Variability Of Soil Properties Research Articles

Internal Piping Erosion Evaluation of Embankment Dam Considering the Spatial Variability of Soil Properties

Internal Piping Erosion Evaluation of Embankment Dam Considering the Spatial Variability of Soil Properties

Effect of 2-D spatial variability of soil properties on cofferdam stability considering degradation of soft soil foundation

Effect of 2-D spatial variability of soil properties on cofferdam stability considering degradation of soft soil foundation

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

Mitigative translocation of Singleton Mint Bush (Prostanthera cineolifera) on the NSW North Coast: Effects of soil texture, horticultural practice and bushfire on the translocation outcome

SummaryTranslocation of the threatened species Singelton Mint Bush (Prostanthera cineolifera R.T.Baker & H.G.Sm.) was carried out for a highway project on the New South Wales North Coast to mitigate development impact on this species. The translocation aimed to augment an existing population with propagated plants to compensate for cleared plants and conduct field research to better understand the effects of soil properties, horticultural practices and a wildfire on translocation results. A novel soil seedbank and fire method applied ex‐situ was used to propagate plants for introduction. After an initial attempt at site‐wide introduction resulted in widespread mortality of seedlings from a wilt disease, three plot‐based experiments were carried out to examine how disease incidence was related to spatial variation in soil properties and horticultural practices. The experiments showed that relatively subtle changes in the clay and sand content of soil strongly affected susceptibility of Singelton Mint Bush seedlings to wilt disease; 12 month old seedlings performed much better than 6 month old seedlings; plants propagated from seed performed better than cuttings, and most of the recipient site represented unsuitable habitat for Singelton Mint Bush. Sufficient Singelton Mint Bush seedlings were established after 3 years, but after a bushfire, the translocated cohort recruited poorly from seed, so that after 6 years, <10% of the total Singelton Mint Bush population in the recipient site was of translocated origin and >90% of in‐situ (pre‐translocation) origin. Lessons learnt from the translocation project were that: (i) microhabitat is as important as macrohabitat in selecting a translocation recipient site; (ii) short‐term survival may not be a reliable indicator of a self‐sustaining population; (iii) translocation incorporating designed experiments can greatly improve understanding of how certain factors influence species performance and (iv) horticultural practices can be as important as ecology in determining the translocation outcome.

Cedrelinga catenaeformis (Tornillo) in natural and agroforestry systems: Dendrometry, soil and macrofauna

Agroforestry and protected areas are alternatives to mitigate the adverse impacts generated by human activities in the tropical forests of the Peruvian Amazon. The aim of this study was to determine the influence of natural and agroforestry systems of Cedrilinga catenaeformis (Tornillo) on soil properties. The physical, chemical, and soil macrofauna properties, as well as dendrometric variables of Tornillo aged 140 and 180 years in natural systems and 7, 17 and 37 years in agroforestry systems, were examined. Results revealed variability in soil properties depending on their origin; however, no marked differences were observed due to tree age or natural versus agroforestry condition. Regarding soil macrofauna, greater diversity was noted in the older agroforestry system, while dendrometric variables exhibited a sigmoidal behavior, except for wood volume, which showed a linear increase. Agroforestry systems of Tornillo associated with Centrosema macrocarpum (Centrosema) maintain soil properties similarly compared to natural systems; however, the soil of the oldest agroforestry system showed higher activity of earthworms Rhinodrilus lavellei, Rhinodrilus pashanasii, Diptera orders, and Julidae, compared to soils of more recent agroforestry systems and natural systems. It was estimated that dendrometric variables of Tornillo show stagnation in the increase of total height at 150 years, commercial height at 165 years, and DBH at 70 years. Meanwhile, total height and DBH showed very strong and strong positive correlations (p < 0.05) with soil N content and CEC in agroforestry systems with 17 and 37 years. Estimating dendrometric variables and understanding the nutritional requirements in the growth and development of Tornillo will contribute to its more effective management in natural and agroforestry ecosystems in the Amazon region.

Nutrient contents and microbial communities as mediators of the effects of land-use in ecosystem functioning in alpine ecosystems from Central Mexico

Abstract Purpose The interplay between microbial taxonomy, functional diversity, and land-use changes across diverse ecosystems must be better understood. This study explores, through a statistical modeling approach, the intricate relationships between microbial communities, soil properties (nutrient contents and physicochemical parameters), and land-use changes, using stable isotopes ratio (δ13C/δ15N) as ecosystem function indicator. Methods Using Structural Equation Modeling (SEM) this study aims to investigate how soil microbial communities, soil properties and ecosystem function are interconnected in three ecosystems undergoing land-use change in Nevado de Toluca, Mexico. Results Multivariate analyses of microbial communities and soil parameters for the different land-uses reveal significant variation in both microbial composition and soil properties (i.e. nutrient contents) mainly explained by the land-use history of the studied ecosystems. Through the SEM approach, it was possible to disentangle the relative contribution of land-use change, microbial community composition and nutrient contents to the ecosystem function defined as the δ13C/δ15N ratio. Conclusions Microbial communities are the main drivers of soil ecosystem functioning. However, through SEM approach it is possible to formally test direct and indirect relationships impacting microbial communities and the resulting functional consequences, contributing to a mechanistic understanding of the ecological implications of land-use change, aiding in sustainable land-use decision-making.

Impact of Randomized Soil Properties and Rock Motion Intensities on Ground Motion

Seismic site response is inevitably influenced by natural variability of soil properties and anticipated earthquake intensity. This study presents the influence of variability in shear wave velocity (Vs) and amplitude of input rock motion on seismic site response analysis. Monte Carlo simulations were employed to randomize the Vs profile for different scenarios. A series of 1-D equivalent linear (EQL) seismic site response analyses were conducted by combining the randomized Vs profile with different levels of rock motion intensities. The results of the analyses are presented in terms of surface spectral acceleration, amplification factors (AFs), and peak ground acceleration (PGA). The mean and standard deviation of these parameters are thoroughly discussed for a wide range of randomized Vs profile, number of Vs randomizations, and intensities of input rock motions. The results demonstrate that both the median PGA and its standard deviations across different number of Vs profile realization exhibit a slight variation. As few as twenty Vs profile realizations are sufficient to compute reliable response parameters. Both rock motion intensity and standard deviation of Vs variability cause significant variation in computed surface parameters. However, the variability in the number of records used to conduct site response has no significant impact on ground response if the records closely match the target spectrum. Incorporating the multiple sources of variabilities can reduce uncertainty when conducting ground response simulations.

Carbon sequestration potential and its main drivers in soils under alfalfa (Medicago sativa L.)

As a perennial forage crop, alfalfa (Medicago sativa L.) has been extensively utilized for the vegetation restoration of degraded soil and provides feedstock for forage. Its high usage can be attributed to its high yield potential and the increasing soil organic carbon (SOC) sequestration of alfalfa cultivation. However, the impact of land conversion to alfalfa on SOC content and its underlying drivers remain unclear. We performed a meta-analysis at the global scale to explore the quantified effects of alfalfa cultivation on SOC content and identify its controlling factors. We employed 1699 pairwise data points from 90 publications based on cropland/abandoned land conversion to alfalfa. Globally, cropland (cropland-alfalfa) and abandoned land (abandoned land-alfalfa) conversion to alfalfa enhanced SOC content by 12.1 % and 13.7 %, respectively. Alfalfa exhibited greater SOC content benefits in the surface soils (0–20 cm) with a lower level of initial SOC (<16 g kg−1), regardless of the land conversion type. Cropland-alfalfa was observed to increase SOC content with fertilization, irrigation, and conventional tillage in the long term (>5 years). Furthermore, abandoned land-alfalfa enhanced SOC content in the absence of alfalfa biomass removal and for longer cultivation durations (>5 years). Boosted regression tree analyses indicated variations in soil properties (75 % for cropland-alfalfa and 65 % for abandoned land-alfalfa) as the primary factors driving changes in SOC content. The dominant drivers were determined as the soil layer (51.6 %), cultivation duration (13.1 %), and initial SOC (12.9 %) for cropland-alfalfa, and initial SOC (43.7 %), soil layer (24.6 %) and cultivation duration (17.1 %) for abandoned land-alfalfa. Land conversion to alfalfa has great potential for SOC sequestration, particularly in low-fertility soils. Therefore, alfalfa cultivation is highly recommended for degraded lands due to its SOC sequestration benefits in vegetation restoration.

