Literature DB >> 21528844

Spatial modeling for groundwater arsenic levels in North Carolina.

Dohyeong Kim1, Marie Lynn Miranda, Joshua Tootoo, Phil Bradley, Alan E Gelfand.   

Abstract

To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21528844      PMCID: PMC3855354          DOI: 10.1021/es103336s

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  18 in total

1.  Well characteristics influencing arsenic concentrations in ground water.

Authors:  Melinda L Erickson; Randal J Barnes
Journal:  Water Res       Date:  2005-10       Impact factor: 11.236

2.  Exposure to arsenic and lead and neuropsychological development in Mexican children.

Authors:  J Calderón; M E Navarro; M E Jimenez-Capdeville; M A Santos-Diaz; A Golden; I Rodriguez-Leyva; V Borja-Aburto; F Díaz-Barriga
Journal:  Environ Res       Date:  2001-02       Impact factor: 6.498

3.  Arsenic in groundwater in eastern New England: occurrence, controls, and human health implications.

Authors:  Joseph D Ayotte; Denise L Montgomery; Sarah M Flanagan; Keith W Robinson
Journal:  Environ Sci Technol       Date:  2003-05-15       Impact factor: 9.028

4.  Arsenic in drinking water and mortality from vascular disease: an ecologic analysis in 30 counties in the United States.

Authors:  R R Engel; A H Smith
Journal:  Arch Environ Health       Date:  1994 Sep-Oct

5.  Validity of spatial models of arsenic concentrations in private well water.

Authors:  Jaymie R Meliker; Gillian A AvRuskin; Melissa J Slotnick; Pierre Goovaerts; David Schottenfeld; Geoffrey M Jacquez; Jerome O Nriagu
Journal:  Environ Res       Date:  2007-10-17       Impact factor: 6.498

6.  Spatial pattern of groundwater arsenic occurrence and association with bedrock geology in greater Augusta, Maine.

Authors:  Qiang Yang; Hun Bok Jung; Charles W Culbertson; Robert G Marvinney; Marc C Loiselle; Daniel B Locke; Heidi Cheek; Hilary Thibodeau; Yan Zheng
Journal:  Environ Sci Technol       Date:  2009-04-15       Impact factor: 9.028

Review 7.  Arsenic contamination in Bangladesh groundwater: a major environmental and social disaster.

Authors:  M G M Alam; G Allinson; F Stagnitti; A Tanaka; M Westbrooke
Journal:  Int J Environ Health Res       Date:  2002-09       Impact factor: 3.411

8.  The relationship of arsenic levels in drinking water and the prevalence rate of skin lesions in Bangladesh.

Authors:  M Tondel; M Rahman; A Magnuson; I A Chowdhury; M H Faruquee; S A Ahmad
Journal:  Environ Health Perspect       Date:  1999-09       Impact factor: 9.031

9.  A Bayesian hierarchical approach for relating PM(2.5) exposure to cardiovascular mortality in North Carolina.

Authors:  Christopher H Holloman; Steven M Bortnick; Michele Morara; Warren J Strauss; Catherine A Calder
Journal:  Environ Health Perspect       Date:  2004-09       Impact factor: 9.031

10.  A framework for widespread replication of a highly spatially resolved childhood lead exposure risk model.

Authors:  Dohyeong Kim; M Alicia Overstreet Galeano; Andrew Hull; Marie Lynn Miranda
Journal:  Environ Health Perspect       Date:  2008-08-14       Impact factor: 9.031

View more
  16 in total

1.  Health risk assessment of groundwater arsenic pollution in southern Taiwan.

Authors:  Ching-Ping Liang; Sheng-Wei Wang; Yu-Hsuan Kao; Jui-Sheng Chen
Journal:  Environ Geochem Health       Date:  2016-01-27       Impact factor: 4.609

2.  Incorporating ToxCast and Tox21 datasets to rank biological activity of chemicals at Superfund sites in North Carolina.

Authors:  Sloane K Tilley; David M Reif; Rebecca C Fry
Journal:  Environ Int       Date:  2017-01-31       Impact factor: 9.621

3.  Can arsenic occurrence rates in bedrock aquifers be predicted?

Authors:  Qiang Yang; Hun Bok Jung; Robert G Marvinney; Charles W Culbertson; Yan Zheng
Journal:  Environ Sci Technol       Date:  2012-02-09       Impact factor: 9.028

4.  Flow and sorption controls of groundwater arsenic in individual boreholes from bedrock aquifers in central Maine, USA.

Authors:  Qiang Yang; Charles W Culbertson; Martha G Nielsen; Charles W Schalk; Carole D Johnson; Robert G Marvinney; Martin Stute; Yan Zheng
Journal:  Sci Total Environ       Date:  2014-05-17       Impact factor: 7.963

5.  Arsenic in North Carolina: public health implications.

Authors:  Alison P Sanders; Kyle P Messier; Mina Shehee; Kenneth Rudo; Marc L Serre; Rebecca C Fry
Journal:  Environ Int       Date:  2011-09-10       Impact factor: 9.621

6.  Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment.

Authors:  Katherine A James; Jaymie R Meliker; Barbara E Buttenfield; Tim Byers; Gary O Zerbe; John E Hokanson; Julie A Marshall
Journal:  Environ Geochem Health       Date:  2014-01-16       Impact factor: 4.609

7.  Bayesian Spatial Design of Optimal Deep Tubewell Locations in Matlab, Bangladesh.

Authors:  Joshua L Warren; Carolina Perez-Heydrich; Mohammad Yunus
Journal:  Environmetrics       Date:  2013-09-01       Impact factor: 1.900

8.  Estimating the High-Arsenic Domestic-Well Population in the Conterminous United States.

Authors:  Joseph D Ayotte; Laura Medalie; Sharon L Qi; Lorraine C Backer; Bernard T Nolan
Journal:  Environ Sci Technol       Date:  2017-10-18       Impact factor: 9.028

9.  Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies.

Authors:  Melissa A Lombard; Molly Scannell Bryan; Daniel K Jones; Catherine Bulka; Paul M Bradley; Lorraine C Backer; Michael J Focazio; Debra T Silverman; Patricia Toccalino; Maria Argos; Matthew O Gribble; Joseph D Ayotte
Journal:  Environ Sci Technol       Date:  2021-03-17       Impact factor: 9.028

10.  Private Well Testing in Peri-Urban African-American Communities Lacking Access to Regulated Municipal Drinking Water: A Mental Models Approach to Risk Communication.

Authors:  Jacqueline MacDonald Gibson; Frank Stillo Iii; Erica Wood; Sydney Lockhart; Wändi Bruine de Bruin
Journal:  Risk Anal       Date:  2021-08-02       Impact factor: 4.302

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.