A novel multistep framework for PM2.5 concentration assessment using probabilistic and spatial methods

Among various environmental challenges, air pollution poses one of the most severe threats to human health and ecological systems. Fine particulate matter (PM2.5) is particularly harmful due to its ability to penetrate deep into the respiratory system and enter the bloodstream. Effective monitoring of PM2.5 is therefore crucial for timely environmental management and informed policy-making. Historical assessments of air quality indices reveal decades of methodological improvements, including refined scaling procedures, component selection, and multi-pollutant aggregation. However, existing indices primarily focus on concentration levels, severity, or frequency of exceedance and fail to capture the temporal distribution of pollution across months. This limitation is significant, as air pollution often exhibits strong seasonal variability that is overlooked by conventional measures. Furthermore, no index specifically addresses the temporal distribution of PM2.5 concentrations or quantifies how evenly they are spread throughout the year. To address this gap, the present study introduces the Standardized Particulate Matter Concentration Index (SPMCI), an adaptation of the established PCI framework, designed to assess the temporal concentration patterns of PM2.5. The SPMCI provides a simple and interpretable measure for classifying PM2.5 distribution behavior into uniform or irregular regimes. Building on this conceptual foundation, the present analysis applies the SPMCI across Pakistan using long-term data from 164 monitoring locations covering the period 1980-2024. Integrating probabilistic characterization, state transitions, steady-state probabilities, and geostatistical modeling into a unified analytical framework, the study reveals noticeable temporal variability, with frequent shifts between uniform and irregular states and high persistence of irregular behavior across major urban corridors. Frequency analysis shows that cities such as Lahore, Faisalabad, and Multan exhibit markedly higher occurrences of irregular years, indicating recurrent episodes of unstable pollution regimes. Spatial analyses further highlight persistent hotspots over central Punjab and strong short-range dependence, while northern high-altitude regions remain comparatively less affected. The proposed probabilistic-spatial formulation demonstrates that the SPMCI effectively captures both the magnitude and organization of PM2.5 variability, offering an interpretable basis for early-warning assessment and spatially targeted mitigation. By providing a coherent representation of long-term fluctuations, spatial gradients, and uncertainty patterns, the framework supports more informed air quality management and strengthens the evidence base for designing region-specific intervention strategies.