4.6 Article

Statistical and geostatistical spatial and temporal variability of physico-chemical parameters, nutrients, and contaminants in the Tenango Dam, Puebla, Mexico

期刊

JOURNAL OF GEOCHEMICAL EXPLORATION
卷 209, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.gexplo.2019.106435

关键词

Reservoirs; Kriging interpolation; Geostatistics; Water quality

资金

  1. Indicadores de integridad ecologica y salud Ambiental 2014-2018 project, from the Universidad Autonoma Metropolitana (UAM)
  2. CONACYT, Mexico

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Human activities such as farming have contributed to the contamination of water bodies. Studying the spatial and temporal variations of parameters that influence water quality is essential for determining whether the water is suitable for human consumption and the ecosystem. This work presents the results from a study of the Tenango Dam, Mexico, which was aimed at determining the spatial and temporal variability of water quality parameters. To this end, five field visits were conducted during 2015. A total of 34 georeferenced water collection stations were installed in order to analyze physico-chemical parameters, nutrients, and metals, namely: temperature, pH, dissolved oxygen, and hardness; nitrates (NO3 (-)), nitrites (NO2-), and phosphorus (P); and cadmium (Cd), chromium (Cr), copper (Cu), and lead (Pb). Statistical analyses were performed in order to determine seasonal variations in these parameters and geostatistical methods (ordinary [OK] and universal kriging [UK]) were applied to define their spatial variability. The functioning of these prediction methods were evaluated with leave-one-out cross validation. The results indicate that the physico-chemical parameters do not present a problem for the dam's water system. Nevertheless, NO3-, NO2-, and metal levels indicate poorer water quality, making it unsuitable for human consumption. The geostatistical analysis shows that seasonality and the anthropogenic activities occurring in the area contributed to increasing or decreasing the values of the variables studied, as well as the geostatistical models' ability to capture their spatial variability patterns.

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