Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia. (June 2019) - Akopov, Andranik S.; Beklaryan, Levon A.; Saghatelyan, Armen K.
Abstract: Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation and configuration of tree clusters in a city. The Pareto optimal solutions for greenery in the city were computed using the suggested heuristic optimisation algorithm, considering the complex absorptive-diffusive interactions between agent-trees (tree clusters) and air pollutants produced by agent-enterprises (factories) and agent-vehicles (car clusters) located in the city. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found the optimal strategy under the budget constraint: planting various types of trees around kindergartens and emission sources. Highlights: We developed an original agent-based model of the interactions between air pollutants and greenery. A decision-making system for greenery planning was developed for Yerevan, Armenia. Two bi-objective optimisation problems were suggested and solved for reducing the air pollution concentration. The Pareto optimal solutions for urban greenery were computed through a genetic optimisation algorithm.