An alternative approach for estimating large-area indoor PM2.5 concentration – A case study of schools

Pei Yi Wong, Hsiao Yun Lee, Ling Jyh Chen, Yu Cheng Chen, Nai Tzu Chen, Shih Chun Candice Lung, Huey Jen Su, Chih Da Wu, Jose Guillermo Cedeno Laurent, Gary Adamkiewicz, John D. Spengler

研究成果: Article同行評審

摘要

Background: For indoor air modelling, difficulties in collecting indoor parameters including life activity patterns and building characteristics are dilemmas when conducting a large-area study. Land-use/land cover information which is easier to obtain could represent as surrogates of emission sources for assessing indoor air quality. Moreover, low-cost sensors and machine learning provide a better way to enhance model accuracy. Objectives: This study proposed an alternative estimation approach to assess daily PM2.5 concentration for indoor environments of schools in a large area by integrating low-cost sensors, land-use/land cover predictors, and machine learning-based modelling approaches. Methods: Indoor PM2.5 data was collected from 145 indoor AirBox sensors in Kaohsiung and Pingtung Counties of Taiwan. Geospatial predictors were extracted from the circular buffers surrounding each AirBox sensor. Spearman correlation analysis and stepwise variable selection procedures were performed to select variables for land-use regression (LUR) and integrated with XGBoost, Random Forest (RF), and LGBM machine learning models. Results: The results revealed that outdoor PM2.5 and distance to the nearest thermal power plant were the main determinants of indoor estimation variations, when there were no indoor sources. When incorporating machine learning, the R2 increased from 0.59 for LUR to 0.85 for LUR-XGBoost while the RMSE decreased from 8.63 to 5.27 μg/m3, which performed better than both LUR-RF and LUR-LGBM. Conclusions: This study demonstrates the value of the proposed alternative approach by incorporating data from a low-cost sensor with LUR model and machine learning algorithm in estimating the spatiotemporal variability of indoor PM2.5 for a large area.

原文English
文章編號109249
期刊Building and Environment
219
DOIs
出版狀態Published - 2022 7月 1

All Science Journal Classification (ASJC) codes

  • 環境工程
  • 土木與結構工程
  • 地理、規劃與發展
  • 建築與營造

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