Evaluation Of Soil Quality Research Articles

Comparison of soil quality assessment methods for different vegetation eco-restoration techniques at engineering disturbed areas.

Scientific assessment of soil quality is the foundation of sustainable vegetation eco-restoration in engineering disturbed areas. This study aimed to find a qualitative and comprehensive method for assessing soil quality after vegetation eco-restoration in engineering disturbed areas. Sixteen soil indicators were used at six vegetation eco-restoration sites as the potential soil indicators. A minimum data set (MDS) and revised minimum data set (RMDS) were determined by principal component analysis. Six soil quality indices (SQIs) of varying scoring functions based on different data sets were employed in this study. Significant positive correlations were observed among all six SQIs, indicating that the effects of different vegetation eco-restoration measures on soil quality could be quantified by all six SQIs. The SQI values of the vegetation concrete eco-restoration slope (VC), frame beam filling soil slope (FB), thick layer base material spraying slope (TB), and external-soil spray seeding slope (SS) were all significantly higher than the SQI value of the abandoned slag slope (AS). It is noteworthy that the SQIs of the VC and TB sites were also significantly higher than the SQI of the natural forest (NF) site. These results indicate that the application of artificial remediation measures can significantly improve the soil quality of the disturbed area at the Xiangjiaba hydropower station. The results of this study also indicate that the SQI-NLRM method is a practical and accurate quantitative tool for soil quality assessment and is recommended for evaluating soil quality under various vegetation eco-restoration techniques in disturbance areas at the Xiangjiaba hydropower station and in other areas with similar habitat characteristics.

Soil Quality Evaluation of Agricultural Land in Qixia City Based on the Smallest Data Set

Soil Quality Evaluation of Agricultural Land in Qixia City Based on the Smallest Data Set

Soil Invertebrates As Sentinels Of Soil Health: A Zoological Approach To Soil Quality Assessment.

Soil invertebrates, a diverse and often inconspicuous component of terrestrial ecosystems, play a pivotal role in evaluating soil health and quality. As living organisms residing within the soil matrix, they are sensitive indicators of the environmental conditions and overall ecosystem dynamics, making them invaluable sentinels in the assessment of soil quality. This scientific endeavor seeks to expound upon the critical importance of employing a zoological approach for comprehensive soil quality evaluation. The utilization of soil invertebrates as bioindicators facilitates the assessment of both abiotic and biotic factors shaping soil quality. Their mobility allows them to respond to changing environmental conditions, while their small size renders them particularly sensitive to local variations. As agents of decomposition, nutrient cycling, and ecosystem stability, soil invertebrates play a major role in the sustainable development of agriculture and forestry practices. The application of a zoological perspective to soil quality assessment not only elucidates the complex web of interactions within soil ecosystems but also contributes to the advancement of sustainable agricultural and environmental practices

Mapping vertical distribution of SOC and TN in reclaimed mine soils using point and imaging spectroscopy

Soil organic carbon (SOC) and total nitrogen (TN) contents in different soil horizons are essential for vegetation growth and crucial indicators to evaluate soil quality in reclaimed mining areas. Compared with conventional wet chemistry methods, soil spectroscopy, including imaging spectroscopy, can be used as a cost and time-efficient soil analysis technique. However, there is a great challenge in combining laboratory point spectra and laboratory hyperspectral imagery for mapping vertical distribution of SOC and TN (0–100 cm) in reclaimed soils. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The main objective of this study is to provide a generic workflow to efficiently evaluate and map reclaimed mine soils in different horizons using imaging spectroscopy and machine learning approaches. A total of 65 soil samples (0–100 cm) were collected from three reclaimed mining lands and one natural site in northern China. Both point soil spectral information and hyperspectral images (350–2500 nm) were obtained under laboratory condition. In order to enhance the relationship between soil quality indicators and spectral features, the stacked feature selection algorithms and three-bands spectral indices were proposed for further modelling. Three machine learning methods (partial least squares regression; PLSR, random forest; RF, and radial basis function model; RBF) based on the point spectra were applied to calibrate and map continuous vertical distribution of SOC and TN. According to the results, thirty spectral bands were identified as important spectral features for SOC and eighteen bands for TN. With feature spectral bands and optimized three-bands spectral indices, the RF model yielded the best predictions for both SOC (R2 = 0.97, RMSE = 7.5 g kg−1) and TN (R2 = 0.78, RMSE = 0.33 g kg−1). It was concluded that imaging spectroscopy can be used to quantify and map soil quality indicators for better monitoring ecological restoration process in reclaimed soil of mining site.

