Power plants remain major contributors to air pollution, and while their impact on air quality and atmospheric chemistry have been extensively studied, there are still uncertainties in quantifying their precise contributions to PM_2.5 and O₃ formation under varying environmental conditions. This study employs the WRF/CMAQ modeling system to quantify the impact of power plant emissions on PM2.5 and O₃ levels across eastern China in June 2019. We investigate the spatial and temporal patterns of pollutant formation, analyze contributions to secondary PM2.5 components, and assess process-specific influences on O₃ concentrations. Results show that power plant emissions contribute up to 2.5–3.0 μg m⁻³ to PM_2.5 levels in central and eastern regions, with lower impacts in coastal and southern areas. O₃ contributions exhibit a more complex pattern, ranging from −4 to +4 ppb, reflecting regional variations in NOx saturation. Among secondary PM_2.5 components, nitrate formation is most significantly influenced by power plant emissions, emphasizing the critical role of NOx. Diurnal O₃ patterns reveal a transition from widespread morning suppression to afternoon enhancement, particularly in southern regions. Process analysis indicates that vertical transport is the primary mechanism enhancing surface O₃ from power plant emissions, while dry deposition acts as the main removal process. This comprehensive assessment provides crucial insights for developing targeted air quality management strategies, highlighting the need for region-specific approaches and prioritized NOx emission controls in the power sector. Our findings contribute to a deeper understanding of the complex relationships between power plant emissions and regional air quality, offering a foundation for more effective pollution mitigation policies.

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation.

The wake of a wind turbine has an important influence on the output power of wind farms. Staggered height layout is an emerging method for the layout optimization of wind farms. In order to study the effect of a staggered height layout on the overall power output of wind farms in depth, we established a combination of two large wind turbines and three small wind turbines arranged laterally between the two large wind turbines, and set four working conditions with different distances between the small wind turbines and the downstream large wind turbines as the research objects. The wind turbine array is analyzed by numerical simulation The layouts add three small wind turbines between the two large wind turbines, and each row of small wind turbines has a different distance from the downstream large wind turbines. The results show that as the distance from the upstream large wind turbine increases, the power of the three small wind turbines on the downstream wind turbine tends to be positive. The numerical simulation suggests that under the inflow wind speed, the closer to the downstream large wind turbine, the higher the wind speed is at the hub height.

Urbanization significantly affects regional climate, and accurately representing urban canopy processes is crucial for simulating heatwaves in highly urbanized regions such as North China. To evaluate the impacts of different urban canopy schemes on surface parameter simulation, this study compares the No Urban Canopy Model (NOUCM), Single-Layer Urban Canopy Model (SLUCM), and Multi-Layer Urban Canopy Model (MLUCM) during a heatwave in North China from 5 to 7 July 2023. The simulations use WRFv4.6.1 and are verified against observations from 141 urban stations. NOUCM shows a positive bias in 10 m wind speed. SLUCM introduces a building drag effect, but its single-layer representation cannot resolve the vertical distribution of drag, leading to a 15.8% increase in RMSE relative to NOUCM. MLUCM explicitly represents the building drag effect and reduces RMSE by 29.5%. For 2 m temperature, all schemes show positive biases. SLUCM produces cooler daytime and warmer nighttime temperatures, with a large nighttime bias. MLUCM more realistically simulates shading and radiation trapping, reducing the daytime RMSE by 18.2% and yielding nighttime biases close to zero. For 2 m specific humidity, SLUCM and MLUCM reduce RMSE by 1.8% and 0.7%, respectively. For urban heat island (UHI) intensity, MLUCM achieves an overall RMSE of 0.721 °C, which is 30.2% and 33.2% lower than NOUCM and SLUCM, respectively. It also produces the smallest daytime and nighttime biases, with good agreement with observations during 12–20 h. SLUCM shows a nighttime UHI bias of +1.094 °C, while NOUCM shows a daytime bias of +1.176 °C. Overall, MLUCM demonstrates superior performance in simulating wind speed, temperature, and UHI intensity, though its advantage in 2 m specific humidity is limited. These findings provide a reference for selecting urban canopy schemes in operational numerical weather prediction over highly urbanized regions.

To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for short-term probabilistic forecasting. Built upon a Transformer encoder, the framework integrates horizon-adaptively weighted power-curve, persistence, and wind-speed cubic-law baselines; propagates wind-speed uncertainty into a power distribution through a probabilistic wind-speed distribution with Monte Carlo integration; and employs spatial graph convolution to model inter-turbine correlations, together with online bias correction and segmented post hoc interval calibration. The framework is validated on two offshore wind farms in coastal China (8350 kW and 10,000 kW units), with all data analyzed at hourly resolution and forecasts issued for 23 hourly steps ahead. For point forecasting, the NMAE reaches 8.74% and 8.24%, with MAE reductions of 19.1% (95% CI: 18.1–19.9%) at Wind Farm A and 7.8% (95% CI: 7.3–8.4%) at Wind Farm B relative to the strongest baselines, both statistically significant under the Diebold–Mariano test (p < 0.001). For probabilistic forecasting, the PICP remains stable at 0.89, balancing coverage and sharpness. The error of PRWind accumulates markedly more slowly with the forecast horizon than that of the baselines, and ablation studies confirm the synergistic effect of the modules. Trained and evaluated independently on the two wind farms, PRWind performs consistently, demonstrating cross-wind-farm stability (rather than zero-shot generalization) conferred by physical guidance, and offering significant engineering value for intra-day and day-ahead offshore wind power dispatch.