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Environmental Science and Pollution Research

, Volume 21, Issue 14, pp 8847–8857 | Cite as

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

  • Somayeh Asadi
  • Marwa HassanEmail author
  • Ataallah Nadiri
  • Heather Dylla
Research Article

Abstract

In recent years, the application of titanium dioxide (TiO2) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO2 on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO2 solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

Keywords

Artificial neural network Neuro-fuzzy Nitrogen oxides Titanium dioxide Photocatalytic pavement 

Notes

Acknowledgments

This work was funded through a grant from the Gulf Coast Research Center for Evacuation and Transportation Resiliency. The authors would like to acknowledge PURETI for donating the materials needed for the construction of the field study and the support of Louisiana Transportation Research Center (LTRC) for granting access to their laboratory.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Somayeh Asadi
    • 1
  • Marwa Hassan
    • 2
    Email author
  • Ataallah Nadiri
    • 3
  • Heather Dylla
    • 4
  1. 1.Department of Civil and Architectural EngineeringTexas A&M University-KingsvilleKingsvilleUSA
  2. 2.Department of Construction ManagementLouisiana State UniversityBaton RougeUSA
  3. 3.University of TabrizTabrizIran
  4. 4.Department of Construction ManagementLouisiana State UniversityBaton RougeUSA

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