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Open AccessArticle

Contribution of Regional PM2.5 Transport to Air Pollution Enhanced by Sub-Basin Topography: A Modeling Case over Central China

1
Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, PREMIC, Nanjing University of Information Science&Technology, Nanjing 210044, China
2
Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2020, 11(11), 1258; https://doi.org/10.3390/atmos11111258
Received: 29 October 2020 / Revised: 19 November 2020 / Accepted: 20 November 2020 / Published: 22 November 2020
(This article belongs to the Special Issue Aerosol Pollution in Asia)

Abstract

The Twain-Hu basin (THB), covering the lower plain of Hubei and Hunan provinces in Central China, has experienced severe air pollution in recent years. However, the terrain effects of such sub-basin on air quality over the THB have been incomprehensibly understood. A heavy PM2.5 pollution event occurred over the THB during 4–10 January 2019. By using the observations and WRF-Chem simulations, we investigated the underlying mechanisms of sub-basin effects on the air pollution with several sensitivity experiments. Observationally, air pollution in the western THB urban area with an average PM2.5 concentration of 189.8 μg m−3, which was more serious than the eastern urban area with the average PM2.5 concentration of 106.3 μg m−3, reflecting a different influence of topography on air pollution over the THB. Simulation results revealed that the terrain effect can contribute 12.0% to increasing the PM2.5 concentrations in the western THB, but slightly mitigate the pollution extent in the eastern THB with the contribution of −4.6% to PM2.5 during the heavy pollution episode. In particular, the sub-basin terrain was conducive to the accumulation of PM2.5 by regional transport with the contribution of 39.1 %, and contrarily lowered its local pollution by −57.0% via the enhanced atmospheric boundary layer height and ventilation coefficients. Given a heavy air pollution episode occurring over the THB, such inverse contribution of terrain effects reflected a unique importance of sub-basin topography in regional transport of air pollutants for air pollution in central China.
Keywords: sub-basin effect; PM2.5 pollution; Twain-Hu basin; regional transport; WRF-Chem sub-basin effect; PM2.5 pollution; Twain-Hu basin; regional transport; WRF-Chem

1. Introduction

For the high PM2.5 concentrations (i.e., the atmospheric particles with aerodynamic diameters equal to or less than 2.5 µm) in ambient atmosphere [1,2] and its negative effects on human health [3,4], PM2.5 variations [5,6], causes [7,8], and the impacts on environment and climate [9,10] have been intensively studied in recent years.
Anthropogenic emissions of air pollutants are the primary driver for the increase in PM2.5 concentrations [11,12], and unfavorable meteorological conditions are the major driver to exacerbate air pollution [13,14,15]. Four regions are closely associated with heavy air pollution on account of their high emission intensity—the North China Plain (NCP) [16,17], the Yangtze River Delta (YRD) [18,19] in East China, the Pearl River Delta (PRD) [20,21] in South China, and Sichuan Basin (SCB) [22,23,24] in Southwest China. Adverse meteorological conditions, including weak surface winds, temperature inversion layer, and low boundary layer height, are conducive to the formation of air pollution [25]. The Chinese government has implemented a series of tailored strategies to restrict pollutant emissions [26,27], Clean Air Action for instance, to improve the atmospheric environment, or utilized wind corridors to better disperse air pollutants [28,29]. Nonetheless, compared to the anthropogenic emissions and meteorological conditions, less attention was paid to the effects of topography on air pollution.
In fact, terrain could change the meteorological conditions further influencing the air quality. Atmospheric circulation changes induced by climate warming of Tibetan Plateau (TP) result in the high frequency of air pollution in central-eastern China [30]. Moreover, it also accentuates PM2.5 accumulations to the SCB with the impact of TP on mid-latitude westerly winds [31]. The thermal effect of the Loess Plateau can modulate the boundary layer structure to suppress the mixed layer growth and then increase air pollutants [32]. Meanwhile, the topographic effects of SCB—a unique deep basin over China intensifies the haze pollution owing to the reducing wind speed and boundary layer height, as well as the raising air temperature and humidity [31]. The lack of ventilation in the Valley of Mexico leads to high levels of air pollutants [33]. However, previous studies have not comprehensively explored the ‘sub-basin’ effects on air pollution, which should also be taken into account for polices of mitigating air pollution.
The Twain-Hu basin (THB), characterized by a sub-basin terrain, with altitudes of ~200 m in a.s.l. over central China (Figure 1a), has become a new center of air pollution [34] due to its rapid urbanization and dense population. Given the important role of topography in air quality, the reasons of PM2.5 pollution over the sub-basin terrain of THB, should be worth investigating. Moreover, due to THB’s unique geographical location (Figure 1), air pollutants of the NCP can be transported easily to the THB driven by prevailing northerly winds of East Asian monsoons in the wintertime [35]. Meanwhile, polluted parcels would be trapped over THB given several mountains locating at the south of THB. Thus, the extent of the terrain impacts on the air pollutants, with regard to the regional transport and local pollution, needs a quantitative assessment. Moreover, how the sub-basin terrain affecting the thermal and dynamic conditions of meteorology and its relationship to air pollution also need to be further investigated.
This paper is organized as follows: Section 2 introduces observation data, method, and numerical experiments conducted in this paper; Section 3 describes the overall heavy air pollution event happened in the THB; Section 4 presents the results of the terrain effects on changes of the air pollutants and meteorology. Section 5 manifests the brief conclusions.

