AIRS and MODIS Satellite-Based Assessment of Air Pollution in Southwestern China: Impact of Stratospheric Intrusions and Cross-Border Transport of Biomass Burning

Abstract

The exacerbation of air pollution during spring in Yunnan province, China, has attracted widespread attention. However, many studies have focused solely on the impacts of anthropogenic emissions while ignoring the role of natural processes. This study used satellite data spanning 21 years from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) to reveal two natural processes closely related to springtime ozone (O₃) and PM_2.5 pollution: stratospheric intrusions (SIs) and cross-border transport of biomass burning (BB). We aimed to assess the mechanisms through which SIs and cross-border BB transport influence O₃ and PM_2.5 pollution in Southwestern China during the spring. The unique geographical conditions and prevalent southwest winds are considered the key driving factors for SIs and cross-border BB transport. Frequent tropopause folding provides favorable dynamic conditions for SIs in the upper troposphere. In the lower troposphere, the distribution patterns of O₃ and stratospheric O₃ tracer (O₃S) are similar to the terrain, indicating that O₃ is more likely to reach the surface with increasing altitude. Using stratospheric tracer tagging methods, we quantified the contributions of SIs to surface O₃, ranging from 6 to 31 ppbv and accounting for 10–38% of surface O₃ levels. Additionally, as Yunnan is located downwind of Myanmar and has complex terrain, it provides favorable conditions for PM_2.5 and O₃ generation from cross-border BB transport. The decreasing terrain distribution from north to south in Yunnan facilitates PM_2.5 transport to lower-elevation border cities, whereas higher-elevation cities hinder PM_2.5 transport, leading to spatial heterogeneity in PM_2.5. This study provides scientific support for elucidating the two key processes governing springtime PM_2.5 and O₃ pollution in Yunnan, SIs and cross-border BB transport, and can assist policymakers in formulating optimal emission reduction strategies.

1. Introduction

Elevated amounts of PM_2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 μm or less) and ozone (O₃) are currently the two major bottleneck issues constraining the continuous improvement of air quality in China. The complexity of their source mechanisms has brought enormous challenges to the prevention and control of air pollution. Although implementing the Air Pollution Prevention and Control Action Plan (APPCAP) has effectively controlled PM_2.5 pollution in most parts of China, the emphasis on reducing PM_2.5 emissions has led to a continuous increase in O₃ concentrations. In some areas, this has resulted in combined pollution from PM_2.5 and O₃ [1,2]. Owing to its harmful impact on human health, vegetation growth, and climate change [3,4], the synergistic control of O₃ and PM_2.5 constitutes a critical priority in China’s long-term air quality management agenda.

The main sources of PM_2.5 depend on local primary emissions, secondary photochemical reactions, and regional transport. Industrial, traffic, and residential sources can directly emit PM_2.5 [5]. Meanwhile, these sources also release other pollutants, which undergo various chemical and physical processes to transform into sulfate, nitrate, and secondary organic aerosols, constituting fine particulate matter [6,7]. Numerous studies have used source apportionment, numerical simulations, and other methods to quantify the contributions of primary emissions and secondary generation to PM_2.5 during high-pollution events [8,9,10,11]. Apart from local sources, regional transport is another significant contributor to local PM_2.5 pollution, and it can directly carry large amounts of pollutants from other areas, exacerbating local air pollution [12,13,14]. Under specific meteorological conditions, regional transport plays a dominant role in PM_2.5 pollution events in regions such as the North China Plain (NCP), the Sichuan Basin (SCB), and the Yunnan Plateau (YP), with contribution rates reaching exceptionally high levels [12,15,16]. Zhang et al. [17] investigated the difference in PM_2.5 transport flux between NCP and SCB in autumn and winter. They observed that NCP was controlled by a cold front weather system and transported PM_2.5 to surrounding areas. In contrast, PM_2.5 in SCB was affected by interbasin transport because of the complex terrain that hindered horizontal diffusion. Unlike the other two regions, PM_2.5 in YP was found to be influenced by biomass burning (BB) in the Indo-China Peninsula during spring, a source of cross-border BB transport [16,18].

Unlike PM_2.5 pollution, O₃ is a secondary pollutant primarily formed through local photochemical reactions. These reactions involve interactions between precursor pollutants, such as nitrogen oxides (NO_x) and volatile organic compounds (VOCs), in the presence of solar radiation. Influenced by anthropogenic and natural emissions, the local photochemical environment plays an important role in shaping O₃ concentrations [19,20]. Additionally, regional transport from neighboring areas and stratospheric intrusions (SIs) are also important O₃ budget sources [21]. Regional transport is a driving factor behind surface O₃ pollution in regions like Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) [15,22,23]. SIs involve the downward transport of O₃ from the stratosphere to the troposphere and even the surface, under the influence of physical mechanisms such as tropopause folding, cut-off lows, subtropical jet streams, and wave breaking [24,25,26,27]. Under specific meteorological conditions such as typhoons, tropical cyclones, and subtropical highs, local circulation intensifies, leading to enhanced regional transport and SIs, impacting the near-surface O₃ concentration [28,29,30].

