Using Artificial Intelligence Models to Evaluate Field Performance of Photocatalytic Asphalt Pavement for Ambient Air Purification

摘要

In recent years, the application of titanium dioxide (TiO_2) as a photocatalyst in asphalt pavementhas received considerable attention due to its ability to purify ambient air from traffic-emittedpollutants via photocatalytic processes. The objective of this study was to utilize ArtificialNeural Network (ANN) and Neuro-Fuzzy (NF) models to predict NO_x concentration in the air asa function of traffic count (T_r) and climatic conditions including humidity (H), temperature (T),solar radiation (S), and wind speed (W) before and after the application of TiO_2 on the pavementsurface. A field study was conducted where a water-soluble nano TiO_2 solution was sprayed ona 0.2 mile of asphalt pavement in Baton Rouge, LA. Two Artificial Intelligence (AI) modelswere developed to predict NO_x concentrations before and after TiO_2 application. Results showedthat the NF model provided a better fitting to NO_x measurements than the ANN model in thetraining, validation, and test steps. Results of a parametric study showed that traffic level,relative humidity, and solar radiation had the most effects on photocatalytic efficiency. Inaddition, the increase in wind speed and relative humidity negatively affected the effectivenessof NO_x reduction efficiency. However, the increase in UV light intensity improved NO_x removalefficiency of the surface coating.