Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method ...Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method was proposed for the degradability identification of MSW.Firstly,the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site.Secondly,a lite convolutional neural network(CNN)model was built with only 3.37 million parameters,and then a total of eight models were trained on these datasets with and without the image augmentation operations,respectively.Finally,a degradability identification system was built for on-site composting,where the images were cut to different sizes of small squares for prediction,and the experiments were conducted to find the best combinations of the trained models and the cutting size.The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher,and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%,which indicated the capability of this cost-effective method to identify the degradability of MSW.展开更多
To perform a systematic survey on the occurrence and removal of micropollutants during municipal wastewater treatment, 943 semi-volatile organic chemicals in 32 wastewater samples including influents of secondary trea...To perform a systematic survey on the occurrence and removal of micropollutants during municipal wastewater treatment, 943 semi-volatile organic chemicals in 32 wastewater samples including influents of secondary treatments, secondary effluents and final effluents（effluents of advanced treatments）, which were collected from seven full-scale municipal wastewater treatment plants（MWTPs） in China, were examined by gas chromatography-mass spectrometry（GC-MS） coupled with an automated identification and quantification system with a database（AIQS-DB）. In total, 196 and 145 chemicals were detected in secondary and final effluents, respectively. The majority of the total concentrations（average removal efficiency, 87.0%±5.9%） of the micropollutants were removed during secondary treatments. However, advanced treatments achieved different micropollutant removal extents from secondary effluents depending on the different treatment processes employed. Highly variable removal efficiencies of total concentrations（32.7%–99.3%） were observed among the different advanced processes. Among them,ozonation-based processes could remove 70.0%–80.9% of the total concentrations of studied micropollutants. The potentially harmful micropollutants, based on their detection frequency and concentration in secondary and final effluents, were polycyclic aromatic hydrocarbons（PAHs）（2-methylnaphthalene, fluoranthene, pyrene, naphthalene and phenanthrene）, phosphorus flame retardants（tributyl phosphate（TBP）, tris（2-chloroethyl）phosphate（TCEP） and tris（1,3-dichloro-2-propyl） phosphate（TDCP））, phthalates（bis（2-ethylhexyl）phthalate（DEHP））, benzothiazoles（benzothiazole,2-（methylthio）-benzothiazol, and 2（3H）-benzothiazolone） and phenol. This study indicated that the presence of considerable amounts of micropollutants in secondary effluent creates the need for suitable advanced treatment before their reuse.展开更多
基金The authors acknowledge that this study was financially supported by the National Key R&D Program of China(Grant No.2020YFD1000300No.2018YFD0200801)+1 种基金National ten thousand talents special support program of China[2018]no.29Innovation and Entrepreneurship Training Program of Hunan Agricultural University(Grant No.2019062x).
文摘Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method was proposed for the degradability identification of MSW.Firstly,the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site.Secondly,a lite convolutional neural network(CNN)model was built with only 3.37 million parameters,and then a total of eight models were trained on these datasets with and without the image augmentation operations,respectively.Finally,a degradability identification system was built for on-site composting,where the images were cut to different sizes of small squares for prediction,and the experiments were conducted to find the best combinations of the trained models and the cutting size.The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher,and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%,which indicated the capability of this cost-effective method to identify the degradability of MSW.
基金supported by the Major Science and Technology Program for Water Pollution Control and Treatment in China(No.2017ZX07106005)
文摘To perform a systematic survey on the occurrence and removal of micropollutants during municipal wastewater treatment, 943 semi-volatile organic chemicals in 32 wastewater samples including influents of secondary treatments, secondary effluents and final effluents（effluents of advanced treatments）, which were collected from seven full-scale municipal wastewater treatment plants（MWTPs） in China, were examined by gas chromatography-mass spectrometry（GC-MS） coupled with an automated identification and quantification system with a database（AIQS-DB）. In total, 196 and 145 chemicals were detected in secondary and final effluents, respectively. The majority of the total concentrations（average removal efficiency, 87.0%±5.9%） of the micropollutants were removed during secondary treatments. However, advanced treatments achieved different micropollutant removal extents from secondary effluents depending on the different treatment processes employed. Highly variable removal efficiencies of total concentrations（32.7%–99.3%） were observed among the different advanced processes. Among them,ozonation-based processes could remove 70.0%–80.9% of the total concentrations of studied micropollutants. The potentially harmful micropollutants, based on their detection frequency and concentration in secondary and final effluents, were polycyclic aromatic hydrocarbons（PAHs）（2-methylnaphthalene, fluoranthene, pyrene, naphthalene and phenanthrene）, phosphorus flame retardants（tributyl phosphate（TBP）, tris（2-chloroethyl）phosphate（TCEP） and tris（1,3-dichloro-2-propyl） phosphate（TDCP））, phthalates（bis（2-ethylhexyl）phthalate（DEHP））, benzothiazoles（benzothiazole,2-（methylthio）-benzothiazol, and 2（3H）-benzothiazolone） and phenol. This study indicated that the presence of considerable amounts of micropollutants in secondary effluent creates the need for suitable advanced treatment before their reuse.
