| Summary: | Critical water resources, such as groundwater, are undergoing a period of intense and global environmental change, driven by climate change, anthropogenic impacts and exploitation, and perturbations to interactions of fundamental processes that are affected by hydrological, mineralogical and biogeochemical factors. Arsenic contamination is a significant threat to these water resources and the populations who depend on them, yet there are few studies directly linking water quality with changes in hydrology and geochemistry in sediments on varying scales. My research explores environmental variability in hydrology and redox processes that regulate soluble arsenic concentrations at the pore scale (µm to mm), and develops methods of upscaling these mechanistic studies to understand heterogeneity in groundwater arsenic levels and their impacts on public health at larger scales (a couple of meters to hundreds of kilometers). Specifically, my research examines the interaction of redox processes in the Earth’s subsurface that drive the release of arsenic into groundwater. Naturally-occurring, or geogenic, arsenic contamination is the main source of arsenic release into groundwater that affects human health, with possible anthropogenic exacerbation of this natural contamination.
Throughout this dissertation, I have developed a suite of data-driven approaches to understand and quantify the highly variable factors that underlie the mechanisms of geogenic arsenic release into groundwater and its migration in the environment. In Chapter 1, I investigate the effects of hydrologic perturbations on formerly uncontaminated aquifers that release arsenic due to increased groundwater pumping in the Red River Delta, Vietnam. To compare the effect of hydrologic processes to measured groundwater arsenic concentrations, I used Monte Carlo simulations in an end-member mixing model and quantified fraction of different recharge sources into an aquifer based on stable water isotopes. I find that changing flow patterns due to groundwater abstraction have increased the extent of arsenic release into groundwater and also changed the location of where arsenic contamination originates. In Chapter 2, I characterize iron mineralogy associated with arsenic release through sampling of sediment cores across a lateral redox gradient in Vietnam with extensive spectroscopy measurements.
Through hierarchical cluster analysis on this data set of X-ray absorption spectroscopy (XAS) measurements of borehole cuttings paired with dissolved groundwater measurements, I reveal signatures of iron mineral reduction that could cause or exacerbate arsenic release. This was upscaled to other deltaic aquifers in South and Southeast Asia based on groundwater data to identify aquifers at risk of arsenic release. I showed that the extent of older and previously pristine aquifers that have been contaminated may have been misclassified and thus underrepresented in deltaic aquifers throughout South and Southeast Asia, disrupting the assumption that older and deeper aquifers are oxidized and thus guarded against arsenic release.
In Chapter 3, I use process-based reactive transport modeling of a laboratory-scale experiment to mechanistically explain the infiltration of contaminated water into uncontaminated aquifers and find that arsenic contamination cannot be explained by the commonly invoked mechanism of iron reducing bacteria only, but instead relies on sulfate reduction and complexation of aqueous arsenic in solution. The role of sulfate reduction in mobilizing arsenic in groundwater is in stark contrast to and undermines the previous use of sulfate reduction as strategy for arsenic remediation.
Finally, in Chapter 4, I quantitatively examine the processes that release arsenic across different arsenic-impacted aquifers, based on the relationships between redox status of iron and arsenic mineralogy and groundwater concentrations. Synthesis of X-ray absorption spectra of the deltaic aquifers of Southeast Asia and the glacial aquifer system in the Northern United States shows that arsenic release occurs in similar geochemical environments in both systems, and is highly generalizable via statistical and unsupervised machine learning approaches.
This dissertation demonstrates that common assumptions behind geogenic arsenic release must be tested: from which aquifers are low in arsenic to the commonly assumed mechanism of arsenic release by iron reducing bacteria. These findings also reveal that the extent of anthropogenic impact on geogenic arsenic contamination is detectable: from changes in recharge sources to changes in mineralogy that affect arsenic concentrations and human health. The next step is to use these data driven and machine learning approaches to quantify the vulnerability of affected aquifers, to mitigate the risk of those currently reliant on contaminated groundwater, to reduce the risks of future contamination and, ultimately, to protect human health.
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