| 基于可见—近红外反射光谱的典型农田重金属污染风险分类研究 |
| |
| 引用本文： | 李全坤,赵万伏,文宇博,郭 超,刘连文,季峻峰.基于可见—近红外反射光谱的典型农田重金属污染风险分类研究[J].高校地质学报,2022,28(6):799-813. |
| |
| 作者姓名： | 李全坤 赵万伏 文宇博 郭 超 刘连文 季峻峰 |
| |
| 作者单位： | 表生地球化学教育部重点实验室，南京大学 地球科学与工程学院，南京 210023 |
| |
| 摘 要： | 对于人为因素或自然因素造成的农田土壤重金属元素污染，需要进行大面积的土壤环境质量调查和分类管控，然而传统的采样测试方法存在工作量大、代价高等问题。可见—近红外（Vis-NIR）反射光谱是一种快速低成本获取土壤理化信息的手段。为研究Vis-NIR反射光谱预测模型划分土壤重金属污染风险类别的能力，文章以典型人为污染地区（浙江温岭）和典型地质高背景地区（广西横县）的390份农田土壤为样本，测定8种重金属元素（As、Cd、Cr、Cu、Hg、Ni、Pb和Zn）的含量和pH值，并测定土壤Vis-NIR光谱。使用偏最小二乘（PLS）和支持向量机（SVM）算法建立回归模型，对土壤重金属含量和pH值进行预测，并基于预测值进行土壤重金属污染风险分类。结果显示，温岭土壤主要污染元素Cd和Cu的光谱模型回归预测偏差（RPD）分别为1.23和1.19，预测机制与有机质有关。横县土壤主要污染元素As和Cd的RPD分别为1.98和1.93，预测机制与铁氧化物和粘土矿物有关。地质高背景土壤重金属与铁氧化物的正相关性普遍较强，使得光谱模型对重金属含量预测准确度较高。温岭和横县土壤pH值的光谱模型RPD分别为1.76和1.68。土壤重金属污染风险光谱分类的总体
准确度分别为75.0%~100%（温岭）和80.0%~100％（横县）。将Vis-NIR光谱与遥感技术相结合，对农田土壤重金属污染风险进行快速分类总体是可行的。
|
| 关 键 词： | 土壤重金属 污染风险分类 Vis-NIR反射光谱 pH值 高光谱遥感 |
Classification of Heavy Metal Contamination Risk in TypicalAgricultural Soils by Visible and Near InfraredReflectance Spectroscopy |
| |
| LI Quankun,ZHAO Wanfu,WEN Yubo,GUO Chao,LIU Lianwen,JI Junfeng.Classification of Heavy Metal Contamination Risk in TypicalAgricultural Soils by Visible and Near InfraredReflectance Spectroscopy[J].Geological Journal of China Universities,2022,28(6):799-813. |
| |
| Authors: | LI Quankun ZHAO Wanfu WEN Yubo GUO Chao LIU Lianwen JI Junfeng |
| |
| Institution: | Key Laboratory of Surface Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023，China; |
| |
| Abstract: | The contamination of heavy metals in agricultural soils caused by natural or anthropogenic factors strengthens the importance of soil environmental quality survey and managements. Nevertheless, the conventional methods of sampling and analysis are time-consuming and costly. Visible and near-infrared (Vis-NIR) reflectance spectroscopy is a rapid and inexpensive alternative to measure soil physical and chemical parameters. This study explored the capacity of Vis-NIR reflectance spectroscopy models for soil heavy metal contamination risk classification. We collected 390 agricultural soils from a typical anthropogenic contaminated area (Wenling, Zhejiang) and a typical high geological background area (Hengxian, Guangxi), examining the concentrations of eight soil heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn), soil pH values, as well as soil Vis- NIR reflectance spectroscopy. Partial least square regression (PLSR) and support vector machine (SVM) algorithms were used to calibrate regression models for predicting the concentrations of soil heavy metals and pH values. Based on the predicted values of spectral models, soil heavy metal contamination risk types were classified. Results showed that, the regression prediction deviation (RPD) values of spectral models for Cd and Cu, the major pollutant elements in Wenling soils, were 1.23 and 1.19, respectively. The spectral prediction mechanism is the correlation between Cd and Cu with organic matter. The two major pollutant elements in Hengxian soils are As and Cd, which had the RPD values of 1.98 and 1.93, respectively. The spectral prediction mechanism is the correlation between As and Cd with iron oxides and clay minerals. In the high geological background soils at Hengxian, there were generally strong positive correlations between soil heavy metals and iron oxides, where spectral models gave more accurate predictions for heavy metals. The RPD values of spectral models for soil pH values were 1.76 at Wenling and 1.68 at Hengxian. Spectral classification of soil contamination risk has reliable overall accuracy (75.0%~100% at Wenling, 80.0%~100% at Hengxian). Therefore, the spectral method combining with remote sensing technology is helpful for rapid classification of heavy metal contamination risk in agricultural soils. |
| |
| Keywords: | soil heavy metals contamination risk classification visible and near infrared (Vis-NIR) reflectance spectroscopy pH values hyperspectral remote sensing
|
|
| 点击此处可从《高校地质学报》浏览原始摘要信息 |
| 点击此处可从《高校地质学报》下载免费的PDF全文 |
|
