【目的】探索建立基于近红外光谱技术的土壤微量元素监测技术。【方法】采集三峡库区(重庆)主要加工甜橙基地果园背景土壤样品168个,随机选取100个作为建模样本,其余为检验样本;测定所有样本的近红外反射光谱和土壤Fe、Mn、Zn全含量;运...【目的】探索建立基于近红外光谱技术的土壤微量元素监测技术。【方法】采集三峡库区(重庆)主要加工甜橙基地果园背景土壤样品168个,随机选取100个作为建模样本,其余为检验样本;测定所有样本的近红外反射光谱和土壤Fe、Mn、Zn全含量;运用最佳光谱预处理方法和偏最小二乘法(partial least square method,PLS)及内部交叉验证方法建立校正模型,并进行模型精度检验。【结果】变量标准化(standard normal variables,SNV)为土壤Fe、Mn、Zn含量近红外光谱预测的最佳光谱预处理方法;运用SNV光谱预处理和偏最小二乘法(PLS)及内部交叉验证法建立的土壤Fe、Mn、Zn含量校正模型,95%置信区间内的预测精度分别为92.65%、95.59%和95.59%。【结论】利用近红外反射光谱技术进行土壤Fe、Mn、Zn含量检测可行且精度较高。展开更多
为了有效去除水中的铅离子,实验制备以MnO2为吸附表面的磁性Fe/Mn纳米复合吸附剂,并进行了吸附实验研究,分析pH、温度等参数对吸附的影响.结果发现,从298 K Langmuir等温吸附曲线可以计算出Fe/Mn复合吸附剂对Pb2+的饱和吸附量(Q0=118.06...为了有效去除水中的铅离子,实验制备以MnO2为吸附表面的磁性Fe/Mn纳米复合吸附剂,并进行了吸附实验研究,分析pH、温度等参数对吸附的影响.结果发现,从298 K Langmuir等温吸附曲线可以计算出Fe/Mn复合吸附剂对Pb2+的饱和吸附量(Q0=118.06 mg/L).复合吸附剂对Pb2+的吸附总量正比于pH(1.5～5)和温度(303～323 K).在研究纳米复合材料对铅离子的吸附动力学实验中发现,纳米复合材料和铅离子之间的吸附动力学符合假二级模型,通过相关热力学研究计算得到纳米材料和铅离子之间为吸热反应.展开更多
Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surf...Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.展开更多
基金financially supported by the Fundamental Research Funds for the Central Universities,China(No.2020CDJDPT001)the Chongqing Natural Science Foundation,China(No.cstc2021jcyj-msxm X0699)。
文摘【目的】探索建立基于近红外光谱技术的土壤微量元素监测技术。【方法】采集三峡库区(重庆)主要加工甜橙基地果园背景土壤样品168个,随机选取100个作为建模样本,其余为检验样本;测定所有样本的近红外反射光谱和土壤Fe、Mn、Zn全含量;运用最佳光谱预处理方法和偏最小二乘法(partial least square method,PLS)及内部交叉验证方法建立校正模型,并进行模型精度检验。【结果】变量标准化(standard normal variables,SNV)为土壤Fe、Mn、Zn含量近红外光谱预测的最佳光谱预处理方法;运用SNV光谱预处理和偏最小二乘法(PLS)及内部交叉验证法建立的土壤Fe、Mn、Zn含量校正模型,95%置信区间内的预测精度分别为92.65%、95.59%和95.59%。【结论】利用近红外反射光谱技术进行土壤Fe、Mn、Zn含量检测可行且精度较高。
文摘为了有效去除水中的铅离子,实验制备以MnO2为吸附表面的磁性Fe/Mn纳米复合吸附剂,并进行了吸附实验研究,分析pH、温度等参数对吸附的影响.结果发现,从298 K Langmuir等温吸附曲线可以计算出Fe/Mn复合吸附剂对Pb2+的饱和吸附量(Q0=118.06 mg/L).复合吸附剂对Pb2+的吸附总量正比于pH(1.5～5)和温度(303～323 K).在研究纳米复合材料对铅离子的吸附动力学实验中发现,纳米复合材料和铅离子之间的吸附动力学符合假二级模型,通过相关热力学研究计算得到纳米材料和铅离子之间为吸热反应.
基金Supported by the National Natural Science Foundation of China(No.50879025)
文摘Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.
