Advances in Meteorology

Advances in Meteorology / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 6680564 | https://doi.org/10.1155/2021/6680564

Chao He, Song Hong, Hang Mu, Peiyue Tu, Lu Yang, Biqin Ke, Jiayi Huang, "Characteristics and Meteorological Factors of Severe Haze Pollution in China", Advances in Meteorology, vol. 2021, Article ID 6680564, 15 pages, 2021. https://doi.org/10.1155/2021/6680564

Characteristics and Meteorological Factors of Severe Haze Pollution in China

Academic Editor: Herminia García Mozo
Received03 Dec 2020
Revised13 May 2021
Accepted05 Jun 2021
Published22 Jun 2021

Abstract

A severe haze pollution incident caused by unfavorable weather conditions and a northern air mass occurred in eastern, northern, northwestern, and southwestern China from January 15 to January 22, 2018. To comparatively analyze variations in PM2.5 pollution, hourly monitoring data and 24 h meteorological data were collected. Air quality observations revealed large spatiotemporal variation in PM2.5 concentrations in Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan. The daily mean PM2.5 concentrations ranged from 111.35 to 227.23 μg·m³, with concentration being highest in Zhengzhou. Hourly mean PM2.5 concentration presented multiple U-shaped curves, with higher values at night and lower values during the day. The ratios of PM2.5 to PM10 were large in target cities and the results of multiscale geographic weighted regression model (MGWR) and Pearson correlation coefficients showed that PM2.5 had a significant positive or negative correlation with PM10, CO, NO2, and SO2. The concentration of PM2.5 was closely related to the combustion of fossil fuels and other organic compounds, indicating the large contribution of secondary aerosols to PM2.5 concentrations. The analysis of meteorological conditions showed that low temperature, low wind speed, and high relative humidity could aggravate the accumulation of regional pollutants in winter. Northwestern trajectory clusters were predominant contributions except in Jinan, and the highest PM2.5 concentrations in target cities were associated with short trajectory clusters in winter. The potential sources calculated by Weight Potential Source Contribution Function (WPSCF) and Weight Concentration-Weighted Trajectory (WCWT) models were similar and the highest values of the WPSCF (>0.5) and the WCWT (>100 μg·m³) were mainly distributed in densely populated, industrial, arid, and semiarid regions.

1. Introduction

Severe haze pollution events occur frequently in winter in China and are dominated by PM2.5 (particulate matter with aerodynamic diameters no larger than 2.5 µm). Particulate matter pollution adversely affects human health and atmospheric visibility [13]. In addition, the direct and indirect radiation lead to climate change and disturb the structure and function of ecosystems [4, 5]. In densely populated and industrially developed areas, daily PM2.5 concentrations in winter frequently exceed the Class II category of the National Ambient Air Quality Standards (NAAQS, 75 μg m³).

A serious haze pollution incident caused by unfavorable weather factors and a northern air mass occurred in eastern, northern, northwestern, and southwestern China from January 15 to January 22, 2018. The most polluted cities were Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan. Many studies have presented long-term measurements and analysis of PM2.5 concentrations in Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan during the last ten years. For example, Zhang et al. [6] reported that the chemical composition of PM10, PM2.5, and PM1 varied in Handan from November 16, 2015, to March 14, 2016, with serious pollution occurring during most of the cold season. A source analysis carried out by Jiang et al. [7] with a positive matrix method showed that the average annual concentrations of PM2.5 and PM10 were the highest in winter and the lowest in summer. Niu et al. [8] studied the temporal and spatial variation and chemical composition of PM2.5 in the Guanzhong Plain from March 2012 to March 2013. The average daily PM2.5 concentration was 134.7 μg·m³, exceeding the Class II category of the NAAQS.

Severe atmospheric pollution is closely related not only to emission sources but also to adverse meteorological conditions, terrain, pollutant transport pathways, and chemical reactions in atmosphere [911]. Human activities and meteorological conditions are the primary factors leading to variation in pollutant concentration [12, 13]. For instance, if the aridity index and average annual temperature increased by 1%, PM2.5 concentrations would increase by 66.9 and 35.7, respectively [14]. But PM2.5-heavy pollution is often accompanied by high relative humidity [15, 16]. Moreover, stable weather conditions, low temperature, and wind speed can also aggravate the accumulation of regional pollutants in winter. PM2.5 and PM10 become very high in the postmonsoon season in Kolkata; PM concentrations are observed to be the lowest during the monsoon seasons; meanwhile, the NO2 and CO concentrations demonstrate similar seasonal fluctuations [17].

Long-distance transportation among regions also plays an important role in PM2.5 pollution. In recent years, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the TrajStat model [18] have become critical tools for studying long-distance transport and potential pollutant sources. Many scholars have traced the transmission path of CO, O3, SO2, PM2.5, PM10, and other gaseous pollutants using HYSPLIT and TrajStat models [1921]. Filonchyk and Yan [22] quantitatively investigated the causes of severe haze during spring and winter seasons in northwest China based on the backward trajectory and the HYSPLIT model. The results showed that the movement of air masses in the north, northwest, and west of China is the main cause of haze during spring and winter in northwest China. However, these studies only focused on the transport direction and passing area in the near surface and did not carry out statistical analyses of PM2.5 concentrations in backward trajectories and clustering trajectories.

Previous studies have focused on the spatial and temporal distribution of PM2.5 in a single city as well as influence factors and potential sources, which often neglects the impact of pollutant transport among cities. To better understand the impact of pollutant transport in different cities, real-time pollutant data and meteorological data of major Chinese cities were collected in this research during the haze period in January 2018. We investigated the correlations between air pollution and meteorological conditions and their spatial variation. We computed Weight Potential Source Contribution Function (WPSCF) and Weight Concentration-Weighted Trajectory (WCWT) models to quantify potential source distributions in different cities. This research aimed to provide a reference for the local government to manage sudden air pollution incidents, propose a method for studying long-distance transport, and identify potential sources of air pollutants in PM2.5 haze pollution.

2. Methods

2.1. Air Quality Observations and Data Quality

Observational pollution data from January 15 to January 22, 2018, were used in this study. Real-time data were provided by the China National Environmental Monitoring Center (http://www.cnemc.cn) after being validated, with hourly concentrations of six major pollutants: PM2.5, PM10, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). We calculated daily mean concentrations of PM2.5 in seven cities (Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan) (Figure 1(b)). We used the threshold of 75 μg·m³ as the highest PM2.5 concentration for acceptable air quality according to the Class II category of the NAAQS (GB3095-2012). Mean PM2.5 concentration in 30 provincial capitals and seven cities was calculated with data from 302 monitoring sites during the pollution period (Figure 1(c)). As shown in Table 1, each city had set up several air quality monitoring sites, most of which were located in urban areas and some in suburban and rural areas as background sites. Daily mean PM2.5 concentrations were calculated when valid data were available for more than 20 h during the day and values were greater than zero [23]. We adopted the same method as the government reports daily concentrations of air pollutants to the public, averaging the concentrations at all sites in each city to represent the daily mean concentration of the city.


City nameLongitudeLatitudeRegionBackground sitesAvailable sites

Changsha (CS)112.9828.20Central110
Wuhan (WH)114.2930.57Central111
Zhengzhou (ZZ)113.6534.76Central19
Fuzhou (FZ)119.3026.08East17
Hangzhou (HZ)120.1630.27East111
Hefei (HF)117.2831.86East110
Jinan (JN)117.0136.67East415
Nanchang (NC)115.9028.68East19
Nanjing (NJ)118.7732.05East413
Shanghai (SH)121.4731.24East110
Beijing (BJ)116.3839.92North112
Hohhot (HT)111.6640.82North08
Shijiazhuang (SJZ)114.4938.05North19
Taiyuan (TY)112.5737.87North210
Tianjin (TJ)117.2039.13North115
Changchun (CC)125.3243.89Northeast110
Harbin (HB)126.6445.74Northeast113
Shengyang (SY)123.4141.80Northeast313
Lanzhou (LZ)91.1329.66Northwest15
Urumqi (UQ)87.6143.79Northwest18
Xi'an (XA)108.9534.26Northwest113
Xining (XN)101.7936.61Northwest25
Yinchuan (YC)106.2738.47Northwest16
Guangzhou (GZ)113.2623.12South112
Nanning (NN)108.3122.81South18
Chengdu (CD)104.0830.66Southwest19
Chongqin (CQ)106.5129.56Southwest117
Guiyang (GY)106.7126.58Southwest110
Kunmin (KM)102.7025.04Southwest28
Lhasa (LS)103.7536.07Southwest06

2.2. Meteorological Data

Hourly meteorological data from January 15 to January 22, 2018, were obtained from the National Meteorological Information Center of the China Meteorological Administration (http://data.cma.cn/) and used to analyze the relationship between meteorological conditions and air pollution. Hourly meteorological data included temperature, relative humidity, 2 min wind speed, 2 min wind direction, and sea-level pressure. Mean values of each meteorological parameter were calculated using data for all monitoring sites in a region.

