Miroslav Černík

Nanomaterials are increasingly applied in modern agriculture to improve crop productivity, nutrient efficiency, and plant protection through fertilizers, pesticides, and delivery systems. However, their growing implementation raises significant concerns regarding environmental fate, ecological toxicity, and potential risks to human health. This review aims to (i) summarize current knowledge on nanomaterial applications in agriculture and (ii) critically evaluate their associated environmental and health risks. The manuscript integrates evidence on nanoparticle behavior in soil–plant–water systems, including transport, transformation, uptake, and accumulation across trophic levels. Particular attention is given to key toxicity mechanisms such as oxidative stress, membrane damage, genotoxicity, and bioaccumulation across terrestrial and aquatic ecosystems. The review also highlights limitations of current studies, which are predominantly short-term and laboratory-based, lacking long-term field validation and standardized risk assessment approaches. Emerging approaches such as artificial intelligence-based optimization and commercial nano-enabled agricultural products, along with existing regulatory frameworks, are further discussed. By integrating agronomic, toxicological, and regulatory perspectives, this review provides a comprehensive systems-level overview of nano-enabled agriculture, emphasizing the urgent need for harmonized risk assessment methodologies, long-term environmental monitoring, and interdisciplinary research.

This work reports, for the first time, the successful degradation of PFOA under standard conditions using nZVI functionalized with a supramolecular compound, demonstrating its effective breakdown into acetate and formate.

Advanced oxidation processes employing sulphate radicals (SO ₄ ^●– ) are effective techniques to degrade trace organic pollutants in wastewaters. Herein, SO ₄ ^●– are generated from peroxymonosulphate (PMS) and peroxydisulphate (PDS) via UV-Vis activation for the degradation of Acid blue 129 (AB 129) dye. The activation was performed using four different UV-Vis lamps, TQ-150, HG-Z1, HG-Z2 and HG-Z3, which emit different electromagnetic radiation wavelengths. Degradation of AB 129 was performed at various conditions, including different oxidant concentrations. In order to evaluate the efficiency of the processes, the kinetic rate constant was calculated via a pseudo-first-order kinetics model. In addition, the detailed oxidative degradation pathway of AB 129 induced by SO ₄ ^●– was determined for the first time to the best of our knowledge. Finally, the ECOSAR model indicates that the toxicity of the treated solution of Acid blue 129 initially increases and then decreases across daphnids, fish, and green algae during degradation.

Despite being banned, Hexachlorocyclohexanes (HCH) still pose significant human health and ecological risks due to their persistence in the environment. While various remedial methods have been investigated since the 1980s, many HCH-contaminated sites remain unresolved. A hybrid-constructed wetland system, Wetland+®, represents a promising nature-based solution (NBS) strategy for treating HCH leachate or drainage water. The system has been built and operates on a full scale at Hajek (Czechia), where several thousand tons of HCH were historically deposited. The system comprises a permeable reactive barrier with Fe(0) chips, a biosorption/biodegradation module, and an aerobic wetland with phytoremediation capabilities. Over 30 months, the system demonstrated an average HCH removal efficiency of 83%, with significantly higher removal rates observed during lower flow and higher water temperature seasons. The system released approximately 8 g and less than 1 g of HCH per day in winter and summer, respectively, compared to an average of 23 g/day in the inflow. The removal efficiency increases over time, as observed in long-term studies, due to the growth of wetland plants. Among the five most abundant HCH isomers, significantly lower efficiency was recorded for the more recalcitrant ε and β isomers. The dominant isomer detected in the system outflow was δ-HCH in winter and spring and ε-HCH in the rest of the year, compared to δ-HCH in the inflow. The Wetland+® system was easy to deploy and sustainable against conventional approaches (WWTP or no-intervention) based on the applied socio-economic survey framework.

Heavy metal contamination in wastewater is a major global problem that requires effective, automated, and sustainable treatment systems capable of linking laboratory-scale adsorption processes to intelligent large-scale operation. This work introduces a novel Expert System ( ES ) that combines a Genetic Algorithm ( GA ), Artificial Neural Networks ( ANN ), and Monte Carlo ( MC ) analysis for the automated removal of heavy metals using adsorbents obtained from Low-Density Concrete waste. This research is significant because adsorption systems are complex to automate due to their nonlinear behaviour and sensitivity to fluctuating operating conditions. By facilitating intelligent process control and optimisation, the system overcomes the main drawback of adsorption technology, which is its lack of automation. Experimental datasets for Pb ²⁺ , Co ²⁺ and Mn ²⁺ removal were used to train the ANN , which predicts removal from inputs (pH, adsorbent mass, contact time, initial concentration). GA then used this trained ANN to optimise these same factors. According to the sensitivity analysis, the most important control variables in heavy metal adsorption were heavy metal type, pH, and adsorbent mass. The ANN model demonstrated strong predictive performance ( R ² = 0.908, RMSE = 7.43 %), enabling GA to optimise process conditions and achieve removal efficiencies of 100 % (Pb ²⁺ ), 87.8 % (Co ²⁺ ), and 71.62 % (Mn ²⁺ ) under identified optimal operating parameters. Operational reliability measured by MC simulations showed Co ²⁺ had a higher variability ( CV : Coefficient of Variation = 5.39 %) than Mn ²⁺ ( CV = 3.29 %) and Pb ²⁺ ( CV = 2.13 %), which had minimal uncertainty. Using resources from the circular economy, the ES provides a framework for a digital twin of an Industry 4.0-compliant water treatment system that enables translation of lab-based procedures into practical applications.

Cadmium (Cd), a highly toxic element, often contaminates drinking water through Cd²⁺ impurities in galvanized pipelines and cadmium-based solders. This study presents a sludge-brick composite for Cd²⁺ decontamination, aligning with circular economy principles. Using Response Surface Methodolo-gy (RSM), key parameters such as pH, mixing time, and sludge proportion (V/V) were optimized, with adsorption capacity (qe) as the response variable. The qe was evaluated by testing 0.1 g of the material in a 10-cc water sample at pH 7 over 1 hour. 50 g of sludge was dispersed in 250 cc of deionized water using ultrasound for 20 minutes. The prepared solution was then combined with an additional 250 cc of deionized water and stirred at 70°C for 2 hours at a speed of 300 rpm. Subsequently, sludge percent-age, the solution's pH and mixing time with 1 g of brick powder were adjusted based on the RSM pat-tern. The results of the RSM modeling indicated that a quadratic model with an R2 value of 0.8 and a predicted R2 of 0.7 provided a reliable framework for response optimization. ANOVA analysis re-vealed that sludge percentage had the greatest impact on adsorption efficiency, with an F-value of 5.17. pH was identified as the second most influential factor, followed by the mixing time of sludge and brick powder. The optimization analysis, achieving a desirability of 1, revealed that the highest Cd²⁺ adsorption performance onto the composite occurs under the following conditions: pH 7, a sludge-brick mixing time of 10 hours, and a sludge proportion of 48% during adsorbent synthesis

The depletion of high-grade mineral deposits and environmental concerns associated with traditional mining practices necessitate alternative strategies for sourcing critical materials. This study explores the innovative use of reactive laser ablation in liquids (RLAL) to recover and repurpose valuable metals from effluents of the former Zlate Hory mine in the Czech Republic, transforming them into multielement nanoparticles (NPs) with broad industrial applications. By leveraging RLAL on two solid targets, Au and Fe, this work demonstrates the formation of multielement NPs that incorporate elements like Al, Fe, and Zn from the mine effluents. These NPs, characterized through various analytical techniques, exhibit unique structural and electrochemical properties. The incorporation of ferrimagnetic AlFe₂O₄ and spinel-structured Zn(Al_1.9Fe_0.1)O₄ into the NPs enhances their catalytic potential, while the presence of Fe-based structures imparts magnetophoretic behavior, making them suitable for various technological applications, including catalysis and data storage. This approach not only provides a sustainable solution for recovering critical elements but also mitigates the environmental impact of former mining activities, aligning with circular economy principles. These findings highlight RLAL as a promising method for transforming waste into high-value nanomaterials, offering a new frontier in sustainable resource management.

There are three components to every environmental protection system: monitoring, estimation, and control. One of the main toxic gases with considerable effects on human health is NO₂, which is released into the atmosphere by industrial activities and the transportation network. In the present research, a NO₂ sensor is designed based on Fe₃O₄ piperidine-4-sulfonic acid grafted onto a reduced graphene oxide Fe₃O₄@rGO-N-(piperidine-4-SO₃H) nanocomposite, due to the highly efficient detection of pollution in the air. In the first phase of the present study, the nanocomposite synthesis is performed in four steps. Afterward, the novel fabricated nanosensor is characterized through energy dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), Raman, surface area analysis, and field emission scanning electron microscopy (FE-SEM). To determine the optimal condition for sensor performance, graphene-based nanosensors are prepared with various weight percentages (wt%) of rGO-N-(piperidine-4-SO₃H) (1 wt%, 5 wt%, 10 wt%, and 15 wt%). During the experimental process, the performance of the sensors, in terms of the sensitivity and response time, is investigated at different NO₂ concentrations, between 2.5 and 50 ppm. The outputs of this study demonstrate that the synthesized nanosensor has the best efficiency at more than a 5 ppm contamination concentration and with at least 15 wt% of rGO-N-(piperidine-4-SO₃H).