Topsoil Samples Research Articles

Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations

Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas stations were collected in Beijing to explore the impact of gas stations on PTE accumulation. The analysis revealed that the average Pollution Index (PI) values for Cd, Hg, Pb, Cu, and Zn in the soil samples all exceeded 1. The random forest (RF) model, achieving an AUC score of 0.95, was employed to predict PTE pollution at 372 unsampled gas stations. Additionally, a Positive Matrix Factorization (PMF) model indicated that gas station operations and vehicle emissions were responsible for 70 % of the lead (Pb) enrichment. Probabilistic health risk assessments showed that the carcinogenic risk (CR) and noncarcinogenic risk (NCR) for PTE pollution to adult females were the highest, at 0.451 and 1.61E-05 respectively, but still within acceptable levels. For adult males at contaminated sites, the Pb-associated CR and NCR were approximately twice as high as those at uncontaminated sites, with increases of 107 % and 81 %, respectively. This study provides new insights for managing pollution caused by gas stations.

A trait-based ecological perspective on the soil microbial antibiotic-related genetic machinery

Antibiotic resistance crisis dictates the need for resistance monitoring and the search for new antibiotics. The development of monitoring protocols is hindered by the great diversity of resistance factors, while the “streetlight effect” denies the possibility of discovering novel drugs based on existing databases. In this study, we address these challenges using high-throughput environmental screening viewed from a trait-based ecological perspective. Through an in-depth analysis of the metagenomes of 658 topsoil samples spanning Europe, we explored the distribution of 241 prokaryotic and fungal genes responsible for producing metabolites with antibiotic properties and 485 antibiotic resistance genes. We analyzed the diversity of these gene collections at different levels and modeled the distribution of each gene across environmental gradients. Our analyses revealed several nonparallel distribution patterns of the genes encoding sequential steps of enzymatic pathways synthesizing large antibiotic groups, pointing to gaps in existing databases and suggesting potential for discovering new analogues of known antibiotics. We show that agricultural activity caused a continental-scale homogenization of microbial antibiotic-related machinery, emphasizing the importance of maintaining indigenous ecosystems within the landscape mosaic. Based on the relationships between the proportion of the genes in the metagenomes with the main predictors (soil pH, land cover type, climate temperature and humidity), we illustrate how the properties of chemical structures dictate the distribution of the genes responsible for their synthesis across environments. With this understanding, we propose general principles to facilitate the discovery of antibiotics, including principally new ones, establish abundance baselines for antibiotic resistance genes, and predict their dissemination.

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Spatial Differentiation Characteristics and Influencing Factors of Surface Soil Cd in Karst Plateau Area of Guiyang City

In order to explore the spatial differentiation characteristics and variation law of soil Cd content in a high geological background area, 14 421 topsoil samples were collected from topsoil in the karst area of Guiyang City. Global Moran's I index, cold hot spot analysis, semi-variance function, and Kriging interpolation were used to reveal the spatial structure and distribution law of soil Cd content. The influence of environmental factors on soil Cd content and its main controlling factors were analyzed through analysis of variance and geographic detector. The results showed that： ① The Cd content of karst surface soil in Guiyang varied from 0.03 to 1.36 mg·kg-1, with an average of 0.440 mg·kg-1, which was 1.77 times and 5.95 times the Guizhou soil Cd background value and Chinese soil Cd background value, respectively. The over-standard rate of soil Cd was 30%, which was 4.29 times that of 7% of soil Cd in China. ② There was a significant spatial positive correlation of soil Cd content, showing an aggregation trend in the global space, whereas in the local region, the northeast and southwest were hot spots, and the north was a cold spot. The nugget coefficient of soil Cd content was 10.37%, indicating that soil Cd was mainly affected by structural factors. ③ In terms of spatial distribution, soil Cd showed different accumulation trends. In some massive soils, such as Xifeng County, Xiuwen County, Qingzhen City, Huaxi District, and Nanming District, the soil ω（Cd）was less than 0.3 mg·kg-1. The soil ω（Cd）was between 0.3 and 0.6 mg·kg-1,and soil Cd in Baiyun District, Wudang District, Guanshan Lake area, and Yunyan area as a whole lied within this range. The soil ω（Cd）between 0.6 and 0.9 mg·kg-1 was concentrated in the southwest of Qingzhen City, the south of Huaxi District, and the north of Kaiyang County, whereas soil ω（Cd） between 0.9 and 1.2 mg·kg-1 was mainly concentrated in the southwest of Qingzhen City. The extreme value of soil Cd content （ > 1.2 mg·kg-1） was mostly distributed in Kaiyang County, Xiuwen County, Qingzhen City, and Huaxi District. ④ The results of analysis of variance and geo-detector showed that different environmental factors had significant effects on the spatial differentiation of soil Cd, but their explanatory power on soil Cd content varied： stratum （0.176 5） > soil type （0.026 0） > organic matter （0.025 1） > altitude （0.010 5） > parent rock （0.007 3） > land use （0.006 4） > pH （0.001 3）, and the interaction between stratum and arbitrary environmental factors was the greatest. Therefore, stratum was the main factor affecting the spatial differentiation of soil Cd content.

