Methane CH4) is the second largest greenhouse gas in the world after CO2. CH4emissions contribute 16% of the world's greenhouse gases. Although global CH4 emissions are much lower than global carbon dioxide emissions, their global warming potential (GWP) over 100 years is 28-36 times greater than that of carbon dioxide. Using satellites to observe methane is an effective means. This paper is aimed at Hebei Province, using TROPOMI onboard Sentinel-5P. The main research content: 1). The concentration of XCH4in Hebei Province has obvious seasonal trend (autumn > winter > summer > spring). 2). Due to the existence of some stable emission sources in the southwest of Hebei Province, there are obvious anomalous areas of stable high value of methane in these areas.

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new algorithm, and the comparison demonstrates the algorithm has an excellent ability to estimate SSA in pollution over land.

TanSat is crucial for detecting global CO2concentration, solar-induced chlorophyll fluorescence, and CO2flux as China's first atmospheric CO2concentration monitoring satellite. This manuscript presents a preliminary attempt to estimate anthropogenic CO2emissions from large sources with TanSat. We identified XCO2(the column-average dry air-mole fraction of CO2) anomalies and quantified CO2emissions from two TanSat observations in Northeast China. The emission rate estimations of the two XCO2plumes are 11.46 kt CO2/h and 10.09 kt CO2/h respectively, and the emission rates of the Multi-resolution Emission Inventory for China (MEIC) are 6.86 kt CO2/h and 3.10 kt CO2/h respectively. The result shows that TanSat has the ability to quantify anthropogenic CO2emissions.

Remote sensing inversion of global PM2.5is an important research topic. In the present study, the Aerosol Optical Depth (AOD) dataset was established by four geostationary satellites to estimate global PM2.5concentrations using improved Geographic Time-Weighted Regression model (IGTWR) models. Then a global hourly PM2.5concentration dataset was obtained in May 2020. The estimated result for PM2.5is verified at ground stations with R of 0.71 and RMSE (Root Mean Square Error) of 26.6 μg/m3. The results indicate that PM2.5has obvious spatial and temporal distribution in the world.

In this paper, surface ozone in China were estimated by the Geographically and Temporally Weighted Regression model with its precursors and AOD data. Based on the GTWR model, the surface ozone was estimated by time, space, ozone precursors and AOD. Taking August 1, 2022 as an example, it was analyzed and confirmed about the feasibility of ozone precursors and AOD data in estimating surface ozone mass concentrations.

Based on the data of air quality monitoring stations and high-resolution remote sensing product data in Xuzhou City, the atmospheric pollutants in 2020 and 2021 were analyzed. The modified A-values method and the model simulation method were used to estimate the remaining atmospheric environmental capacity (RAEC) of PM10and PM2.5pollutants in Xuzhou, Jiangsu Province. The results show that the excessive PM10and PM2.5pollutants are the main problems of atmospheric pollution in Xuzhou, and the annual emissions still need to be reduced are 10.80×104t/a and 5.98×104t/a, respectively. Among them, the situation is the most serious in Tongshan District, which still needs to cut annual PM10and PM2.5emissions by 7.41×104t/a and 3.70×104t/a, respectively. Xinyi City has the smallest annual PM10emission reduction, which is 0.89×104t/a. Suining County needs to cut the smallest PM2.5emissions, at 0.67×104t/a. In addition, there are significant quarterly differences in RAEC, with the first quarter > the fourth quarter > the second quarter > the third quarter. Except for the third quarter, the excess atmospheric environmental capacity was the most serious in Xuzhou urban area, the other three quarters were the highest in Tongshan District.

The OCO-2 satellite retrieval of CO2inferred CO2concentrations from atmospheric spectral absorption signals in three bands at 0.76 µm, 1.61 µm, and 2.06 µm. The accuracy of CO2inversion by OCO-2 satellite is directly dependent on the accuracy of the molecular absorption model used in the retrieval algorithm. For gases with low anisotropy and background concentrations like CO2, the accuracy of the molecular absorption model is critical. Therefore, it is essential to establish a fast (usually costly to calculate at line-by-line spectral resolution) and accurate molecular absorption model. In this study, we train an artificial neural network with data from the HITRAN CO2line-by-line absorption database and can build an accurate, precise and effective prediction model for the CO2absorption coefficient. This model only requires the thermodynamic state of CO2as input to obtain an accurate molecular absorption coefficient. Using this prediction model instead of OCO-2 ABSCO V5.2 input to RTRETRIEVALFRAMEWORK MODEL, obtained the retrieval result with the official product R2reaching 0.991. It proves the feasibility of the artificial neural network training model instead of ABSCO.

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new parameterization scheme of the aerosol model, which optimized the independent SSA value into mixed combination of three basic aerosol components, then constructed lookup table to obtain hourly SSA from the Advanced Himawari Image (AHI) sensor. The comparison is encouraging the retrieved SSA results agree well with AERONET, especially for high aerosol loading (AOD > 0.3 at 470nm), with correlation coefficient of R = 0.50, RMSE = 0.02, and approximately 93% of retrieval results falling within the 5% EE envelope. Under normal conditions, R = 0.46, RMSE = 0.04, and approximately 68% of retrieved SSA fall in within the uncertainty of$\Delta$SSA = ±0.05. The comparison demonstrates the algorithm has the excellent ability to estimate SSA in pollution over land.

The Greenhouse Gases Observing Satellite (GOSAT) is the world's first spacecraft to measure the concentrations of CO2from space and it has high-precision hyperspectral atmospheric CO2monitoring from 2009. Several atmospheric CO2products, such as ACOS, NIES, OCFP and SRFP products from full physics retrieval algorithm, both provide XCO2(the column-average dry-air mole fraction of atmospheric CO2) of GOSAT. These products have different characteristics and advantages and have different performance in different regions. In order to obtain the XCO2data set with high precision, low uncertainty and high coverage, the maximum likelihood estimation (MLE) method is used to fuse GOSAT XCO2products. The algorithm takes into account the uncertainty of each product on each pixel, and is applied from April 2009 to December 2015. The validation result between fusion XCO2and Total Carbon Column Observing Network XCO2shows R = 0.843 and RMSE = 3.248.

Mining cities are an essential part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. In the present study, the Aerosol Optical Depth (AOD) retrieval of major mining urban agglomerations in China from the Gaofen-1 satellite data. Then a new hybrid model based on CTM (chemical transport model) Transport Model 5 (TM5) and GTWR (Geographic Time-Weighted Regression model) is proposed for PM2.5 and PM10mass concentration estimation. According to the different transformation stages and urban structure of mining cities, the temporal and spatial analysis of particulate matter characteristics is carried out in mining urban agglomerations. The estimated result for PM2.5 is verified at ground stations with R2 of 0.956 and RMSE (Root Mean Square Error) of 10.377 μg/m3, Moreover, the estimated result for PM10is verified at ground stations with R2 of 0.926 and RMSE of 16.669 μg/m3, The results indicate that PM2.5 and PM10have distinct spatial and temporal distribution patterns as Chinese mining cities are undergoing different types of transformation processes.

In this paper, surface-level ozone in China was estimated by using the source-sink analysis and machine learning model. By analyzing the source and sink of surface ozone, it is clear that ozone mass concentration is influenced by background value, regional and local chemical generation, deposition, chemical removal and Interregional transport comprehensively. Then, the light gradient boosting machine (LGBM) model was used to integrate various corresponding satellite-based variables, numerical model-based meteorological variables and land variables to obtain the high spatial resolution surface mass concentration of ozone in China. Taking June, July, August, 2021 as example, the feasibility of the Tropospheric Monitoring Instrument (TROPOMI) data, the European Centre for Medium-Range Weather Forecasts (ECWMF) data and LGBM model in estimating surface-level ozone mass concentration was analyzed and confirmed.

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. In 2013, China launched Gaofen-1 satellite, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. In this paper, we calculated AOD at 100m from Gaofen-1 and AQUA data based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm over Beijing, China. The experimental results are compared with the Aerosol Robotic Network (AERONET) for preliminary validation. The correlation coefficient is about 0.9 and a root-mean-square error (RMSE) of about 0.13. The experimental results show that the method have higher accuracy, and further validation work is continuing.

Aerosols playa significant role in earth-atmospheric radiant balance and global climate changes. FengYun-4A(FY-4A) is the first three-axis stabilized geostationary satellite in China, the Advanced Geosynchronous Radiation Imager (AGRI) is one of the four payloads onboard the satellite. In fact, FY-4A AOD products is not officially available so far, and there are few researches on AOD retrieval of FY-4A. Therefore, AGRI data was used to develop a new algorithm for retrieval of AOD over land in this paper. We used MCD43C2 datasets to obtain the band surface reflectance, established the surface reflectance ratio database. Next used 6SV model to build lookup table (LUT), calculated hourly AOD eventually. To validate our algorithm, the AGRI -derived AOD was quantitatively compared with AERONET ground-based measurements. It shows the better accuracy and coverage than JMA-AOD.

The global long-term aerosol optical depth (AOD) dataset has a great significance for the study of global climate change. Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) satellites can provide global observation from 1978 to present. In this paper, we have improved the algorithm for the retrieval of the AOD over land proposed by Xue et al in 2017 [1]. We obtain 0.64μm band surface reflectance by utilizing a linear relationship between the surface reflectance at the wavelength of3.75μm and 0.64μm, which has been verified in the Moderate Resolution Imaging Spectroradiometer (MODIS). Considering difference of spectral response between AVHRR's bands and MODIS's bands, we calibrate this empirical relationship by fitting the simulated surface reflectance of the relative AVHRR and MODIS bands. A radiative transfer model for Lambertian surface and the look-up table (LUT) method are applied to NOAA-7, 9,11,14, 18 over China mainland (15° - 60° N, 70° - 140° E) from 1982 to 2011. Comparison of retrieval AVHRR AOD against AErosol RObotic NETwork (AERONET) data shows good consistency with more than 60% points within uncertainty of$\pm$(0.05+0.25xAOD).

In this paper, PM2.5 and PM10 in mainland China were estimated by using the Geographically and Temporally Weighted Regression model and FY-4 AOD data. Based on the GTWR model, the PM was estimated by BLH, RH, time, space and AOD. Taking June 2, 2019 as an example, the feasibility of FY-4 data in estimating PM2.5 and PM10 mass concentrations was analyzed and confirmed.

In this paper, through the analysis and comparison of three commonly used atmospheric environmental capacity estimation methods, we find that the existing methods have many limitations in the aspects of data base and the factors considered. Taking Xuzhou City in Jiangsu Province as an example, based on the air pollution multi-source model, environmental impact assessment and primary pollutant simulation are carried out by using remote sensing data, ground monitoring station data, pollution emission inventory data and meteorological data. In addition, the iterative algorithm of multi-pollutant environmental capacity with the joint constraint of$\text{PM}_{2.5}$and$\mathrm{O}_{3}$is established to recalculate the urban atmospheric environmental capacity.