Sanitary landfill leachates usually have characteristics that depend on the region where they are generated and according to the age of the landfill, which is why a unique treatment for their sanitation has not been found. However, the adsorption preceded by the Fenton process has been proven to be highly efficient at removing contaminants. In this study, the adsorptive capacity of two types of activated carbon, granular and powdered, was analyzed to determine which was more efficient in the adsorption stage in the Fenton-adsorption process. Likewise, its behavior was analyzed using three isotherm models (Langmuir, Freundlich and Temkin), testing the raw leachate and the Fenton-treated one with both carbons. The adsorption that is carried out on the carbons is better adjusted to the Freundlich and Temkin models. It concludes that multilayers, through the physical adsorption, carry out the adsorption of pollutants on the surface of the carbons. The results show that, statistically, granular activated carbon is more efficient at removing chemical oxygen demand (COD), and powdered activated carbon removes color better. Finally, an adsorption column was designed for the Fenton-adsorption process that was able to remove 21.68 kgCOD/kg carbon. Removal efficiencies for color and COD were >99%.

Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

In this work, 10 chemometric models based on Raman spectroscopy were constructed to predict the physicochemical properties of honey produced in the state of Campeche, Mexico. The properties of honey studied were pH, moisture, total soluble solids (TSS), free acidity, lactonic acidity, total acidity, electrical conductivity, Redox potential, hydroxymethylfurfural (HMF), and ash content. These proprieties were obtained according to the methods described by the Association of Official Analytical Chemists, Codex Alimentarius, and the International Honey Commission. For the construction of the chemometric models, 189 honey samples were collected and analyzed in triplicate using Raman spectroscopy to generate the matrix data [X], which were correlated with each of the physicochemical properties [Y]. The predictive capacity of each model was determined by cross validation and external validation, using the statistical parameters: standard error of calibration (SEC), standard error of prediction (SEP), coefficient of determination of cross-validation (R²_cal), coefficient of determination for external validation (R²_val), and Student’s t-test. The statistical results indicated that the chemometric models satisfactorily predict the humidity, TSS, free acidity, lactonic acidity, total acidity, and Redox potential. However, the models for electric conductivity and pH presented an acceptable prediction capacity but not adequate to supply the conventional processes, while the models for predicting ash content and HMF were not satisfactory. The developed models represent a low-cost tool to analyze the quality of honey, and contribute significantly to increasing the honey distribution and subsequently the economy of the region.