Quantum Computing–Based Optimization of Urban Air Pollution Control Strategies

Abstract

Multiple sources of pollution, nonlinear interactions, and competing environmental and economic goals are characteristics of complicated optimisation problems in urban air pollution control. When dealing with such high-dimensional decision spaces, conventional optimisation strategies frequently encounter scaling issues. This study suggests an optimisation structure for urban air pollution management methods based on quantum computing. In order to minimise pollutant concentrations while meeting operational and regulatory restrictions, the suggested method formulates air pollution reduction as a constrained optimisation problem in which industry rules, traffic management policies, and emission reduction strategies are optimised. To effectively explore the solution space, a hybrid quantum–classical optimisation model is created employing quantum-inspired algorithms. The framework's efficiency is compared with traditional optimisation techniques with respect to of convergence effectiveness and solution quality using simulated municipal air quality & emission data. The results show that for complicated pollution management scenarios, the quantum-based optimisation strategy achieves better optimisation performance and faster convergence. The results show how quantum computing paradigms can be a useful tool for solving large-scale environmental optimisation issues and assisting with data-driven the air quality in cities management.