Depending on the geochemical forms, heavy metal (HM) accumulation is one of the most serious environmental problems in the world and poses negative impacts on soil, plants, animals, and humans. Although the use of biochar to remediate contaminated soils is well known, the huge quantities of waste used and its recycling technique to sustain soil in addition to its use conditions are determinant factors for its characteristics and uses. A pot experiment was conducted in a completely randomized block design to evaluate metal forms and their availability under the application of garden waste biochar (GB) pyrolyzed at different temperatures, and a sequential extraction procedure was designed to fractionate Pb, Cd, Zn, and Cu of the contaminated soil. The results show that the TCLP-extractable Pb, Cd, Zn, and Cu were significantly decreased depending on the biochar addition rate, pyrolysis temperature, and tested metal. The acid extractable fraction was significantly decreased by 51.54, 26.42, 16.01, and 74.13% for Pb, Cd, Zn, and Cu, respectively, at the highest application level of GB₄₀₀ compared to untreated pots. On the other hand, the organic matter bound fraction increased by 76.10, 54.69, 23.72, and 43.87% for the corresponding metals. The Fe/Mn oxide bound fraction was the predominant portion of lead (57.25–62.84%), whereas the acid fraction was major in the case of Cd (58.06–77.05%). The availability of these metals varied according to the application rate, pyrolysis temperature, and examined metals. Therefore, the GB is a nominee as a promising practice to reduce HM risks, especially pyrolyzed at 400 °C by converting the available fraction into unavailable ones.

Biochar is crucial for agricultural output and plays a significant role in effectively eliminating heavy metals (HMs) from the soil, which is essential for maintaining a soil–plant environment. This work aimed to assess machine learning models to analyze the impact of soil parameters on the transformation of HMs in biochar–soil–plant environments, considering the intricate non-linear relationships involved. A total of 211 datasets from pot or field experiments were evaluated. Fourteen factors were taken into account to assess the efficiency and bioavailability of HM–biochar amendment immobilization. Four predictive models, namely linear regression (LR), partial least squares (PLS), support vector regression (SVR), and random forest (RF), were compared to predict the immobilization efficiency of biochar-HM. The findings revealed that the RF model was created using 5-fold cross-validation, which exhibited a more reliable prediction performance. The results indicated that soil features accounted for 79.7% of the absorption of HM by crops, followed by biochar properties at 17.1% and crop properties at 3.2%. The main elements that influenced the result have been determined as the characteristics of the soil (including the presence of different HM species and the amount of clay) and the quantity and attributes of the biochar (such as the temperature at which it was produced by pyrolysis). Furthermore, the RF model was further developed to predict bioaccumulation factors (BAF) and variations in crop uptake (CCU). The R² values were found to be 0.7338 and 0.6997, respectively. Thus, machine learning (ML) models could be useful in understanding the behavior of HMs in soil–plant ecosystems by employing biochar additions.

The beneficial utilization of potentially increasing urban green waste (UGW) is critical for sustainable urban development in China. In this study, UGW was pyrolyzed at different temperatures, and the resulting biochar was used to amend Cd-contaminated soils to grow cabbage. Our results showed that the Cd adsorption capacity of UGW-biochar was positively correlated with the surface area, O/C, and (O+N)/C value of biochar. Furthermore, UGW-biochar was incorporated into three Cd-contaminated soils, including one acidic soil and two neutral soils, to assess its impact on the availability of Cd. The most substantial reduction in the concentration of available Cd was observed in the acidic soil, of the three tested soils. In the neutral soils, a more substantial reduction was found in the heavily Cd-contaminated soil compared to the lightly Cd-contaminated soil. UGW-biochar amendments to the three Cd-contaminated soils resulted in an increase in the cabbage biomass in acidic soil, whereas in neutral soils, it increased in lightly contaminated soils but decreased in heavily contaminated soils. Additionally, the Cd bioaccumulation factor (BCF), translocation factor (TF), and removal efficiency (RE), as impacted by the biochar application, were calculated in the lightly Cd-contaminated soil–cabbage system. The BCF decreased from 5.84 to 3.80 as the dosage of the UGW-biochar increased from 0% to 3%, indicating that the UGW-biochar immobilized Cd and reduced its bioaccumulation in cabbage roots. Based on our investigations, UGW-biochar effectively immobilizes Cd by reducing its mobility and bioavailability in a lightly contaminated environment matrix.

