INVESTIGADORES
RAJAL Veronica Beatriz
congresos y reuniones científicas
Título:
Human Health Risks from Water-Borne Pathogens using Quantitative Microbial Risk Assessment (QMRA) in Arenales River, Argentina
Autor/es:
ARTI KUNDU; HUGO R. POMA; VERÓNICA B. RAJAL; STEFAN WUERTZ
Reunión:
Congreso; UC Global Health; 2012
Resumen:
The main objective of this research was to quantify the human health risks associated with specific water-borne pathogens in the Arenales River in Argentina using risk assessment techniques. The approach used to quantify risks is called Quantitative Microbial Risk Assessment (QMRA). QMRA is a multi-step computational approach. The water-borne pathogens included in the study were Giardia, Ascaris lumbricoides and Entamoeba coli in the Arenales River. In this analysis, the “Hockey-stick “distribution was used for fitting the parasites data. The QMRA approach was based on concentrations of pathogens in surface waters and calculations were done using the standard first-order Monte Carlo method. The end result was calculated both in terms of illness and infection. In addition, three modes of exposure were used in the study: ingestion by adults when swimming, ingestion by children when swimming, and inhalation by secondary contact. The scenario considered was 100 recreational users at a given recreational site for a given day and repeated for 1000 days. So, the 100 users faced the same pathogen concentration on any day but different concentrations on different days. The risk was calculated as Individual’s Illness Risk (IIR), which is a simple arithmetic mean. The risk calculated exceeded the current EPA criteria limit for HCGI (highly credible gastrointestinal illness), which is 0.8% for freshwater. Entamoeba coli had an IIR above the acceptable limit for children (2.863%) in the Arenales river. The risks associated with other protozoans were below HCGI criteria limits. This study can be used to assess which pathogens are most important in terms of human health risk assessment. Stakeholders and agencies involved can more accurately predict health risks and make informed decisions.