Estimation of PM_2.5 Transport Fluxes in the North China Plain and Sichuan Basin: Horizontal and Vertical Perspectives

Abstract

In this study, the PM_2.5 pollution transport budget in the atmospheric boundary layer (ABL) of Beijing–Tianjin–Hebei (BTH) and Chengdu–Chongqing (CY) was quantitatively evaluated from the perspective of horizontal and vertical exchange. Based on the aircraft meteorological data relay (AMDAR) observation data, the study found that the vertical exchange process of pollutants is mainly influenced by the combined effects of meteorological conditions and topographical factors. Meteorological factors determine the direction and intensity of the vertical exchange, while the complexity of the terrain affects the exchange pattern through local circulation and air flow convergence. The characteristics of the pollution transport budget between the BTH and CY regions show that the BTH region has a net output of pollutants throughout the year, while the CY region has a net input of pollutants. The total transport budget of the four typical representative seasons in BTH is negative. It indicated that BTH, as the region with the highest intensity of air pollution emission in China, is dominated by outward transport of air pollutants to surrounding regions. Due to the influence of topographic and meteorological conditions in the CY region, the air pollutants tend to accumulate in the basin rather than diffuse. The transport budget relationship of the four seasons is positive and the input of air pollutants can be obviously simulated. Combined with the results of the vertical wind profile, Beijing is more vulnerable to the prevailing cold air sinking in the northwest in winter, which is characterized by the inflow of the free troposphere (FT) into the ABL. As for Chongqing, it is blocked by mountains so that the gas convection at the top of the ABL is obvious. This horizontal convergence phenomenon induces upward vertical movement, which makes Chongqing show a strong characteristic of the ABL transport to the FT.

1. Introduction

In China, serious urban air pollution has attracted increasing attention due to its high frequency and adverse effects on human health, visibility and climate change. In order to reduce the occurrence of air pollution processes, the Chinese government has implemented the action plan for the prevention and control of air pollution since September in 2013, thus leading to the obvious improvement of the air quality around the country. However, regional air pollution incidents still occur occasionally. Many studies have shown that PM_2.5 can be transported for a long distance through the atmospheric circulation. It has played a crucial role in the formation, accumulation and diffusion of PM_2.5 pollution [1,2]. In addition, regional air pollution, long-distance transport and chemical transformation not only occur within the ABL but also are affected by the exchange between the ABL and the FT [3,4,5]. Therefore, it is imperative to study the characteristics of pollution transport from the perspective of exchange between the ABL and the FT.

Previous studies have revealed the transport characteristics between ABL and FT based on observational data [6,7]. Ref. [8] investigated the transport characteristics of aerosol particles from the FT to the ABL in the Arctic in summer and found that the FT is a potentially important source of aerosol particles in the boundary layer. Ref. [9] analyzed the evolution and transport mechanism of the residual layer during several heavy pollution processes in Beijing using the observation data of a cloud altimeter and Doppler wind radar. It showed that the increase in PM_2.5 concentrations during the heavy pollution period in Beijing is mainly affected by the southwest transport, and the pollutants in the residual layer are transported to the near surface with the downdraft, resulting in pollution events. Ref. [10] found that the air pollutants in the FT can be entrained into the local boundary layer through cloud-induced mixing at the cloud top. Some studies focusing on the aerosol budget in the ocean boundary layer in remote areas showed that the entrainment of free atmospheric aerosol particles is the main contributor to the concentration of marine aerosol particles [11,12]. Although progress has been made, due to the lack of continuous observation data of the vertical profile of particles and wind field, the studies based on observations are not suitable for the quantitative evaluation of the vertical exchange of air pollutants between the ABL and FT under long-term conditions.

In recent years, the calculation of transboundary transport flux of pollutants based on a three-dimensional numerical model has become an important method to study the transport characteristics of air pollutants [13]. For example, ref. [14] quantitatively evaluated the horizontal net inflow and net outflow fluxes of inland and coastal cities at different altitudes in 2016 by using WRF-CAMX models and found that the largest average inflow and outflow fluxes of inland cities and coastal cities were in April and January, respectively. Ref. [15] quantitatively evaluated the transport flux results at different altitudes in Beijing in January and July of 2015 based on a simulation technique and proposed the major transport pathways. However, most studies on transport flux mainly focused on the quantitative evaluation of horizontal transport characteristics, without considering the impact of the exchange between the ABL and the FT. In addition, the variations of ABL height were also ignored, thus leading to a bias in the calculation of horizontal transport flux within the ABL. In general, it is necessary to further study the relationship between pollution budget within the ABL from the perspective of horizontal and vertical exchange.

