Soil Analysis Research Articles

Heavy Metal Analysis and Health Risk Assessment of Groundwater and Soil in and Around Peenya Industrial Area, Bengaluru

Heavy Metal Analysis and Health Risk Assessment of Groundwater and Soil in and Around Peenya Industrial Area, Bengaluru

Prioritization of Hydrological Restoration Areas Using AHP and GIS in Dulcepamba River Basin in Bolivar–Ecuador

In this study, we performed a preliminary soil analysis and collected environmental data for the Dulcepamba River Basin in Bolivar–Ecuador, before carrying out its hydrological restoration (HR). A geographic information system (GIS) and the multicriterion Analytical Hierarchy Process (AHP) decision-making method were used. The comprehensive evaluation included morphological aspects, soil properties, climatic conditions, vegetation, and land use. The terrain conditions were investigated using indicators such as the flow capacity, topographic moisture, soil resistance, sediment transport, current density, curve number, NDVI, precipitation, and distance to rivers. The results and analysis are presented in a series of maps, which establish a starting point for the HR of the Dulcepamba watershed. The key factors for assessing soil degradation in the watershed include land use, vegetation cover, sedimentation, humidity, and precipitation. Of the studied territory, 10.7 do not require HR, while 20.28% demand HR in the long term. In addition, 30.67% require HR in the short term, and 33.35% require HR immediately. Based on the findings, it is suggested that authorities consider the environmental remediation of the watershed and propose various HR measures. This analytical approach could prove valuable as a tool for the environmental restoration of watersheds in Ecuador.

Mineral and particle-size chemometric classification using handheld near-infrared instruments for soil in Northeast Brazil

The characterization of soil variations crucial for agriculture is challenging due to soil having different mineral composition and particle-size distribution. Traditional methods are costly and time-consuming for large-sized areas. Spectroscopic techniques coupled with chemometrics are alternative ways to overcome these drawbacks. Miniaturized near-infrared (NIR) spectrophotometers provide fast, cost-effective spectra acquisition for assessing soil chemistry and distribution despite challenges like overlapped bands and reduced spectral range. This study presents a pattern recognition strategy to address these limitations, enhancing the use of handheld NIR instruments for soil analysis. The study analyzed 176 soil samples from 15 soil groups in the Northeast region of Brazil. First, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and energy-dispersive X-ray fluorescence spectrometry (EDXRF) were employed to characterize the samples, providing complementary vibrational and elemental information, respectively. Common Dimension Analysis (ComDim) identified links between ATR-FTIR and EDXRF data, aiding soil characterization. The Common Components (CCs) from ComDim were used in Partitioning Around Medoids (PAM) clustering, resulting in five distinct classes based on their mineral composition. These classes showed significant differences in clay and sand contents. With the use of ComDim-PAM, samples were labeled for classification via Partial-least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) using NIR spectra (spectral range: 908–1676 nm) from two handheld instruments (Hand 1 and Hand 2). The SVM models outperformed the PLS-DA models, especially by including variable selection for Hand 1, with test accuracy exceeding 90%. These findings highlight the method's advantages for fast and cost-effective assessment, classification, and soil mapping based on their mineral and particle-size distribution.

Comparative Analysis of Japanese Soils: Exploring Power Generation Capability in Relation to Bacterial Communities

Comparative Analysis of Japanese Soils: Exploring Power Generation Capability in Relation to Bacterial Communities

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R2, RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R2, RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.

Characteristics Of Physical And Chemical Properties Of Soil After Gold Processing (Case Study Of Unlicensed Gold Mining In The District Sumbawa Regency, Indonesia)

The purpose of this study was to analyze the physical and chemical characteristics of the soil used for unlicensed gold processing in West Sumbawa Regency. This research was carried out in Kuang Village, Taliwang District, West Sumbawa Regency in September 2023. The criteria for soil sampling location is that the activity of the spindle has been inactive for 5 years, using mercury as a material for gold extraction and is in the home environment. This study was conducted by quantitative descriptive method or direct observation in the field and soil analysis in the Laboratory of soil chemistry and Biology, Faculty of Agriculture, University of Mataram. Furthermore, soil sampling was carried out using purposive sampling method or based on purpose with consideration of potential pollution. Based on the results, it can be seen that the physical characteristics of soil including clay with volume weight, specific gravity and porosity are quite dense soil with poor infiltration, while the chemical characteristics of soil include low fertility of both C-organic, CEC, soil pH, N and P soil. The concentration of Hg found in soil ranges from 1.42-15.83 ppm. In this case, mercury contaminated soil needs to be remediated using environmentally friendly materials, cheap and the availability of many in nature. Efforts that can be done is the use of green technology using phytoremediator plants and soil improvement materials, namely biochar. Therefore, it is necessary to conduct further research aimed at reducing the availability and mobility of mercury in the soil

