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Calibrating a simulated exposure distribution using measurement error models

Author

Listed:
  • Jiwoong Yu
  • Xueyan Zheng
  • Kwan-Young Bak
  • Kiyoung Lee
  • Woojoo Lee

Abstract

Indirect exposure assessment based on average environmental concentrations in microenvironments and time spent in each environment has been considered an important way of assessing personal exposure to air pollutants. Using this indirect approach, the exposure simulator generates personal exposure values or the distribution of personal exposure for air pollutants. To match the simulator with the actual exposure measurements well, some calibration is necessary. However, unlike simulators generating personal exposure values, research evaluating the validity of the second type of simulator is rare. This study aims to develop a method for calibrating a simulator that generates an exposure distribution. To describe the relationship between the actual exposure measurements and the simulator, we introduce measurement error models (MEMs) and explain how the coefficients in the models can be used for calibrating the exposure distribution. We illustrate the proposed method using a Korea Simulation Exposure Model for fine particulate matter (KoSEM-PMII).

Suggested Citation

  • Jiwoong Yu & Xueyan Zheng & Kwan-Young Bak & Kiyoung Lee & Woojoo Lee, 2025. "Calibrating a simulated exposure distribution using measurement error models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 52(14), pages 2707-2719, October.
    • Handle: RePEc:taf:japsta:v:52:y:2025:i:14:p:2707-2719
    DOI: 10.1080/02664763.2025.2481460
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