Using river distance and existing hydrography data can improve the geostatistical estimation of fish tissue mercury at unsampled locations

Environmental Science and Technology

Volumn 45, Issue 18, 2011, Pages 7746-7753

  Money, Eric S ^a,b   Sackett, Dana K ^c   Aday, D Derek ^c   Serre, Marc L ^a  

^a IL   (United States)

^b Duke University   (United States)

^c NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL APPROACH; BASIN WIDE; BAYESIAN MAXIMUM ENTROPIES; ESTIMATION ERRORS; FISH MOVEMENTS; FISH TISSUE; GENERAL POPULATION; GEO-STATISTICS; GEOSTATISTICAL ESTIMATION; HUMAN HEALTH CONCERNS; NORTH CAROLINA; RESOURCE CONSTRAINT; SECONDARY VARIABLES; YOUNG CHILDREN;

AIR POLLUTION CONTROL; ESTIMATION; HEALTH RISKS; MERCURY (METAL); RIVERS; TISSUE;

FISH;

MERCURY;

ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; BIOACCUMULATION; CHILD HEALTH; DATA SET; ESTIMATION METHOD; FISH; FOOD CONSUMPTION; FUNCTIONAL MORPHOLOGY; GEOSTATISTICS; HEALTH RISK; HYDROGRAPHY; IDENTIFICATION METHOD; MERCURY (ELEMENT); NEUROLOGY; POLLUTION EFFECT; RIVER; STREAM;

ANIMAL TISSUE; ARTICLE; BAYES THEOREM; BIOACCUMULATION; CONTAMINATED FISH; CONTROLLED STUDY; GEOSTATISTICAL ANALYSIS; HYDROGRAPHY; NONHUMAN; PH; RIVER; RIVER BASED BAYESIAN MAXIMUM ENTROPY; RIVER BASIN; TIME; UNITED STATES;

ANIMALS; ENVIRONMENTAL MONITORING; FISHES; GEOGRAPHY; MERCURY; MODELS, THEORETICAL; NORTH CAROLINA; RIVERS; WATER POLLUTANTS, CHEMICAL;

NORTH CAROLINA; UNITED STATES;

EID: 80052723390     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es2003827     Document Type: Article

References (47)

1. Morel, F. M. M.; Kraepiel, A. M. L.; Amyot, M. The chemical cycle and bioaccumulation of mercury Annu. Rev. Ecol. Syst. 1998, 29, 543-566 (Pubitemid 29033042)
2. Nriagu, J.; Becker, C. Volcanic emissions of mercury to the atmosphere: global and regional inventories Sci. Total Environ. 2003, 304, 3-12 (Pubitemid 36344077)
3. Pacyna, E. G.; Pacyna, J. M.; Steenhuisen, F.; Wilson, S. Global anthropogenic mercury emission inventory for 2000 Atmos. Environ. 2006, 40, 4048-4063 (Pubitemid 43903339)
4. Gilmour, C. C.; Elias, D. A.; Kucken, A. M.; Brown, S. D.; Palumbo, A. V.; Schadt, C. W.; Wall, J. D. The sulfate-reducing bacterium Desulfovibrio desulfuricans ND132 as a model for understanding bacterial mercury methylation. Appl. Environ. Microbiol. 2011, 77 (12), 3938 - 3951.
5. Brigham, M. E.; Wentz, D. A.; Aiken, G. R.; Krabbenhoft, D. P. Mercury cycling in stream ecosystems. 1. Water column chemistry and transport Environ. Sci. Technol. 2009, 43, 2720-2725
6. Ullrich, S. M.; Tanton, T. W.; Abdrashitova, S. A. Mercury in the aquatic environment: a review of factors affecting methylation Crit. Rev. Environ. Sci. Technol. 2001, 31, 241
7. Bloom, N. S. On the chemical form of mercury in edible fish and marine invertebrate tissue Can. J. Fish. Aquat. Sci. 1992, 49, 1010-1017
8. Scheuhammer, A. M.; Meyer, M. W.; Sandheinrich, M. B.; Murray, M. W. Effects of environmental methylmercury on the health of wild birds, mammals, and fish Ambio 2007, 36, 12-18 (Pubitemid 46587111)
9. Mergler, D.; Anderson, H. A.; Hing Man Chan, L.; Mahaffey, K. R.; Murray, M.; Sakamoto, M.; Stern, A. H. Methylmercury exposure and health effects in humans: a worldwide concern Ambio 2007, 36, 3-11 (Pubitemid 46587110)
10. U.S. Environmental Protection Agency. 2008 National listing of fish advisories; EPA-823-F-07-003; USEPA: Washington, DC, 2009.
11. Scudder, B. C.; Chasar, L. C.; Wentz, D. A.; Bauch, N. J.; Brigham, M. E.; Moran, P. W.; Krabbenhoft, D. P. Mercury in fish, bed sediment, and water from streams across the United States, 1998-2005: U.S. Geological Survey Scientific Investigations Report 2009-5109; U.S. Geological Survey: Reston, VA, 2009; 74 p.
12. Qian, S. S.; Warren-Hick, J.; Keating, D.; Moore, R. J.; Teed, R. S. A predictive model of mercury in stream fish Environ. Sci. Technol. 2001, 35 (5) 941-947
13. Paller, M. H.; Bowers, J. A.; Littrell, J. W.; Guanlao, A. V. Influences on mercury bioaccumulation factors for the Savannah River Arch. Environ. Contam. Toxicol. 2004, 46 (2) 236-243 (Pubitemid 38199193)
14. Delhomme, J. P. Kriging in the Hydrosciences Adv. Water Res. 1978, 1 (5) 251-266
15. Cressie, N. The origins of kriging Math. Geol. 1990, 22 (3) 239-252
16. Stein, M. L. Interpolation of spatial data: Some theory for kriging; Springer: New York, NY, 1999; 247 p.
17. th International Symposium on Spatial Data Quality; ITC Enschede: The Netherlands, 2007.
18. Li, Z.; Zhang, Y-K; Schilling, K.; Skopec, M. Cokriging estimation of daily suspended sediment loads J. Hydrol. 2006, 327, 389-398 (Pubitemid 44093534)
19. Seo, D. J.; Krajewski, W. F.; Azimi-Zonooz, A.; Bowles, D. S. Stochastic interpolation of rainfall data from rain gauges and radar using cokriging Water Resour. Res. 1990, 26 (5) 915-924
20. Jager, H. I.; Sale, M. J.; Schmoyer, R. L. Cokriging to assess regional stream quality in the southern blue ridge province Water Resour. Res. 1990, 26 (7) 1401-1412
21. Goovaerts, P. Geostatistics for natural resources evaluation; Oxford University Press: London, 1997.
22. Christakos, G.; Li, X. Y. Bayesian maximum entropy analysis and mapping: A farewell to kriging estimators? Math. Geol. 1998, 30, 435-462 (Pubitemid 28431427)
23. Pang, W.; Christakos, G.; Wang, J.-F. Comparative spatiotemporal analysis of fine particulate matter pollution Environmetrics 2010, 21, 305-317
24. Christakos, G. A Bayesian/maximum-entropy view on the spatial estimation problem Math. Geol. 1990, 22 (7) 763-776
25. Money, E.; Carter, G.; Serre, M. L. Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey Water Res. 2009a, 43 (7) 1948-1958
26. Money, E. S.; Carter, G.; Serre, M. L. Modern space/time geostatistics using river distances: Data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan river in New Jersey Environ. Sci. Technol. 2009b, 43 (10) 3736-3742
27. Miller, D. R.; Akagi, H. pH affects mercury distribution, not methylation Ecotoxicol. Environ. Saf. 1979, 3 (1) 36-38 (Pubitemid 9206487)
28. Walters, D. M.; Blocksom, K. A.; Lazorchak, J. M.; Jicha, T.; Angradi, T. R.; Bolgrien, D. W. Mercury contamination in fish in midcontinent great rivers of the United States: Importance of species traits and environmental factors Environ. Sci. Technol. 2010, 44 (8) 2947-2953
29. Southworth, G. R.; Peterson, M. J.; Bogle, M. A. Bioaccumulation factors for mercury in stream fish Environ. Pract. 2004, 6, 135-143 (Pubitemid 41271434)
30. N.C. Dept. of Environment and Natural Resources. North Carolina Water Quality Assessment and Impaired Waters List (2006 Integrated 303(d) and 303(b) reports); NCDENR: Raleigh, NC, 2006.
31. NCDENR. Cape Fear basinwide assessment report; NCDENR: Raleigh, NC, 2004.
32. NCDENR. Lumber river basinwide assessment report; NCDENR: Raleigh, NC, 2007.
33. Sackett, D. K.; Aday, D. D.; Rice, J. A.; Cope, W. G. A state-wide assessment of mercury dynamics in North Carolina waterbodies and fish Trans. Am. Fish. Soc. 2009, 138, 1323-1341
34. N.C. Division of Water Quality. 2002/2003 Eastern Regional Mercury Study summary; NCDWQ: Raleigh, NC, 2004.
35. NCDWQ. Eastern Regional Mercury Study extension report; NCDWQ: Raleigh, NC, 2007.
36. th Annual Conference of the International Association for Mathematical Geology; Buccianti, A.; Nardi, G.; Potenza, R., Eds.; De Frede Editore: Naples, Italy, 1998, 1, 117-122.
37. De Nazelle, A.; Arunachalam, S.; Serre, M. L. Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina Environ. Sci. Technol. 2010, 44, 5707-5713
38. Serre, M. L.; Christakos, G. Modern geostatistics: Computational BME in light of uncertain physical knowledge-the equus beds study Stoch. Envrion. Res. Risk Assess. 1999, 13 (1) 1-26 (Pubitemid 129514557)
39. Christakos, G.; Bogaert, P.; Serre, M. L. Temporal GIS: Advanced Functions for Field Based Applications; Springer: New York, 2002, 217 p.
40. Money, E. S. Modern Space/Time Geostatistics Using River Distances: Theory and Applications for Water Quality Estimation and Mapping, Ph.D. Dissertation, University of North Carolina at Chapel Hill, Chapel Hill, NC, 2009.
41. Ver Hoef, J. M.; Peterson, E. E.; Theobold, D. Spatial statistical models that use flow and stream distance Environ. Ecol. Stat. 2006, 13, 449-464 (Pubitemid 44680510)
42. Ver Hoef, J.; Peterson, E. A moving average approach for spatial statistical models of stream networks. J. Am. Stat. Assoc. 2010, 105 (489), 6 - 18.
43. Food and Drug Administration. FDA Consumer Advisory; FDA: Washington, DC, 2001.
44. Williams, L. K. Health effects of methylmercury and North Carolina's advice on eating fish; N.C. Dept. of Health and Human Services: Raleigh, NC, 2006.
45. USEPA. Notice of Availability of Water Quality Criterion for the Protection of Human Health: Methylmercury; Federal Register 66 (5); USEPA: Washington, DC, 2001, p 1334-1359.
46. National Atmospheric Deposition Program: Mercury Deposition Network; http://nadp.sws.uiuc.edu/mdn/ (accessed July 13, 2011).
47. Wente, S. P. A statistical model and national data set for partitioning fish-tissue mercury concentration variation between spatiotemporal and sample characteristic effects. U.S. Geological Survey Scientific Investigation Reports, USGS 2004-5199; U.S. Geological Survey: Reston, VA, 2004, 15 p.