State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, PR China; School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, PR China.
School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, PR China.
Chemosphere. 2021 Oct;280:130599. doi: 10.1016/j.chemosphere.2021.130599. Epub 2021 Apr 27.
A novel method of predicting heavy metal concentration in lake water by support vector machine (SVM) model was developed, combined with low-cost, easy to obtain nutrients and physicochemical indicators as input variables. 115 surface water samples were collected from 23 sites in Chaohu Lake, China, during different hydrological periods. The particulate concentrations of heavy metals in water were much higher than the dissolved concentrations. According to Nemerow pollution index (Pi), pollution degrees by Fe, V, Mn and As ranged from heavy (2 ≤ Pi < 4) to serious (Pi ≥ 4). The concentrations of most heavy metals were the highest during the medium-water period and the lowest during the dry season. Non-metric Multidimensional Scaling Analysis confirmed heavy metal concentrations had slight spatial difference but relatively large seasonal variation. Redundancy Analysis indicated the close associations of heavy metals with nutrient and physicochemical indicators. When both nutrient and physicochemical indicators were used as input variables, the simulation effects for most elements in total and particulate were relatively better than those obtained using only nutrient or only physicochemical indicators. The simulation effects for As, Ba, Fe, Ti, V and Zn were generally good, based on their training R values of 0.847, 0.828, 0.856, 0.867, 0.817 and 0.893, respectively, as well as their test R values of 0.811, 0.836, 0.843, 0.873, 0.829 and 0.826, respectively; and meanwhile, in both the training and test stages, these metals also had relatively lower errors. The spatial distribution of heavy metals in Chaohu Lake was then predicted using the fully trained SVM models.
一种基于支持向量机（SVM）模型的预测湖水重金属浓度的新方法被开发出来，该方法结合了低成本、易于获取的营养物质和物理化学指标作为输入变量。从中国巢湖的 23 个地点采集了 115 个地表水样本，这些样本采集于不同的水文时期。水中重金属的颗粒浓度远高于溶解浓度。根据内梅罗污染指数（Pi），Fe、V、Mn 和 As 的污染程度从重（2≤Pi<4）到严重（Pi≥4）不等。大多数重金属的浓度在中水期最高，在旱季最低。非度量多维尺度分析证实重金属浓度具有轻微的空间差异，但季节性变化较大。冗余分析表明重金属与营养物质和物理化学指标密切相关。当营养物质和物理化学指标都作为输入变量时，大多数元素的总浓度和颗粒浓度的模拟效果都比仅使用营养物质或仅使用物理化学指标的效果要好。As、Ba、Fe、Ti、V 和 Zn 的模拟效果通常较好，其训练 R 值分别为 0.847、0.828、0.856、0.867、0.817 和 0.893，测试 R 值分别为 0.811、0.836、0.843、0.873、0.829 和 0.826，同时在训练和测试阶段，这些金属的误差也相对较低。然后利用完全训练的 SVM 模型对巢湖重金属的空间分布进行了预测。