This study aims to establish methods for calculating horizontal transport fluxes within the atmospheric boundary layer and vertical exchanges between the atmospheric boundary layer and the free atmosphere. By integrating the two dimensions of horizontal transport and vertical exchange, it constructs a budget equation for the transport and exchange of pollutants within the atmospheric boundary layer. This will enable the quantification of the transport and exchange of pollutants in the atmospheric boundary layer of typical regions. Firstly, considering the dynamic changes of the ABL, the spatiotemporal changes of PM_2.5 horizontal transport flux within the ABL in BTH and CY are comprehensively discussed. Secondly, considering the influence of the top slope and the height of the ABL over time on the local boundary layer and the surrounding atmosphere, based on the vertical exchange flux estimation scheme, the results of the vertical exchange flux between the ABL and the FT in the two regions are quantitatively evaluated. Finally, based on the horizontal and vertical flux calculation schemes, the pollution transport budget relationship between the two regions is studied. The research results will contribute to an understanding of air pollutant transport within the ABL, as well as the exchange between the ABL and FT. This could provide support for the improvement of regional joint prevention and control mechanisms of our country.

2. Data and Methods

2.1. Data Collection

Hourly surface PM_2.5 concentration data were obtained from the China National Environmental Monitoring Center (CNEMC). The website is https://air.cnemc.cn:18007/ (accessed on 12 December 2024). These data were used to evaluate the accuracy of the air quality model simulation. Hourly surface meteorological parameters, including wind speed at 10 m (WS10), temperature at 2 m (T2), and relative humidity (RH), were obtained from the Chinese National Meteorological Center. Vertical meteorological parameters were obtained from the aircraft meteorological data relay (AMDAR), which includes information about the state of the aircraft flight, latitude and longitude, WS, WD and temperature (T). AMDAR data has the advantage of high spatial and temporal resolution and is considered as effective supplementary data for studying the vertical structure of the ABL [16]. This study uses AMDAR data as meteorological observation data to identify the meteorological factors that affect the vertical exchange characteristics of typical cities in the two regions.

2.2. Model Configuration

In this study, simulations were conducted during January, April, July and October of 2018, serving as the representative months for winter, spring, summer and autumn, respectively. Weather Research and Forecasting (WRF, version 3.5.1) and Comprehensive Air Quality Model with Extensions (CAMx, version 6.3.0) models were used to simulate meteorological conditions and variations in PM_2.5 concentrations [17] and for subsequent calculations of the PM_2.5 horizontal transport flux and vertical exchange flux for study areas. The background fields required for the WRF simulation are derived from the 1° × 1°, 6 h resolution global analysis data provided by the National Centers for Environmental Prediction (NECP) of the United States. The land use data and topographic data used are the global topographic and land use data from the United States Geological Survey (USGS). The detailed configuration of the WRF parameterization schemes for the Beijing–Tianjin–Hebei region and the Chengdu–Chongqing region is shown in

Table 1. Parameterization schemes setting of WRF model in the BTH region.

Parametric Scheme Category	Parametric Variable	Solution Selection
Projection scheme	map_proj	Lambert
Nested scheme	feedback	Two-way feedback
Microphysical scheme	mp_physics	Lin
Shortwave radiation scheme	ra_sw_physics	Dudhia
Longwave radiation scheme	ra_lw_physics	RRTM
Land surface process scheme	sf_surface_physics	Noah
Cumulus cloud parameterization scheme	cu_physics	Greel-3D
Boundary layer scheme	bl_pbl_physics	YSU

and

Table 2. Parameterization schemes setting of WRF model in the CY region.

Parametric Scheme Category	Parametric Variable	Solution Selection
Projection scheme	map_proj	Lambert
Nested scheme	feedback	Two-way feedback
Microphysical scheme	mp_physics	WSM3
Shortwave radiation scheme	ra_sw_physics	Dudhia
Longwave radiation scheme	ra_lw_physics	RRTM
Land surface process scheme	sf_surface_physics	SLAB
Cumulus cloud parameterization scheme	cu_physics	Greel-3D
Boundary layer scheme	bl_pbl_physics	MYJ

.

A two-level nested modeling domain was chosen with a spatial resolution of 36 km × 36 km for domain 1 (Do1) and 12 km × 12 km for domain 2 (Do2), as shown in Figure 1. Do1 covered most of China and part of East Asia and Southeast Asia. Notably, in order to ensure the accuracy of the simulation, Do2 was set for BTH and CY separately. The details of other model configurations applied in this study can be found in [18]. Furthermore, we used the high-resolution emission inventory for the BTH region, which was developed in our previous work [19,20], and updated the inventory for the year of 2018. Emission data outside the BTH region were obtained from the Multi-resolution Emission Inventory of China (MEIC) with a resolution of 0.5° × 0.5°. The website is http://www.meicmodel.org/ (accessed on 28 September 2024). The emission inventory of air pollutants adopts an estimation method that combines activity level data with emission factors. The emission inventory of air pollutants mainly covers five sectors: power, industry, civil use, transportation and agriculture. The species included are SO₂, NOx, CO, NMVOC, NH₃, PM_2.5, PM₁₀, BC, OC and CO₂.

