Tao Tang

Urban nitrogen dioxide (NO₂) emission is a major source of total NO₂ emission, yet brings large uncertainty of emission estimates because of its complicated internal environment. Current top-down methods, e.g. exponentially modified Gaussian (EMG) have strict theoretical assumptions of atmospheric dispersion condition and simplify the whole urban region as an isolated point source, which differs from actual emission situation, resulting in poor temporal representativeness and large uncertainty. This paper constructs a remote sensing estimation method for urban NO₂ emissions based on the cross-sectional flux method which is insensitive to meteorological assumptions, and improved it with considerations of NO₂ lifetime. Compared with ground-based observation, the mean actual percentage error (MAPE) of this work decreases by 42.58% compared with EMG’s MAPE. On the total scale of annual stocktake, the MAPE of this work is reduced by 39.79% compared with the EMG method. This method weakens the impact of meteorology condition and provide higher temporal representativeness. The retrieved NO₂ emission based on this method of New York City, Las Vegas, Chicago, Wuhan, Xian and Paris shows a 61.55±28.25kt/yr differences compared with Emissions Database for Global Atmospheric Research (EDGAR) inventory results, with overestimation up to 135.39 kt/yr (Xi’an). For all study regions, a clearly temporal pattern of NO₂ emission is found, with the monthly emission during ozone season increases 5.76 kt/month compared with non-ozone season. The cross-sectional flux method, once improved, demonstrates greater accuracy in estimating urban NO₂ emissions, and is expecting to be applied to different gaseous emission to provide more reliable results.

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.

Accurately estimating carbon emissions is crucial under the Paris Agreement, especially for thermal power plants, the largest source of fossil fuel emissions. While traditional methods are validated by satellite remote sensing, the effectiveness of satellites like the Orbiting Carbon Observatory (OCO) for national-level carbon inventories remains debated. This study evaluates satellite capabilities in monitoring emissions from thermal power plants, considering frequency, column-averaged dry-air mole fraction of CO ₂ (XCO ₂ ) precision, and spatial resolution. A correction strategy is proposed for global carbon stocktake using satellite data. The study reveals that the OCO-2 v11.1 and OCO-3 v10 satellites, with their approximate 1 part per million (ppm) XCO ₂ precision, substantially underestimate total U.S. power plant emissions by 70% ( ± 12%) due to their inability to detect emissions from smaller facilities. Improving precision to 0.5 ppm can narrow this gap to 52% ( ± 17%). Further reductions in this discrepancy can be achieved by enhancing monitoring frequency, XCO ₂ precision, and spatial resolution. Specifically, with a precision of 0.7 ppm, a spatial resolution of 0.5 km, and daily monitoring, the error can be decreased to less than 20%. A parallel analysis of the planned Copernicus Anthropogenic CO ₂ Monitoring Mission estimates that it could detect 52% of total U.S. power plant emissions, while TanSat-2 Global is projected to detect 44%. The findings highlight current limitations in satellite-based global carbon stocktake but indicate future potential improvements with higher spatiotemporal resolution and precision in upcoming satellite missions.

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.

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.