Análisis de la calidad de aguas de vertido en el Distrito de Riego Arenal-Tempisque (DRAT)
The Arenal-Tempisque Irrigation District (DRAT by its acronym in Spanish) is the government agency that regulates the distribution of irrigation water in the agricultural farms in the province of Guanacaste. The DRAT monitors the wastewater quality used in irrigation every six months, through operat...
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| Autores principales: | , |
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| Formato: | Online |
| Lenguaje: | spa |
| Publicado: |
Universidad de Costa Rica
2021
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| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/ingenieria/article/view/44123 |
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| Sumario: | The Arenal-Tempisque Irrigation District (DRAT by its acronym in Spanish) is the government agency that regulates the distribution of irrigation water in the agricultural farms in the province of Guanacaste. The DRAT monitors the wastewater quality used in irrigation every six months, through operational reports. However, it isn’t a common practice in irrigation districts to develop studies where the parameters behavior is analyzed over time.
This study developed a multivariate and geostatistical evaluation for the characterization of two microbiological and twenty physical-chemical variables of water quality and related to spatial components such as location, area and distance. Besides, a descriptive statistical analysis was carried out for 12 sampling points evaluated in a period of ten years (2008-2018). A Principal Component Analysis (PCA) was applied, where eigenvector values did not show appreciable differences to visualize the weight of the variables independently. However, the analysis allowed grouping the parameters that explain the behavior within the studied system such as: the concentration of salts (CP1); the active substances to methylene blue and the content of total nitrates (CP2); and the biological dynamics (CP3); that can be related to agricultural practices.
In addition, a hierarchical cluster analysis was carried out for the locations under study and a regression of partial least squares (PLS2) with respect to the geostatistical variables, obtaining that the results of both tests were in agreement with the PCA.
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