Optimising the network of air quality monitoring stations

Abstract

This study explores the application of genetic algorithms (GAs) for optimizing the network of air quality monitoring stations. Recognizing the complexity of accurately assessing air pollution levels across diverse urban and rural landscapes, the research focuses on finding the most effective station placements to maximize coverage and data fidelity while minimizing costs. The methodology entails simulating natural selection processes, including selection, crossover, and mutation, to evolve a population of potential solutions. Each candidate solution in the population represents a unique configuration of monitoring stations. A fitness function evaluates the efficiency of each configuration based on criteria such as population coverage, proximity to pollution sources, and installation and operational expenses. The research employs a genetic algorithm developed in Python, which iteratively refines the population of solutions over thousands of generations. The algorithm's performance is assessed through experimental validation, with an emphasis on the adaptability of the approach to accommodate various environmental, economic, and regulatory constraints. Results indicate that GAs can effectively balance multiple optimization objectives, leading to a network design that is both cost-efficient and comprehensive in its monitoring capabilities. The outcome of the study is an optimized network that significantly improves upon the initial state in terms of coverage and cost-effectiveness. The study concludes that genetic algorithms offer a promising avenue for addressing the challenges of air quality monitoring network design. The flexibility and global search capabilities of GAs make them suitable for the complex, multi-objective nature of the task. Moreover, the findings suggest potential for further improvements and applications of GAs in environmental monitoring and other complex systems optimization scenarios.