variation of PM2.5 in Shenyang during the five months were remarkable, and those for November were the smallest ones. It was observed that the daily variation trend for PM2.5 concentration was similar to that of NO2, CO, and SO2. The peak level of PM2.5 may indicate the burning of fossil fuels such as coal. PM2.5 has the highest concentrations at noon in December and January. This might be related to the reduction of NO2 and SO2, probably indicating that the secondary formation was an important reason for the increase of PM2.5 concentrations. The daily variation of PM2.5 is similar to that of SO2, demonstrating that PM2.5 levels are related to coal combustion during the study period. PM2.5 reached a concentration peak during the morning. Compared with NO2 and CO, levels of PM2.5 showed a similar increasing trend. This indicates that traffic emissions increased PM2.5 concentrations during the morning peak hours. The PM2.5 level began to decline, which may be due to the decrease of boundary layer height and the increase of NO2 and CO emissions after 20:00 h. During the night, wind speeds are usually higher than during daytime, reducing the concentrations of pollutants. According to our findings, changes in boundary layer height and meteorological parameters may be the main driving factors to air pollution in the area. For future research, it is necessary to perform more measurements on the above-mentioned parameters in order to elaborate more accurate analyses.

3.3 Transmission path

The seventy-two hour backward trajectories of SLL (background) and SLXL (severe pollution) from November 1st, 2017 to March 31st, 2018 were calculated using the HYSPLIT model. Figure 5 shows the clustering trajectories (i.e., transport path) in two monitoring points. Five main backward clustering trajectories were obtained at both SLXL and SLL points. Clustering from the two points can summarize the four directions of the trajectory: south, southwest, northwest, and north. During this study, five clusters were identified for SLL: Cluster 1 – Northwest (short; NWs), Cluster 2 – Southwest (SW), Cluster 3 – North (N), Cluster 4 – Northwest (Long; NWl), and Cluster 5 – South (S). Southern air, which accounted for 12.07% of the total air mass, initiates in the southern coastal area of the Liaoning Province. In this case, the mean PM2.5 concentration was the highest detected (64.26 μg/m3). The southern wind carried the polluted air mass to SLL, and in 26.59% of the trajectories, PM2.5 concentrations exceeded 75 μg/m3. Fine particle mass concentration in the S air was heavier, with highest PM2.5/PM10 value of 0.88. The N air mass came from the Central Siberian Plateau and passed through the Daxinganling area and the northern part of the Liaoning Province before reaching SLL. This accounted for 28.76% of the air mass. The level of air pollutants was low (mean PM2.5 concentration was 30.46 μg/m3), aerosol contribution to PM in SLL was also low (PM2.5/PM10, 0.60), and the air was relatively clean. NWs, SW, and NWl clusters originated from the central and western parts of Siberia and passed through eastern Mongolia and the Mongolian plateau before reaching SLL. These clusters accounted for 35.54, 15.87, and 7.77% of the air mass, respectively. These air masses were much cleaner than corresponding S air. Average PM2.5 concentrations were 44.19, 57.2, and 43 μg/m3, respectively. The PM2.5 concentration of SW air mass was slightly higher than that of the other two, because SW air mass passed through the Xilingol Desert in Inner Mongolia, where there is less vegetation and bare sandy land. When the wind blows, it is easy to cause a higher concentration of particulate matter in the air above. The SW air mass may have carried additional dust and aerosols (PM2.5/PM10, 0.69). Finally, the PM2.5/PM10 ratio of the NWs and NWl air masses was higher than 0.6.

Figure 5 The back trajectory of SLL and SLXL air masses from November 2017, to March 31st, 2018.
Figure 5

The back trajectory of SLL and SLXL air masses from November 2017, to March 31st, 2018.

