Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution

Levels of potentially toxic and essential elements in Tocantins River sediment: health risks at Brazil's Savanna-Amazon interface

The field study aims to address identified research gaps by providing valuable information on the concentration, spatial distribution, pollution levels, and source apportionment of toxic and essential elements in sediment samples from four sampling sites (P1: Beira Rio (urban area), P2: Bananal (rural area), P3: Embiral (rural area), P4: Cidelândia (rural area) distributed along the middle Tocantins River, Brazil. Samples were collected in 2023 from river sections and analyzed using various contamination índices (geoaccumulation index, contamination factor, enrichment factor, pollution load index, sediment pollution index, potential ecological risk coefficients, and integrated risk index). Results indicated that the levels of aluminum, iron, manganese, and selenium exceeded legal standards in that year. Chromium, nickel, copper, zinc, and lead exceeded guidelines, mainly in section P1 for aluminum and section P3 for nickel and lead. Rainy months showed increased presence, indicating seasonal variability. The geoaccumulation index indicated low pollution levels, with lead and nickel notably present near urban and industrial areas. The enrichment factor highlighted elevated concentrations of lead and zinc in industrial areas. Both PLI and SPI indices raise concerns regarding Pb (P4) and Zn (P3) concentrations at specific times of the year. Overall, potential ecological risks were deemed low for most sites. Continuous monitoring and interventions are crucial to preserve water and environmental quality in the region.

Effectiveness of soil remediation intervention in abandoned used lead acid battery (ULAB) recycling sites to reduce lead exposure among the children

Lead (Pb) is a neurotoxin, and children are vulnerable due to their evolving physiology and high-risk behaviours. Soil remediation interventions have proven effective in reducing Pb exposure. The primary objective is to measure the effectiveness of soil remediation at abandoned used lead acid battery (ULAB) recycling sites, nearby household cleaning, and community awareness in reducing blood lead levels (BLLs) in children. Additionally, this study aims to examine associations of Pb exposure with hematological, cardiovascular, renal, immunological, and endocrinological parameters in children aged 0-12 years. This study employs a quasi-experimental design, with abandoned ULAB sites as intervention sites and two control sites in Bangladesh. The intervention includes soil remediation coupled with community education. Data will be collected prior to the intervention and at a 12-month follow-up, including a comprehensive Pb exposure survey and collect environmental, turmeric samples, and blood from the child. Pb concentrations in environmental samples and turmeric samples will be determined using XRF analyser. Child BLL will be measured using Graphite Furnace Atomic Absorption Spectrometry (GF-AAS) and proposed biochemical parameters will be analysed using routine laboratory methods. This study could provide valuable insights for designing targeted interventions in similar settings and mitigating exposure to Pb.

Machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.

Comparison between Variable-Selection Algorithms in PLS Regression with Near-Infrared Spectroscopy to Predict Selected Metals in Soil

Soil is one of the Earth's most important natural resources. The presence of metals can decrease environmental quality if present in excessive amounts. Analyzing soil metal contents can be costly and time consuming, but near-infrared (NIR) spectroscopy coupled with chemometric tools can offer an alternative. The most important multivariate calibration method to predict concentrations or physical, chemical or physicochemical properties as a chemometric tool is partial least-squares (PLS) regression. However, a large number of irrelevant variables may cause problems of accuracy in the predictive chemometric models. Thus, stochastic variable-selection techniques, such as the Firefly algorithm by intervals in PLS (FFiPLS), can provide better solutions for specific problems. This study aimed to evaluate the performance of FFiPLS against deterministic PLS algorithms for the prediction of metals in river basin soils. The samples had their spectra collected from the region of 1000-2500 nm. Predictive models were then built from the spectral data, including PLS, interval-PLS (iPLS), successive projections algorithm for interval selection in PLS (iSPA-PLS), and FFiPLS. The chemometric models were built with raw data and preprocessed data by using different methods such as multiplicative scatter correction (MSC), standard normal variate (SNV), mean centering, adjustment of baseline and smoothing by the Savitzky-Golay method. The elliptical joint confidence region (EJCR) used in each chemometric model presented adequate fit. FFiPLS models of iron and titanium obtained a relative prediction deviation (RPD) of more than 2. The chemometric models for determination of aluminum obtained an RPD of more than 2 in the preprocessed data with SNV, MSC and baseline (offset + linear) and with raw data. The metals Be, Gd and Y failed to obtain adequate models in terms of residual prediction deviation (RPD). These results are associated with the low values of metals in the samples. Considering the complexity of the samples, the relative error of prediction (REP) obtained between 10 and 25% of the values adequate for this type of sample. Root mean square error of calibration and prediction (RMSEC and RMSEP, respectively) presented the same profile as the other quality parameters. The FFiPLS algorithm outperformed deterministic algorithms in the construction of models estimating the content of Al, Be, Gd and Y. This study produced chemometric models with variable selection able to determine metals in the Ipojuca River watershed soils using reflectance-mode NIR spectrometry.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

