Quantitative Soil Characterization for Biochar-Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization

Rashid, Muhammad Saqib1; Wang, Yanhong1; Yin, Yilong1; Yousaf, Balal2; Jiang, Shaojun1; Mirza, Adeel Feroz3; Chen, Bing4; Li, Xiang1; Liu, Zhongzhen1

2024-08-01

10.3390/toxics12080535

TOXICS   影响因子和分区

2305-6304

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 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 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.

biochar cadmium transformation remediation prediction models

SCI

英语

其他

Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province[2023SDZG08] ; Low Carbon Agriculture and Carbon Neutralization Research Center, GDAAS[XTXM202204] ; High-level Guangdong Agricultural Science and Technology Demonstration City Construction Fund City Institute Cooperation Project["2220060000054","2220060000050"]

Environmental Sciences & Ecology ; Toxicology

Environmental Sciences ; Toxicology

WOS:001307500600001

MDPI

Web of Science

1.Guangdong Acad Agr Sci, Key Lab Plant Nutr & Fertilizer South Reg, Guangdong Key Lab Nutrient Cycling & Farmland Cons, Inst Agr Resources & Environm,Minist Agr, Guangzhou 510640, Peoples R China
2.Silesian Tech Univ, Fac Energy & Environm Engn, Dept Technol & Installat Waste Management, PL-44100 Gliwice, Poland
3.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China
4.Guangdong Acad Agr Sci, Key Lab Anim Nutr & Feed Sci South China, Guangdong Prov Key Lab Anim Breeding & Nutr, Collaborat Innovat Ctr Aquat Sci,Inst Anim Sci,Min, Guangzhou 510640, Peoples R China

Rashid, Muhammad Saqib,Wang, Yanhong,Yin, Yilong,et al. Quantitative Soil Characterization for Biochar-Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization[J]. TOXICS,2024,12(8).

Rashid, Muhammad Saqib.,Wang, Yanhong.,Yin, Yilong.,Yousaf, Balal.,Jiang, Shaojun.,...&Liu, Zhongzhen.(2024).Quantitative Soil Characterization for Biochar-Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization.TOXICS,12(8).

Rashid, Muhammad Saqib,et al."Quantitative Soil Characterization for Biochar-Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization".TOXICS 12.8(2024).