Chemometric assessment of spatial and temporal variations in water quality of an artificial water reservoir in the semiarid Midwest of Argentina

CID, F. D.; ANTÓN, R. I.; PARDO ALMUDÍ, R.; CAVIEDES-VIDAL E.

Antwerp, Belgium

Congreso; XII International Conference on Chemometrics in Analytical Chemistry; 2010

University of Antwerp

The Embalse La Florida, a water reservoir (33‹07Œ S - 66‹02Œ W; 1030 m.a.s.l.; surface area: 651.86 ha; water capacity: 100.97 hm3) located on the Rio Quinto basin in the San Luis province, Argentina, is an important part of the largest water system of this province. It provides drinking water to around 70 % of the human population of the San Luis province and irrigation water to one-fifth of the territory of this province. Main tributaries are the Rio Grande and Rio Trapiche rivers. The shores have different degrees of human disturbance, the northern coast is a low perturbed area, whereas the southern coast is highly perturbed by human activities. Dam refill depends mainly on water rainfall along the basin. Rainfall regime is seasonal in this semiarid region of Argentina. It comprises summer rainy season (October-April) and a winter dry period (May-September). This seasonality of rainfall has strong effects on the tributary rivers discharge and subsequently on the concentration of pollutants and compounds in the reservoir. The main objective of this work is to analyze spatial and temporal variation of the water quality of the Embalse la Florida water reservoir using chemometric techniques. Water samples were collected during 1998, 1999 and 2000 from 8 points along the reservoir for both, the rainy and dry seasons. Parameters and analytes determined were: air and water temperature, turbidity, pH, conductivity, alkalinity, hardness, calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride, sulphate, nitrate, phosphate, fluoride, nitrite, ammonium, iron, chromium, arsenic, lead cadmium, COD, BOD, and bacteriological studies. Data matrix (320 observations) was analyzed using Principal Component Analysis. True n-dimensional PCA methods, such as PARAFAC or TUCKER3, allowed clear and significant grouping of the observations at the different sampling points (8), analytes or chemical parameters (19), physical parameters (7) and bacteriological data (1). In conclusion, models derived from these multivariate statistical analyses, demonstrated to be powerful and useful tools for understanding the spatial and temporal variations in water quality in this reservoir.