Environmental Science and Pollution Research - Trace copper ion (Cu(II)) in water and wastewater can trigger peroxymonosulfate (PMS) activation to oxidize organic compounds, but it only works under... 相似文献
Environmental Science and Pollution Research - High temperature environment causes reduction in productivity in broilers by disrupting the intestinal barrier function. This study aimed to... 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
The continuous increase in waste generation warrants global management of waste to reduce the adverse economic, social, and environmental impact of waste while achieving goals for sustainability. The complexity of waste management systems due to different waste management practices renders such systems difficult to analyze. System dynamics (SD) approach aids in conceptualizing and analyzing the structure, interactions, and mode of behavior of the complex systems. The impact of the underlying components can therefore be assessed in an integrated way while the impact of possible policies on the system can be studied to implement appropriate decisions. This review summarizes various applications of SD pertinent to the waste management practices in different countries. Practices may include waste generation, reduction, reuse/recovery, recycling, and disposal. Each study supports regional-demanding targets in environmental, social, and economic scopes such as expanding landfill life span, implementing proper disposal fee, global warming mitigation, energy generation/saving, etc. The interacting variables in the WMS are specifically determined based on the defined problem, ultimate goal, and the type of waste. Generally, population and gross domestic product can increase the waste generation. An increase in waste reduction, source separation, and recycling rate could decrease the environmental impact, but it is not necessarily profitable from an economic perspective. Incentives to separate waste and knowledge about waste management are variables that always have a positive impact on the entire system.
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
Cassava starch waste hydrolysates (CSWHs) with different degrees of polymerisation, i.e., CSWHs-1, CSWHs-2 and CSWHs-3, were prepared through the hydrolysis of cassava starch waste with thermostable a-amylase from Thermococcus sp. HJ21. The prepared CSWHs were then used as a carbon source for curdlan production with Alcaligenes faecalis ATCC 31749. The amount of curdlan produced and the glucosyltransferase activity during curdlan synthesis increased more obviously when CSWHs-2 was used as the carbon source than when glucose was used. Using both carbon sources, the maximum curdlan production was observed at day 5, and the maximum glucosyltransferase activity was observed at day 4. Glucosyltransferase activity decreased thereafter, and biomass continued to increase until the end of the experiment (day 6). Results indicated that the enhanced curdlan production with CSWHs as the carbon source was highly correlated with glucosyltransferase activity. 相似文献
