Environmental Chemistry Letters - Branched allylic sulfones are scaffolds widely distributed in bioactive molecules and organic functional materials. The synthesis of allylic sulfones has been... 相似文献
● MSWNet was proposed to classify municipal solid waste.● Transfer learning could promote the performance of MSWNet.● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions. 相似文献
● A novel framework integrating quantile regression with machine learning is proposed.● It aims to identify factors driving observations to upper boundary of relationship.● Increasing N:P and TN concentration help fulfill the effect of TP on CHL.● Wetter and warmer decrease potential and increase eutrophication control difficulty.● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships. 相似文献
● Medium poly Al salts dominated the PAC residual salts with a rational dosage. ● Settlement flocculation effect under medium poly Al salts showed a better trend. ● Complex of medium poly Al salts and enzymes promoted cell activity. ● Medium poly Al salts were beneficial to the effluent indexes. With the widespread introduction of pre-coagulation prior to the biological unit in various industrial wastewater treatments, it is noteworthy that long-term accumulation of residual coagulants has certains effect on both micro and macro characteristics of activated sludge (AS). In this study, the morphology distributions of residual aluminum salts (RAS) and their effects on the removal efficiency of AS were investigated under different PAC concentrations. The results showed that the dominance of medium polymeric RAS, formed under an appropriate PAC dose of 20 mg/L enhanced the hydrophobicity, flocculation, and sedimentation performances of AS, as well as the enzymatic activity in cells in the sludge system, improving the main pollutants removal efficiency of the treatment system. Comparatively the species composition with monomer and dimer / high polymer RAS as the overwhelming parts under an over-dosed PAC concentration of 55 mg/L resulted in excessive secretion of EPS with loose flocs structure and conspicuous inhibition of cellular activity, leading to the deterioration of physico-chemical and biological properties of AS. Based on these findings, this study can shed light on the role of the RAS hydrolyzed species distributions, closely relevant to Al dosage, in affecting the comprehensive properties of AS and provide a theoretical reference for coagulants dosage precise control in the pretreatment of industrial wastewater. 相似文献
● A systematic framework was developed to identify i-PPCPs for landfill leachate.● The wide-scope target analysis offered a basis for comprehensive i-PPCP screening.● Source-specificity and representativeness analysis helped to refine i-PPCPs.● Erythromycin, gemfibrozil and albendazole were identified as i-PPCPs for leachate. Identifying potential sources of pharmaceuticals and personal care products (PPCPs) in the environment is critical for the effective control of PPCP contamination. Landfill leachate is an important source of PPCPs in water; however, it has barely been involved in source apportionment due to the lack of indicator-PPCPs (i-PPCPs) in landfill leachates. This study provides the first systematic framework for identifying i-PPCPs for landfill leachates based on the wide-scope target monitoring of PPCPs. The number of target PPCPs increased from < 20 in previous studies to 68 in the present study. Fifty-nine PPCPs were detected, with median concentrations in leachate samples ranging from below the method quantification limit (MQL) to 41 μg/L, and 19 of them were rarely reported previously. A total of 29 target compounds were determined to be PPCPs of high concern by principal component analysis according to multiple criteria, including occurrence, exposure potential, and ecological effect. Coupled with source-specificity and representativeness analysis, erythromycin, gemfibrozil, and albendazole showed a significant difference in their occurrence in leachate compared to other potential sources (untreated and treated municipal wastewater and livestock wastewater) and correlated with total PPCP concentrations; these were recommended as i-PPCPs for leachates. Indicator screening procedure can be used to develop a sophisticated source apportionment method to identify sources of PPCPs from adjacent landfills. 相似文献