THE IMPORTANCE OF SPATIALLY AND TEMPORALLY VARYING RECHARGE ON THE ESTIMATION OF CONTAMINANT TRANSPORT PARAMETERS

Toms River, New Jersey is the location of a statistically significant childhood cancer cluster with an excess incidence for all malignant cancers combined, brain and central nervous system (CNS) cancers, and leukemia. The community's concern focused on the possibility that exposure to environmental contaminants may be related to the incidence of these childhood cancers. The Reich Farm site, is a possible source of contaminants to the aquifer that serves a major well field for Toms River. Contaminants in the aquifer from the Reich Farm site include TCE, PCE and styrene-acrylonitrile (SAN) trimer. Groundwater modeling was undertaken to establish the historical relationship between the Reich Farm site and the municipal well field and to aid in the management and protection of the aquifer and well field to ensure both water quality and quantity.

Groundwater flow from the Reich Farm Superfund site to the municipal well field for Toms River was modeled for a thirty-year time period using FRAC3DVS. Model calibration exercises indicated that a physically based spatial and temporally variable recharge was necessary to account for dramatic fluctuations in water levels due to seasonal variations. A physically based approach using the hydrologic model HELP3 in conjunction with GIS was developed to estimate the spatially and temporally varying recharge distribution. Input included detailed land use and land cover (LULC) and soil information. Transient flow model calibration was facilitated by approximately 9 years of head data from continuous well records.

The accurate simulation of the transient groundwater flow system was essential for the subsequent prediction of contaminant migration from the Reich Farm site to the municipal wells. The contaminant transport parameters were estimated using optimization with two heuristic search algorithms (a dynamically dimensioned search and a parallelized micro genetic algorithm) and a gradient based multi-start PEST algorithm. For the detailed, transient flow model with spatially and temporally varying recharge, the estimated transverse dispersivity coefficients were estimated to be significantly less than that reported in the literature for the more traditional approach that uses steady-state flow with averaged, less physically based recharge values.