2.3. Model Performance Evaluation

In order to evaluate the performance of the WRF-CAMX model, correlation coefficient (COR), normalized mean deviation (NMB) and normalized mean error (NME) are selected as statistical parameters, and their abbreviations are defined as follows:

$$COR={\displaystyle \frac{{\sum }_{i=1}^N\left(Sim\left(i\right)-\overline{Sim}\right)\left(Obs\left(i\right)-\overline{Obs}\right)}{\sqrt{{\sum }_{i=1}^N{\left(Sim\left(i\right)-\overline{Sim}\right)}^2{\sum }_{i=1}^N{\left(Obs\left(i\right)-\overline{Obs}\right)}^2}}}$$

(1)

$$NMB={\displaystyle \frac{{\sum }_{i=1}^N\left(Sim\left(i\right)-Obs\left(i\right)\right)}{{\sum }_{i=1}^{N}Obs\left(i\right)}}\times 100\%$$

(2)

$$NME={\displaystyle \frac{{\sum }_{i=1}^N\left|Sim\left(i\right)-Obs\left(i\right)\right|}{{\sum }_{i=1}^{N}Obs\left(i\right)}}\times 100\%$$

(3)

In this study, the meteorological observation data of two stations in Beijing and Chengdu are selected for verification of the two cities. The meteorological parameters are 10 m wind speed, relative humidity and 2 m temperature on the ground. As shown in

Table 3. Comparison of simulated and observed meteorology parameters in Beijing and Chengdu.

City-Meteorological Parameters	Date	COR	NMB	NME
Beijing-Temperature unit: k	January	0.88	−0.10%	0.28%
	April	0.81	0.30%	0.48%
	July	0.73	0.80%	0.93%
	October	0.94	−0.20%	0.38%
Beijing-RH unit: %	January	0.83	−12.33%	18.49%
	April	0.82	−22.41%	23.31%
	July	0.75	−17.34%	21.16%
	October	0.90	−10.04%	14.03%
Beijing-WS10 unit: m/s	January	0.77	20.83%	30.28%
	April	0.62	28.32%	36.12%
	July	0.51	33.56%	43.86%
	October	0.78	31.05%	41.33%
Chengdu-Temperature unit: k	January	0.84	−0.18%	0.40%
	April	0.74	−0.51%	0.93%
	July	0.68	−0.87%	1.04%
	October	0.89	−0.26%	0.30%
Chengdu-RH unit: %	January	0.71	−19.25%	19.87%
	April	0.70	−21.53%	25.77%
	July	0.68	−24.18%	35.93%
	October	0.75	−14.46%	14.68%
Chengdu-WS10 unit: m/s	January	0.73	32.53%	37.77%
	April	0.66	35.87%	45.24%
	July	0.50	46.31%	56.22%
	October	0.67	37.89%	40.98%

, the wind speed simulation results of the two cities are higher than the observation results, and the simulation results of the North China Plain are significantly better than those of the Sichuan Basin. The correlations of January, April, July and October in Beijing are 0.77, 0.62, 0.51 and 0.78, respectively, and they are 0.73, 0.66, 0.50 and 0.67 in Chengdu. The error analysis indexes NMB and NME of Beijing and Chengdu are 20.83~33.56%, 30.28~43.86% and 32.53~46.31%, 37.77~56.22%, respectively. Compared with the research results of previous studies, it is found that the wind speed simulated by the WRF model is generally overestimated [21], in which the wind speed deviation of basin cities is significantly higher than that of plain cities, which is mainly caused by the difference of local terrain conditions and the insufficient resolution of urban underlying surface data [22], but the simulation results are basically consistent with the previous research results [23]. The temperature simulations are much better than the wind speed, which could well reproduce the variations of observed temperature. Relative humidity can promote the suction growth effect of particulate matter and increase its specific surface area. The verification results show that the COR of RH in two cities ranges from 0.68 to 0.90, with NMB and NME at −24.18%~−10.04% and 14.03%~35.93%, respectively.

Table 4. Verification of PM_2.5 (unit: μg/m³) simulated concentration in typical cities.

City	Date	COR	NMB	NME
Beijing	January	0.84	−6.17%	34.13%
	April	0.72	−10.30%	35.48%
	July	0.68	−12.80%	38.93%
	October	0.89	−1.54%	30.68%
Shijiazhuang	January	0.74	−24.82%	31.07%
	April	0.70	−27.41%	33.34%
	July	0.64	−30.43%	36.61%
	October	0.72	−21.60%	40.77%
Chongqing	January	0.69	14.29%	32.29%
	April	0.62	−24.23%	35.21%
	July	0.52	−37.65%	40.68%
	October	0.73	−10.91%	34.97%
Chengdu	January	0.70	−19.60%	31.05%
	April	0.64	−21.51%	33.39%
	July	0.55	−24.78%	38.83%
	October	0.71	−22.29%	34.79%