2.3. Multiscale Geographically Weighted Regression

MGWR is a significant improvement on GWR because it allows one to study relationships at varying spatial scales and covariate-specific bandwidths to be optimized [24]. MGWR can be defined aswhere represents the geographical coordinates of i city, βbwi is the regression coefficient, and ɛi represents a random error term. We used the MGWR2.2 software to undertake all calibrations (https://sgsup.asu.edu/sparc/mgwr). The spatial kernel function type is Bisquare, the bandwidth search type is Golden, and the parameter initialization type is GWR estimation [25].

2.4. Trajectory Data

In this study, 72 h backward trajectories arriving at the centers of Handan (114.51°E, 36.62°N), Zhengzhou (113.64°E, 34.75°N), Xi’an (108.95°E, 34.27°N), Chengdu (104.07°E, 30.66°N), Yuncheng (111.02°E, 35.04°N), Xiangyang (112.17°E, 32.07°N), and Jinan (116.99°E, 36.67°N) were calculated every 6 h (at 00 h, 06 h, 12 h, and 18 h Coordinated Universal Time) during the pollution period. Data were obtained from the National Centers for Environmental Prediction (NCEP) reanalysis data and the HYSPLIT model (version 4.9) developed by the National Oceanic and Atmospheric Administration Air Resources Laboratory (NOAA ARL, https://ready.arl.noaa.gov/HYSPLIT.php).

2.5. Inverse Distance Weighted Interpolation

The inverse distance weighted interpolation method is based on the principle of similarity. The spatial variation of PM2.5 mass concentration in China during the pollution period was drawn in ArcGIS 10.3. We used the coordinate information of 1,436 air quality monitoring stations in 338 cities as the “input point feature” and used daily PM2.5 mass concentration data at each site as the Z value field. We set the maximum number of adjacent features and the minimum number of adjacent features to 15 and 10, respectively.

2.6. Backward Trajectory Statistics and Calculation

Trajectory cluster calculation was carried out with TrajStat [26] http://www.meteothinker.com/downloads/index.html). TrajStat provides two clustering options, Euclidean distance or angle distance. In this study, we used angle distance as we intended to use the backward trajectories to determine how the air mass reached the center of the monitoring point. The angular distance between two backward trajectories is defined aswhere , , and . is the mean angle between two backward trajectories. The variables and define the position of the study site.

2.7. WPSCF Analysis

The Potential Source Contribution Function (PSCF) algorithm identifies source regions based on airflow trajectories analysis and has been widely used to identify potential source areas for high-concentration pollutants at receptor sites [27]. The area covered by the backward trajectory is divided into equal grid cells. The PSCF value for the cell is defined aswhere is the total number of trajectory endpoints that fall in the grid cells and is the total number of trajectory endpoints for which the monitored pollutant concentration exceeds a threshold value in the cells (Kong et al., 2013; [28]). In this study, the grid cell size was 0.5° 0.5 latitude-longitude and we defined 75 μg·m³ as the threshold value of PM2.5 mass concentration. To account for uncertainty, PSCF values were multiplied by an arbitrary weight function [29, 30]. The weighting function reduced PSCF values when the total number of the endpoints in a cell was fewer than three times the average number of endpoints for all cells. We calculated WPSCF values to identify the possible source areas of PM2.5 in a region.

2.8. WCWT Analysis

The PSCF method calculates the proportion of pollution trajectories in a grid, reflecting the potential influence of the grid on the receptor site. Whether pollutant concentrations at the monitoring site are only slightly higher or much higher than the criterion, grid cells have the same PSCF value and it can be difficult to distinguish moderate pollution sources from major sources. In the Concentration-Weighted Trajectory (CWT) method [31], each grid cell is assigned a weighted concentration by averaging the sample concentrations that have associated trajectories crossing that grid cell as follows:where is the average weighted concentration in the cell, is the index of the trajectory, is the total number of trajectories, is the concentration observed on the arrival of trajectory , and is the time spent in the cell by trajectory . The influence coefficient is also used in the WCWT method ().