Factor analysis in residual soils of the Iberian Pyrite Belt (Spain): comparison between raw data, log-transformation data and compositional data

The Iberian Pyrite Belt (IPB) is a metallogenic province in SW Spain and Portugal hosting the largest concentration of massive sulfide deposits worldwide. Exploration campaigns in both the Spanish and Portuguese sectors of the IPB have increased recently due to the rise in metal prices. Within this framework, distinguishing geochemical features associated with natural phenomena and isolating geogenic anomalies from anthropogenic ones can pose a challenge. This contribution uses the residual soil geochemical database of the IPB (Spain) to examine numerous variables, encompassing major, minor and trace elements. Some of these variables commonly exhibit high correlations owing to consistent geochemical behaviour. However, the influence of anthropogenic factors tends to elevate data variability, occasionally masking the natural relationships that govern their distributions. We apply different treatments of data to develop factor analysis using log-transformed data, and centred log-ratio (clr) transformed data to compare and improve the geochemical interpretation of this important zone. Factor analysis has been developed with these results to compare with previously published research on factor analysis in raw data. Factor score interpolated maps were also generated using both lognormal and clr-transformed data to visualize better the distribution and the different geochemical associations in the IPB. This study shows the importance of the different data treatments and the improvement of the clr-transformed multivariate analysis to reduce the dilution or overestimation of the results of some elements that cause erroneous interpretations of the data.

Impact of electro-osmotic, activation energy and chemical reaction on Sisko fluid over Darcy–Forchheimer porous stretching cylinder

This study provides numerical solution for two-dimensional electro-osmotically motivated electro-magneto-hydrodynamic Sisko fluid through an elongating porous cylinder. The electro-osmotic, activation energy with chemical reaction are also interpreted for this flow model. The numerical equations that govern this flow problem are renewed into dimensionless form by applying appropriate transformations exploiting Runge–Kutta–Fehlberg fifth order through shooting technique method. The after-effects are illustrated as graphs for the concerned physical parameters and their impact on convection and conduction is also studied. The velocity rises for electro-osmotic parameter whereas drops for magnetic and porous parameter. The temperature shows an increasing profile for the curvature, electric and thermal conductivity parameter while decreases for Prandtl number. The concentration of nanoparticles in the fluid boosts for activation energy but deflates for curvature parameter. The present findings appear to be in good accord when compared to earlier published studies. Numerous opportunities and applications are presented by applying electro-osmotic forces to non-Newtonian fluid flow, especially in the fields of nanotechnology, electro-kinetics and micro-fluids. The current study can be applied to design effective electro-magnetic devices, particularly in certain thermal transport characteristic regime. The main findings demonstrate the great utility of electro-osmosis in micro-fluidic devices, chemical analysis, soil analysis and cement slurries for managing flow and heat transmission.

Soil Analysis and Crop Prediction System

In the vast tapestry of the earth's bounty, where agriculture reigned supreme, a quest unfolded – a pursuit of sustainable practices and optimal crop selection, crucial threads in the fabric of food security and economic prosperity. For in those regions where the farmer's toil was the heartbeat of livelihood, an intricate dance between soil, environment, and crop suitability posed a formidable challenge, one that traditional knowledge and experience alone could not fully unravel. Thus, a clarion call echoed through the fertile fields, beckoning a new era of innovation – a comprehensive soil analysis and crop prediction system that would harness the power of machine learning, weaving data into a tapestry of enlightened cultivation. Like a grand symphony, this system harmonized a multifaceted approach, blending the rigorous physicochemical analysis of soil samples with the rhythmic cadence of climatic data and the echoes of historical crop yield. From the soil's pH to its nutrient composition, texture, and moisture content, each parameter was meticulously evaluated, a prelude to the orchestration that would follow.This earthly aria was then enriched by the melodious whispers of meteorological data – temperature, rainfall patterns, and humidity levels, each note tailored to the region's unique symphony.But it was the machine learning ensemble, a grand chorus of supervised and unsupervised algorithms, that truly breathed life into this composition. Trained on a comprehensive dataset of soil analysis, climatic conditions, and historical crop yield records, these algorithms learned to decipher the intricate patterns and harmonies that bind input variables to crop performance, their synthetic synapses firing in perfect synchronicity.and from this symphony emerged the system's crowning aria – a resonant recommendation of crops tailored to the specific soil and environmental conditions of each location, a harmonious blend of predicted yield potential and optimal growing conditions.

