Currently, the New York State Department of Transportation (NYSDOT) uses a conservative approach of classifying all paint waste as hazardous from bridges undergoing rehabilitation which were constructed before 1989. This practice stems from the fact that there is no approved reliable, fast, and efficient method for classifying paint waste in-situ as non-hazardous. The main objective of this study was to develop a model that can predict the leachability of trace metals in paint waste generated during bridge rehabilitation. A statistically significant number of bridge sites were sampled based on hypothesis testing. Samples were then evaluated for total concentration of Resource Conservation and Recovery Act (RCRA) metals (i.e., Ag, As, Ba, Cd, Cr, Hg, Pb, and Se), iron as well as zinc. Leaching studies included the U.S. Environmental Protection Agency (U.S. EPA) toxicity characteristic leaching procedure (TCLP) and the multiple extraction procedure (MEP). Interestingly, although elevated Pb (5 to 168,090 mg kg-1) and other metal concentrations were observed in the paint samples, leaching results revealed only up to 22.6 mg L-1 for Pb and 9.52 mg L-1 for Cr. The relatively low concentrations observed are attributed to the use of iron-based abrasives (steel grit) in the paint removal process. In New York State, steel grit is typically applied as an abrasive material to remove paint during bridge rehabilitation. Although magnetic separation is applied to collect and reuse the steel grit, the fraction remaining in the paint waste ranges from 5 to 80% by weight. Using the suite of analyses, ferrihyidrite was observed to be an important mineral surface on the steel grit; spherical particle aggregates ranged from 20 to 200 nm in diameter. In addition, sequential extraction revealed trace metal sorption to the iron oxide surface may be the dominant mechanism responsible for the reduced leaching observed. The sorption process was further modeled using the diffuse layer model. Based on an understanding of mechanistic processes along with a demonstrated analysis of variables through principal component analysis (PCA), statistically-based models for leaching from paint waste were developed. Results of this work assist in better understanding and predicting the mobility of trace metals as well as in addressing disposal and management of paint waste during bridge rehabilitation
