Seafood-borne illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of when and where risks are present. We aim to determine the contribution of factors that affect concentrations in oysters () using ten years of surveillance data for environmental and climate conditions in the Great Bay Estuary of New Hampshire from 2007 to 2016. A time series analysis was applied to analyze concentrations and local environmental predictors and develop predictive models. Whereas many environmental variables correlated with concentrations, only a few retained significance in capturing trends, seasonality and data variability. The optimal predictive model contained water temperature and pH, photoperiod, and the calendar day of study. The model enabled relatively accurate seasonality-based prediction of concentrations for 2014-2016 based on the 2007-2013 dataset and captured the increasing trend in extreme values of concentrations. The developed method enables the informative tracking of concentrations in coastal ecosystems and presents a useful platform for developing area-specific risk forecasting models.