27 September 2019
Agent-based modeling of air pollution dynamics using a case of Yerevan, Armenia
Urban greenery such as vertically greenery systems can effectively reduce air pollution concentrations in a natural and eco-friendly way. An agent-based model of interactions between vertically greening facades of buildings and air pollutants is developed using a case study of Yerevan, Armenia. We developed the original simulation model of the diffusion of air pollution plumes (e.g. dust, SO2, NOx, VOCs, etc.) emitted by agent-enterprises and agent-vehicles in the city with taking into account interacting between agent-pollutants and urban vertically greenery systems. This model is based on combining methods of agent-based modelling and simplified Gaussian dispersion models for air pollutants. First of them allow to implement the complex diffusion-absorption mechanism of interactions between air pollutants and vertical greenery systems planting around social important objects (e.g. kindergartens). The Gaussian dispersion models allow predicting the dispersion of air pollution plumes emitted by agent-enterprises and agent-vehicles located in the city with the most precision. In the developed simulation model, air pollution concentrations estimated in protected urban areas depend on radiuses of personal spaces of agent-pollutants, their locations and the total number. The spatial dynamics of agent-pollutants are described by differential equations with variable structures with taking into account the Gaussian distribution of their trajectory after emitting. Such approach allows modelling the most realistic scenarios of interactions between air pollutants and greenery in scale of the whole city with keeping up satisfactory computational complexity. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found optimal configurations of vertically greenery systems located near kindergartens (e.g. coordinates of buildings that should be greened, the best kinds of plants for effective absorption of air pollutants, optimal densities of planting, etc.).
Last edited: 17 October 2019
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