shows the comparison and verification results of simulated and observed PM_2.5 concentrations in four representative months in typical cities (Beijing, Shijiazhuang, Chongqing and Chengdu). The results show that the simulated values of PM_2.5 concentrations in the four cities are highly correlated with the observed values, of which the COR in Beijing is 0.68~0.89, that in Shijiazhuang is 0.64~0.74, that in Chongqing is 0.52~0.73 and that in Chengdu is 0.55~0.71. The error analysis results show that, for the PM_2.5 concentration simulated based on the CAMX model, the observed values of NMB, NME range from −37.65~−1.54%, 30.68~40.77%, respectively. In conclusion, the error analysis of the WRF meteorological model and CAMx model are within a reasonable range, and the simulation results can be used for subsequent research. The atmospheric boundary layer (ABL) height plays a critical role in accurately assessing transport flux budgets. To validate the reliability of ABL height estimates, we compared daily mean ABL heights derived from two methods: measurements obtained using a single-lens ceilometer at the Beijing Tower Station (39.98° N, 116.39° E) in January 2018 and calculations based on the potential temperature profile approach. As illustrated in Figure 2, the ABL heights determined by the potential temperature profile method exhibited strong agreement with ceilometer observations, with a correlation coefficient of 0.92. Additionally, the normalized mean bias (NMB) and normalized mean error (NME) were 4.57% and 12.67%, respectively. The minimal discrepancies and high correlation confirm the robustness of ABL height estimations using AMDAR data.

The simulation results have certain uncertainties, mainly in the following aspects: (1) the USGS terrain data used in the WRF model simulation has a certain difference from the actual terrain height, and there is a certain error in the underlying surface compared to the real situation [24]; (2) the initial field data input in WRF is the FNL (the final operational global analysis data) reanalysis meteorological data, which cannot completely replace the real meteorological field; (3) different boundary layer schemes have different turbulent mixing methods and calculation methods for atmospheric boundary layer height, which leads to certain differences in the simulation of physical quantities such as heat, water vapor and momentum in the vertical direction, resulting in certain errors in the model’s simulation of the atmospheric boundary layer height [25]; (4) the error of AMDAR data mainly comes from sensor measurement deviations (such as temperature and wind speed affected by flight thermal effects and navigation accuracy), spatial representativeness limitations (uneven route coverage, sparse low-altitude data), discontinuous time sampling, data compression or loss during transmission and systematic deviations caused by differences in aircraft models and flight stages. All these error factors will affect the uncertainty of the entire research results; (5) for the entire ABL-FT exchange flux, the correlation coefficient and relative deviation between the simulation results and the observational results are approximately 0.67 (p < 0.01) and 45%, respectively. The differences in the exchange fluxes mainly result from the two components of horizontal and vertical motions at the top of the boundary layer [26].

2.4. Horizontal Transport Flux Within the ABL

In this study, the WRF-CAMX model is used to study the trans regional transport flux. First, the target area should be selected, and then its adjacent areas should be divided according to China’s administrative boundary demarcation method. The purple curve represents the administrative boundary line of area A and area B (Figure 3). The GIS tool was used to pick up the adjacent grids at the red line as the calculation object of cross-border transport flux.

PM_2.5 flux is defined as the mass of PM_2.5 flowing through the vertical section in a certain period of time. Based on the WRF-CAMX model, the vertical plane is discretized into multiple vertical grid elements. In order to calculate the PM_2.5 flux in the ABL, according to the hourly boundary layer height data of each discrete grid simulated by the model, the PM_2.5 concentration and meteorological elements of each grid below the corresponding boundary layer height are extracted, and the cross-border transport flux within the boundary layer between the target area and the surrounding adjacent areas is obtained by integration. The specific calculation formula is as follows:

$${Flux}_{horizontal}={\sum }_{k=1}^h{\sum }_{l}L\times H_k\times c\times v\times n$$

(4)

In this formulation, where h (m) represents the atmospheric boundary layer (ABL) top height, l (m) denotes the dividing line between two neighboring cities, the grid width is defined as L (m) and H_k (m) corresponds to the vertical distance separating layers k and k + 1. The PM_2.5 concentration within a vertical grid cell is given by c (μg/m³), and v signifies the wind vector. Additionally, n is the normal vector associated with the vertical grid cell. Based on the above calculation formula, PM_2.5 inflow flux, outflow flux and net flux of all tanks from the same height above the ground to the top of the ABL at the boundary line are calculated with PM_2.5 inflow target area as positive and outflow target area as negative. Net flux refers to the vector sum of PM_2.5 inflow and outflow fluxes. A positive value represents net inflow flux and a negative value represents net outflow flux.

The PM_2.5 exchange flux between the ABL and the FT was defined as the mass of PM_2.5 passing through the ABL top in a certain period of time. The quantification of ABL-FT PM_2.5 exchange flux in this study was based on a mass budget equation derived by [27], as given below:

$${Flux}_{ABL-FT}=c_h\times {\overrightarrow{U}}_h\times \overrightarrow{n}\times L\times dt-c_h\times w_h\times L^2\times dt+c_h\times L^2\times {\displaystyle \frac{\partial h}{\partial t}}\times d$$

(5)

where c is the PM_2.5 concentration, h is the ABL height, $\overrightarrow{U}$(u,v) is the horizontal wind vector, w is the vertical wind vector, n = (∂h/∂x, ∂h/∂y) is the unit normal vector perpendicular to the ABL top surface, subscript h indicates quantities at the ABL top, L is the grid width and ∂h/∂t is the temporal variation in ABLH. Positive or negative values indicate a downward or upward output of ABL PM_2.5, respectively. A more detailed description of vertical exchange fluxes can be found in the previous studies [28].

