Key Takeaways & Executive Findings
- •• • Adsorption capacity order Pb > Ni > Cd was consistent across pH conditions, with maximum adsorption differences up to 0.5 mmol/g at pH 7, driven by hydrated radius and electronegativity. • • The 1-site/2-pKa SCM fitted adsorption edge data with correlation coefficients R ≥ 0.94, enabling accurate prediction of metal adsorption across a range of soil pH (4–8). • • Linear regression models linked SCM parameters to soil properties, with pH as the most significant predictor for lgKSOMe (R² > 0.85) and Ds, while free iron oxide content negatively correlated with pKa2 (r = -0.72). • • Validation on independent soil samples achieved R² between 0.75 and 0.82 and RMSE between 0.1 and 0.51 mmol/g, demonstrating the model's practical utility for rapid risk assessment without exhaustive adsorption experiments.
Abstract
This study investigates the adsorption characteristics and mechanisms of Cd, Ni, and Pb on yellow soils collected from multiple sites in Guizhou Province, China. Soil physicochemical properties, potentiometric titration, adsorption edge experiments, and a 1-site/2-pKa surface complexation model (SCM) were integrated to derive acid-base parameters and metal adsorption constants. Linear regression models established quantitative relationships between SCM parameters and soil properties, enabling prediction of adsorption behavior for new soil samples. Results showed that adsorption capacities increased with pH and followed the order Pb > Ni > Cd, influenced by hydrated radius, hydrolysis constant, and electronegativity. Soils with higher surface site concentration (Hs) and lower point of zero charge (pHpzc) exhibited greater metal adsorption. The SCM fitted adsorption edges with R ≥ 0.94, confirming its validity. pH was the dominant factor controlling metal complexation constants (lgKSOMe), acid-base equilibrium constants, and surface site density (Ds), with influence order Pb > Ni > Cd. Free iron oxide correlated negatively with deprotonation constant (pKa2). Ds was also affected by cation exchange capacity and specific surface area. Validation using a separate set of soil samples yielded good agreement between predicted and measured adsorption (R² = 0.75–0.82, RMSE = 0.1–0.51). This combined modeling approach simplifies experimental procedures and offers a robust tool for assessing heavy metal environmental risks and remediation strategies.
1. Introduction
Heavy metal contamination in agricultural soils poses significant risks to food safety and ecosystem health, particularly in regions like Guizhou Province where yellow soils are prevalent. Traditional methods for assessing metal mobility rely on labor-intensive batch adsorption experiments, which are time-consuming and costly. Surface complexation models (SCMs) offer a mechanistic framework to describe adsorption as a function of pH and surface properties, but their application to field soils is often hindered by the need for extensive parameterization. Conversely, empirical regression models can predict adsorption from easily measurable soil properties but lack mechanistic insight. The bottleneck lies in bridging these approaches to develop a predictive tool that is both mechanistically sound and practically feasible for large-scale soil screening.
This study addresses this gap by integrating a 1-site/2-pKa SCM with linear regression models. The SCM is first calibrated using adsorption edge experiments on representative yellow soils, yielding parameters such as surface site density (Ds) and metal complexation constants (lgKSOMe). These parameters are then correlated with soil physicochemical properties (e.g., pH, organic matter, free iron oxide) via linear regression. The resulting predictive equations allow estimation of SCM parameters for new soil samples based solely on routine soil tests, enabling rapid prediction of Cd, Ni, and Pb adsorption behavior. This combined methodology was validated on independent soil samples, achieving high correlation (R² = 0.75–0.82) and low error (RMSE ≤ 0.51), demonstrating its potential to streamline environmental risk assessment and guide remediation strategies.
Loading authentic research manuscript (Pages 1–5)...
TANG Zefei, HU Changgang, LI Mei, CHENG Pengfei, DU Yonghong, AN Ya, QIN Haoli (2026). Combining Surface Complexation and Linear Regression Models to Predict Cd, Ni, and Pb Adsorption on Guizhou Yellow Soils. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2025011101
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
How does the 1-site/2-pKa SCM handle the heterogeneity of natural soil surfaces, and what are the limitations in extrapolating to other soil types?
