Since the conventional processes employed in most wastewater treatment plants (WWTPs) worldwide are not designed to entirely remove or oxidize emerging pollutants, which, due to their incidence and persistence, can cause damage to both the environment and human health, several options for their degradation and removal have emerged. Coupling the advanced Fenton oxidation process as a polishing or tertiary wastewater treatment alternative within conventional WWTP processes stands out among the treatment options. Therefore, the main objective of this research was to evaluate, at the laboratory level, the ability of the advanced Fenton oxidation process to oxidize triclosan, ibuprofen, DEET (N, N-diethyl-meta-toluamide), carbamazepine, caffeine, and acesulfame-K, which represent several groups of emerging pollutants in real wastewater from the second settling tank of a municipal WWTP. The compound used as a catalyst (Fe²⁺) supplier in the advanced Fenton oxidation process was ferrous sulfate heptahydrate (FeSO₄•7H₂O). The results obtained upon application showed that the advanced Fenton oxidation process could simultaneously oxidize and remove practically the total concentration of the above-mentioned emerging pollutants, except for DEET (85.21%), in conjunction with the chemical oxygen demand (COD), total suspended solids (TSS), and fecal coliforms (FC, pathogen group) in the effluent generated by the advanced Fenton oxidation process.

The controlling hydrogeochemical processes of an intermontane aquifer in central Mexico were identified through multivariate statistical analysis. Hierarchical cluster (HCA) and k-means clustering analyses were applied to Na⁺, K⁺, Ca²⁺, Mg²⁺, F⁻, Cl⁻, SO₄²⁻, NO₃⁻, HCO₃⁻, As, pH and electrical conductivity in 40 groundwater samples collected from shallow and deep wells, where As and F⁻ are contaminants of concern. The effectiveness of each hierarchical and k-means clustering method in explaining solute concentrations within the aquifer and the co-occurrence of arsenic and fluoride was tested by comparing two datasets containing samples from 40 and 36 wells, the former including ionic balance outliers (>10%). When tested without outliers, cluster quality improved by about 5.4% for k-means and 7.3% for HCA, suggesting that HCA is more sensitive to ionic balance outliers. Both algorithms yielded similar clustering solutions in the outlier-free dataset, aligning with the k-means solution for all 40 samples, indicating that k-means was the more robust of the two methods. k-means clustering resolved fluoride and arsenic concentrations into four clusters (K1 to K4) based on variations in Na⁺, Ca²⁺, As, and F⁻. Cluster K2 was a Na-HCO₃ water type with high concentrations of As and F. Clusters K1, K3, and K4 exhibited a Ca-HCO₃, Na-Ca-HCO₃, and Ca-Na-HCO₃ water types, respectively, with decreasing As and F concentrations following the order K2 > K3 > K1 > K4. The weathering of evaporites and silicates and Na-Ca ion exchange with clays were the main processes controlling groundwater geochemistry. The dissolution of felsic rocks present in the aquifer fill is a likely source of As and F⁻, with evaporation acting as an important concentration factor.

Domestic and industrial wastewaters are complex matrices that contain a large variety of pollutants. Consequently, the conventional processes at wastewater treatment plants (WWTPs) cannot remove these. These pollutants remain in the effluent and are discharged into different environmental compartments worldwide, generating a range of negative impacts on the environment and human health. In this chapter, general features and the application of the most common advanced oxidation processes (AOPs) for the treatment of domestic and industrial wastewater are described. Also, the feasibility of scaling up advanced oxidation processes for pollutants removal (emerging and conventional) and the advantages and complications of each type of advanced oxidation process when applied to wastewater treatment (domestic and industrial) are shown.

In this study, a carbon paste electrode modified with silver and magnetite nanoparticles was used to quantify arsenic via square-wave anodic stripping voltammetry. Under optimized parameters, a deposition potential of −0.4 V for a period of 70 s, the setup achieved a linear quantification range from 0 to 40 µg L⁻¹ of As (III) (R² = 0.982). Electrochemical characterization was performed using cyclic voltammetry and electrochemical impedance spectroscopy (EIS). The evaluated arsenic quantification framework encompasses the World Health Organization (WHO) limit and the Mexican maximum permissible level for arsenic in drinking water (10 µg L⁻¹), as well as arsenic concentrations frequently reported in groundwater wells throughout northern Mexico. Key advantages of this electrode include its low fabrication cost, ease of operation, and analytical stability spanning a 30-day period. Additionally, selectivity tests revealed that Cu(II) masked the arsenic stripping signal at a 1:1 Cu(II): arsenic concentration ratio, whereas no modification of the target signal was observed in the presence of cadmium.

As the industry prioritizes energy efficiency, the implementation of Energy Management Systems has become a strategic necessity. One of the basic aspects for this type of system is the evaluation of energy performance that requires the collection of energy data as an essential input, so this project has as objective design a measurement device focused on the acquisition of voltage, current, power, power factor and electrical consumption through an iterative and progressive approach with elements adapted from a cascade model for determining the significant uses of energy and energy indicators of the production area of a bottling plant. The results of the measuring equipment has a difference of 10% with respect to the standard equipment so the significant uses of energy and the energy performance indicators by production line were determined.