Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy

Regional Inversion of Soil Heavy Metal Cr Content in Agricultural Land Using Zhuhai-1 Hyperspectral Images

With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite images are susceptible to environmental factors such as atmospheric and soil background, presenting a significant challenge in the accurate estimation of soil heavy metal concentrations. In this study, typical chromium (Cr)-contaminated agricultural land in Shaoguan City, Guangdong Province, China, was taken as the study area. Soil sample collection, Cr content determination, laboratory spectral measurements, and hyperspectral satellite image collection were carried out simultaneously. The Zhuhai-1 hyperspectral satellite image spectra were corrected to match laboratory spectra using the direct standardization (DS) algorithm. Then, the corrected spectra were integrated into an optimal model based on laboratory spectral data and sample Cr content data for regional inversion of soil heavy metal Cr content in agricultural land. The results indicated that the combination of standard normal variate (SNV)+ uninformative variable elimination (UVE)+ support vector regression (SVR) model performed best with laboratory spectral data, achieving a high accuracy with an R2 of 0.97, RMSE of 5.87, MAE of 4.72, and RPD of 4.04. The DS algorithm effectively transformed satellite hyperspectral image data into spectra resembling laboratory measurements, mitigating the impact of environmental factors. Therefore, it can be applied for regional inversion of soil heavy metal content. Overall, the study area exhibited a low-risk level of Cr content in the soil, with the majority of Cr content values falling within the range of 36.21-76.23 mg/kg. Higher concentrations were primarily observed in the southeastern part of the study area. This study can provide useful exploration for the promotion and application of Zhuhai-1 image data in the regional inversion of soil heavy metals.

Organic amendments for in situ immobilization of heavy metals in soil: A review

There is a growing need for soil remediation due to the increase in heavy metals (HMs) migrating into the soil environment, especially those from man-made sources dominated by industry and agriculture. In situ immobilization technology, because of its lower life cycle environmental footprint, can achieve "green and sustainable remediation" of soil heavy-metal pollution. Among the various in situ immobilization remediation agents, organic amendments (OAs) stand out as they can act as soil conditioners while acting as HMs immobilization agents, and therefore have excellent application prospects. In this paper, the types and remediation effects of OAs for HMs in situ immobilization in soil are summarized. OAs have an important effect on the soil environment and other active substances in soil while interacting with HMs in soil. Based on these factors, the principle and mechanism of HMs in situ immobilization in soil using OAs are summarized. Given the complex differential characteristics of soil itself, it is impossible to determine whether it can remain stable after heavy-metal remediation; therefore, there is still a gap in knowledge regarding the compatibility and long-term effectiveness of OAs with soil. In the future, it is necessary to develop a reasonable HMs contamination remediation program for in situ immobilization and long-term monitoring through interdisciplinary integration techniques. These findings are expected to provide a reference for the development of advanced OAs and their applications in engineering.

Estimation of multi-media metal(loid)s around abandoned mineral processing plants using hyperspectral technology and extreme learning machine

Hyperspectral techniques are promising alternatives to traditional methods of investigating potentially toxic metal(loid) contamination. In this study, hyperspectral technology combined with partial least squares regression (PLSR) and extreme learning machine (ELM) established estimation models to predict the contents of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb) and tin (Sn) in multi-media environments (mine tailings, soils and sediments) surrounding abandoned mineral processing plants in a typical tin-polymetallic mineral agglomeration in Guangxi Autonomous Region. Four spectral preprocessing methods, Savitzky-Golay (SG) smoothing, continuum removal (CR), first derivative (FD) and continuous wavelet transform (CWT), were used to eliminate noise and highlight spectral features. The optimum combinations of spectral preprocessing and machine learning algorithms were explored, then the estimation models with best accuracy were obtained. CWT and CR were excellent spectral pretreatments for the hyperspectral data regardless of the applied algorithms. The coefficients of determination (R2) of estimation models for the best accuracy of various metals (loid) are as follows: Cu (CWT-ELM:0.85), Zn (CR-PLSR:0.93), As (CWT-ELM: 0.86), Cd (CR-PLSR: 0.89), Pb (CWT-PLSR: 0.75) and Sn (CR-ELM: 0.81). In contrast, ELM models had higher accuracy with R2 > 0.80 (except Cd and Pb). In conclusion, ELM-based spectral estimation models are able to predict metal (loid) concentrations with high accuracy and efficiency, providing a potential new combinatorial approach for estimating toxic metal contamination in multi-media environments.

Source, fate and budget of Dechlorane Plus (DP) in a typical semi-closed sea, China

Dechlorane Plus (DP), which has severe effects on marine ecosystems, has been proposed for listing under the Stockholm Convention as a persistent organic pollutant (POPs). This study was the first comprehensive investigation of the concentration and fate of DP in the Bohai Sea (BS) based on determination of river estuary water, river estuary sediment, surface seawater, bottom seawater, and sea sediments samples. The highest water DP levels were found in river estuary in Tianjin in North China due to the huge usage of DP in recent years, and spatial distribution analysis indicates it was mainly affected by regional high urbanization and emission of E-waste. The spatial distribution of DP in the BS was mainly affected by a combination of coastal hydrodynamics and land anthropogenic activities. On the basis of multi-box mass balance, simulations of DP in seawater showed an increase from 2014 to 2025, before leveling off at 184 pg L ^-1 by a constant DP input to the BS. Riverine discharge almost contributed to the total input (∼99%) and dominated the DP levels in the BS. Degradation of DP accounted for 55.3% and 78.1% of total DP output in seawater and sediment, respectively, indicating that degradation mainly affected decline of DP in the environment.

Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil

Heavy metal contamination in soil disturbs the chemical, biological, and physical soil conditions and adversely affects the health of living organisms. Visible and near-infrared spectroscopy (VNIRS) shows a potential feasibility for estimating heavy metal elements in soil. Moreover, deep learning models have been shown to successfully deal with complex multi-dimensional and multivariate nonlinear data. Thus, this study implemented a deep learning method on reflectance spectra of soil samples to estimate heavy metal concentrations. A convolutional neural network (CNN) was adopted to estimate arsenic (As), copper (Cu), and lead (Pb) concentrations using measured soil reflectance. In addition, a convolutional autoencoder was utilized as a joint method with the CNN for dimensionality reduction of the reflectance spectra. Furthermore, artificial neural network (ANN) and random forest regression (RFR) models were built for heavy metal estimation. Principal component analysis was utilized for dimensionality reduction of the ANN and RFR models. Among these models, the CNN model with convolutional autoencoder showed the highest accuracies for As, Cu, and Pb estimates, having R² values of 0.86, 0.74, and 0.82, respectively. The convolutional autoencoder disentangled the relevant features of multiple heavy metal elements and delivered robust features to the CNN model, thereby generating relatively accurate estimates.

Predicting copper contamination in wheat canopy during the full growth period using hyperspectral data

The rapid and efficient determination of heavy metal content in food crops is essential for human health and environmental protection. The use of hyperspectral data has become a popular way to predict heavy metal content in plants; however, many challenges remain. One challenge is that lab conditions differ from actual agricultural production conditions. Another challenge is that spectral data characteristics are not universally applicable to all situations. Therefore, in this study, the field test method was adopted to conduct experiments during the full growth period of wheat, and the spectrum data of wheat canopy were processed by the first derivative method to screen-sensitive spectral bands as the basis for the prediction model of the copper content in wheat. The results showed that the copper content increased with an increase in the soil copper content, and there were dissimilar subtle differences in the spectral reflectance of wheat canopy under different stressed soil copper concentrations; sensitive spectral indices and wavelengths were screened based on good correlation with the copper content in the wheat canopy. Different optimal predicting models in different periods were built and verified. The established linear regression models, which were based on NDVI/SIPI and W728, were the most suitable predicting models during the tillering stage with R² = 0.669 and 0.818; Rg, W741, and multiple bands were the most suitable predicting models during the jointing stage with R² = 0.548, 0.830, and 0.868; the optimal model during the heading stage was based on W480 (R² = 0.625). This study demonstrated that the constructed models had good potential for estimating the copper content in wheat leaves during full growth periods, and this method had the potential to be applied to the actual agricultural production process.

Effect of Sewage Irrigation on the CT-Measured Soil Pore Characteristics of a Clay Farmland in Northern China

Sewage irrigation has a strong influence on the physical, chemical, and biological properties of soil. However, the effects of sewage irrigation on the pore characteristics of soil are not well understood. This study compares the effects of sewage irrigation and groundwater irrigation on computed tomography (CT)-measured pore parameters and examines the relationships between CT-measured pore parameters and soil physicochemical and microbial properties. Intact soil cores were collected from S1 irrigated with sewage for 25 years, S2 irrigated with sewage for 52 years, and CK irrigated with groundwater. Various soil pore characteristics were determined, including the total pore number, macropore number (>1 mm diam.), coarse mesopore number (0.264⁻1 mm diam.), total porosity, macroporosity, coarse mesoporosity, and circularity. The results indicated that sewage irrigation significantly affected soil pore number and porosity. Compared with S1 and S2, CK exhibited a higher average total pore number (91), macropore number (40), coarse mesopore number (51), total porosity (2.08%), macroporosity (1.90%), and coarse mesoporosity (0.18%) throughout the 50⁻350 mm layer. At depths of 200⁻350 mm, S2 exhibited the lowest average total pore number (33), macropore number (13), coarse mesopore number (21), total porosity (0.42%), macroporosity (0.35%), and coarse mesoporosity (0.07%) among the three sites. In addition, the average pore numbers and porosity at depths of 200⁻350 mm decreased with increasing sewage irrigation time. There were significant positive correlations between pore features (including pore numbers and porosity) and soil properties (phosphorus content and fungi numbers). Our results suggest that decreased macropore numbers and macroporosity in the sewage-irrigated farmland may strongly intensify the accumulation of metals and nutrients in the upper layer. The findings of this study are useful for understanding the negative effects of sewage irrigation on soil pore structure and are critical for developing sustainable strategies in agriculture.