2. Data and Methods

2.1. Data

Hourly surface PM2.5 dataset over the THB, mainly including eight cities of Xiangyang (XY), Jingmen (JM), Jingzhou (JZ), Changde (CD), Suizhou (SZ), Xiaogan (XG), Wuhan (WH), and Xianning (XN), with terrain altitudes less than 200 m, was collected from the Chinese Ministry of Environmental Protection to discuss the heavy air pollution.
The 1° × 1° global final (FNL) reanalysis dataset from the National Center for Environment Prediction (NCEP), with a time resolution of 6 h, was used to be the initial and boundary fields of meteorological conditions for the numerical modeling. Based on observations, the vertical profile specified in chemistry routines was utilized to be the initial and lateral boundary conditions for chemistry.
Local time in China (UTC + 8:00 h) was used in this study.

2.2. Method of Filling the Terrain

In this study, we used the method of Zhang et al. 2018 [31] to fill the THB with equation:
H X = H W ( H W H E ) L o n X L o n W L o n E L o n W
where HX is the terrain height, and Lon is the longitude. Subscripts X, W, and E represent the location of filled point, west boundary of the basin, and east boundary of the high mountains, respectively. The basin-filled terrain was displayed in Figure 1c.

2.3. Model Configuration and Validation

In this study, the online-coupled WRF-Chem simulation [37] was conducted to investigate the terrain effects of the THB on the air pollution event. The simulated time was from 4 January to 10 January 2019, with hourly model outputs, and the first 24 h are considered as the spin-up. The physics parameterization schemes used comprised the Lin microphysics scheme [38], the RRTM long-wave radiation scheme [39], the Goddard shortwave scheme [40], the Mesoscale Model (MM5) similarity surface layer, the YSU boundary layer scheme [41], and the unified Noah land surface scheme [42]. In addition, the CBMZ mechanism [43,44] was employed to simulate air pollutant concentrations by using Multi-resolution Emission Inventory of China of 2016 (MEIC; http://www.meicmodel.org/; last accessed on 20 Nov. 2020). Detailed information of model configuration can be found in Hu et al., 2020 [45].
Some statistical metrics involving the correlation coefficient (R), the root mean square error (RMSE), mean bias (MB) and normalized mean bias (NMB) were calculated to evaluate the simulation results. As shown in Table 1, the simulation results reasonably captured the observed changes of meteorological parameters and PM2.5 concentrations, which therefore could be used to further study the terrain effects of the THB on air pollution event.

