In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Vi...In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Village of Xiqing District into 3 groups： compostable materials, recyclable materials and toxics on the basis of the constructed secondary classification mode of domestic waste. The study focused on waste generation strength and classification features, compared the waste generation features between rural and urban residents, and analyzed the re- lation between waste generation strength and economic and cultural factors. The re- sults indicated that the average generation speed of urban domestic waste was 423.08 g/（d.capita）, and that of rural domestic waste was 629.89 g/（d.capita）, there was significant difference between rural and urban compost generation strength （P= 0.00002）, while the generation strength of recyclable materials and toxics between rural and urban areas had no significant difference （P=0.471 and P=0.099, respec- tively）. Secondary classification mode is an effective source classification mode for domestic wastes and has positive effects on waste reduction and treatment.展开更多
This study defines and compares four scenarios for MSW （municipal solid waste） management： Scenario 1, unsorted waste taken to a landfill （baseline scenario）; Scenario 2, sorted waste used for home or communal co...This study defines and compares four scenarios for MSW （municipal solid waste） management： Scenario 1, unsorted waste taken to a landfill （baseline scenario）; Scenario 2, sorted waste used for home or communal composting; Scenario 3, sorted waste used for anaerobic digestion; and Scenario 4, sorted waste taken to a composting centre. The results of this study suggest that Scenario 1 would emit the highest levels of GHG （greenhouse gas） emissions, 692 x 103 tonnes CO2eq per year. Scenario 3 would have the lowest levels of GHG emissions, 195 x 103 tonnes CO2eq per year. Compared with the baseline scenario, it yields a 72% reduction of GHG emissions with a total savings of 498 ~ 103 tonnes CO2eq per year. The second-best option is Scenario 2, followed closely by Scenario 4, both yield 66.6% reductions with deviation by 0.03%. The deviation is due to transportation, which emission is negligibly small. The amounts of GHG savings for Scenario 2 and 4 are 461.3 ×10^3 tonnes CO2eq per year and 461×10^3 tonnes CO2eq per year, respectively It is evident from these results that anaerobic digestion has the highest potential for reducing GHG emissions.展开更多
In this study, methods to classify advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding ...In this study, methods to classify advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding the classification of opinion spam documents, which is very rare in foreign studies; however, there are no studies that classify advertising reviews from Korean reviews. In this study, the Naive Bayes Classifier was used to classify review documents and the POS （Part-of-Speech）-Tagging and bigram methods were used to extract specific words. The frequency calculation methods for the probability value of specific words were： （1） The general number of appearances of words （2） the frequency calculation of specific words through the suggested Latent Semantic Analysis （LSA）, and by recalculating the result from （1） in （2）, the performances of each method were compared. As a result, the methods from （2） showed 88.43% accuracy which is 8.89% higher than 79.54% which was the previous result from using the POS-Tagging ＋ Bigram method. Therefore, it was proved that the method suggested in this study is effective at classifying or extracting advertising reviews from Korean product review documents.展开更多
基金Supported by Agricultural Scientific and Technological Achievement Transformation and Popularization Project of Tianjin(201003010)~~
文摘In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Village of Xiqing District into 3 groups： compostable materials, recyclable materials and toxics on the basis of the constructed secondary classification mode of domestic waste. The study focused on waste generation strength and classification features, compared the waste generation features between rural and urban residents, and analyzed the re- lation between waste generation strength and economic and cultural factors. The re- sults indicated that the average generation speed of urban domestic waste was 423.08 g/（d.capita）, and that of rural domestic waste was 629.89 g/（d.capita）, there was significant difference between rural and urban compost generation strength （P= 0.00002）, while the generation strength of recyclable materials and toxics between rural and urban areas had no significant difference （P=0.471 and P=0.099, respec- tively）. Secondary classification mode is an effective source classification mode for domestic wastes and has positive effects on waste reduction and treatment.
文摘This study defines and compares four scenarios for MSW （municipal solid waste） management： Scenario 1, unsorted waste taken to a landfill （baseline scenario）; Scenario 2, sorted waste used for home or communal composting; Scenario 3, sorted waste used for anaerobic digestion; and Scenario 4, sorted waste taken to a composting centre. The results of this study suggest that Scenario 1 would emit the highest levels of GHG （greenhouse gas） emissions, 692 x 103 tonnes CO2eq per year. Scenario 3 would have the lowest levels of GHG emissions, 195 x 103 tonnes CO2eq per year. Compared with the baseline scenario, it yields a 72% reduction of GHG emissions with a total savings of 498 ~ 103 tonnes CO2eq per year. The second-best option is Scenario 2, followed closely by Scenario 4, both yield 66.6% reductions with deviation by 0.03%. The deviation is due to transportation, which emission is negligibly small. The amounts of GHG savings for Scenario 2 and 4 are 461.3 ×10^3 tonnes CO2eq per year and 461×10^3 tonnes CO2eq per year, respectively It is evident from these results that anaerobic digestion has the highest potential for reducing GHG emissions.
文摘In this study, methods to classify advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding the classification of opinion spam documents, which is very rare in foreign studies; however, there are no studies that classify advertising reviews from Korean reviews. In this study, the Naive Bayes Classifier was used to classify review documents and the POS （Part-of-Speech）-Tagging and bigram methods were used to extract specific words. The frequency calculation methods for the probability value of specific words were： （1） The general number of appearances of words （2） the frequency calculation of specific words through the suggested Latent Semantic Analysis （LSA）, and by recalculating the result from （1） in （2）, the performances of each method were compared. As a result, the methods from （2） showed 88.43% accuracy which is 8.89% higher than 79.54% which was the previous result from using the POS-Tagging ＋ Bigram method. Therefore, it was proved that the method suggested in this study is effective at classifying or extracting advertising reviews from Korean product review documents.
