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The current approach to life cycle carcinogenic impact assessment (LCCA) is hindered by its static and linear characteristics. This situation prevents the accurate prediction of the incidence, associated damage, and potential economic burden of cancer. This study explores a highly comprehensive pathway for LCCA assessment. It uses the impacts of Tracheal, bronchus, and lung (TBL) predicted by the LCCA of China's coal power industry through a screened statistical regression model as the research target. The latest global burden of disease estimates is utilized to quantify the health damage from TBL incidence, whereas an approach combining the actual cost of health and human capital is applied to further assess the economic burden of TBL. Findings indicate that the traditional and static LCCA method, which relies on animal toxicity data, can lead to underestimations in actual LCCA. The interaction among spatiotemporal meteorological factors, epidemiological cancer disease burden, and socioeconomic behaviors allows exhibits nonlinearity due to the changes in the combined toxicity of mixed key substances. Following the active implementation of ultralow emission and energy-saving transformations in China's coal power industry, the national percentage of TBL cancer incidence caused by pollutants from the coal power industry decreased from 25.2 % in 2004 to 11.5 % in 2020. Results indicate that the established dynamic LCCA model based on temporal and spatial climate, socioeconomic, and epidemiological cancer data can be feasibly employed for the accurate impact evaluation and mitigation of carcinogens in practical applications.
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http://dx.doi.org/10.1016/j.scitotenv.2024.170851 | DOI Listing |
PLoS 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.
View Article and Find Full Text PDFJ Colloid Interface Sci
September 2025
State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China.
Commercial V-W/TiO catalysts are extensively applied for NO emission control in coal-fired power plants. However, their limited operating temperature range and low active site utilisation significantly restrict NO removal efficiency, particularly during boiler load fluctuations. This study introduces atomically dispersed Ce-V/TiO catalysts synthesised using a dual-site coordination strategy, enhancing active site dispersion.
View Article and Find Full Text PDFToxicol Rep
December 2025
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bang Phli, Samut Prakarn 10540, Thailand.
This comprehensive study investigated fly ash particulate matter (PM) from Thailand's Mae Moh Coal-fired Power Plant, focusing on its major toxin composition and toxicological effects in mice. Chemical composition analysis using inductively coupled plasma-mass spectrometry identified iron (Fe) as the predominant heavy metal (101,067.31 ± 8246.
View Article and Find Full Text PDFACS Omega
August 2025
School of Energy and Environment, Anhui University of Technology, Maanshan 243002, China.
Accurate assessment of coal quality is essential for optimizing combustion efficiency and reducing pollutant emissions in coal-fired power plants. In this study, we developed a laser-induced breakdown spectroscopy (LIBS)-based framework, combined with advanced machine learning techniques to predict key coal quality parameters, including elemental carbon, ash content, volatile matter, total sulfur, and calorific value. After applying spectral preprocessing methods.
View Article and Find Full Text PDFACS Omega
August 2025
School of Energy and Power Engineering, Northeast Electric Power University, No. 169, Changchun Road, Chuanying District, Jilin, Jilin 132012, China.
Traditional dust removal technologies have relatively low capture efficiencies for PM2.5 (particulate matter ≤2.5 μm) emitted by coal-fired power plants.
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