Escalating herbicide pollution in natural water bodies necessitates further exploration of effective remediation strategies. This study investigated the electro-degradation of Terbutryn (TBT) at concentrations comparable to those encountered in agricultural practices. Anodic oxidation (AO), electro-Fenton (EF), and photoelectron-Fenton (PEF) were employed for TBT abatement. AO achieved moderate removal (68%), EF significantly improved efficiency (99%), and PEF surpassed both, reaching near complete removal (99.4%) by combining EF with UV light-induced •OH generation. Statistical analysis confirmed that optimizing treatment conditions was crucial. All three factors (current density, Fe²⁺ concentration, and initial TBT concentration) independently affected the PEF process ability to remove TBT pollutants. However, the interplay between these factors was even more important. Sufficient Fe²⁺ was critical for high TBT concentrations, and a balance between current density, Fe²⁺, and initial TBT concentration was necessary. Excessive levels of any could hinder COD removal. High-performance liquid chromatography (HPLC) was employed to monitor the degradation profile of by-products, including desthiomethyl Terbutryn, 2-hydroxy Terbutryn, and cyanuric acid. The analysis of these degradation products facilitated the proposal of a degradation pathway for Terbutryn. PEF stands out as a viable approach for TBT removal, especially in high-TBT wastewater.

In this study, anodic oxidation (AO) was evaluated using Ti/IrO₂–SnO₂–Sb₂O₅ electrodes in chloride, sulfate, and mixed electrolytes, along with electro-Fenton (EF) and photoelectro-Fenton (PEF) at pH 3.0, for the degradation of alachlor and isoproturon, each 50 mg L⁻¹. Active chlorine species were monitored using UV–Vis, while the removal of both herbicides was quantified using High Performance Liquid Chromatography (HPLC), along with the reduction in Total Organic Carbon (TOC), mineralization current efficiency (MCE), and specific energy per TOC removed (EC_TOC). The results show that electrolyte composition influences AO more than current density. In a chloride medium, isoproturon was eliminated within minutes, whereas alachlor required mixed electrolytes of Cl⁻/SO₄²⁻, allowing simultaneous combination of HClO/ClO⁻, ^●OH, and S₂O₈²⁻/SO₄^●−, or coupling with EF. An optimal current density of ~30 mA cm⁻² limited voltage rise and radical scavenging. EF introduced measurable mineralization (15% TOC), whereas PEF achieved rapid alachlor reduction and TOC reductions of up to 76% at low Fe²⁺. Overall, sequential AO followed by PEF maximized mineralization per unit of energy, and the mixed electrolytes provided a controllable pathway to scale up oxidant speciation generation.

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.