Correlation-network analysis of coherent daily PM10 variability regimes in Riyadh

Understanding city-wide PM 10 fluctuations, rather than relying solely on individual monitoring sites, is essential for accurately characterizing air quality dynamics, assessing public health risks, and supporting effective monitoring and management strategies. This study investigates daily PM 10 variability across Riyadh, Saudi Arabia, using a correlation-based network framework combined with community detection analysis applied to standardized daily PM 10 observations aggregated from hourly monitoring data collected at 16 monitoring stations from April 2015 to December 2017. The analysis quantifies spatial coherence, identifies dominant connectivity structures, and delineates coherent variability regimes across the urban domain. Results indicate strong network-wide coherence, with Pearson correlation coefficients computed between standardized daily PM 10 time series from different monitoring stations predominantly exceeding 0.75 and persisting under relatively high correlation thresholds. These findings indicate strong synchronization in daily PM 10 variability across the monitoring network and consistency with regional-scale atmospheric forcing. Network density remained high even under relatively stringent correlation thresholds, indicating persistent spatial coherence in daily PM 10 variability across the monitoring network. Centrality analysis reveals pronounced spatial heterogeneity, with betweenness centrality spanning more than an order of magnitude. This highlights a limited number of key locations that disproportionately influence network connectivity. Community detection identifies three distinct network communities corresponding to coherent PM 10 variability regimes, which, when projected into geographic space, form spatially contiguous zones that distinguish urban, transitional, and peripheral patterns. The urban core exhibits stronger connectivity and higher centrality, consistent with enhanced mixing and emission-driven resuspension, whereas peripheral zones show weaker connectivity, reflecting greater sensitivity to regional dust transport and synoptic-scale processes. Overall, the findings demonstrate that correlation network analysis provides an effective framework for characterizing the spatial organization and regime structure of daily PM 10 variability in arid urban environments.