To assess the performance of the WRF model for the meteorological simulations, the simulated surface temperature, RH, WD, and WS were compared with the observed data. Table 1 and Fig. S2 present the statistics of the model performance in Wuhan and other cities like Beijing, Hefei and Changsha. Overall, the WRF model was able to reproduce the main characteristics of the four meteorological variables in these four cities. The simulated temperatures at 2 m were close to the observations, with correlation coefficients (r) of 0.75–0.91 and average mean biases (MB) of approximately −1.8 to 1.4°C. The variations of RH were also reproduced by the model with r values of approximately 0.72–0.84, except for more underestimation of RH in SouthC than that over the NCP. RH is a diagnostic quantity rather than an output of the model and is inferred based on the simulated temperature, surface pressure, and water vapor mixing ratio. As suggested by Yang and Duan (2016), one possible reason for the underestimation of RH in the WRF model is due to the overestimation of the surface temperature under insignificant change in water vapor pressure. WRF overestimated the surface temperature, with mean biases from 0.5°C to 1.4°C, in southern cities of China. The correlations between the simulated WSs and the observations, with r values ranging from 0.32 to 0.71, were relatively lower than those of temperature and RH, which might be related to the difficulty in simulating the weak atmospheric circulation at the urban scale. The WRF model was able to reproduce the evolution of the winds in these cities, especially for the period with prevailing strong winds.
Site Factors MO MM MB r MFB(%) MFE (%) Wuhan T (°C) 7.7 9.1 1.4 0.84 18.4 26.7 RH (%) 58.3 46.2 −12.1 0.77 −20.7 25.4 WS (m s−1) 2.4 3.3 0.9 0.33 34.2 53.2 Changsha T (°C) 8.6 9.1 0.5 0.85 4.4 32.4 RH (%) 62.2 50.8 −11.4 0.84 −20.7 25.2 WS (m s−1) 2.4 3.0 0.6 0.63 26.0 45.3 Hefei T (°C) 5.5 6.1 0.6 0.91 −15.6 17.4 RH (%) 68.3 56.3 −12 0.80 −18.2 24.7 WS (m s−1) 1.9 3.2 1.3 0.71 58.3 62.4 Beijing T (°C) −0.3 −2.1 −1.8 0.75 63.3 −55.8 RH (%) 44.3 40.8 −3.5 0.72 −0.8 31.6 WS (m s−1) 2.0 2.9 0.8 0.32 36.3 59.6
Table 1. Statistical parameters of the three simulated and observed meteorological parameters [temperature (T), relative humidity (RH), and wind speed (WS)] in Wuhan, Changsha, Hefei and Beijing in January 2014. The definitions of the abbreviations MO, MM, MB, r, MFB and MFE are given (Table S1 in Electronic Supplementary Material).
Figure S2. Temporal variation of the hourly simulated and observed Temperature (T), relative humidity (RH), wind direction, and wind speed (WS) at Wuhan and its surrounding cities including Changsha in Hunan Province, Hefei in Anhui Province, and Beijing. The black dots and red lines represent observed and simulated data, respectively.
Figure 2 displays the simulated monthly average concentrations of PM2.5 over Central and East China and the observed PM2.5 concentrations in 117 cities during January 2014. As shown by the observations and simulations, PM2.5 values over 150 μg m−3 were mainly distributed across the south of the NCP region, Central China, north of SouthC, and the Sichuan Basin. The average PM2.5 concentrations in January 2014 over the typical cities in the SouthC region (e.g., Changsha, Nanchang, Guangzhou), at 93.1 μg m−3 to 153.9 μg m−3, were relatively higher than those over the typical cities in the NCP region (e.g., Beijing, Tianjin, Shijiazhuang, Jinan, Zhengzhou), at 93.5 μg m−3 to 138.4 μg m−3. The model showed good performance for the magnitude and spatial distribution of PM2.5 concentrations over Central and East China. Figure 3 compares the hourly simulated and observed PM2.5 concentrations in Wuhan and the 15 provincial capital cities, with corresponding statistical parameters shown in Table S1. The simulated PM2.5 values were consistent with observations in most cities, with r values of approximately 0.48–0.73 and MBs within ±20 μg m−3, specifically for periods with sharp increases and decreases of the PM2.5 concentrations over the NCP, including Beijing, Tianjin, Shijiazhuang, Jinan, and Zhengzhou, as well as East China, including Shanghai, Hangzhou, and Nanjing. The mean fraction biases (MFBs) of −10.5% to 22.6% and mean fraction errors (MFEs) of 31.6%–54.2% in all cities also suggest a good performance of NAQPMS, based on a previous study of the evaluation of PM simulation with chemical transport models (Boylan and Russell, 2006). Therefore, the high-precision simulated data of the PM2.5 concentrations served as a good basis for analyzing the variations in the spatiotemporal distributions of the PM2.5 pollution and quantifying the impacts of regional transport to the haze formation in Wuhan.
