Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NO_x), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant emissions. Previous bottom-up emission inventories exhibit considerable discrepancies and lack a comprehensive and reliable database. To develop a high-precision emission inventory for individual cities, this study utilizes high-resolution single-pass observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite to quantify the emission rates of NO_x. The Exponentially Modified Gaussian (EMG) model is validated for estimating NO_x emission strength using real plumes observed in satellite single-pass observations, demonstrating good consistency with existing inventories. Further analysis based on the results reveals the existence of a weekend effect and seasonal variations in NO_x emissions for the majority of the studied cities.

Coal mines are a major global source of methane emissions, accounting for 10% of global methane emissions. As the world’s largest coal producer and consumer, China has various coal mine types, yet significant uncertainty exists in its methane emissions due to a lack of systematic ground-based data. Therefore, accurately quantifying methane emissions from coal mining activities is crucial. Existing inventories struggle to capture complex and anomalous emissions, while medium-resolution satellites lack facility-level precision. High-spatial-resolution satellite observations offer detailed insights. With a spatial resolution of 60 m and spectral channels from 381 to 2493 nm, the EMIT satellite can finely characterize facility-level methane plumes. This study uses data from 88 methane emission plumes captured by the EMIT satellite to quantify the methane emission characteristics of 32 coal mines located in Inner Mongolia, Ningxia Hui Autonomous Region, and Shanxi Province, China. Principal Component Analysis reveals that mine size, coal type, and processing stage are key factors influencing methane emissions, with emission rates varying significantly under different conditions. Data indicate varying methane emission rates across production stages. The median methane emission rate in gas treatment/utilization is double that of ventilation shafts and chemical plants. Larger coal mines show a decreasing trend in the unit methane emission rate with scale increase, with super-large mines emitting only one-tenth that of medium-sized mines. For large coal mines, bituminous coal mines emit nearly double that of anthracite coal mines. Bottom-up emission inventory evaluation results for the 32 coal mines studied show that EDGAR v8.0 and GFEI v2 underestimated annual methane total emissions, capturing only about half of the emissions quantified through satellite observations. The average emission intensity of the 32 coal mines estimated by satellite data is 0.48 kg/GJ, which is higher than the emission intensities reported by EDGAR v8.0 (0.24 kg/GJ) and GFEI v2 (0.18 kg/GJ). Overall, high-resolution satellite data offer new insights into facility-level emissions, revealing the complexity of methane emissions from coal mines and underscoring the need for tailored mitigation strategies that consider different mine types and operational stages.

Fusing multiple satellite aerosol optical depth (AOD) products to produce high-quality aerosol records is necessary for climate-related research. The Bayesian maximum entropy (BME) approach has unique advantages in this regard. However, the large accuracy differences and redundancy characteristics between aerosol products are not clearly considered by BME resulting in limited fusion quality and large computational consumption. Therefore, in this study, we try to explicitly introduce a priori knowledge about the multi-source AOD product accuracy, called the accuracy ranking matrix, into the BME fusion process to test whether the above issues can be alleviated. Eight publicly released aerosol products over China are used in the fusion experiments, and the fusion results are validated with ground-based measurements. Results suggest that, compared with fusion directly, the fusion accuracy and efficiency are significantly improved by introducing the accuracy ranking matrix, the proportion of satisfying the Global Climate Observing System (GCOS) accuracy requirements increases by 12.2%, and the consumption time decreases by 74.6%. Specifically, the accuracy improvement in vegetated and clean air conditions outperforms that in arid and polluted conditions, with the most significant improvement in clean conditions, the GCOS fraction increased by 22.9%. Moreover, the spatial distribution of fusion results in typical regions indicates that the introduction of the accuracy ranking matrix makes the AOD spatial variation smoother. These results demonstrate that introducing the accuracy ranking matrix can generate higher quality AOD distributions with improved efficiency, which is expected to guide the fusion of AOD products or other remote sensing products.

Carbon dioxide (CO ₂ ) is the most important atmospheric contributor for global warming. Satellite remote sensing is a commonly used method for high-precision CO ₂ detection, but it often suffers from striping issues, which hinders its ability to achieve full coverage. Therefore, the limited availability of data poses challenges for global carbon accounting. In this paper, we generate a global seamless and high-resolution dataset of column-averaged dry air CO ₂ mole fractions (XCO ₂ ) from 2017 to 2020 by integrating multiple data sources using deep learning techniques Feedforward Neural Network (FNN). The data sources primarily include satellite, ground-based, and reanalysis XCO ₂ products, satellite vegetation index data, and meteorological data. The spatial resolution of the dataset is 0.1°, and the temporal resolution is one day. Moreover, this paper also investigated the importance of features in deep learning models and examined the spatiotemporal variations of global XCO ₂ . The results demonstrate that the FNN approach with time series features yields an improved dataset (R=0.98, RMSE=0.82 ppm) compared to the other methods. In an FNN, the known model data (CT XCO ₂ ) are considered as the most important features. We find that the global XCO ₂ has increased by approximately 7.5 ppm from 2017 to 2020. This seamless and fine-scale dataset provides valuable support for understanding global carbon cycling and formulating carbon emission reduction policies.

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R² = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2’s AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality.