Modeling arsenic content in Brazilian soils: What is relevant?

Assessment of potentially toxic elements in sediment cores from reservoirs in the Upper Tiete River Basin, Brazil

This study evaluated concentrations of potentially toxic elements (PTEs)-As, Sb, Co, Cr, Zn, U, and Th-in sediment cores from the Jundiai and Taiaçupeba reservoirs in the Upper Tiete Water Basin, Sao Paulo, Brazil. These reservoirs are vital for supplying water to the São Paulo metropolitan area but face risks from agricultural and industrial activities. The research aimed to determine whether PTE enrichment in sediments is due to natural or anthropogenic factors, assess the influence of sediment geochemistry and grain size, and evaluate risks to public health and biota. Granulometric analysis and enrichment factors were used to interpret the results, with As, Cr, and Zn compared to sediment quality guidelines. Significant Zn contamination was found in the Taiaçupeba reservoir, exceeding the Probable Effects Level (PEL), suggesting mining-related contamination. This highlights the need for further research on Zn's spatial distribution, ecological risks, and bioavailability in the Taiaçupeba reservoir. Conversely, Sb, Co, Cr, U, and Th were linked to natural processes. Arsenic showed a local geologic anomaly in both reservoirs. This research emphasizes the importance of geochemistry as a critical tool for interpreting PTEs in trace element environmental monitoring. Geochemical parameters, including Hf, Ta, Sc, and K, and rare earth elements, were essential for understanding sedimentary dynamics and anthropogenic impacts. This approach enhances the effectiveness of PTE impact assessments and can be applied to other dam reservoirs worldwide.

Arsenic, cadmium, and chromium concentrations in contrasting phosphate fertilizers and their bioaccumulation by crops: Towards a green label?

Potentially toxic elements such as arsenic (As), cadmium (Cd), and chromium (Cr) are severely regulated in fertilizers and deserve continuous investigation. Phosphate-derived Cd has been a stepping-stone toward achieving sustainable and safe worldwide food production, especially after a new regulation aiming for reduced limits of Cd in P fertilizers (EU, 2019/1009). Three pot experiments were conducted to assess the variability of As, Cd, and Cr concentrations - with a particular focus on Cd - from monoammonium phosphates (MAP 1, MAP 2, and MAP 3 from different geographic origins) and their accumulation in limed and unlimed soils, and contrasting crops, representing staple food and significant sources of these elements for humans (i.e., potato, tobacco, and rice). A diverse array of sensitive techniques for trace elements determination were used to reveal the highest level of Cd of MAP 3 (20.71 mg kg-1 MAP), which loaded the highest amounts of this element to the soil matrix and solution, plant shoots, and xylem sap, contrasting with results for MAP 1 (0.87 mg kg-1 MAP), which has almost ten times less Cd than that required for low-Cd labeling of P fertilizers (≤20 mg Cd kg-1 P2O5). MAP 3 also had the highest Cr concentration (139.3 mg kg-1 MAP). Among crops, rice accumulated 16-fold less Cd than potato plants. Liming decreased Cd in tobacco and potato shoots up to 35%. Moreover, reductions of about 20% were also observed for Cd accumulation in tubers and sap. Conversely, Cd from MAP 3 was always much more accumulated in soil solution, achieving up to 20 μg L-1, while values < 5 μg L-1 (i.e., a groundwater limit) were obtained from MAP 1. Our findings may be used as a reference in developing green labels for fertilizers in scenarios where Cd accumulation represents a potential risk for soil and human health.

The soil conservation agenda of Brazil: A review of "edge-to-edge" science contributions

Soil conservation adheres to various United Nations Sustainable Development Goals while in Brazil is a constitutional obligation. To attain the goals and fulfil the obligation, laws, policies, governance and science must be imbricated to deliver suitable conservation solutions for the long term, namely appropriate to positively influence other downstream chains such as the food chain. However, in Brazil, a major world producer and exporter of food, weaknesses were recently diagnosed by judicial authorities concerning soil governance and coordinated land use policies. Integrated scientific assessments on soil conservation and mitigation of degraded soil are also lacking in this country. This was enough motivation and the purpose to present here a holistic view over the soil conservation agenda and promoting policies in Brazil, based on a literature review that followed the guidelines and criteria of PRISMA approach. We termed this analysis a review hinged on "edge-to-edge" science contributions for two reasons. Firstly, the intent of retrieving from the recently published literature solely papers centered on a relevant soil conservation topic (e.g., soil characterization, here called an "edge") but with complementary analyses over boundary topics (frontier "edges", such as soil degradation). Secondly, the intent of underlining the urgency to assist decision-makers with scientific evidence in all dimensions of the soil conservation agenda ("edge-to-edge" science), namely soil characterization (e.g., quality reference values), soil degradation assessment (e.g., anthropogenic-related soil erosion or contamination), soil degradation consequences focused on the carbon cycle (e.g., net CO2 emissions and climate warming), sustainable management practices and production systems (e.g., no-tillage agriculture and integrated crop-livestock-forestry systems), and scientific evaluation of existing laws as well as of governance and policy programs with potential implications on soil quality (e.g., the Forest Code). Thus, this literature review addressed all these topics following a multidisciplinary discourse, which produced an extensive but comprehensive document about soil conservation in Brazil.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers

Digital soil mapping relies on relating soils to a particular set of covariates, which capture inherent soil spatial variation. In digital mapping of soil classes, the most commonly used covariates are topographic attributes, RS attributes, and maps, including geology, geomorphology, and land use; in contrast, the subsurface soil characteristics are usually ignored. Therefore, we investigate the possibility of using soil diagnostic characteristics as covariates in a mountainous landscape as the main aim of this study. Conventional covariates (CC) and a combination of soil subsurface covariates with conventional covariates (SCC) were used as covariates, and random forest (RF), Multinomial Logistic Regression (LR), and C5.0 Decision Trees (C5) were used as different machine learning algorithms in digital mapping of soil family classes. Based on the results, the RF model with the SCC dataset had the best performance (KC = 0.85, OA = 90). In all three models, adding soil covariates to the sets of covariates increased the model performance. Soil covariates, slope, and aspect were selected as the principal auxiliary variables in describing the distribution of soil family classes.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Application of machine learning in groundwater quality modeling - A comprehensive review

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.

Arsenite and arsenate toxicity in the earthworm Eisenia andrei (Bouché 1972) in natural soil and tropical artificial soil

Inorganic forms of As (arsenite - As(III) and arsenate - As(V)) are prevalent in soil and recognized for their high toxicity. Once in the soil, these forms of As can compromise key organisms for ecological processes, such as earthworms. The aim of the study was to evaluate the toxicity of arsenite and arsenate in the Californian earthworm Eisenia andrei exposed in natural soil and tropical artificial soil (TAS). Adverse effects were evaluated using avoidance test, acute toxicity test, and a sublethal concentration test to assess biochemical parameters. LC50 values for arsenite were 21.27 mg/kg in natural soil and 19.0 mg/kg in TAS and for arsenate were 76.18 mg/kg in natural soil and above 120 mg/kg in TAS. In the avoidance test, this behavior was shown to be significantly higher in the natural soil and for earthworms exposed to arsenite, while total antioxidant capacity, glutathione levels, lipid damage, and DNA damage were significantly higher in animals exposed to arsenite, but without differences in relation to the two types of soil tested. Animals exposed to As(V) showed increased activity of enzymes related to glutathione metabolism. The results obtained in the present study show the impact of As exposure on the health of the Californian earthworm E. andrei, especially in the form of arsenite, and alert the public authorities that legal limits should, whenever possible, consider the soil properties and also the different chemical species of the contaminants.

Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India

This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree-based machine learning models namely Random Forest, Optimized Forest, CS Forest, SPAARC, and REP Tree algorithms have been applied to classify water samples. As per the guidelines of the World Health Organization (WHO), the arsenic concentration in water should not exceed 10 μg/L. The groundwater quality parameter was ranked using a classifier attribute evaluator for training and testing the models. Parameters obtained from the confusion matrix, such as accuracy, precision, recall, and FPR, were used to analyze the performance of models. Among all models, Optimized Forest outperforms other classifier as it has a high accuracy of 80.64%, a precision of 80.70%, recall of 97.87%, and a low FPR of 73.33%. The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples.

Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem. Several methods have been used to determine the contents of PTEs in soils, but most are laborious, costly and generate waste. In this study, we use a methodological framework to optimize the prediction of levels of PTEs in soils. We used a total set of 120 soil samples, collected at a depth of 0-10 cm. The covariate database is composed of variables measured by proximal sensors, physical and chemical soil characteristics, and morphometric data derived from a DEM with a spatial resolution of 30 m. Five machine learning algorithms were tested: Random Forests, Cubist, Linear Model, Support Vector Machine and K Nearest Neighbor. In general, the Cubist algorithm produced better results in predicting the contents of Pb, Zn, Ba and Fe compared to the other tested models. For the Al contents, the Support Vector Machine produced the best prediction. For the Cr contents, all models showed low predictive power. The most important covariates in predicting the contents of PTEs varied according to the studied element. However, x-ray fluorescence measurements, textural and morphometric variables stood out for all elements. The methodology structure reported in this study represents an alternative for fast, low-cost prediction of PTEs in soils, in addition to being efficient and economical for monitoring potentially contaminated areas and obtaining quality reference values for soils.

Environmental and human-health risks of As in soils with abnormal arsenic levels located in irrigated agricultural areas of Paracatu (MG), Brazil

The municipality of Paracatu (Brazil) is notorious for its large irrigated agricultural area and by abnormal arsenic (As) levels in selected soils of the region. Concerns regarding As exposure via ingestion of water and food are frequent, yet little is known about the behavior of arsenic in irrigated agricultural soils, as well as on As bioaccessibility/bioavailability in agroecosystems of this region. This work evaluated total and available As in agricultural soils cultivated under irrigation and in soils under native vegetation in Paracatu. We also assessed reactive arsenic fractions and As bioaccessibility in the soil, as well as arsenic levels in plant shoots to estimate As risks in these agroecosystems. Soil (different depths) and plant tissue samples were collected in 6 irrigated agricultural areas (CA1 to CA6) and 4 reference areas (RA1 to RA4). Total soil-As did not differ between soil depths, reinforcing that the source of As in agricultural soils is natural. This was evident when counterpointing arsenic and phosphorus contents at different soil depths, as both accumulate on the surface of oxidic soils when added to agroecosystems by anthropogenic routes (e.g., phosphate fertilization for P and irrigation for As). Available As levels in soils and plants were very small (below detection limit). Furthermore, all soils presented very low oral As bioaccessibility. Our findings revealed that the irrigated soils are not As polluted due to the low enrichment and accumulation of arsenic, as well as the prevalence of low ecological risks. There is no non-carcinogenic risk for the local population, except for children in RA2. The estimated carcinogenic risk for children followed the order RA2 > CA3 > CA4 > RA3 > CA2, and for adults, RA2 > CA3. Ultimately, the strategy of comparing the behavior of P and As in the soils of this study proved to be efficient in showing that there are no major risks to humans and the environment in the investigated area. However, periodic monitoring of As bioavailability in these areas is recommended.

Rare earth elements (REEs): geochemical patterns and contamination aspects in Brazilian benchmark soils

Rare earth elements have been increasingly used in modern societies and soils are likely to be the final destination of several REE-containing (by)products. This study reports REE contents for topsoils (0-20 cm) of 175 locations in reference (n = 68) and cultivated (n = 107) areas in Brazil. Benchmark soil samples were selected accomplishing a variety of environmental conditions, aiming to: i) establishing natural background and anthropogenic concentrations for REE in soils; ii) assessing potential contamination of soils - via application of phosphate fertilizers - with REE; and, iii) predicting soil-REE contents using biomes, soil type, parent material, land use, sand content, and biomes-land use interaction as forecaster variables through generalized least squares multiple regression. Our hypotheses were that the variability of soil-REE contents is influenced by parent material, pedogenic processes, land use, and biomes, as well as that cultivated soils may have been potentially contaminated with REE via input of phosphate fertilizers. The semi-total concentrations of REE were assessed by inductively coupled plasma mass spectrometry (ICP-MS) succeeding a microwave-assisted aqua regia digestion. Analytical procedures followed a rigorous QA/QC protocol. Soil physicochemical composition and total oxides were also determined. Natural background and anthropogenic concentrations for REE were established statistically from the dataset by the median plus two median absolute deviations method. Contamination aspects were assessed by REE-normalized patterns, REE fractionation indices, and Ce and Eu anomalies ratios, as well as enrichment factors. The results indicate that differences in the amounts of REE in cultivated soils can be attributed to land use and agricultural sources (e.g., phosphate-fertilizer inputs), while those in reference soils can be attributed to parent materials, biomes, and pedogenic processes. The biomes, land use, and sand content helped to predict concentrations of light REE in Brazilian soils, with parent material being also of special relevance to predict heavy REE contents in particular.

Seven potential sources of arsenic pollution in Latin America and their environmental and health impacts

This review presents a holistic overview of the occurrence, mobilization, and pathways of arsenic (As) from predominantly geogenic sources into different near-surface environmental compartments, together with the respective reported or potential impacts on human health in Latin America. The main sources and pathways of As pollution in this region include: (i) volcanism and geothermalism: (a) volcanic rocks, fluids (e.g., gases) and ash, including large-scale transport of the latter through different mechanisms, (b) geothermal fluids and their exploitation; (ii) natural lixiviation and accelerated mobilization from (mostly sulfidic) metal ore deposits by mining and related activities; (iii) coal deposits and their exploitation; (iv) hydrocarbon reservoirs and co-produced water during exploitation; (v) solute and sediment transport through rivers to the sea; (vi) atmospheric As (dust and aerosol); and (vii) As exposure through geophagy and involuntary ingestion. The two most important and well-recognized sources and mechanisms for As release into the Latin American population's environments are: (i) volcanism and geothermalism, and (ii) strongly accelerated As release from geogenic sources by mining and related activities. Several new analyses from As-endemic areas of Latin America emphasize that As-related mortality and morbidity continue to rise even after decadal efforts towards lowering As exposure. Several public health regulatory institutions have classified As and its compounds as carcinogenic chemicals, as As uptake can affect several organ systems, viz. dermal, gastrointestinal, peptic, neurological, respiratory, reproductive, following exposure. Accordingly, ingesting large amounts of As can damage the stomach, kidneys, liver, heart, and nervous system; and, in severe cases, may cause death. Moreover, breathing air with high As levels can cause lung damage, shortness of breath, chest pain, and cough. Further, As compounds, being corrosive, can also cause skin lesions or damage eyes, and long-term exposure to As can lead to cancer development in several organs.

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.