Figure 2. The monthly mean concentrations of simulated PM2.5 (background color) and the observed values of 117 cities (solid circles) in the first modeling domain in January 2014.
Figure 3. Comparison between the hourly observed and simulated PM2.5 concentrations in Wuhan and the surrounding 15 capital cities over Central and East China. The red lines with open circles and black lines represent simulated and observed data, respectively.
Number Site MO (μg m−3) MM (μg m−3) MB (μg m−3) r MFB (%) MFE (%) 1 Beijing 93.5 95.6 2.1 0.70 4.9 54.2 2 Nanchang 109.2 137.6 28.4 0.48 22.6 40.2 3 Tianjin 120.2 133.3 13.2 0.71 1.1 46.1 4 Hefei 155.5 159.6 4.1 0.71 1.7 31.6 5 Shijiazhuang 126.3 136.5 10.1 0.59 9.3 47.9 6 Nanjing 124.7 123.2 −1.5 0.69 −7.2 36.4 7 Jinan 130.7 148.4 17.7 0.70 14.6 42.5 8 Shanghai 89.6 101.8 12.2 0.73 6.5 51.8 9 Zhengzhou 138.4 157.1 18.7 0.63 7.2 38.4 10 Hangzhou 109.7 104.9 −4.8 0.70 −4.2 36.2 11 Wuhan 172.9 181.5 8.6 0.56 3.2 33.6 12 Guangzhou 93.1 96.8 3.7 0.27 1.1 44.8 13 Changsha 153.9 166.5 12.6 0.59 5.2 37.6 14 Chengdu 181.5 161.1 −20.5 0.52 −10.5 35.4 15 Xi’an 151.6 147.4 −4.2 0.63 9.1 41.4 16 Taiyuan 95.0 110.6 15.6 0.48 21.7 50.4
Table S1. Statistical parameters of the simulated and observed PM2.5 concentrations in Wuhan and its surrounding 15 capital cities over Central and East China in January 2014. The definitions of the abbreviations MO, MM, MB, r, MFB and MFE are provided below (Text S1).
Wuhan experienced a persistent haze pollution period for nearly the entire month of January 2014, which was significantly longer than that in Beijing with 18 polluted days. A detailed comparison of several statistics of the PM2.5 concentrations and the corresponding meteorological variables observed in Wuhan and Beijing during this period is shown in Table 2. The number of severely polluted days with a daily PM2.5 average ≥150 μg m−3 was 19, which was far more than that of Beijing at 4 days. The monthly average PM2.5 concentration reached 172.8 μg m−3 in Wuhan—almost twice as much as the 94.6 μg m−3 found in Beijing during January 2014, and much higher than the 121.0 μg m−3 also reported in Beijing during January 2013 (Zheng et al., 2015b). These results suggest that the haze pollution in Wuhan was far worse and persisted for a longer period compared to Beijing during January 2014.
Wuhan Beijing Mean value 172.8 μg m−3 94.6 μg m−3 Polluted days 30 days 18 days Peak PM2.5 347.0 μg m−3 538.0 μg m−3 Daily increase 116.8 μg m−3 d−1 215.6 μg m−3 d−1 Hourly increase 103.0 μg m−3 h−1 230.0 μg m−3 h−1 Daily decrease −132.6 μg m−3 d−1 −242.9 μg m−3 d−1 Hourly decrease −91 μg m−3 h−1 −192 μg m−3 h−1 Non-haze days 1 day (72.0 μg m−3) 13 days (38.4 μg m−3) Slightly polluted 5 days (95.2 μg m−3) 7 days (95.6 μg m−3) Moderately polluted 6 days (133.1 μg m−3) 7 days (126.0 μg m−3) Heavily polluted 19 days (233.5 μg m−3) 4 days (270.2 μg m−3) T 7.8°C −0.3°C RH 58.4% 44.3% WS 2.4 m s−1 2.0 m s−1
Table 2. General information on the PM2.5 concentration and corresponding meteorological elements in Wuhan and Beijing in January 2014. The days with daily PM2.5 concentrations over 75 μg m−3, 115 μg m−3, and 150 μg m−3 are defined as slightly, moderately, and heavily polluted days, respectively.
In view of the temporal variation in the PM2.5 values in Fig. 4, the daily values showed cyclic variations lasting approximately 4–10 days in Wuhan and approximately 3–7 days in Beijing. During the processes of accumulation among pollutants, the maximum rates of increase in the PM2.5 concentrations within one day and one hour in Wuhan were 116.8 μg m−3 d−1 and 103.0 μg m−3 h−1, respectively—much lower than the corresponding values of 215.6 μg m−3 d−1 and 230.0 μg m−3 h−1 in Beijing. The peak value of the hourly PM2.5 concentrations in Wuhan was 347.0 μg m−3 and occurred at 0800 LST 27 January, which was much lower than the observed peak value of 538.0 μg m−3 in Beijing. Additionally, the rates of decrease in the PM2.5 concentrations within one day and one hour in Wuhan were −132.6 μg m−3 d−1 and −91 μg m−3 h−1, approximately half the values of −242.9 μg m−3 d−1 and −192 μg m−3 h−1 in Beijing during the stages of decreasing PM2.5 concentrations. Wuhan’s minimum daily PM2.5 concentration was 72.0 μg m−3 in January, while it was only 9 μg m−3 in Beijing. The result suggests that the fluctuation of PM2.5 concentrations in Wuhan was much less than that in Beijing in January 2014.
Figure 4. The temporal variation in the observed hourly wind field and PM2.5 concentrations in Beijing and Wuhan in January 2014; the color represents the wind speed (WS). Five haze episodes were defined as Ep. 1–Ep. 5 based on the daily variation in the PM2.5 concentrations in Wuhan.
For a more detailed analysis, the haze pollution in January 2014 was divided into five episodes (Ep. 1–Ep. 5), shown in Table 3, based on the periodical variations of the daily PM2.5 concentrations, and we defined the sharp increasing and slow decreasing stages of PM2.5 concentrations as the early and later stages of the five episodes, except Ep. 5a and Ep. 5b (two special processes). As can be seen from Fig. 4, the sharp increases in the PM2.5 concentrations over Beijing were often accompanied by southerlies with low WSs of < 2 m s−1, and the rapid decreases in the PM2.5 concentrations corresponded to strong northerly winds. However, quite a different behavior was found for the relationship between the PM2.5 concentrations and the winds in Wuhan. The rapid increases in the PM2.5 values and the occurrence of the peak values in Wuhan were strongly associated with strong northerly winds. The above results suggest that the characteristics of the PM2.5 pollution (the change cycle, fluctuation amplitude, and relationship with winds) in Wuhan were distinct from those in Beijing in January 2014.