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Zanthoxylum bungeanum forest is an important economic forest on the Loess Plateau. The rapid assessment of its soil organic carbon (SOC) and total nitrogen (TN) concentrations is critical for the evaluation of soil quality. Soil samples of different forest ages and soil depths were collected for laboratory analyses and spectral measurements, and the visible and near-infrared reflectance (VNIR) spectral data were processed by mathematical transformations, such as first-order derivative (FD) and second-order derivative, multiple scattering correction, and logarithm of the reciprocal. The importance bands of SOC and TN were screened on the basis of competitive adaptive reweighted sampling. In addition, partial least squares regression and support vector machine regression models were constructed, and the applicability of SOC and TN to the models were compared. Results showed that forest age and soil depth remarkably affect SOC and TN concentrations. Among the calibration and validation models, the combination of the SVMR model with Savitzky–Golay smoothing and FD estimated SOC and TN with the highest accuracy, and the validation accuracy of the surface SOC and TN was higher than that of the bottom depths. Meanwhile, the importance bands involved in the modeling were mainly distributed in 400–600 nm and 1000–2400 nm. The combination of the FD transformation and the SVMR model is suitable for determining SOC and TN concentrations in Z. bungeanum forests, and the model has great potential for the quantitative evaluation of soil quality in the Loess Plateau.

Changes in soil quality under long‐term rice‐crayfish farming in the Dongting Lake Plain, China

AbstractRice‐crayfish (RC) integrated cultivation, as a sustainable agricultural system, could significantly improve soil fertility. However, the soil quality under the long‐term RC farming system is still unclear. To examine the effects of RC cultivation on soil quality in paddy fields, this study utilized an integrated soil quality index (SQI) for examining the soil quality at different soil profiles (0–10, 10–20, and 20–30 cm depth) in rice monoculture (RM) and RC farming systems (after 1, 6, 10, and 15 years) scattered in Nanxian County, Hunan Province, China. The minimum datasets for evaluating soil quality were assembled using the soil pH, bulk density, total nitrogen, microbial biomass carbon, microbial biomass nitrogen, and β‐d‐glucosidase. The findings demonstrated that soil quality was greatly impacted by RC farming, which improved with the increase of years under RC management. The soil quality of 10‐ and 15‐year RC farming systems were significantly higher than RM system. Besides, soil quality declined with increasing soil depth. The SQI value, which may be used as an indicator to assess the soil quality of RC systems, was dominated by total nitrogen, which contributed 27.60% to the SQI value. On the whole, long‐term RC farming affected the soil quality of paddy fields through management methods including non‐rice season flooding, feeding, and no tillage. Through this study, it was found that MDS and SQI composed of appropriate physical, chemical, and biological indicators could more objectively analyze the soil quality of long‐term RC system and provide guidance for sustainable development of RC farming.

Partial substitution of nitrogen fertilizers by organic products of rural waste co-composting impacts on farmland soil quality

Substituting nitrogen fertilizers with organic fertilizer is considered as an important measure to address agricultural non-point source pollution. However, little is known about the impact on soil environment of rural multi-source waste compost products to substitute nitrogen fertilizers. Here we examined the response of soil quality to the alternative application of organic products which previously made from co-compost of swine manure, human excrement and rice straw. According to the soil quality evaluation model, we observed that compost product application improved soil quality, wherein 40% compost product instead of nitrogen fertilizer showed a best effect on promoting soil quality. This was strongly related to microbial diversity and their network complexity. Also, compost product application shifted the composition of bacterial and fungal communities, significantly increasing relative abundances of dominant bacteria phyla (e.g. Proteobacteria, Actinobacteriota and Gemmatimonadota) and fungal phyla (e.g. Ascomycota), which indicated that compost product application facilitate the growth of microorganisms eutrophic taxa of microorganisms. Collectively, this study gives an optimal solution to replace nitrogen fertilizers by co-composted organic product and provides a deeper understanding of the impact of rural waste co-composted product on agricultural soils along with their value for agricultural utilization.

Spatio-temporal variation in soil erosion on sloping farmland based on the integrated valuation of ecosystem services and trade-offs model: A case study of Chongqing, southwest China

Mastering the spatio-temporal evolution of soil erosion on regional sloping farmland is crucial for optimizing erosion control planning and carrying out soil quality evaluations. To simulate the potential or net soil erosion on sloping farmland in Chongqing, China, this study analyzed the temporal and spatial variation features of soil erosion using the integrated valuation of ecosystem services and trade-offs (InVEST) model and other models. The InVEST model was applied to calculate the soil erosion modulus on sloping farmland, hotspot analysis was used to identify hot and cold spots of sloping farmland erosion, and the chord diagram visualization model was used to reflect the transformation quantity and flow direction relationship between different erosion levels of sloping farmland transfer processes in different periods. The results showed that soil erosion on sloping farmland in Chongqing presents a distribution pattern of high values of soil erosion in the northeast and low values in the southwest. Very strong and severe erosion is concentrated in the low mountainous hilly areas in the northeast and southeast of Chongqing, while slightly and lightly eroded areas are distributed in the western and central flatter areas. The soil erosion grade of most sloping farmland is light and middle; in addition, the annual average soil erosion modulus shows a decreasing trend. Only in 2000 was it in the middle erosion grade. The areas with strong, very strong and severe erosion showed a downward trend, with the proportion decreasing from 18 % to 11 %. The average erosion modulus with slope > 25° was the largest (7553 t·km−2·a-1) and presented strong erosion. Soil erosion in sloping farmland mainly occurs between 400 and 1600 m of elevation. This research can effectively improve soil erosion control and quality control of sloping farmland and provide a reference for the sustainable development of land use in the region.

Using deep neural networks for evaluation of soil quality based on VIS–NIR spectroscopy

Using deep neural networks for evaluation of soil quality based on VIS–NIR spectroscopy

Performance of soil quality indicators in estimation and distribution of rapeseed yield.

This research aimed to evaluate the quality of soils for rapeseed crop production by Boolean and fuzzy-analytical hierarchy process (FAHP) approach in northwest of Iran. To this purpose, the physical, chemical, and topography quality indicators of land were selected based on agricultural considerations that were obtained from 83 fields. The spatial distribution of soil quality indicators was prepared using inverse distance weighting (IDW) technique. Also, validation of the developed model was performed using composite operator. The results showed that physical and chemical properties were key deciding parameters for the evaluation of soil quality. In the developed models, clay, sand, silt, soil organic matter, pH, calcium carbonate equivalent, electrical conductivity, and elevation were selected as modeling parameters. AHP technique showed that soil texture and elevation had the strongest and weakest influences on rapeseed yield, respectively. By dividing lands into four suitability categories, FAHP could more easily classify lands into soil quality classes where 36.3% of the study area was permanently unsuitable, 39.7% was marginally suitable, 22.6% was moderately suitable, and 1.4% was suitable. The comparison results of soil quality and rapeseed yield map by composite operator showed that FAHP with 77% agreement provided better results than Boolean approach with 39% agreement. Finally, this research will provide a reasonable record in ensuring crop yield security, agronomic use and management of rapeseed as well as increasing crop income. Hence, FAHP was introduced as an efficient approach.

Positive Effects of Organic Amendments on Soil Microbes and Their Functionality in Agro-Ecosystems.

Soil microbial characteristics are considered to be an index for soil quality evaluation. It is generally believed that organic amendments replacing chemical fertilizers have positive effects on changing microbial activity and community structure. However, their effects on different agro-ecosystems on a global scale and their differences in different environmental conditions and experimental durations are unclear. This study performed a meta-analysis based on 94 studies with 204 observations to evaluate the overall effects and their differences in different experimental conditions and duration. The results indicated that compared to chemical fertilizer, organic amendments significantly increased total microbial biomass, bacterial biomass, fungal biomass, Gram-positive bacterial biomass and Gram-negative bacterial biomass, and had no effect on the ratio of fungi to bacteria and ratio of Gram-positive bacteria to Gram-negative bacteria. Meanwhile, land use type, mean annual precipitation and soil initial pH are essential factors affecting microbial activity response. Organic-amendment-induced shifts in microbial biomass can be predominantly explained by soil C and nutrient availability changes. Additionally, we observed positive relationships between microbial functionality and microbial biomass, suggesting that organic-amendment-induced changes in microbial activities improved soil microbial functionality.

Investigation of Amended Sandy Soil's Water Retention Characteristics

Purpose: Soil water retention studies are important for evaluating soil quality and water movement in soils. This study assesses the water content capacity and changes in the retention curve of sandy soil after using zeolite X, activated charcoal, and rice husk ash as amendments. Design/Methodology/Approach: Sandy soil samples were collected from the study area, Keta District of Ghana, and a core borer and a pot experiment were used to determine the pressure head and water content of the soil. One-way analysis of variance (One-way ANOVA) was used to analyze the data. Water retention curve analyses were conducted using COMSOL multi-physics simulation and experimental work. Research Limitation/Implication: The amendments used were restricted to zeolite X, activated charcoal, and rice husk ash. Practical Implications: The study contributes to the field of sustainable agriculture and land management by exploring amendments that can potentially reduce water usage in irrigation, mitigate soil erosion, and enhance soil fertility. This aligns with global efforts to promote sustainable and efficient resource use. Social Implications: These findings can be implemented for cases of poor yield by farming communities by applying zeolite X, activated charcoal, and rice husk ash to improve crop productivity.

