Inorganic arsenic in drinking water from groundwater sources is one of the potential causes of arsenic-contaminated environments, and it is highly toxic to human health even at low concentrations. The purpose of this study was to develop a magnetic adsorbent capable of removing arsenic from water. Fe₃O₄-monolithic resorcinol-formaldehyde carbon xerogels are a type of porous material that forms when resorcinol and formaldehyde (RF) react to form a polymer network, which is then cross-linked with magnetite. Sonication-assisted direct and indirect methods were investigated for loading Fe₃O₄ and achieving optimal mixing and dispersion of Fe₃O₄ in the RF solution. Variations of the molar ratios of the catalyst (R/C = 50, 100, 150, and 200), water (R/W = 0.04 and 0.05), and Fe₃O₄ (M/R = 0.01, 0.03, 0.05, 0.1, 0.15, and 0.2), and thermal treatment were applied to evaluate their textural properties and adsorption capacities. Magnetic carbon xerogel monoliths (MXRF600) using indirect sonication were pyrolyzed at 600 °C for 6 h with a nitrogen gas flow in the tube furnace. Nanoporous carbon xerogels with a high surface area (292 m²/g) and magnetic properties were obtained. The maximum monolayer adsorption capacity of As(III) and As(V) was 694.3 µg/g and 1720.3 µg/g, respectively. The incorporation of magnetite in the xerogel structure was physical, without participation in the polycondensation reaction, as confirmed by XRD, FTIR, and SEM analysis. Therefore, Fe₃O₄-monolithic resorcinol-formaldehyde carbon xerogels were developed as a potential adsorbent for the effective removal of arsenic with low and high ranges of As(III) and As(V) concentrations from groundwater.

This work analyzes the influence of whitening a greenhouse roof on the microclimate and yield of a tomato crop. In the west sectors of two multi-span greenhouses, a whitening concentration of 0.250 kg L⁻¹ was used as a control. In an autumn–winter cycle, a lower (0.125 kg L⁻¹) and an increased (0.500 kg L⁻¹) concentration were used in the east sectors of greenhouses 1 and 2. In a spring–summer cycle, the whitening concentrations in the east were varied depending on outside temperature. The effect of whitening on photosynthetic activity, production, plants’ morphological parameters, and the quality of the fruits were also analyzed. To evaluate the effect on microclimate, solar and photosynthetically active (PAR) radiations, air and soil temperatures, and heat flux in the soil were measured in greenhouse 1. Results show that excessive whitening leads to reductions of inside PAR radiation that decreases photosynthesis and crop yield. A whitening concentration of 0.500 kg L⁻¹ is proposed at the beginning of the autumn–winter crop cycle, washing the cover when inside temperature drops to 35 °C. At the end of the spring–summer cycle, a concentration of 0.125 kg L⁻¹ is recommended when inside temperature increases to 35 °C.

Arsenic contamination of groundwater is still a global problem due to the toxicity at low dose on human health confirmed by epidemiological studies. Magnetic xerogel monoliths (MXs) were synthesized by the sol-gel polymerization using resorcinol, formaldehyde, alkaline catalyst and magnetite. The varying molar ratios of magnetite and resorcinol (M/R) in the gel were evaluated for As(V) removal from groundwater. The surface chemistry, structure and morphology of MXs related to arsenic adsorption were characterized by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy and point of zero charge. Batch adsorption experiments were carried out to investigate the effects of Fe contents, initial pH and adsorbent dose on As(V) removal performance. The MXs with molar ratio of M/R at 0.15 gave the maximum As(V) adsorption capacity and removal with values of 62.8 µg/g and 86.7%, respectively. The adsorption data were well described by the Elovich equation of the kinetic model and the Freundlich isotherm. The thermodynamic studies demonstrated that the adsorption process was endothermic and spontaneous in nature. MXs showed to be a good alternative for As(V) removal from groundwater and achieving the efficient desorption, and thus fulfilled the Mexican standard for drinking water.

Arsenic contamination demands innovative, sustainable remediation. This study presents a fuzzy approach for synthesizing a magnetic biochar nanocomposite from pecan shell agricultural waste for efficient arsenic removal. Using a Multi-Input Fuzzy Rules Emulated Network (MiFREN), a systematic investigation of the synthesis process revealed that precursor type (biochar), Fe:precursor ratio (1:1), and iron salt type were the most significant parameters governing material crystallinity and adsorption performance, while particle size and N₂ atmosphere had a minimal effect. The MiFREN-identified optimal material, the magnetic biochar composite (FS7), achieved > 90% arsenic removal, outperforming the least efficient sample by 50.61%. Kinetic analysis confirmed chemisorption on a heterogeneous surface (q_e = 12.74 mg/g). Regeneration studies using 0.1 M NaOH demonstrated high stability, with FS7 retaining > 70% removal capacity over six cycles. Desorption occurs via ion exchange and electrostatic repulsion, with post-use analysis confirming structural integrity and resistance to oxidation. Application to real groundwater from the La Laguna region proved highly effective; FS7 maintained selectivity despite competing ions like ${Na}^{+}$, ${Cl}^{-},$ and ${SO}_4^{2-}$. By integrating AI-driven optimization with reusability and real contaminated water, this research establishes a scalable framework for transforming agricultural waste into a high-performance adsorbent, supporting global Clean Water and Sanitation goals.

Arsenic contamination in water demands effective, low-cost removal methods. This study introduces nanomagnetite-coated biochar derived from pecan nutshells for efficient arsenic adsorption. Utilizing a solvothermal method, uniform magnetite crystals were grown on biochar in a controlled process at 200 °C. The resulting bioadsorbent, characterized by XRD, SEM, and FTIR, exhibited a narrow size distribution and consistently high arsenic removal rates (97.30–98.76%). Biochar with varied particle sizes, synthesized at a short reaction time (6 h), showed the highest removal efficiency of arsenic (98.76%) and adsorption capacity (7.974 mg/g). This approach offers a sustainable for arsenic remediation, and ease of magnetic separation.

In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system using the European Pepper Moth, Duponchelia fovealis (Lepidoptera: Crambidae), as a study model. A prototype water trap equipped with an infrared digital camera controlled using a microprocessor served as the attraction and capture device. Images captured by the system in the laboratory were processed to detect objects. Subsequently, these objects were labeled, and size and shape features were extracted. A machine learning model was then trained to identify the number of insects present in the trap. The model achieved 99% accuracy in identifying target insects during validation with 30% of the data. Finally, the prototype with the trained model was deployed in the field for result confirmation.