Estimating the Soil Copper Content of Urban Land in a Megacity Using Piecewise Spectral Pretreatment

• Published in: Land (Basel) Vol. 13; no. 4; p. 517
• Main Authors: Liu, Yi, Shi, Tiezhu, Lan, Zeying, Guo, Kai, Zhuang, Dachang, Zhang, Xiangyang, Liang, Xiaojin, Qiu, Tianqi, Zhang, Shengfei, Chen, Yiyun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Analysis; Analytical chemistry; Cities; Contamination; Copper; Cu (copper); Estimation; Heavy metals; Influence; Infrared spectra; Infrared spectroscopy; Land pollution; Megacities; Metals; Methods; Near infrared radiation; Near infrared spectroscopy; Noise reduction; Particle size; Pretreatment; Soil contamination; Soils; spectral pretreatment; Spectrum analysis; Support vector machines; Topsoil; urban land; Wavelengths; China; United States > US
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land13040517

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model.