Spatial distribution, bioaccessibility, and human health implications of potentially toxic elements in mining-impacted topsoils in Obuasi Municipality of Ghana.

Potential toxic elements emanating from extracted ores during gold processing present occupational and unintentional health hazards in communities, the general populace, and the environment. This study investigated the concentrations and potential health effects of metal content in the topsoils of Obuasi municipality, which has been mined for gold over the past century. Surface topsoil samples, sieved to 250µm, were initially scanned for metals using x-ray fluorescence techniques, followed by confirmation via ICP-MS. In vitro bioaccessibility assays were conducted using standard methods. The geoaccumulation indices (Igeo) indicate high enrichment of As (Igeo = 6.28) and Cd (Igeo = 3.80) in the soils, especially in the eastern part of the municipality where illegal artisanal mining is prevalent. Additionally, the southern corridor, situated near a gold mine, exhibited significant levels of As and Mn. Results obtained for the total metal concentrations and contamination indices confirmed the elevation of the studied potential toxic elements in the Obuasi community. A hazard index value of 4.42 and 3.30 among children and adults, respectively, indicates that indigens, especially children, are susceptible to non-cancer health effects.

Texture and clay mineralogy as main drivers of the priming effect in temperate forest soils

Abstract Background and aims This work aimed to determine how the soil parameters affect the magnitude and direction of priming effect (accelerated or decreased decomposition of native SOM under addition of new organic substrates, PE) in temperate acidic forest soils. Methods Thirteen topsoil samples were incubated for 163 days with the addition of maize residues. Soil respiration was measured and natural isotope labelling was used in order to separate the respiration sources (SOM, maize and PE). The effect of soil parameters on PE was studied using linear regression and structural equation modelling (SEM). Results Soils with high C/N ratio showed the lowest magnitude of cumulative PE (R2 = 0.321, p < 0.05) and the longest negative PE period. A positive relationship was found between PE and the pH (R2 = 0.511, p < 0.05). SEM analysis showed that pH and C/N ratio has direct (β = 0.50) and indirect (β = 0.20, via modifying soil texture and mineralogy) effect on PE. Soil texture and mineralogy had a significant effect on PE: texture affects the proportions of soil respiration sources and PE was reduced by the dithionite–citrate–bicarbonate–extractable Al (AlDCB, R2 = 0.454, p < 0.05), silt + clay (R2 = 0.421, p < 0.05), non-swelling clay mineral (R2 = 0.575, p < 0.05) and illite (R2 = 0.522, p < 0.05) contents. SEM analysis also highlighted that the AlDCB, illite and silt + clay contents has a great effect (β=−0.59) on the PE. Conclusion The silt + clay content and mineral composition of the soil, including the Al oxide and illite contents may thus significantly inhibit the magnitude of PE, and consequently the decomposition of SOM under acidic conditions.

Soil texture and vegetation root density assessment on regulating erosion across river floodplains

Over the last few decades, floodplain management with best management practices has been utilized to treat areas susceptible to soil erosion and degradation. A major emphasis has been placed on the role of the above-ground vegetation to regulate soil erosion, but less attention has been directed to the floodplain soil types and root interactions. The goal of the current study was to quantify the effectiveness of soil texture and vegetation root density in reducing soil erosion in the highly agricultural Turkey River watershed in Iowa. For the purposes of this study, twenty-four topsoil samples were removed from various locations across the lower, i.e., active, and higher elevation river floodplain soils of five identified field sites along the Turkey River longitudinal profile. The topsoil sampling process was designed based on site-specific flood inundation maps. Using detailed particle size analyses and topsoil erodibility experiments, results indicated that the threshold values for the onset of erosion increased longitudinally, from upstream to downstream, matching the pattern identified for silt and clay particles in floodplain soils. Statistical analysis confirmed that there is a strong linear correlation between the threshold values for erosion to occur and the fine particle content in floodplain soils, as well as the existence of vegetation characterized by dense and well-developed root systems. Overall, the fine particle content of floodplains’ surface soils and the existence of vegetation with dense and well-developed roots determined the threshold values for erosion, whereas the presence of vegetation with non-dense and non-well-developed root systems had a negligible effect, similar to bare soil, on controlling soil erosion. The findings of the current research can be applied by watershed management authorities to protect floodplain areas at risk and prevent further soil degradation and water pollution.

Soil organic carbon fractions and storage potential in Finnish arable soils

AbstractUnderstanding the factors affecting the total amount and distribution of soil organic carbon (OC) across different functional carbon pools is important to better define the future management of soil OC stocks. The interactions between soil management practices, local physicochemical soil properties and climate are essential for determining the OC content of the soil. Nevertheless, how these factors affect the total amount of OC and its distribution across carbon pools, i.e., more labile particulate (POC) and more stable mineral‐associated (MAOC) organic carbon, is only partly known. In this study, we assessed topsoil (0–20 cm) samples from 93 arable farms in the southern half of Finland to determine the total amount of OC, and its distribution in MAOC and POC, along with relevant soil properties (amount of clay and silt, aluminium and iron oxides and pH), climate (precipitation and temperature) and fertilization (mineral versus organic). The fertilization did not affect the total soil carbon content (12–58 g OC kg−1 soil). The share of OC in the MAOC fraction (on average 86% of total OC) was relatively stable across the large range of OC contents and clay contents (2%–68%). We assessed the highest feasible MAOC of the soils with boundary line analyses and their OC saturation state with Hassink's equation (Hassink, 1997). Only soils with the lowest clay content (<10% clay) were assumed to be carbon‐saturated, suggesting that most of the studied soils have a capacity to accrue more MAOC. Simple linear regression showed that clay, aluminium and iron oxides explained 9%, 21% and 22% of the variation in MAOC, respectively. Multiple regression analyses including the amount of clay, clay+silt, aluminium and iron oxides, pH, type of fertilization, precipitation and temperature as explanatory variables explained 33%–53% of the variation in OC and MAOC. In all soils, aluminium oxides were important explanatory variable for MAOC, whereas Fe oxides were significant only in soils with higher clay content (>30%). In soils with a low clay content (<30%), pH had added value in explaining MAOC. Altogether, it seems that various climatic, edaphic and soil management‐related factors are context‐dependently controlling OC and that soil textural information alone is not necessarily an adequate predictor to assess the MAOC saturation state of the soil.

Soil biological fertility evolution in a chronosequence under long‐term rice cultivation after land reclamation in China

AbstractLand use significantly affects soil biological fertility through impacts on carbon (C) and nitrogen (N) cycling. The present study investigated the effects of long‐term rice cultivation after tidal flat reclamation on soil C and N metabolism, microbial biomass and biological fertility. Eighteen composite topsoil (0–20 cm) samples were identified in a chronosequence of coastal reclamation areas (0–700 years old) in subtropical monsoon climate zone, namely tidal flat (T0), salt marsh soil (S10) and paddy soil (P50, P100, P300 and P700). Using ANOVA analysis, mono‐exponential regression model, and multiple linear regressions, soil organic matter (SOM), total nitrogen (TN), cumulative C mineralization content (Ct) and N mineralization content (Nt), basal soil respiration (BSR) and microbial biomass C and N (MBC and MBN) in the P50‐P700 samples were significantly higher than those in the T0 and S10 samples, whereas C metabolic quotients (qCO2) in the P50‐P700 were significantly lower than those in the T0 and S10 samples. The time to steady state for SOM and TN are 357 years and 80 years, respectively; 133 and 221 years for Ct and Nt, respectively; and 318 and 183 years for MBC and MBN, respectively. Also, a soil biological fertility index (SBFI) was calculated on the basis of SOM, BSR, Ct, MBC, qCO2 and qCM. P100‐P300 samples had the highest SBFI score (28.7) and ranked in the class V (very high) of biological fertility, achieving steady‐state conditions after 146 years. SBFI was significantly positively correlated with SOM, TN, MBC, MBN, BSR, Ct and Nt, whereas it was significantly negatively correlated with pH, qCO2 and C mineralization quotient (qCM). MBC and qCM were two independent variables with considerable positive effects on SBFI. Long‐term rice cultivation could facilitate C and N accumulation and enhance biological fertility in soils via microbial activity, especially within 300 years. Our findings demonstrate that rice cultivation has the potential to enhance soil C and N accumulation. Carbon‐related SBFI is suitable for assessing soils under long‐term rice cultivation, mainly because the rice paddy field is an intensive and conservative system.

Impact of Metals Scrap Yard on the Presence of some Heavy Metals Concentration within Gombe Metropolis, Gombe State, Nigeria, using Atomic Absorption Spectrometry

The Metal scrap yards play a crucial role in the recycling and reuse of metals, contributing significantly to the economy and conservation of natural resources. However, the processing and storage of metal scraps can lead to the release of heavy metals such as lead, chromium and cadmium, into the environment. The presence of heavy metals in metal scrap yards poses a significant risk to nearby communities, workers and the environment, also causing air and water pollution, soil contamination, and health problem. Study of impact of metals scrap yard on the presence some of some heavy metals in soil samples was done using Atomic Absorption Spectrometer (AAS). Top soil sample were collected from eight metals scrap yards and their corresponding control areas. One gram (1 g) each of the soil samples was digested and then heated slowly and steadily, the solution appears colorless. The samples were allowed to cool and ready for analysis. The result obtained was, the mean elemental concentration element was of the order ofMn (41.022±41.202)> Cu (4.416±4.274) >Pb (2.685±1.399) > Mg (0.500±10.122) > Cr (0.460±0.187) > Cd (0.125±0.027) > Co (0.04±0.000)> Ni (0.000±0.000). The comparison of the elemental concentration of the heavy metals of (Mn, Mg, Cu, Pd, Cr, Cd, Co and Ni) in the study area ware shows the metal scrap yard has impact in the presence of heavy metals in soil. In addition, the Contamination factor (CF), and Pollution load index (PLI) of each trace element in soil of the study area and their respective control area were all < 1 which implies that they all have low contamination factors, less polluted and low contaminated.

Mapping Key Soil Properties of Cropland in a Mountainous Region of Southwestern China

Soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK) are important indicators for evaluating soil fertility. Exploring the content and spatial distribution of these indicators is of great significance for optimizing cropland management measures and developing sustainable agriculture. Yunnan Province is one of the most important agricultural regions in southwestern China, characterized by large variations in the topography and an uneven distribution of soil fertility. In this study, the data of 8571 topsoil (0–20 cm) samples selected from Yunnan Province and a portion of related spatial data were used to carry out electronic mapping of the spatial distribution of soil sand content, clay content, silt content, SOC, TN, TP, TK content, and C:N ratio at 1 km resolution using the Random Forest (RF) model. The results indicated that the average measured contents of SOC, TN, TP, TK, and C:N ratio in the topsoil of Yunnan Province were 18.78 ± 0.09 g/kg, 1.78 ± 0.01 g/kg, 0.98 ± 0.01 g/kg, 13.89 ± 0.08 g/kg, and 10.56 ± 0.02, respectively. The spatial analysis showed that higher SOC was mainly distributed in northern and eastern Yunnan, and the spatial distribution pattern of TN and TP was similar to that of SOC. While higher TK was mainly distributed in southwestern Yunnan Province. There was a significant positive correlation between SOC and TN and TP contents with correlation coefficients of 0.889 and 0.463, however, there was a significant negative correlation between SOC and TK content with correlation coefficients of −0.060. It was also indicated that elevation, temperature, precipitation, clay content, sand content, and silt content were the most important factors affecting SOC, TN, TP, and TK content. The present study provided an understanding of soil nutrients characteristics and their affecting factors, which is helpful to optimize agricultural management practices and develop sustainable agriculture according to soil fertility.

Total and Extractable Micronutrients in Tropical Acid Soils of Lampung, Indonesia

ABSTRACT The study aimed to determine the total and available amounts of iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the humid tropical acid soils of Lampung, Indonesia, and to identify the key factors affecting them. Topsoil samples were taken from 10 agricultural fields and 6 natural fields in 16 sub-districts of Lampung, Indonesia. The average total contents of Fe (26.36 g kg−1), Cu (8.97 mg kg−1), and Zn (53.24 mg kg−1) are generally below the world-soil average contents and those in other acid soils, which may be due to eluviation of Fe, Cu, and Zn into subsoil induced by high rainfall and the low natural contents of especially Cu and Zn in the parent rocks (acid volcanic rocks and sandstone). In contrast, the average total Mn content (1.20 g kg−1) is 2 to 8 times higher than that commonly found in acid soils and the world-soil average content, indicating that the parent materials may contain high Mn-bearing minerals. The total Fe, Mn, and Zn are positively correlated to the clay content, exchangeable-Ca and -Mg, and exchangeable-Mg, respectively, whereas the total Cu has no correlation to any soil properties. The amounts of available Fe, Mn, Cu, and Zn account for on average 0.38%, 6.20%, 21.50%, and 4.40% of the total contents, respectively. Around 6%, 12.5%, and 56% of the soil samples exhibit Mn, Cu, and Zn deficiency, respectively, and 25% face Fe toxicity. Total Fe and Mn are the main factors determining available Fe and Mn, respectively, whereas exchangeable-Mg and organic-C content mainly control available Cu and Zn, respectively.

Quantitative source apportionment and health risk assessment of trace elements in surface soils around the Shazand chemical industrial district, northwestern of Iran

A comprehensive study aimed at measuring trace elements pollution in the Shazand chemical industrial district, located in northwestern Iran, was conducted. Employing principal component analysis (PCA) and positive matrix factorization (PMF), we identified sources of trace elements and assessed associated health risks for inhabitants. The results showed that the mean concentrations of Pb, Cd, Ni, Zn, and As of 190 topsoil samples exceeded the background values, except for Cu. The total noncancer risk values for adults and children were 0.72 and 7.95, respectively, and the total carcinogenic risks were 8.46 E – 05 and 2.69 E – 04, respectively. The noncarcinogenic risk for children was above the threshold level, but low for adults. The total potential carcinogenic health risks for children exceeded the threshold of 1 E – 04 indicating a high carcinogenic risk for them, whereas the risk for adults was considered acceptable. Furthermore, As was the primary hazardous metal to human health, followed by Cd and Ni. According to the results of the PMF model, four factors were extracted for trace elements, including natural source, transportation sources, industrial sources, and agricultural sources, which contributed about 27.9%, 17.51%, 29.15%, and 25.44% to pollution of the area, respectively. Anthropogenic sources contributed far more to the potential health risks posed to inhabitants of the study area. These findings can assist governmental agencies in implementing targeted control strategies to prevent the contamination of heavy metals.

Examining the Effect of Agrochemicals on Soil Microbiological Activity, Micronutrient Availability, and Uptake by Maize (Zea mays L.) Plants

Agricultural practices profoundly influence soil microbial populations and physicochemical properties, vital for crop growth and quality. This study aims to explore the impact of diverse agrochemical applications on soil microbial dynamics, physicochemical properties, and maize yield and proximate properties. Topsoil samples, collected at depths of 1 to 15 cm, were transported to Jimma University for maize cultivation. Over 120 days, soil and maize samples were collected at specified intervals for analysis, including soil pH, microbial populations, and nutrient content. Statistical analysis using one-way ANOVA (p < 0.05) was conducted. Soil bacterial and fungal populations were measured on days 5, 10, 20, 40, 80, and 120. The highest total mesophilic bacterial count (TMBC) was in compost-treated pots (G) and the lowest in those receiving macronutrient fertilizers and glyphosates (B). The highest total mesophilic fungal count (TMFC) was in pots with glyphosates and compost (F), and the lowest was in pots treated with macronutrient fertilizers and glyphosates (B). Pots treated with macronutrient fertilizers and glyphosates (B), macronutrient fertilizers (A), and micronutrient fertilizers (C) showed the lowest Fe and Zn levels. Maize in pots treated with macronutrient fertilizer combined with glyphosate (B) exhibited the lowest protein, fats, and carbohydrates. Notably, compost-treated soils showed the highest bacterial and fungal counts, Fe, and Zn concentrations, while micro-mineral fertilizer combined with glyphosate (B) depleted the soil. Agrochemical treatments negatively affected maize yield quality, indicating complex treatment-related changes in soil parameters.