Soil pollution with cadmium (Cd) poses serious health and environmental consequences. The study investigated the incubation of several soil samples and conducted quantitative soil characterization to assess the influence of biochar (BC) on Cd adsorption. The aim was to develop predictive models for Cd concentrations using statistical and modeling approaches dependent on soil characteristics. The potential risk linked to the transformation and immobilization of Cd adsorption by BC in the soil could be conservatively assessed by pH, clay, cation exchange capacity, organic carbon, and electrical conductivity. In this study, Long Short-Term Memory (LSTM), Bidirectional Gated Recurrent Unit (BiGRU), and 5-layer CNN Convolutional Neural Networks (CNNs) were applied for risk assessments to establish a framework for evaluating Cd risk in BC amended soils to predict Cd transformation. In the case of control soils (CK), the BiGRU model showed commendable performance, with an $R^2$ value of 0.85, indicating an approximate 85.37% variance in the actual Cd. The LSTM model, which incorporates sequence data, produced less accurate results ($R^2=0.84)$, while the 5-layer CNN model had an R² value of 0.91, indicating that the CNN model could account for over 91% of the variation in actual Cd levels. In the case of BC-applied soils, the BiGRU model demonstrated a strong correlation between predicted and actual values with $R^2$ (0.93), indicating that the model explained 93.21% of the variance in Cd concentrations. Similarly, the LSTM model showed a notable increase in performance with BC-treated soil data. The $R^2$ value for this model stands at a robust $R^2 \left(0.94\right)$, reflecting its enhanced ability to predict Cd levels with BC incorporation. Outperforming both recurrent models, the 5-layer CNN model attained the highest precision with an $R^2$ value of 0.95, suggesting that 95.58% of the variance in the actual Cd data can be explained by the CNN model’s predictions in BC-amended soils. Consequently, this study suggests developing ecological soil remediation strategies that can effectively manage heavy metal pollution in soils for environmental sustainability.

Three successive vegetable pot experiments were conducted to assess the effects on the long-term immobilization of heavy metals in soil and crop yield improvement after the addition of peanut shell biochar and an alkaline mineral to an acidic soil contaminated with lead and cadmium. Compared with the CK treatment, the change rates of biomass in the edible parts of the three types of vegetables treated with B0.3, B1, B3, B9, R0.2 and B1R0.2 were −15.43%~123.30%, 35.10%~269.09%, 40.77%~929.31%, −26.08%~711.99%, 44.14%~1067.12% and 53.09%~1139.06%, respectively. The cadmium contents in the edible parts of the three vegetables treated with these six additives reduced by 2.08%~13.21%, 9.56%~24.78%, 9.96%~35.61%, 41.96%~78.42%, −4.19%~57.07% and 12.43%~65.92%, respectively, while the lead contents in the edible parts reduced by −15.70%~59.47%, 6.55%~70.75%, 3.40%~80.10%, 55.26%~89.79%, 11.05%~70.15% and 50.35%~79.25%, respectively. Due to the increases in soil pH, soil cation-exchange capacity and soil organic carbon content, the accumulation of Cd and Pb in the vegetables was most notably reduced with a high dosage of 9% peanut shell biochar alone, followed by the addition of a low dosage of 1% peanut shell biochar blended with 0.2% alkaline mineral. Therefore, the addition of a low dosage of 1% peanut shell biochar blended with 0.2% alkaline mineral was the best additive in increasing the vegetable biomass, whereas the addition of 9% peanut shell biochar alone was the worst. Evidently, the addition of 0.2% alkaline mineral can significantly reduce the amount of peanut shell needed for passivating heavy metals in soil, while it also achieves the effect of increasing the vegetable yield.

Sorption is the most important process influencing the amount of herbicide retained in soils. The special properties of biochar could influence the soil retention of pollutants through adsorption. However, the detailed sorption mechanisms as influenced before and after applying biochar to soils with different properties are unclear. This study examined the sorption characteristics of metolachlor using soil samples collected from South China. Sorption experiments were conducted using a batch equilibration method. After comparing the metolachlor sorption constants observed for all soil samples, our results showed that the application of biochar significantly increased the capacity of metolachlor adsorption. Without biochar, sorption capacity (K_f) was positively related to soil organic matter and to a soil particle size of 0.002–0.02 mm in soils developed from granite, of 0.002–0.02 mm in soils developed from delta shockwaves, and of 0.002–0.02 mm, together with complex iron oxide and total iron content, in soils developed from arenaceous shale. Moreover, sorption capacity (K_f) with biochar was positively related to peroxidase in soils developed from granite and to dissociative iron oxide and total iron content in soils developed from arenaceous shale. Our results show that biochar greatly affects metolachlor sorption behavior, probably because of qualitative differences in the structural characteristics of soils with different developmental parent materials and properties.