优化光谱指数的露天煤矿区土壤重金属含量估算

doi:10.3964/j.issn.1000-0593(2019)08-2486-09

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摘要：光谱学提供了对土壤中许多元素进行定量分析和快速无损检测的方法。可见光和近红外反射光谱（Vis-NIR）为研究土壤重金属污染提供了一个有用的工具。于新疆准东露天煤矿区采集51个0～10 cm深度的土壤样品，在实验室中分别测定样品的有机质（SOM）含量、重金属砷（As）含量与高光谱；使用基于JAVA语言自主开发的两波段组合软件V1.0（No: 2018R11S177501）计算不同高光谱数据变换形式（原始反射率（R），倒数（1/R），对数（lgR）和平方根（ $\sqrt{R}$ ）下Vis-NIR区域（400~2 400 nm）所有两波段组合得到的优化光谱指数（NPDI）与As的相关性，在最优光谱指数（|r|≥0.73和p=0.001）中通过变量重要性准则（VIP）进一步筛选VIP≥1的指数作为模型自变量，基于地理加权回归（GWR）模型估算As含量并使用四个交叉验证度量标准：相对分析误差（RPD），决定系数（R²），均方根误差（RMSE）和最小信息准则（ACI）评价模型精度，从而探讨优化光谱指数方法应用于高光谱检测露天煤矿区土壤重金属砷含量的可行性。结果表明：(1）研究区As含量离散度较高，所有样品中SOM含量均小于2％，且As含量与SOM含量在0.01的显著性水平上无显著相关性（|r|=0.113）。（2）As含量与单波段光谱反射率的相关性很低（|r|≤0.228），而通过R，1/R，lgR， $\sqrt{R}$ 计算的NPDIs与As含量的相关性在近红外（NIR，780~1 100 nm）和短波红外（SWIR，1 100~1 935 nm）光谱中发现最高的相关系数和最低的p值（|r|≥0.73和p=0.001），在长波近红外（LW-NIR）区域基于R形成的NPDIs与As含量相关性最高（|r|=0.74）。（3）VIP方法分别筛选NPDI_{R（1 417/1 246）}，NPDI_{1/R（799/953，825/947）}、NPDI_{sqrt-R（1 023/1 257，1 008/1 249，1 021/1 250，1 020/1 247）}和NPDI_{lgR（801/953，811/953，817/951，825/947，828/945）}为GWR模型自变量。(4）从4个预测模型的表现可以看出，Model-a（R）与其他三个模型（Model-b（1/R），Model-c（ $\sqrt{R}$ ）和Model-d（lgR））相比，它具有最高的验证系数（R²=0.831，RMSE=4.912 μg·g^-1，RPD=2.321）和最低的最小信息准则值（AIC=179.96）。优化光谱指数NPDI_{R(1 417/1 246)}有助于快速准确地估算As含量，为进一步获取地表土壤重金属污染分布信息提供理论支持和应用参考，促进露天煤矿区环境污染快速有效调查和生态可持续发展。

关键词：土壤重金属；优化光谱指数；地理加权回归模型；露天煤矿区

Abstract：Spectroscopy is regarded as a quick and nondestructive method to classify and analyze quantitatively many of elements of the soil. Visible and near-infrared re?ectance spectroscopy offers a conductive tool for investigating soil heavymetal pollution. In this work, 51 soil samples with depths of 0～10 cm were collected, which were in the Eastern Junggar coal-field mining area, Xinjiang. The soil organic matter (SOM) content, Arsenic (As) content and indoor hyperspectra were measured in the laboratory. The significant relationship between As content and hyperspectral data was conductive analysis of NPDIs, which were calculated from Vis-NIR region. For calculating the indices, on the basis of the raw spectral reflectance (R), its three mathematical transformations were calculated, i. e., the reciprocal (1/R), logarithm (lgR) and root mean square method (sqrt-R/ $\sqrt{R}$ ), respectively. The two band combination of optimized indices software V1.0 (No: 2018R11S177501, independently developed based on the JAVA) was used during the calculation of the indices. NPDIs were calculated using all possible combinations of available bands (i nm and j nm) in the full spectral region (400～2 400 nm). In the optimal spectral indices (|r|≥0.73 and p=0.001), an index of VIP≥1 was further selected as a model independent variable by the Variable importance in projection (VIP) selection method. The main goal of this work is to obtain optimized spectral index (NPDI) related to soil heavy metal As, to estimate As concentration in soil based on geographically weighted regression (GWR) model, and to investigate the plausibility of using optimized spectral index for hyperspectral detection of heavy metal Arsenic in soil of coal mining areas. To assess the performance of the soil heavy metal contents prediction models, four cross-validation metrics were used; Residual Prediction Deviation (RPD), the Coefficient of Determination (R²), the Root Mean Square Error (RMSE) and Akaike Information Criterion (ACI). The results of this study are as follows: (1) As has the largest dispersion in the study area, SOM contents in all samples are less than 2%, and the As concentration has no significant correlation with the SOM content at a significance level of 0.01 (|r|=0.113). (2) Single-bandreflectance shows low correlation with As contents, lower than 0.228. However, the highest correlation coefficient and lowest p-values (|r|≥0.73 and p=0.001) between As and NPDIs calculated by original and transformed reflectance (R, 1/R, lgR, $\sqrt{R}$ ) are found in theNear-infrared (NIR, 780～1 100 nm) and Shortwave-infrared (SWIR, 1 100～1 935 nm) long wavelength infrared. The original spectral region formed with long wave length near-infrared (LW-NIR) regions show highest correlation with As contents (|r|=0.74). (3) VIP value of NPDI_{R（1 417/1 246）}，NPDI_{1/R（799/953，825/947）}，NPDI_{sqrt-R（1 023/1 257，1 008/1 249，1 021/1 250，1 020/1 247）} and NPDI_{lgR（801/953，811/953，817/951，825/947，828/945）} higher than 1, thus these NPDIs are chosen as independent variables. (4) From the four prediction model (GWR) performances it can be seen, the Model-a (R) showed superior performance to other three models (Model-b (1/R), Model-c ( $\sqrt{R}$ ) and Model-d (lgR)), and it has the highest validation coefficients (R²=0.831, RMSE=4.912 μg·g^-1, RPD=2.321) and lowest AIC value (AIC=179.96). The hyperspectral optimized index NPDI_{R(1 417/1 246)} may help to quickly and accurately evaluate Arsenic contents in soil, furthermore, the results provide theoretical and data support to accesse the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.

Key words：Heavy metal; Optimized spectral indices; GWR model; Coal mine field

收稿日期: 2018-06-07 修订日期: 2018-10-25

中图分类号:	X53
	X87

基金资助: 国家自然科学基金项目（41761077）和新疆维吾尔自治区自然科学基金项目(2017D01C065)资助

通讯作者: 尼格拉·塔什甫拉提，张飞 E-mail: ngr.t@hotmail.com；zhangfei3s@163.com

作者简介: 亚森江·喀哈尔，1993年生，新疆大学资源与环境科学学院硕士研究生 e-mail： ysj_0801@163.com; 茹克亚·萨吾提，1987年生，新疆大学资源与环境科学学院博士研究生 e-mail： 13079931238@163.com

引用本文:

亚森江·喀哈尔，茹克亚·萨吾提，尼加提·卡斯木，尼格拉·塔什甫拉提，张飞，阿不都艾尼·阿不里，师庆东. 优化光谱指数的露天煤矿区土壤重金属含量估算[J]. 光谱学与光谱分析, 2019, 39(08): 2486-2494.
Yasenjiang Kahaer, Rukeya Sawut, Nijat Kasim, Nigara Tashpolat, ZHANG Fei, Abdugheni Abliz, SHI Qing-dong. Estimation of Heavy Metal Contents in Soil Around Open Pit Coal Mine Area Based on Optimized Spectral Index. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2486-2494.

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