Seasonally apportioning the potential sources and transport pathways of atmosphere particulate matter in Hefei City, the most rapidly developing megalopolis in China

doi:10.21203/rs.3.rs-1518822/v1

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Seasonally apportioning the potential sources and transport pathways of atmosphere particulate matter in Hefei City, the most rapidly developing megalopolis in China

https://doi.org/10.21203/rs.3.rs-1518822/v1

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Trajectories cluster analysis (CA), potential source contribution function (PSCF) method and concentration weighted trajectories (CWT) method were integrated to identify the potential sources of particulate matter (PM₁₀ and PM_2.5) during 2018 to 2020 in Hefei city of China. From 2018 to 2020 PM₁₀ and PM_2.5 were annually averaged at 66.74 ± 33.05 µg·m^− 3 and 38.08 ± 23.05 µg·m^− 3, respectively. Correspondingly, the mean concentrations in spring, summer, autumn and winter were 73.41 ± 30.04, 45.80 ± 16.50, 72.75 ± 35.10 and 81.81 ± 38.17 µg·m^− 3 for PM₁₀ while 38.73 ± 15.73, 24.26 ± 10.73, 37.16 ± 19.60 and 61.42 ± 32.16 µg·m^− 3 for PM_2.5, with the highest in winter while the lowest in summer. CA shows that Hefei’s atmosphere particulate matter pollution was mainly affected by those air flows derived from the north and northwest China in spring, autumn and winter, while south and southeast during the summer. The pressures of backjectories were in the range of 700 ~ 950 hPa during four seasons, wherein summer had a higher pressure than that of other seasons. This means that the influence of near surface air mass was stronger in summer. PSCF and CWT analysis indicate that winter had the largest number of potential contribution sources. In spring, autumn and winter several cities from the Fen-Wei Plain and ‘2 + 26’ cities were identified as the major contributor. In addition, some places in Anhui province were also the considerably potential contributor. It is found the air pollution transport pathways reaching Hefei City have changed recently. This study could benefit the prevention and control of air pollution in city clusters and metropolitan area in China.

Particulate matter

Potential source

Backward trajectory

Transport pathway

Hefei

Many cities in China are facing severe air pollution resulted from economic development, population growth and urbanization [1–4]. Particulate matter (PM) pollution, represented by PM₁₀ and PM_2.5, has been of great concern [5, 6], because it can not only lead to various environment problem like decreasing in atmospheric visibility and increasing in haze day and climate change but also human health impairment as respiratory and cardiovascular disease [7, 8].

Owing to the efforts on air pollution control in China, the concentrations of PM₁₀ and PM_2.5 have been decreased gradually in many places [9, 10]. Nevertheless, air pollution by PM still was at unfavorable level, especially in winter [11]. For example, PM_2.5 is much higher than the specified value (10 µg·m^− 3) by WHO in almost all cities. The challenge of air pollution control is that the urban air pollution is not only affected by local pollution sources, but also related with the contribution of external sources [12, 13]. Therefore, it becomes greatly important to determine the potential sources and diffusion pathways for air quality improvement.

Statistical method like clustering analysis on air mass trajectories has been proved to be an effective method to identify the sources and prevalent transport pathways of airborne pollutants [14–15]. Trajectory clustering can sort the air-mass into representative groups based on velocity or angle of air flow. Potential sources contribution function (PSCF) improves the resolutions of source areas via giving high resolution data [16]. However, it is good at angular resolution but poor radial resolution because of trajectories convergence as they approach the receptor [17, 18]. These days trajectory clustering, PSCF and WCT are usually integrated to characterize the sources of atmospheric pollutant to obtain much more information [18, 19].

Hefei, one megalopolis located in East China, is the most rapidly developing city in China with the population up to ~ 9.4 million in 2020. To date, many comprehensive studies on PM in China have been concentrated at Beijing–Tianjin–Hebei Province, the Yangtze River Delta and the Pearl River Delta. However, in the Yangtze River Delta, the work has been mainly carried out towards those areas with high economic development level like Shanghai, Nanjing and Hangzhou. Previously, Luo et al. [20] investigated the sources of PM₁₀ and PM_2.5 during 2014 in Hefei using backward trajectory model. As we know, due to the action of defending the blue sky, which was launched in 2017 throughout the country, the status of air pollution in China has greatly changed. Consequently, the timely study is of great importance. This study is conducted to better understand detailed transport pathways and potential sources of PM₁₀ and PM_2.5 in Hefei during 2018 ~ 2020, which can help to figure out the variation in air pollution transport pathways with endpoint at the Yangtze River Delta during recent years. The result is potential to benefit the prevention and control of air pollution in city clusters and metropolitan area in China. We determined the main air mass transport pathways by using cluster analyses and press profile via backward trajectories and identified the significant seasonal sources based on hourly resolution data of PM using PSCF method and WCT method.

2.1 Environmental monitoring data

Hourly concentrations of PM₁₀ and PM_2.5 in Hefei were obtained from the Department of Ecology and Environment of Anhui Province (available at https://sthjt.ah.gov.cn/site/tpl/5371). The averaged data from 10 national monitoring sites (Luyang District, Amber Hills, Sanli Street, Yaohai District, Yaohai District, Baohe District, Binhu New District, Middle Changjing Road, Dongpu Reservoir, High-tech Zone and Pear Plaza) was officially used as the representative of air pollution in Hefei City (Fig. 1) by PM.

[Fig. 1 Location of Hefei City]

2.2 Trajectory computation

In this study, 72-hour back-trajectories arriving at the receptor (117.17E, 31.52 N [20] were computed hourly (00:00–23:00 Beijing time) using MeteoInfo map integrated with TrajStat (Version 2.4.5 downloaded from http://www.meteothink.org/). Daily meteorological data were available at “ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1/’ in the global data assimilation system from National Centers for Environmental Prediction”.

2.3 Trajectory clustering

Air trajectories can be grouped by their specific similarity like angle or speed. In this study the originated angle distance was selected to carry out trajectory clustering.

Potential source contribution function analysis

Potential source contribution function (PSCF) is a widely used method, identifying the potential contributor with high pollutant concentration to the receptor site. For given geographic grid cell (i, j), it is defined as [9] the following equation.

$${PSCF}_{ij}={x}_{ij}/{y}_{ij}$$

in which ${y}_{ij}$ is the number of trajectory endpoint falling in grid cell (i, j) and ${x}_{ij}$ is the number of trajectory endpoint with pollutant concentration over a threshold. Hence, threshold is the boundary between local emission and regional transmission. As the pollutant concentration was higher than the threshold, the pollution is mainly contributed by the regional transmission. In this study, the grid cell was set as 0.5×0.5° and the averaged concentrations of PM during each season were selected as the thresholds for the corresponding season [16]. In case of grid cell with few endpoints, the result of PSCF analysis will have uncertainty. To mitigate this uncertainty, ${PSCF}_{ij}$ values were multiplied by a weight coefficient (${W}_{ij}$) to be $W{PSCF}_{ij}$ [17]. The values of ${W}_{ij}$ were determined as follows.

$$W\left({n}_{ij}\right)=\left\{ \begin{array}{c}1.00 {y}_{ij}<{3n}_{ave } \\ 0.70 {1.5n}_{ave}<{y}_{ij}\le {3n}_{ave} \\ 0.42 {n}_{ave}<{y}_{ij}\le {1.5n}_{ave} \\ 0.17 {y}_{ij}\le {n}_{ave} \end{array}\right.$$

2.4 Concentration weighted trajectory analysis

Those grid cells, with sample concentrations either only slightly higher or much higher than the threshold, are likely to have the same PSCF value, resulting in difficulty in recognizing the moderate sources from the strong ones. Alternatively, concentration weighted trajectory (CWT) analysis was proposed. For CWT, each grid cell is given a weighted concentration by averaging the sample concentration, which is associated with trajectories that cross the grid cell, by following equation.

