Comparison of the impacts of the experimental parameters and soil properties on the prediction of the soil sorption of Cd and Pb

Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities

“…Soil CEC, a measure of the amount of total exchangeable cations and the total negative surface charge of soil, showed considerable influences on the predicted adsorption according to the SHAP analysis (Figure ). Indeed, extensive studies have reported positive correlations between CEC and the adsorption of heavy metals on soil. ,− Soil pH is another important factor, in agreement with many experimental studies. , At low pH, H + in soil can compete strongly with metal ions for active adsorption sites leading to attenuated adsorption. , Soil pH also affects the charge status of soil surfaces; higher soil pH usually leads to more negatively charged sites and thus better adsorption. Clay minerals tend to have small particle sizes with very high specific surface areas and have the ability to sequester heavy metals through complexation reactions and electrostatic attraction. , Soil OC offers functional groups to complex with metal ions, which can significantly enhance the adsorption capacity.…”

“…Soils have different properties such as pH, cation-exchange capacity (CEC), clay content, and organic carbon content (OC). ,− This heterogeneity makes the adsorption of heavy metals vary considerably. , Batch experiments are traditional ways to determine adsorption on soils, which are time-consuming and inefficient. , An alternative approach is to estimating the adsorption capacity using models . Adsorption isotherms, such as the Freundlich and Langmuir equations, are a traditional way to model adsorption equilibrium .…”

Influence of Soil Type on the Reliability of the Prediction Model for Bioavailability of Mn, Zn, Pb, Ni and Cu in the Soils of the Republic of Serbia

“…The synergy of agricultural practice, science and computer technologies through machine learning and artificial intelligence (AI) forms highly predictive mathematical models that are based on patterns and structures hidden in large and high-dimensional data sets [21]. One of the statistical techniques used in the formation of prediction models for machine learning and AI is multiple linear regression [20][21][22][23][24]. Freundlich's model is obtained by applying multiple linear regression, which makes it one of the potential models that can be used for artificial intelligence and machine learning.…”

“…Freundlich's model is obtained by applying multiple linear regression, which makes it one of the potential models that can be used for artificial intelligence and machine learning. Models formed based on the Freundlich equation (or Freundlich models) were used to predict the sorption of TEs in soil [23][24][25][26][27][28][29], the bioavailability of TEs [30][31][32][33][34][35], and the bioaccumulation of TEs in plants [12,[36][37][38][39][40].…”

“…Predictive factors that influence the above mentioned processes in the soil and the soil-plant system, such as the numerous physical and chemical properties of the soil and the mechanisms of the adoption of TEs by the plants, are used to form the training set. The physical and chemical properties include the content of clay fraction, organic matter (total SOM and/or water-soluble DOM), cation exchange capacity (CEC), total DTPA, EDTA, soluble and water-soluble forms of TEs, iron/manganese/aluminum oxide, pH, texture class, liquid-solid ratio for TEs and chemical speciation TEs [13,18,23,24,36,[41][42][43][44], and have been used in prediction models by many authors [18,40,[45][46][47][48][49][50][51][52][53][54][55][56][57]. The most commonly used quantitative prediction variables are pH, SOM and total content of TEs [36,58].…”