The adsorption of As(V) and As(III) (0.01–1 mM) on a calcined oxidic lithologic material substrate with pH-dependent surface variable charges, chemically modifiable, was investigated. The substrate was prepared via thermal treatment using a natural lithologic material rich in amphoteric oxides of Fe, Al, Mn and Ti. The calcined substrate was treated with acid media (HCl 0.1) to homogenize the positive charge density on the oxide surface via oxide protonation so that anion adsorption would be favored. A batch experiment was performed on the acid-treated substrate (activated) and non-activated substrate. L-type isotherms were obtained, which fit the Freundlich model. Isotherm constants showed that there was a greater affinity between the activated substrate and As(V) (K = 10.58) compared to As(III) (K = 5.45). The adsorption capacity of the activated substrate was two times greater than that of the non-activated substrate, As(V) (K_act = 10.58 and K_noact = 5.45) vs. As(III) (K_act = 5.45 y K_noact = 2.44), which was due to the greater positive charge density on the activated surface, created by the protonation of the surface oxides. Protons were liberated during the adsorption reaction (As(V): 2.17 × 10⁻³ and As(III): 0.96 × 10⁻³ mmol/mL). The forms H₂AsO₄⁻ and H₃AsO₃ deprotonated when adsorbed by the surface groups $\mathrm{M}-\mathrm{O}{\mathrm{H}}_2^{+}$ (M: Fe, Al). Kinetic data showed a second-order process for As(V) adsorption and a first-order process for As(III) adsorption. The adsorption rate on the activated substrate was two times greater compared with the non-activated substrate: As(V) (k_act = 3.78 × 10⁻⁵ L/mg·min and k_noact = 2.16 × 10⁻⁵ L/mg·min) vs. As(III) (k_act = 0.055 h⁻¹ and k_noact = 0.027 h⁻¹). The tested substrate is potentially useful as a low-cost natural material for arsenic removal from contaminated water.

INTRODUCTION: Pollution is the most obvious factor in the deterioration of environmental quality. This issue affects different media and is closely related to the destructive effects on the environment, the accelerated greenhouse effect, the deterioration of ecosystems, and environmental quality. OBJECTIVES: Determine and characterize the sedimentable particulate matter due to anthropic and natural incidence Using Geographic Information Systems in the Central Ecuadorian Highlands. METHODS: 22 monitoring points were established, these collected sedimentable particulate matter (SPM) through the passive gravimetric method that consists of particulate matter (PM) being deposited by gravity in Petri dishes with filter paper inside for one month. The samples were analyzed and compared with the maximum permissible limits (LMP) of the standards: international (WHO) and national (Agreement 097-A). RESULTS: Showed that all samples comply with the regulations established in Agreement 097-A reformed in 2018 (1 mg / cm2 / month), however, 21 of the 22 samples collected do not comply with WHO regulations being above the maximum permissible limit that is (0.5 mg / cm2 / month), being the SPM a factor that affects air quality in the area. CONCLUSION: A map of concentration and dispersion of the SPM was elaborated with the help of Geographic Information Systems (GIS) where 3 types of ranges to the concentration of the MP were categorized: high, medium, and low, also the areas with the highest degree of concentration of particulate matter were identified, turning out to be the north and south of the study area.

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations.

Esta investigación examina la exposición ocupacional a partículas químicas en procesos de manufactura, empleando como estudio de caso la empresa MINABRADEC CIA. LTDA., situada en el Parque Industrial de Riobamba y dedicada a la producción de materiales abrasivos. Evaluamos si la concentración de material particulado volátil (MPV) y sedimentable (MPS) en el entorno laboral supera los límites de la normativa y su potencial impacto potencial en la salud de los trabajadores. Para ello, se consideraron seis puntos de muestreo con el uso de que fueron analizados con instrumentos especializados como microscopio de barrido, balanza analítica SARTORIUS y DustTrak™. Los resultados obtenidos revelan concentraciones de MP2.5 (106.61 µg/m³) y MP10 (168.33 µg/m³), así como de MPS (1.183 mg/cm²/mes), que superan los parámetros establecidos por la Organización Mundial de la Salud (OMS) y la normativa TULSMA. Esta evidencia sugiere la presencia de riesgos químicos significativos en el área de trabajo, subrayando la necesidad de implementar estrategias de control ambiental, monitoreo continuo y mejoras en las condiciones laborales para mitigar potenciales impactos en la salud ocupacional.

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15.