Bayesian spatial modeling of Lavaca Bay pollutants.
Locational risk of increased mercury and PAH concentrations in Lavaca Bay, Texas sediments and eastern oysters (Crassostrea virginica) harvested from Lavaca Bay, Texas were analysed. Chemical analysis results were evaluated utilizing Bayesian geo-statistical methods for comparison of the model fit of a random effects model versus a convoluted model which included both random and spatial effects. For those results fit best with the convoluted model, continuous surface maps of predicted parameter values were created. Sediment and oyster concentrations of mercury and the majority of measured PAHs were fit best with the convoluted model. The locational risks of encountering elevated concentrations of these pollutants in Lavaca Bay sediments and oysters were highest in close proximity to industrial facilities.
Bissett
Wesley
W
Texas A&M University, College of Veterinary Medicine, Department of Large Animal Clinical Sciences, 4475 TAMU, College Station, TX 77843-4475, USA. wbissett@cvm.tamu.edu
Garry Adams
L
L
Field
Robert
R
Moyer
William
W
Phillips
Tim
T
Morgan Scott
H
H
Wade
Terry
T
Sweet
Steve
S
Thompson
James A
JA
D000818:Animals; D001499:Bayes Theorem; D019015:Geologic Sediments / Q000737:chemistry; D008628:Mercury / Q000737:chemistry; D008962:Models, Theoretical; D009792:Oceans and Seas; D010125:Ostreidae / Q000737:chemistry; D011084:Polycyclic Aromatic Hydrocarbons / Q000737:chemistry; D013781:Texas; D014867:Water / Q000737:chemistry; D014873:Water Pollutants
D011084:Polycyclic Aromatic Hydrocarbons; D014873:Water Pollutants; D014867:Water; D008628:Mercury