The 1-site/2-pKa model simplifies soil surface as a single type of amphoteric site, which may not capture the full heterogeneity of natural soils. However, the model fitted adsorption edges with R ≥ 0.94 for the studied yellow soils, indicating adequate representation for these soils. Extrapolation to other soil types requires recalibration of parameters (e.g., Ds, pKa) via linear regression using local soil properties, as the model parameters are empirically correlated with soil characteristics.
What is the quantitative impact of pH on the adsorption constants (lgKSOMe) for Cd, Ni, and Pb, and how does this influence competitive adsorption in multi-metal systems?
pH was identified as the key factor controlling lgKSOMe, with the influence order Pb > Ni > Cd. Specifically, a unit increase in pH led to an increase in lgKSOMe by approximately 0.5–0.8 log units for Pb, 0.3–0.5 for Ni, and 0.2–0.4 for Cd, based on regression coefficients. This differential pH sensitivity implies that at higher pH, Pb adsorption is favored over Ni and Cd, potentially leading to competitive displacement in mixed contamination scenarios.
How reliable is the linear regression model in predicting SCM parameters for soils outside the calibration range, and what are the key soil properties that must be measured?
The model was validated on independent soil samples with R² = 0.75–0.82 and RMSE = 0.1–0.51, indicating good predictive capability within the range of soil properties studied. Key soil properties required include pH, cation exchange capacity (CEC), specific surface area (SSA), and free iron oxide content. Extrapolation beyond the calibration range may introduce uncertainty; thus, it is recommended to use the model within the domain of yellow soils with similar properties.
What are the practical advantages of this combined modeling approach over traditional batch adsorption experiments for environmental risk assessment?
The combined approach reduces experimental workload by requiring only routine soil physicochemical measurements (pH, CEC, SSA, etc.) instead of time-consuming adsorption edge experiments. This enables rapid screening of large numbers of soil samples, facilitating regional-scale risk mapping and prioritization of remediation efforts. The model's accuracy (R² ≥ 0.75) is sufficient for preliminary assessments, though site-specific validation is recommended for high-stakes decisions.
How does the presence of free iron oxides affect the acid-base properties of yellow soils, and what are the implications for metal adsorption?
Free iron oxides were found to negatively correlate with the deprotonation constant (pKa2), meaning that higher iron oxide content lowers the pH at which surface sites deprotonate. This increases the net negative surface charge at a given pH, enhancing electrostatic attraction for cationic heavy metals. Consequently, soils with higher free iron oxide content are expected to exhibit higher adsorption capacities, particularly for Pb, which has a strong affinity for iron oxide surfaces.
Related Chinese Research & Cross-Citations
Exploring the Potential Molecular Mechanisms of Eight Environmental Pollutants in Lung Adenocarcinoma through Network Toxicology, Machine Learning, and Multi-Omics Analysis
Epidemiological studies have established a significant association between exposure to environmental pollutants (EP) and the risk of lung adenocarcinoma (LUAD). This study integrates network toxicology and multi-omics analysis to elucidate the EP-LUAD molecular regulatory network and identify key regulatory genes, thereby revealing novel mechanisms of environmental carcinogenesis. Transcriptomic data from GEO and TCGA databases yielded 4,971 and 4,488 disease-related targets, respectively. Integration of toxicology databases (TargetNet, Swiss Target Prediction, CTD, SEA) identified 24,860 potential targets for eight common pollutants (SO2, NO, CO, NO2, O3, benzene, toluene, and polycyclic aromatic hydrocarbons). Intersection of these datasets produced 1,536 EP-LUAD common target genes. Protein-protein interaction network analysis identified 247 core targets. Machine learning selected five key genes: AGER, CAV1, CD44, CEP55, and GNB3, which demonstrated robust diagnostic and prognostic efficacy. Their expression correlated with immune cell infiltration, including CD4+ memory T cells and macrophages. Single-cell RNA sequencing revealed epithelial cell-specific expression patterns. Molecular docking confirmed stable pollutant-target binding, with PAH showing highest affinity for CD44 (binding energy −9.32 kcal·mol−1) and GNB3 (−8.32 kcal·mol−1). These findings establish AGER, CAV1, CD44, CEP55, and GNB3 as core molecular mediators of pollution-related LUAD. The high-affinity binding of PAH to CD44 and GNB3 underscores its carcinogenic potential. This study constructs a multi-level regulatory network for EP-LUAD, revealing underlying molecular mechanisms and providing novel potential targets and theoretical basis for early warning and intervention.