The trajectory of SLXL generated 5 clusters: cluster 1 – northwest (short; NWs), cluster 2 – north (short; Ns), cluster 3 – north (long; Nl), cluster 4 – northwest (long; NWl), and cluster 5 – South (S). Same as SLL trajectory, the S air mass of SLXL accounted for 6.78% of total air masses, and the mean PM2.5 concentration was 73.5 μg/m3. These air masses started in southern Shandong. The PM2.5 concentration was greater than 75 μg/m3 with a fraction of 37.5% in these air masses and an average PM2.5/PM10 ratio of 0.59. Nl clustering started in the Central Siberian Plateau and passed through the eastern Mongolian and Mongolian Plateaus, possibly consisting of coarse aerosols (PM2.5/PM10 value: 0.47), including 19.17% of all trajectories. However, this air was much cleaner; PM2.5 concentration was 40.04 μg/m3 and PM10 was 76.69 μg/m3, respectively. NWs and NWl clusters demonstrated the influence of the plains of Central Siberia. In these cases, the PM2.5 average concentrations were 61.02 and 62.51 μg/m3, respectively. In addition, it was determined that 28.67 and 29.46% of the NWs and NWl cluster trajectories, respectively, were polluted. Moreover, SLXL was influenced by the air mass in southern Siberia (Ns air mass). The mean PM2.5 concentration was 71.44 μg/m3, with the PM2.5/PM10 value of 0.59, which was close to that determined for the S air mass.

By analyzing the backward trajectory of the model output, it can be found that the environmental conditions during the heating season may be affected by pollutant emissions and long-distance transportation in the region. The result is consistent with a previous study [29].

3.4 Potential source distribution

The PSCF and CWT were analyzed using the backward trajectory calculated by the model and the PM2.5 or PM10 hourly monitoring concentration. This information was used to determine the potential source and contribution to the receptor site. Figures 6 and 7 show the contribution of pollution distribution in potential contaminant sources in SLXL, which was the most polluted region. Where the results of PSCF and CWT are large can be considered as potential sources of contamination in this study. PSCF in Figure 6 shows that the eastern Inner Mongolia, northwestern Jilin, and Liaoning are the main potential sources of contribution for PM2.5 during the heating season, and the weight potential source contribution function (WPSCF) is greater than 0.3. In particular, the main source of high PM10 concentration is the central and western urban agglomeration of Liaoning Province. Most of the Beijing-Tianjin-Hebei area and Inner Mongolia displayed low PM2.5 and PM10 contribution rates (WPSCF was less than 0.3). The Bohai Sea area in the south and the coastal cities are the potential sources for Shenyang particulate matter. (WPSCF was higher than 0.4). Overall, these findings are in accordance with findings reported by Wang et al. [35].

Figure 6 PSCF maps for PM2.5 and PM10 in SLXL from 1 November 2017 to 31 March 2018.
Figure 6

PSCF maps for PM2.5 and PM10 in SLXL from 1 November 2017 to 31 March 2018.

Figure 7 CWT maps for PM2.5 and PM10 in SLXL from 1 November 2017 to 31 March 2018.
Figure 7

CWT maps for PM2.5 and PM10 in SLXL from 1 November 2017 to 31 March 2018.

According to the CWT diagram in Figure 7, exogenous PM2.5 and PM10 are mainly from Mongolia, Anhui, parts of the Beijing-Tianjin-Hebei region, western Jilin, and parts of Inner Mongolia. The contribution rate of PM2.5 in the above areas is greater than 80 μg/m3, and that of PM10 is greater than 120 μg/m3. The Yellow Sea is a potential source of particulate matter in Shenyang, contributing more than 120 μg/m3 to PM2.5 and more than 150 μg/m3 to PM10 in SLXL. As can be seen from the figure, the northwest source region has the widest distribution area and the farthest origin, which can be traced back to Mongolia and Russia. In addition, aerosols from the ocean also contributed.

3.5 Comparison with other cities

The concentrations of PM2.5 in seven major cities in China, including Beijing [36,37], Guangzhou [38], Xi’an [33], Shanghai [39], Chengdu [40], Jinan [41], and Qingdao [42], were summarized and compared with those in Shenyang. The results are displayed in Table 2. The mean 24 h PM2.5 concentration was widely different in varied cities, and it is related to different factors such as seasonality and detection methods. This paper analyzed the measured data in winter, and the purpose is to better compare the PM2.5 concentration levels in Shenyang and other cities in China. The results show that the mean PM2.5 concentration in Shenyang was lower than other cities surveyed in the article. Among these cities, Shenyang’s PM2.5 levels are far lower than those of Beijing, Xi’an, and Chengdu. In addition, Shenyang’s PM2.5 concentration is about half that of Guangzhou and Shanghai. Also, PM2.5 concentrations in Xi’an and Chengdu were 5.2 and 3.7 times higher than that determined in Shenyang, respectively. The annual mean PM2.5 concentration in Jinan was 101 μg/m3, which was 1.8 times higher than that found in Shenyang.

