A multi-model approach toward understanding iron fouling at rock-fill drainage sites along roadways in New Hampshire, USA

Abstract

Abstract Factors affecting iron fouling in wet areas adjacent to roadways were investigated by collecting field rock cut and aqueous physicochemical data; developing exploratory predictive models; and developing geochemical models. Basic data included the identification of iron fouling from aerial imagery and field visits at 374 New Hampshire rock cut locations, and their associated rock-fill sites. Based on field water quality measurements from wet areas at 36 of the rock-fill sites, the occurrence of iron fouling was associated with higher values of specific conductance, lower concentrations of dissolved oxygen and lower pH compared to areas without iron fouling. A statistical model, using boosted regression trees, was developed to predict the occurrence of iron fouling in wet areas adjacent to roadways where rock-fill from nearby rock cuts was used in roadway construction. The model was used to develop a continuous iron fouling probability map for the state of New Hampshire that can be used to better understand the occurrence of iron fouling. Geochemical models illustrate how iron fouling of waters increases along roadways built with fill from sulfidic rock cuts as a result of acid generation from pyrite dissolution and ferrous iron (Fe2+) oxidation and increases in areas with greater specific conductance from deicing runoff caused by cation exchange. More iron is precipitated as goethite in simulations that include pyrite, and in simulations with deicing salts added, indicating that rock-fill sites with rocks that contain pyrite and water with greater salt content could have enhanced iron fouling.

Keywords

• Iron fouling
• Boosted regression trees
• Rock-fill
• Water chemistry
• Water quality
• Geochemical modeling