Dynamic Programming-Based Decision-Making Model for Selecting Optimal Air Pollution Control Technologies for an Urban Setting

Increasing population, rapid urbanization and hasty industrialization are assisting gradual shift in human residence from rural to urban. Any city trying to become a smart and sustainable one must provide a better quality of life for its citizens and address the problem of degrading air quality and increasing pollutant emissions. The solution for these problems requires selection and decision-making among a number of candidate emission-control-alternatives that need to satiate a number of technical constraints, policy criteria and regulations. For a given pollutant and emission source, a number of control technologies are available; hence, it is coveted to choose the best combination among them to reduce emissions to a desired standard. Because of its applicability, flexibility and ease of computation in solving large-scale practical problems, dynamic programming-based approach appears to be the appropriate and feasible choice for optimal air pollution control technology selection for an urban metropolitan area. Current study develops a dynamic programming (DP) model that determines the optimal selection strategy, after defining different parameters, at a minimized total cost. The usage and applicability of the proposed model were illustrated with a representative case study of a simulated city with three major sources of pollution. It is inferred that DP is ideal for the ‘multiple sources—multiple control technologies—single air pollutant’ optimization problem.