Indirect caffeine modeling in an urban river

  • Luis Otávio Miranda Peixoto Departamento de Hidráulica e Saneamento. Universidade Federal do Paraná (UFPR), Avenida Coronel Francisco H. dos Santos, n° 100, Bloco H, CEP: 81530-000, Curitiba, PR, Brazil.
  • Luana Mayumi Takahasi Marques Departamento Acadêmico de Química e Biologia. Universidade Tecnológica do Paraná (UTFPR), Rua Deputado Heitor de Alencar Guimarães, n° 5000, Bloco EC, CEP: 81280-340, Curitiba, PR, Brazil.
  • Alinne Mizukawa Departamento de Hidráulica e Saneamento. Universidade Federal do Paraná (UFPR), Avenida Coronel Francisco H. dos Santos, n° 100, Bloco H, CEP: 81530-000, Curitiba, PR, Brazil.
  • Julio Cesar Rodrigues de Azevedo Departamento Acadêmico de Química e Biologia. Universidade Tecnológica do Paraná (UTFPR), Rua Deputado Heitor de Alencar Guimarães, n° 5000, Bloco EC, CEP: 81280-340, Curitiba, PR, Brazil.

Abstract

Caffeine is used worldwide as a chemical tracer to identify anthropic pressures on urban water resources. Nevertheless, its quantification demands great financial investments. This research created a model that would indirectly determine a range of possible caffeine concentrations along an urban river, without the need for extensive laboratory work. The model is based on Canonical Correlation Analysis (CCA), which can correlate two sets of different-sized independent and dependent variables in order to generate a single empirical equation. This equation takes as input the concentrations of ammonia nitrogen and orthophosphate, as well as the total population and the population inhabiting irregular housing areas. From the model’s results, it was possible to elaborate a spectrum of possible concentrations of caffeine along the Atuba River (Curitiba-Brazil). The tendency of water quality degradation of this river was also predicted. This model could become a useful preliminary analysis for water resource managers and researchers alike.

Keywords: caffeine, canonical correlation analysis, water quality modeling.


Published
29/03/2022
Section
Papers