Previous Article in Journal
Wind-Induced Water Transport and Circulation Structure in the Laptev Sea–East Siberian Sea
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting

1
Computer Engineering Department, Munzur University, 62000 Tunceli, Turkey
2
Faculty of Education and Liberal Arts, INTI International University, 71800 Nilai, Negeri Sembilan, Malaysia
3
Computer Programming, Siverek Vocational School, Harran University, Sanlıurfa 63200, Turkey
4
Medical Documentation and Secretary Training, Siverek Vocational School, Harran University, Sanlıurfa 63200, Turkey
5
Department of Finance and Banking, Recep Tayyip Erdogan University, Fener Street, 53100 Rize, Turkey
6
Accounting and Tax Applications program, Siverek Vocational School, Harran University, Şanlıurfa 63300, Turkey
*
Authors to whom correspondence should be addressed.
Atmosphere 2025, 16(9), 1003; https://doi.org/10.3390/atmos16091003
Submission received: 10 July 2025 / Revised: 16 August 2025 / Accepted: 19 August 2025 / Published: 24 August 2025
(This article belongs to the Section Air Quality)

Abstract

Particulate matter, particularly PM2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health.
Keywords: air quality; machine learning; deep learning; convolutional neural network; recurrent neural network air quality; machine learning; deep learning; convolutional neural network; recurrent neural network

Share and Cite

MDPI and ACS Style

Utku, A.; Can, U.; Alpsülün, M.; Balıkçı, H.C.; Amoozegar, A.; Pilatin, A.; Barut, A. Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting. Atmosphere 2025, 16, 1003. https://doi.org/10.3390/atmos16091003

AMA Style

Utku A, Can U, Alpsülün M, Balıkçı HC, Amoozegar A, Pilatin A, Barut A. Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting. Atmosphere. 2025; 16(9):1003. https://doi.org/10.3390/atmos16091003

Chicago/Turabian Style

Utku, Anıl, Umit Can, Mustafa Alpsülün, Hasan Celal Balıkçı, Azadeh Amoozegar, Abdulmuttalip Pilatin, and Abdulkadir Barut. 2025. "Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting" Atmosphere 16, no. 9: 1003. https://doi.org/10.3390/atmos16091003

APA Style

Utku, A., Can, U., Alpsülün, M., Balıkçı, H. C., Amoozegar, A., Pilatin, A., & Barut, A. (2025). Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting. Atmosphere, 16(9), 1003. https://doi.org/10.3390/atmos16091003

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop