MULTIVARIATE STATISTICAL ANALYSIS AND USE OF GEOGRAPHIC INFORMATION SYSTEMS IN RAW WATER QUALITY ASSESSMENT

Henrique Kosby Corrêa; Roberta Plangg Riegel; Darlan Daniel Alves; Daniela Montanari Migliavacca Osório; Gustavo Marques da Costa; Chaudhery Mustansar Hussain; Daniela Müller de Quevedo

doi:10.5327/Z2176-947820190431

Authors

Henrique Kosby Corrêa Universidade FEEVALE https://orcid.org/0000-0003-4560-3641
Roberta Plangg Riegel Universidade FEEVALE https://orcid.org/0000-0002-0267-1671
Darlan Daniel Alves Universidade FEEVALE https://orcid.org/0000-0002-2633-2033
Daniela Montanari Migliavacca Osório Universidade FEEVALE https://orcid.org/0000-0002-9923-4514
Gustavo Marques da Costa Universidade Feevale https://orcid.org/0000-0002-7343-8527
Chaudhery Mustansar Hussain New Jersey Institute of Technology https://orcid.org/0000-0002-1964-4338
Daniela Müller de Quevedo Universidade FEEVALE https://orcid.org/0000-0003-2169-9781

DOI:

https://doi.org/10.5327/Z2176-947820190431

Keywords:

principal component analysis; Spearman’s rank correlation coefficient; water pollution; land use.

Abstract

This study aimed to apply a methodology for evaluating raw water quality
and its relationship with land uses and occupations through multivariate
statistical analysis and Geographic Information System. Hydrogenic potential,
water temperature, dissolved oxygen, biochemical oxygen demand,
chemical oxygen demand, total nitrogen, total phosphorus, and E. coli were
monitored from August 2012 until March 2013. The geoprocessing tool
enabled delimiting the contribution areas of each sampling site, as well as
the individual identification of land use of each area. Principal Component
Analysis resulted in: domestic sewage, domestic sewage/agriculture, and
industrial discharge. Significant correlations were identified between the
variable urban area and hydrogenionic potential (ρ = 0.446; p = 0.049),
dissolved oxygen (ρ = -0.625; p = 0.003), total nitrogen (ρ = 0.649; p = 0.002),
and E. coli (ρ = 0.932; p < 0.001). The methodology enabled to identify the
contribution of land use factors as to water quality.