Over the last decade we have gained a detailed understanding of the static geochemical characteristics of arsenic and manganese contaminated aquifers and have characterized the rapid response of sediment to artificial chemical perturbations in incubation experiments. However, remarkably little is known about the basic aspects of hydrogeology that are vital for understanding the evolution of groundwater chemistry along flow paths that are contaminated by these metals. We do not know how the solute fluxes that drive manganese and arsenic mobility enter the aquifer, what patterns groundwater flow follows, or how solutes mix across different flow paths. Little is known about deeper groundwater flow, and basic issues such as the significance of regional flow and groundwater pumping are still controversial. In the proposed research, we will combine networks of sensors and geophysical imaging techniques with three-dimensional groundwater flow and transport models to characterized changing subsurface conditions. We will observe how subsurface conditions may be altered by installation of community supply wells, the most common approach to providing safe water, and we will develop predictive models for future shifts in groundwater chemistry. Our results will provide insight into the processes that cause metal mobilization from sediments, and will enable better management of contaminated groundwater.

Public Health Relevance

This project will address critical issues of toxic metals and how humans are exposed. We will address how human activity alters metal solubility in drinking water, how communities can monitor changes in hydrogeology that will change concentrations of toxic metals in drinking water and whether deep wells are an effective intervention to reduce toxic metal exposure.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Hazardous Substances Basic Research Grants Program (NIEHS) (P42)
Project #
5P42ES016454-03
Application #
8377619
Study Section
Special Emphasis Panel (ZES1-LWJ-M)
Project Start
Project End
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
3
Fiscal Year
2012
Total Cost
$191,072
Indirect Cost
Name
Harvard University
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
von Stackelberg, Katherine; Li, Miling; Sunderland, Elsie (2017) Results of a national survey of high-frequency fish consumers in the United States. Environ Res 158:126-136
Valeri, Linda; Mazumdar, Maitreyi M; Bobb, Jennifer F et al. (2017) The Joint Effect of Prenatal Exposure to Metal Mixtures on Neurodevelopmental Outcomes at 20-40 Months of Age: Evidence from Rural Bangladesh. Environ Health Perspect 125:067015
Wagner, Peter J; Park, Hae-Ryung; Wang, Zhaoxi et al. (2017) In Vitro Effects of Lead on Gene Expression in Neural Stem Cells and Associations between Up-regulated Genes and Cognitive Scores in Children. Environ Health Perspect 125:721-729
Claus Henn, Birgit; Bellinger, David C; Hopkins, Marianne R et al. (2017) Maternal and Cord Blood Manganese Concentrations and Early Childhood Neurodevelopment among Residents near a Mining-Impacted Superfund Site. Environ Health Perspect 125:067020
Sun, Ryan; Carroll, Raymond J; Christiani, David C et al. (2017) Testing for gene-environment interaction under exposure misspecification. Biometrics :
Lee, Jane J; Kapur, Kush; Rodrigues, Ema G et al. (2017) Anthropometric measures at birth and early childhood are associated with neurodevelopmental outcomes among Bangladeshi children aged 2-3years. Sci Total Environ 607-608:475-482
Rahman, Mohammad L; Valeri, Linda; Kile, Molly L et al. (2017) Investigating causal relation between prenatal arsenic exposure and birthweight: Are smaller infants more susceptible? Environ Int 108:32-40
Tamayo Y Ortiz, Marcela; Téllez-Rojo, Martha María; Trejo-Valdivia, Belem et al. (2017) Maternal stress modifies the effect of exposure to lead during pregnancy and 24-month old children's neurodevelopment. Environ Int 98:191-197
Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi et al. (2017) On longitudinal prediction with time-to-event outcome: Comparison of modeling options. Biometrics 73:83-93
Wang, Zhaoxi; Claus Henn, Birgit; Wang, Chaolong et al. (2017) Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment. Environ Health 16:81

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