Applying statistical methodologies to simulate the fate and transport of engineered nanoparticles in the subsurface

The escalating production and consumption of engineered nanomaterials (ENMs) may lead to their increased release into groundwater. A number of studies have revealed the potential human health effects and aquatic toxicity of nanomaterials. Predicting the distribution of ENMs in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. A Modular Three-Dimensional Multispecies Transport Model (MT3DMS) was modified to evaluate the transport and retention of nC60 nanoparticles. Hypothetical scenarios for the introduction of nanomaterials into the subsurface environment were investigated, including the release from an injection well and a waste site. Under the conditions evaluated, the mobility of nC60 nanoparticles was found to be very sensitive to the release scenario, release concentration, aggregate size, collision efficiency factor, and dispersivity. Response surface methodology (RSM) was applied to develop simple relationships between key factors that control ENM transport (collision efficiency factor, particle size, hydraulic gradient, and initial release concentration) and key parameters that describe ENM concentration distributions in porous media (maximum standardized concentration, the mass percentage of injected nanoparticle attached in the aquifer, x-centroid of aqueous phase nC60 plume, and x-centroid of attached phase nC60 distribution). All regression equations that were developed for a mildly heterogeneous site (Bachman Road, MI) had high R-squared values (greater than 0.9). In a highly heterogeneous site (Columbus AFB site), only the regression equation for the percentage of nanoparticles attached had a R-squared value of more than 0.9. This work represents the first effort to apply Response Surface Methodology (RSM) to model the distribution of engineered nanomaterials in porous media. Furthermore, Autoregressive integrated moving average (ARIMA) modeling was successfully applied to predict conservative tracer transport in two field sites with different levels of heterogeneity and to forecast the aqueous phase plume far front and attached phase far front of nC60 nanoparticles in the subsurface based on numerically generated data. This work represents the first effort to apply ARIMA modeling to predict the transport of tracer and nanomaterials in the subsurface