Rashid Muhammad Saqib, Wang Yanhong, Yin Yilong, Yousaf Balal, Jiang Shaojun, Mirza Adeel Feroz, Chen Bing, Li Xiang, Liu Zhongzhen
Key Laboratory of Plant Nutrition and Fertilizer in South Region, Ministry of Agriculture, Guangdong Key Laboratory of Nutrient Cycling and Farmland Conservation, Institute of Agricultural Resources and Environment, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Department of Technologies and Installations for Waste Management, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland.
Toxics. 2024 Jul 24;12(8):535. doi: 10.3390/toxics12080535.
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 R2 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 (R2=0.84), while the 5-layer CNN model had an 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 R2 (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 R2 value for this model stands at a robust R2 (0.94), 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 R2 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.
镉（Cd）污染土壤会带来严重的健康和环境后果。该研究对多个土壤样本进行了培养，并进行了土壤定量表征，以评估生物炭（BC）对镉吸附的影响。目的是使用依赖于土壤特性的统计和建模方法来开发镉浓度的预测模型。通过pH值、粘土、阳离子交换容量、有机碳和电导率，可以保守地评估与生物炭在土壤中吸附镉的转化和固定相关的潜在风险。在本研究中，应用长短期记忆网络（LSTM）、双向门控循环单元（BiGRU）和五层卷积神经网络（CNNs）进行风险评估，以建立一个评估生物炭改良土壤中镉风险的框架，从而预测镉的转化。在对照土壤（CK）的情况下，BiGRU模型表现出色，R²值为0.85，表明实际镉含量的方差约为85.37%。包含序列数据的LSTM模型产生的结果准确性较低（R² = 0.84），而五层CNN模型的R²值为0.91，表明该CNN模型可以解释实际镉含量变化的91%以上。在施加生物炭的土壤中，BiGRU模型显示预测值与实际值之间具有很强的相关性，R²为0.93，表明该模型解释了镉浓度方差的93.21%。同样，LSTM模型在处理生物炭改良土壤数据时性能显著提高。该模型的R²值达到了稳健的0.