Concentration Contribution Ep. Stage WS Wuhan AdjCity NCP EastC SouthC Others Ep. 1 early (1/1–1/3) 2.3 58.0 48.7 13.3 1.8 36.4 39.3 later (1/4) 1.4 59.2 9.4 24.3 0.0 0.0 8.0 Ep. 2 early (1/5–1/8) 3.3 38.3 49.3 66.0 38.2 9.2 10.5 later (1/9–1/11) 2.1 69.1 33.0 7.8 10.8 0.2 0.5 Ep. 3 early (1/12–1/13) 1.6 55.6 24.9 94.9 10.8 0.0 15.7 later (1/14–1/16) 2.4 58.6 60.5 3.1 30.8 2.3 1.4 Ep. 4 early (1/17–1/19) 2.4 37.8 43.7 105.1 28.3 3.6 32.0 later (1/20) 1.9 19.8 9.4 19.7 0.1 0.1 19.1 Ep. 5 early (1/25) 2.5 26.2 17.7 182.1 8.2 16.3 38.1 later (1/26–1/29) 2.1 65.4 55.9 16.5 18.3 9.6 21.8 Ep. 5a (1/22–1/24) 3.0 30.9 41.6 2.2 15.3 43.3 2.6 Ep. 5b (1/30–1/31) 2.9 48.9 57.0 3.5 1.1 53.1 9.2
Table 3. The mean wind speed (WS, m s−1) and concentration contribution (μg m−3) of the PM2.5 concentrations over Wuhan from the local, nearby cities (AdjCity), North China Plain (NCP), East China (EastC), South China (SouthC), and other regions (Others) during different stages of the five haze episodes (Ep.) in Wuhan.
To further explore the influence of the planetary boundary layer structure on the PM2.5 pollution, Fig. 5 displays the evolution of the simulated horizontal wind, potential temperature, RH in the vertical direction, and the PBLH in Wuhan. As can be seen, the variation of the peak value of the daily PBLH was strongly consistent with thermal stratification during the entire study period, except for Ep. 5a and Ep. 5b, which indicated that the strong thermal stratifications suppressed the development of the PBLH. Also, in Ep. 5a and Ep. 5b, different source regions of air masses driven by the prevailing southerly winds may have been responsible for the strong thermal stratifications with high PBLH. Besides, higher daily maximum PBLH tended to occur during the build-up stages of the PM2.5 pollution, except for Ep. 2, which was almost opposite to the finding in Miao et al. (2019). The PBLH often decreased during the later stages of PM2.5 episodes, which would not have been favorable for the vertical dispersion of air masses and would have led to the local accumulation of air pollutants. Furthermore, Wuhan was influenced by strong northerly winds from the surface layer to the upper layer (at least 2000 m) during the stages of increasing PM2.5 concentrations, and there were relatively weak WSs with WDs shifting rapidly during the stages of decreasing PM2.5 concentrations. High PBLH and strong winds are often considered to be conducive to the diffusion of the air pollutants, which further illustrates the special features of the haze episodes that occurred in Wuhan during January 2014.
Figure 5. The vertical distribution of the simulated planetary boundary layer height (PBLH), potential temperature (θ), horizontal winds (V), relative humidity (RH), and PM2.5 concentrations in the vertical direction over Wuhan in January 2014.
Based on the sea-level weather patterns from the simulation data (see Fig. 6) and the corresponding synoptic weather maps of the Korea Meteorological Administration (see Fig. S3), the impact of the large-scale atmospheric circulation conditions on the formation of the PM2.5 pollution in Wuhan was investigated. Overall, there were two types of weather patterns leading to the increase of the PM2.5 concentrations in Wuhan.
Figure 6. The simulated sea-level weather patterns and wind fields at (a) 1500 LST 2 January 2014 (Ep. 1), (b) 2100 LST 7 January 2014 (Ep. 2), (c) 1400 LST 12 January 2014 (Ep. 3), (d) 2200 LST 16 January 2014 (Ep. 4), and (e) 1900 LST 24 January 2014 (Ep. 5).
Figure S3. Sea-level weather pattern over eastern China at (a) 0200 LST 3 January 2014 (Ep. 1), (b) 2000 LST 7 January 2014 (Ep. 2), (c) 0800 LST 12 January (Ep. 3), (d) 1400 LST 17 January (Ep. 4), and (e) 0800 LST 25 January (Ep. 5) from the synoptic weather maps of the Korea Meteorological Administration.
