The seed of Moringa oleifera Lam. (Moringaceae) comes from a tropical plant containing important quantities of edible oils and water-soluble proteins with excellent coagulation properties which can be used in wastewater treatment. These proteins are non-toxic natural organic polymers and biodegradable. The main objective of this work was to use the powder of seeds of M. oleifera Lam. as a natural coagulant for the adsorption of organic pollutants from slaughterhouse wastewater. The effects of adsorption time, initial pH, slaughterhouse wastewater concentration and dosage of seed in powder were evaluated. The results indicated that 180 min is necessary for a high adsorption. The greatest removal efficiency measured as chemical oxygen demand (COD) was 5,614 mg/L (64%) and this was reached using 7 g/L powder of seed, pH 9 and 8,772 mg/L of pollutant in wastewater. This study elucidates that the sorption process follows second-order kinetics and the values of constant are in the range 0.0427 ≤ k₂ (g pollutant organic adsorbed/g of M. oleifera min) ≤ 0.0516, with correlation coefficients above 0.98. The adsorption data were fitted to the Langmuir and Freundlich isotherm equations. The maximum adsorption capacity of 0.523 g COD/g powder suggests a good affinity between the organic pollutants and the powder.

In this paper, electrocoagulation (EC) treatment for the removal of chemical oxygen demand (COD) from cold meat industry wastewater is modeled and optimized using computational techniques. Methods such as artificial neural networks (ANNs) and response surface methodology (RSM), based on the Box–Behnken design using three levels, were employed to calculate the best control parameters for pH (5–9), current density (2–6 mA/cm²) and EC time (20–60 min). Analysis of variance (ANOVA) and 3D graphs revealed that pH and current density are the main parameters used for depicting the EC process. The developed models successfully describe the process with a correlation coefficient of R² = 0.96 for RSM and R² = 0.99 for ANN. The models obtained were optimized applying the moth-flame optimization (MFO) algorithm to find the best operating conditions for COD removal. ANN-MFO was used and showed superior COD removal (92.91%) under conditions of pH = 8.9, current density = 6.6 mA/cm² and an EC time of 38.62 min. The energy consumption with these optimal conditions was 6.92 kWh/m³, with an operational cost of $3.14 (USD)/m³. These results suggest that the proposed computational model can be used to obtain more effective and economical treatments for this type of effluent.

This paper describes an organosolv pretreatment of corncob waste to improve its anaerobic digestion for biogas production. Through a thermochemical process based on the use of ethanol and acetic acid, it was possible to separate the fractions of lignin, considered to be a natural inhibitor of anaerobic digestion processes. In addition, with this organosolv pretreatment, the available sugars in the carbohydrates present as monosaccharides, or simple sugars, were depolymerised, facilitating the digestion process. The obtained results include the chemical characterisation of the corncob, the hydrolysate, and the mixture with cow manure, finding that these substrates have potential to be used in anaerobic digestion. The total reducing sugars consumed were 96.8%, and total sugars were 85.75%. It was clearly observed that with the use of pretreatment with organosolv, the production of biogas was superior, because 484 NmL/gVS was obtained compared to the other reported treatments. It was also observed that adding the hydrolysate organosolv increased the production because the values of the control without hydrolysate were 120 NmL/gVS in the bottle experiment. When the experiment was scaled to the 5L reactor, the total volumes of biogas that were accumulated in 15 days of production were 5050 NmL/gVS and 1212 NmL/gVS with and without hydrolysate, respectively. This indicates that the organosolv pretreatment of corncob waste is effective in improving biogas production.

Polycyclic aromatic hydrocarbons (PAHs) and quinones in the gas phase and as submicron particles raise concerns due to their potentially carcinogenic and mutagenic properties. The majority of existing studies have investigated the formation of quinones, but it is also important to consider both the primary and secondary sources to estimate their contributions. The objectives of this study were to characterize PAHs and quinones in the gas and particulate matter (PM₁) phases in order to identify phase distributions, sources, and cancer risk at two urban monitoring sites in the Guadalajara Metropolitan Area (GMA) in Mexico. The simultaneous gas and PM₁ phases samples were analyzed using a gas chromatography–mass spectrometer. The lifetime lung cancer risk (LCR) due to PAH exposure was calculated to be 1.7 × 10⁻³, higher than the recommended risk value of 10⁻⁶, indicating a potential health hazard. Correlations between parent PAHs, criteria pollutants, and meteorological parameters suggest that primary sources are the main contributors to the Σ₈ Quinones concentrations in PM₁, while the secondary formation of 5,12-naphthacenequinone and 9,10-anthraquinone may contribute less to the observed concentration of quinones. Additionally, naphthalene, acenaphthene, fluorene, phenanthrene, and anthracene in PM₁, suggest photochemical degradation into unidentified species. Further research is needed to determine how these compounds are formed.

Temporal delays and spatial randomness between ground-based data and satellite overpass involve important deviations between the empirical model output and real data; these are factors poorly considered in the model calibration. The inorganic matter-generated turbidity in Lake Chapala (Mexico) was taken as a study case to expose the influence of such factors. Ground-based data from this study and historical records were used as references. We take advantage of the at-surface reflectance from Landsat-8, sun-glint corrections, a reduced NIR-band range, and null organic matter incidence in these wavelengths to diminish the physical phenomena-related radiometric artifacts; leaving the spatio-temporal relationships as the principal factor inducing the model uncertainty. Non-linear correlations were assessed to calibrate the best empirical model; none of them presented a strong relationship (<73%), including that based on hourly delays. This last model had the best predictability only for the summer-fall season, explaining 71% of the turbidity variation in 2016, and 59% in 2017, with RMSEs < 24%. The instantaneous turbidity maps depicted the hydrodynamic complexity of the lake, highlighting a strong component of spatial randomness associated with the temporal delays. Reasonably, robust empirical models will be developed if several dates and sampling-sites are synchronized with more satellite overpasses.

The spatial assessments of water supply quality from wells, springs, and surface bodies were performed during the dry and rainy seasons in six municipalities in the eastern regions of Michoacán (Central Mexico). Different physicochemical parameters were used to determine the supplies’ Water Quality Index (WQI); all of the communities presented good quality. The analysis indicates that many water quality parameters were within limits set by the international standards, showing levels of “excellent and good quality” according to WQI, mainly during the dry season (except at San Pedro Jácuaro and Irimbo communities in the rainy season). However, some sites showed “poor quality” and “unsuitable drinking water” related to low pH levels ( 1) in the rainy season for three target groups. At the same time, the carcinogenic risk (1 × 10⁻³) exceeded the acceptability criterion in the rainy season, which suggests that the As mainly represents a threat to the health of adults, children, and infants.