$${c}_{ij}=\frac{k}{\sum _{k=1}^{M}{\tau }_{ijk}}\sum _{k=1}^{M}{c}_{k}{\tau }_{ijk}$$

where ${c}_{ij}$ is the average weighted concentration in the grid cell (i,j), k is the index of the trajectory, M is the number of trajectories, ${c}_{k}$ is the concentration observed on arrival of trajectory k, and ${\tau }_{ijk}$ is the retention time of trajectory k in the cell. Weight coefficient, used by PSCF, also was introduced into CWT to eliminate grid cells with small ${y}_{ij}$ based on the criterion described by Eq. (2).

3.1 Characteristics of PM₁₀ and PM_2.5

Daily concentraiton’s fluctuation of PM₁₀ and PM_2.5 in Hefei City is shown in Fig. 2. From 2018 to 2020 the annual averaged concentrations of PM₁₀ and PM_2.5 were 66.74 ± 33.05 µg·m^− 3 and 38.08 ± 23.05 µg·m^− 3, respectively. Of three years, PM₁₀ (58.84 ± 28.67 µg·m^− 3) and PM_2.5 (32.05 ± 18.64 µg·m^− 3) had the lowest concentrations in 2020 (p > 0.05). Also, the levels of PM in 2020 were either lower than or similar to the criterion as 70 µg·m^− 3 for PM₁₀ and 35 µg·m^− 3 for PM_2.5 required by the Class II standard of the Chinese Ambient Air Quality Standards (GB 3095 − 2012). This means that the air quality of Hefei city has been improved considerably recently. However, the concentraion of PM₁₀ was still higher than the annual average concentrations of 64 µg·m^− 3 of 168 Chinese cities in 2020 [11]. As to the ratio of PM_2.5 to PM₁₀, it did not fluctuate significantly over the study period with averages of 0.60 ± 0.71 in 2018, 0.56 ± 0.18 in 2019 and 0.57 ± 0.19 in 2020. The value of PM_2.5/PM₁₀ were lower than those, calculated with the data from [11], of Beijing (0.67), shanghai (0.78) and Tianjin (0.71), while similar to that of Nanjing (0.55) and Guangzhou (0.53).

[Fig. 2 Daily variation of particulate matter in Hefei city between 2018 and 2020]

As shown in Fig. 3 (a), between 2018 and 2020 the averaged concentrations of PM in spring, summer, autumn and winter were 73.41 ± 30.04, 45.80 ± 16.50, 72.75 ± 35.10 and 81.81 ± 38.17 µg·m^− 3 for PM₁₀ while 38.73 ± 15.73, 24.26 ± 10.73, 37.16 ± 19.60 and 61.42 ± 32.16 µg·m^− 3 for PM_2.5. PM₁₀ and PM_2.5 had the same seasonal concentration variation, the highest in winter while the lowest in summer. Meanwhile, the pollution level of PM₁₀ and PM_2.5 in spring was nearly the same to that of autumn (p < 0.001).

Person correlationship analysis suggests that in Hefei city PM_2.5 was significantly related with PM₁₀ (p < 0.001) with correlation coefficient (r) as 0.557 in spring, 0.822 in summer, 0.688 in autumn and 0.899 in winter. Based on the record by Shen [21] on Hefei, the values of r in spring, summer, autumn and winter during 2015–2017 were 0.811, 0.913, 0.925 and 0.947, respectively. It means that the relationship between PM₁₀ and PM_2.5 had been weakened by a small margin. Therefore, the pollution sources contributing PM₁₀ and PM_2.5 also had changed correspondingly. On the other hand, the values of PM_2.5/PM₁₀ were roughly the equivalent during spring (0.56 ± 0.19), summer (0.53 ± 0.14) and autumn (0.53 ± 0.17) (p > 0.05). And the winter had the highest ratio as 0.75 ± 0.16. This indicates that the atmospheric pollution type of Hefei by PM in winter was different from other seasons.

