Madhavan Vasudevan

Madhavan Vasudevan

Ph.D. Student

Development of smart roof top deflector system to control air pollution in street canyons

Air pollution is projected to cause up to 9 million premature deaths annually by 2060 in the absence of aggressive control measures. A combination of factors including meteorology, high traffic volumes and nature of built infrastructure frequently result in formation of air pollution hot spots in street canyon like structures, where buildings flank streets on both the sides, typically seen in cities. Passive control interventions aim to engineer the wind patterns in a microenvironment to alter the path of the wind and thereby pathway connecting the source and receiver of a pollution source. Vegetation in street canyons using trees, hedges, shrubs, low boundary walls and modified roof geometries are popularly explored passive intervention systems.

The roof-top deflector system developed in this PhD project, also enhances mixing of air between above building roof region and inside street canyon region apart from only altering the pathways, thereby also reduces residence time of polluted air inside street canyons. These roof-top deflectors will be flexible installations which will prevent them from counterproductive consequences under contrasting wind patterns. A holistic improvement in the air quality in the street canyon by targeting all human-occupied regions will be considered using the deflectors. CFD modelling will employed for the analysis of the same.

The deflector performance will be evaluated under theoretical and real-time conditions and will be correlated for providing design guidelines on optimal utilization of the same. A real-time street canyon offers a combination of complex features including street junctions, modified roof shapes, oblique and gusty winds resulting in innumerable choices for study of deflector performance. Therefore, this study will be focussed on developing a systematic methodology for realizing optimal deflector usage mode in terms of its shape, size, position, orientation under specific complex conditions and utilizing that knowledge in developing case- specific solution for a real-time street canyon.


Supervisor(s): Prof. Aonghus McNabola