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Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration
- Source :
- Environment International, Vol 182, Iss , Pp 108343- (2023)
- Publication Year :
- 2023
- Publisher :
- Elsevier, 2023.
-
Abstract
- Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.
Details
- Language :
- English
- ISSN :
- 01604120
- Volume :
- 182
- Issue :
- 108343-
- Database :
- Directory of Open Access Journals
- Journal :
- Environment International
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.04181bf66c004ddfbf2d2a3465033e8b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.envint.2023.108343