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.