3. Results and Discussion

3.1. Air Pollution Characteristics

In 30 Chinese provincial capitals during the pollution period, mean concentrations of PM2.5, PM10, O3, CO, NO2, and SO2 were 93.7 μg·m−3, 124.2 μg·m³, 31.6 μg·m³, 1.5 mg·m³, 61.6 μg·m³, and 27.3 μg·m³, respectively. These results can be explained by the polluted air mass in northern China and adverse weather conditions during the pollution period. Northern cities receive relatively weak effects from monsoons, slower wind speeds, and small amount of precipitation, resulting in drier air and more sandstorms, which more often occur in winter, so it is easier for pollutants to enter the air. In contrast, lower temperatures are not beneficial to pollutants diffusion, retain pollutants near the surface, and lead to high concentrations [32]. A similar phenomenon was observed in the previous study [33]. Table 2 shows the average concentrations of the air quality index (AQI) and variations in six major pollutants. The areas with the most severe air pollution (AQI ≥ 115) were distributed in central, northwestern, and northeastern China. These patterns may be caused by excessive pollutant emissions from coal-fired heating, biomass burning, and industrial combustion in winter [34, 35]. The AQI of Shijiazhuang, Jinan, Taiyuan, Zhengzhou, Xi’an, and Chengdu reached 170.5, 200.5, 116.2, 260.0, 228.8, and 117.1, respectively. And the air quality levels reached a moderate or severe pollution grade, which have significant impacts on human health.


CityAQIPM2.5PM10CONO2O3_8hSO2

Beijing (BJ)75.0342.5591.241.0552.4627.0610.61
Chengdu (CD)117.1189.08128.191.2753.2727.4612.10
Fuzhou (FZ)53.4535.2253.560.8435.9142.416.68
Guangzhou (GZ)133.04101.0296.381.43102.5037.3816.59
Guiyang (GY)67.8348.0068.601.0334.2330.5824.74
Harbin (HB)99.7975.5472.271.1746.6135.2145.03
Hangzhou (HZ)109.9381.54115.961.1265.7317.6313.84
Hefei (HF)153.29125.7033.671.5567.3920.8710.36
Hohhot (HT)86.7443.81119.151.6451.2533.2038.17
Jinan (JN)200.52156.94241.902.0480.5822.2143.91
Kunming (KM)59.2335.5063.680.9735.5542.8817.43
Lhasa (LS)54.8627.7764.500.7629.9357.496.36
Lanzhou (LZ)99.2358.35140.021.9262.7034.4841.42
Nanchang (NC)84.2861.1472.751.5647.5919.4013.32
Nanjing (NJ)181.68141.39179.421.5879.6521.3317.13
Nanning (NN)105.3877.01113.261.3662.8644.9816.18
Shanghai (SH)101.2576.2842.541.0271.3037.2513.09
Shenyang (SY)64.4444.6667.851.1938.8525.6831.91
Shijiazhuang (SJZ)170.48133.20204.432.5368.1514.4642.72
Taiyuan (TY)116.1677.84155.671.7058.3325.5888.23
Tianjin (TJ)73.8350.3375.411.7541.0213.8813.06
Urumqi (UQ)141.87151.2786.593.5365.5611.8411.67
Wuhan (WH)146.77128.2235.611.5361.3917.8811.03
Xi’an (XA)228.80181.62246.632.5797.2018.7232.61
Xining (XN)86.7452.42115.642.0126.0535.5522.19
Yinchuan (YC)57.2132.6475.061.3619.0127.9429.65
Changchun (CC)78.7055.1269.681.0942.0633.3145.43
Changsha (CS)130.39124.4224.441.2949.0227.8814.54
Zhengzhou (ZZ)260.03222.70212.382.2683.2721.0229.23
Chongqin (CQ)82.5861.2085.421.3643.4111.778.49

3.2. Spatial Analysis of PM2.5

Figure 2 shows the spatial distribution of daily mass concentrations of PM2.5 during the pollution period. Elevated concentrations were mainly located in central, northwestern, and eastern China and concentrated in southern Hebei, western Shandong, southern Shanxi, northern Henan, central Shaanxi, and central Sichuan because of the high emissions from fossil fuel combustion and adverse weather conditions. Overall, PM2.5 pollution in central and eastern China is more severe than in northern China due to distinct emissions sources, weather features, and source apportionment [36]. PM2.5 pollution in Handan (Hebei Province), Zhengzhou (Henan Province), Xi’an (Shaanxi Province), Yuncheng (Shanxi Province), Chengdu (Sichuan Province), Xiangyang (Hubei Province), and Jinan (Shandong Province) was highest among major polluted cities, with daily mean PM2.5 concentrations of 212.6 ± 95.5 μg·m³, 227.2 ± 109.9 μg·m³, 186.7 ± 59.0 μg·m³, 207.8 ± 83.1 μg·m³, 111.4 ± 45.4 μg·m³, 219.3 ± 87.8 μg·m³, and 163.5 ± 64.7 μg·m³, respectively.

