Bayesian predictive modeling of indoor ultrafine particles to enhance mid-cost monitoring.

Autor: Hyun Y; School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul 03722, Republic of Korea., Rim D; Department of Architectural Engineering, Pennsylvania State University, University Park, PA, 16802, USA., Wallace L; Wallace Research, Santa Rosa, CA, 95409, USA., Choi JI; School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul 03722, Republic of Korea. Electronic address: jic@yonsei.ac.kr.
Jazyk: angličtina
Zdroj: Environmental research [Environ Res] 2025 Jan 15; Vol. 265, pp. 120384. Date of Electronic Publication: 2024 Nov 23.
DOI: 10.1016/j.envres.2024.120384
Abstrakt: Monitoring airborne nanoparticles has a vital role in indoor air quality control due to their hazardous effects on human health. Detecting particles becomes more challenging as their sizes decrease. While research-grade instruments like the scanning mobility particle sizer (SMPS) can provide detailed and useful information, they are not practical for personal use due to their size and cost. This study aims to provide a comparable prediction of the temporal size distribution of ultrafine particles (UFPs, <100nm) using mid-cost measurements from a handheld particle sizer, which is more economical but has a narrower detectable size range. To achieve this, the study builds upon a computational modeling approach based on a mass-balance equation to estimate the time-varying particle size distribution, while accounting for particle evolution processes such as coagulation, deposition, and ventilation. The analytical model for indoor UFPs requires prior information regarding particle dynamic behavior, such as the size-resolved deposition rate and source emission rate. This study estimates, rather than pre-determines, the model parameters required for the temporal prediction of indoor UFP size distribution by applying Bayesian parameter inference with the analytical model of indoor aerosol. The results indicate that the present model reasonably predicts the temporal evolution of particle distributions, comparable to that of the SMPS. Furthermore, this study demonstrates the identifiability of model parameters, considering both the entire and detectable size ranges, through variance-based global sensitivity analysis.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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