Network analysis of ground-level ozone: Implications for environmental policy and air quality management

Abstract

As network science emerges as a transformative tool in the ‘Big Science’ era, this study harnesses this tool to model ground-level ozone distribution dynamics across US states under different regulatory frameworks from 1980 to 2017. The evolution of these regulations provides a unique natural experiment to analyze how network-driven models evolve amidst varied environmental policies. By constructing a network from ozone monitoring sites connected based on Pearson correlation coefficients, we analyzed the structural evolution of air quality networks. Techniques like community detection highlighted localized and temporal variations in ozone levels, influenced by meteorological and energy consumption data. Our findings reveal that geographical and regulatory factors significantly shape the network structure. This research demonstrates how network science can elucidate the complex interdependencies in environmental systems and suggests that integrating these insights could refine air quality regulations, promoting more effective management strategies in line with advanced environmental modeling needs.