Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers

JURADO ZAVALETA, MARCELO A.; ALCARAZ, MIRTA R.; PEÑALOZA, LIDIA G.; BOEMO, ANALÍA; CARDOZO, ANA; TARCAYA, GERARDO; AZCARATE, SILVANA M.; GOICOECHEA, HÉCTOR C.

MICROCHEMICAL JOURNAL

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2021 vol. 162

0026-265X

In Argentina, both surface and ground water are used for a diverse priority purposes, such as drinking and basichygiene, but they are also utilized as receivers of different types of industrial and urban and suburban effluentsthat affect their natural composition. This activity accompanied by the increase of the population and climatechanges have activated the alarms of organism water management forced to implement strict quality controlsprevious to its use. In this work, a systematic evaluation of a set of physicochemical and biological parametersmeasured in 19 sampling sites during the period 2017?2019 is presented. Principal component analysis (PCA)and matrix augmentation-PCA (MA-PCA) were applied as exploratory analysis tools to visualize and interpret theinformation contained in the dataset. Both studies allowed to detect the relevant variables and to differentiate thesamples based on pollution areas. These models led to similar conclusions; nonetheless, MA-PCA provided a morestraightforward overview of the spatiotemporal variation of the samples in comparison to classical PCA. Finally,a significant and sensitive discriminant model (93% non-error rate) was developed to analyze and predict theself-depuration of the rivers. The excellent predictive ability achieved by this model makes its applicationsuitable for the monitoring of the water quality.