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Abstract
Portable X-ray fluorescence (pXRF) spectrometers provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils. The current method for improving pXRF measurement accuracy is soil sample preparation, which inevitably consumes significant amounts of time. To eliminate the influence of sample preparation on PXRF measurements, this study evaluates the performance of pXRF measurements in the prediction of eight heavy metals’ contents through machine learning algorithm linear regression (LR) and multivariate adaptive regression spline (MARS) models. Soil samples were collected from five industrial sites and separated into high-value and low-value datasets with pXRF measurements above or below the background values. The results showed that for Cu and Cr, the MARS models were better than the LR models at prediction (the MARS-R2 values were 0.88 and 0.78; the MARS-RPD values were 2.89 and 2.11). For the pXRF low-value dataset, the multivariate MARS models improved the pXRF measurement accuracy, with the R2 values improved from 0.032 to 0.39 and the RPD values increased by 0.02 to 0.37. For the pXRF high-value dataset, the univariate MARS models predicted the content of Cu and Cr with less calculation. Our study reveals that machine learning methods can better predict the Cu and Cr of large samples from multiple contaminated sites.
Highlights
Predictive models using Portable X-ray fluorescence (pXRF) measurements were created and found to be applicable at the farm and national scales, and the results showed that the multiple linear regression (MLR) model had good performance for predicting Zn, while the multivariate adaptive regression spline (MARS) model had better performance in the prediction of Cu and
The R2 and RPD values of the MARS models for predicting Cr (0.88, 2.89) and Cu (0.77, 2.11) were larger than those of the linear regression (LR) models for Cr (0.8, 2.22) and Cu (0.73, 1.94), which indicated that the MARS models were better than the LR models at predicting Cu and Cr
Models for Cr (0.8, 2.22) and Cu (0.75, 2.00), which indicated that the MARS models were better than the LR models at predicting Cu and Cr
Summary
They can occur in living organisms through biomagnification and bioaccumulation and present in high amounts in the environment, which leads to potential risks for human health and the environment [1–4]. Heavy metals can cause adverse effects on humans through the inhalation of respirable dust particles, the ingestion of foods from living organisms exposed to heavy metals, and dermal absorption [1–4]. Portable X-ray fluorescence (pXRF) spectrometers can provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils and have been widely used to assess environmental risks, predict soil properties, and evaluate soil fertility, among other uses [5–9]. According to the Chinese Standard Technical Guidelines for the Investigation on Soil Contamination of Land for Construction [10], the heavy metal rapid detector is recommended for the qualitative and quantitative analysis of heavy metals in soils in situ. The pXRF instrument can help to guide the selection of samples to be analyzed in the laboratory and make investigative and remediative decisions [11,12]
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