Enterococci predictions from partial least squares regression models in conjunction with a single-sample standard improve the efficacy of beach management advisories.
Beach health advisories are issued if enterococci (ENT) densities exceed the 30-d geometric mean or single-sample water quality criteria. Current ENT enumeration procedures require 1 day of incubation; therefore, beach managers make policy decisions using 1-day-old data. This is tantamount to using a model that assumes ENT density on day t is equal to ENT density on day t-1. Research has shown that ENT densities vary over time scales shorterthan a day, calling into question the usefulness of the current model for decision-making. We created Dynamic Partial Least Square Regression (DPLSR) models for ENT at water quality monitoring stations within two adjacent marine recreational sites, Huntington State Beach (HSB) and Huntington City (HCB) Beach, California, using publicly available environmental data and tested whether these models overcome the drawbacks of the current model. The DPLSR models provide a better prediction of ENT than the current models based on comparisons of root-mean-square errors of prediction and the numbers of type 1 and 2 errors. We compared outcomes in terms of predicted illness, swimmers deterred from entering the water, and net benefits to swimmers for hypothetical management scenarios where beach advisories were issued based on (a) the previously collected sample's ENT density in conjunction with the two water quality criteria, and (b) predictions from DPLSR models in conjunction with the single-sample standard. At both HSB and HCB the DPLSR scenario produced a more favorable balance between illness prevention and recreational access. The results call into question the current method of beach management and show that model-informed decision-making and elimination of the geometric mean standard will aid beach managers in achieving more favorable outcomes in terms of illness and access than are presently achieved using 1-day-old measurements, especially at beaches where water quality problems are chronic.
Hou
Deyi
D
Environmental Water Studies, Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305-4020, USA.
Rabinovici
Sharyl J M
SJ
Boehm
Alexandria B
AB
D000818:Animals; D001493:Bathing Beaches / Q000592:standards*; D002140:California; D016983:Enterococcus / Q000302:isolation & purification* / Q000502:physiology; D004782:Environmental Health; D005544:Forecasting; D006801:Humans; D016018:Least-Squares Analysis; D008954:Models, Biological; D010140:Pacific Ocean; D011050:Policy Making; D011998:Recreation; D012623:Seawater / Q000382:microbiology*; D014871:Water Microbiology