Estimating Local Air Pollutant Contribution Ratio Based on Concentration Variability Among Monitoring Stations

Quantifying the relative contributions of local emissions and regional transport is critical for urban air quality management. Chemical transport models (CTMs) are widely applied for source apportionment, but they require detailed emission inventories, extensive input data, and substantial computational resources, which limit their operational use. In contrast, urban monitoring networks provide continuous and readily available observations. This study develops an observation-based framework that estimates regional contribution ratios (RCs) from inter-station concentration variability, quantified by the coefficient of variation (CV), using WRF–CAMx results as a reference. Using Linyi as the primary case, with Xi’an and Beijing for comparison, concentration-stratified regression was applied to establish CV–RC relationships. Results show a consistent nonlinear relationship between CV and RC, with coefficients of determination (R2) up to 0.86 for PM10 (daily), 0.81 for NO2 (hourly), and 0.78–0.79 for O3. CV decreases markedly with increasing concentration; for PM2.5, values decline from ~0.17–0.18 to 0.05–0.06 (≈65–70%), indicating enhanced spatial homogeneity under regional influence. The relationship is most stable within a 10–15 km spatial scale. Application-based evaluation for January 2022 shows moderate agreement between estimated and modeled RC (R = 0.55–0.65), reflecting pollutant-dependent uncertainties, partly associated with biases in the model-derived reference RC. These results demonstrate that inter-station concentration variability provides a first-order, computationally efficient indicator of the balance between local emissions and regional transport.