3.3. Daily Mean PM2.5 Concentrations

Daily mean PM2.5 concentrations in the seven major polluted cities in Figure 3(b) exceeded the Class II category of the NAAQS. Hourly data were used to examine daily variability in PM2.5 and identify potential emission sources [37]. Trends in hourly mean PM2.5 concentrations in polluted cities were similar and exhibited multiple U-shaped curves, with higher values in the early morning (0:00–05:00) and at night (19:00–23:00) and lower values during the middle of the day (12:00–15:00). Such daily patterns can be explained by enhanced emissions from heating, unfavorable meteorological conditions, and variations in topography.

A comparative study of seven major cities found that the PM2.5 hourly concentrations showed a steady trend from day to night in Xi’an and Chengdu, which can be explained by the static stability of the atmosphere [38]. During the pollution episode, the mean wind speed was 1.65 and 1.62 m·s−1 in Xi’an and Chengdu, respectively, illustrating that the weak wind was conducive to the diffusion of pollutants. The temperature was 2.2°C and 9.3°C in Xi’an and Chengdu, respectively, and relative humidity was 61% and 87%, respectively. Thus, low temperature, low wind speed, and high relative humidity may have led to the accumulation of PM2.5 in winter.

High concentrations of PM2.5 appeared at midday (12:00–13:00) in Handan and Yuncheng (Figure 3(a)), which can be explained by high emissions from coal heating, cooking, and transportation [4, 37, 39]. The lowest PM2.5 concentrations were observed in the afternoon, when the boundary layer becomes larger and the wind speed increased. After 17 : 00, PM2.5 concentrations started to increase in Handan, Zhengzhou, Xi’an, Yuncheng, and Xiangyang because of decreasing wind speed and increasing vehicle emissions. PM2.5 pollution emitted from diesel truck traffic, which is allowed only during nighttime, additionally increased PM2.5 burden because the emission factors of heavy-duty vehicles are six times higher than those from light-duty vehicles [40].

3.4. Correlations between PM2.5 and Other Gaseous Pollutants

The Pearson correlation coefficient (r) was used to investigate the relationship between PM2.5 and PM10, CO, NO2, O3, and SO2 using hourly data (Table 3). The analysis results showed a strong positive correlation (r > 0.9) between PM2.5 and PM10 in the seven polluted cities, indicating that a significant fraction of the PM2.5 was secondary PM, such as ammonium sulfate, secondary organic aerosol, or fugitive dust, which typically have broader regional distributions than anthropogenic primary pollutants [39]. We found large values of the ratio of PM2.5 to PM10 in Handan (0.67), Zhengzhou (0.79), Xi’an (0.70), Yuncheng (0.65), Chengdu (0.68), Xiangyang (0.87), and Jinan (0.64), which indicates the large contribution of secondary aerosols to PM2.5 concentration in these regions. The ratio of PM2.5 to PM10 also shows that PM2.5 is the main component of PM10. Similar results were reported in the previous study [41]. A positive correlation between PM2.5 and CO was observed (r > 0.6), which revealed that the CO emission process is accompanied by the emission of fine particles.


HandanZhengzhouXi’anYunchengChengduXiangyangJinan

PM100.97870.96140.93210.98800.98200.95830.9416
CO0.82770.85940.75930.60570.80680.86500.7180
NO20.88180.46810.45620.43730.55330.48290.4970
O3-8h−0.0681−0.1522−0.2227−0.37350.4369−0.00070.0873
SO20.63860.07820.38070.36950.64120.19490.3145

Note. .

The correlation coefficients between PM2.5 and NO2 and SO2 in Handan were very high (PM2.5 and NO2: r = 0.88; PM2.5 and SO2: r = 0.64). This may be caused by the large amount of emissions from power plants, urban dust, and the combustion of fossil fuels. In Chengdu, the correlation coefficients of PM2.5 with NO2 and SO2 were 0.55 and 0.64, respectively, mainly due to adverse weather conditions restricting diffusion and chemical conversion of traffic pollutants. However, correlations between PM2.5 and NO2, O3, and SO2 were lower in other cities (Table 3).