Dietary cadmium health risk assessment for the Chinese population

Cadmium (Cd) has high rates of soil-to-plant transference, coupled with its non-biodegradability and persistence; long-term management of Cd in agriculture is thus required to ensure better soil and food security and safety. Identifications of regions with high soil Cd concentration or high dietary Cd intakes are critical public health priorities. Human health risk assessment for dietary Cd intake was thus undertaken by employing three approaches: FCA (food chain approach), TDA (total diet approach), and FQA (food quality approach). The correlation between green/total vegetable consumption rates and dietary Cd intake from vegetables was statistically significant. For consumption, the hazard quotients (HQs) calculated by FCA and TDA were all less than 1 except for Hunan and Sichuan province. For rice consumption, the HQs derived by FCA or TDA approach for eight provinces exceeded 1. Residents in Hubei, Guangxi, Jilin, Zhejiang, Liaoning, Shanghai, Sichuan, and Guangxi were more vulnerable due to their notable higher consumption rates.Weighted rankings of the health risk levels were determined to derive the comparative risk management priority. For Cd intake from vegetables, four provinces/cities have high relative priority; for Cd intake from grains, three provinces have high relative priority. The comparative risk management priority for Hunan and Sichuan was high for dietary intake from vegetables or rice. Weighted average HQs were derived to determine the integrated dietary Cd intake health risk levels for dietary intake from vegetables or grains. The risk levels for Hunan, Guangxi, Sichuan, and Zhejiang are high, so effective measures should be taken to reduce Cd dietary intakes to ensure health protection.It is envisaged that the methodology employed in this study could provide useful insights into how various approaches can be integrated to determine human health risk levels for Cd intake, so more effective and targeted measures can be taken accordingly for the relevant regions.

Heavy metal stabilization remediation in polluted soils with stabilizing materials: a review

The remediation of soil contaminated by heavy metals has long been a concern of academics. This is due to the fact that heavy metals discharged into the environment as a result of natural and anthropogenic activities may have detrimental consequences for human health, the ecological environment, the economy, and society. Metal stabilization has received considerable attention and has shown to be a promising soil remediation option among the several techniques for the remediation of heavy metal-contaminated soils. This review discusses various stabilizing materials, including inorganic materials like clay minerals, phosphorus-containing materials, calcium silicon materials, metals, and metal oxides, as well as organic materials like manure, municipal solid waste, and biochar, for the remediation of heavy metal-contaminated soils. Through diverse remediation processes such as adsorption, complexation, precipitation, and redox reactions, these additives efficiently limit the biological effectiveness of heavy metals in soils. It should also be emphasized that the effectiveness of metal stabilization is influenced by soil pH, organic matter content, amendment type and dosage, heavy metal species and contamination level, and plant variety. Furthermore, a comprehensive overview of the methods for evaluating the effectiveness of heavy metal stabilization based on soil physicochemical properties, heavy metal morphology, and bioactivity has also been provided. At the same time, it is critical to assess the stability and timeliness of the heavy metals' long-term remedial effect. Finally, the priority should be on developing novel, efficient, environmentally friendly, and economically feasible stabilizing agents, as well as establishing a systematic assessment method and criteria for analyzing their long-term effects.

