HR: 1340h
AN: H53A-1209    [Abstracts]
TI: Measurement Errors in Microbial Water Quality Assessement: the Case of Bacterial Aggregates
AU: * Plancherel, Y
EM: yves@soest.hawaii.edu
AF: University of Hawaii at Manoa, 1000 Pope Rd. Department of Oceanography Marine Science Building, Honolulu, HI 96822 United States
AU: Cowen, J P
EM: jcowen@soest.hawaii.edu
AF: University of Hawaii at Manoa, 1000 Pope Rd. Department of Oceanography Marine Science Building, Honolulu, HI 96822 United States
AB: The quantification of the risk of illness for swimmers, bathers, or consumers exposed to a polluted water body involves the measurement of microbial indicator organism densities. Depending on the organism targeted, there exist two widely used (traditional) techniques for their enumeration: most probable number (MPN) and membrane filtration (MF). Estimation of indicator organism density by these traditional methods is subject to large measurement error, which translates into poorly constrained relationships between indicator organism density and illness rate. Neither the MPN nor the MF method can discriminate multiple cells that form an aggregate. Mathematical formulations and computer simulations are used to investigate the effects that bacterial clumps have on the measurement error of the concentrations. The first case considered is that of the formation of clusters induced during the membrane filtration process assuming a randomly distributed population of cells growing into colonies. The computer simulations indicate that this process induces a typical measurement error $<$15% with the MF method. Replication of the MF measurements does not reduce this type of error. The second case describes a mathematical framework for the modeling of particle-associated bacteria. When aggregates harboring bacteria are present in a sample, an additional measurement error of 5-35% is expected. Empirical results from laboratory and field experiments enumerating aggregated bacteria using the MF method agree well with these model values. Furthermore, the data reveal that this type of error depends on the microbial indicators used (Enterococcus, {\it C. perfringens}, Heterotrophic Plate Count bacteria) and highlights the importance of small bacterial clusters ($<$5 $\mu$m).
UR: http://www2.hawaii.edu/~plancher/
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
DE: 1871 Surface water quality
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting