Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Seawater intrusion pattern recognition supported by unsupervised learning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-13) Narváez Montoya, Christian Felipe; Mahlknecht, Jürgen; emimmayorquin; Mora Polanco, Abrahan Rafael; Bertrand, Guillaume; Bonasia, Rosanna; School of Engineering and Sciences; Campus Monterrey; Torrés Martínez, Juan AntonioClimate change and anthropogenic activities have negatively affected the world's water resources in the last 200 years. Seawater intrusion, one of the leading causes of groundwater contamination, particularly affects coastal systems. Coastal aquifers are naturally connected to seawater, with the saltwater forming a wedge beneath the freshwater due to the difference in densities. Seawater migrates further inland when the aquifer hydraulic head decreases concerning the sea level, and is driven by groundwater overexploitation or sea level rise. The resulting salty water is extracted for public water supply, irrigation, or industrial purposes. Thus, monitoring and understanding this process is essential for developing appropriate water management strategies. Environmental tracers, such as the major ions (Mg2+, Ca2+, Na+, K+, SO4 2-, HCO3-, NO3-, and Cl-), are recognized as valuable tools for identifying seawater intrusion and other salinization sources. In this context, unsupervised learning has supported the multivariate analysis to characterize the variability and range of major ions at different spatial and temporal scales. However, complex case studies with multiple processes triggering high salinity concentrations make pattern recognition of salinization sources challenging with traditional unsupervised techniques. This research identifies seawater intrusion and triggering factors for two complex case studies: the Caplina/Concordia aquifer system in the hyper-arid Atacama Desert and the Yucatan Peninsula, one of the world's largest coastal karst lowland areas. For this, novel network-based clustering was applied to major ions water quality datasets sourced from governmental institutions. The outcomes show that the Caplina/Concordia water samples associated with seawater intrusion were identified up to 5.5 km inland in zones with hydraulic heads less than 6 m.a.s.l. These findings align with a developed hydrogeological model and underscore overexploitation as a key driver for seawater intrusion. On the other hand, in the Yucatan Peninsula, water samples were indicated to be associated with seawater intrusion in zones with hydraulic heads less than 5 m.a.s.l. The natural extensive seawater wedge is the product of low hydraulic gradients, facilitating the extraction of seawater-groundwater mixture (upconing) to more than 100 km from the coast. Furthermore, gypsum dissolution and nitrate pollution are also critical concerns for water quality in the peninsula. Additionally, the thesis advocates for improved open science in water research, urging journals and researchers to make raw data publicly available.
- Assessment of nitrate and sulfate contamination in groundwater using isotopic and hydrogeochemical tools in three aquifer systems of Northern Mexico(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12-01) Torres Martínez, Juan Antonio; MAHLKNECHT, JURGEN; 120939; Mahlknecht, Jürgen; qro /|bqrotbecerra, emipsanchez; Ramírez Orozco, Aldo Iván; Aguilar Barajas, Ismael; Knappett, Peter; Escuela de Ingeniería y Ciencias; Campus Monterrey; Mora Polanco, Abrahan RafaelNitrate and sulfate comprise a significant portion of ionic charge in most natural waters. Groundwater pollution from nitrate is one of the most prevalent environmental problems. Over the past decades, groundwater quality has deteriorated worldwide due to the intensive use of fertilizers in agriculture, the release of untreated urban sewage and industrial wastewater (e.g., mining, smelting, steel manufacturing, kraft pulp, paper mills, and flue gas desulphurization circuits), and natural sources (natural fertilization, bacterial production, atmospheric deposition). These pollution sources contributed to adverse human and biota effects. Furthermore, arid or semi-arid areas are mainly dependent on groundwater resources, which, together with accelerated population growth, generates water stress and often leads to groundwater quality deterioration. To assess these issues, the origin and biogeochemical transformations of nitrate and sulfate in groundwater have been widely studied since the 1970s. A successful tool for tracing pollution sources is the use of the dual-stable isotopic compositions of nitrate (δ15NNO3 and δ18ONO3) and sulfate (δ34SSO4 and δ18OSO4). Unfortunately, despite the ability of the dual-isotope plot to trace the origin of NO3- and SO42- contamination, uncertainties remain because two or more sources may sometimes overlap, hindering the correct differentiation of the origin. For this reason, this Ph.D. research aims to identify and quantify nitrate and sulfate sources in groundwater within three semi-arid areas of Northern Mexico with multiple potential sources using a multi-tracer approach combined with a Bayesian isotope mixing model. The study cases were a highly urbanized industrial area (Monterrey Metropolitan Area), an intensive livestock-agricultural area (Comarca Lagunera Region), and a coastal agricultural aquifer (La Paz aquifer). The approach followed in this research is a useful tool for estimating the contribution of different nitrate and sulfate sources, allowing establishing effective pollution management strategies in contaminated aquifers.