Compared with plains such as the BTH, the YRD, and the PRD, Yunnan boasts a unique and complex topography as a high-altitude plateau with substantial elevation variations among its cities. While Yunnan’s overall altitude is relatively high, a considerable difference in elevation exists among its cities, creating a topographical landscape with higher elevations in the northwest and lower elevations in the southeast, resulting in a stair-step descent from south to north. Additionally, the climate of Yunnan is characterized by its complexity in both the horizontal and vertical dimensions, featuring a combination of boreal, temperate, and tropical climates, and temperature variations are notably influenced by changes in elevation. In contrast to the other major city clusters, such as BTH, the YRD, and the PRD, where PM_2.5 pollution is concentrated in winter and O₃ pollution is concentrated in summer [12,20,31], Yunnan experiences PM_2.5 and O₃ pollution primarily during spring. High-frequency tropopause folding provides favorable dynamic conditions for the downward transport of O₃-rich air mass from the stratosphere to the troposphere during springtime, influencing near-surface O₃ concentrations [25,30,32]. Additionally, BB is active in the spring over the Indo-China Peninsula (ICP), and the prevailing westerly winds transport BB-generated PM_2.5 and O₃ to Yunnan [33,34]. Numerous studies of pollution cases have confirmed the existence of transport channels between Yunnan and the ICP [16,18]. Given the unique topographic conditions and geographical location of Yunnan, these two distinct transport processes, SIs and cross-border BB transport, exert a more profound impact on PM_2.5 and O₃ pollution in Yunnan during the spring. However, current emission reduction policymaking has predominantly focused on reducing local anthropogenic sources, and there remains a notable lack of research on the impact of these two natural sources on the mechanism of springtime O₃ pollution in Yunnan.

Satellite remote sensing technology can provide vertical profile data, real-time observations, and extensive spatiotemporal coverage, which can help to better understand the mechanisms governing PM_2.5 and O₃ pollution [35,36,37]. Instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Ozone Monitoring Instrument (OMI), and Cloud–Aerosol Lidar and Infrared Satellite Observations (CALIPSO) have been widely used to retrieve the spatiotemporal distribution of Aerosol Optical Depth (AOD), which quantifies the solar attenuation caused by aerosols [38,39,40]. Shaik et al. [41] and others have confirmed the accuracy of MODIS-derived AOD by comparing it to ground-based AOD, showing strong agreement with a correlation coefficient (R) of 0.86 and a root mean square error (RMSE) of 0.14. Additionally, satellite-based AOD has been instrumental in revealing the impact of BB on the optical properties and vertical structure of aerosols [35,42]. Satellite-based vertical profiles of O₃, obtained from instruments like the Atmospheric Infrared Sounder (AIRS), are crucial for investigating SIs [43,44]. Ma et al. [45] used vertical AIRS profiles of O₃ to provide evidence of SIs in cities such as Chaoyang, Shijiazhuang, and Hangzhou during the spring and summer of 2019. By characterizing the three-dimensional spatiotemporal distribution of pollutants, satellite observations can significantly enhance our understanding of pollutant transport processes and largely compensate for a lack of vertical observation data.

To study and quantify the impact of SIs and cross-border BB transport on O₃ and PM_2.5 concentrations in Yunnan province during spring, 21-year satellite observations from MODIS and AIRS were used to investigate the spatial and temporal variations in O₃ and PM_2.5. The Whole Atmosphere Community Climate Model (WACCM) dataset was used to quantify the contributions of SIs and cross-border BB transport to O₃ and PM_2.5 concentrations under complex topographic conditions. Finally, the sources of PM_2.5 and O₃ in Yunnan during spring were determined by using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate air mass trajectories. The objectives of this study are to: (1) analyze the general characteristics of O₃ and AOD; (2) explain the mechanisms of SIs and cross-border BB transport; and (3) investigate the influence of SIs and cross-border BB transport on O₃ and PM_2.5 under complex topographic conditions.

2. Materials and Methods

2.1. Studied Area

Two nested domains were used in this study: the 16°–36°N, 85°–125°E region, and Yunnan province (Figure 1). Previous studies indicate that the westerly jet stream is an important midlatitude circulation in region d01 during the spring [33]. This area is known for frequent tropopause folding and is also a hotspot for BB during the spring [32].

2.2. Satellite Datasets

We used two types of satellite datasets from different instruments, MODIS and AIRS.

Table 1. Specifications of MODIS and AIRS instruments.

Instrument/ Satellite	Wavelength Bands	Number of Bands	Spectral Resolutions	Temporal Resolution	Spatial Resolution
MODIS/Aqua	0.4–14.4 µm	Bands 1–19 and 26	0.4 µm to 2.2 µm	Multiple times daily	250 m, 500 m and 1000 m
		Bands 20–25 and 27–36	3.66 µm to 14.4 µm
AIRS/Aqua	0.4–15.4 µm	2378 infrared channels	649 to 1136 cm−1 (longwave) 1213 to 1613 cm−1 (midwave) 2181 to 2665 cm−1 (shortwave)	Twice daily	1° × 1°
		4 visible/near-infrared channels	0.4 µm to 0.94 µm

and

Table 2. Attributes of the satellite datasets used in this study.

Instrument/ Satellite	Data Provider	Data Product	Temporal Resolution	Spatial Resolution	Parameters Used	Reference
MODIS/Aqua	LAADS	MYD04_L2	Daily (2003–2023)	10 km × 10 km	AOD (550 nm)	(Levy et al., Sayer et al.) [46,47]
AIRS/Aqua	GESDISC	AIRS3STM 7.0	Monthly (2003–2023)	1° × 1°	O3 profile	(Aumann et al.) [48]

, respectively, present their instrument specifications and the attributes of their corresponding datasets.

2.2.1. MODIS

MODIS is part of the National Aeronautics and Space Administration (NASA) and is deployed on two satellites, Aqua and Terra, which are in near-polar orbits. This positioning allows for global observations multiple times a day. MODIS captures data in 36 inter-aligned spectral bands ranging from 0.4 µm to 14.4 µm and acquires them at three spatial resolutions: 250 m, 500 m, and 1000 m, providing global data over a wide range. The daily level 2 Collection 6.1 AOD dataset (MYD04_L2) is used to provide AOD data, which are available from the website: https://ladsweb.modaps.eosdis.nasa.gov/, accessed on 3 November 2023. Each file in this dataset corresponds to a 5-min MODIS orbital fragment with a spatial resolution of 10 × 10 km, containing a total of 203 × 135 pixels. The MODIS retrieval algorithm for aerosol properties is divided into the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is used for retrieving AOD over the ocean and vegetated land, while the DB algorithm is designed for bright surfaces, like deserts and urban areas, and dark surfaces, including vegetated regions. The latest MODIS product merges the strengths of the DT and DB algorithms into a new combined dataset that relies on the Normalized Difference Vegetation Index (NDVI). The combined dataset is generated as follows: (1) if NDVI > 0.3, DT retrievals are used; (2) if NDVI < 0.2, DB retrievals are used; (3) if 0.2 ≤ NDVI ≤ 0.3, the average of the DT and DB retrievals is used, or the one with the recommended quality assurance (QA = 3 for DT and QA ≥ 2 for DB) is selected. This approach enhances spatial coverage over land, particularly in transitional regions with low vegetation that are dark enough for the DT algorithm to be applicable [49]. Consequently, we used the combined dataset to analyze the general characteristics of AOD, namely, “AOD_550_Dark_Target_Deep_Blue_ Combined”.

2.2.2. AIRS

AIRS was launched with the Aqua satellite on 4 May 2002, as part of NASA. This sensor is specifically designed to obtain infrared spectral information in the atmosphere; it has a high spectral resolution and provides all-day vertical profile observations. Furthermore, the AIRS and MODIS instruments are concurrently deployed on the Aqua satellite, establishing a dual-satellite configuration that comprehensively analyzes both surface and atmospheric data. In this study, the AIRS Version 7 Level 3 standard product with 1° × 1° spatial resolution and monthly temporal resolution was used to investigate the spatiotemporal distribution of O₃. The vertical profile data are divided into 24 pressure levels, from 1000 hPa to 1 hPa, and can be obtained from the website: https://airs.jpl.nasa.gov/data/get-data/standard-data/, accessed on 3 November 2023.

2.3. WACCM Simulation

2.3.1. WACCM Datasets

The WACCM is a global climate model developed by the National Center for Atmospheric Research (NCAR), falling under the Atmospheric Chemistry-Climate Model (AOGCM) category. This model aims to simulate and study physical and chemical processes in the atmosphere, covering entire pressure levels from the surface to the top of the atmosphere. This model uses anthropogenic emissions from the latest Copernicus Atmosphere Monitoring Service (CAMS) inventory. Open-flame emissions are derived from FINN-v1. The spatial resolution of the datasets is 0.9° × 1.25°, and the vertical profile is divided into 88 levels, which can be obtained from the website: https://www.acom.ucar.edu/waccm/download.shtml, accessed on 3 November 2023. The stratospheric O₃ tracer (O₃S) and the CO tracer (CO01) represent the contribution of SIs to tropospheric O₃ and the impact of global BB emissions on local CO.

2.3.2. Model Evaluation

O₃ and PM_2.5 observations from site data and AOD observations from the MODIS dataset are performed to assess the performance of the WACCM dataset at eight sites in Yunnan from 2020 to 2023. The hourly O₃ and PM_2.5 data from 2020 to 2023 used in this study were obtained from the China Environmental Monitoring Center’s national urban air quality real-time release platform (https://air.cnemc.cn:18007/, accessed on 3 November 2023). Figure S1 and Table S1 show that the WACCM dataset had good performance for O₃, PM_2.5, and AOD, with averaged R of 0.86, 0.88, and 0.67, respectively. The averaged RMSEs are 14.21 μg/m³, 23.17 μg/m³, and 0.21, and the mean biases (MBs) are 11.27 μg/m³, 12.29 μg/m³, and 0.14, respectively. Overall, the above discussion can prove the reliability of WACCM data and support the subsequent analysis of our study objectives.