[Fig. 3 Seasonal variation of particulate matter in Hefei city between 2018 and 2020]

[Fig. 4 Correlationship between PM₁₀ and PM_2.5 with respect to season n Hefei city between 2018 and 2020]

3.2 Trajectories cluster

Trajectories were categorized into six clusters by the present study for each season (Fig. 5). And the analysis on PM corresponding to six clusters is summarized in Fig. 6 and Table 1. The pathways of air flow in summer were different from that of other seasons. The trajectories mainly came from the southern inland and the East China Sea during summer while from the north China and the northwest China in spring, autumn and winter. Comparatively, the migration pathway of air mass was the shortest in summer. Shi et al. [22] identified the seasonal trajectories reaching Hefei city during 2001 ~ 2005 and found they were mainly sourced from the northwest of Hefei city during spring, summer and winter, which was in accordance with the present study. However, the migration pathway got short during 2018–2020 regardless of season. Also, the air mass originated from Mongolia became rare.

[Fig. 5 Clusters of back trajectories of air flow for Hefei City between 2018 and 2020]

[Fig. 6 PM₁₀ and PM_2.5 portioned by trajectory cluster of air flow for Hefei City between 2018 and 2020]

The air mass of trajectories in spring accounted for 15.22%, 19.72%, 23.83%, 3.72%, 18.84% and 8.67% for cluster 1 to 6, respectively. Cluster 1 (northwest→southwest) had the highest PM₁₀ as 93.37 ± 39.22 µg·m^− 3, followed by cluster 4 (northwest) as 89.29 ± 44.17 µg·m^− 3 and cluster 3 (northwest→south) as 81.04 ± 41.80 µg·m^− 3. Also, cluster 1 was coupled with the highest PM_2.5 of 44.09 ± 19.11 µg·m^− 3. The levels of PM_2.5 from cluster 3 and 4 were almost the same, which was slightly smaller than that of cluster 1. In spring PM₁₀ and PM_2.5 in Hefei were averaged at 73.41 and 38.73 µg·m^− 3, respectively. Concentrations of PM₁₀ at cluster 1, 3 and 4 were higher than the average concentration of winter in Hefei, while PM_2.5 at cluster 1 to 5. Therefore, air mass from cluster 1, 3 and 4 is considered as the major transport pathway that PM migrating to Hefei in spring. The air mass of cluster 1 started from Fen-wei Plain and went through several cities of the “2 + 26” cities. Fen-wei Plain is one of the key prevention and control areas of air pollution in China. “2 + 26” cities are air pollution transmission channel of Beijing Tianjin Hebei region. Cluster 3 started from the east of Hubei while cluster 4 passed the north of Anhui. Both eastern Hubei and northern Anhui had a high concentration of PM. The ratios of PM_2.5 to PM₁₀ associated with cluster 1, 3 and 4 were 0.47, 0.52 and 0.48, respectively. While, the trajectories in cluster 5 had the highest value of PM_2.5/PM₁₀as 0.61. Nonetheless, those trajectories had no high pollutant concentration and were not the significant pollution transport pathway.

