Uncertainty analysis of field trials of low-cost Bi-TiO2-P25 solar photocatalyst for sustainable water treatment

Ayushi Arora*, Giulio Calvani, Efthalia Chatzisymeon, Neil Robertson, Paolo Perona

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Solar photocatalysis has the potential to reduce chemical and microbial contaminants in water and make it safer for consumption in an effective and sustainable manner. This has been studied and proven well under laboratory-scale conditions. In our previous work, the developed solar photocatalyst, Bi- (Formula presented.) -P25 has been tested for its efficiency in reducing total coliform from natural waters under solar light. Along with the promising results (up to 99% reduction of total coliform and 99.9% reduction of Escherichia coli in 2 hours), the uncertainties due to environmental factors associated with the process were also observed. The reaction rate was largely impacted by the change in sunlight intensity over the treatment or different initial concentrations of contaminants in natural water. Therefore, it becomes essential to understand how the performance would be impacted in such varied conditions to predict the optimum results. This paper discusses how the treatment time could be impacted by uncertainties affecting either the kinetic rate constant, or the initial concentration of bacteria, or both, as well as external factors such as solar intensity and other randomly varying factors during the treatment. Mathematically exact results are derived and future development trends, and challenges are discussed while providing a prospective outlook for the deployment of solar photocatalysis at pilot scale.

Original languageEnglish
Article number1536359
JournalFrontiers in Environmental Science
Volume13
Early online date17 Mar 2025
DOIs
Publication statusE-pub ahead of print - 17 Mar 2025

Keywords / Materials (for Non-textual outputs)

  • derived distribution simulations
  • photocatalytic oxidation
  • solar photocatalysis
  • stochastic process
  • sustainable water treatment

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