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Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy

Yongsheng Hong, Yiyun Chen, Ruili Shen, Songchao Chen, Gang Xu, Hang Cheng, Long Guo, Zushuai Wei, Jian Yang, Yaolin Liu, et al.
(2021) ENVIRONMENTAL POLLUTION. 291.
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Abstract
Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.
Keywords
Urban and suburban soil Cd contamination, Visible-to-near-infrared spectroscopy, Boruta algorithm, Synthetic minority over-sampling technique, Machine learning, DIFFUSE-REFLECTANCE SPECTROSCOPY, POTENTIALLY TOXIC ELEMENTS, HEAVY-METAL CONCENTRATIONS, NIR SPECTROSCOPY, ORGANIC-CARBON, QUANTITATIVE-ANALYSIS, RANDOM FOREST, HUMAN HEALTH, RIVER DELTA, PREDICTION

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Citation

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MLA
Hong, Yongsheng, et al. “Diagnosis of Cadmium Contamination in Urban and Suburban Soils Using Visible-to-near-Infrared Spectroscopy.” ENVIRONMENTAL POLLUTION, vol. 291, 2021, doi:10.1016/j.envpol.2021.118128.
APA
Hong, Y., Chen, Y., Shen, R., Chen, S., Xu, G., Cheng, H., … Mouazen, A. (2021). Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy. ENVIRONMENTAL POLLUTION, 291. https://doi.org/10.1016/j.envpol.2021.118128
Chicago author-date
Hong, Yongsheng, Yiyun Chen, Ruili Shen, Songchao Chen, Gang Xu, Hang Cheng, Long Guo, et al. 2021. “Diagnosis of Cadmium Contamination in Urban and Suburban Soils Using Visible-to-near-Infrared Spectroscopy.” ENVIRONMENTAL POLLUTION 291. https://doi.org/10.1016/j.envpol.2021.118128.
Chicago author-date (all authors)
Hong, Yongsheng, Yiyun Chen, Ruili Shen, Songchao Chen, Gang Xu, Hang Cheng, Long Guo, Zushuai Wei, Jian Yang, Yaolin Liu, Zhou Shi, and Abdul Mouazen. 2021. “Diagnosis of Cadmium Contamination in Urban and Suburban Soils Using Visible-to-near-Infrared Spectroscopy.” ENVIRONMENTAL POLLUTION 291. doi:10.1016/j.envpol.2021.118128.
Vancouver
1.
Hong Y, Chen Y, Shen R, Chen S, Xu G, Cheng H, et al. Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy. ENVIRONMENTAL POLLUTION. 2021;291.
IEEE
[1]
Y. Hong et al., “Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy,” ENVIRONMENTAL POLLUTION, vol. 291, 2021.
@article{8720112,
  abstract     = {{Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.}},
  articleno    = {{118128}},
  author       = {{Hong, Yongsheng and Chen, Yiyun and Shen, Ruili and Chen, Songchao and Xu, Gang and Cheng, Hang and Guo, Long and Wei, Zushuai and Yang, Jian and Liu, Yaolin and Shi, Zhou and Mouazen, Abdul}},
  issn         = {{0269-7491}},
  journal      = {{ENVIRONMENTAL POLLUTION}},
  keywords     = {{Urban and suburban soil Cd contamination,Visible-to-near-infrared spectroscopy,Boruta algorithm,Synthetic minority over-sampling technique,Machine learning,DIFFUSE-REFLECTANCE SPECTROSCOPY,POTENTIALLY TOXIC ELEMENTS,HEAVY-METAL CONCENTRATIONS,NIR SPECTROSCOPY,ORGANIC-CARBON,QUANTITATIVE-ANALYSIS,RANDOM FOREST,HUMAN HEALTH,RIVER DELTA,PREDICTION}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy}},
  url          = {{http://doi.org/10.1016/j.envpol.2021.118128}},
  volume       = {{291}},
  year         = {{2021}},
}
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