Table 1

Statistical analysis on trajectories of air flow for each cluster in Hefei City between 2018 and 2020
Season	Cluster	NT^a	PNT ^b (%)	PM₁₀^c (µg·m^− 3)	PM_2.5^c (µg·m^− 3)	PM_2.5/PM₁₀
Spring	1	1035	15.22	93.37 ± 39.22	44.09 ± 19.11	0.47
	2	1281	19.72	68.66 ± 30.62	39.00 ± 22.52	0.57
	3	1611	23.83	81.04 ± 41.80	42.41 ± 19.23	0.52
	4	900	13.72	89.29 ± 44.17	42.77 ± 21.99	0.48
	5	1228	18.84	66.62 ± 37.56	40.50 ± 22.88	0.61
	6	563	8.67	71.60 ± 52.51	30.88 ± 16.85	0.43
Summer	1	1054	15.60	53.37 ± 21.56	27.44 ± 12.71	0.51
	2	1888	28.84	44.00 ± 20.94	22.53 ± 9.43	0.51
	3	1421	21.66	50.33 ± 21.84	26.19 ± 13.63	0.52
	4	690	10.54	48.20 ± 24.59	23.49 ± 11.89	0.49
	5	917	14.01	54.79 ± 27.32	26.67 ± 14.10	0.49
	6	650	9.55	44.60 ± 22.38	21.67 ± 9.84	0.49
Autumn	1	279	4.32	83.22 ± 54.70	43.80 ± 25.79	0.53
	2	727	11.26	81.01 ± 39.13	50.71 ± 35.39	0.63
	3	1282	19.46	67.16 ± 33.68	33.71 ± 17.70	0.50
	4	1736	26.06	73.61 ± 42.12	37.08 ± 24.00	0.50
	5	1797	27.83	63.07 ± 29.05	35.30 ± 18.13	0.56
	6	715	11.07	99.40 ± 64.29	42.16 ± 27.26	0.42
Winter	1	1015	15.26	89.18 ± 43.19	71.11 ± 42.86	0.80
	2	2041	31.83	70.67 ± 43.40	59.37 ± 40.16	0.84
	3	1259	19.68	89.97 ± 47.81	72.53 ± 42.14	0.81
	4	872	13.59	71.82 ± 36.21	70.75 ± 39.39	0.99
	5	823	12.45	94.75 ± 40.30	69.93 ± 35.48	0.74
	6	461	7.19	70.01 ± 45.77	70.00 ± 43.66	1.00
^a, number of trajectories ^b, percentage of trajectories ^c, mean ± std

In summer, trajectory values of PM_2.5/PM₁₀ among clusters did not obviously vary with the values of ~ 0.50. There were four clusters (1(northwest→east), 3 (east-southeast), 4 (north→east) and 5 (southeast)) with PM₁₀ higher than averaged summer concentration of Hefei while 3 (1,3 and 5) for PM_2.5. Cluster 1, 3 and 5 were regarded to be the predominant trajectories that simultaneously affected Hefei’s PM₁₀ and PM_2.5. Moreover, the trajectories from cluster 4 were also the major transport pathway that PM₁₀ reached Hefei. Trajectories from clusters 1, 3, 4 and 5 had relationship with the city groups including Shanghai-Suzhou-Wuxi-Changzhou-Nanjing-Maanshan, Jiaxing-Suzhou-Wuxi-Changzhou-Nanjing-Wuhu, Chuzhou-Maanshan-Wuhu and Wenzhou-Lishui-Jinhua-Hangzhou-Huangshan-Chizhou-Tongling, respectively. However, the cluster 2 with the highest percentage of trajectory as 28.84% was not the major pathway of PM migrating to Hefei with heavy pollution.

In autumn, mean trajectory concentrations of PM₁₀ and PM_2.5 were 83.22 ± 54.70 and 43.80 ± 25.79 µg·m^− 3 in cluster 1, 81.01 ± 39.13 and 50.71 ± 35.39 µg·m^− 3 cluster 2, 67.16 ± 33.68 and 33.71 ± 17.70 µg·m^− 3 cluster 3, 73.61 ± 42.12 and 37.08 ± 24.00 µg·m^− 3 cluster 4, 63.07 ± 29.05 and 35.30 ± 18.13 µg·m^− 3 cluster 5 and 99.40 ± 64.29 and 42.16 ± 27.26 µg·m^− 3 in cluster 6. As a result, cluster 1, 2, 4 and 6 had the trajectories with high PM₁₀ level while cluster 1, 2 and 6 for PM_2.5. The corresponding trajectories of cluster 1, 2, 4 and 6 were observed to pass either Wei-fen plain or “2 + 26” cities. As to PM_2.5/PM₁₀, the highest was present in cluster 2 with 0.63 while the smallest from cluster 6 as 0.42.

For winter, the ratio of PM_2.5/PM₁₀ in trajectories was significantly higher than that of other seasons. Of those six clusters, cluster 4 and 6 had the highest ratio of around 1.00, while the smallest for cluster 5 as 0.74. And the ratio values of cluster 1, 2 and 3 were 0.80, 0.84 and 0.81. Trajectories of cluster 2, with the highest air mass fraction of 31.83%, were not associated with the concentrations of neither PM₁₀ nor PM_2.5 higher than the mean concentration of PM in winter. The highest trajectory concentration of PM appeared at Cluster 5 for PM₁₀ as 94.75 ± 40.30 µg·m^− 3 and cluster 3 for PM_2.5 as 72.53 ± 42.14 µg·m^− 3. Specifically, there were three clusters (1, 3 and 5) containing trajectories with high pollution of PM₁₀ and 5 clusters (1, 3, 4, 5 and 6) for PM_2.5. In winter the air mass all passed the places (Wei-fen Plain and “2 + 26” cities) with heavy PM pollution in China.

Figure 7 demonstrates the press profiles of the air mass clustered reaching Hefei. Comparatively, air flow among seasons occurred with the minimum height in summer. In spring the trajectories in cluster 3 with the highest ratio and shortest moving distance were observed to have the smallest height, coupled with pressure of 840 ~ 950 hPa. Meanwhile, the height of those trajectories with the longest transport path changed considerably. Over summer the heights of flow masses in cluster 1 and 6 varied similarly, and decreased gradually. The initial height of trajectories from cluster 4 which had short pathway was the highest. Also those trajectories, derived from the East China Sea, of cluster 3 and 5 almost started at the same height. However, the air flows grouped into cluster 5 was increased and decreased successively as they moved. Coming to autumn, cluster 5 with the highest ratio contained the trajectories with the maximum height. And the height of trajectories in cluster 1 moving with the long way was stable firstly and then rose slightly. Thereafter, it declined dramatically. Regarding winter, the range of initial heights was the smallest among four seasons. And all trajectories had the transport height decreased gradually regardless of cluster. It should be noted that the press profiles were not related with the concentrations of PM in trajectories regardless of season.

[Fig. 7 Pressure profile of back trajectories for air flow in Hefei City between 2018 and 2020]

3.3 Potential contributor sources identification

3.3.1PSCF analysis

WPSCF maps for PM₁₀ and PM_2.5 of Hefei City between 2018 and 2020 are provided in Figs. 8 and 9.

[Fig. 8 WPSCF maps for PM10 of Hefei City between 2018 and 2020]

[Fig. 9 WPSCF maps for PM2.5 of Hefei City between 2018 and 2020]

In Spring, PM₁₀’s WPSCF over 0.60 were mainly located in two large zones from 10 provinces (Anhui, Hubei, Hunan, Jiangxi, Zhejiang, Guangxi, Henan, Shanxi, Shaanxi and Huhhot). And the main potential contributors of atmospheric PM₁₀ in Heifei were distributed in the neighboring region of Anhui, Jiangxi and Zhejiang, the middle north of Fujian, the border of Hunan and Hubei, the middle east and south of Hubei, the middle north of Hunan, the border of Shanxi and Henan, etc. Otherwise, the area and value of WPSCF for PM_2.5 were smaller than that of PM₁₀ in spring. The significant contributors came from the northeast of Jiangxi, the junction of Anhui, Jiangxi and Zhejiang, the north of Fujian and the northeast of Guangxi, which also could contribute PM₁₀ considerably to Hefei.

In summer, the value of WPSCF, exceeding 0.70, for PM₁₀ mainly appeared in Anhui, Jiangsu and Zhejiang province. And the major cities included Tongling, Xuancheng, Wuhu and Huangshan in Anhui province, Changzhou, Wuxi and Suzhou in Jiangsu province and Huzhou, Jiaxing, Shaoxing, Hangzhou, Quzhou, Jinhua, Lishui in Zhejiang province. Moreover, there were several patches from Jiangxi, Fujian, Shandong and Henan province with WPSCF as 0.70–0.80, being the significant potential contributor. Comparatively, PM_2.5’s WPSCF was mainly within the range of < 0.70. And the places, away from the southeast of Hefei, including Tongling, Chizhou, Wuhu, Xuancheng in Anhui province and Huzhou and Hangzhou in Zhejiang Province were recognized as the considerable contributors of PM_2.5.

As shown in Fig. 8(c), the sources likely affecting Heifei’ PM₁₀ scattered in Huangshi, Ezhou, Huanggang, Xianning, Suizhou, Jingmen, Shiyan, Nanyang, Sanmenxia, Shangluo,Weinan, Yanan, Yulin and Qingyang Cities. Moreover, there were large areas with WPSC between 0.50 and 0.60 from the middle-lower Yangtze Plain, the Northeast Plain, the Loess Plateau and the Inner Mongolian Plateau. As to PM_2.5, its major contributors were from southern Hubei (Xianning and Huanggang) and western Jiangxi (Jiujiang, Yichun and Xinyu). Additionsally, Huaibei, Shangluo, Sanmenxia and Qingyang can also be considered as the significant contributor.

Within winter, WPSC values both of PM₁₀ and PM_2.5 the highest of four seasons. The area of PM’s contributors to Hefei was remarkably increased. And the contribution was stronger in northern China than southern China. Specifically, the contributors of PM₁₀ were mainly seated at west, northwest and southwest of Hefei city. The places, between the line of Yinchuang-Zhengzhou city and the line of Zhongwei-Pingdingshan city, covering Henan, Shanxi, Shaanxi and Gansu province, were found to be the significant contributor of PM₁₀ with the largest area. Moreover, northeast Hubei province, southeastern Hubei province, Jianghan Plain and northeast Hunan province were also the strong contributor. On the other hand, PM_2.5 was mainly contributed by southeastern Shandong, eastern Henan, northern Anhui, southeastern Hubei, northeast Hunan and southern Shanxi.

3.3.2 WCWT analysis

Figure 10 shows WCWT map distributions of PM₁₀. The results were very similar to that of WPSCF method. In spring the areas with WCWT over 100 µg·m^− 3 covering the border of Shanxi and Shaanxi province (Lvliang, Linfen, Yulin and Yanan city), the border of Shanxi and Henan province (Yuncheng, Jincheng, Sanmenxia, Jiyuan, Luoyang and Jiaozuo city), the middle of Henan province (Zhengzhou, Xuchang and Pingdingshan city), the center of Hebei province (Boding city), the junction of Anhui, Jiangxi and Zhejiang province (Huangshan Jingdezheng, Shangrao and Quzhou city). In summer, WCWT was observed in the range of < 70 µg·m^− 3. And the places of the south of Anhui province, the south of Jiangsu province and the middle and northern west of Zhejiang province were identified as the main potential contributor. Otherwise, during autumn WCWT of 100–157 µg·m^− 3 was found mainly in Henan and Hubei province. Moreover, several patches from Anhui, Shanxi and Shaanxi province were also the significant contributor of PM₁₀. Over winter, there an enormous region with WCWT of PM₁₀ over 90 µg·m^− 3. The major contributors were located almost within the whole of Henan province, the middle east of Hubei province, large area of Anhui province, the southwest of Shanxi province, the southwest of Shandong province, west of Zhejiang province, the neighboring region of Hubei, Hunan and Guangxi province as well as the middle of Guangxi province.

[Fig. 10 WCWT maps for PM10 of Hefei City between 2018 and 2020]

WCWT distribution of PM_2.5 is given in Fig. 11. The main contributors of PM_2.5 with WCWT > 40 µg·m^− 3 were widely distributed in the central and eastern China. And the neighboring region of Anhui with Jiangxi and Zhejiang Province, the northwest part of Henan Province, and the middle, northwest and east places of Shandong Province were observed to contribute WCWT as 50 ~ 60 µg·m^− 3. Comparatively, summer had the smallest PM_2.5 WCWT (< 40 µg·m^− 3) among four seasons. And, the areas with WCWT higher than 30 µg·m^− 3 were within the large region of southern Anhui province, the north of Zhejiang province and the south of Jiangsu province. Into autumn, the contribution area of PM_2.5 started to expand with WCWC mainly as 40 ~ 70 µg·m^− 3. Over winter, WCWT of 90 ~ 100 µg·m^− 3 was concentrated at the southeast of Hubei Province, the east of Henan Province and the southwest of Shandong Province.