Coal seam in-situ inorganic analysis based on least angle regression and competitive adaptive reweighted sampling algorithm by XRF-visNIR fusion

The fusion of X-ray fluorescence spectroscopy (XRF) and visible near infrared spectroscopy (visNIR) has been widely used in geological exploration. The outer product analysis (OPA) has a good effect in the fusion. The dimension of the spectral matrix obtained by OPA is large, and the Competitive Adaptive Reweighted Sampling (CARS) cannot cover the whole spectrum. As a result, the selected variables by the method are inconsistent each time. In this paper, a new feature variable screening method is proposed, which uses the Least Angle Regression (LAR) to select the high dimensional spectral matrix first, and then uses CARS to complete the secondary selection of the spectral matrix, forming the LAR-CARS algorithm. The purpose is to make the sampling method cover all the spectral data. XRF and visNIR tests were carried out on three cores in two boreholes, and a cross-validation set, validation set and a test set were established by combining the results of wavelength dispersion X-ray fluorescence spectrometer and ITRAX Core scanner in the laboratory. The quantitative model was established with the Extreme Gradient Boosting (XGBoost) and LAR-CARS was compared to these other algorithms (LAR, Successive Projections Algorithm, Monte Carlo uninformative variables elimination and CARS). The results showed that the RMSEP values of the models established by the LAR-CARS for six rock-forming elements (Si, Al, K, Ca, Fe, Ti) were relatively small, and the RPD ranges from 1.424 to 2.514. All these results show that the high-dimensional matrix formed by XRF and visNIR integration combined with LAR-CARS can be used for quantitative analysis of rock forming elements in in-situ coal seam cores, and the analysis results can be used as the basis for judging lithology. The research will provide necessary technical support for digital mine construction.

Geochemical fingerprinting and magnetic susceptibility to unravel the heterogeneous composition of urban soils

The typically high heterogeneity of urban soil properties challenges their characterization and interpretation. The objective of this study was to investigate if proximally sensed volume-specific magnetic susceptibility and/or geochemical soil properties can uncover differences in anthropogenic, lithogenic and pedological contributions in, and between, urban soils. We also tested if volume-specific magnetic susceptibility can predict heavy metal enrichment. Data on 29 soil properties of 103 soil horizons from 16 soils from Ghent, Belgium, were analyzed by factor analysis. A correlation analysis, and in-depth analysis of five contrasting urban soils supplemented insights gained from the factor analysis. The factor analysis extracted four factors: 29.2 % of the soil property variability was attributed to fossil fuel combustion and industrial processes, with high (>0.80) loadings for S, organic carbon, magnetic susceptibility, and Zn. Furthermore, 26.0 % of the variability was linked to parent material differences, with high loadings (>0.80) for K, Rb and Ti. In absence of geogenic carbonates, increased soil alkalinity due to anthropogenic input of CaCO₃ explained 17.0 % of the variability. Lastly, 4.7 % of the variability resulted from variable Zr contents by local geology. Elemental analysis by XRF, possibly combined with magnetic susceptibility measurements, helped to explain lateral or vertical differences related to (1) the nature of anthropogenic influence, for instance burning (e.g., by the S and Zn content) or the incorporation of building rubble (e.g., by the Ca content); (2) the particle size distribution (e.g., by the K, Rb or Ti content); (3) lithology (e.g., by the Zr content); or (4) pedology, such as organic matter build-up (e.g., by the S content) or leaching of alkalis (e.g., by the Ca content). Even though artifacts and soil translocation were common in the studied soils, volume-specific soil magnetic susceptibility measured on fine earth predicted the total heavy metal pollution loading index well (Pearson correlation = 0.85).

Quantitative Assessment and Spatial Analysis of Metals and Metalloids in Soil Using the Geo-Accumulation Index in the Capital Town of Romblon Province, Philippines

The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow's pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as "clean". Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.

