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Mapping and modelling of public supply water quality at Northeastern São Paulo State

Grant number: 14/10034-2
Support Opportunities:Regular Research Grants
Duration: October 01, 2014 - September 30, 2016
Field of knowledge:Health Sciences - Collective Health - Public Health
Principal Investigator:Sergio Dovidauskas
Grantee:Sergio Dovidauskas
Host Institution: Instituto Adolfo Lutz (IAL). Coordenadoria de Controle de Doenças (CCD). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil


The Attention to Health Regional Net 13 includes the northeastern São Paulo State, involving four Health Regional Departments (Araraquara, Barretos, Franca, and Ribeirão Preto). In this region of 3,3 million inhabitants distributed in 90 municipal districts and that is considered under industrialization process, the water supply is achieved from subterranean source (70% of the municipal districts), surface source (15%), or both (15%). The groundwater can be accessed from 3 aquifers (Bauru, Serra Geral e Guarani) and 1 aquitard (Passa Dois), and the surface water may have more diverse origin by virtue of regional hydrography. While several hydrogeochemical and environmental studies involving hydric resources have been established in SãoPaulo State, this research proposal attempts to approach the Public Health perpesctive looking for situations that could be classified as at risk or harm through mapping and modelling of physico-chemical and microbiological data from water analysis results furnished by Drinking Water Quality Surveillance Program of the São Paulo State. Currently this Program examines 8 parameters (temperature, pH, free residual chlorine, apparent colour, turbidity, fluoride, total coliform, Escherichia colli), and 17 new parameters (conductivity, total dissolved solids, nitrate, nitrite, ammonium, cloride, clorite, clorate, bromide, bromate, dichromate, sulfate, orthophosphates, magnesium, calcium, sodium, potassium) will be added and examinated in this work. These 25 parameters will be investigated simultaneously with available data of other variables (demographic, geographical, sanitation, hydric resources, and industrial activity). In mapping, that will be concerned with pattern recognition and classification, the cluster analysis and principal component analysis should be initially used. Among several available techniques in modelling, the linear discriminant analysis and the SIMCA method (Soft Independent Modelling of Class Analogy) wil be used a priori. (AU)

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