Development of a soil heavy metal estimation method based on a spectral index: Combining fractional-order derivative pretreatment and the absorption mechanism

Analysis of four heavy metal concentrations in sediments fromthe Jiaozhou Bay, China by visible and near infrared spectroscopy (225-975 nm)

As an important component ofbiogeochemical cyclein coastal ecosystems, sediments are the sink of heavy metals. Therefore, distribution and dynamics of heavy metals in sediments could assess ecological quality and predict ecological risks. In the new era, rapid and green technology are highly needed, especially that could determine multi-parameters simultaneously. Here, we explored a new method to rapidly determine concentrations of heavy metals in sediments by visible and near infrared reflectance spectroscopy (VIRS).We sampled sediments in the Jiaozhou Bay, China, collected their reflectance spectra, and measured concentrations of four heavy metals (As, Cr, Cu, and Zn). Heavy metal models were established and evaluated using substances highly correlated with heavy metals. This study provides an effective reference for rapid analysis of As, Cr, Cu, and Zn simultaneously in sediments, at least in the Jiaozhou Bay, and for ecological environment protection and resource development of the Jiaozhou Bay.

Heavy Metal Concentration Estimation for Different Farmland Soils Based on Projection Pursuit and LightGBM with Hyperspectral Images

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10-13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP-LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality.

Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics

Though soil is widely known as one of the most valuable resources for the world, its quality is going to be lower because of unsustainable economic development and social progress. Therefore, it is important for us to monitor and evaluate the quality of soil, especially its heavy metal contents which is too scarce to identify in soil spectra easily but poisonous enough to affect human health in a long run. Most of the existing estimation methods have based the characteristic bands on statistical analysis to a large extent, which is hard to accurately explain the retrieval mechanism. In this paper, the absorption characteristics of heavy metal are studied based on the soil spectra, and the distribution pattern is mapped in a large-scale continuous space, for environmental monitoring and further decision support. Taking Yitong County, China as the study area. After spectra continuum removal, the heavy metal contents were estimated by 11 features including the absorption depth, absorption area, and band ratio around 2200 nm, which showed the best performance. For arsenic (As), the best model yields Rp2 value of 0.8474, and the RMSEP value is 36.1542 (mg/kg). It is concluded that As is adsorbed by organic matter, clay minerals, and iron/manganese oxides in soil, and the adsorption of As by first two components is greater than that of the last. For airborne spectra after continuum removal, combining the spectral absorption characteristic parameters and the highly correlated bands is more accurate than using the spectral absorption characteristic parameters or bands alone. AdaBoost is presented for the heavy metal estimation, and the fitting ability of the method is found to be stronger than that of the traditional classical methods, with the Rp2 values of 0.6242 and the RMSEP value of 43.6481 (mg/kg). In summary, these results will provide a prospective basis for the rapid estimation of soil heavy metals, the risk assessment of soil heavy metals and soil environmental monitoring in a large scale.

Human health-risk assessment of heavy metal-contaminated soil based on Monte Carlo simulation

Soil contamination soils of by heavy metals (HMs) poses serious threats to the soil environment and enters the human body through exposure pathways such as ingestion and skin contact, posing a threat to human health. The purpose of this study was to analyze the sources and contributions of soil HMs, and to quantitatively assess the human health risks of soil HMs to different populations (i.e. children, adult females and adult males), and to analyze the human health risks caused by various sources of sensitive populations. 170 topsoil (0-20 cm) were collected from Fukang, Jimsar and Qitai on the northern slope of Tianshan Mountains in Xinjiang, China, and the contents of Zn, Cu, Cr, Pb and Hg were determined. This study used the Unmix model and a health-risk assessment (HRA) model to assess the human health risks of five HMs. The results showed that: (1) The mean values of Zn and Cr were lower than the background values of Xinjiang, the mean values of Cu and Pb were slightly higher than the background values of Xinjiang but lower than the national standard, and the mean value of Hg and Pb was higher than the background value of Xinjiang and the national standard. (2) The sources of soil HMs in the region were mainly traffic, natural, coal, and industrial sources. Moreover, the HRA model combined with Monte Carlo simulation showed similar trends in the health-risk status of all population groups in the region. Probabilistic HRA revealed that noncarcinogenic risks were acceptable for all populations (HI < 1) while carcinogenic risks were high (children: 77.52%; female: 69.09%; male: 65.63%). For children, carcinogenic risk from industrial and coal sources exceeded the acceptable threshold by 2.35 and 1.20 times, respectively, and Cr was the main element contributing to human carcinogenic risk. These findings suggest that carcinogenic risks from coal-based Cr emissions cannot be ignored, and the study area should aim to control Cr emissions from industrial sources. The results of this study provide support for the prevention of human health risks and the control of soil HMs pollution across different age groups.

Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery

Widespread soil contamination endangers public health and undermines global attempts to achieve the United Nations Sustainable Development Goals. Due to the lack of relevant studies and low precision of spaceborne spectroscopy, estimating soil heavy metal concentrations is challenging. In this study, we developed a coupled retrieval to qualify the heavy metal nickel (Ni) concentration in agricultural soil from spaceborne hyperspectral imagery. The retrieval couples spectral feature extraction from multi-scale discrete wavelet transform (DWT) and dimension reduction (DR), optimal band combination algorithm to five machine learning retrieval models using tree-based ensemble learning, neural network-based, and kernel-based. The comparison between the retrievals and Ni measurements shows that the DWT combined with t-distributed stochastic neighbor embedding (tSNE) coupled extreme gradient boosting (XGboost) retrieval model exhibited the best prediction for the validation dataset. Moreover, due to the integration of six statistical indicators of model performance and the fitted slope of the regression line, the retrieval framework can produce more robust and accurate predictions than those that rely on correlation coefficients. The demonstrated potential of spaceborne hyperspectral remote sensing to provide accurate quantitative measurements of soil heavy metal concentrations will serve as a reference for agricultural plot applications worldwide.

Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentrations of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (R_p²) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA; R_p² of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA; R_p² of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the requirements of practical application.

Combining Fractional Derivatives and Machine Learning: A Review

Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is that fractional derivatives inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches.

Inversion study of cadmium content in soil based on reflection spectroscopy and MSC-ELM model

Heavy metal pollution in saline-alkali land has a significant impact on the ecological environment and human health. Rapid and accurate inversion of cadmium (Cd) element content in the saline-alkali land is important for environmental protection, saline-alkali soil improvement and conversion of saline-alkali land to cultivated land. Using traditional chemical detection methods to detect the content of heavy metal elements requires a long testing time and has the drawback of high prices. In this paper, we select the saline-alkali land of Zhenlai County as the study area and combine visible-NIR spectroscopy with machine learning models to invert the Cd content in the saline-alkali land. We preprocess the original reflection spectra using fractional order derivatives (FOD), then construct six three-band spectral indices (TBIs) and obtain the corresponding optimal band combination parameters by the optimal band combination (OBC) algorithm. To address the shortcomings of two-hidden-layer extreme learning machine (TELM), this paper introduces new weight parameters among the nodes of the first hidden layer, further extends it to multiple layers on this basis, and proposes the MSC-ELM model. The improved model is compared with several models, such as random forest (RF), partial least squares (PLS) and extreme learning machine (ELM). And the model performance is analyzed and compared by introducing several performance indicators, such as root mean square error (RMSE) and the ratio of the performance to interquartile (RPIQ). The experimental results show that the FOD transformation can eliminate the baseline drift and reduce the spectral noise. The constructed TBIs can effectively enhance the correlation with Cd content relative to the original single band, reduce redundant information and enhance the spectral features. The MSC-ELM model achieves better performance metrics compared to the other models and obtains the optimal prediction performance. This study provides an accurate and rapid method for the detection of Cd content in saline soil, which is important for the improvement and ecological recovery of saline soil.

Estimation of SO42- ion in saline soil using VIS-NIR spectroscopy under different human activity stress

SO₄^2- ion is an important indicator of soil salinization degree, but there are few researches on quantitative inversion of SO₄^2- content based on hyperspectral and fractional-order derivative (FOD). This study aimed to improve the prediction accuracy of SO₄^2- content in arid regions using visible and near-infrared (VIS-NIR) spectroscopy. The study area was divided into three regions according to different human activity stress, namely, lightly affected region (Region A), moderately affected region (Region B) and severely affected region (Region C). The combination estimation method of spectral transformations (R, R, 1/R, lgR, 1/lgR), FOD, significance test band (STB), and partial least squares regression (PLSR) were been constructed, and four models (FULL-PLSR, FOD-FULL-PLSR, IOD-STB-PLSR, FOD-STB-PLSR) were also used to compare and analyze the estimation accuracy. Simulation results show that the optimal prediction model of three regions is FOD-STB-PLSR, its spectral transformation is established by R, 1/R and R in Region A, B, and C, respectively. Its RPD is 2.4701, 3.4679 and 1.9781, and its optimal FOD derivative is located at 1.8-, 1.1- and 1.1-order, respectively. It means that FOD can fully extract VIS-NIR spectroscopy details, the higher-order FOD is more capable of extracting characteristic data than low-order FOD, and the predictive ability of the best estimation model is very good, extremely strong and relatively good in Region A, B and C, respectively. Compared with the best IOD-STB-PLSR of each region, the RPD of the optimal FOD-STB-PLSR model has increased more than 38%, 32%, and 19%, respectively. This study shows that the proposed FOD-STB-PLSR model is suitable for estimating the SO₄^2- ion content of saline soil under different human activity stresses, and the study can provide a certain technical reference value for the monitoring of saline soil in arid areas.