The Spatiotemporal Variability of Soil Available Phosphorus and Potassium in Karst Region: The Crucial Role of Socio-Geographical Factors

The contents of soil available phosphorus (AVP) and potassium (AVK) in karstic mountainous agricultural areas have changed rapidly in recent decades. This temporal variation displays strong spatial heterogeneity due to these areas’ complex topography and anthropogenic activities. Socio-geographical factors can reflect the changes in the natural environment caused by human beings, and our objective is to enhance understanding of their role in explaining the changes of AVP and AVK. In a typical karst region (611.5 km2) with uniform soil parent material and low climatic variability, 255 topsoil samples (138 in 2012 and 117 in 2021) were collected to quantify the temporal AVP and AVK changes. Random forest (RF) and partial dependence plot analyses were conducted to investigate the responses of these changes to socio-geographical factors (distance from the nearest town center [DFT] and village density [VD]), topography, biology, and landscape pattern indexes. The mean values of AVP (48.25 mg kg−1) and AVK (357.67 mg kg−1) in 2021 were significantly (p < 0.01) higher than those in 2012 (28.84 mg kg−1 and 131.67 mg kg−1, respectively). Semi-variance analysis showed strong spatial autocorrelation for AVP and AVK, ranging from 7.29% to 10.95% and 13.31% to 10.33% from 2012 to 2021, respectively. Adding socio-geographical factors can greatly improve the explanatory power of RF modeling for AVP and AVK changes by 19% and 27%, respectively. DFT and VD emerged as the two most important variables affecting these changes, followed by elevation. These three variables all demonstrated clear nonlinear threshold effects on AVP and AVK changes. A strong accumulation of AVP and AVK was observed at DFT < 5 km and VD > 20. The AVP changes increased dramatically when the elevation ranged between 1298 m and 1390 m, while the AVK changes decreased rapidly when the elevation ranged between 1350 m and 1466 m. The interaction effects of DFT and VD with elevation on these changes were also demonstrated. Overall, this study examined the important role of socio-geographical factors and their nonlinear threshold and interaction effects on AVP and AVK changes. The findings help unravel the complex causes of these changes and thus contribute to the design of optimal soil phosphorus and potassium management strategies.

Molecular and Morphological Assessment of the Impact of Gas Flares on the Fungal Abundance and Diversity in soils of Niger Delta Area, Nigeria