Studies on Soil Physical Quality of PT Coal Mine Reclamation Land, Sumber Bara Abadi, Kutai Kartanegara Regency, Indonesia

Evaluation of the physical quality of soil on coal mine reclaimed land is needed to monitor and evaluate reclamation activities aimed at restoring and improving soil quality. The results of the evaluation of the quality of the physical properties of the soil are presented as an index of the physical quality of the soil which can be used as input for taking further soil management actions. This research aims to determine the characteristics of the physical properties of the soil and assess the physical quality of the soil on coal mine reclaimed land as outlined in the soil physical quality index (IKFT). This research was carried out from August to November 2022 on the reclaimed coal mine land of PT. Sumber Bara Abadi, Sumber Sari Village, Sebulu District, Kutai Kartanegara Regency, East Kalimantan, Indonesia. The method used in this research was a survey. Determining the location of soil samples was carried out using a purposive method based on reclaimed land cover, reclamation age, and slope. Data analysis was carried out directly in the field and soil analysis in the soil laboratory of the Faculty of Agriculture, Mulawarman University. The assessment of the IKFT is carried out after the analysis of the physical characteristics of the soil has been determined. The IKFT is measured by following a three-step procedure, namely identification of indicator data sets, interpretation of indicators, and integration of all indicator scores into one overall IKFT value. The research results show that (1) the physical characteristics of the soil are: soil texture varies between clay, clay loam, and sandy clay loam; Soil porosity is classified as poor and good; stability of aggregate is classified as less stable aggregate to very stable; Soil permeability is relatively slow and moderate; bulk density is classified as medium and slightly dense; surface rocks are abundant; the depth of the solum is classified as shallow and medium; and (2) the physical quality of the soil on reclaimed coal mine land is classified as rather low and moderate with limiting factors including a large amount of surface rock, large bulk density, low solum depth, and low aggregate stability.

Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset

AbstractInfrared spectroscopy is increasingly being adopted as a technology for soil analysis. However, laboratories worldwide are equipped with different infrared spectrometers, leading to variations that hinder the global application of soil spectroscopy. This study evaluates the transferability of soil spectra from a global dataset collected using four mid‐infrared spectrometers. To evaluate the efficacy of five spectral transfer functions (direct standardization, piecewise direct standardization, spectral space transformation [SST], principal components‐canonical correlation analysis [PC‐CCA], and domain‐invariant partial least square [DIPLS] regression), two datasets were used: dataset A (n = 224; standardized samples) was scanned using one primary spectrometer and three secondary spectrometers; dataset B (n = 1904; legacy samples) was scanned only using the primary spectrometer. The first set of chemometrics models was developed using dataset A to compare the performance of different spectrometers. The second set of models was developed using dataset B to evaluate the effectiveness of spectral transfer functions. Both models were developed using partial least squares regression. Spectral transfer functions developed using dataset A indicate that the PC‐CCA method was the best in converging spectra collected from four instruments into a similar space projected using Uniform Manifold Approximation and Projection. Spectral transfer did not result in consistent improvement in the prediction of soil properties compared to the direct use of spectra collected from different spectrometers. These findings carry significant implications for the utilization of legacy models, enabling laboratories to concentrate on acquiring new samples and spectral measurements using established protocols without the need for spectral transfer.

Ecological Studies of Saurashtra Coast and Neighbouring Islands II. Beyt Island

The plant ecology Beyt island, a narrow crooked strip of sand rock to the east of Okhamandal point and about 3 km to the north of the mainland of Okhamandal situated 22°35' : 72°21' in the Gulf of Cutch is presented in this paper. Based on the types of situations and edaphic features the vegetation of the island has been distinguished into 3 main categories : (1) Strand, (2) Salt pans, slacks and mud formations and (3) Inland mixed scrub forest. Main features of thewvegetation and analysis of soils are described under the'above mentioned heads. The flora is a mixture of coastal and inland types. Further, the affinity of the flora with the adjacent lands of Cutch and Okhamandal to Diu coastal area has been studied. A list of the plants occurring in the island is enumerated.

Ecological Studies of Saurashtra Coast and Neighbouring Islands. V. Jafarabad to Bhavnagar Coastal Area

This paper is an ecological account of the plant communities and habitats observed in the coastal areas of Saurashtra from Jafarabad to Bhavnagar between 20°52': 21°45' and 71°25': 72°12'; ecological aspects are presented on an ecosystem basis. Plant communities grouped under ecosystems and analyses of soils are described to study soil features in relation to vegetation. A list of the coastal plants is prepared to record their distribution.

Ecological Studies of Saurashtra Coast and Neighbouring Islands: I. Diu Island

The plant ecology of Diu, an island of irregular outline, situated in 20°43' N & 71°02' E and separated from the southern extremity of the peninsula of Saurashtra by a narrow channel on western shore of India is presented in this paper. Several plant communities are grouped into three ecosystems : Rock-strand, Strand and Inland sandy plain. The community type is a sub-division of an ecosystem, that is, it is recognised by the features of both its plant cover and its habitat. Plant communities under each of the three abovementioned ecosystems are described and also analysis of rocks and soils presented in tabular form to study soil features in relation to vegetation.

Path to autonomous soil sampling and analysis by ground-based robots

Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components – sensing, sampling, mobility, and autonomy – and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).

Quantification of trace heavy metals in environmental water, soil and atmospheric particulates with their bioaccessibility analysis

In this work, sulfhydryl (SH) functionalized magnetic covalent organic framework (COF) was synthesized by using 4-aldehyde phenyl butadiyne (DEBD) and 1,3,5-tris(4-aminophenyl) benzene (TAPB) as the monomers and ethanedithiol as the modifier, with the aid of thiol-alkyne “click” reaction. The prepared Fe3O4@COFTAPB-DEBD@SH exhibited relatively strong magnetism (32.8 emu g−1), good stability and selectivity to target analytes with a high sulfhydryl content (0.24 mmol g−1). Based on Fe3O4@COFTAPB-DEBD@SH, a method combining magnetic solid phase extraction with inductively coupled plasma mass spectrometry (ICP-MS) was developed for the quantitative analysis of trace metals. Under the optimal conditions, the method merited fast desorption kinetics (<2 min), adsorption kinetics (<20 min), fast phase separation (<1 min), high enrichment factor (100), and the detection limits for Cd, Hg, Pb and Bi were determined to be 1.18, 0.51, 4.91 and 0.39 ng L−1, respectively. A good resistance to complex matrices was demonstrated for the method in the analysis of soil, atmospheric particles and simulated pulmonary fluids samples. Certified reference materials (coal fly ash GBW08401 and soil GBW07427) were employed to validate the accuracy of the method. Four target metals in the range of 12.9–215 ng L−1, 0.06–24.6 μg g−1 and 0.52–33.1 ng m−3 were found in local water, soil and atmospheric particulates (PM), respectively. Additionally, artificial lysosome solution and gamble's solution were used to simulate human pulmonary fluid and the bioaccessibility of Cd, Hg, Pb and Bi in PM2.5 was evaluated to be 58.6–73.1 % and 1.3–7.1 %, respectively.

X-ray Fluorescence Core Scanning for High-Resolution Geochemical Characterisation of Soils

X-ray fluorescence (XRF) core scanners are commonly used for fine-scale geochemical analysis in sediment studies, but data are semi-quantitative and require calibration to convert geochemical element counts to concentrations. Application of XRF core scanning in soil science remains largely untapped. This study employed an ITRAX core scanner to scan grassland soil cores and developed a novel calibration method based on a chemometric approach to characterise soil geochemistry. As soil samples are collected based on depth sampling, this study investigated whether higher resolution element concentrations could be inferred from lower resolution reference samples and if regression models from multiple cores could apply to a new core at the same resolution. Reference concentrations were obtained for all cores at 10 cm intervals, with validation conducted at 1 cm for a single core. Two calibration curve types were proposed: one based on the single core’s 10 cm data to validate references at 1 cm intervals; and another using all cores, with each core serving as a test item after exclusion from the training set. Various preprocessing measures and feature selection techniques were tested. Results showed successful calibration for elements Ca, P, Zn, Sr, and S, with high R2 values of 0.94, 0.93, 0.93, 0.92 and 0.91, respectively. The study presents a novel method for calibrating XRF core scanning element counts, demonstrating its potential for high-resolution soil analysis.