Ab initio calculation of the adsorption of As, Cd, Cr, and Hg heavy metal atoms onto the illite(001) surface: Implications for soil pollution and reclamation

Elucidating the mechanisms of heavy metal (HM) adsorption on clay minerals is key to solving HM pollution in soil. In this study, the adsorption of four HM atoms (As, Cd, Cr, and Hg) on the illite(001) surface was investigated using density functional theory calculations. Different adsorption configurations were investigated and the electronic properties (i.e., adsorption energy (E_ad) and electron transfer) were analyzed. The E_ad values of the four HM atoms on the illite(001) surface were found to be As > Cr > Cd > Hg. The E_ad values for the most stable adsorption configurations of As, Cr, Cd, and Hg were -1.8554, -0.7982, -0.3358, and -0.2678 eV, respectively. The As atoms show effective chemisorption at all six adsorption sites, while Cd, Cr, and Hg atoms mainly exhibited physisorption. The hollow and top (O) sites were more favorable than the top (K) sites for the adsorption of HM atoms. The Gibbs free energy results show that the illite(001) surface was energetically favorable for the adsorption of As and Cr atoms under the influence of 298 K and 1 atm. After adsorption, there was a redistribution of positions and reconfiguration of the chemical bonding of the surface atoms, with a non-negligible influence around the upper surface atoms. Bader charge analysis shows electrons were transferred from the surface to the HM atoms, and a strong correlation between the valence electron variations and the adsorption energy was observed. HM atoms had a high electronic state overlap with the surface O atoms near the Fermi energy level, indicating that the surface O atoms, though not the topmost atoms around the surface, significantly influence HM adsorption. The above results show illite(001) preferentially adsorbed As among all four investigated HM atoms, indicating that soils containing a high proportion of illite might be more prone to As pollution.

The New Hyperspectral Analysis Method for Distinguishing the Types of Heavy Metal Copper and Lead Pollution Elements

In recent years, the problem of heavy metal pollution in agriculture caused by industrial development has been particularly prominent, directly affecting food and ecological environmental safety. Hyperspectral remote sensing technology has the advantages of high spectral resolution and nondestructive monitoring. The physiological and biochemical parameters of crops undergo similar changes under different heavy metal stresses. Therefore, it is a great challenge to explore the use of hyperspectral technology to distinguish the types of the heavy metal copper (Cu) and lead (Pb) elements. This is also a hot topic in the current research. In this study, several models are proposed to distinguish copper and lead elements by combining multivariate empirical mode decomposition (MEMD) transformation and machine learning. First, MEMD is introduced to decompose the original spectrum, which effectively removes the noise and highlights and magnifies the weak information of the spectrum. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and iteratively retaining informative variables (IRIV) were used to screen the characteristic bands and were combined with extreme learning machine (ELM), support vector machine (SVM), and general regression neural network (GRNN) algorithms to build models to distinguish the types of Cu and Pb elements. The quality of the model was evaluated using accuracy (A), precision (P), recall (R), and F-score. The results showed that the MEMD-SPA-SVM, MEMD-CARS-SVM, MEMD-SPA-ELM, MEMD-CARS-ELM, and MEMD-IRIV-ELM models intuitively and effectively distinguished the types of Cu and Pb elements. Their accuracy and F-scores were all greater than 0.8. To verify the superiority of these models, the same model was constructed based on first derivative (FD) and second derivative (SD) transformations, and the obtained classification and recognition accuracy (A) and F-score were both lower than 0.8, which further confirmed the superiority of the model established after MEMD transformation. The model proposed in this study has great potential for applying hyperspectral technology to distinguish the types of elements contaminated by Cu and Pb in crops.

Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative

Soil total nitrogen (TN) is a vital nutrient element that affects the growth and rubber production of rubber trees. Especially in the coastal environment, soil nutrients will show significant differences. Using hyperspectral technology to detect soil nitrogen ion content in the offshore environment can provide technical support for nutrient management. Preprocessing hyperspectral data is a crucial step in accurate spectral model estimation. At the same time, it is considered that the traditional first-order and second-order derivatives are easily unbalanced between the signal-to-noise ratio, resulting in the loss of adequate information. Therefore, this work focuses on the feasibility of fractional order derivative (FOD) combined with partial least squares regression (PLSR) to estimate its TN content. By collecting soil samples from rubber plantations, the TN content of the soil samples was determined, and the spectral reflectance was measured. The FOD of the original spectrum was preprocessed with an interval of 0.2, and 11 spectral curves were obtained. Then, successive projections algorithm (SPA) was used to extract spectral features, and partial least squares regression (PLSR) models of soil TN content were established. The research results show that compared with the traditional integer derivative, FOD has a tremendous advantage in balancing spectral information and noise and can provide more abundant characteristic variables, which helps establish a more robust estimation model. In the range of orders 0–2, the model established by the 1.8-order is the best. Under that circumstance, the determination coefficients of validation (R2v) is 0.649, and the ratio of the performance to deviation (RPD) is 1.72. Combined with FOD, it is feasible and practical to establish an accurate and rapid estimation model of soil TN content, which can provide an important reference for large-scale detection of soil TN content in rubber plantations.

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure.