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The potential of satellite-based CO emission estimation from power plants is gaining increasing attention. However, the limited spatiotemporal coverage of current satellite-derived XCO data poses significant challenges to tracking CO variations on a large scale and over extended periods. In view of this, this study uses satellite-derived NO data as a suitable proxy and tracks CO emissions from 38 selected power plants globally by integrating near-synchronously observed TROPOMI NO data and OCO-2 XCO data. The results show that our method significantly increases the effective observation frequency by almost 200 times compared to using OCO-2 data alone. Compared to the emissions reported by the power plants, the correlation coefficient of the method used in this study (0.78) is higher than that of the emission inventory estimates (0.43-0.62), resulting in an accuracy improvement of approximately 1.8-2.3 Mt/yr per power plant. The use of satellite-derived NO data significantly enhances the ability to remotely estimate CO emissions from power plants, which gives us confidence in studying anthropogenic point-source CO emissions across different spatial and temporal scales. This enhances the understanding of their variability and mitigation potential, supporting the development of refined carbon inventories and advanced carbon cycle assimilation systems.
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http://dx.doi.org/10.1021/acs.est.5c01100 | DOI Listing |
Environ Monit Assess
September 2025
Department of Environment and Life Science, KSKV Kachchh University, Bhuj, Gujarat, 370 001, India.
India's energy demand increased by 7.3% in 2023 compared to 2022 (5.6%), primarily met by coal-based thermal power plants (TPPs) that contribute significantly to greenhouse gas emissions.
View Article and Find Full Text PDFWaste Manag
September 2025
Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery, School of Environmental Science & Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
As one of the major sources of greenhouse gas (GHG) emissions, the municipal solid waste (MSW) management system was regarded as a key contributor to the construction of a low-carbon society. Understanding the evolution of waste treatment facilities and the corresponding GHG emissions was essential for assessing the low-carbon competitiveness of local communities. In this study, facility-level data were used to estimate GHG emissions from the waste management system in the Yangtze River Delta (YRD) and analyze their temporal and spatial variations.
View Article and Find Full Text PDFSci Total Environ
September 2025
Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering, "Giulio Natta" - Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
The outdoor storage of wood chips, used in biomass thermal power plants, may lead to different diffuse gaseous emissions. These emissions can contain different molecules, often with a non-negligible odour potential. Despite this need, these solid area sources are particularly complex to be characterised, due to their very high heterogeneity determined by a complex phenomenon of self-heating.
View Article and Find Full Text PDFSci Rep
September 2025
Fukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology (AIST), Fukushima, 9630298, Koriyama, Japan.
The increasing adoption of the Internet of Things (IoT) in energy systems has brought significant advancements but also heightened cyber security risks. Virtual Power Plants (VPPs), which aggregate distributed renewable energy resources into a single entity for participation in energy markets, are particularly vulnerable to cyber-attacks due to their reliance on modern information and communication technologies. Cyber-attacks targeting devices, networks, or specific goals can compromise system integrity.
View Article and Find Full Text PDFPLoS One
September 2025
School of Chemical Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Coal blending in thermal power plants is a complex multi-objective challenge involving economic, operational and environmental considerations. This study presents a Q-learning-enhanced NSGA-II (QLNSGA-II) algorithm that integrates the adaptive policy optimization of Q-learning with the elitist selection of NSGA-II to dynamically adjust crossover and mutation rates based on real-time performance metrics. A physics-based objective function takes into account the thermodynamics of ash fusion and the kinetics of pollutant emission, ensuring compliance with combustion efficiency and NOx limits.
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