This study investigates the dispersion of natural gas leakages in urban environments under varying wind conditions (Beaufort levels 1, 2, and 6) and street layouts, with a focus on the implications for mobile leak detection at a height of 0.3 m above ground. Through numerical simulations, we analyze how urban canyons influence wind field and methane (CH₄) concentration distributions, highlighting the impact of wind speed and urban geometry on gas dispersion. The key findings indicate that urban structures significantly affect gas dispersion patterns, with higher wind speeds facilitating better dispersion and reducing the risk of high-concentration gas buildups. The study underscores the need to consider both meteorological conditions and urban design in enhancing gas leak detection and safety measures in cities. The results contribute to improving emergency response strategies and urban planning for mitigating the risks associated with gas leaks.

The increasing deployment of turbines installed offshore is critical for sustainable energy development, yet accurate performance assessment remains challenging due to complex environmental influences, diverse turbine control strategies, and issues with data quality. Traditional performance metrics and power curve models often fail to provide reliable cross-turbine comparisons because they neglect multivariate environmental factors and turbine-specific biases. To address these limitations, this study develops a novel multivariate environmental factor-driven power assessment framework employing segmented long short-term memory (LSTM) models. A hybrid data cleaning method, combining bidirectional quartile analysis with the power curtailment detection, is proposed to effectively identify outliers, including subtle anomalies within typical data ranges. Samples are segmented based on rated wind speed to reflect differences in control strategies, and turbine-specific operational parameters are excluded to ensure unbiased comparisons among turbines. The proposed method achieves substantial improvements in predictive accuracy, with decreases of 9.39% in mean absolute error (MAE) and 11.75% in root mean square error (RMSE), compared to conventional binning approaches. When applied to three 5.5 MW offshore wind turbines, the proposed method reveals significant differences among the units. Turbine A demonstrates the highest performance, while turbines B and C exhibit reductions of 14.35% and 8.29%, respectively. Operational state analysis shows that turbine B experiences substantially longer maintenance durations, indicating severe faults that adversely affect its operational reliability and power output. These findings provide valuable insights for maintenance prioritization and performance benchmarking among wind turbines.