2.5. ABL-FT PM_2.5 Exchange Flux

The total ABL-FT vertical exchange flux within the boundary layer can be further denoted as Flux_vertical, given by:

$${Flux}_{vertical}={\sum }_{i=1}^n{Flux}_{i(u,v,w)}+{\sum }_{i=1}^n{Flux}_{i\left(h\right)}$$

(6)

where n is the number of target city grids. The vertical exchange flux can then be clearly categorized into two components: one is determined by motion perpendicular to the slope of the ABL top, which transfers mass across the ABL surface; while the other is the coiling effect caused by the change in boundary layer height, which allows PM_2.5 entrainment into the ABL or retention in the FT. It is worth noting that, due to the significant deviation of the WRF model in simulating the nocturnal boundary layer, especially its characteristic low-level atmospheric wind speed gradually increasing slowly with height, the height of the atmospheric boundary layer has been systematically underestimated by the system [6]. We used the potential temperature profile method based on AMDAR data to calculate the ABLH, with the budget relationship within the ABL of the target city subsequently evaluated using the horizontal and vertical flux calculation methods detailed above (Figure 4).

3. Results and Discussion

3.1. Horizontal Transport Flux of Two Regions

Figure 5a–d shows the PM_2.5 inflow, outflow and net flux within the ABL between BTH and its surrounding areas. The BTH region shows seasonal PM_2.5 flux variations, acting as a pollution source with net outflow year-round. Inflow peaks in April (5.47 kt/d), outflow in April (−5.97 kt/d). January sees net inflow from Shanxi (1.09 kt/d) and Inner Mongolia (0.51 kt/d), with outflow to Shandong (−1.21 kt/d); April/July show dominant Shandong inflow (1.91/1.55 kt/d) and outflow to Inner Mongolia (−2.58 kt/d in July); October features Shanxi/Shandong inflow (1.14/1.00 kt/d) and Bohai Bay outflow (−1.21 kt/d). Net flux is most negative in October (−1.08 kt/d).

For the CY region, the seasonal variation of PM_2.5 inflow and outflow fluxes is significant (Figure 5e,f). The CY region mainly receives pollution (positive net flux except April), peaking in January (1.68 kt/d). Inflow maximizes in January (6.06 kt/d), outflow in January (−4.38 kt/d). January brings significant inflow from the Yunnan–Guizhou Plateau (3.28 kt/d) and Yangtze River (1.34 kt/d), with outflow to Yunnan–Guizhou (−2.26 kt/d). Other months maintain Yunnan–Guizhou (1.41–1.49 kt/d) and Yangtze River (0.47–0.70 kt/d) inflows, with Fenwei Plain as the main outflow destination (−0.45 to −0.74 kt/d).

The differences of transport characteristics between the two regions are mainly reflected in three aspects: pollution emissions, climatic conditions and geographical location. The pollution emission intensity of the BTH region is higher than that of the surrounding areas, especially in the southern cities of Hebei, where there are many heavy industrial enterprises, the industrial structure is heavy and the pollution load is large, which has a significant impact on the pollution transmission in Shandong, Henan and other regions. Compared with the BTH region, the pollution emission intensity of the CY region is not significantly different from that of neighboring regions and is lower than that of the Triangle of Central China. From the perspective of climate conditions, the BTH region has a temperate monsoon climate, especially in autumn and winter with heavy pollution. Influenced by the northwest monsoon climate, the pollutants in the BTH region are transmitted in the southeast direction with the cold northwest wind direction, affecting the air quality in downstream areas. The CY region has a subtropical monsoon climate. Due to the blocking effect of the high mountains on the Qinghai–Tibet Plateau, the cold air is greatly weakened after crossing the Qinba Mountains. Therefore, it is difficult for the local pollutants discharged from the CY region to be transmitted downstream through the cold air in the northwest. In terms of geographical location, the BTH region is located in the north of the North China Plain. The terrain is high in the northwest and low in the southeast. The Yanshan–Taihang Mountain structure from the northwest gradually transforms to the southeast into a plain. This topographic condition is more conducive to the transport of pollutants from the northwest to the southeast. The CY region is located in the Sichuan Basin, surrounded by the Qinghai–Tibet Plateau, Daba Mountain, Huaying Mountain and Yunnan–Guizhou Plateau. The altitude around the region is mostly between 1000 m and 3000 m. the middle basin is low, with an altitude of 250 m to 750 m. Under such topographical conditions, pollutants are more likely to converge at the basin bottom, which is not conducive to the diffusion of pollutants.

