Reducing Ambient NOx with an Environmental Artificial Tree (EAT)

The objective of the proposed investigation is to perform numerical optimization, followed by experimental evaluation of an optimized leaf surface texture for the development of an environmental artificial tree (EAT) for reducing ambient nitrogen oxides (NOx). Previous investigations have shown adding titanium dioxide nanoparticles to paint (enviropaint) when applied to a building surface has the potential to absorb ambient NOx. We aim to establish an innovative method for the development of an artificial tree for reducing ambient NOx. We will use numerical optimization to identify a leaf surface geometry that can increase contact duration between the ambient air and the leaf surface, maximizing residence time. We then follow with an experimental verification of the optimized surface geometry painted with the enviropaint for ambient NOx reduction. The output of the investigations provides the baseline design criteria for the development of various-shaped artificial trees for reducing local ambient NOx. The proposed project is aligned with sustainability in transportation, reducing the environmental impacts of transportation, and is aligned with technology innovation and technology transfer.

Principal Investigator: 
Hamid Rahai
PI Contact Information:

CSU Long Beach

January 2024 to December 2024
Project Number: 



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