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Toxics
Special Issues
Heavy Metals and Pesticide Residue Remediation in Farmland
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Heavy Metals and Pesticide Residue Remediation in Farmland

A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Toxicity Reduction and Environmental Remediation".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 1094

Special Issue Editor

Dr. Ying Huang

E-Mail Website
Guest Editor
Department of Agricultural Resources and Environment, Hunan Agricultural University, Changsha, China
Interests: heavy metal remediation; photocatalytic degradation; plant nutrition; safety utilization of polluted farmland

Special Issue Information

Dear Colleagues,

With the intensification of agricultural production, heavy metal and pesticide residue pollution in farmland has become increasingly prominent. These contaminants not only threaten the ecological health of farmland soils and water but can also accumulate in crops, endangering food safety and ultimately human health through the food chain. Currently, public concern over food safety is growing, particularly regarding heavy metals and pesticide residues. Therefore, there is an urgent need for research on remediation technologies targeting heavy metals and organic pollutants in farmland to ensure agricultural product safety.

This Special Issue focuses on pollution prevention and control technologies for heavy metal and pesticide residues in farmland. We invite contributions on the following key research areas:

Heavy metal pollution prevention and control technologies in paddy fields;
Safe utilization of polluted farmland;
Pesticide residue degradation technologies;
Pollution monitoring and risk assessment;
Integrated prevention and control technologies for combined pollution.

This Special Issue will provide the opportunity for researchers to discuss challenges and achievements regarding the remediation of heavy metals and organic pollutants in farmland. Laboratory and field studies with novel research outcomes are warmly welcomed.

Dr. Ying Huang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • heavy metal
  • pesticide residues
  • photocatalytic degradation
  • pollution evaluation
  • rice
  • combined pollution
  • phytoremediation
  • source, transfer, and accumulation

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Published Papers (2 papers)

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Research

16 pages, 2122 KB  
Article
Synergistic Toxicity Reduction of Cadmium in Rice Grains by Foliar Co-Application of Nano-Silica and Surfactants
by Jihao Kang, Pengyue Yu, Zhi Huang, Zhenglong Tong, Ruimin Chang, Zhiyan Xie, Shiyu Gui and Ying Huang
Toxics 2025, 13(12), 1047; https://doi.org/10.3390/toxics13121047 - 2 Dec 2025
Viewed by 222
Abstract
Cadmium (Cd) accumulation in rice poses a serious threat to global food safety and human health. Foliar application of nano-silica (Si) offers a promising remediation strategy, but its efficacy is often limited by poor droplet retention on hydrophobic leaf surfaces. This study hypothesized [...] Read more.
Cadmium (Cd) accumulation in rice poses a serious threat to global food safety and human health. Foliar application of nano-silica (Si) offers a promising remediation strategy, but its efficacy is often limited by poor droplet retention on hydrophobic leaf surfaces. This study hypothesized that surfactants could overcome this barrier by enhancing the foliar performance of nano-Si. Through field experiments, we evaluated the synergistic effects of five surfactants (Polyvinylpyrrolidone (PVP) powder, Aerosol OT (AOT), Rhamnolipid (RH), Didecyldimethylammonium bromide (DDAB), and Alkyl Polyglycoside (APG)) when combined with nano-silica. The results demonstrated that all surfactants significantly improved wetting and retention, with alkyl polyglycoside (APG) and polyvinylpyrrolidone (PVP) being the most effective. These improvements translated into a remarkable suppression of Cd translocation within rice plants. The PVP–nano-Si combination emerged as the most potent treatment, reducing grain Cd content by 50% and achieving the lowest levels of As and Cr among all treatments. Furthermore, this synergistic effect was linked to a significant increase in grain concentrations of manganese (Mn) and zinc (Zn), which exhibit a competitive relationship with Cd. The findings reveal that surfactant co-application not only optimizes the physical application of nano-Si but also triggers beneficial nutrient–Cd interactions, providing a novel and efficient strategy for mitigating Cd contamination in rice. This study provides critical theoretical support for developing efficient and environmentally friendly foliar barrier technologies and supports safe production of rice in lightly to moderately contaminated paddy fields. Full article
(This article belongs to the Special Issue Heavy Metals and Pesticide Residue Remediation in Farmland)
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15 pages, 1072 KB  
Article
Comparison of Artificial Neural Network and Multiple Linear Regression to Predict Cadmium Concentration in Rice: A Field Study in Guangxi, China
by Junyang Zhao, Fuhai Zheng, Baoshan Yu, Guanchun Qin, Shunpiao Meng, Yuhang Qiu and Bing He
Toxics 2025, 13(8), 645; https://doi.org/10.3390/toxics13080645 - 30 Jul 2025
Viewed by 646
Abstract
The translocation of cadmium (Cd) in the soil-rice system is complicated; therefore, most of the soil-plant models of Cd have not been extensively studied. Hence, we studied the back-propagation artificial neural network model (BP-ANN) and multiple regression model (MLR) to predict the cadmium [...] Read more.
The translocation of cadmium (Cd) in the soil-rice system is complicated; therefore, most of the soil-plant models of Cd have not been extensively studied. Hence, we studied the back-propagation artificial neural network model (BP-ANN) and multiple regression model (MLR) to predict the cadmium (Cd) content in rice grain and soil through testing soil parameters. In this study, 486 pairs of rice grains and corresponding soil samples of 456 vectors were used for training + validation, and 30 vectors were collected from the southwestern karst area of Guangxi Province as a test data set. In this study, the Cd content in rice was successfully predicted by using the factors soil available cadmium (ACd), total soil cadmium (TCd), soil organic matter (SOM), and pH, which have a more significant impact on rice, as the main prediction variables. Root mean square error (RMSE), Relative Percent Difference (RPD), and correlation coefficient (R2) were used to assess the models. The R2, RPD, and RMSE values for RCd medium obtained by the MLR model with pH, TCd, and ACd as entered variables were 0.551, 2.398, and 0.049, respectively. The R2 and RMSE values for RCd medium obtained by the BP-ANN model with pH, TCd, and ACd as entered variables were 0.6846, 2.778, and 0.104, respectively. Therefore, it was concluded that BP-ANN was useful in predicting RCd and had better performance than MLR. Full article
(This article belongs to the Special Issue Heavy Metals and Pesticide Residue Remediation in Farmland)
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