3.2. Vertical Exchange Flux Between the ABL and the FT

3.2.1. Quantitative Evaluation of the Vertical Exchange Flux

Figure 6a–d show the seasonal variation of PM_2.5 vertical exchange flux between the ABL and the FT in the BTH region in January, April, July and October 2018. For January, the total net inflow, net outflow and net flux were 6.88 kt/d, −7.15 kt/d and −0.27 kt/d, respectively. From the characteristics of vertical exchange fluxes of the three subitems, the vertical term is downward input, while the BTH region as a whole has an output process from the boundary layer to the FT due to the strong horizontal divergence. For April, the total net inflow, net outflow and net flux were 13.17 kt/d, −14.11 kt/d and −0.94 kt/d, respectively. For July, the total net inflow, net outflow and net flux were 11.24 kt/d, −12.76 kt/d and −1.52 kt/d, respectively. The variation characteristics of each subitem in April and July are the same. The vertical direction shows upward output, while the horizontal direction shows horizontal convergence. As the horizontal convergence intensifies the vertical upward movement, the overall performance is that the boundary layer outputs PM_2.5 to the FT. In October, the total net inflow, net outflow and net flux were 9.08 kt/d, −9.76 kt/d and −0.68 kt/d, respectively. From the vertical exchange characteristics of the four seasons, the seasonal variation characteristics of the vertical exchange flux in the BTH region are consistent, showing a strong output from the boundary layer to the FT as a whole. From the vertical exchange intensity of the four seasons, ranking of the net inflow flux intensity in the BTH region is April > July > October > January, and that of the net outflow flux intensity is April > July > October > January. The highest value is 1.91 times and 1.97 times the lowest value, respectively.

As shown in Figure 6e,f, the total net inflow, net outflow and net flux in the CY region in January were 6.42 kt/d, −7.82 kt/d and −1.40 kt/d, respectively. For April, the total net inflow, net outflow and net flux were 10.59 kt/d, −10.08 kt/d and 0.50 kt/d, respectively. As for July, the total net inflow, net outflow and net flux were 9.09 kt/d, −8.06 kt/d and 1.02 kt/d. respectively. In October, the total net inflow, net outflow and net flux were 7.14 kt/d, −7.26 kt/d and −0.12 kt/d, respectively. From the vertical exchange characteristics of the four seasons, the seasonal variation of the vertical exchange flux in the CY region is relatively significant. In autumn and winter, it is mainly manifested as the output of the ABL to the FT, while in spring and summer, affected by the subtropical high-pressure downdraft, it is manifested as the input of the FT to the boundary layer. From the vertical exchange intensity of the four seasons, the seasonal variation characteristics of the CY region and BTH region are basically consistent. The ranking of net inflow flux intensity is April > July > October > January, and that of the net outflow flux intensity is April > July > January > October. The highest value is 1.64 times and 1.38 times the lowest value, respectively.

From the comparison results of the vertical terms of the two regions, the BTH region is positive in winter, while the CY region is negative, which indicates that the North China Plain is more easily controlled by the northwest cold high than the Sichuan Basin in winter, and the downdraft effect is significant. Ref. [29] studied the vertical exchange characteristics between the boundary layer and the free atmosphere in winter in the entire North China Plain. The results show that the vertical exchange in winter in the North China Plain and its surrounding areas is mainly controlled by the terrain, land sea characteristics and the prevailing northwest wind. The systematic subsidence of the cold high is the main reason for the transport fluxes from the FT to the ABL in the North China Plain.

As for the horizontal term, the exchange results of the two regions are consistent in winter, both of which are negative values, showing that the ABL flows out to the FT. The exchange fluxes of the two regions are positive in spring and summer, which shows that the FT flows into the ABL. Interestingly, we found that the time variation term of the ABL obtained from the simulation calculation was not significant. This is because the ABL rose in the daytime, the air in the FT was sucked into the ABL and the height of the ABL dropped in the evening. The air at the top of the ABL stayed in the FT or the residual layer, and the upward and downward fluxes offset each other.

On the whole, the vertical exchange fluxes of the four representative seasons in the BTH region are all negative, indicating that they are characterized by the outflow of the ABL into the FT. The autumn and winter in the CY region are the same as those in the BTH region, while the spring and summer are characterized by the inflow of the FT into the ABL.

3.2.2. Analysis on the Difference of Vertical Exchange Flux Results

Based on the aircraft AMDAR meteorological observation data, combined with the vertical wind field information inside and outside the ABL of Beijing and Chongqing in January 2018, this study analyzed the meteorological conditions that affect the vertical exchange characteristics of the ABL and the FT.

