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<p>This study aimed to predict PM<sub>2.5</sub> concentrations using Aerosol Optical Depth (AOD) data and compare its effect on mortality with ground-based exposure. Linear regression model including meteorological covariates was used in order to predict PM<sub>2.5</sub> for each pixel. The predicted PM was visualized using Inverse Distance Weighting (IDW) interpolation. The aggregated PM was corrected for bias using standardization method and observed data which was eventually used in the effect size estimation model. In the model, we estimated both city-specific and pooled cumulative relative risk across different lags by ground-based and bias-corrected data. We also estimated attributable risk and number by both exposures. There was inconsistency between the effects of ground-based and bias-corrected data. In Ahvaz, for example, the cumulative RR during 14 days was statistically significant based on bias-corrected data (CRR<sub>0 − 14</sub>: 1.016; CI95%: 1.004, 1.028), while no significant effect was observed by the ground-based exposure. The number of deaths in this city was mainly explained by PM<sub>2.5</sub> concentration ranged 15–100&#xa0;µg/m³, and 0–15&#xa0;µg/m³ range resulted in negligible effect. Except for Urmia, a considerable uncertainty in the effects was observed by ground-based data, especially in Sanandaj, Kermanshah and Ilam. Totally, bias-corrected data had greater certainty in the estimation of mortality risk, particularly in the city with the highest pollution level while there was a considerable uncertainty in the effects by ground-based data.</p>

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Fine Particulate Matter and its Impact on Mortality Using Ground and MODIS-Based Data: A Study of Comparison Between the Two Exposures

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Reza Rezaee,
  • Omid Aboubakri,
  • Afshin Maleki,
  • Esmaeil Ghahramani,
  • Afshin Azarniusheh,
  • Gholamreza Goudarzi,
  • Mahdi Safari

摘要

This study aimed to predict PM2.5 concentrations using Aerosol Optical Depth (AOD) data and compare its effect on mortality with ground-based exposure. Linear regression model including meteorological covariates was used in order to predict PM2.5 for each pixel. The predicted PM was visualized using Inverse Distance Weighting (IDW) interpolation. The aggregated PM was corrected for bias using standardization method and observed data which was eventually used in the effect size estimation model. In the model, we estimated both city-specific and pooled cumulative relative risk across different lags by ground-based and bias-corrected data. We also estimated attributable risk and number by both exposures. There was inconsistency between the effects of ground-based and bias-corrected data. In Ahvaz, for example, the cumulative RR during 14 days was statistically significant based on bias-corrected data (CRR0 − 14: 1.016; CI95%: 1.004, 1.028), while no significant effect was observed by the ground-based exposure. The number of deaths in this city was mainly explained by PM2.5 concentration ranged 15–100 µg/m³, and 0–15 µg/m³ range resulted in negligible effect. Except for Urmia, a considerable uncertainty in the effects was observed by ground-based data, especially in Sanandaj, Kermanshah and Ilam. Totally, bias-corrected data had greater certainty in the estimation of mortality risk, particularly in the city with the highest pollution level while there was a considerable uncertainty in the effects by ground-based data.