title: Using river distance and existing hydrography data can improve the geostatistical estimation of fish tissue mercury at unsampled locations.
abstract: Mercury in fish tissue is a major human health concern. Consumption of mercury-contaminated fish poses risks to the general population, including potentially serious developmental defects and neurological damage in young children. Therefore, it is important to accurately identify areas that have the potential for high levels of bioaccumulated mercury. However, due to time and resource constraints, it is difficult to adequately assess fish tissue mercury on a basin wide scale. We hypothesized that, given the nature of fish movement along streams, an analytical approach that takes into account distance traveled along these streams would improve the estimation accuracy for fish tissue mercury in unsampled streams. Therefore, we used a river-based Bayesian Maximum Entropy framework (river-BME) for modern space/time geostatistics to estimate fish tissue mercury at unsampled locations in the Cape Fear and Lumber Basins in eastern North Carolina. We also compared the space/time geostatistical estimation using river-BME to the more traditional Euclidean-based BME approach, with and without the inclusion of a secondary variable. Results showed that this river-based approach reduced the estimation error of fish tissue mercury by more than 13% and that the median estimate of fish tissue mercury exceeded the EPA action level of 0.3 ppm in more than 90% of river miles for the study domain.
authors.affiliation: Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599-7438, USA.
authors.forename: Eric S
authors.initials: ES
authors.lastname: Money
authors.forename: Dana K
authors.initials: DK
authors.lastname: Sackett
authors.forename: D Derek
authors.initials: DD
authors.lastname: Aday
authors.forename: Marc L
authors.initials: ML
authors.lastname: Serre
mesh_terms: D000818:Animals; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005399:Fishes; D005843:Geography / Q000706:statistics & numerical data*; D008628:Mercury / Q000032:analysis*; D008962:Models, Theoretical; D009657:North Carolina; D045483:Rivers; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D014874:Water Pollutants, Chemical; D008628:Mercury