Integrated water system modelling to support water management in the Cuenca Basin

En este capítulo se evalúa la calidad biológica del agua en relación con los productos químicos descargados a través de la descarga de aguas residuales durante la estación seca y lluviosa. El área de tierras bajas de la cuenca del río Cuenca en los Andes del sur del Ecuador, incluida la ciudad de Cuenca, constituyó el área de estudio. Para realizar una evaluación integrada de la calidad del agua, se recolectaron datos de macroinvertebrados, condiciones fisicoquímicas y características morfológicas en 43 sitios en el río Cuenca y sus afluentes. Para evaluar la calidad biológica del agua se utilizaron el Índice Andino de Bióticos (ABI) y el Grupo de Trabajo de Monitoreo Biológico adaptado a Colombia (BMWP-Col). Ambos índices biológicos fueron más altos aguas arriba que aguas abajo de la ciudad. Además. Estos índices indicaron mejores condiciones durante la estación lluviosa que en la estación seca, debido a la presencia de familias más sensibles. Los índices biológicos se relacionan más con la saturación de oxígeno que con la demanda biológica de oxígeno (DBO5) de cinco días, las concentraciones de nutrientes y cloruro. La relación entre DBO5 y las concentraciones de nutrientes con la variación de ambos índices biológicos fue más clara en la estación seca que en la lluviosa. Sin embargo, en algunos sitios, estos índices fueron influenciados más por aspectos morfológicos que por contaminantes. Ambos índices biológicos muestran patrones similares a lo largo de los ríos, generalmente el BMWP-Col obtuvo una puntuación más alta que el índice ABI. Se demostró que el índice posterior era más adecuado para la región de los altos Andes como indicador de la calidad del agua. Estos resultados podrían usarse para monitorear la implementación de acciones de restauración de ríos, como determinar las prioridades para dividir los sistemas de transporte de agua de alcantarillado y precipitación y las necesidades de mejores instalaciones de tratamiento de aguas residuales en ubicaciones específicas.In this chapter is evaluated the biological water quality in relation to chemicals discharged through sewage outfall during doth dry and rainy season. The lowland area of the Cuenca River basin in the southern Andes of Ecuador, including the city of Cuenca, constituted the study area. To perform an integrated water quality assessment, date were collected of macroinvertebrates, physicochemical conditions and morphological characteristics in 43 sites in the Cuenca River and its tributaries. The Andean Biotic Index (ABI) and the Biological Monitoring Working Party adapted to Colombia (BMWP-Col) were used to evaluate the biological water quality. Both biological indexes were higher upstream than downstream form the city. Moreover. These indexes indicated better conditions during the rainy season than in the dry season, based on the presence of more sensitive families. The biological indexes related more to the oxygen saturation than to the five-day biological oxygen demand (BOD5), nutrients and chloride concentrations. The relationship between BOD5 and nutrient concentrations with the variation of both biological indexes was clearer in the dry season than in the rainy season. However, in some sites, these indexes were influenced more by morphological aspects than by pollutants. Both biological indexes shoed similar patterns along the rivers, generally the BMWP-Col scored higher than the ABI index. The later index was shown to be more suitable for the high Andes region as an indicator of water quality. These results could be used to monitor the implementation of river restoration actions, such as determining priorities for splitting sewer and precipitation water transport systems and needs for improved wastewater treatment facilities in specific locations.Doctor of Applied Biological SciencesGhen

Effects of land use and water quality on greenhouse gas emissions from an urban river system

Rivers act as a natural source of greenhouse gases (GHGs) that can be released from the metabolisms of aquatic organisms. Anthropogenic activities can largely alter the chemical composition and microbial communities of rivers, consequently affecting their GHG emissions. To investigate these impacts, we assessed the emissions of CO2, CH4, and N2O from Cuenca urban river system (Ecuador). High variation of the emissions was found among river tributaries that mainly depended on water quality and neighboring landscapes. By using Prati and Oregon Indexes, a clear pattern was observed between water quality and GHG emissions in which the more polluted the sites were, the higher were their emissions. When river water quality deteriorated from acceptable to very heavily polluted, their global warming potential (GWP) increased by ten times. Compared to the average estimated emissions from global streams, rivers with polluted water released almost double the estimated GWP while the proportion increased to ten times for very heavily polluted rivers. Conversely, the GWP of good-water-quality rivers was half of the estimated GWP. Furthermore, surrounding land-use types, i.e. urban, roads, and agriculture, significantly affected the river emissions. The GWP of the sites close to urban areas was four time higher than the GWP of the nature sites while this proportion for the sites close to roads or agricultural areas was triple and double, respectively. Lastly, by applying random forests, we identified dissolved oxygen, ammonium, and flow characteristics as the main important factors to the emissions. Conversely, low impact of organic matter and nitrate concentration suggested a higher role of nitrification than denitrification in producing N2O. These results highlighted the impacts of land-use types on the river emissions via water contamination by sewage discharges and surface runoff. Hence, to estimate of the emissions from global streams, both their quantity and water quality should be included

Simulación para Estimación de Muertes por Cáncer de Pulmón por Contaminación Ambiental de PM2.5. //Simulation to estimate deaths from lung cancer due to environmental contamination of PM2.5

El objetivo de este estudio es estimar el número de muertes por cáncer de pulmón  provocadas por la contaminación ambiental debida a la exposición de las personas a material particulado fino menor a 2.5 µm (PM2.5).  Para cumplir con este fin, se realizó un estudio con enfoque deductivo en el que se efectuaron simulaciones del modelo de evaluación de la morbilidad ambiental desarrollado por la Organización Mundial de la Salud.  Se evaluó la exposición de la población a la contaminación por PM2.5, basado en datos monitoreados en 12 estaciones de calidad de aire del Distrito Metropolitano de Quito en los grupos de población expuestas a PM2.5, y la incidencia  en la salud, estimada en la tasa de mortalidad en la población. Para el período de análisis 1990-2020 el total de muertes por neoplasias pulmonares es de 3058 ± 24 de los cuales 523 ± 32 se asociarían con las concentraciones de PM2.5; equivalente al 17.1%, CI=95% [15.9%-18.3%] y un Riesgo Relativo de 1.2046 [1.0688, 1.394]. Estos resultados fueron obtenidos a través de un software desarrollado para el efecto.   En conclusión, los valores obtenidos en la presente simulación se encuentran dentro del intervalo de confianza en relación a otros estudios similares.AbstractThe objective of this study is to simulate the estimation of the number of deaths from lung cancer caused by environmental pollution due to human exposure to fine particulate matter less than 2.5 µm (PM2.5). To achieve this goal, the study was conducted with deductive approach.  A simulation environmental model to assess morbidity developed by the World Health Organization was applied, based on population exposure to PM2.5 pollutant. This was done with data obtained from 12 air quality stations of the Metropolitan District of Quito and the population groups exposed to PM2.5, determining the impact on health.  The final simulation was calculated using the death rate in the population. For the period 1990-2020, the total number of deaths due to lung neoplasms was 3058 ± 24.  The number of these deaths associated to PM2.5 pollution was 523 ± 32, which supposes a Relative Risk of 523 ± 32, equivalent to  17.1%, CI=95% [15.9%-18.3%]. These results were obtained through software developed for this purpose. In conclusion, the values obtained in the present simulation are within the confidence interval of other similar studies