Deterministic and stochastic survival models of injured ozonated Giardia cysts

Micha Peleg1

  • 1Department of Food Science, University of Massachusetts, Amherst, MA, 01003, USA. micha.peleg@foodsci.umass.edu.

Insights

Delayed inactivation of Giardia cysts after ozone disinfection can be modeled using modified microbial survival models. Both deterministic and stochastic approaches, including Weibull and lognormal distributions, accurately describe cyst mortality patterns.

Area of Science:

  • Environmental microbiology
  • Water treatment technologies
  • Public health

Background:

  • Giardia cysts exhibit delayed inactivation post-ozonation, a phenomenon not explained by traditional disinfection models.
  • Sublethal ozone exposure can injure cysts, leading to prolonged mortality after ozone dissipation.

Purpose of the Study:

  • To investigate and model the delayed inactivation of Giardia cysts following sublethal ozonation.
  • To adapt microbial survival models for injured microorganisms and assess their applicability to Giardia cyst inactivation.

Main Methods:

  • Analysis of Giardia cyst survival data after exposure to sublethal ozone levels in lake water.
  • Application of modified general microbial survival models, including Weibull and lognormal distribution functions (CDF).
  • Development and application of a stochastic model based on a Markov chain for injured cysts.

Main Results:

  • Delayed inactivation of Giardia cysts was observed, extending beyond ozone dissipation.
  • Weibull and lognormal distribution functions provided an excellent fit to the experimental survival data.
  • A stochastic model, with a linearly increasing mortality rate, accurately described the survival patterns and explained increasing scatter in survival curves.

Conclusions:

  • Modified deterministic and stochastic models effectively describe delayed microbial inactivation, particularly for injured microorganisms.
  • The Weibull and lognormal distributions are suitable for modeling Giardia cyst mortality times.
  • The stochastic model provides a framework for understanding the variability in microbial survival curves during disinfection processes.