Prediction of the undrained base heave and post uplift in braced excavation of a typical subway station in thick soft clay

Construction of a typical subway station usually involves a narrow long excavation with a width of approximately 20 m. An undrained base heave is a common problem in deep excavation areas with soft clay, which may induce considerable post uplift and damage the safety of the retaining system. Post uplift involves complex interactions between soil, pile post foundation, and supported struts, and an accurate prediction of post uplift is challenging. This paper aimed to establish a practical method for predicting the base heave and post uplift in the construction of typical subway stations in areas with thick soft clay. Series soil–water coupled finite element method (FEM) analysis was conducted to investigate the characteristics of the base heave with different soil properties and wall stiffnesses. The results showed that the calculated base heave can be fitted by a normalized function, regardless of large variations in soil properties and wall design parameters. The normalized function was then applied to a soil–post–strut interaction analysis model to predict the post uplift at different excavation depths. The application of the proposed method to a real subway station project showed that the predicted results were in good agreement with the field observations.

Relationship of selected properties of Cambisols to altitude and forest ecosystems of four vegetation grades

Currently, little is known about the spatial variability of significant soil properties and their relationships to forest ecosystems of different vegetation grades. This work evaluates the variability of the properties of the upper layer of Cambisol taxa and their relationship to altitude and forest ecosystems of 2nd to 5th forest vegetation grades selected in the Western Carpathians using PCA and regression analysis. The content of clay, total carbon and total nitrogen, humus, energy, and ash in the soils varied between 5.43 and 11.53 %, 21–65 mg g−1, 1.9–4.7 mg g−1, 36–112 mg g−1, 438.4–5845.7 J g−1 and 852.9–946.3 mg g−1, and C/N, pHH2O, and pHKCl values ranged between 11.2 and 16.7, 4.0–5.8 and 3.1–4.6. PCA showed that EAC in the 3rd oak-beech vegetation grade had significantly higher pH values and significantly lower energy content, ESC in the 4th beech vegetation grade had a significantly higher ash content and a significantly lower energy content, and DC in the 5th fir-beech vegetation grade had a significantly higher content of Ct, Nt, and humus. Linear regression revealed a strong negative correlation between the energy content and soil reaction (R2 for pHH2O = 0.48; R2 for pHKCl = 0.38) for all Cambisol taxa. Ct content and ash show a strong negative correlation (R2 = 0.78). The positive relationship between altitude and FVGs was found only for the soil Ct (R2 = 0.87), Nt (R2 = 0.81), and humus content (R2 = 0.87). A strong negative linear relationship between altitude and FVGs showed the ash content (R2 = 0.77). In turn, the oscillatory, polynomial course had a relationship between the clay content (R2 = 0.65) and energy (R2 = 0.75) to altitude and FVGs. Recognizing significant soil variables and better understanding their impact on the development of forest ecosystems is a prerequisite for distinguishing areas with the highest risk of their damage under conditions of various anthropogenic interventions and climate change. Therefore, this topic continues to require increased research efforts. For this reason, a better understanding of the relationships between soil properties and ecologically differentiated communities of forest ecosystems will allow us to identify areas with the highest risk of ecological changes that could lead to the degradation of European forests in the future.

Geophysical methods reveal the soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie

Abstract Purpose The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. Methods We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Results EMI revealed high spatial heterogeneities in ECa distribution across the land-lake interfaces, with higher values in the wetland and transition zones compared to the upland zone. Soil ECa maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa &gt; 40 mS/m). ERT and GPR showed vertical variation in soil properties with clear stratigraphic boundaries, and correlation of ERT profiles with lithologs from piezometers revealed the stratigraphic units of silt–clay and till sequence down to 3.5 m depth which are consistent with the surficial geology of the study area. Conclusions These results validate the use of multiple geophysical methods for extrapolating soil properties and mapping stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

Abstract Purpose Machine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples. Materials and methods A spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique. Results and discussions The generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance. Conclusions This study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

Vegetation communities and soil properties along the restoration process of the Jinqianghe mine site in the Qilian Mountains, China.

The study explores the impact of mine grassland restoration on plant communities and soil properties in alpine grasslands, a subject of significant interest due to the observed relationship between grassland changes, plant communities, and soil properties. While prior research has mainly focused on the consequences of grassland degradation on plant diversity and soil characteristics, the specific effects of varying restoration degrees in alpine mining grasslands at the regional scale remain poorly understood. To address this knowledge gap, we established 15 sampling plots (0.5m×0.5m) across five different restoration degrees within alpine mining grasslands in the Qilian Mountains, China. Our objective was to assess the variations in plant diversity and soil properties along these restoration gradients. We conducted comprehensive analyses, encompassing soil properties [soil water content (SWC), available nitrogen (AN), total phosphorus (TP), nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total nitrogen (TN), available phosphorus (AP), soil organic carbon (SOC), nitrate nitrogen, soil pH, and electrical conductivity (EC)], plant characteristics (height, density, frequency, coverage, and aboveground biomass), and plant diversity indices (Simpson, Shannon-Wiener, Margalef, Dominance, and Evenness indexes). Our findings included the identification and collection of 18 plant species from 11 families and 16 genera across the five restoration degrees: Very Low Restoration Degree (VLRD), Low Restoration Degree (LRD), Moderate Restoration Degree (MRD), High Restoration Degree (HRD), and Natural Grassland (NGL). Notably, species like Carex duriuscula, Cyperus rotundus, and Polygonum viviparum showed signs of recovery. Principal component analysis and Pearson correlation analysis revealed that soil pH, SWC, SOC, NO3-N, and AN were the primary environmental factors influencing plant communities. Specifically, soil pH and EC decreased as restoration levels increased, while SWC, AN, TP, NH4-N, TN, AP, SOC, and NO3-N exhibited a gradual increase with greater restoration efforts. Furthermore, the HRD plant community demonstrated similarities to the NGL, indicating the most effective natural recovery. In conclusion, our study provides valuable insights into the responses of plant community characteristics, plant diversity, and soil properties across varying restoration degrees to environmental factors. It also elucidates the characteristics of plant communities along recovery gradients in alpine grasslands.

A Study of the Multivariate Spatial Variability of Soil Properties, and their Association with Vine Vigor Growing on a Clayish Soil

A Study of the Multivariate Spatial Variability of Soil Properties, and their Association with Vine Vigor Growing on a Clayish Soil

Assessment of Selected Physical and Chemical Soil Properties and Organic Carbon Stock, Under Different Land-Uses in Melka Gura Subwatershed of North Shewa Zone, Oromia, Ethiopia

A study conducted in Melka Gura Subwatershed to assess soil properties across three types of land uses plantation forest, grazing land, and cultivated land from 2022 to 2023 found that all land uses had loam soil, except for the plantation forest, which had sandy loam. The mean soil BD in the study area ranged from 1.06 to 1.39 g/cm3. Conversely, plantation forest soil and grazing land had the highest mean AWC (194.8 mm/m) at 0–20 cm depth. The highest mean values of sand, silt, TP, pH, AP, exchangeable basic cations, CEC, SOC, TN, and SOC stock (68.62-ton ha-1) were registered in the soil of plantation forest land. The highest mean values of BD (1.39 g/cm3), clay content (19%), Ex. A, and Ex. Al were recorded in the soil of cultivated land. The average values of clay, BD, FC, PWP, pH, PBS, and exchangeable bases all indicated an increasing trend from the surface toward the subsurface. The highest mean value of SOCS (68.62 tons’ ha-1) was discovered in plantation forest land and the lowest in cultivated land. The differences in soil management practices could be the major reasons for variations in soil properties across land-use types. Further studies are needed on the effects of land-use change on fertility-related soil properties in relation to integrated soil and water conservation practices to reduce further land degradation.

Probabilistic failure envelopes of tripod bucket-supported offshore wind turbines in spatially variable clay based on a novel continuous method

Marine geologic conditions play key roles in offshore geotechnical preliminary design and construction decisions. Tripod bucket foundations are considered promising geotechnical foundations for offshore engineering. Their bearing characteristics are often determined based on tripod foundation system failure modes in uniform clay without considering spatially variable soil properties. Therefore, this study investigates the combined bearing capacity of tripod bucket foundations in spatially variable clay based on comprehensive finite element analysis with Monte Carlo simulation (MCS) using a novel simplified modified swipe (SMS) method considering the effects of bucket spacing and foundation skirt length. The SMS method can directly represent the ultimate combined bearing capacity of the whole space efficiently and accurately. The results show that a reduced bucket spacing or increased skirt length changes the failure mode of the foundation from independent caisson failure to global failure. Moreover, the spatial variation in soil leads to an asymmetric failure mode, which is more remarkable for foundations with small bucket group effects. An independent safety factor design approach is adopted to represent failure envelopes with different recurrence probabilities, which should be cost-effective, especially for foundations sensitive to soil variability. Finally, an application based on a convex polynomial optimization strategy for representing failure envelopes is demonstrated.

Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices

Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China’s Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data.

A new contact and road model for multi-body dynamic simulation of wheeled vehicles on soft-soil terrain

AbstractIn this paper, a new high-performance and memory-efficient contact and road model is developed. Specifically, the road is modeled as a rectangular structured grid of deformable springs in the vertical direction, thus enabling fast execution. The new road model stands out due to its ability to handle large road scenarios by allocating computer memory dynamically for each spring, resulting in efficient memory utilization. Furthermore, each spring represents a small road patch that entails various information, such as the soil elevation, the soil properties, and the soil compaction, allowing for complicated simulations incorporating spatially varying soil properties and phenomena related to the multi-pass effect. In addition, using the new contact model, complex terrain geometries are handled in a computationally efficient way by approximating locally the irregular road profile with a suitable equivalent plane. For this, two different strategies are proposed, namely the radial basis function (RBF) interpolation method and the 3D enveloping contact model. Finally, the proposed techniques are implemented in Altair MotionSolve, a comprehensive multi-body simulation software for complex mechanical systems. In particular, a single-wheel test bed is initially examined followed by a four-wheeled rover model and the next-generation NATO reference mobility model (NG-NRMM). In all cases, the proposed model is validated by using available experimental data. Lastly, a case involving both wheeled and tracked vehicles is also examined by using a shared road model.

The effects of co-existing acridine on adsorption-desorption behavior of carbazole in soils: Co-sorption and mechanism insight

Carbazole (CBZ) and acridine (ACR) are polycyclic aromatic nitrogen heterocycles (PANHs) widely found in combined contaminated soils, while investigations on organic-organic interactions have been very limited. In this study, batch experiments were carried out on five soils with different properties, taking CBZ as a representative of PANHs and ACR as a co-existing contaminant. The adsorption isotherms of CBZ (50–1000 μg/L) were nonlinear. Soil organic matter (SOM) and cation exchange capacity (CEC) showed positive correlations with CBZ adsorption-desorption coefficients. The adsorption mechanisms of CBZ involved hydrogen bonding, π-π interaction, and cation-π bonding. Different concentrations of ACR had varying effects on CBZ. The adsorption of CBZ was inhibited with 250 μg/L ACR. The cooperative adsorption was observed on three soils with increasing ACR concentration (1000 μg/L) and led to more pronounced nonlinear isotherms. The S-shaped isotherms of ACR indicated that ACR was adsorbed to the soil surface in a perpendicular configuration. New adsorption sites were created allowing for increased CBZ adsorption through π-π interaction with ACR. Therefore, variations in soil properties and potential impacts of co-existing contaminants should be well considered when assessing the combined pollution of site soil. This will contribute to a more accurate estimation of environmental and health risks.

Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons

Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R2 = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.