Firstly, northerly winds were observed over Wuhan in the early stages of all haze episodes (Fig. 4), so we first analyzed the characteristics of weather patterns in upstream regions, including the whole of Central and East China and coastal areas in China (Fig. S3). It was found that a very similar configuration of the atmospheric circulation conditions over East Asia occurred in the early stages of Ep. 1–Ep. 2 and Ep. 5. Taking Ep. 2 for example, a strong and cold anticyclone from Mongolia moved southward, accompanied by a cyclonic system stagnating over the East China Sea. Such a configuration of weather systems led to a large pressure gradient and prevailing northerly winds over Central and East China. According to the observations in Fig. 4, the daily WS of 1.2 m s−1 on 6 January in Beijing increased to 2.3 m s−1 on 7 January, and the average value in Wuhan reached approximately 3.3–3.7 m s−1. During this period, Beijing experienced haze pollution, with observed daily mean PM2.5 values of 124.7 μg m−3 and 104.6 μg m−3 on 6 and 7 January, respectively. The strong northerly winds brought clean air masses from Mongolia to the NCP region, resulting in a significant decrease in the PM2.5 concentrations on 8 January (daily PM2.5 concentrations of 14.7 μg m−3 in Beijing). Simultaneously, the high concentrations of air pollutants over Beijing moved southward under the influence of the regional northerly winds, which were probably related to the rapid increase in the PM2.5 concentrations in Wuhan. Though the locations of the cyclonic and anticyclonic systems were slightly different, prevailing northwesterly winds in the northwest of the NCP and northerly winds in the NCP occurred during the stages of rapidly increasing PM2.5 concentrations in Ep. 1 and Ep. 5, respectively. In Ep. 3 and Ep. 4, a high-pressure system moving southeast was blocked by the strong weather system downstream and would have similarly resulted in strong winds over the NCP. Wuhan was in front of the surface high in Ep. 2 and Ep. 3, or controlled by sparse isobars in Ep. 1, Ep. 4 and Ep. 5, as shown in Fig. 6. Overall, the blocking high-pressure system moving southeastward was the key and typical weather pattern in the formation of the strong northerly winds in the NCP, and then the long-range transport of the polluted air masses from the NCP to Wuhan area.
The other type of atmospheric circulation controlled SouthCand Central China and occurred in Ep. 5a and Ep. 5b (see Fig. S4). The low-pressure system was located in Southwest China, and the high-pressure system was in the East China Sea. Such a configuration of two strong weather systems would have resulted in a large pressure gradient and prevailing southerly air flows over SouthC and Central China. According to the observations, persistent southerly winds dominated Wuhan, with average WSs of 3.0 m s−1 and 2.9 m s−1 in Ep. 5a and Ep. 5b, respectively.
Figure S4. Sea-level weather pattern over eastern China at (a) 0200 LST 24 January 2014 and (b) 1100 LST 31 January 2014 from the synoptic weather maps of the Korea Meteorological Administration.
The results highlight two typical configurations of the synoptic systems related to Wuhan’s increase in PM2.5 concentrations and a strong link between the severe haze pollution over Wuhan in January 2014 to the regional haze pollution over the NCP and SouthC. This finding also indicates that special attention should be paid to the above configurations of synoptic systems and concurrent regional haze pollution over the NCP and SouthC with regards to the forecasting and control of haze pollution in Wuhan.
The high-precision simulation data of the PM2.5 concentrations were used to investigate the formation and dissipation mechanism of the haze episodes in Wuhan.
Figure 6 shows the distribution of simulated PM2.5 concentrations and winds during the sharp increase in PM2.5 concentrations in Ep. 1–Ep. 5. Taking Ep. 2 as an example, before the occurrence of the first synoptic system configuration mentioned in section 3.2, the NCP region, with the highest emission intensity of air pollutants in China, was suffering from regional haze pollution due to adverse meteorological conditions. A weak low-pressure band (not shown here) was the synoptic flow pattern associated with the occurrence of the haze episodes over the NCP region during January 2014. The weak WS, high RH, and shallow PBLH under this typical synoptic condition frequently leads to low-visibility events in winter (Ye et al., 2015). In addition, high RH provides favorable conditions for the hygroscopic growth of particles (Eichler et al., 2008), and aqueous or heterogeneous reactions of gaseous pollutants such as SO2 and NOx (Xie et al., 2015) that lead to significant increases in secondary pollutants in the NCP. As shown in Fig. 7b, the polluted air masses in NCP were transported to the Wuhan area when the northerly winds moved towards the south quickly. Video S1 and video S2 in the Electronic Supplementary Material provide detailed information on the evolution of the regional PM2.5 concentrations and wind fields in the horizontal and vertical directions during 6–8 January. As for the other episodes, similar pollutant transport processes are displayed in Figs. 7a, c, d and e. It should be noted that mountains, with a maximum altitude of 1777 m, are located in the north of the Wuhan city-cluster. In this case, the simulation results suggest that the air pollutants over the NCP can be transported directly to Wuhan due to the impetus of the strong weather system with high WSs. A different influence of the mountains on pollutant transport has been reported. In some cases with relatively weak northerly winds, the highest mountain peak in the north of the Wuhan city-cluster tends to change the transport pathway of the polluted air mass to a northwest or northeast route (Zhou et al., 2016; Bai et al., 2018).
Figure 7. The distribution of simulated PM2.5 concentrations (units: μg m−3) and winds at the same time as Fig. 6 during stages of increasing PM2.5 concentrations in Ep. 1–Ep. 5 over Wuhan in the first modeling domain.
To further identify the key source region contributing to Wuhan’s rapid increase in PM2.5 concentrations during January 2014, the source-tagged method described in section 2.2 was conducted. As shown in Fig. 1, the entire model domain was divided into six source regions based on administrative boundaries, including Wuhan, surrounding cities of Wuhan, the NCP, EastC, SouthC, and “AdjCity”. Figure 8 and Table 3 display the daily contributions of the six source regions to the PM2.5 concentration levels in Wuhan. During the early stage of the five episodes, the contribution rates from long-range regional transport were approximately 49.7%–84.9%, and the concentration contributions of PM2.5 reached approximately 90.8–244.7 μg m−3, with a mean value of 150.0 μg m−3. On the other hand, the contribution from local emissions was < 75 μg m−3 during the same period. This result suggests that long-range regional transport was the main factor for Wuhan’s rapid increase in PM2.5 concentrations, and was the direct trigger of the build-up of severe haze pollution in Wuhan during January 2014.
Figure 8. The observed daily average concentrations (black dotted lines) and mean contribution rate (column diagram) of PM2.5 over Wuhan among local, nearby cities (AdjCity), the North China Plain (NCP), East China (EastC), South China (SouthC), and other regions (Others).
From Fig. 8 and Table 3 we can see that the NCP was the most important source region. The average contribution of the NCP region to the PM2.5 concentrations in Wuhan reached 38.3% and 92.3 μg m−3 during the stages with rapidly increasing PM2.5 concentrations in the five episodes—especially Ep. 4 and Ep. 5, which showed concentration contributions of 105.1 μg m−3 and 182.1 μg m−3, respectively. If the contributions from the NCP were removed, the numbers of moderate and heavy pollution days in Wuhan during January 2014 were reduced by 4 days and 9 days, respectively. This result highlights the important impact of regional haze pollution over the NCP on the formation of haze episodes in Wuhan. Special attention should be paid to the NCP region with regards to mitigating and controlling haze pollution on a nationwide scale.
As described in section 3.2, the rates of decrease in PM2.5 concentrations in Wuhan in the five episodes were much slower than those observed in Beijing. This phenomenon was a key factor for connecting the five episodes to forming the haze pollution lasting almost an entire month in Wuhan. We investigated the characteristics of the meteorological conditions during the later stages. Figures 9a and c and 10a and c show the spatial distributions of the sea level pressure, wind fields and PM2.5 values. There were mainly two types of circulation fields found to be favorable to the decrease in the PM2.5 concentrations during the five episodes over Wuhan.
Figure 9. The simulated sea-level weather patterns and wind fields at (a) 1100 LST 3 January in the first modeling domain (D1), (b) 1100 LST 4 January in the second modeling domain (D2), (c) 1600 LST 10 January in D1, and (d) 1900 LST 11 January in D2.
Figure 10. The distribution of average simulated PM2.5 concentrations (units: μg m−3) and winds during the last two days of the haze episodes on (a) 3 January in the first modeling domain (D1) and (b) 4 January in the second modeling domain (D2) of episode 1 (Ep. 1), and (c) 10 January in D1 and 11 January in D2 of episode 2 (Ep. 2). The black dots represent Wuhan.
One situation, occurring in Ep. 1 and Ep. 4, was that prevailing northerly winds that dominated the whole of Central and East China with a strong high-pressure system from Siberia and Mongolia area moving southeastward. With the dry and clean air toward Wuhan, air pollutants dispersed partly. Ye et al. (2015) found that this typical atmospheric circulation reduces pollutant concentrations effectively over Beijing at the leading edge of the northerly cold advection in wintertime. Figure 4 also shows a similar situation in which the hourly PM2.5 concentrations in Beijing experienced a sharp decrease from 205 μg m−3 to 18 μg m−3 within 8 h and remained at values lower than 20 μg m−3 under the influence of northerly winds with daily WSs of approximately 2.3–3.5 m s−1 on 7 and 8 January. The second situation, occurring in Ep. 2, Ep. 3 and Ep. 5, was that a high-pressure system stagnated over eastern coastal areas of China and resultant regional easterly winds were conducive to removing the particle concentrations in Wuhan. This second typical situation is tightly linked to the mitigation of air pollution in EastC under the control of clean maritime airstreams (Shu et al., 2017). However, these two strong circulation fields did not lead to the expected decrease in Wuhan’s PM2.5 concentrations.
The main reason for the ineffectiveness of the two strong atmospheric conditions in removing pollutants from Wuhan was the city’s geographic limitations—specifically, the city being located in the center of regional haze pollution covering large areas of China (Fig. 2). When the clean air brought by the strong atmospheric circulation system was close to Wuhan, the circulation system often changed. As shown in Fig. 9, Wuhan was in front of the surface high on 3 January and was behind the offshore high-pressure system on 10 January, and then controlled by sparse isobars in the following days. The transitions of weather patterns were also found in the other episodes. Therefore, winds shifted from a northerly direction to a weak northeasterly one in Ep. 1, to a weak northeasterly one in Ep. 4, and to a weak southerly one in Ep. 5, respectively shown in Fig. 10 and Fig. S5. Additionally, prevailing easterly winds that were also carrying clean air weakened in Ep. 2 and Ep. 3. The results suggest that the change in the circulation system led to the end of the prevailing northerly winds and sea winds. By comparison, the high PM2.5 concentrations in Beijing could be eliminated immediately by the strong northerly winds due to its location at the edge of the regional haze (see Fig. 2) and lasted shorter (3–7 days) than that (4–10 days) in Wuhan. On the other hand, the variations of the PM2.5 concentrations of the two cities were strongly related under the influences of cold air from Siberia. From Fig. 5, the WS gradually decreased in the later stages of the episodes from the ground to a height of approximately 1.5 km, and the WDs shifted rapidly on the last day of the episodes on 4, 11, 16, 21 and 29 January. Referring to Ep. 2 as an example, the evolution of the PM2.5 pollution under weak winds is shown in Video S1 and Video S2. The stable weather conditions and weak winds were responsible for the accumulation of air pollutants and the maintenance of high PM2.5 concentrations in Wuhan during the later stages of the episodes. This would have been another key factor for the 30 haze days seen in Wuhan, as well as the long-range regional transport during the early stages of the episodes.
Figure S5. The distribution of the PM2.5 concentrations (units: μg m−3) and winds (units: m s−1) during the last two days of haze sub-episodes on (a) 15 January in the first modeling domain (D1) and (b) 16 January in the second modeling domain (D2) of sub-episode 3 (Ep.3); (c) 20 January in D1 and (d) 21 January in D2 of sub-episode 4 (Ep. 4); and (e) 28 January in D1 and (f) 29 January in D2 of sub-episode 5 (Ep. 5). The black dots represent Wuhan.
Additionally, there were a few cases that were not related to the regional transport of particles from the NCP, but rather to the transport from SouthC. As shown in Fig. 4, southerly and southeasterly winds occurred in Wuhan in Ep. 5 from 22 to 24 January (see Ep. 5a) and 30 and 31 January (see Ep. 5b), with daily average winds of approximately 2.6–3.6 m s−1 and 2.5–3.3 m s−1 and hourly peaks of 4.6 and 4.9 m s−1, respectively. According to the surface atmospheric circulation (Fig. S4), SouthC was controlled by a similar atmospheric circulation during these two periods. The low-pressure system was located in Southwest China, and the high-pressure system was in the East China Sea. Figures 5 and 11 show prevailing and persistent southerly winds were observed over SouthC and Wuhan from the ground to a height of 2.5 km. On 24 and 30 January, polluted air masses in SouthC with PM2.5 concentrations higher than 150 μg m−3 and 300 μg m−3, respectively, were transported to the Wuhan area by strong southerly winds. This further highlights the severity and complexity of PM2.5 pollution in Wuhan—especially pertaining to the city’s centrality among multiple city clusters. The variation in the PM2.5 pollution over Wuhan during the wintertime is strongly associated with the regional PM2.5 pollution over the extended areas of China, which makes pollution control in Wuhan more complex and difficult.
Figure 11. The distribution of simulated PM2.5 concentrations (units: μg m−3) and wind fields in the vertical direction from South China to Wuhan via Changsha in Hunan Province at (a) 0300 LST 24 January and (b) 0000 LST 30 January.
Source-tagged modeling analyses were also conducted during the later stages of the five episodes. As shown in Fig. 8 and Table 3, the emissions from Wuhan and its surrounding cities contributed approximately 42.9%–84.8% of the PM2.5 concentrations during these periods, thus becoming the dominant sources of the PM2.5 pollution in Wuhan. In addition, the emissions from the NCP had important impacts on Wuhan’s PM2.5 concentrations during the later stages of Ep. 1 and Ep. 4, with average contributions of 24.1% and 29.0% respectively. Furthermore, the emissions from East China had an important influence during the later stages of Ep. 2 and Ep. 5, with average contributions of 21.0% and 10.7% respectively. Moreover, South China played a key role in the occurrence of haze days over Wuhan in Ep. 5a and Ep. 5b, contributing 30.3% and 31.8% of the PM2.5 concentrations respectively. The southern adjacent cities such as Xianning and Huangshi, contributed 31.1% and 33.0% with regional southerly winds. The contributions from local emissions were 30.9 μg m−3 and 48.9 μg m−3 or < 30%, respectively. The results above confirm the important roles played by the emissions from Wuhan and its surrounding cities in retaining high PM2.5 concentrations in Wuhan under weak winds and stable weather conditions during the later stages of the episodes. Both the high pollutant concentrations in source regions and the conducive wind field (e.g., relatively strong WS) are key factors in the occurrence of pollutant transport to Wuhan. Furthermore, the results also suggest that the haze pollution in Wuhan was aggravated by complex regional sources, including from the NCP, EastC and SouthC, and AdjCity. These key source regions could change rapidly with the meteorological conditions because of Wuhan’s location in the center of the regional haze pollution of China in that month. The change in the WD could only change the source regions of PM2.5 pollution in Wuhan, but it proved difficult to bring clean air quickly. Therefore, pollution control in Wuhan is a more complex issue compared to other regions, which raises concerns for rolling out effective national pollution control policies considering China’s diverse geography and regional climates.
|WS (m s−1)||2.4||3.3||0.9||0.33||34.2||53.2|
|WS (m s−1)||2.4||3.0||0.6||0.63||26.0||45.3|
|WS (m s−1)||1.9||3.2||1.3||0.71||58.3||62.4|
|WS (m s−1)||2.0||2.9||0.8||0.32||36.3||59.6|