Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km ) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.
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http://dx.doi.org/10.1111/gcb.16828 | DOI Listing |
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFHeliyon
January 2025
Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing, 100091, China.
Railway bridges with lower beam bottom clearances in windblown sand areas tend to accumulate sand particles on the sides of the beams, which seriously impacts railway safety. To investigate the effect of beam clearance height on wind-sand movement near the surface, and to determine the minimum clearance height for railway bridges in such areas, computational fluid dynamics using the Euler-Euler two-phase flow model was employed to simulate the wind-sand flow field beneath bridges with different heights. The results indicated that as clearance height increased, both the high-speed area above the bridge and acceleration area under the bridge increased, while the turbulence area on the leeward side remained unchanged.
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January 2025
State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
Vegetation assimilation of atmospheric gaseous elemental mercury (GEM) represents the largest dry deposition pathway in global terrestrial ecosystems. This study investigated Hg accumulation mechanisms in deciduous broadleaves and evergreen needles, focusing on how ecophysiological strategies─reflected by δC, δO, leaf mass per area, and leaf dry matter content-mediated Hg accumulation. Results showed that deciduous leaves exhibited higher total Hg (THg) concentrations and accumulation rates (THg), which were 85.
View Article and Find Full Text PDFSci Rep
January 2025
Northwest Sichuan Gas District of Southwest Oil and Gasfield Company, Jiangyou, 621700, China.
With the wide application of hydrogen-doped natural gas (HBNG) in end users, laying pipelines in urban, comprehensive pipe corridors has become increasingly common. However, the leakage and diffusion of hydrogen-doped natural gas in confined or semi-confined spaces (e.g.
View Article and Find Full Text PDFVet Q
December 2025
College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.
Foot-and-Mouth Disease is a highly contagious transboundary animal disease. FMD has caused a significant economic impact globally due to direct losses and trade restrictions on animals and animal products. This study utilized multi-distance spatial cluster analysis, kernel density analysis, directional distribution analysis to investigate the spatial distribution patterns of historical FMD epidemics.
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