High-resolution monitoring of biogeochemical gradients in a tar oil-contaminated aquifer

Abstract

The detailed understanding of in situ biodegradation of petroleum hydrocarbons in porous aquifers requires knowledge on biogeochemical gradients, the distribution of individual redox species and microorganisms. The generally limited spatial resolution of conventional monitoring wells, however, hampers appropriate characterization of small-scale gradients and thus localization of the relevant processes. Groundwater sampling across a BTEX plume in a sandy aquifer by means of a novel high-resolution multi-level well (HR-MLW) is presented here. The presence of distinct and steep biogeochemical gradients is demonstrated in the centimeter and decimeter scale, which could not be resolved with a conventional multi-level well. The thin BTEX plume with a vertical extension of only 80 cm exhibited a decline of contaminant concentrations by two orders of magnitude within a few centimeters in the upper and lower fringe zone. The small-scale distribution of sulfate, sulfide and Fe(II) in relation to the contaminants and elevated δ34S and δ18O values of groundwater sulfate strongly indicated sulfate and iron reduction to be the dominant redox processes involved in biodegradation. High microbial activities and biomass especially at the plume fringes and the slope of chemical gradients supported the concept that the latter are regulated by microbial processes and transverse dispersion, i.e. vertical mixing of electron donors and acceptors. Transverse dispersion therefore was suggested to be a driving factor controlling biodegradation in porous aquifers, but not exclusively limiting natural attenuation processes at this site. Broad overlapping zones of electron donors and electron acceptors point towards additional factors limiting anaerobic biodegradation in situ. The identification of small-scale gradients substantially contributed to a better understanding of biodegradation processes and hence is a prerequisite for the development of reliable predictive mathematical models and future remediation strategies.