Environmental Science and Pollution Research - At present, the contradiction between survival and ecology necessitates the integration of crop planting, chemical fertilizer application, and... 相似文献
Environmental Science and Pollution Research - The development of cyanobacterial blooms can have adverse effects on water bodies and may produce cyanotoxins. Several physical and chemical methods... 相似文献
Environmental Science and Pollution Research - Buildings account for nearly 2/5ths of global energy expenditure. Due to this figure, the 90s witnessed the rise of green buildings (GBs) that were... 相似文献
Environmental Science and Pollution Research - The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the... 相似文献
Environmental Science and Pollution Research - Arsenic (As) indelibly exists in the environment and may reach to a food chain. Flavors and herbs are recognized sources of natural antioxidants that... 相似文献
Plastic pollution is a major environmental issue worldwide, calling for advanced methods to recycle waste plastics in the context of the circular economy. Here we review methods and strategies to convert waste plastics into value-added carbon materials, with focus on sources, properties, pretreatment of waste plastics, and on preparation of carbon materials. Pretreatment techniques include mechanical crushing, plastic stabilization and electrospinning. Carbon materials such as carbon nanotubes, graphene, carbon nanosheets, carbon spheres and porous carbon are prepared by oxygen-limited carbonization, catalytic carbonization, the template-based method, and pressure carbonization. We emphasize the conversion of polyethene terephthalate, polyethylene, polypropylene, polystyrene, halogenated plastics, polyurethane and mixed plastics.
Environmental Chemistry Letters - The demand for lithium is growing rapidly with the increase in electric vehicles, batteries and electronic equipments. Lithium can be extracted from brines, yet... 相似文献
Environmental Chemistry Letters - Electrochemical reduction of carbon dioxide (CO2) is promising to alleviate carbon emissions and produce fuels and materials in a circular way, yet effective... 相似文献
Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing. 相似文献