Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions

Effect of cadmium stress on the bacterial community in the rhizosphere of mulberry (Morus alba L.)

Mulberry has a good tolerance to cadmium (Cd) and is considered a candidate plant for phytoremediation. The rhizosphere microbial community plays an important role in phytoremediation. Nevertheless, little information on the rhizosphere microbial community mechanisms in mulberry during the phytoremediation of Cd-contaminated soil is available. In this study, the remediation efficiency of mulberry in pots subjected to three simulated Cd pollution levels and their rhizosphere bacterial communities during the remediation process were analyzed. "Yuesang 11" was used as the test mulberry variety, and three simulated Cd pollution levels were set by adding three concentrations of Cd (Cd5, 5 mg kg-1; Cd3, 3 mg kg-1; Cd2, 2 mg kg-1). The results showed that the elimination rates of Cd in the rhizosphere soil were 81.7%, 85.3%, and 57.9% under the stress of the Cd2, Cd3, and Cd5 conditions, respectively. Meanwhile, 3,082,583 high-quality sequence reads and 976 operational taxonomic units were successfully obtained from the mulberry rhizosphere soil by high-throughput absolute quantification sequencing and further assigned to 11 bacterial phyla and 26 families. Of these, decreased abundances of 19 bacteria at the family level and increased abundances of seven bacteria under Cd stress were revealed by comparative analysis. Based on the alpha diversity indices (Chaol, Shannon and Simpson) and principal component analysis, the rhizosphere bacterial diversity of the Cd5 condition was significantly decreased, but that of the Cd2 and Cd3 conditions was not different from that of soil without Cd (CK). Likewise, redundancy analysis showed that the abundances of Acidobacteria Gp2, Acidobacteria Gp13, and Sphingobacteria were significantly positively associated with the elimination rates of Cd. This study suggested that the mulberry rhizosphere contains a relatively stable bacterial community consisting of diverse Cd-resistant bacteria, providing a scientific basis for remediating heavy-metal polluted soils using mulberry.

Modelling potentially toxic elements in forest soils with vis-NIR spectra and learning algorithms

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible-near infrared (vis-NIR: 350-2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis-NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis-NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R² = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R² = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis-NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results.

Atmospheric pressure glow discharge optical emission spectrometry coupled with laser ablation for direct solid quantitative determination of Zn, Pb, and Cd in soils

A device utilizing atmospheric pressure glow discharge as the second excitation source coupled with laser ablation (LA) for direct solid sampling was developed, with few operating costs and low gas consumption. This new device was first utilized for the highly sensitive determination of Zn, Pb, and Cd elements in complex matrix soil samples. It also provided a new method for monitoring these three trace elements in soil samples. Good linearity was observed in the quantitative results for Zn, Pb, and Cd detection, and the respective linear correlation coefficients (R²) were 0.9953, 0.9897, and 0.9961. Moreover, the limit of detection (LOD) of 0.68, 2.71, and 0.31 mg kg^-1 were achieved for Zn, Pb, and Cd, respectively; the LOD of Zn reduced by more than one order of magnitude compared to that observed in laser-induced breakdown spectroscopy results. In addition, the quantitative analysis results showed good agreement with the certified values and those obtained of ICP optical emission spectrometry, proving the detection accuracy and practicability of the developed device.

Quantitative analysis of pH value in soil using laser-induced breakdown spectroscopy coupled with a multivariate regression method

The quantitative analyses of pH value in soil have been performed using laser-induced breakdown spectroscopy (LIBS) technology. The aim of this work was to obtain a reliable and accurate method for rapid detection of pH value in soil. Seventy-four samples were used as a calibration set, and 24 samples were used as a prediction set. To eliminate the matrix effect, the multivariate models of partial least-squares regression (PLSR) and least-squares support vector regression (LS-SVR) were used to construct the models. The intensities of nine emission lines of C, Ca, Na, O, H, Mg, Al, and Fe elements were used to fit the models. For the PLSR model, the correlation coefficient was 0.897 and 0.906 for the calibration and prediction set, respectively. Furthermore, the analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficients for calibration and prediction set were improved to 0.991 and 0.987. The prediction mean absolute error was pH 0.1 units, and the root mean square error of the prediction was only 0.079. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method for determining pH value in soil.

Univariate and multivariate analyses of strontium and vanadium in soil by laser-induced breakdown spectroscopy

Univariate and multivariate analyses of strontium (Sr) and vanadium (V) elements in soil have been performed using laser-induced breakdown spectroscopy technology. Thirty-three samples were used as a calibration set, and 11 samples were used as a prediction set. The results demonstrated that the correlation coefficients of the calibration curves method were poor due to the matrix effect. Then, the multivariate models of partial least-squares regression and least squares support vector regression (LS-SVR) were used to construct models. The analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficient is 0.999 for Sr and 0.983 for V. The average relative errors for the prediction set are lower than 7.45% and 2.88% for Sr and V, respectively. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method in the quantitative determination of elemental Sr and V in complex matrices like soil.

Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model

The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard–Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares–support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.