2.4. HYSPLIT Model

The HYSPLIT model is a Lagrangian trajectory model developed by the National Oceanic and Atmospheric Administration (NOAA) to calculate simple air parcel trajectories as well as complex simulations for transportation, dispersion, chemical transformation, and deposition. The HYSPLIT model has been widely used to track aerosol sources and identify BB emission smoke delivery routes [18,35]. The Global Data Assimilation System (GDAS) from the National Centers for Environmental Prediction (NCEP) is used to generate meteorological conditions at an interval of 6 h with 1° horizontal resolution and 23 vertical levels from 1000 hPa to 20 hPa, and its data can be obtained from the website: https://www.ready.noaa.gov/data/archives/gdas1/, accessed on 3 November 2023. Two representative cities, Kunming (25.04°N, 102.66°E) and Xishuangbanna (22.02°N, 100.80°E), were selected as the model-simulated trajectory termination or particle receiving points. The starting height was set to 500 m above ground level, and simulations were conducted at 00:00, 06:00, 12:00, and 18:00 UTC during spring from 2020 to 2023 for 72 h backward in time. After running the HYSPLIT model, we obtained trajectory information, including the number of trajectories and endpoints within each grid cell that the trajectory passed through.

Potential Source Contribution Function (PSCF) analysis is widely used to identify spatial patterns in pollution sources and quantitatively estimate the relative contribution strengths of different source areas [18,31,50]. To assess the pollution levels in the study region from various source areas, it was essential to establish threshold values for PM_2.5. According to the daily average primary limit of the Ambient Air Quality Standards of China (GB 3095-2012) [51], the PM_2.5 threshold value was set to 35 μg/m³. The position where each trajectory endpoint falls is recorded as (i, j). Mij represents the number of trajectory endpoints where the measured PM_2.5 parameter exceeds the threshold value within the ij-th grid cell, and Nij represents the total number of trajectory endpoints within the ij-th grid cell. The PSCF value of each grid cell in the study area was determined by calculating the ratio of M_ij and N_ij, as shown in Equation (1):

$${\mathrm{P}}_{\mathrm{i}\mathrm{j}}=\frac{{\mathrm{M}}_{\mathrm{i}\mathrm{j}}}{{\mathrm{N}}_{\mathrm{i}\mathrm{j}}}$$

(1)

When N_ij is small, the uncertainty of the conditional probability P_ij increases. To reduce this uncertainty, we referred to the study by Polissar [50] and introduced a weight function, W_ij, which is multiplied by P_ij, as follows:

$${\mathrm{W}}_{\mathrm{i}\mathrm{j}}=\left\{\begin{array}{l}1.00\\ 0.70\\ 0.42\\ 0.17\end{array}\right.....\begin{array}{c}150<{\mathrm{N}}_{\mathrm{i}\mathrm{j}}\\ 15<{\mathrm{N}}_{\mathrm{i}\mathrm{j}}\le 150\\ 5<{\mathrm{N}}_{\mathrm{i}\mathrm{j}}\le 15\\ {\mathrm{N}}_{\mathrm{i}\mathrm{j}}\le 5\end{array}$$

(2)

$$\mathrm{W}\mathrm{P}\mathrm{S}\mathrm{C}\mathrm{F}={\mathrm{W}}_{\mathrm{i}\mathrm{j}}\times {\mathrm{P}}_{\mathrm{i}\mathrm{j}}$$

(3)

Concentration-Weighted Trajectory (CWT) analysis was employed to determine the potential source area contributions of PM_2.5. PM_2.5 concentrations in the target cities can be obtained from the China Environmental Monitoring Center’s national urban air quality real-time release platform (https://air.cnemc.cn:18007/, accessed on 3 November 2023). The CWT value of each grid cell in the study area was obtained by calculating the weighted value of pollutant concentration in different trajectories, as shown in Equation (4):

$${\mathrm{C}}_{\mathrm{i}\mathrm{j}}=\frac{1}{{\displaystyle \sum _{\mathrm{l}=1}^{\mathrm{M}}{\mathrm{T}}_{\mathrm{i}\mathrm{j}\mathrm{l}}}}{\displaystyle \sum _{\mathrm{l}=1}^{\mathrm{M}}{\mathrm{C}}_{\mathrm{l}}{\mathrm{T}}_{\mathrm{i}\mathrm{j}\mathrm{l}}}$$

(4)

where: C_ij represents the average weighted PM_2.5 concentration of the ij-th grid cell (μg/m³); l is the number of trajectories; M is the total number of trajectories; C_l represents the pollutant concentration along trajectory l; and T_ijl represents the time spent by trajectory l in the ij-th grid cell. Similar to P_ij, we used W_ij to decrease the error in C_ij, as follows:

$$\mathrm{W}\mathrm{C}\mathrm{W}\mathrm{T}={\mathrm{W}}_{\mathrm{i}\mathrm{j}}\times {\mathrm{C}}_{\mathrm{i}\mathrm{j}}$$

(5)

3. Results

3.1. General Characteristics of AOD and O₃

To compare the difference in the effects of cross-border BB transport and SIs on PM_2.5 and O₃ in Yunnan and the entire region, this section analyzes satellite observations from MODIS and AIRS in d01 and d02, as shown in Figure 1.

We analyzed the seasonal variation characteristics of AOD using MODIS satellite observations from 2003 to 2023, aiming to indirectly obtain the variation characteristics of PM_2.5 from an observational perspective. Figure 2 depicts the seasonally averaged AOD over Yunnan province from 2003 to 2023. In the Yunnan region, AOD exhibits distinctive characteristics, with its peak occurring during the spring season. This feature contrasts notably with other regions, such as BTH and the YRD, where the maximum AOD can be observed during the summer season [52,53]. The average AOD during spring has consistently exceeded the 21-year mean value, with 16 years recording maximum AOD values in spring surpassing 0.8 and even reaching 1.1. This phenomenon became more pronounced after 2015, with AOD values in spring significantly surpassing those of other seasons. Notably, the high AOD values during spring in Yunnan are more pronounced compared with the overall region, with the average falling within a range of 0.35–0.47.

Additionally, PM_2.5, PM₁₀, and O₃ and AOD exhibit similar seasonal variation characteristics, with their peak values appearing in spring. However, there are significant differences in the annual variation trends of these elements in Yunnan province between 2015 and 2023, with PM_2.5 and PM₁₀ decreasing by 4 μg/m³ and 12 μg/m³, respectively, and O₃ increasing by 13 μg/m³. The APPCAP plan’s implementation in 2013 is responsible for these variations. Reductions in anthropogenic emissions have led to a sustained decrease in PM_2.5 and PM₁₀ emissions. Besides the increase in radiative flux due to the decrease in PM_2.5 exacerbating O₃ pollution, the change in pollutant emission structure caused by China’s PM_2.5 control-focused emission reduction measures may be a significant reason for the recent continuous rise in O₃ pollution [54]. Meanwhile, AOD did not change significantly, fluctuating around a value of 1. Previous studies have shown that PM_2.5 and PM₁₀ contribute to the total aerosol load, thus affecting AOD in the atmosphere [55,56,57]. Figure S3 shows that there is a positive relationship between PM_2.5, PM₁₀, and AOD in spring and summer over Yunnan, with an R ranging from 0.56 to 0.75, indicating that PM_2.5 and PM₁₀ significantly contribute to springtime and summertime AOD in Yunnan. However, the correlations between PM_2.5, PM_10, and AOD are low in autumn and winter, which are attributed to various meteorological factors such as humidity, wind speed, and atmospheric stability. For instance, high humidity can cause hygroscopic particle growth, increasing AOD, but this does not necessarily indicate a proportional increase in surface PM_2.5 or PM₁₀. Additionally, AOD signifies the cumulative extinction capacity of all aerosol particles within the vertical atmospheric column. However, PM_2.5 and PM₁₀ site observations can only reflect surface pollution levels. The differences mentioned above can lead to variations in the correlation between satellite observations of AOD and surface observations of PM_2.5 or PM₁₀ in different seasons.

In the context of significant annual reductions in PM_2.5 and PM₁₀ pollution, the consistently high levels of PM_2.5 in spring are particularly notable. Consequently, the impact of cross-border BB transport occurring during spring has been further amplified. Furthermore, the overall increase in spring AOD in Yunnan and the entire region confirms the homogeneity of AOD sources in these two areas.

Figure 3 depicts the spatiotemporal distribution of O₃ in Yunnan and the entire region. A tongue-shaped plume of high O₃ can be seen extending from 100 hPa downward to the lower troposphere, with O₃ concentrations higher than 80.0 ppbv. The downward transport of stratospheric O₃-enriched air accumulates at the top of the boundary layer owing to the blocking effect of that layer, forming an area of high O₃ values. Previous studies have proved that similar phenomena can indicate the occurrence of SIs [21,25]. The latitude cross-section also further supports that SIs transport O₃-enriched air from the stratosphere to the troposphere, forming a tropospheric O₃ hotspot above 500 hPa (Figure 4a).

Figure 4b shows a band of O₃-enriched air extending from Northern India to Yunnan above 500 hPa, possibly indicating that cross-border BB transport exacerbates O₃ pollution in the lower troposphere. Figure 5d–f depicts the spatial distribution of O₃ and wind fields at altitudes of 303 hPa, 512 hPa, and 713 hPa, confirming the existence of the hotspot above 500 hPa. The strong westerly jet at 313 hPa and 512 hPa provides favorable meteorological conditions for the horizontal transport of pollutants from the source area in Myanmar to Yunnan, indirectly supporting the possibility that cross-border BB transport affects the formation of O₃ hotspots (Figure 5e,f). Figure S4 shows that Kunming and Xishuangbanna’s five and four major pathways have been identified. The trajectories from central Myanmar have the highest proportion, accounting for 58.98% in Kunming and 38% in Xishuangbanna, further indicating the influence of cross-border BB on pollutants in Yunnan. Consequently, cross-border BB transport in the lower troposphere may further exacerbate tropospheric O₃ pollution.

