Abstract
Abstract. It is important for the sustainable development of soil and monitoring the soil quality to obtain the heavy metal contents. Visible and near-infrared (Vis–NIR) spectroscopy provides an alternative method for soil heavy metal estimation. A total of 80 soil samples collected in Xuzhou city of China were utilized as data sets for calibration and validation to establish the relationship between the soil reflectance and soil heavy metal content. To amplify the weak spectral characteristic, improve the estimation ability, and explore the characteristic band regions, the preprocessing method of fractional order derivative (FOD) (intervals of 0.25, range of 0–2) and the wavebands selection method of interval partial least squares regression (IPLS) are introduced in this paper. Combining these two methods, for Chromium (Cr), the best estimation model yields Rp2 and RMSRp values of 0.97 and 2.20, respectively, when fractional order is 0.5. This paper explores the potential that FOD conducts the most appropriate order to preprocess spectra and IPLS selects the feature band regions in estimating soil heavy metal of Cr. The results show that FOD and IPLS can strengthen the soil information and improve the accuracy and stability of soil heavy metal estimation effectively.
Highlights
With the intensification of human activities and the expansion of urban scale, many industries have brought serious environmental problems, especially the heavy metal pollution in the soil, which has attracted an increasing number of attention
The samples spectra were conducted by the preprocessing method of fractional order derivative (FOD)
After FOD pretreatment, 80 soil samples were divided into calibration set and validation set by SPXY. 53 samples were selected as calibration set, and the remaining samples were selected as validation set
Summary
With the intensification of human activities and the expansion of urban scale, many industries have brought serious environmental problems, especially the heavy metal pollution in the soil, which has attracted an increasing number of attention. Soil heavy metals can be absorbed and enriched by crops, which pose a great threat to human health through the migration and enrichment of the food chain. It is vital to obtain heavy metal concentration in soil rapidly. The traditional methods of obtaining the soil heavy metal contamination are mainly laboratory chemical analysis, which are costly and timeconsuming. It is difficult to achieve large-scale, dynamic and rapid soil heavy metal content estimation. The relationship between the visible and near-infrared reflectance (380-2500 nm), and soil properties has been widely utilized as an inexpensive and rapid estimation tool of soil heavy metal (Tan et al, 2020; Wu et al, 2007; Viscarra et al, 2016)
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