2.4. Numerical Experiments

In order to investigate the terrain effects on air pollution in the THB, a series of numerical experiments including E1 (control experiment, CE), E2 (transport experiment, TE), E3 (filling-hgt experiment, FHE) via filled basin (Figure 1c), and E4 (filling-hgt & transport experiment, FHE&TE) were evaluated, respectively. The differences between E1 and E3 (i.e., CE minus FHE) represent the impacts of topography on total PM2.5 concentrations, the experiments of E2 with E4 can evaluate the terrain influence on regional PM2.5 transport from upstream regions to the THB. Moreover, the potential effects of topographic effects on local PM2.5 pollution over THB can be identified by the differences between (E1 minus E2) and (E3 minus E4). More detailed information can be found in Table 2.

3. Descriptions of a Heavy Air Pollution Episode

As shown in Figure 2, the THB region suffered a heavy air pollution from 4 January to 10 January 2019. All eight cities experienced high PM2.5 concentrations exceeding 150 μg m−3 with longer duration time for the western cities (XY, JM, JZ, and CD in Figure 1) compared with those in the eastern cities (SZ, XG, WH, and XN in Figure 1). During the episode process, the mean PM2.5 concentrations in the western and eastern cities were 189.8 and 106.3 μg m−3, respectively, both well above the ‘polluted’ threshold of 75 μg m−3 following the ambient air quality standards of China (GB3095-2012). In addition to investigate the terrain effect on the overall PM2.5 concentrations, how sub-basin terrain affecting the regional transport of PM2.5 and its local pollution needs a quantitative evaluation, considering the special geographical location of THB.

4. Results and Discussion

4.1. Topographic Effects on the PM2.5 Change in the THB

4.1.1. Potential Effects on Overall Air Quality to the THB

In order to explore the sub-basin effects on the air quality over the THB, we conducted the simulation experiments in real and changed terrain (E1 and E3 in Table 2). Figure 3 displayed the spatial distribution of PM2.5 concentrations and wind fields averaged from 4 January to 10 January 2019 in the aforementioned simulations. Overall, the simulated PM2.5 pollution was more serious in the western cities compared with the eastern cities (Figure 3a), consistent with the observations (Figure 2). By comparison of Figure 3a,b, the sub-basin terrain was more conducive to the accumulation of regionally transported PM2.5 from upwind areas to the western cities in view of the big terrain gap of the northwestern THB and obvious terrain blocking of the southwestern THB (Figure 1).
Figure 4 further illustrates the differences of near-surface PM2.5 concentrations between the E1 and E3, which further revealed that such sub-basin terrain could deteriorate the air quality with the increase in PM2.5 by 10–40 μg m−3 in the western cities. It was worth noting that the topography could slightly mitigate the air pollution in small areas of eastern cities with the PM2.5 decrease by 0–10 μg m−3, mostly because of the topographic block of the Dabie Mountain (Figure 1) weakening the invasion of air pollutants from outside, considering horizontal transport of PM2.5 was the dominant process in the long-range transport from the upwind sources over northern China to the receptor region of THB over central China [45]. Unexpectedly, the sub-basin terrain could also worsen the air quality of downwind areas which located in the southwest to the THB with increasing PM2.5 concentrations by 30–50 μg m−3.
On average, the sub-basin topography forced an increase in PM2.5 concentrations by 17.4 μg m−3, corresponding to 12.0% in contribution proportion in the western THB (Table S1), while the PM2.5 level in the eastern THB was contrarily alleviated with the contribution of −4.6 %. Overall, the sub-basin topography played an important role in worsening the air quality of the THB with an average contribution of 5.2% in the typical eight cities.
Vertically, the PM2.5 concentrations averaged regionally over the THB, shown nonlinear decreases with height in both of the experiments, E1 and E3 (Figure 5). Moreover, since the effect of sub-basin topography, the PM2.5 concentrations mostly concentrated below 2.4 km in the E1 (blue dotted line), and were more obvious below 1.9 km in the E3 (purple dotted line) without the basin effect, suggesting the great influence of the sub-basin terrain exerting on the diffusion height of the air pollutants. The vertical difference of PM2.5 (red dotted line) among E1 and E3 further demonstrated that the deviation increased stably in the near the ground and then obviously went up between 0.6 and 0.9 km, after which it declined monotonously from 1.0 to 3.0 km with peak PM2.5 difference of 15.2 μg m−3 at 0.9 km, possibly due to the peak difference of PM2.5 flux (Figure S1), terrain contribution accounting for 28.1 %. As a whole, the THB’s topography could aggravate the air quality in different heights, especially at 0.9 km, in the planetary boundary layer height (PBLH).

4.1.2. Potential Effects on Regional Transport and Local Pollution of PM2.5 in the THB

Figure 6 presents the distribution of near-surface PM2.5 differences—the differences between PM2.5 concentrations simulated by the control experiments and various sensitivity experiments (Table 2) with regard to the regional transport (RT) and local pollution (LP) respectively. It was found that the sub-basin terrain was generally in favor of the PM2.5 accumulation of RT to THB with notable PM2.5 increase by 5 to 60 μg m-3 from east to west. The sub-basin terrain contributed about 39.1% to the RT of PM2.5 (Table S2), with higher contribution in the western cities (48.6 %) compared with the eastern cities (29.0 %), confirming that the western cities could be more influenced by regional PM2.5 transport from NCP triggered by East Asian winter monsoon whereas led to the mitigation of PM2.5 pollution in the eastern cities (Figure 4) under such basin topography—the several mountains in the south and Dabie Mountain in the northeast (Figure 1a) that blocked the dispersion of air pollutants in the THB and meantime moderated the invasion of air pollutants from the outside to the eastern cities, respectively. In particular, such sub-basin topography could unexpectedly alleviate the PM2.5 concentrations of LP by −45–−5 μg m−3 during the heavy pollution process over the most areas of the THB, with contributions of 57.0% (58.1% and 55.9% in the western and eastern cities, respectively) (Table S2).
Given that the important role of the sub-basin terrain in PM2.5 of regional transport to the THB, we further investigated the terrain contribution to the RT of PM2.5 under different PM2.5 pollution levels: light air pollution (75 μg m−3 ≤ PM2.5 < 150 μg m−3) and heavy air pollution (PM2.5 ≥ 150 μg m−3), according to the ambient air quality standards of China (GB3095-2012). Generally, the sub-basin topography contributed 48.3% to the RT of PM2.5 in the light pollution level over THB, while such contribution slightly descended to 41.8% in the heavy pollution level, which implied the prevailing role of the sub-basin terrain during the air pollution process over the THB.

4.2. Underlying Mechanisms of Sub-Basin Terrain Influencing the Air Pollution Event

Figure 7 shows the differences of meteorology including PBLH, ventilation coefficient (VC), air temperature (T) and relative humidity (RH) between the experiments E1 and E3. In real terrain, the PBLH increased by about 30–140 m over most of the THB region (Figure 7a), resulting from the elevated height of the temperature inversion layer forced by the sub-basin terrain (Figure S2). However, the spatial distribution of such increase in PBLH exhibited great differences, with higher enhancement up to 100–140 m in the eastern cities and smaller increase by 30–60 m in the western cities where the PBLH even declined by 10–30 m, which attributed to the obvious differences of PM2.5 pollution between the western and eastern cities (Figure 2 and Figure 4). Positive changes of VC, ranging from 120–360 m−2 s−2 over most of the THB in the real terrain (Figure 7b), could favor the regional pollutant transport from upwind areas to THB as well as the removal of local pollutants. Higher temperature and lower RH were contrarily noticed in real terrain, respectively (Figure 7c,d). Although enhanced temperature would accelerate chemical reaction of secondary aerosol, lower RH can effectively inactivate the secondary aerosol formation [46], leading to an offset of PM2.5 concentration changes. The obvious precipitation only happened in the end of air pollution, and THB’s terrain effect on precipitation could contribute 30.7% reducing rainfall over the THB, reflecting the decreased contribution of PM2.5 precipitation washout to air pollution by sub-basin topography, which indicated that PM2.5 concentrations can be better cleared away without sub-basin terrain.
The meteorology alteration by topography revealed the complexity of sub-basin effects on the air quality over THB and we did only investigate the terrain effect without the impact of underlying surface on the regional air pollution, which could be further studied.
Horizontal and vertical transports of PM2.5 concentrations were two major processes in this heavy air pollution episode, and the horizontal transport of PM2.5 was the dominant process in the long-range transport from the upwind sources over northern China to the receptor region of THB [45], while vertical transport was the process of vertical mixing of air pollutants including PM2.5 from the upper layer downwards to the near-surface layer leading to air pollution over the receptor region of THB, which could be more important for sub-basin effects in the local scale and a direct way.
However, it was worth noting that sub-basin terrain could mitigate the local PM2.5 pollution over THB via the increased PBLH and VC. Nevertheless, under such topography, air pollution was still exacerbated with high levels of PM2.5 persisting over THB, suggesting the important effect of sub-basin terrain on driving PM2.5 deterioration of regional (horizontal) transport to worsen the air quality in the THB, which was more significant in the western cities.

