Study of Indoor Air Quality Impact During Building Demolition: A Case Study of the 2009 L’Aquila Earthquake Reconstruction

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Published: Jun 19, 2024
DOI: https://doi.org/10.12681/monubasin.8318
Keywords:
Earthquake Reconstruction Indoor Air Quality Impact Particulate Matter Prediction Statistical Analysis Deep Learning
Wei-Shiang Lin
Stefano Sfarra
Yuan Yao
Abstract

This study focuses on the 2009 earthquake in L’Aquila, Italy, as a case study. A user-friendly household monitoring method was adopted for systematic data collection, aiming to enhance the comprehensive understanding of particle pollution. Multivariate statistical methods, such as principal component analysis and partial least squares regression, were implemented for data analysis. Mutual information was also utilized to explore the relationships between variables. Additionally, a deep learning model was developed to predict the diffusion patterns of dust around demolished buildings, attempting to forecast the quantity of suspended particles. Given the data’s potential correlation with time series, autoregressive integrated moving average models were employed as well to forecast particle counts. By combining traditional statistical techniques with advanced analytical tools, it is expected to gain deeper insights into the complex dynamics of air quality during the building demolition process.

Article Details
  • Section
  • Part IV - Methodologies for Characterization and Damage Assessment

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Author Biographies
Wei-Shiang Lin

Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan

Stefano Sfarra

Department of Industrial and Information Engineering and Economics, University of L’Aquila, L’Aquila 67100, Italy

Yuan Yao

Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan

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