To further discuss the relationship between PM2.5 and other pollutants, we investigated the impact of other pollutants on PM2.5 by using the statistical advantage of the MGWR model that each regression coefficient was based on local regression. The model regression results were shown in Tables 4 and 5. In terms of the number of effective parameters, from the analysis of global regression results, the goodness of fit (R2) was 0.908 (), and the residual sum of squares (RSS) was 51.56 (Table 4). According to the local regression results, the local R2 of all selected cities exceeds 0.80 (). These regression results showed that the MGWR model uses fewer parameters to get the regression results closer to the true value, which could be used to evaluate the relationship between PM2.5 and other pollutants. It can be clearly found from Table 5 that the relationship between PM2.5 concentration and other pollutants obtained by the MGWR model was similar to that obtained by the Pearson correlation coefficient. The MGWR analysis results showed a strong positive correlation (the regression coefficient > 1.0, ) between PM2.5 and PM10 in the seven polluted cities. In all selected cities, except Yuncheng (regression coefficient is −0.055, ), PM2.5 had a significant positive correlation with CO. There was a significant positive correlation between PM2.5 and O3 in Handan and Zhengzhou, which was contrary to the Pearson correlation coefficient. The main reason was that the MGWR model was more sensitive to nonlinear relationship than the Pearson correlation coefficient in the regression process. In addition, in all cities, except Jinan (the regression coefficient of SO2 is −0.380, ), PM2.5 has a significant positive correlation with NO2 and SO2.


Descriptive indexValue

Residual sum of squares (RSS)5.156
Log-likelihood−12.673
AICc517.92
R20.908
Adjust R20.889


Handan (0.881)Zhengzhou (0.880)Xi’an (0.894)Yuncheng (0.877)Chengdu (0.932)Xiangyang (0.876)Jinan (0.822)

PM101.0501.0801.1971.1691.1591.1981.029
CO0.1510.1140.059−0.0550.0870.0770.191
NO20.1420.1480.1440.1240.1970.1500.146
SO20.3540.3420.2360.2390.3260.253−0.380
O3-8h0.0410.031−0.040−0.010−0.021−0.0240.047

Note. , ; , . Numbers in parentheses represent local R2.
3.5. Meteorological Condition Analysis

The multiscale interaction of meteorological conditions affects air quality in a complex way [13]. Previous studies have shown that meteorological factors play an important role in the daily variation of pollutant concentrations [13]. The Pearson product-moment correlation coefficient between the hourly mean concentrations of major pollutants and local meteorological parameters (wind speed temperature, relative humidity, and sea-level pressure) in the seven cities is shown in Figure 4. In general, the correlation coefficients showed little difference among cities, indicative of regional pollution characteristics [20]. PM2.5, CO, PM10, and SO2 concentrations were positively correlated with temperature in Handan, Yuncheng, Chengdu, Xiangyang, and Jinan, while O3 concentrations were negatively correlated with temperature in all cities. This can be explained by the fact that solar radiation is the main stimulus for the chemical reactions of NO2 and O3 and temperature, which are affected by atmospheric turbulence and influence regional pollutant concentrations [3].

Generally, pollutant concentrations decrease with increasing wind speed. Deng et al. [42] and Wang et al. [43] found that low primary pollutant concentrations result in high O3 concentrations and cause a positive correlation between wind speed and O3 concentration. We found a positive correlation between O3 concentration and wind speed in Handan, Xi’an, and Jinan (Figure 4(b)). We also found a negative correlation between pollutant concentrations and wind speed in Zhengzhou and a positive correlation between PM2.5, CO, PM10, NO2, and O3 with wind speed in Xi’an, possibly because strong winds can stir up dust. O3 concentration was weakly positively correlated with relative humidity in the seven cities (Figure 4(c)). Primary pollutant concentrations were negatively correlated with relative humidity in Handan, Yuncheng, Xiangyang, and Jinan and positively correlated in other cities (Figure 4(c)), which indicates that lower relative humidity is unfavorable for scrubbing gaseous pollutants. Pollutant concentrations were negatively correlated with sea-level pressure in all cities except Xiangyang, where a positive correlation was observed, possibly for two reasons. First, the atmosphere would be stable under the low air pressure, leading to the inversion layer taking place easily and the air convection slowing down, thus resulting in higher concentrations of atmospheric pollutants. The increase in air pressure may have led to the enhancement of air advection as well as an increase in wind speed, which plays a positive role in the diffusion of air pollutants.

PM2.5 concentrations in the study cities were influenced not only by local emissions but also by the surrounding pollution region. To further study the influence of wind speed and direction on pollutant diffusion, the wind rose and the distribution of hourly PM2.5 concentrations, wind speed, and wind direction in the target cities were calculated (Figures 5 and 6). During the pollution episode, the lowest wind speeds were found in Handan. And the wind speeds associated with north and northwesterly winds were low in Handan, Zhengzhou, and Yuncheng, while weak winds were associated with the south and southeasterly directions in Xi’an, Chengdu, and Xiangyang. In addition, east winds were weak in Jinan (Figure 5). As shown in Figure 6, high PM2.5 concentrations were associated with weak winds, less than 2–3 m·s−1. Previous studies have shown that low wind speeds can stimulate the accumulation of gaseous pollutants [44], indicative of the influence of regional transport from the surrounding polluted regions.

3.6. Statistical Analysis of Trajectory Clustering

Cluster analysis is a widely applied multivariate statistical analysis technique. According to the similarity principle of trajectory space, large numbers of backward trajectories are divided into different transport groups or clusters [45]. The calculated backward trajectories were divided into five main trajectory clusters from the total spatial variance using the HYSPLIT and TrajSat models. Main transport pathways were divided into nine categories according to the results of the trajectory clusters (Figure 7): long northwest (LNW: Handan C2, Zhengzhou C3, C4, and C5, Xi’an C2 and C3, Yuncheng C2, C3, and C4, Xiangyang C3 and C5, and Jinan C3 and C4), short northwest (SNW: Xi’an C4 and C5 and Jinan C1), long north (LN: Handan C3), short north (SN: Handan C4 and C5, Zhengzhou C2, Chengdu C4, and Xiangyang C4), short southwest (SSW: Handan C1, Zhengzhou C1, Xi’an C1, and Jinan C2 and C5), long southwest (LSW: Yuncheng C1 and C5 and Xiangyang C2), long west (LW: Chengdu C1 and C3), short east (SE: Chengdu C2), and short south (SS: Chengdu C5 and Xiangyang C1).

The northwestern trajectory clusters (LNW and SNW) passing through some natural sources of aerosol emissions including northern Xinjiang, southern Inner Mongolia, northwestern Gansu, and central Shannxi were predominant and accounted for dominant trajectories 72.9%, 55.1%, 80.2%, 90.6%, 38.5%, and 41.2% of clusters, respectively, in Handan, Zhengzhou, Xi’an, Yuncheng, Xiangyang, and Jinan. LW pathways made a large contribution of 53.1% in Chengdu. Particle matters accumulated more in short trajectories. As Table 6 shows, the highest mean PM2.5 concentrations were associated with SSW (Handan C1, Xi’an C1, and Jinan C2 and C5), SN (Zhengzhou C2 and Xiangyang C4), LSW (Yuncheng C1 and C5), and SS (Chengdu C5) pathways. Our results are consistent with those from a study from Perrone et al. [46], which found that a longer airflow trajectory had a faster speed not conducive to particle deposition according to the principle of dynamics.


CityCluster1 (C1)Cluster2 (C2)Cluster3 (C3)Cluster4 (C4)Cluster5 (C5)

Handan12.5072.928.334.172.08
304.00207.99109.92227.69211.25
Zhengzhou10.4212.5038.5421.8816.67
255.14270.89208.07330.65205.90
Xi’an19.7912.5035.4215.6316.67
200.36182.73192.90198.91164.13
Yuncheng5.2117.7135.4237.504.17
235.56233.98191.93200.75228.65
Chengdu11.4632.2941.677.297.29
112.90119.31120.7998.50135.93
Xiangyang8.3334.3823.9618.7514.58
198.17208.89192.95261.73261.04
Jinan6.2514.5862.507.299.38
162.11183.07189.33143.35191.00

3.7. Source Analysis

Figures 8 and 9 show the 72 h backward trajectories and potential sources of PM2.5 with WPSCF and WCWT. The warm-colored areas of the map represent the main potential sources of pollution that had a significant effect on PM2.5 concentration. The cold-colored regions represent minor potential sources of pollution. Figures 8 and 9 show areas with WPSCF > 0.5 and WCWT > 100 μg·m³ (i.e., where main potential sources were concentrated). PM2.5 pollution in these regions was mainly caused by artificial emissions. In areas with WPSCF < 0.5 and WCWT < 100 μg·m³ (i.e., secondary potential sources), PM2.5 pollution was related to the presence of arid and semiarid areas and desert areas, such as the Badain Jaran Desert, the Ulan Buh Desert, the Kubuqi Desert, and the Tengger Desert in Inner Mongolia, where dust storms occur frequently [47, 48].

The identity and distribution of the main potential sources of PM2.5 in each polluted city differed. Our results suggest that regional transport from the northwest and south of Handan plays a dominant role in the formation of pollution events. Pollution sources were mainly distributed in southern Hebei, south of Shanxi, and north of Shaanxi, which are the main coal-burning areas in winter. These anthropogenic emissions were transported to Handan through low-level air mass. In addition, Handan is a developed industrial city with many power plants and energy-consuming factories producing a large amount of industrial emissions. For Zhengzhou, high WPSCF and WCWT values were concentrated in northern Shaanxi and southern Shanxi because of Zhengzhou’s large population density and high energy consumption, which depend heavily on fossil fuels. The high potential source regions for Xi’an were mainly distributed around Xi’an and were caused by motor vehicle emissions, thermal power plants, and coal burning for heating in winter. Ningxia and Gansu may also be important potential sources of PM2.5 pollution in Xi’an since these areas are the main sources of dust in winter. Central Gansu, Ningxia, and Shaanxi were the main potential sources of pollution in Yuncheng. The high potential source regions for Chengdu were located in surrounding areas due to unfavorable meteorological conditions in winter. High WPSCF and WCWT values were mainly concentrated in the south of Henan where population density and biomass burning are high. The high potential source regions for Jinan were distributed in southwestern Shandong, northeastern Henan, east of Shandong, and south of Hebei [50].

4. Conclusions

Air pollution is a serious environmental and societal problem in China. This study mainly analyzed the characteristics of a severe air pollution incident that occurred from January 15 to January 22, 2018, in seven major polluted cities in China. We also analyzed the effects of meteorological conditions and identified potential pollution sources. This study provides an important scientific basis for the design of pollution control strategies in this region.

The most serious air pollution was observed in Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan, where mean PM2.5 concentrations were 212.6 μg·m³, 227.2 μg·m³, 186.7 μg·m³, 207.8 μg·m³, 111.4 μg·m³, 219.3 μg·m³, and 163.5 μg m³, respectively. Hourly variation in mean PM2.5 concentration showed a multiple U-shaped trend with higher values at night and lower values during the day. The ratio of PM2.5 to PM10 was large in all cities, indicating the large contribution of secondary aerosols to PM2.5 concentrations in these regions. PM2.5 concentrations showed positive correlations with PM10, CO, NO2, and SO2 and negative correlations with O3.

Pollutant concentrations were generally negatively correlated with sea-level pressure and wind speed in Handan, Xi’an, Zhengzhou, and Yuncheng and PM2.5, PM10, CO, and SO2 concentrations were positively correlated with temperature, while the opposite occurred for NO2 and O3 concentrations in Handan, Xi’an, Zhengzhou, Yuncheng, and Jinan. Correlations with relative humidity showed significant regional differences. PM2.5, PM10, CO, and SO2 were negatively correlated with relative humidity in Handan, Yuncheng, Chengdu, and Jinan, while NO2 and O3 were negatively correlated with wind speed. We identified calm meteorological conditions as one of the main factors causing the haze event.

The analysis of transport contributions indicated that the northwestern trajectory yielded the greatest PM2.5 contributions, ranging between 41.2% and 90.6% in target cities, whereas the highest mean PM2.5 concentrations were generally associated with SSW, SN, LSW, and SS pathways. The potential pollution sources calculated by WPSCF and WCWT models were very similar and the highest values of WPSCF (>0.5) and WCWT (>100 μg·m³) were distributed in densely populated and industrial areas.

Data Availability

Data used in this paper can be obtained from Chao He (he_chao@whu.edu.cn) upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by funding from the China Scholarship Council (Grant no. 201506270052), Shenzhen Basic Research Project, China (Grant no. JCYJ20150630153917252), and Collaborative Innovation Center of Geospatial Technology in Wuhan University. The authors gratefully acknowledge the National Urban Air Quality Real-Time Publishing Platform for help with daily mean PM10 concentrations data and the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT model used in this publication.

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