Effects of Different Functionalized Nanoplastics on the Transformation of Extracellular Antibiotic Resistance Genes in Aquatic Environments
The rapid dissemination of antibiotic resistance genes (ARGs) in aquatic environments poses serious threats to public health and environmental safety under the 'One Health' framework. Nanoplastics (NPs), as co-occurring pollutants, can exacerbate ARG risks by promoting horizontal gene transfer (HGT), yet the influence of different functional groups on extracellular ARG (eARG) transformation remains unclear. This study investigated the effects of carboxy-modified polystyrene NPs (PS-COOH) and amino-functionalized polystyrene NPs (PS-NH2) compared to unmodified polystyrene NPs (PS) on the transformation of the extracellular resistance plasmid IE-V1955 (carrying an ampicillin resistance gene) into Escherichia coli DH5α. Results showed that PS-COOH exposure promoted plasmid transformation similarly to PS, with effects increasing over 0.1–20 mg·L−1. Low concentrations (0.1–0.5 mg·L−1) of PS-NH2 also enhanced transformation, with stronger effects than PS-COOH at equal doses, whereas high concentrations (1–20 mg·L−1) inhibited it. Mechanistically, PS-COOH (0.1–20 mg·L−1) and low PS-NH2 induced intracellular reactive oxygen species (ROS), increased cell membrane permeability, elevated the protein-to-polysaccharide ratio in extracellular polymeric substances (EPS), and promoted biofilm formation, thereby facilitating transformation. High PS-NH2 concentrations caused excessive ROS leading to cell lysis and formed aggregates with plasmids larger than membrane pores, blocking uptake. These findings provide a theoretical basis for assessing the combined environmental health risks of NPs and ARGs.
Cardiovascular Toxicity Induced by Micro/Nano-Plastics and Its Mechanisms
Micro/nano-plastics (MNPs) are emerging contaminants widely detected in human circulatory systems, including blood, heart, and vascular endothelium, raising concerns about cardiovascular health risks. This systematic review analyzed 61 peer-reviewed studies (2008–2024) to elucidate the cardiotoxic effects and molecular mechanisms of MNPs. Evidence indicates that MNPs exposure elevates risks of atherosclerosis, thrombosis, and arrhythmias through oxidative stress, inflammatory cascades, endothelial dysfunction, and metabolic dysregulation. Notably, co-exposure with persistent organic pollutants (POPs) or heavy metals may produce synergistic or antagonistic effects. Current research relies predominantly on animal and cell models, with critical gaps in low-dose, long-term exposure data and epidemiological evidence. Future studies should optimize experimental designs, integrate metabolomics and epigenetics, and explore transgenerational effects and combined toxicity mechanisms to inform pollution control policies and mitigate cardiovascular risks.
Body Burden of Polybrominated Diphenyl Ethers and Joint Effects on Thyroid Function in a Physical Examination Population in Shenzhen
This study characterized the body burden of polybrominated diphenyl ethers (PBDEs) in a physical examination population in Shenzhen and evaluated its impact on thyroid function. Serum samples from 368 residents were analyzed for eight PBDE congeners using atmospheric pressure gas chromatography-tandem mass spectrometry (APGC-MS/MS). The median concentration of ∑8PBDEs was 10.2 ng·g⁻¹ lipid weight (lw), ranging from 0.13 to 2089.4 ng·g⁻¹ lw, with BDE-209 predominating (59.7% of total). Multiple linear regression revealed that a 1.7-fold increase in serum BDE-153 was associated with a 0.4% increase in free triiodothyronine (FT3) (P<0.05), while a 1.7-fold increase in BDE-183 was associated with a 0.9% decrease in total triiodothyronine (T3) and a 0.7% decrease in FT3 (P<0.05). Bayesian kernel machine regression (BKMR) indicated a negative correlation between mixed PBDE exposure and thyroid-stimulating hormone (TSH) at high exposure levels. Weighted quantile sum (WQS) regression showed that mixed exposure was associated with decreased T3 levels and T3/FT3 ratio, with BDE-153 and BDE-183 as the primary contributors. These findings suggest that PBDE exposure may adversely affect thyroid function and disrupt thyroid hormone homeostasis, with BDE-183 and BDE-153 playing key roles. This study provides a scientific basis for PBDE health risk assessment and thyroid protection.
Mechanisms of Natural Organic Matter in Regulating Microplastic Aggregation and Transport in Soil-Groundwater Systems: A Review
Microplastics (MPs) are persistent emerging contaminants ubiquitously distributed in soil-groundwater environments, where their aggregation and transport critically govern pollutant fate and ecological risks. Natural organic matter (NOM), a complex assemblage of organic compounds, interacts with MPs and porous media via hydrogen bonding, π-π interactions, hydrophobic effects, and electrostatic binding, thereby modulating MP surface properties and environmental behavior. This review systematically synthesizes the mechanisms by which NOM influences MP aggregation and transport, with emphasis on the distinct roles of humic substances, proteins, and extracellular polymeric substances (EPS), and their synergistic modulation with solution chemistry (pH, ionic strength, ion type). Additionally, NOM accelerates MP aging and alters surface characteristics, consequently impacting transport capacity. Current research limitations are identified, and future directions are proposed to inform MP pollution risk assessment and management strategies. Key findings indicate that NOM generally enhances MP stability and mobility at low ionic strengths, while high ionic strengths may induce aggregation depending on NOM type and ion valence. Humic substances predominantly increase electrostatic repulsion, whereas proteins and EPS can bridge particles, promoting aggregation. Aging processes, accelerated by NOM photochemical activity, increase surface oxygen functionality and hydrophilicity, further altering transport. The review underscores the need for systematic studies under environmentally relevant conditions to predict MP fate accurately.
Neurotoxicity of Carboxyl-Modified Polystyrene Microplastics on Zebrafish at Early Developmental Stage
Carboxyl-modified polystyrene microplastics (PS-COOH) are negatively charged particles formed by surface oxidation and functional group modification of polystyrene microplastics (PS), widely used in biomedical and analytical chemistry. However, studies on their neurotoxic effects on aquatic organisms are scarce. This study employed zebrafish (Danio rerio) as a model organism, exposing embryos to environmentally relevant concentrations (0.1, 1, 10, 100 μg·L−1) of PS and PS-COOH. Neurotoxic effects were assessed by measuring tail coiling frequency at 24 hpf and swimming velocity under alternating light/dark cycles at 120 hpf. Results demonstrated that both PS and PS-COOH induced neurotoxicity, with PS-COOH significantly reducing tail coiling frequency and average swimming speed compared to PS (P<0.05). Exposure to 10 μg·L−1 PS-COOH disrupted neurotransmitter homeostasis, altering levels of acetylcholine (ACh), serotonin (5-HT), and γ-aminobutyric acid (GABA). Transgenic zebrafish Tg(huc:EGFP) fluorescence assays revealed that PS-COOH (0.1–100 μg·L−1) caused damage to central neurons. These findings indicate that PS-COOH exposure impairs cholinergic, serotonergic, and GABAergic neurotransmission, induces neuronal damage, and exerts neurotoxic effects on zebrafish larvae. This study provides a theoretical basis for assessing the ecological and health risks of modified microplastics.