Quantifying the impact of the uncertainty arising from spatial allocation on public health using CMAQ

The spatial allocation of emissions in air quality models introduces uncertainties that significantly impact pollution exposure assessments. This study quantified the effects of emission allocation uncertainty on atmospheric concentrations and exposure levels using the CMAQ modeling system. The research focused on the Afşin-Elbistan Power Plant (AP), with substantial emissions of SO₂ (∼300,000 t/y) and PM_2.5 (∼6000 t/y), evaluating the variability in concentrations from emission allocation in gridded inventories. 13 model simulations were conducted, including a base case (c0) where emissions were spatially allocated based on intersection ratios and 12 scenario cases (c1–c12) where emissions were assigned to different grids for 2018. Results showed significant variability in pollution levels and population exposures across scenario cases. In the Maximum Impact Zone (MIZ), annual mean PM_2.5 concentrations ranged from 5.0 to 41.3 μg/m³, with differences up to 24.9 μg/m³ from the base case. SO₂ exhibited even greater variability, with maximum differences reaching 338.2 μg/m³. The 95 % probability range of uncertainty for PM_2.5 was estimated at −45 % to +96 %, while for SO₂, it reached −84 % to +240 %. Grids A–F represent six selected regions with high population density, used to evaluate differences in concentration and exposure across scenarios. In Grid A-F, meteorology influenced these patterns, with low wind speeds causing pollutant build-up in Grid A, while pollutant transport affected Grids D–F in summer. Annual population exposure in Grid C ranged from 1.0 to 2.1 kg/y for PM_2.5 and from 3.9 to 16.7 kg/y for SO₂. This paper highlights the importance of not only absolute emission inventories but also spatial emission allocation in air quality models to enhance regulatory effectiveness and protect public health.