Environmental Science and Pollution Research - The aerodynamic noise around the high-speed railway bridge is studied by the train-bridge-flow field numerical model and theory analysis. With the... 相似文献
Environmental Science and Pollution Research - In the paper, the pollution of playgrounds in Lublin with heavy metals was assessed. Since playgrounds are a place of activity of children—the... 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
Environmental Chemistry Letters - Wastewater from the uranium mining industry contains toxic arsenate (AsO43–), selenate (SeO42–), and molybdate (MoO42–) that can be removed by... 相似文献
Environment, Development and Sustainability - This study attempts to introduce haze pollution into the environmental efficiency evaluation framework and measures PM2.5 environmental efficiency in... 相似文献
A process combining catalyzed Fe(0)-carbon microelectrolysis (IC-ME) with activated carbon (AC) adsorption was developed for advanced reclaimed water treatment. Simultaneous nitrate reduction and chemical oxygen demand (COD) removal were achieved, and the effects of composite catalyst (CC) addition, AC addition, and initial pH were investigated. The reaction kinetics and reaction mechanisms were calculated and analyzed. The results showed that CC addition could enhance the reduction rate of nitrate and effectively inhibit the production of ammonia. Moreover, AC addition increased the adsorption capacity of biorefractory organic compounds (BROs) and enhanced the degradation of BRO. The reduction of NO3?–N at different pH values was consistently greater than 96.9%, and NH4+–N was suppressed by high pH. The presence of CC ensured the reaction rate of IC-ME at high pH. The reaction kinetics orders and constants were calculated. Catalyzed iron scrap (IS)-AC showed much better nitrate reduction and BRO degradation performances than IS-AC and AC. The IC-ME showed great potential for application to nitrate and BRO reduction in reclaimed water.
Environmental Science and Pollution Research - Nitrosamines (NAms) are potent genotoxic and carcinogenic but widely detected in drinking water. This study aimed to investigate the occurrence of... 相似文献
Environmental Geochemistry and Health - Effective supply of environmental public services (EPS) is important to guarantee the mitigation of residential pollution exposure risk. This study analyzes... 相似文献