5. Conclusions

From 4 January to 10 January 2019, the THB experienced a heavy air pollution event, which was more serious in the western cities, with an average PM2.5 concentration of 189.8 μg m−3, compared with the eastern cities (the average PM2.5 of 106.3 μg m−3). In this study, four simulation experiments—including E1 (control experiment, CE), E2 (transport experiment, TE), E3 (filling-hgt experiment, FHE), and E4 (filling-hgt & transport experiment, FHE&TE)—were conducted by WRF-Chem model to evaluate the effects of the sub-basin topography on the air quality over THB, central China.
The comparison of E1 and E3 simulations revealed that the sub-basin terrain totally contributed 5.2% PM2.5 worsening the air quality of typical 8 cities over THB. In particular, such terrain can contribute 12.0% to the PM2.5 concentrations in the western cities, but slightly mitigated the air pollution in eastern cities with the contribution of −4.6 %. Vertically, the terrain effects were more substantial at around 0.9 km with the peak PM2.5 difference of 15.2 μg m−3, contributing about 28.1 %.
Differences of the E2 and E4 experiments indicated that the sub-basin terrain was conducive to the PM2.5 accumulation of RT with the average contribution of 39.1 %, which was more significant in the western cities (48.6 %) compared with the eastern cities (29.0 %), while the THB’s topography could mitigate the local PM2.5 pollution by 57.0% over THB via the increased PBLH and VC. However, the air quality was still exacerbated with high levels of PM2.5 persisting over THB, suggesting the outstanding important role of deteriorated PM2.5 of regional transport by sub-basin terrain over the THB.
This study revealed that the meteorology altered by topography can alleviate the local PM2.5 pollution over THB. However, a substantial increase in PM2.5 concentrations exceptionally reflected the important role of sub-basin topography driving increasing PM2.5 of regional transport to worsen the air quality over the THB.
Note that in this study near-surface PM2.5 concentrations were resulted from the combined action of physical and chemical processes (e.g., advection, dry deposition, turbulent diffusion, cloud and aerosol chemistry, wet scavenging, etc.) without separating every process to evaluate their difference of contribution to PM2.5 before and after filling the sub-basin terrain, which could be further studied.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4433/11/11/1258/s1, Figure S1: Vertical distribution of difference of PM2.5 flux averaged over the THB during the air pollution event in real terrain and changed terrain simulations; Figure S2: Vertical distribution of temperature averaged over the THB during the air pollution event in real terrain (E1; black dotted line) and changed terrain (E3; red dotted line) simulations; Table S1: PM2.5 change and terrain contribution based on the E1 and E3 simulations; Table S2: Terrain contribution to regional transport of PM2.5 and local pollution.

Author Contributions

Conceptualization, T.Z. and Y.B.; Data curation, X.S.; Formal analysis, W.H.; Investigation, Y.G.; Resources, L.S.; Visualization, W.H.; Writing—original draft, W.H.; Writing—review and editing, T.Z. and Y.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research has been supported by the National Natural Science Foundation of China (grant no. 41830965, 42075186, 91744209), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_0951).

Acknowledgments

This study was jointly funded by the National Natural Science Foundation of China (41830965; 42075186; 91744209), the National Key R&D Program Pilot Projects of China (2016YFC0203304), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_0951).

Conflicts of Interest

The authors declare no conflict of interest.

Data Availability

The data used in this paper can be provided by Weiyang Hu ([email protected]) upon request.

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Figure 1. (a) The geographical location of the THB surround by six mountains over China [36] with the red frame covering the WRF-Chem simulation domain; (b) the terrain heights and the locations of Xiangyang (XY), Jingmen (JM), Jingzhou (JZ), Changde (CD), Suizhou (SZ), Xiaogan (XG), Wuhan (WH), and Xianning (XN) with the black dash line indicating the lower lands (mainly less than 200 m a.s.l.) around the THB; (c) the filled THB using the method of Zhang et al., 2018 [31].
Figure 1. (a) The geographical location of the THB surround by six mountains over China [36] with the red frame covering the WRF-Chem simulation domain; (b) the terrain heights and the locations of Xiangyang (XY), Jingmen (JM), Jingzhou (JZ), Changde (CD), Suizhou (SZ), Xiaogan (XG), Wuhan (WH), and Xianning (XN) with the black dash line indicating the lower lands (mainly less than 200 m a.s.l.) around the THB; (c) the filled THB using the method of Zhang et al., 2018 [31].
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Figure 2. Hourly changes of PM2.5 concentrations at 8 cities of XY, JM, JZ, CD, SZ, XG, WH, and XN (as shown in Figure 1), with the black frame indicating the heavy air pollution event over the THB.
Figure 2. Hourly changes of PM2.5 concentrations at 8 cities of XY, JM, JZ, CD, SZ, XG, WH, and XN (as shown in Figure 1), with the black frame indicating the heavy air pollution event over the THB.
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Figure 3. Distributions of the averaged near-surface PM2.5 concentrations (color shading) and wind fields (arrows) during 4–9 January 2019 in (a) control experiment (CE) and (b) rising-hgt experiment (RHE).
Figure 3. Distributions of the averaged near-surface PM2.5 concentrations (color shading) and wind fields (arrows) during 4–9 January 2019 in (a) control experiment (CE) and (b) rising-hgt experiment (RHE).
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Figure 4. Distribution of near-surface PM2.5 difference between real terrain and changed terrain simulations (CE and RHE, respectively).
Figure 4. Distribution of near-surface PM2.5 difference between real terrain and changed terrain simulations (CE and RHE, respectively).
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Figure 5. Vertical distribution of the averaged PM2.5 levels in real terrain (E1; blue dotted line) and changed terrain (E3; purple dotted line) simulations and their difference (red dotted line).
Figure 5. Vertical distribution of the averaged PM2.5 levels in real terrain (E1; blue dotted line) and changed terrain (E3; purple dotted line) simulations and their difference (red dotted line).
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Figure 6. Distribution of near-surface PM2.5 differences concerning (a) regional transport and (b) local pollution, respectively.
Figure 6. Distribution of near-surface PM2.5 differences concerning (a) regional transport and (b) local pollution, respectively.
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Figure 7. Near-surface differences of (a) planetary boundary layer height (PBLH; m), (b) ventilation coefficient (VC; m2 s −1), (c) air temperature (T; °C), and (d) relative humidity (RH; %) between real terrain (E1) and changed terrain (E3) simulations.
Figure 7. Near-surface differences of (a) planetary boundary layer height (PBLH; m), (b) ventilation coefficient (VC; m2 s −1), (c) air temperature (T; °C), and (d) relative humidity (RH; %) between real terrain (E1) and changed terrain (E3) simulations.
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Table 1. Statistical metrics between observed and simulated meteorological parameters and PM2.5 concentrations averaged over the THB.
Table 1. Statistical metrics between observed and simulated meteorological parameters and PM2.5 concentrations averaged over the THB.
VariablesRRMSEMBNMB (%)
T2 (K)0.791.091.431.16
RH (%)0.857.62−6.71−7.51
SLP (hPa)0.931.35−1.20−0.12
WS10 (m s−1)0.680.270.063.18
PM2.5 (μg m−3)0.8027.21−6.39−6.05
Table 2. Experimental designs in this study.
Table 2. Experimental designs in this study.
ExperimentsDescription
E1Base case
E2CE with closing all anthropogenic emissions over the THB
E3CE with filling topography of the THB
E4FHE with closing all anthropogenic emissions over the THB
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