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首页> 外文期刊>The Science of the Total Environment >Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest
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Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest

机译:通过反射光谱估算围城农业土壤中铅和锌浓度:分数阶衍生物和随机林的影响

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摘要

Heavy metal contamination of peri-urban agricultural soil is detrimental to soil environmental quality and human health. A rapid assessment of soil pollution status is fundamental for soil remediation. Heavy metals can be monitored by visible and near-infrared spectroscopy coupled with chemometric models. First and second derivatives are two commonly used spectral preprocessing methods for resolving overlapping peaks. However, these methods may lose the detailed spectral information of heavy metals. Here, we proposed a fractional-order derivative (FOD) algorithm for preprocessing reflectance spectra. A total of 170 soil samples were collected from a typical peri-urban agricultural area in Wuhan City, Hubei Province. The reflectance spectra and lead (Pb) and zinc (Zn) concentrations of the samples were obtained in the laboratory. Two calibration methods, namely, partial least square regression and random forest (RF), were used to establish the relation between the spectral data and the two heavy metals. In addition, we aimed to explore the use of spectral estimation mechanism to predict the Pb and Zn concentrations. Three model evaluation parameters, namely, coefficient of determination (R-2), root mean squared error, and ratio of performance to inter-quartile range (RPIQ), were used. Overall, the spectral reflectance decreased with the increase in Pb and Zn contents. The FOD algorithm gradually removed spectral baseline drifts and overlapping peaks. However, the spectral strength slowly decreased with the increase in fractional order. High fractional-order spectra underwent more spectral noises than low fractional-order spectra. The optimal prediction accuracies were achieved by the 0.25- and 0.5-order reflectance RF models for Pb (validation R-2 = 0.82, RPIQ = 2.49) and Zn (validation R-2 = 0.83, RPIQ = 2.93), respectively. A spectral detection of Pb and Zn mainly relied on their covariation with soil organic matter, followed by Fe. In summary, our results provided theoretical bases for the rapid investigation of Pb and Zn pollution areas in peri-urban agricultural soils. (C) 2018 Elsevier B.V. All rights reserved.
机译:围城市农业土壤的重金属污染对土壤环境质量和人类健康有害。对土壤污染状况的快速评估是土壤修复的基础。可以通过与化学计量模型相结合的可见和近红外光谱来监测重金属。第一和第二衍生物是用于解析重叠峰的两个常用的光谱预处理方法。然而,这些方法可能失去重金属的详细光谱信息。这里,我们提出了一种用于预处理反射谱的分数阶数衍生物(FOD)算法。湖北省武汉市典型的围城农业区收集了170种土样。在实验室中获得了样品的反射光谱和铅(Pb)和锌(Zn)浓度。两种校准方法,即部分最小二乘回归和随机森林(RF),用于建立光谱数据和两个重金属之间的关系。此外,我们旨在探讨光谱估计机制的使用来预测PB和Zn浓度。使用三种模型评估参数,即使用判定系数(R-2),根均方误差和差异间隙范围(RPIQ)的比率。总体而言,光谱反射率随着PB和Zn含量的增加而降低。 FOD算法逐渐去除光谱基线漂移和重叠峰。然而,光谱强度随着分数阶的增加而缓慢降低。高分阶光谱接受比低分馏光谱更多的光谱噪声。通过0.25-和0.5阶反射率RF模型来实现最佳预测精度,用于PB(验证R-2 = 0.82,RPIQ = 2.49)和Zn(验证R-2 = 0.83,RPIQ = 2.93)。 PB和Zn的光谱检测主要依赖于其与土壤有机物的调节,其次是Fe。总之,我们的结果为围城市农业土壤中的PB和ZN污染区域进行了快速调查的理论基础。 (c)2018年elestvier b.v.保留所有权利。

著录项

  • 来源
    《The Science of the Total Environment》 |2019年第2期|1969-1982|共14页
  • 作者单位

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China|Chinese Acad Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Jiangsu Peoples R China;

    Hubei Acad Environm Sci Wuhan 430072 Hubei Peoples R China;

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China|Chinese Acad Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Jiangsu Peoples R China;

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China|Chinese Acad Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Jiangsu Peoples R China;

    Anhui Univ Finance & Econ Sch Publ Finance & Adm Bengbu 233030 Peoples R China;

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China;

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China;

    Cent China Normal Univ Sch Urban & Environm Sci Wuhan 430079 Hubei Peoples R China|Cent China Normal Univ Key Lab Geog Proc Anal & Simulat Hubei Prov Wuhan 430079 Hubei Peoples R China;

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China|Chinese Acad Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Jiangsu Peoples R China;

    Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Proximal soil sensing; Soil heavy metal; Spectral derivative; Predictive model; Estimation mechanism;

    机译:近端土壤感应;土壤重金属;光谱衍生物;预测模型;估计机制;

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