Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks
- Author(s)
- Abed Bashardoost, Mohammad Saadi Mesgari, Mina Karimi
- Abstract
Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.
- Organisation(s)
- Department of Geography and Regional Research
- External organisation(s)
- K. N. Toosi University of Technology (KNTU)
- Journal
- Frontiers in Environmental Science
- Volume
- 12
- DOI
- https://doi.org/10.3389/fenvs.2024.1399339
- Publication date
- 07-2024
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 507003 Geoinformatics, 102001 Artificial intelligence, 102035 Data science
- Keywords
- ASJC Scopus subject areas
- General Environmental Science
- Sustainable Development Goals
- SDG 3 - Good Health and Well-being, SDG 11 - Sustainable Cities and Communities
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/a8bf2a52-349d-4311-9a1a-143b312aa330