Micro- and nano-plastics pollution and its potential remediation pathway by phytoremediation.

MAIN CONCLUSION: This review proposed that phytoremediation could be applied for the decontamination of MPs/NPs. Micro- and nano-plastics (MPs < 5 mm; NPs < 100 nm) are emerging contaminants. Much of the recent concerns have focused on the investigation of their pollution and their potential eco-toxicity. Yet little review was available on the decontamination of MPs/NPs. Recently, the uptake of MPs/NPs by plants has been confirmed. Here, in view of the current knowledge, this review introduces MPs/NPs pollution and highlights the updated information about the interaction between MPs/NPs and plants. This review proposed that phytoremediation could be a potential possible way for the in situ remediation of MPs/NPs-contaminated environment. The possible mechanisms, influencing factors, and existing problems are summarized, and further research needs are proposed. This review herein provides new insights into the development of plant-based process for emerging pollutants decontamination, as well as the alleviation of MPs/NPs-induced toxicity to the ecosystem.

Differential effects of polystyrene microplastics on the adsorption of cadmium and ciprofloxacin by tea leaf litter-derived magnetic biochar: Influencing factors and mechanisms.

Water pollution involves the coexistence of microplastics (MPs) and traditional pollutants, and how can MPs influence the adsorption of other pollutants by biochar during the treatment process remains unclear. This study aimed to investigate the influence of polystyrene microplastics (PS MPs) on the adsorption of cadmium (Cd) and ciprofloxacin (CIP) by magnetic biochar (MTBC) in the single and binary systems. MTBC was prepared using tea leaf litter; the effects of time, pH, and salt ions on the adsorption behaviors were investigated; and X-ray photoelectronic spectroscopy (XPS) and density flooding theory analysis were conducted to elucidate the influence mechanisms. Results indicated that PS MPs reduced the pollutants adsorption by MTBC due to the heterogeneous aggregation between PS MPs and MTBC and the surface charge change of MTBC induced by PS MPs. The effects of PS MPs on heavy metals and antibiotics adsorption were distinctly different. PS MPs reduced Cd adsorption on MTBC, which were significantly influenced by the solution pH and salt ions contents, suggesting the participation of electrostatic interaction and ion exchange in the adsorption, whereas the effects of PS MPs on CIP adsorption were inconspicuous. In the hybrid system, PS MPs reduced pollutants adsorption by MTBC with 66.3% decrease for Cd and 12.8% decrease for CIP, and the more remarkable reduction for Cd was due to the predominated physical adsorption, and CIP adsorption was mainly a stable chemisorption. The influence of PS MPs could be resulted from the interaction between PS MPs and MTBC with changing the functional groups and electrostatic potential of MTBC. This study demonstrated that when using biochar to decontaminate wastewater, it is imperative to consider the antagonistic action of MPs, especially for heavy metal removal. PRACTITIONER POINTS: Magnetic biochar (MTBC) was prepared successfully using tea leaf litter. MTBC could be used for cadmium (Cd) and ciprofloxacin (CIP) removal. Polystyrene microplastics (Ps MPs) reduced Cd/CIP adsorption by MTBC. Ps MPs effects on Cd adsorption were more obvious than that of CIP. Ps MPs changed the functional groups and electrostatic potential of MTBC, thus influencing MTBC adsorption.

Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.