Organic amendment substitution improves the sustainability of wheat fields changed from cotton and vegetable fields

Long-term excessive use of chemical fertilizers and continuous cropping in vegetable and cotton production lead to a decline in farmland soil quality and crop productivity, endangering sustainable agricultural development. Organic amendment substitution (OAS) or farmland use change or can potentially enhance soil quality and crop yield. However, it remains unclear whether their combined application can improve the sustainability of wheat fields when changing from cotton and vegetable fields. In this study, continuous cropping cotton and vegetable fields were changed to wheat fields (C-W and V-W), and then field fertilization experiments (non-fertilization, NF; chemical fertilizer alone, CF; chemical fertilizer reduced by 20%, RF; and organic amendment substituted 20% of inorganic nitrogen, OAS) were conducted for three years (2018–2020) to explore their effects on wheat productivity and soil quality. The findings revealed that the combined farmland use change and fertilization improved soil quality in wheat fields compared to pre-change cotton and vegetable fields. OAS improved soil quality index (SQI-TDS by 0.36–0.82 and 0.32–0.82; SQI-IDS by 0.37–0.90 and 0.40–0.88) by improving soil properties and increasing microbial diversity compared to NF, CF and RF in C-W and V-W. Meanwhile, OAS increased nutrient use efficiency, crop productivity index (CPI, by 13.69%−72.46% and 8.52%−41.45%) and the sustainability index (SI, by 14.14%−49.35% and 12.56%−40.00%). Moreover, microbial biomass carbon and nitrogen (MBC and MBN), pH, available nitrogen (AN), soil organic carbon (SOC) and ACE index were key soil factors affecting yield. These indicators can be used to construct important dataset to simplify soil quality evaluation systems. Overall, the present study revealed the mechanism by which OAS improved wheat field sustainability by increasing wheat productivity and soil quality after farmland use changes, contributing to sustainable agricultural development. Moreover, it provides valuable reference methods and evaluation ideas for future research on farmland soil quality and sustainability evaluation.

Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.

Spatial prediction of soil sand content at various sampling density based on geostatistical and machine learning algorithms in plain areas

Accurate prediction of the spatial distribution of soil sand content is a pre-requisite for land use management, soil quality evaluation and erosion control, as it determines the transport and movement of soil water, fertilizer, air and heat. Digital soil mapping (DSM) is extensively employed for predicting soil properties. However, practical research is required to address the challenge of selecting an optimal prediction model that is both cost-effective and accurate at a specific sampling density. In this study, topsoil samples were collected from 2,848 sampling points in the eastern plains of China (107,200 km2). The performance of different prediction models for mapping soil sand content was compared at 12 levels of sampling density. Moreover, the geographical detector, a statistical method used to assess the spatial stratified heterogeneity of variables, was adopted to determine the major drivers of spatial variation in soil sand content. The results indicated that climate factors are the major drivers of the spatial variability in soil sand content. For the 100% sample size (26.57 samples/103 km2), the geostatistical models that did not depend on environmental variables (ordinary kriging, sequential Gaussian simulation) performed best, followed by the machine learning models (random forest, cubist and support vector machine) and the geostatistical model with environmental variables (co-kriging). Sampling density had a considerable impact on model accuracy, and the advantages of machine learning models became apparent when sampling densities were below 20% (5.31 samples/103 km2). Therefore, the best combination of prediction model and sampling density should be selected to obtain maps of soil sand content economically and accurately. This study provides a valuable reference for the selection of prediction methods in the practical application of DSM.

Use of Spatial Variability of Soil Quality Index Models and Soil Properties for Soil Quality Evaluation in the Irrigated Perimeter, Semi-Arid Region of Morocco

Use of Spatial Variability of Soil Quality Index Models and Soil Properties for Soil Quality Evaluation in the Irrigated Perimeter, Semi-Arid Region of Morocco

Effects of Deep Vertical Rotary Tillage Management Methods on Soil Quality in Saline Cotton Fields in Southern Xinjiang

A long-term high-saline soil environment will limit the improvement of soil quality and cotton yield. Modified tillage management measures can improve soil quality, and the establishment of a soil quality evaluation system will facilitate evaluation of the soil quality and land production potential in southern Xinjiang. The objective of this study was to determine the effects of different tillage management methods on soil quality in saline cotton fields in southern Xinjiang. A three-year experiment was conducted in Tumushuke, Xinjiang, with different deep vertical rotary tillage depths (DTM20, 20 cm; DTM40, 40 cm; DTM60, 60 cm) and conventional tillage (CTM, 20 cm). The soil quality index (SQI) under different tillage management methods was established by using the full dataset (TDS) with a scoring function for eight indicators, including physicochemical properties of the soil from 0 to 60 cm, to evaluate its impact on the soil quality of the saline farmland in southern Xinjiang. The results of the study showed that deep vertical rotary tillage management can effectively optimize soil structure; reduce soil bulk density (BD), soil solution conductivity (EC), and pH; and promote the accumulation of soil organic carbon (SOC) and total nitrogen (TN) in the soil. However, the average diameter of soil water-stable aggregates (MWD) in a 0–60 cm layer becomes smaller with an increasing depth of tillage. This does not reduce crop yields but does promote soil saline leaching. In addition, the significant linear relationship (p < 0.001) between seed cotton yield and soil quality indicated that improving soil quality was favorable for crop yield. The principal component analysis revealed BD, MWD, pH, and EC as limiting sensitive indicators for seed cotton yield, while SOC and TN were positive sensitive indicators. The soil quality index (SQI) values of DT40 and DTM60 were significantly higher than that of CTM by 11.02% and 15.27%, respectively. Overall, the results show that DTM60 is the most suitable tillage strategy to improve soil quality and seed cotton yield in this area, and this approach will provide a reliable theoretical basis for the improvement of saline farmland.

Soil Quality Evaluation of Typical Vegetation and Their Response to Precipitation in Loess Hilly and Gully Areas

The selection of suitable tree species and the reasonable allocation of planting areas are important measures for improving soil quality. This study aimed to investigate the characteristics of typical vegetation type soil quality differences and their dominant factors in loess hilly–gully areas after returning farmland to the forest (grassland). The soil quality status and dominant factors of arbors, shrubs and grasslands in the study area were comprehensively analyzed using the soil quality index (SQI) and structural equation modeling (SEM). The results showed the following: (1) In the study area, the shrub forest had a high capacity for air permeability, water retention and nitrogen fixation. (2) The soil quality of the three vegetation types improved with increasing precipitation, and the soil quality indicator of shrubs was the highest, indicating a better soil quality improvement. However, the soil quality of the arbors and grasslands showed a greater percentage increase. In the precipitation range of 400–410 mm, the soil quality of shrub forests was significantly higher than that of arbors and grasslands. (3) Structural equation modeling analysis indicated that precipitation, vegetation and soil factors are closely related to soil quality. Further analysis showed that soil bulk density, porosity, capillary water-holding capacity, soil organic carbon and total phosphorus were the dominant factors affecting the soil quality in the study area. The purpose of this study was to evaluate quantitatively the soil quality after different vegetation types under different precipitation gradients, to clarify the variation trend of soil quality at different vegetation types with different precipitation gradients and to provide a scientific basis and data support for the quantitative evaluation of vegetation restoration and selection of tree species and vegetation configuration within different precipitation gradients in loess hilly and gully regions in the future.

Evaluation of soil quality through simple additive soil quality index (SQI) of Tehsil Charsadda, Khyber Pakhtunkhwa, Pakistan

In the study, the Simple Additive Soil Quality Index (SQI) is used to evaluate soil quality in Tehsil Charsadda, District Charsadda, Pakistan. Organic matter (OM), electrical conductivity (EC), phosphorus (P), acidity and basicity (pH), estimated nitrogen (EN), and soil texture were the parameters evaluated. The whole research region was split into 1*1 km grids for data collection, and samples were gathered from each grid along with XY coordinates. An augur was used to collect 172 samples from a depth of 30 to 45 cm. The collected samples were analyzedat the Agriculture Department in Charsadda. The index value for each soil quality indicator was determined, and then normalizationwas performed. Each SQ parameter was assigned a threshold value based on expert judgmentand a review of the literature. The index values of each SQ parameter were added up to determine the SQI value. Using the Simple Additive Soil Quality Index (SQI), the area under poorly fertile soil accounts for 4835.52 ha (10.91%), moderately fertile soil accounts for 11853.90 ha (30.33%), and highly fertile soil accounts for nearly half of the research area (17338.1 ha (48.27%). The exceptionally productive soil is located in a large patch along the western boundary of the study area. Patches of poor fertile soil are generally found in thecentral, northern, and northeastern parts of the study area.

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria.

Potentially toxic elements (PTEs) occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational errors. As a result, to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the entire study area. Furthermore, the multilayer perceptron artificial neural network (MLP-NN) was used to forecast the most important toxic components influencing soil quality. Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic element ingestion than dermal exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing toward the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil.