Calidad de agua de la quebrada Mamarramos. Santuario de Flora y Fauna Iguaque, Colombia

Cerón-Vivas, Alexandra; Gamarra, Yolanda; Villamizar, Maryory; Restrepo, Ricardo; Arenas, Reynaldo

doi:10.24850/j-tyca-2019-06-04

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Estos grupos tróficos se caracterizan por aferrarse a sustratos de distintos tamaños para alimentarse (Hamada et al, 2019), de esa forma estos grupos tróficos representan, la heterogeneidad y la estabilidad del tipo de sustrato, que es de interés cómo medida de la variación ambiental (Saal et al, 2020). La diversidad de macroinvetebrados es un buen indicador de calidad de agua, y posee mayor eficiencia cuando es incluido en índices integrales donde los macroinvertebrados se clasifican a nivel taxonómico de familia (Prat et al, 2009), de esta forma el número efectivo de familia de orden 2, que es una forma de determinación de la diversidad está incluido dentro del índice integral desarrollado, ofreciendo el potencial de proporcionar criterios de diversidad para la evaluación de la calidad del agua (Villamizar et al, 2019). Valor mínimo Fórmula, X = valor de la métrica NEF orden 2 5,4 0,03 100*(X/5,4) Tolerancia Ephemeroptera 0,3 0,10 100*(X/0,2) Tolerancia Trichoptera 0,3 0,02 100*(X/0,3)…”

Section: Diseño Del íNdice Integralunclassified

Construction of an integral index based on macroinvertebrates to determine the quality of water with agro-industrial influence

Niño-de-Guzman-Tito¹,

Vásquez-Ramos²

2022

Sci. agropecu

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The physicochemical and biological indices have been used in isolation; if the parameters of these indices were applied in an integrated manner, they would bring together in a single measure the functional and structural variability of the biotic and abiotic components of water quality. The aim of this study was to build a comprehensive water quality index. Eleven sampling points were selected considering different degrees of agro-industrial intervention. 21 abiotic variables and 27 biological metrics were measured. Macroinvertebrates were quantitatively collected and identified to family taxonomic level. Using Principal Component Analysis, after standardization and exclusion of uncorrelated variables (VIF ≤ 10), the abiotic gradient was determined, which represented the abiotic variables that explained the disturbances in the water; with the abiotic gradient and the biological metrics, a Pearson correlation was performed, and those biological metrics that presented a high and non-redundant correlation were selected (Pearson 0.6 ≤ r ≤ 0.8); with the selected biological metrics, we proceeded to formulate and categorize the index; finally, by means of simple linear regression, the proposed index was compared with five other indexes (ICA, ICOMO, EPT, BMWP/col. and ASPT). The results showed that the abiotic gradient was defined by CP 1 which explained 65.5% of the accumulated variance, represented by altitude (r = 0.411), iron (r = 0.345) and dissolved oxygen (r = 0.329). The biological metrics used for the index design were: % scrapers, % swimmers, NEF of order 2, Ephemeroptera and Trichoptera tolerance. It was concluded that the integral index presents a higher predictive level (R2 = 0.87) of water quality, compared to the other indices: ASPT (R2 = 0.79), BMWP/col. (R2 = 0.68), EPT (R2 = 0.61), ICOMO (R2 = 0.35) and ICA (R2 = 0.27).

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Section: Diseño Del íNdice Integralunclassified

Construction of an integral index based on macroinvertebrates to determine the quality of water with agro-industrial influence

Niño-de-Guzman-Tito¹,

Vásquez-Ramos²

2022

Sci. agropecu

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Effect of agricultural activities on surface water quality from páramo ecosystems

Rey-Romero

Domínguez

Oviedo-Ocaña

2022

Environ Sci Pollut Res

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Páramos are high mountain ecosystems strategic for water provision in South America. Currently, páramos are under threat due to agricultural intensification that impairs surface water sources. This research analyzed the effect of agriculture (spring onion— Allium fistulosum , potato— Solanum tuberosum , and livestock farming) on water quality in páramo ecosystems. A Hydrographic Unit upstream of the Jordan river catchment (Colombia) was selected and monitored in two different rainfall regimes, following the paired catchments and upstream-downstream approaches to compare water quality from natural and anthropic areas. Twenty-two parameters related to agricultural activities were analyzed (nutrients, salts, organic matter, sediments, and pathogens). The studied agricultural activities increased loads of surface water in quality in nitrates (0.02 to 2.56 mg N-NO 3 /L), potassium (0.13 to 1.24 mg K/L), and Escherichia coli (63 to 2718 FCU/100 mL), generating risks on the human health and promoting eutrophication. Total nitrogen and organic matter in the rainy season were higher than dry. BOD 5 , COD, turbidity, and E. coli were above international standards for direct human consumption. However, water could be used for irrigation, livestock watering, and aquatic life ambient freshwater. The results show that a small land-use change of almost 15% from natural páramo vegetation to agricultural uses in these ecosystems impairs water quality, limiting its uses, and the need to harmonize small-scale livelihoods in the páramo with the sustainability of ecosystem service provision. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-21709-6.

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Multivariate prediction of nitrogen concentration in a stream using regression models

2021

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Calidad de agua de la quebrada Mamarramos. Santuario de Flora y Fauna Iguaque, Colombia

Cited by 6 publications

References 8 publications

Construction of an integral index based on macroinvertebrates to determine the quality of water with agro-industrial influence

Construction of an integral index based on macroinvertebrates to determine the quality of water with agro-industrial influence

Effect of agricultural activities on surface water quality from páramo ecosystems

Multivariate prediction of nitrogen concentration in a stream using regression models

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