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Sci Total Environ. 2019 Feb 15;651(Pt 2):1969-1982. doi: 10.1016/j.scitotenv.2018.09.391. Epub 2018 Oct 1.

Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest.

Author information

1
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China; State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China.
2
Hubei Academy of Environmental Sciences, Wuhan 430072, China.
3
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China; State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China. Electronic address: chenyy@whu.edu.cn.
4
School of Public Finance and Administration, Anhui University of Finance and Economics, Bengbu 233030, China.
5
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China.
6
School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China; Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China.
7
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China. Electronic address: yfliu610@sina.com.

Abstract

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 (R2), 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 R2 = 0.82, RPIQ = 2.49) and Zn (validation R2 = 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.

KEYWORDS:

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

PMID:
30321720
DOI:
10.1016/j.scitotenv.2018.09.391
[Indexed for MEDLINE]

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