Potential human health risks of toxic/harmful elements by consumption of Pseudevernia furfuracea

The potential human health risks of some toxic/harmful elements related to the consumption of Pseudevernia furfuracea (L.) Zopf. were investigated. The toxic/harmful elements (cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn)) were determined in P. furfuracea. According to the analysis result, the maximum (max.) toxic/harmful element value was 62 ± 3.1 mg/kg for Mn and minimum (min.) value was 0.19 ± 0.01 mg/kg for Cd. The estimated daily exposure doses (EDEXDs) for men, women and children were dietary (bread) > dietary (tea) > dermal. For dietary (bread) and dietary (tea) non-carcinogenic (HQ) risk was children > women > men. For dermal, HQ risk was women > children > men. Hazard index (HI) value for men was >1 for Cr. HI value for men was 1.36 for Cr. HI value for women was >1 for Cr and Mn. HI values for women were 1.54 for Cr and 1.01 for Mn. Also, the HI value for children was >1 for Cr, Mn, and Pb. HI values for children were 3.44 for Cr, 2.24 for Mn, and 1.66 for Pb. This situation showed that there was a non-carcinogenic risk. Carcinogenic risk values were dietary (bread) > dietary (tea) > dermal. The total max. carcinogenic value was 1.97E-03 for Cr while the total min. carcinogenic value was 1.31E-05 for Pb. As a result, it has been determined that there may be a risk of cancer due to the consumption of lichen as bread and this situation may adversely affect human health.

Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China

Soil organic carbon (SOC) plays an important role in the global carbon cycle and soil fertility supply. Rapid and accurate estimation of SOC content could provide critical information for crop production, soil management and soil carbon pool regulation. Many researchers have confirmed the feasibility and great potential of visible and near-infrared (Vis-NIR) spectroscopy in evaluating SOC content rapidly and accurately. Here, to evaluate the feasibility of different spectral bands variable selection methods for SOC prediction, we collected a total of 330 surface soil samples from the cotton field in the Alar Reclamation area in the southern part of Xinjiang, which is located in the arid region of northwest China. Then, we estimated the SOC content using laboratory Vis-NIR spectral. The Particle Swarm optimization (PSO), Competitive adaptive reweighted sampling (CARS) and Ant colony optimization (ACO) were adopted to select SOC feature bands. The partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) inversion models were constructed by using full-bands (400-2400 nm) spectra (R) and feature bands, respectively. And we also analyzed the effects of spectral feature band selection methods and modeling methods on the prediction accuracy of SOC. The results indicated that: (1) There are significant differences in the feature bands selected using different methods. The feature bands selected methods substantially reduced the spectral variable dimensionality and model complexity. The models built by the feature bands selected by CARS, PSO and ACO methods showed the different potential of improvement in model accuracy compared with the full-band models. (2) The CNN model had the best performance for predicting SOC. The R² of the optimal CNN model is 0.90 in the validation, which was improved by 0.05 and 0.04 in comparison with the PLSR and RF model, respectively. (3) The highest prediction accuracy was archived by the CNN model using the feature bands selected by CARS (validation set R² = 0.90, RMSE = 0.97 g kg^-1, RPD = 3.18, RPIQ = 3.11). This study indicated that using the CARS method to select spectral feature bands, combined with the CNN modeling method can well predict SOC content with higher accuracy.

Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing

X-ray fluorescence (XRF) spectroscopy offers a fast and efficient method for analysing soil elemental composition, both in the laboratory and the field. However, the technique is sensitive to spectral interference as well as physical and chemical matrix effects, which can reduce the precision of the measurements. We systematically assessed the XRF technique under different sample preparations, water contents, and excitation times. Four different soil samples were used as blocks in a three-way factorial experiment, with three sample preparations (natural aggregates, ground to ≤2 mm and ≤1 mm), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 s). The XRF spectra were recorded and gave 540 spectra in all. Elemental peaks for Si, K, Ca, Ti, Fe and Cu were identified for analysis. We used analysis of variance (anova) with post hoc tests to identify significant differences between our factors and used the intensity and area of the elemental peaks as the response. Our results indicate that all of these factors significantly affect the XRF spectrum, but longer excitation times appear to be more defined. In most cases, no significant difference was found between air-dry and 10% water content. Moisture has no apparent effect on coarse samples unless ground to 1 mm. We suggested that the XRF measurements that take 60 s from dry samples or only slightly moist ones might be an optimum option under field conditions.

