Juan Antonio Torres-Martínez

Unsafe water sources contaminated with fecal bacteria and antibiotic-resistant pathogens represent a critical public health challenge in rapidly urbanizing regions. This study investigates the occurrence, distribution, and antimicrobial resistance (AMR) patterns of Escherichia coli across interconnected water sources in Guwahati, India, including river water, wells, municipal supply, and groundwater. A total of 87 samples were analyzed using membrane filtration, biochemical identification, and automated confirmation, followed by antibiotic susceptibility testing and phenotypic detection of β-lactamase production. Total coliforms were detected in 64.4% of samples, while E. coli was confirmed in 51.7%. According to World Health Organization risk thresholds, 93.3% of river water samples and 50% of groundwater samples fell within the very-high-risk category (>1000 CFU/100 mL), indicating widespread fecal contamination in sources used for domestic purposes. Antibiotic-resistant E. coli was identified across all water types, with 14.1% of isolates exhibiting multidrug resistance (MAR index > 0.2). Notably, 20.6% of isolates produced β-lactamases, including one extended-spectrum β-lactamase (ESBL) producer, while no carbapenemase-producing strains were detected. The co-occurrence of multidrug-resistant E. coli across surface and groundwater systems is consistent with the interconnected nature of urban water contamination and suggests the potential for widespread exposure through drinking water pathways, though direct transfer between water systems was not experimentally confirmed in this study. These findings underscore the urgent need for improved wastewater management, protection of groundwater resources, and strengthened antibiotic stewardship within a One Health framework to mitigate the environmental dissemination of AMR.

Across the semiarid landscapes of northern Mexico, groundwater systems that sustain both regional food production and population centers are approaching critical thresholds owing to sustained overextraction and widespread nitrate contamination. The Meoqui–Delicias aquifer (MDA) exemplifies this challenge, supplying water to approximately 265,000 people, one of the country’s largest irrigation districts, and more than 90,000 head of cattle while experiencing a severe groundwater deficit of ~165 Mm3·yr-1. Moreover, 82% of the sampled wells exceeded the natural nitrate background levels (>3 mg L-1 as N), raising concerns regarding drinking-water security and ecosystem health.Identifying nitrate sources in such systems is inherently challenging because of the overlapping isotopic signatures of manure, sewage, synthetic fertilizers, and soil nitrogen, further complicated by active biogeochemical transformations. To address this complexity, groundwater samples were first classified using hydrochemical clustering based on self-organizing maps, which revealed two statistically coherent groups. Bayesian mixing models were then constrained using robust isotope end members from the literature. Each model was run with 300,000 MCMC iterations (200,000 burn-in, thinning interval of 100, three parallel chains), with convergence verified using Gelman–Rubin, Heidelberg–Welch, and Geweke diagnostics. A systematic sensitivity analysis (±10–20% perturbations of source signatures) demonstrated the greater robustness and stability of the δ15N vs δ11B model compared to the conventional δ15N vs δ18O pairing model.The results revealed a marked contrast between the isotopic approaches. The traditional δ15N vs δ18O model aggregates manure and sewage as the dominant combined source (65 ± 20%, ~4.5 mg L-1 N), with secondary contributions from soil nitrogen (22 ± 19%) and fertilizers (13 ± 14%). In contrast, the incorporation of boron isotopes effectively resolved source overlap, identifying livestock manure as the primary contributor (52 ± 12%, ~3.5 mg L-1 N), followed by soil nitrogen (37 ± 14%), with minor inputs from fertilizers (6 ± 8%) and negligible sewage contributions (5 ± 7%). Isotopic evidence further indicated that nitrification dominated nitrogen cycling in approximately 60% of the samples, whereas denitrification was restricted to riparian zones. Stable water isotopes confirm that meteoric recharge is modified by evaporation during irrigation return flows.Overall, this study demonstrates that boron-enhanced Bayesian isotope mixing models provide a robust and transferable framework for nitrate source apportionment in complex semiarid aquifers, delivering quantitative discrimination where conventional isotope approaches remain ambiguous and offer direct relevance for targeted groundwater management and nutrient-mitigation strategies.

Coastal aquifers are critical freshwater resources that are increasingly threatened by seawater intrusion driven by the combined impacts of climate change and intensive human activities. The introduction of harmful substances, rising sea levels and groundwater overextraction increase salinization processes. Coastal Groundwater contamination is a complex environmental challenge that requires robust, scalable tools for reliable assessment and prediction. This talk presents recent advances in data-driven approaches for groundwater contamination analysis, with a particular focus on the application of probabilistic, supervised and unsupervised learning techniques in coastal aquifer systems. Drawing on case studies from arid to semi-arid regions of Mexico and Peru, the presentation demonstrates how Bayesian networks, clustering algorithms and random forest models can be applied to multi-parameter hydrogeological and hydrochemical datasets to identify sources of salinization and improve the characterization of seawater intrusion processes. These approaches have proven effective in handling data scarcity and uncertainty. By incorporating artificial intelligence and probabilistic frameworks into hydrogeological assessments, the proposed methods enhance contaminant source identification, support the development of early-warning tools, and enable more informed decision-making. The talk concludes with recommendations for advancing groundwater sustainability through interdisciplinary, data-driven strategies.

Tracer hydrology in Latin America and the Caribbean has made significant progress in recent decades, largely through the sustained support of the International Atomic Energy Agency (IAEA). Current practises show water stable isotope applications and precipitation‐groundwater monitoring at the core of most networks, providing valuable insights into recharge mechanisms, groundwater to surface water connectivity, pollution tracking, and climate variability. Despite these advances, critical challenges persist, including short and fragmented monitoring records, limited capture of extreme events, restricted data accessibility, and persistent barriers related to funding, analytical capacity, and weak policy integration. Improving science communication emerges as an urgent need to transform technical findings into actionable knowledge that informs decision‐makers and empowers communities. Opportunities exist to build on IAEA's legacy by sustaining long‐term networks, diversifying tracer applications, mobilising citizen science in monitoring efforts, expanding modelling and laboratory capacity, and advocating for FAIR data sharing across end‐users. Strengthened collaboration across the region, improved communication, and deeper policy engagement can elevate tracer hydrology into a pillar of regional water governance and hydro‐climate resilience.

Tracer hydrology in Latin America and the Caribbean has made significant progress in recent decades, largely through the sustained support of the International Atomic Energy Agency (IAEA). Current practices show water stable isotope applications and precipitation-groundwater monitoring at the core of most networks, providing valuable insights into recharge mechanisms, groundwater to surface water connectivity, pollution tracking, and climate variability. Despite these advances, critical challenges persist, including short and fragmented monitoring records, limited capture of extreme events, restricted data accessibility, and persistent barriers related to funding, analytical capacity, and weak policy integration. Improving science communication emerges as an urgent need to transform technical findings into actionable knowledge that informs decision-makers and empowers communities. Opportunities exist to build on IAEA’s legacy by sustaining long-term networks, diversifying tracer applications, mobilizing citizen science in monitoring efforts, expanding modeling and laboratory capacity, and advocating for FAIR data sharing across end-users. Strengthened collaboration across the region, improved communication, and deeper policy engagement can elevate tracer hydrology into a pillar of regional water governance and hydro-climate resilience.