Estudio de tratabilidad y coexposición a arsénico y fluoruros en agua de bebida

María de Lourdes Rivera Huerta Juana Enriqueta Cortés Muñoz Alejandra Martín-Domínguez FABIÁN ALMAZÁN GARCÍA (2013)

Se llevó a cabo un estudio de tipo ecológico (encuesta y datos de monitoreo del agua), a fin de evaluar la co-exposición a estas especies químicas y su posible asociación con las tasas de mortalidad por enfermedades endócrinas. Con la información disponible para cuatro municipios del estado de Guanajuato y once de Hidalgo, se estimó que el agua de consumo, aporta al organismo humano entre el 56 y el 78% de los fluoruros, y prácticamente el 100% del arsénico. Aunque este tipo de estudios no son suficientemente robustos para determinar causalidad, se identificaron asociaciones significativas entre las concentraciones de arsénico y fluoruros en agua y las tasas de mortalidad debidas a enfermedades endócrinas y metabólicas, Diabetes mellitus tipo II, enfermedades hipertensivas y del aparato urinario e insuficiencia renal, lo que evidencia las necesidades de tratamiento del agua para consumo humano.

Working paper

Flúor Arsénico Agua potable Tratamiento del agua Estudios de factibilidad Guanajuato Hidalgo INGENIERÍA Y TECNOLOGÍA

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Mustafa Kamal Timothy Joseph Krupnik (2024)

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

Article

Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE