(57e) Agent Based Approach for Prescriptive Mitigation of Exposure to Air Pollution in Urban Setting

Ambient air pollution has a severe effect on human health. Quantifying exposure to individuals is the first step in developing mitigation strategies which in turn depend on air quality data that is available with high spatio-temporal resolution. Existing infrastructure for monitoring air quality that uses static monitors is sparsely distributed and prohibitively expensive to expand. This results in gaps in capturing variability in air quality. Dispersion and CFD based models have been used extensively to fill such gaps. However these models are computationally intensive. Alternatively, hyperlocal air quality information obtained using mobile monitoring is considered in tandem with an agent based modeling framework. In this framework, a system is represented as a collection of agents interacting with each other and with the environment. At the heart of this framework is a mass balance equation, which determines the exchange of mass between the agents and the environment. The model captures high spatio-temporal variation in the air quality. Unlike dispersion and CFD based models which require complex differential-algebraic equations, the agent based framework takes a rule based approach, thus making it computationally less demanding. It also enables the model to capture emergent behavior in the system. In this work, we explore the applications of an agent based framework in analysis and prescription of mitigation strategies in an urban set up. The following applications are studied in detail under the framework:

1. Estimating exposure of an individual to air pollution as a function of their location and time activity information.
2. Determining the route between any two locations in city with the least exposure.
3. Dynamic traffic rerouting

A rule based approach makes it feasible to make such complex decisions for large urban road networks. The results are validated against field data.