School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, 150030, China; Joint Laboratory of Northeast Agricultural University and Max Planck Institute of Colloids and Interfaces (NEAU-MPICI), Harbin, 150030, China.
School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, 150030, China; Joint Laboratory of Northeast Agricultural University and Max Planck Institute of Colloids and Interfaces (NEAU-MPICI), Harbin, 150030, China.
Environ Res. 2022 May 15;208:112694. doi: 10.1016/j.envres.2022.112694. Epub 2022 Jan 8.
Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R are 0.940 and 0.976) than the published NN-LFER model (R are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log C, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.
详细预测水中有机污染物的吸附量对于水资源的清洁开发和管理至关重要。本研究采用克里金（Kriging）和多参数线性自由能关系模型（polyparameter linear free energy relationship model）耦合，预测生物炭和树脂对有机污染物的吸附容量。该模型基于包含 73 种有机化合物在 50 种生物炭和 30 种聚合物树脂上的 1750 个吸附实验数据集。Kriging-LFER 模型在吸附方面表现出更高的准确性和预测性能（R 分别为 0.940 和 0.976），优于已发表的神经网络-LFER 模型（R 分别为 0.870 和 0.880）。采用局部敏感性分析方法评估每个变量对树脂吸附系数的影响，发现最敏感的参数是 V 和 log C，可用于指导参数优化。通过蒙特卡罗方法进行数据不确定性分析。预测在 95%置信区间下，吸附系数的范围将在 0.062 到 0.189 之间。Kriging-LFER 模型对于理解各种参数的重要性、减少实验数量、调整实验改进方向以及评估有机污染物在环境中的归宿具有重要意义。