Fungal abundance and diversity in soils of three communities flow stations (ID, IR and OB) owned by an unnamed oil exploration firm operating in the Niger Delta area for about five decades were determined using nucleic acid analytical methods. Top soil (0-15 cm) samples were collected from mini pits using sterile bottles at 50, 100 and 150 meters from the flare barrier of each community flow station and soil samples from the control was taken at 2.5 kilometers during the dry and wet seasons. The fungal properties were determined first using the cultural method of serial dilution method which revealed the presence of the following fungi; Aspergillus sp, and penicillium chrysogenum at 50 m away from flare in all the three flow stations during the dry seasons and in the wet season Aspergillus sp, penicillium chrysogenum, Mucor sp., yeast cells, A. niger, and Rhizopus sp. At 100 m away from flare, Mucor sp, A. niger and rhizopus were isolated in all three-flow station in the dry season and in the wet season, Yeast cells, A. niger, Aspegillus sp. and Rhizopus were isolated. P. chrysogenum, Aspergillus sp. and yeast cells were isolated at 150 m away from flare in the dry season and in the same distance, yeast cells, A. niger aggregate, A. niger and P. chrysogenum were isolated. At the control site, during the dry season, A. niger, P. chrysogenum, Aspergillus sp., Rhizopus sp. and Botryodiplodia threobome were isolated in the dry and A. niger, P. chrysogenum and yeast in the wet season. Fungal DNA was extracted using Norgen fungal genomic DNA extraction kits for the molecular analysis to establish the true characteristics and species identity of the isolates earlier identified with cultural methods. This revealed the presence of the following fungi upon sequence blast on NCBI database; Aspergillus fumigatus (Aspergillus fumigatus brown 2 (abr2) gene, complete cds strain), Aspergillus fumigatus (Aspergillus fumigates strain IHM 15988 pigment biosynthesis protein), Fusarium phyllophilum , Fusarium graminearum, Aspergillus oryzae (a strain of Aspergillus niger) Aspergillus oryzae (niger). Findings from this work have shown that molecular technique gave the exact identity of fungal species isolated from study area. All six (6) molecularly identified fungi were isolated from the three-study flow station and in both seasons. The Aspergillus group was dominant as a result of their ability to adapt to the flare environment

Development of near-infrared spectroscopy (NIRS) for estimating organic matter, total carbon, and total nitrogen in agricultural soil

The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0–20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky–Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil (R2 of calibration and validation higher than 0.80).

Comparative analysis of machine learning algorithms for identifying cobalt contamination in soil using spectroscopy

Cobalt (Co) has been recognized as one of the most hazardous elements by the United Nations Environmental Program; however, it has received limited attention in previous studies of identifying heavy metal contamination and has been limited to small, site-scale datasets and few machine learning algorithms. To fill this research gap, eight machine learning algorithms were combined with visible and near-infrared reflectance spectroscopy in this study to develop a large-scale model for classifying Co content in soil. In total, 18,675 topsoil samples were used to train and validate the models. Spectral preprocessing, principal component analysis, and hyper-parameter tuning were utilized to improve the tested models’ performance, which was evaluated using multiple indicators. The optimal model was then applied to the United States soil spectral dataset. The results show that the eXtreme Gradient Boosting (XGB) performed the best on both the training and testing sets, with an area under the curve value of 0.901 on the training set and 0.904 on the testing set. The application of XGB revealed that Utah, Arizona, New Mexico, North Dakota, Arkansas, Mississippi, and Alabama were at higher risk of Co contamination. In summary, XGB can be used to effectively identify areas of potential Co contamination. The approach presented here not only fills a gap in terms of large-scale identification of Co contamination but can also greatly assist environmental managers in risk assessment and reclamation strategies.

Distribution of some natural and artificial radionuclides in soil from the city of Bitola (Macedonia) and its environs.

The aim of the study is a thorough investigation of the radioactivity level in soils of the town of Bitola (Macedonia) and its environs. Topsoil samples collected from 58 locations within a 5×5 km grid were analysed. Serving as a screening, gross alpha and beta activity measurements were performed using gas-flow proportional counter. Gamma-spectrometric measurements revealed the presence of three natural (40K, 226Ra, 232Th) and one artificial radionuclide (137Cs) in the samples. The activity concentrations of these radionuclides were consistent with the results of similar studies in neighbouring areas. Spatial distribution maps and factor analyses have revealed that the activity concentrations of the natural radionuclides are strongly influenced by geology and have no significant influence from human activities. A correlation of 137Cs activity concentrations with terrain elevation was also observed. The aim of the study is a thorough investigation of the radioactivity level in soils of the town of Bitola (Macedonia) and its environs. Topsoil samples collected from 58 locations within a 5×5 km grid were analysed. Serving as a screening, gross alpha and beta activity measurements were performed using gas-flow proportional counter. Gamma-spectrometric measurements revealed the presence of three natural (40K, 226Ra, 232Th) and one artificial radionuclide (137Cs) in the samples. The activity concentrations of these radionuclides were consistent with the results of similar studies in neighbouring areas. Spatial distribution maps and factor analyses have revealed that the activity concentrations of the natural radionuclides are strongly influenced by geology and have no significant influence from human activities. A correlation of 137Cs activity concentrations with terrain elevation was also observed.