A Novel Hybrid Approach: Integrating Bayesian SPDE and Deep Learning for Enhanced Spatiotemporal Modeling of PM 2.5 Concentrations in Urban Airsheds for Sustainable Climate Action and Public Health

Daniel Patrick Johnson (), Niranjan Ravi, Gabriel Filippelli and Asrah Heintzelman
Additional contact information
Daniel Patrick Johnson: Department of Geography, Indiana University, Indianapolis, IN 46202, USA
Niranjan Ravi: Department of Electrical and Computer Engineering, Indiana University, Indianapolis, IN 46202, USA
Gabriel Filippelli: Department of Earth and Environmental Science, Indiana University, Indianapolis, IN 46202, USA
Asrah Heintzelman: Department of Earth Sciences, Indiana University, Indianapolis, IN 46202, USA

Sustainability, 2024, vol. 16, issue 23, 1-28

Abstract: This study introduces a novel hybrid model combining Bayesian Stochastic Partial Differential Equations (SPDE) with deep learning, specifically Convolutional Neural Networks (CNN) and Deep Feedforward Neural Networks (DFFNN), to predict PM 2.5 concentrations. Traditional models often fail to account for non-linear relationships and complex spatial dependencies, critical in urban settings. By integrating SPDE’s spatial-temporal structure with neural networks’ capacity for non-linearity, our model significantly outperforms standalone methods. Accurately predicting air pollution supports sustainable public health strategies and targeted interventions, which are critical for mitigating the adverse health effects of PM 2.5 , particularly in urban areas heavily impacted by climate change. The hybrid model was applied to the Pleasant Run Airshed in Indianapolis, Indiana, utilizing a comprehensive dataset that included PM 2.5 sensor data, meteorological variables, and land-use information. By combining SPDE’s ability to model spatial-temporal structures with the adaptive power of neural networks, the model achieved a high level of predictive accuracy, significantly outperforming standalone methods. Additionally, the model’s interpretability was enhanced through the use of SHAP (Shapley Additive Explanations) values, which provided insights into the contribution of each variable to the model’s predictions. This framework holds the potential for improving air quality monitoring and supports more targeted public health interventions and policy-making efforts.

Keywords: PM2.5 concentrations; air quality modeling; Bayesian SPDE; deep learning; Convolutional Neural Networks (CNN); Deep Feedforward Neural Networks (DFFNN); hybrid models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/23/10206/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/23/10206/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:23:p:10206-:d:1526630

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().