TY - JOUR
T1 - Quantitative microbial risk assessment of a non-membrane based indirect potable water reuse system using Bayesian networks
AU - Zhiteneva, Veronika
AU - Carvajal, Guido
AU - Shehata, Omar
AU - Hübner, Uwe
AU - Drewes, Jörg E.
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - Risk-based approaches are used to define performance standards for water and wastewater treatment to meet health-based targets and to ensure safe and reliable water quality for desired end use. In this study, a screening level QMRA for a non-membrane based indirect potable reuse (IPR) system utilizing the sequential managed aquifer recharge technology (SMART) concept was conducted. Ambient removals of norovirus, Campylobacter and Cryptosporidium in advanced water treatment (AWT) steps were combined in a probabilistic QMRA utilizing Bayesian networks constructed in Netica. Results revealed that all pathogens complied with disease burden at the 95th percentile, and according to the assumptions taken about pathogen removal, Cryptosporidium was the pathogen with the greatest risk. Through systematic sensitivity analysis, targeted scenario analysis, and backwards inferencing, critical control points for each pathogen were determined, demonstrating the usefulness of Bayesian networks as a diagnostic tool in quantifying risk of water reuse treatment scenarios.
AB - Risk-based approaches are used to define performance standards for water and wastewater treatment to meet health-based targets and to ensure safe and reliable water quality for desired end use. In this study, a screening level QMRA for a non-membrane based indirect potable reuse (IPR) system utilizing the sequential managed aquifer recharge technology (SMART) concept was conducted. Ambient removals of norovirus, Campylobacter and Cryptosporidium in advanced water treatment (AWT) steps were combined in a probabilistic QMRA utilizing Bayesian networks constructed in Netica. Results revealed that all pathogens complied with disease burden at the 95th percentile, and according to the assumptions taken about pathogen removal, Cryptosporidium was the pathogen with the greatest risk. Through systematic sensitivity analysis, targeted scenario analysis, and backwards inferencing, critical control points for each pathogen were determined, demonstrating the usefulness of Bayesian networks as a diagnostic tool in quantifying risk of water reuse treatment scenarios.
KW - Bayesian network (BN)
KW - Disease burden
KW - Health-based targets
KW - Quantitative microbial risk assessment (QMRA)
KW - Water reuse
UR - http://www.scopus.com/inward/record.url?scp=85103127809&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2021.146462
DO - 10.1016/j.scitotenv.2021.146462
M3 - Article
AN - SCOPUS:85103127809
SN - 0048-9697
VL - 780
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 146462
ER -