An assessment of fecal indicator bacteria-based water quality standards.
Fecal indicator bacteria (FIB) are commonly used to assess the threat of pathogen contamination in coastal and inland waters. Unlike most measures of pollutant levels however, FIB concentration metrics, such as most probable number (MPN) and colony-forming units (CFU), are not direct measures of the true in situ concentration distribution. Therefore, there is the potential for inconsistencies among model and sample-based water quality assessments, such as those used in the Total Maximum Daily Load (TMDL) program. To address this problem, we present an innovative approach to assessing pathogen contamination based on water quality standards that impose limits on parameters of the actual underlying FIB concentration distribution, rather than on MPN or CFU values. Such concentration-based standards link more explicitly to human health considerations, are independent of the analytical procedures employed, and are consistent with the outcomes of most predictive water quality models. We demonstrate how compliance with concentration-based standards can be inferred from traditional MPN values using a Bayesian inference procedure. This methodology, applicable to a wide range of FIB-based water quality assessments, is illustrated here using fecal coliform data from shellfish harvesting waters in the Newport River Estuary, North Carolina. Results indicate that areas determined to be compliant according to the current methods-based standards may actually have an unacceptably high probability of being in violation of concentration-based standards.
Gronewold
Andrew D
AD
Nicholas School of the Environment, Duke University, Durham, North Carolina 27708, USA. adg12@duke.edu
Borsuk
Mark E
ME
Wolpert
Robert L
RL
Reckhow
Kenneth H
KH
D001499:Bayes Theorem; D015169:Colony Count, Microbial; D003198:Computer Simulation; D004755:Enterobacteriaceae / Q000302:isolation & purification*; D004784:Environmental Monitoring / Q000379:methods* / Q000592:standards* / Q000706:statistics & numerical data; D005243:Feces / Q000382:microbiology*; D008962:Models, Theoretical; D009657:North Carolina; D045483:Rivers / Q000382:microbiology*