| 阳宗海湖滨湿地环境因素对沉积物砷赋存形态的影响及浓度水平预测 |
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| 引用本文： | 李梦莹,刘云根,侯磊,郑毅,齐丹卉,赵蓉,任伟.阳宗海湖滨湿地环境因素对沉积物砷赋存形态的影响及浓度水平预测[J].环境科学研究,2018,31(9):1554-1563. |
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| 作者姓名： | 李梦莹 刘云根 侯磊 郑毅 齐丹卉 赵蓉 任伟 |
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| 作者单位： | 1.西南林业大学水科学与工程中心, 云南 昆明 650224 |
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| 基金项目： | 国家自然科学基金项目（No.41761098，21767027）；云南省一流学科（生态学）建设经费 |
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| 摘 要： | 湖滨湿地独特的水文条件区别于其他生态系统，环境因素变化频繁，对沉积物中污染物形态影响显著.以阳宗海南岸湖滨湿地表层沉积物为研究对象，探究不同季节的S-TAs（沉积物中总砷）、不同形态砷质量分数及环境因素时空分布特征，以及环境因素与不同形态砷分布的关系，同时基于逐步回归和BP神经网络模型对沉积物中4种不同形态砷（弱酸提取态砷、可还原态砷、可氧化态砷、残渣态砷）质量分数进行预测和比较.结果表明:①夏季ρ（W-TAs）（W-TAs为水体总砷）、w（S-TAs）（S-TAs为沉积物总砷）略高，且ρ（W-TAs）处于GB 3838-2002《地表水环境质量标准》Ⅲ级限值（0.05 mg/L）和Ⅴ类限值（0.1 mg/L）之间，冬季物理指标pH、ρ（DO）、Eh（氧化还原电位）、电导率（TDS）、w（OM）均较高，沉积物pH（记为S-pH）、ρ（DO）与Eh存在明显的季节性差异（P < 0.05）.②湖滨湿地沉积物中活性砷（弱酸提取态砷、可还原态砷、可氧化态砷）质量分数之和所占比例为17.70%~62.59%，80%采样点的活性砷的质量分数较低，对生态风险影响较小，S-pH、ρ（DO）、Eh对不同形态砷的质量分数影响显著（P < 0.05），同时，不同季节湖滨湿地对砷均有明显的拦截作用.③与逐步回归模型相比，BP神经网络预测模型是通过输入层到输出层的计算完成，增强了非线性、自适应性处理能力，不同砷形态质量分数的实测值与预测值的拟合度高达0.999 5，而逐步回归仅为0.374 9，神经网络更准确地预测了不同形态砷的质量分数及时空变化规律.研究显示，湖滨湿地环境因素的变化对沉积物砷赋存形态具有显著影响，因BP神经网络比数理统计线性回归模型更能准确地反映沉积物不同形态砷与环境因子间复杂的非线性关系，预测效果更精确.
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| 关 键 词： | 湖滨湿地 沉积物 不同形态砷 环境因素 神经网络 |
| 收稿时间： | 2018/1/8 0:00:00 |
| 修稿时间： | 2018/3/12 0:00:00 |
Effects and Concentration Predictions of Environmental Factors on the Speciation of Arsenic in the Sediments of Yangzonghai Lakeside Wetland |
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| LI Mengying,LIU Yungen,HOU Lei,ZHENG Yi,QI Danhui,ZHAO Rong and REN Wei.Effects and Concentration Predictions of Environmental Factors on the Speciation of Arsenic in the Sediments of Yangzonghai Lakeside Wetland[J].Research of Environmental Sciences,2018,31(9):1554-1563. |
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| Authors: | LI Mengying LIU Yungen HOU Lei ZHENG Yi QI Danhui ZHAO Rong and REN Wei |
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| Affiliation: | 1.Research Center of Water Science and Engineering, Southwest Forestry University, Kunming 650224, China2.College of Ecology and Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China |
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| Abstract: | The unique hydrological conditions of lakeside wetland are different from other ecosystems, and their environmental factors change frequently and have great influence on the speciation of contaminants in the sediments. The Yangzonghai lakeside wetland located in Yunnan Province was selected as the research object, and sediment samples were collected. The contents and distribution characteristics of environmental factors, total arsenic (S-TAs) and each species of arsenic of these samples were determined, and correlation analyses between species of arsenic and environmental factors were conducted. The concentrations of four species of arsenic in sediments as functions of environmental factors were predicted and compared based on both the stepwise regression and back-propagation network (BP neural network) models. The results showed that the concentrations of total arsenic in water (W-TAs) and sediment (S-TAs) were slightly higher in summer, and the contents of arsenic were near the level Ⅲ value and level Ⅴ value of environmental quality standard for water. The contents of other physical factors (pH, DO, Eh and TDS) were higher in winter, and there were obvious seasonal differences among sediment pH (S-pH), dissolved oxygen (DO) and oxidation-reduction potential (Eh). The proportion of active arsenic forms (weak acid extracted, reducible and oxidable arsenic) in the sediments of the lakeside wetland ranged from 17.70% to 62.59%, and near 80% of the sampling points showed relatively low contents of active arsenic, correspoding to low risk of ecological hazards. In the different seasons, the lakeside wetland had a significant retention effect on arsenic, and the correlation analyses showed that S-pH, DO and Eh had great influence on the distribution of arsenic speciation. Compared with the stepwise regression model, the BP neural network model was applied through the calculation of the input layer to the output layer, which enhances the ability of nonlinear and adaptive processing. The fitting degree of measured values and predicted values was up to 0.9995, while that of the stepwise regression was only 0.3749. Thus, the BP neural network model could better reflect the relationship between arsenic speciation and environmental factors and could more accurately predict their contents. The study showed that the environmental factors of the lakeside wetland had a certain influence on the occurrence forms of arsenic. Due to the complex nonlinear relationship between arsenic speciation and environmental factors, the BP neural network model was more accurate than use of mathematical statistics. |
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| Keywords: | lakeside wetland sediment arsenic speciation environmental factors neural network |
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