Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs）...Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.展开更多
Accurate soil organic carbon storage(SOCS)estimation is crucial for sustaining ecosystem health and mitigating climate change impacts.This study investigated the accuracy and variability of SOCS predictions,focusing o...Accurate soil organic carbon storage(SOCS)estimation is crucial for sustaining ecosystem health and mitigating climate change impacts.This study investigated the accuracy and variability of SOCS predictions,focusing on the role of pedotransfer functions(PTFs)in estimating soil bulk density(BD).Utilizing a comprehensive dataset from the Korean Rural Development Administration(RDA database),which includes 516 soil horizons,we evaluated 36 widely-used BD PTFs,well-established formulas that estimate BD by considering soil properties,including soil organic carbon(SOC),soil organic matter(OM),sand,gravel,silt,and clay.These PTFs demonstrated varying levels of precision,with root mean squared errors(RMSE)ranging from 0.177 to 0.377 Mg m^(-3) and coefficients of determination(R^(2))from 0.176 to 0.658;hence,the PTFs have been classified into excellent,moderate,and poor-performing groups for predicting BD.Further,a novel PTF based on an exponential function of SOC was developed,showing superior predictive power(R^(2)=0.73)compared to existing PTFs,using an independent validation dataset.Our findings reveal significant differences in SOCS predictions and observations among the PTFs,with a p-value<0.05.The highest concentrations of SOCS were noted in forest soils,considerably above the national average,highlighting the importance of tailored soil management practices to enhance carbon sequestration.These findings are crucial for refining PTF precision to improve the accuracy of national SOCS estimates,supporting effective land management and climate change mitigation strategies.展开更多
基金Supported by the Innovative Talent Funds for Project 985 under Grant No WLYJSBJRCTD201102the Fundamental Research Funds for the Central Universities under Grant No CQDXWL-2013-014+1 种基金the Natural Science Foundation of Chongqing under Grant No CSTC2006BB5240the Program for New Century Excellent Talents in Universities of China under Grant No NCET-07-0903
文摘Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.
基金supported by Korea Environmental Industry&Technology Institute(KEITI)through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Project,funded by Korea Ministry of Environment(MOE)[2022003460002].
文摘Accurate soil organic carbon storage(SOCS)estimation is crucial for sustaining ecosystem health and mitigating climate change impacts.This study investigated the accuracy and variability of SOCS predictions,focusing on the role of pedotransfer functions(PTFs)in estimating soil bulk density(BD).Utilizing a comprehensive dataset from the Korean Rural Development Administration(RDA database),which includes 516 soil horizons,we evaluated 36 widely-used BD PTFs,well-established formulas that estimate BD by considering soil properties,including soil organic carbon(SOC),soil organic matter(OM),sand,gravel,silt,and clay.These PTFs demonstrated varying levels of precision,with root mean squared errors(RMSE)ranging from 0.177 to 0.377 Mg m^(-3) and coefficients of determination(R^(2))from 0.176 to 0.658;hence,the PTFs have been classified into excellent,moderate,and poor-performing groups for predicting BD.Further,a novel PTF based on an exponential function of SOC was developed,showing superior predictive power(R^(2)=0.73)compared to existing PTFs,using an independent validation dataset.Our findings reveal significant differences in SOCS predictions and observations among the PTFs,with a p-value<0.05.The highest concentrations of SOCS were noted in forest soils,considerably above the national average,highlighting the importance of tailored soil management practices to enhance carbon sequestration.These findings are crucial for refining PTF precision to improve the accuracy of national SOCS estimates,supporting effective land management and climate change mitigation strategies.