Table 2

Average daily concentration of PM2.5 in eight Chinese cities including Shenyang

Cities PM2.5 (μg/m3) Location Research period
Qingdao 85 Urban 2015/11–2016/1
Beijing 118.5 Urban/rural 2005/3–2006/2
80.6 Urban 2012/8–2012/9
Guangzhou 108.3 Urban 2012/12–2013/2
Xi’an 194.1 (mean) Urban 2006/3–2007/3
288.7 (winter)
Shanghai 83.4 (mean) Urban 2011/12–2012/1
104.3 (winter)
Chengdu 206.8 Urban 2009/11–2010/1
Ji’nan 101 Urban 2013/3–2014/2
Shenyang 55 Urban 2017/11–2018/3

4 Conclusion

  1. The survey showed that the main pollutant in Shenyang in winter was PM2.5. In 25.2% of the research days, PM2.5 concentration during daytime was higher than the one indicated in the NAAQS limit. Among the 11 monitoring sites, SLXL usually has the highest pollution level. It is because Tiexi District is developed in heavy industrial area with many factories. It was also determined that low wind speeds and the presence of southerly or easterly winds may have contributed to the accumulation and transport of pollutants. The daily variation curve of O3 shows a single peak shape and reaches its peak around 14:00. According to the results on daily variation of the six pollutants, coal heating and main traffic emissions are the main causes of air pollution in Shenyang.

  2. HYSPLIT model was used to calculate the back trajectories of SLL and SLXL, in order to identify the transport pathways of the pollutants. During the study period, pollutants were mainly affected by local and long-distance transport. The southern air mass mainly originated in the northern part of Jiangsu, Shandong, and the southern Liaoning Province, accounting for 6.78–12.07% of the total air mass. The average PM2.5 concentration varied between 64.26 and 73.5 μg/m3, and the proportion of over-standard trajectories was between 26.39 and 37.5%. The northern air mass mainly comes from Siberia, Mongolia, and other regions. The air mass from the Siberian plateau is relatively clean, diluting pollutants to some extent. The increase in particulate matter is mainly caused by increased desert aerosols from Inner Mongolia and aerosol transport from the Bohai Sea and the Yellow Sea.

  3. Use PSCF and CWT to comprehensively analyze potential sources of emissions and their contribution to PM levels. In the winter, Mongolia, eastern Inner Mongolia, northwestern Jilin, and most of Liaoning Province were the main potential source regions of PM2.5, contributing with more than 80 μg/m3 PM2.5. In addition, the Beijing-Tianjin-Hebei region, the Yellow sea and Bohai sea, and Shandong and Anhui provinces also had a certain influence on the concentration of particulate matter in Shenyang. Therefore, the concentration of contaminants in Shenyang in winter may be determined by local emissions (such as coal, automobiles, anthropogenic, and industrial sources) and cross-regional transmission from surrounding areas such as Jilin, Hebei, and Inner Mongolia.

In this work, the transport path and potential sources of winter pollutants in Shenyang were deeply studied, which could provide scientific support for the joint prevention and control of regional air pollution in Shenyang and other cities. In future work, simulation of other seasons or the whole year can be added and the results of multiple monitoring stations can be selected for analysis and research. The future studies can further analyze the causes of heavy pollution weather in other seasons.

  1. Funding information: This research was financially funded by Aeronautical Science Foundation of China (Grant No. 2017ZA54001), Industry-University Cooperation and Education Project (Grant No. 201802303005), Natural Science Foundation of Liaoning Province (No. 2021-MS-079), and National Key R&D Program of China (No. 2017YFC0212500).

  2. Author contributions: Yunfeng Ma designed the experiments. Qiyao Liu and Yushan Bian carried them out, analyzed results, and wrote the paper. Lei Feng and Di Zhao verified the experimental results. Shuai Wang, Huijie Zhao, and Kunyu Gao prepared the experimental data. Zhengqing Xu provided software support for the experiments.

  3. Conflict of interest: The authors declare no conflict of interest.

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Received: 2021-05-07
Revised: 2021-07-13
Accepted: 2021-08-11
Published Online: 2021-09-30

© 2021 Yunfeng Ma et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2020-0292
Keywords for this article
pollution sources; pollution characteristics; HYSPLIT; TrajStat; PSCF; CWT
Creative Commons
BY 4.0

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  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
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