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 SO 2 (∼300,000 t/y) and PM 2.5 (∼6,000 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/m 3 , with differences up to 24.9 μg/m 3 from the base case. SO 2 exhibited even greater variability, with maximum differences reaching 338.2 μg/m 3 . The 95% probability range of uncertainty for PM 2.5 was estimated at −45% to +96%, while for SO 2 , 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 SO 2 . 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. • PM 2.5 concentrations exhibited variations of up to 24.9 μg/m 3 among the cases. • SO 2 concentrations deviated by up to 338.2 μg/m 3 due to allocation changes. • The 95% probability range of uncertainty for PM 2.5 was estimated at −45% to +96%. • The 95% probability range of uncertainty for SO 2 was estimated at −84% to +240%. • Annual population exposure was as high as 2.1 kg/y for PM 2.5 and 16.7 kg/y for SO 2 .

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Publication Details

Journal
Atmospheric Environment X
Published
2025-04-01
DOI
https://doi.org/10.1016/j.aeaoa.2025.100338
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
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article

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

Mete Tayanç, Alper Ünal, Fulya Cingiroglu, Ezgi Akyüz
Atmospheric Environment X
Air Quality and Health Impacts
article

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

Mete Tayanç, Alper Ünal, Fulya Cingiroglu, Ezgi Akyüz
article en

Abstract

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 SO 2 (∼300,000 t/y) and PM 2.5 (∼6,000 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/m 3 , with differences up to 24.9 μg/m 3 from the base case. SO 2 exhibited even greater variability, with maximum differences reaching 338.2 μg/m 3 . The 95% probability range of uncertainty for PM 2.5 was estimated at −45% to +96%, while for SO 2 , 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 SO 2 . 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. • PM 2.5 concentrations exhibited variations of up to 24.9 μg/m 3 among the cases. • SO 2 concentrations deviated by up to 338.2 μg/m 3 due to allocation changes. • The 95% probability range of uncertainty for PM 2.5 was estimated at −45% to +96%. • The 95% probability range of uncertainty for SO 2 was estimated at −84% to +240%. • Annual population exposure was as high as 2.1 kg/y for PM 2.5 and 16.7 kg/y for SO 2 .

Atmospheric Environment XVol. 26
University of Washington (US), Istanbul Technical University (TR), Seattle University (US), Marmara University (TR)
Istanbul Teknik Üniversitesi, Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi
Openalex Percentile: Top 83%
Air Quality and Health Impacts
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