The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM_2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control.

A directional polarimetric camera (DPC) is a key payload on board China’s Gaofen 5B (hereafter denoted as GF-5B) satellite, a hyperspectral observation instrument for monitoring aerosols. On the basis of the dark dense vegetation (DDV) algorithm, this study applied DDV algorithm to DPC measurements. First, the reflectance of vegetation in three channels (0.443, 0.49, and 0.675 μm) was analyzed, and inversion channels were identified. Subsequently, the decrease in normalized difference vegetation index associated with various view angles was simulated, and the optimal view angle for extracting dark pixels was determined. Finally, the top-of-atmosphere reflectance at different view angles was simulated to determine the optimal view angle for aerosol inversion. The inversion experiments were conducted by using DPC data collected over North China from November 2021 to January 2022. The results revealed that DDV algorithm could monitor pollution from 30 December 2021 to 4 January 2022, and the inversion results were strongly correlated with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product and AERONET station data (R > 0.85).

Carbon dioxide (CO₂) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO₂ levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO₂ concentrations and to support the development of climate policies, this study proposes a method based on random forest models to generate a continuous monthly dataset of CO₂ column concentration (XCO₂) across the entire Chinese region from 2004 to 2023. The study integrates XCO₂ satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO₂ profile data. Using the random forest algorithm, a complex relationship model was established between XCO₂ concentrations and various environmental variables. The goal of this model is to provide XCO₂ estimates with enhanced spatial coverage and accuracy. The XCO₂ concentrations predicted by the model show a high level of consistency with satellite observations, achieving a correlation coefficient (R-value) of 0.9959 and a root mean square error (RMSE) of 1.1631 ppm. This indicates that the model offers strong predictive accuracy and generalization ability. Additionally, ground-based validation further confirmed the model’s effectiveness, with a correlation coefficient (R-value) of 0.956 when compared with TCCON site observation data.

Carbon dioxide and methane are the two most important greenhouse gases and are closely related to global warming and extreme weather events. To master their spatial and temporal variations, the CO₂ and CH₄ concentration data monitored by the GOSAT satellite in 2020 and 2021 were used to map and analyse the annual, seasonal and monthly changes in CO₂ and CH₄ concentrations in the world and major countries/regions. The results demonstrate that (1) in 2021, the average annual CO₂ concentration over the global land area was 412.74 ppm, an increase in 0.64% compared with the same period last year, and there were spatial differences in the distribution of CO₂ concentration, with high values mostly concentrated in the middle latitudes of the Northern Hemisphere; (2) compared with 2020, the CO₂ concentration in China, the United States, India, the European Union and other countries/regions increased significantly; (3) in 2020 and 2021, the quarterly CO₂ trend of the global and major countries/regions was the same, which was higher in the first (January, February, March) and second (April, May, June) quarters, significantly lower in the third (July, August, September) quarter, and gradually increased in the fourth (October, November, December) quarter. Further work on long time series and validation needs to be conducted.

Large volumes of atmospheric pollutants injected into the troposphere and stratosphere from volcanic eruptions can exert significant influence on global climate. Through utilizing multi-satellite observations, we present a large-scale insight into the long-range transport and transformation of sulfur dioxide (SO₂) emissions from the Hunga Tonga-Hunga Ha’apai eruption on 15 January 2022. We found that the transport of volcanic emissions, along with the transformation from SO2 to sulfate aerosols, lasted for two months after the Tongan eruption. The emitted volume of SO₂ from the volcano eruption was approximately 183 kilotons (kt). Both satellite observation and numerical simulation results show that the SO₂ and volcanic ash plumes moved westward at a rate of one thousand kilometers per day across the Pacific and Atlantic Ocean regions and that SO₂ transformation in the atmosphere lasted for half a month. The transport and enhancement of aerosols is related to the conversion of SO₂ to sulfate. CALIPSO lidar observations show that SO₂ reached an altitude of 25–30 km and transformed into sulfate in the stratosphere after 29 January. Sulfate aerosols in the stratosphere deceased gradually with transport and fell back to the background level after two months. Our study shows that satellite observations give a good characterization of volcanic emissions, transport, and SO₂-sulfate conversion, which can provide an essential constraint for climate modeling.