Crop Monitoring System Using IOT

Currently, the low agricultural output is causing farmers to suffer. Low crop growth is primarily caused by knowledge of soil fertility and crop selection, even though proper crop selection is the primary booster key for enhancing agricultural yield by doing soil analysis and taking metrological elements into consideration. Farmers with limited experience in conventional farming are finding it difficult to make wise crop selection choices in the current environment. The low soil fertility is caused by the same crop being chosen for each seasonal cycle. The goal of this research is to use IoT devices to create an accurate and efficient system. Compared to the outdated manual laboratory testing procedures, which are subject to human errors, such a system is predictable. A crop's proper selection is mostly important in the agricultural field. As a contribution, we offer Smart Crop Selection (SCS), an IOT-based approach that is based on soil and metrological factor data. These variables include soil moisture content, temperature, and precipitation. Because these issues are not given as much care as they should, existing IoT-based systems are less efficient than our proposed model. Real-time sensory data is transmitted to Firebase cloud for analysis in the suggested model

Lead Speciation, Bioaccessibility, and Sources for a Contaminated Subset of House Dust and Soils Collected from Similar United States Residences.

Residential lead (Pb) exposure is of critical concern to families globally as Pb promotes severe neurological effects in children, especially those less than 5 years old, and no blood lead level is deemed safe by the US Center for Disease Control. House dust and soils are commonly thought to be important sources of Pb exposure. Probing the relationship between house dust and soil Pb is critical to understanding residential exposure, as Pb bioavailability is highly influenced by Pb sources and/or species. We investigated paired house dust and soil collected from homes built before 1978 to determine Pb speciation, source, and bioaccessibility with the primary goal of assessing chemical factors driving Pb exposure in residential media. House dust was predominately found to contain (hydro)cerussite (i.e., Pb (hydroxy)carbonate) phases commonly used in Pb-based paint that, in-turn, promoted elevated bioaccessibility (>60%). Pb X-ray absorption spectroscopy, μ-XRF mapping, and Pb isotope ratio analysis for house dust and soils support house dust Pb as chemically unique compared to exterior soils, although paint Pb is expected to be a major source for both. Soil pedogenesis and increased protection from environmental conditions (e.g., weathering) in households is expected to greatly impact Pb phase differences between house dust and soils, subsequently dictating differences in Pb exposure.

Utilization of graphene as an alternative sustainable amendment in improving soil health through accelerated decomposition of oil palm mulch and enhanced nutrient availability

Graphene has unique properties for improving soil health properties such as nutrient availability, soil physical and chemical properties, and controlled release of essential elements. This research aimed at determining the impact of graphene amendment on the decomposition of oil palm frond mulching and on soil health status. The study was conducted using a factorial experiment in completely randomized design with two main factors: (i) covering conditions: cover with plastic sheet and no cover, and (ii) graphene application that included T1 (control): oil palm frond mulching (OFM), T2: OFM + graphene (G), T3: OFM + G + chemical fertilizer, and T4: OFM + G + goat manure. The results indicated that there were significant differences among graphene applications, between cover conditions, and in interactions between graphene applications and cover conditions for all soil characteristics in the most observed month. In the third month of soil analysis, the treatment of graphene applications showed higher electrical conductivity (T2: 151.7 ± 6.8 µS cm−1), available phosphorus (T3: 9.0 ± 6.7 mg kg−1), exchangeable potassium (T2: 67.1 ± 24.9 mg kg−1), and exchangeable calcium (T3: 95.4 ± 5.1 mg kg−1), compared to control. The cover condition showed suitable soil pH (5.0 ± 0.2), higher soil available phosphorus (7.1 ± 5.0 mg kg−1), and exchangeable calcium (599.1 ± 235.2 mg kg−1), but the no-cover condition presented higher soil organic matter (0.7% ± 0.2%), exchangeable potassium (60.3 ± 19.1 mg kg−1), and exchangeable magnesium (96.7 ± 11.4 mg kg−1). Correlation results indicated that most soil characteristics were correlated under graphene applications. Principal component analysis showed that the treatments of graphene application dominated most soil characteristics. The results suggest that graphene has potential for improving soil health properties and can be applied as an alternative sustainable amendment to accelerate the decomposition of oil palm frond mulch and enhance nutrient availability for oil palm. In addition, the authors suggest that further investigations should consider more soil health parameters in long-term field studies for a better understanding and to provide recommendations to farmers.