The higher O₃ concentrations in the lower troposphere of Yunnan may also be attributable to the region’s topographical conditions, as its elevated altitude facilitates the transport of stratospheric O₃ closer to the surface layer. A comprehensive analysis of satellite data has revealed that SIs and cross-border BB transport are two significant sources influencing air quality in the Yunnan region. However, there are still large uncertainties in studies of the quantitative regulation mechanism of springtime O₃ pollution over Yunnan.

3.2. Occurrence of SIs and the Cross-Border BB Transport in Yunnan

Satellite observations have confirmed the significant impact of SIs and cross-border BB transport on air quality during spring in Yunnan. However, the specific mechanisms and quantifications of each process have not been thoroughly investigated. This knowledge gap undermines the effectiveness of strategies aimed at reducing anthropogenic emissions. Many studies have demonstrated the accuracy of WACCM simulations and their applications in research related to O₃ and CO pollution [58,59,60]. WACCM simulations were performed to investigate the governing dynamic mechanisms of SIs and cross-border BB transport from 2020 to 2023.

In Figure 6, noticeable seasonal characteristics were observed for PM_2.5, CO, and CO01 concentrations in both Yunnan and Myanmar, where high values all occurred in later winter and early spring (February–April). In comparison, the high PM_2.5 and CO values in Yunnan are primarily concentrated between 800 hPa and the surface layer, while pollutants in Myanmar accumulate closer to the ground. Additionally, the maximum PM_2.5 and CO concentrations in Myanmar can reach 191 μg/m³ and 1768 ppbv, whereas in Yunnan, these values reach 73 μg/m³ and 679 ppbv (Figure 6). This indicates that Yunnan is situated downwind from Myanmar and is significantly influenced by cross-border BB transport. The vertical distribution characteristics of CO01 further confirm this inference. Many previous studies have demonstrated that the prevailing southwest winds in spring over ICP transport PM_2.5 to Yunnan, which is located downwind [16,18]. A hotspot of PM_2.5, CO, and CO01 concentrations was noticed between 700 and 800 hPa over Guangdong (Figure 6c,f,i), which indicated the important role of BB in air quality in that area [35].

Figure 7 further provides a time–height cross-section plot of monthly mean O₃ and O₃S profiles for Yunnan, Myanmar, India, and Guangdong from 2020 to 2023. A tongue-shaped plume of high O₃ exceeding 108 ppbv is transported downward to 500 hPa. A pool of O₃ with concentrations higher than 90 ppbv appears above the boundary layer. However, the vertical distribution characteristics of O₃S indicate that SIs have a relatively limited impact on the high concentrations of lower tropospheric O₃ in Yunnan and Myanmar. In comparison with the distribution of PM_2.5 and CO, both CO01 and O₃ in Yunnan exhibit high-value areas at 900 hPa, indicating that BB also plays a significant role in the accumulation of pollutants in this layer (Figure 6a,e). In Guangdong, the intensity of SIs is relatively weak, with less than 30 ppbv O₃S transported to the troposphere. In addition, topography may be another factor influencing the contribution of SIs in Yunnan. The higher altitude facilitates the transport of stratospheric O₃ to the surface layer.

Notably, unlike the vertical distribution of pollutants in Yunnan and Myanmar, the pollutant hotspots in Guangdong were elevated in altitude but did not extend to the ground. Many studies have shown that there are two main reasons why these pollutant hotspots are elevated in altitude in Guangdong: the long-range horizontal transport of pollutants generated from BB in ICP and cross-latitudinal SI transport [26,35,61,62]. In our previous study, a strong inversion layer observed above the ABL blocked O₃ from penetrating the boundary layer in Hong Kong. Under favorable atmospheric circulation conditions (i.e., Saddle Point or Col), O₃ plumes can penetrate the boundary layer and affect the near-surface [21,26]. The differences between the WACCM-modeled O₃ concentration and the AIRS measurements are primarily due to inaccuracies in the WACCM simulations and AIRS measurements. Firstly, limitations in the vertical resolution of the WACCM simulations and parameterization schemes hinder accurate depictions of atmospheric layer variations, leading to inaccuracies [63]. Secondly, the vertical satellite data are derived from O₃ column concentrations, which may introduce errors during the inversion process. Therefore, more vertical observations need to be conducted to assess the WACCM simulation results and AIRS satellite data in the future [64].

Overall, our results indicate that SIs and cross-border BB transport are two major key mechanisms for determining spatiotemporal variations in PM_2.5 and O₃ in Yunnan during spring.

3.3. Mechanisms of SIs and Cross-Border BB Transport

Surface PM_2.5 reaches its maximum in Xishuangbanna, Eastern Myanmar, Northern Laos, and Northwestern Vietnam and gradually decreases along the northeastern direction, as shown in Figure 5. As altitude increases, PM_2.5 and O₃ concentrations exhibit opposite trends. Owing to topographical effects, the influence of cross-border BB transport is limited in higher-altitude areas. Conversely, the vertical downward transport of O₃ from the stratosphere is more likely to reach the surface layer in higher-altitude regions, resulting in an increase in O₃ concentrations with elevation. As mentioned in the previous section, cross-border BB transport may exacerbate PM_2.5 pollution in Yunnan during the spring.

Figure 8 shows pressure–longitude cross-sections along 23°N and 26°N for averaged PM_2.5 and vertical circulation in spring from 2020 to 2023. Owing to the impact of BB, high PM_2.5 concentrates on the surface of Myanmar, with a value of 150 μg/m³. In Yunnan, the wind speed and elevation gradually increase from south to north. Vertically, surface PM_2.5 concentrations decrease with increasing elevation. A stronger westerly jet transports PM_2.5 to the downstream area, and the towering, mountainous terrain hinders further byproduct transport from BB. Therefore, Xishuangbanna, which has the lowest elevation in Yunnan and is closest to Myanmar, faces severe PM_2.5 pollution challenges.

To further track potential sources of PM_2.5 pollution in Yunnan, the PSCF and CWT approaches embedded in the HYSPLIT model were used to track air pollutant source areas for the target cities, Kunming and Xishuangbanna. A higher WPSCF value indicates a higher likelihood of the region being a potential PM_2.5 source area. Figure 9a shows that, for Xishuangbanna, regions with WPSCF values greater than 0.4 are primarily located in Myanmar, covering nearly half of the country’s area. Additionally, in the central part of Myanmar, the WPSCF value exceeds 0.6. This suggests that Myanmar is a substantial potential source area for PM_2.5 in Xishuangbanna, confirming the significant impact of cross-border BB transport on PM_2.5. Compared with Xishuangbanna, the impact of cross-border BB transport on Kunming is much smaller. The high-WPSCF-value areas for Kunming are mainly located in the southwestern region of Yunnan province (Figure 9b). Figure 9c,d show that the high-value WCWT areas are consistent with those for WPSCF. In spring, Myanmar serves as a potential source area for Xishuangbanna, with PM_2.5 contributions reaching 40–55 μg/m³. Meanwhile, the PM_2.5 contribution to Kunming mainly falls within a range of 20–40 μg/m³, with high-value areas primarily located in the southwestern region of Yunnan province and at the southwestern border of Yunnan with Myanmar. The spatial heterogeneity of WPSCF and WCWT between Xishuangbanna and Kunming is essentially driven by the difference in elevation (1.1 km for Xishuangbanna and 2.0 km for Kunming).

To further evaluate the relative SI contribution, we analyzed the horizontal distribution characteristics of the O₃S/O₃ ratio at different altitudes, which is used to describe the percentage of O₃ transported from the stratosphere to different levels. At a certain level, a higher ratio value of O₃S/O₃ signifies the greater impact of SI on O₃ concentrations at this level. Figure 10 shows that the O₃S/O₃ ratio increases with altitude, which is consistent with the SI characteristics. At 713 hPa and 512 hPa, an O₃S/O₃ hotspot can be seen over Northwest Yunnan, which agrees with the topographic feature, as shown in Figure 1b. In the higher troposphere (i.e., 303 hPa), the ratio exhibits a zonal distribution, increasing with higher latitudes. On the one hand, the tropopause height decreases with increasing latitude. On the other hand, the tropopause folding frequency demonstrates a similar zonal distribution, which provides favorable dynamic conditions for SIs (Figure S5). Owing to the significant influence of the Qinghai–Tibet Plateau’s topography, hot spots are centered in the northwest part of Yunnan. Therefore, the complex terrain of the Yunnan region will have a significant impact on pollutant transport, exerting opposing effects on SIs and cross-border BB transport.

3.4. Relationships between Topography and Air Pollutants

As discussed above, topography has a significant impact on pollutant transport in Yunnan. Thus, we expect that topography has a close relationship with O₃ and PM_2.5, which are examined in this section.

To explore the influence of topography, we analyzed the relationships between altitude and anomalous values of individual pollutant values during various months (February–May) over Yunnan and Myanmar. The altitudes in Yunnan and Myanmar vary significantly. Yunnan has an average elevation of 2.3 km and a diverse internal topography, while Myanmar features predominantly gentle terrain, with most areas situated at elevations below 1 km. Figure 11 shows a strong negative relationship between altitude and anomalous PM_2.5 in Yunnan with an R higher than 0.67, which indicates that the effect of cross-border transport is gradually weakened by increasing altitude. In comparison, the connection between altitude and the cross-border transport impacts of BB in Myanmar is not prominently evident. This is primarily due to the lower altitudes in Myanmar and the predominantly local sources of BB. The significant negative correlation between CO (CO01) and altitude further substantiates the substantial impact of cross-border BB transport on air quality in Yunnan (Figure S6). The significant positive correlation between O₃S and altitude in Yunnan indicates that stratospheric O₃-enriched air is more readily transported downward to reach high-altitude cities. At the same time, the differences in R and slope between different months also prove the varying intensity of the influence of SIs and cross-border BB transport on PM_2.5 and O₃ at different altitudes. Areas at lower altitudes are more susceptible to PM_2.5 impacts from horizontal transport because of BB, while higher-altitude regions are more influenced by vertical O₃ transport from SIs.

Owing to the severe polluted status in March–April, we calculated the averaged March–April PM_2.5, O₃, and O₃S concentrations from 2020 to 2023 for 16 cities in Yunnan. We then sorted them by altitude, from highest to lowest (Figure 12). A close link between O₃, O₃S, PM_2.5, and topography was found. As the altitude decreases, there is a significant increase in PM_2.5 concentration and a decrease in the proportion of O₃S. Under the influence of topography, SIs can contribute 8–31 ppbv to surface O₃ in these cities. Notably, Xishuangbanna, the closest city in Yunnan to BB sites, has a further higher PM_2.5 (close to 300 μg/m³) than other cities (100–200 μg/m³), resulting from cross-border BB transport.

It should be noted that the WACCM products cannot directly track the impact of cross-border BB transport on O₃. Therefore, it is not possible to calculate the quantitative contribution of cross-border BB transport using WACCM data. Furthermore, the less significant trends observed in O₃ and O₃S can be attributed to the absence of constraints on surface O₃ and O₃S in the simulation results. As a workaround, O₃ and O₃S from levels close to surface pressure were used as substitutes for surface concentrations.

4. Discussion

Currently, Yunnan province faces severe PM_2.5 and O₃ pollution in the spring. A comprehensive analysis of MODIS and AIRS satellite data over the past 21 years has revealed the presence of two atmospheric pollutant transport processes associated with springtime in Yunnan, specifically SIs and BB. The implementation of APCAAP in 2013 led to a reduction in local emissions, amplifying the relative contribution of two unique transport processes during springtime.

In this study, we focused on assessing the impact of SIs and BB on O₃ and PM_2.5 during the spring season in Yunnan. Two factors affecting SIs and cross-border BB transport were identified: the prevalent southwest winds and complex topographical conditions. Yunnan is situated in the downwind region of Myanmar and features complex terrain. This unique geographical position provides an ideal environment for SIs and the cross-border transport of BB emissions, allowing PM_2.5 and O₃ from Myanmar to be transported to Yunnan. The complex topography within Yunnan, with higher elevations in the north and lower elevations in the south, facilitates the easier transport of PM_2.5 to border cities with lower elevations, such as Xishuangbanna. Cities farther from Myanmar, owing to higher elevations, obstruct some PM_2.5 transport, weakening cross-border BB transport and resulting in the spatial distribution of PM_2.5 concentrations decreasing with elevation.

High-frequency tropopause folding in Yunnan provides favorable dynamic conditions for SIs. The distribution of O₃ and O₃S exhibits a northwest-southeast trend similar to the terrain. This can be partly attributed to the higher tropopause folding frequency in the northwest, influenced by the Qinghai–Tibet Plateau, as well as the high-altitude terrain in the northwest, which facilitates the transport of stratospheric O₃ to the surface. The contribution of SIs to surface O₃ in Yunnan’s cities ranges from 6 to 31 ppbv, accounting for 10% to 38% of surface O₃.

Notably, these two unique natural processes in Yunnan contribute differently to surface O₃ and PM_2.5 concentrations during springtime. SIs are solely responsible for the downward transport of stratospheric O₃, thereby contributing to surface O₃ pollution. High-altitude sites are more susceptible to the effects of SIs, as evidenced by the positive correlation between O₃S and altitude. Conversely, the cross-border BB transport affects both O₃ and PM_2.5 concentrations through the transport of PM_2.5, O₃, and O₃ precursors. The impact of cross-border BB transport on O₃ and PM_2.5 decreases with increasing altitude owing to topographic obstruction, as indicated by the negative correlation between altitude and O₃ as well as PM_2.5. Moreover, it should be noted that in addition to the processes mentioned above (see also [65]), the temporal evolution of the tropopause height (e.g., foldings) exhibits persistent behavior (memory effect). This means that a small fluctuation in the past is followed by a drastically enhanced fluctuation in the future. The response time varies from 4 months to 6 years [66]. This behavior has also been found in surface O₃, PM_2.5, and AOD [67].

This study underscores the significant regulatory role of SIs and cross-border BB transport as non-local emissions on local PM_2.5 and O₃ pollution in Yunnan, which will pose a substantial challenge for policymakers in the future. Hence, we recommend that the impact of these two distinct natural transport processes be taken into consideration when formulating local emission reduction strategies. It is essential to comprehensively consider the influence of terrain conditions on pollutant transport in cities at different altitudes.