The ABL is an important factor affecting the vertical diffusion of pollutants. The low boundary layer suppresses the turbulent movement, resulting in the increase in the near surface pollutant concentration. In addition, the ABL plays an important role in pollution transport through the vertical exchange of aerosols between the ABL and FT. Strong turbulent mixing in the ABL couples the surface and FT, resulting in vertical transport and redistribution of pollutants. As shown in Figure 7a, the inside and outside of Beijing’s ABL in January are mainly affected by the northwest cold air. Beijing is located in the northwest of the North China Plain, which is more vulnerable to the influence of the northwest cold high prevailing in winter. In particular, the average wind speed at the top of the boundary layer is greater than 10 m/s on 2–5, 7–11, 14–18, 23–26 and 28–30 January, and the periodic invasion of the cold high-pressure system over north China will lead to systematic subsidence, which is manifested in the FT flowing into the ABL. In addition, high wind speed increases the transport intensity of PM_2.5 from the FT to the ABL, resulting in a positive monthly average vertical exchange intensity. In winter, Beijing is mainly affected by the alternating warm and humid air in the southwest and cold air in the northeast. When Beijing is affected by the warm and humid air in the southwest, the height of the ABL is generally low, and the average height of the ABL is less than 600 m. When it is invaded by the cold air in the northeast, the height of the ABL increases significantly, and the average height of the boundary layer is about 800 m. On the whole, the monthly average ABL height in winter in Beijing is maintained at about 700 m. Compared with the cold air flow in the northwest, the southwest warm and humid air flow has a lower wind speed, which reduces the friction resistance between the air flow and the ground and weakens the kinetic energy of vertical turbulence and the warmer air is very easily forms an inversion layer at high altitude, which inhibits the increase in the height of the ABL and is prone to pollution. At the same time, under such adverse weather conditions, pollutants trapped at a high altitude will penetrate the top of the boundary layer and enter the FT, which is shown as the vertical output of PM_2.5 from the ABL to the FT.

Compared with Beijing, the ABL height in Chongqing is relatively low, and the monthly average boundary layer height is less than 500 m (Figure 7b). In general, the meteorological conditions in Chongqing are more complex than those in Beijing. In January, the near surface of Chongqing is mainly controlled by the northeast wind, while the upper air is mainly affected by the northeast wind, southeast wind, northwest wind and southwest wind. In addition, due to the special terrain of Sichuan Basin, the surrounding area is surrounded by plateau mountains of 1000~3000 m, so the cold air from the north in winter is blocked and it struggles to reach the interior of the basin. The wind speed is significantly lower than that of cities in the North China Plain, and the average wind speed near the ground is less than 2 m/s and the average wind speed at the top of the boundary layer is about 4 m/s. According to the distribution of the vertical wind vector, on January 1, at an altitude of about 600 m, the air flows in the southwest and northeast directions were affected at the same time. On 8, 20 and 27 January, the air flows in the northwest and northeast converged at the same time. On 13 January, the air flows in the southeast and southwest converged at an altitude of more than 300 m. On 15 January, the air flows in the east and west directions were transmitted to Chongqing at the same time. Because the strong horizontal convergence induces upward vertical movement, Chongqing presents the characteristics of strong ABL transport to the FT. In addition, due to the blocking effect of mountains, the high altitude in Chongqing is not significantly affected by the downdraft of cold air, resulting in a small temperature difference in the basin and a stable structure of the ABL, which is not conducive to the diffusion of pollutants, and heavy pollution weather easily occurs. At the same time, it also weakens the intensity of pollution transported from the FT to the ABL.

In addition, the backward trajectory model is used to cluster the air flow trajectories at the atmospheric boundary layer top of typical cities in the BTH and CY regions. The backward trajectory model is widely applied in atmospheric transport research and pollution process analysis [30]. In this study, the starting heights for backward trajectory calculations were selected as the average boundary layer heights of the two cities (600 m and 500 m), and the trajectory running time was set as a 36 h backward trajectory with a time interval of 6 h. The clustering analysis was based on the binary K-means method of trajectory wind direction angles. The meteorological data used in the backward trajectory model were the global data assimilation system (GADS) data provided by the National Centers for Environmental Prediction of the United States, with four time periods per day, namely 00:00, 06:00, 12:00 and 18:00 UTC, and the horizontal resolution was 1° × 1°.

As can be seen from Figure 7c, the air flow at the top of the atmospheric boundary layer over Beijing in winter mainly comes from the northwest direction, of which 22% comes from the long-distance transmission of Inner Mongolia, and 60% and 18% from Outer Mongolia and Russia. From the initial height of the air flow track, the air flow from Inner Mongolia is about 1000 m high, and the air flow moves along the southeast direction and gradually sinks to 600 m high. The height of the air flow from Outer Mongolia is about 2300 m, and the air flow trajectory is similar to that of Inner Mongolia. The initial altitude of the air flow from Russia was about 2000 m. During its movement, the air flow first lifted in altitude to several hundred meters and then continued to move southeast. In the first 6 h before it arrived in the Beijing area, it quickly sank and finally dropped to about 600 m above sea level. Affected by the sinking movement of cold air in northwest China, Beijing in winter shows the characteristics of free atmosphere transport into the atmospheric boundary layer.

Different from Beijing, the air flow at the top of the ABL in Chongqing mainly comes from the southwest in winter (Figure 7d), 91% of which comes from the Yunnan–Guizhou Plateau and 9% from the Qinghai–Tibet Plateau. From the perspective of the initial altitude of the air flow track, the initial altitude of the air flow from the Yunnan–Guizhou Plateau is significantly lower than that from the Qinghai–Tibet Plateau, which is mainly due to the high altitude of the Qinghai–Tibet Plateau with an average altitude above 4000 m, which is also basically consistent with the initial altitude of the air flow from the Qinghai–Tibet Plateau. Due to the barrier of the plateau, the northwest monsoon climate is not significant, which also leads to the climate characteristics of warm winter, less snow and more fog. Under the influence of the warm and humid air flow from southwest China, that of Chongqing is transported from the atmospheric boundary layer to the free atmosphere in winter.

3.3. PM_2.5 Transport Budget Relationship Within the ABL

Figure 8a shows the quantitative assessment results of the transport budget of pollution transport in the ABL in the BTH region in January, April, July and October 2018. On the whole, the ranking of the total net flux intensity in the ABL is generally July > October > April > January. The total net fluxes in January, April, July and October are −1.18 kt/d, −1.43 kt/d, −2.49 kt/d and −1.76 kt/d, respectively. From the perspective of the transport budget of pollution transport, the total net flux in the four typical representative months is negative, indicating that the BTH region is mainly used as a “source” to transport pollutants to the surrounding areas. Figure 8b shows the quantitative assessment results of the revenue expenditure relationship of pollution transport in the ABL in the CY region in January, April, July and October 2018. On the whole, the ranking of the total net flux intensity in the ABL is generally July > October > January > April. The total net fluxes in January, April, July and October were 0.28 kt/d, 0.25 kt/d, 1.75 kt/d and 0.49 kt/d, respectively. From the perspective of the transport budget of pollution transport, the trend is opposite to that of the BTH region. The total net flux in the four typical representative months is positive, indicating that the CY region, as a “convergence”, is mainly affected by the pollution transport in the surrounding areas.

From the comparison results of quantitative assessment of the pollution transport budget in different regions, the characteristics of the pollution transport budget in the BTH and CY regions show an obvious opposite trend. Among them, the total transport budget of the four typical representative seasons in the BTH region is negative, which indicates that the BTH region, as the region with the highest intensity of air pollution emission in China, has a very obvious process of transporting pollution from the ABL to the outside. Due to the influence of topographic conditions in the CY region, pollutants easily accumulate in the basin and do not easily diffuse. The total transport budget of the four typical representative seasons is positive, and the process of transporting pollution from the surrounding areas to the boundary layer is more significant. The quantitative assessment results of the pollution transport budget between the two regions have laid a foundation for the study of pollution transport at a large regional scale in China. Especially for the BTH region, where the pollution is relatively serious, stricter air pollution control measures should be taken to reduce its impact on the surrounding areas.

4. Conclusions

In this study, we quantitatively evaluated the PM_2.5 transport budget in the BTH and CY regions in January, April, July and October 2018 from the horizontal and vertical perspectives.

The BTH region shows the strongest PM_2.5 horizontal net flux in October (−1.08 kt/d), followed by July (−0.96 kt/d), January (−0.91 kt/d) and April (−0.49 kt/d). The CY region exhibits the highest net flux in January (1.68 kt/d), with subsequent values in July (0.73 kt/d), October (0.61 kt/d) and April (−0.26 kt/d). Vertical exchange fluxes reveal consistent seasonal patterns. The BTH region consistently shows strong ABL-to-FT output throughout the year. The CY region primarily shows ABL-to-FT output in autumn and winter, but FT-to-ABL input dominates in spring and summer due to subtropical high downdrafts. We analyze AMDAR aircraft data to understand vertical exchange flux differences. Winter observations show opposite trends between Beijing and Chongqing. Cold air subsidence from northwest cold highs drives FT-to-ABL flow in the North China Plain. Mountain barriers prevent cold air from reaching the Sichuan Basin, where high-altitude convergence induces upward vertical motion. PM_2.5 transport budget analysis reveals consistent regional roles. The BTH region consistently acts as a pollution “source” with negative net fluxes in all months. Conversely, the CY region serves as a pollution “convergence” with positive net fluxes, receiving pollutants from surrounding areas.

For the air pollution control in the BTH and the CY region, it is suggested to adopt regional collaborative control measures. In the BTH region, the focus should be on optimizing the energy structure (such as expanding the use of clean energy to replace coal) and strengthening industrial emission standards, while also strengthening control of transportation sources (promoting the use of new energy vehicles); in the CY region, considering the unfavorable diffusion conditions caused by the terrain, it is necessary to strengthen collaborative control of dust and fine particulate matter precursors (SO₂, NOx, NH₃, etc.) and establish a cross-regional joint prevention and control mechanism. In the future, it is necessary to combine high-precision monitoring (such as the integration of satellite remote sensing and ground stations) and dynamic source analysis technology to formulate differentiated emission reduction strategies and at the same time promote policy coordination among regions and the construction of information sharing platforms to achieve long-term improvement of air quality.