| 基于BP神经网络的复合污染体系中沉积物吸附阿特拉津的规律 |
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| 引用本文： | 李鱼,王倩,高茜,王岙.基于BP神经网络的复合污染体系中沉积物吸附阿特拉津的规律[J].吉林大学学报(地球科学版),2011(Z1):315-321. |
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| 作者姓名： | 李鱼 王倩 高茜 王岙 |
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| 作者单位： | 华北电力大学能源与环境研究院;吉林大学环境与资源学院;吉林省卫生监测检验中心; |
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| 基金项目： | 国家自然科学基金项目(50879025) |
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| 摘 要： | 建立了Cu2+和阿特拉津(AT)复合污染体系中沉积物各活性组分及其交互作用对AT吸附量影响的BP神经网络模型,模型预测值和实验值的相关系数达到0.97,各集合的平均偏差均小于10%。模型显示,AT在沉积物上的主要吸附位是铁氧化物。沉积物吸附AT时铁氧化物、锰氧化物、有机质之间存在显著的交互作用,铁氧化物、锰氧化物交互作用贡献率为-130%～80%,铁氧化物、有机质交互作用贡献率为5%～28%,锰氧化物、有机质交互作用贡献率为-200%～-70%。各活性组分吸附AT的能力及其交互作用受Cu2+的影响较大,其中:Cu2+对AT在铁氧化物上的吸附表现为拮抗作用,对AT在锰氧化物上的吸附表现为协同作用,而对AT在有机质上的吸附影响不显著,同时Cu2+减弱了铁氧化物-锰氧化物和锰氧化物-有机质的交互作用影响,增强了铁氧化物-有机质的交互作用影响。
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| 关 键 词： | 沉积物 BP神经网络 Cu2+ 阿特拉津 交互作用 复合污染 |
The Atrazine Adsorption Characteristics of Surficial Sediments Under Composite Contamination System Based on the BP Artificial Neural Network Model |
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| LI Yu,WANG Qian,GAO Qian,WANG Ao, .Research Academy of Energy , Environmental Studies,North China Electric Power University,Beijing ,China .College of Environment , Resources,Jilin University,Changchun ,China .Jilin Provincial Center for Sanitary Inspection , Test,Changchun ,China.The Atrazine Adsorption Characteristics of Surficial Sediments Under Composite Contamination System Based on the BP Artificial Neural Network Model[J].Journal of Jilin Unviersity:Earth Science Edition,2011(Z1):315-321. |
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| Authors: | LI Yu WANG Qian GAO Qian WANG Ao Research Academy of Energy Environmental Studies North China Electric Power University Beijing China College of Environment Resources Jilin University Changchun China Jilin Provincial Center for Sanitary Inspection Test Changchun China |
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| Affiliation: | LI Yu1,2,WANG Qian1,GAO Qian1,WANG Ao2,3 1.Research Academy of Energy and Environmental Studies,North China Electric Power University,Beijing 102206,China 2.College of Environment and Resources,Jilin University,Changchun 130012,China 3.Jilin Provincial Center for Sanitary Inspection and Test,Changchun 130062,China |
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| Abstract: | A BP artificial neural network model(ANN) was established to predict the influence of the active components of sediment and their interactions on the adsorption of atrazine(AT) under atrazine-copper co-contamination system.The results showed that the correlation coefficient between model predictions and experimental values reached 0.97 and the average deviations of all sets were less than 10%.The model calculation showed that Fe oxides are the main adsorption sites for AT sorption on sediment.The effects of... |
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| Keywords: | sediments BP artificial neural network Cu2 atrazine interaction co-contamination |
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