Field methods for rapidly characterizing paint waste during bridge rehabilitation

Abstract

For Department of Transportation (DOT) agencies, bridge rehabilitation involving paint removal results in waste that is often managed as hazardous. Hence, an approach that provides field characterization of the waste classification would be beneficial. In this study, an analysis of variables critical to the leaching process was conducted to develop a predictive tool for waste classification. This approach first involved identifying mechanistic processes that control leaching. Because steel grit is used to remove paint, elevated iron concentrations remain in the paint waste. As such, iron oxide coatings provide an important surface for metal adsorption. The diffuse layer model was invoked (logKMe=4.65 for Pb and logKMe=2.11 for Cr), where 90% of the data were captured within the 95% confidence level. Based on an understanding of mechanistic processes along with principal component analysis (PCA) of data obtained from field-portable X-ray fluorescence (FP-XRF), statistically-based models for leaching from paint waste were developed. Modeling resulted in 96% of the data falling within the 95% confidence level for Pb (R2 0.6–0.9, p⩽0.04), Ba (R2 0.5–0.7, p⩽0.1), and Zn (R2 0.6–0.7, p⩽0.08). However, the regression model obtained for Cr leaching was not significant (R2 0.3–0.5, p⩽0.75). The results of this work may assist DOT agencies with applying a predictive tool in the field that addresses the mobility of trace metals as well as disposal and management of paint waste during bridge rehabilitation.