Revealing XRF data quality level, comparability with ICP-ES/ICP-MS soil PTE contents and similarities in PTE induced health risk

Portable X-ray fluorescence spectroscopy (XRF) was recognized as an efficient and promising tools to study the contents of chemical elements in various media including soils under the impact of anthropogenic activities. However, the quality of data and the equality of chemical elements with other common analytical methods such as aqua-regia extraction vary depending on site, sample conditions, and analysis time. In this study, we examine the adequacy of XRF and ICP-ES/ICP-MS aqua-regia extractable (AR) results obtained for lab-type pretreated samples (N = 15) for Ti, Fe, Mn, Co, V, Pb, Zn, Cu, Cr, Mo, Sr, and As contents in soils under the impact of copper smelter and assess the equality of PTE contents induced health risk. The obtained results suggested that XRF reached definitive data quality level for As, Zn, and Mn and screening (quantitative) data quality level established for Cu, Pb, Fe, Mo, Cr, V, and Ti. Moreover, in some cases (i.e., for Ti) XRF overperformed AR indicating the high efficiency of XRF application when sparingly soluble mineral matrices are presented. At the same time, PTE induced health risk assessment at the established data quality level showed that equality of non-carcinogenic children health risk was observed for As, Zn, Cu, Pb, Mn, and V. The latter indicating the applicability of XRF to generate reliable base for risky sites identification and characterization.

Prediction of Heavy Metal Concentrations in Contaminated Sites from Portable X-ray Fluorescence Spectrometer Data Using Machine Learning

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.

Estimation of metal elements content in soil using x-ray fluorescence based on multilayer perceptron

X-ray fluorescence (XRF) is widely used to rapidly detect heavy metals in soil. Spectra processing has been an important research topic to improve accuracy. In this study, 80 soil samples were analyzed by XRF under indoor conditions, where different preprocessing and quantitative analysis methods were compared in terms of prediction accuracy. Denoising algorithms were used to preprocess the soil spectra before establishing prediction models for As, Pb, Cu, Cr, and Cd in soil. The influence of denoising methods on the prediction effects of different models was compared and discussed. The results on five heavy metal spectra show that the proper spectral preprocessing method can effectively improve the prediction performance of the model. The multilayer perceptron model provides promising analysis and modeling for the five metal elements. The determination coefficients (R²) of the models were 0.857, 0.976, 0.977, 0.995, and 0.886, respectively. The proposed method provides the potential to support accurate quantitation by XRF analysis.

X-ray fluorescence spectrometry applied to digital mapping of soil fertility attributes in tropical region with elevated spatial variability

Portable X-ray fluorescence (pXRF) spectrometry offers valuable information for prediction models of soil fertility attributes spatial variation, although this approach is yet scarce in tropical regions. This study aims to predict and build spatial variability maps of soil pH, remaining phosphorus (P-Rem), soil organic matter (SOM) and sum of bases (SB) using pXRF results through stepwise multiple linear regression (SMLR) and Random Forest (RF) in a highly variable tropical area. Composite samples from soil A horizon were collected at 90 points throughout the campus of the Federal University of Lavras, Minas Gerais, Brazil, for pH, P-Rem, SOM, SB and pXRF analyses. RF predictions showed the highest accuracies, especially for P-Rem and SB (R² values of 0.66 and 0.55, respectively). Attributes that showed higher R² in punctual predictions also exhibited higher R² in spatial predictions. Data obtained from pXRF in tandem with RF can be used to assist prediction models for soil fertility attributes, consequently enabling the digital mapping of such attributes and helping to improve the knowledge about the spatial variability of such attributes in soils of tropical climate. This technique can therefore assist in the identification and orientation of adequate management practices in tropical agricultural practices.

Regional human health risk assessment of cadmium and hexachlorocyclohexane for agricultural land in China

Widespread pollution of agricultural soil is posing great risks to food safety and human health. The absence of human health-based Generic Assessment Criteria (GAC) for agricultural land means Chinese farmers struggle to manage these risks efficiently and effectively. Cadmium (Cd) and hexachlorocyclohexane (HCH), two of the most concerned contaminants, demonstrate threshold toxicity meaning that background exposure (MDI_oral) is considered when deriving soil Generic Assessment Criteria (GAC). The CLEA (Contaminated Land Exposure Assessment) model was used to derive GAC for Cd and HCH that reflect differences in diet and soil characteristics across 19 provinces/cities. For both cadmium and alpha-HCH, Sichuan had the lowest GAC of 0.379 mg kg^-1 and 0.0136 mg kg^-1, respectively, resulting from its significant high MDI_oral values, which are approximately six to nine times larger than the average MDI_oral for all the 19 provinces/cities. Jiangxi province had the highest GAC of 1.230 mg kg^-1 and 0.0866 mg kg^-1, respectively, for cadmium and alpha-HCH, caused by its notable low MDI_oral values and low vegetable consumption rate. Human health risk assessment based on regional GAC for Cd revealed that agricultural land with very high to high risks is located in southern China, while very low-risk land is located in northern China. For HCH, alpha- and gamma-HCH pose negligible health risks, but beta-HCH poses some health risk in some of the provinces/cities. When applying the regional GAC for beta-HCH, agricultural land in Beijing and Sichuan posed the highest risk, and those in Heilongjiang and Jiangxi had the lowest risk. This reflects the significant influence of background and vegetable consumption pathway on the GAC. Regional GACs could simplify and speed up risk assessment of agricultural land in different regions of China, by avoiding the need to calculate site-specific assessment criteria, thus saving time and money by avoiding over or under remediation.

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

vis-NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible-near infrared (vis-NIR: 350-2500 nm) and X-ray fluorescence (XRF: 0.02-41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis-NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis-NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis-NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis-NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models' accuracies as compared with the single vis-NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis-NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

DNA damage and oogenesis anomalies in Pimelia latreillei (Coleoptera: Tenebrionidae) induced by heavy metals soil pollution

The present study used Pimelia latreillei as a biomonitoring insect for heavy metals soil pollution in a populated industrial area at Zawya Abd El-Qader, Alexandria, Egypt. Comet assay and histological analysis were applied to evaluate the potential risk of heavy metals. X-ray analysis of the soil samples collected from the polluted site revealed significantly increased metal percentages compared with the reference site. Moreover, a significant increase in metal percentages was detected by the X-ray analysis in insect ovaries collected from the polluted site. The Tail DNA length was significantly greater in the insects collected from the polluted site-47.6% compared with 11.4% at the reference site. Pronounced disruptions in oogenesis were observed through histological and ultrastructure investigations in insects collected from the polluted site. The study summarized the potential utility of insect biomonitors in predicting the effect of heavy metals soil pollution on occupational health.

Identification of trace metals and potential anthropogenic influences on the historic New York African Burial Ground population: A pXRF technology approach

The New York African Burial Ground (NYABG) is the country’s oldest and largest burial site of free and enslaved Africans. Re-discovered in 1991, this site provided evidence of the biological and cultural existence of a 17^th and 18^th Century historic population viewing their skeletal remains. However, the skeletal remains were reburied in October 2003 and are unavailable for further investigation. The analysis of grave soil samples with modern technology allows for the assessment of trace metal presence. Portable X-ray fluorescence (pXRF) spectrometry provides a semi-quantitative and non-destructive method to identify trace metals of this population and in the surrounding environment. Sixty-five NYABG soil samples were analyzed on a handheld Bruker Tracer III- SD XRF with 40 kV of voltage and a 30μA current. Presence of As, Cu, and Zn can potentially decipher the influence of the local 18^th Century pottery factories. Elevated levels of Sr validate the assumed heavy vegetative diets of poor and enslaved Africans of the time. Decreased levels of Ca may be due in part to the proximity of the Collect Pond, the existing water table until the early 19^th Century, and Manhattan’s rising sea level causing an elevated water table washing away the leached Ca from human remains. These data help us reconstruct the lives of these early Americans in what became New York City.

Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China

To verify the feasibility of portable X-ray fluorescence (PXRF) for rapidly analyzing, assessing and improving soil heavy metals mapping, 351 samples were collected from Fuyang District, Hangzhou City, in eastern China. Ordinary kriging (OK) and co-ordinary kriging (COK) combined with PXRF measurements were used to explore spatial patterns of heavy metals content in the soil. The Getis-Ord index was calculated to discern hot spots of heavy metals. Finally, multi-variable indicator kriging was conducted to obtain a map of multi-heavy metals pollution. The results indicated Cd is the primary pollution element in Fuyang, followed by As and Pb. Application of PXRF measurements as covariates in COK improved model accuracy, especially for Pb and Cd. Heavy metals pollution hot spots were mainly detected in northern Fuyang and plains along the Fuchun River in southern Fuyang because of mining, industrial and traffic activities, and irrigation with polluted water. Area with high risk of multi-heavy metals pollution mainly distributed in plain along the Fuchun River and the eastern Fuyang. These findings certified the feasibility of using PXRF as an efficient and reliable method for soil heavy metals pollution assessment and mapping, which could contribute to reduce the cost of surveys and pollution remediation.

A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and four integrated industry types by five different machine learning approaches. Multinomial naive Bayesian (NB) methods achieved an accuracy of 87% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 260,000 enterprises. The relationship between the different industry classes and measurements of soil cadmium (Cd) and mercury (Hg) concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of Cd, elevated concentrations also occurred in some areas because of excessive fertilization and coal mining. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites.

An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing

Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils.

Identifying heavy metal pollution hot spots in soil-rice systems: A case study in South of Yangtze River Delta, China

The soil-rice system in China is subjected to increasing concentrations of heavy metals (HMs) which derived from various sources. It is very critical to investigate the concentrations, spatial characteristics and hot spots of HMs content in the soil-rice system. This study presents work completed on 915 soil-rice sample pairs collected from South of Yangtze River Delta, China. These samples were evaluated for HM concentrations. Ordinary Kriging and the Getis-Ord index were used to explore spatial distributions and pollution hot spots. Averaged HMs content in soil is shown to be Zn > Cr > Pb > Cu > Ni > As > Hg > Cd, and concentrations in rice arrange as Zn > Cu > Cr > Ni > As > Cd > Pb > Hg. Compared with Chinese maximum permissible limits, mean content of all HMs in farmland soil are at safe levels and averaged content of all HMs in rice were also at safe levels except As and Ni. Ni was most polluted HM in soil Most of and showed relatively high content in farmland soil in southeastern part. As and Ni are the most polluted in rice, with highest content distributed in the northwestern and southern area, respectively. The majority of HMs pollution hot spots in soil clustered in the central area. Pollution hot spots of Ni and As in rice are mainly concentrated in the central part and southeastern part, correspondingly. Our results found a weak link between content and spatial pattern of pollution status of HMs in soil and rice. The results are anticipated to contribute to more efficient and accurate control of HMs pollution in soil-rice system, and assist decision-makers achieve a balance between cost and regulation of HM pollution.

Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R² = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R² = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R² = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R² = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R² = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R² = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China

Trace elements pollution has attracted a lot of attention worldwide. However, it is difficult to identify and apportion the sources of multiple element pollutants over large areas because of the considerable spatial complexity and variability in the distribution of trace elements in soil. In this study, we collected total of 2051 topsoil (0⁻20 cm) samples, and analyzed the general pollution status of soils from the Yangtze River Delta, Southeast China. We applied principal component analysis (PCA), a finite mixture distribution model (FMDM), and geostatistical tools to identify and quantitatively apportion the sources of seven kinds of trace elements (chromium (Cr), cadmium (Cd), mercury (Hg), copper (Cu), zinc (Zn), nickel (Ni), and arsenic (As)) in soil. The PCA results indicated that the trace elements in soil in the study area were mainly from natural, multi-pollutant and industrial sources. The FMDM also fitted three sub log-normal distributions. The results from the two models were quite similar: Cr, As, and Ni were mainly from natural sources caused by parent material weathering; Cd, Cu, and Zu were mainly from mixed sources, with a considerable portion from anthropogenic activities such as traffic pollutants, domestic garbage, and agricultural inputs, and Hg was mainly from industrial wastes and pollutants.

Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China

Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China

Heavy metal (HM) contamination and accumulation is a serious problem around the world due to the toxicity, abundant sources, non-biodegradable properties, and accumulative behaviour of HMs. The degree of soil HM contamination in China, especially in the Yangtze River Delta, is prominent. In this study, 1822 pairs of soil and crop samples at corresponding locations were collected from the southern Yangtze River Delta of China, and the contents of Ni, Cr, Zn, Cd, As, Cu, Hg, and Pb were measured. The single pollution index in soil (SPI) and Nemerow composite pollution index (NCPI) were used to assess the degree of HM pollution in soil, and the crop pollution index (CPI) was used to explore the degree of HM accumulation in crops. The bioaccumulation factor (BAF) was used to investigate the translocation of heavy metals in the soil-crop system. The health risks caused by HMs were calculated based on the model released by the U.S. Environmental Protection Agency. The SPIs of all elements were at the unpolluted level. The mean NCPI was at the alert level. The mean CPIs were in the following decreasing order: Ni (1.007) > Cr (0.483) > Zn (0.335) > Cd (0.314) > As (0.232) > Cu (0.187) > Hg (0.118) > Pb (0.105). Only the mean content of Ni in the crops exceeded the national standard value. The standard exceeding rates were used to represent the percentage of samples whose heavy metal content is higher than the corresponding national standard values. The standard exceeding rates of Cu, Hg, and Cd in soil were significantly higher than corresponding values in crops. Meanwhile, the standard exceeding rates of Ni, As, and Cr in crops were significantly higher than corresponding values in soil. The chronic daily intake (CDI) of children (13.8 × 10^-3) was the largest among three age groups, followed by adults (6.998 × 10^-4) and seniors (5.488 × 10^-4). The bioaccumulation factors (BAFs) of all crops followed the order Cd (0.249) > Zn (0.133) > As (0.076) > Cu (0.064) > Ni (0.018) > Hg (0.011) > Cr (0.010) > Pb (0.001). Therefore, Cd was most easily absorbed by crops, and different crops had different capacities to absorb HMs. The hazard quotient (HQ) represents the potential non-carcinogenic risk for an individual HM and it is an estimation of daily exposure to the human population that is not likely to represent an appreciable risk of deleterious effects during a lifetime. All the HQs of the HMs for the different age groups were significantly less than the alert value of 1.0 and were at a safe level. This indicated that citizens in the study area face low potential non-carcinogenic risk caused by HMs. The total carcinogens risks (TCRs) for children, adults, and seniors were 5.24 × 10^-5, 2.65 × 10^-5, and 2.08 × 10^-5, respectively, all of which were less than the guideline value but at the alert level. Ingestion was the main pathway of carcinogen risk to human health.

Assessment of the potential health risks of heavy metals in soils in a coastal industrial region of the Yangtze River Delta

Soil heavy metal contamination is a serious environmental problem. Human beings may be directly exposed to heavy metals in soils through the inhalation of soil particles, dermal contact, and oral ingestion, which can seriously threaten health. This study assesses the health risks associated with heavy metals in soils by determining the concentrations of eight heavy metals (Cr, Pb, Cd, Hg, As, Cu, Zn, and Ni) based on 2051 surface-soil samples collected from the southern Yangtze River Delta of China. The mean concentrations were higher than the corresponding background values in Zhejiang Province and China as a whole, indicating an accumulation of heavy metals. The health risk assessment suggests that the non-carcinogenic and carcinogenic risks in the study area were not significant. The non-carcinogenic risk for children was the highest, followed by those for adults and seniors; the non-carcinogenic risk for the entire population was less than 1.0, the predetermined threshold. Carcinogenic risk for adults was the highest, followed by those for seniors and children; a few sample points had a value larger than the threshold of 1.0E-04. Arsenic represented the greatest contribution to non-carcinogenic and carcinogenic risk. Meanwhile, ingestion of heavy metals in soil was the main exposure pathway for carcinogenic risk, followed by inhalation and dermal exposure. The spatial method of Getis-Ord was used to identify hot spots of health risk. Hot spots with high hazard index (HI) and total carcinogenic risk (TCR) for children, adults, and seniors were mainly distributed in core urban areas, such as Jiangbei, Haishu, Yinzhou, Jiangdong, and the urban areas of some other counties, which coincided with industrial, mining, and urban areas of the study area and were strongly influenced by anthropogenic activities. These results provide a basis for heavy metal control in soil, source identification, and environment management in the Yangtze River Delta and other rapidly developing industrial regions in China.