[Fig. 11 WCWT maps for PM2.5 of Hefei City between 2018 and 2020]

As above-discussed, the potential contributor sources to Hefei city seasonally varied. And the places from Shanxi, Hebei, Henan and Anhui Province were identified as the major contributor. Dring 2001 ~ 2005, dust storm episode could significantly affect Hefei’s PM concentration, especially in spring [22]. Interestingly, inner Mongolia where dust storm episode frequently previously did not seem to be the important contributor by the present study. A study by Yu et al. [23] shows that Anqing, a neighboring city of Hefei, was mainly contributed by the sources from the east of Hubei province, north of Jiangxi province and south of Hunan province, which was coupled with a planar area involved in Shanxi, Henan, southern Shandong and northern Anhui province. And there are several places as the same contributor to both Anqing and Hefei.

In the present study, transport pathway and potential contributor sources of particulate matter in Hefei City during 2018 ~ 2020 were identified. The pollution level of particulate matter in Hefei City had been decreased considerably but still was unfavorable. PM₁₀ and PM_2.5 had the highest concentration in winter while the lowest in summer. The comparation with pervious study shows that the relationship between PM₁₀ and PM_2.5 had been weakened by a small margin, indicating the pollution sources of particulate matter had changed slightly. And the seasonal variation of PM_2.5/PM₁₀ shows that the atmospheric pollution type of Hefei by particulate matter in winter may be different from that of other seasons. During spring, autumn and winter, Hefei was mainly affected by landlocked air mass derived from either north China or northwest China. The trajectories passed Fen-Wei Plain and ‘2 + 26’ City had high PM concentration. Spatiotemporal distribution of backjectories’ pressure profile suggests that the influence of near surface air mass was stronger in summer. Except for summer, the places from Shanxi, Hebei, Henan and Anhui Province were identified as the major contributor.

Acknowledgements

This reseaerch was supported by the Open Fund by Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Anhui Province. The authors are grateful for the support.

Author Declarations

1. Ethics Declaration statement

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2. Consent to Participate

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3. Consent for publication

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Funding

This work was supported by [Nanjing University of Information Science & Technology and the Human Resources and Social Security Department of Anhui Province]. Author Siping Niu has received research support from Nanjing University of Information Science & Technology and the Human Resources and Social Security Department of Anhui Province.

Competing Interests

The authors declare that The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Siping Niu: Conceptualization, Methodology, writing, Reviewing and Editing. Tiantian Wang: Data collection and analysis. Jianghua Yu: Reviewing. All authors read and approved the final manuscript.

Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Seasonally apportioning the potential sources and transport pathways of atmosphere particulate matter in Hefei City, the most rapidly developing megalopolis in China

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Method

2.1 Environmental monitoring data

2.2 Trajectory computation

2.3 Trajectory clustering

2.4 Concentration weighted trajectory analysis

3. Results And Discussion

3.1 Characteristics of PM₁₀ and PM_2.5

3.2 Trajectories cluster

3.3 Potential contributor sources identification

3.3.2 WCWT analysis

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

Seasonally apportioning the potential sources and transport pathways of atmosphere particulate matter in Hefei City, the most rapidly developing megalopolis in China

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Method

2.1 Environmental monitoring data

2.2 Trajectory computation

2.3 Trajectory clustering

2.4 Concentration weighted trajectory analysis

3. Results And Discussion

3.1 Characteristics of PM10 and PM2.5

3.2 Trajectories cluster

3.3 Potential contributor sources identification

3.3.2 WCWT analysis

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

3.1 Characteristics of PM₁₀ and PM_2.5