A Survey on Air Pollution using Deep Learning

Abstract

Air pollution remains a significant environmental and public health issue, with particulate matter being a major factor in declining air quality. This research is an attempt to examine the various methodologies for monitoring, classifying, and forecasting air quality using statistical approaches, time-series analysis, and machine learning techniques. Traditional models such as regression analysis, ARIMA, and time-series decomposition have been commonly employed for air quality evaluation. However, advancements in artificial intelligence (AI) have introduced more precise predictive models, including support vector machines (SVM), deep convolutional neural networks (DCNN), and long short-term memory (LSTM) networks. Furthermore, the impact of meteorological factors on pollutant dispersion and the effectiveness of urban greening strategies in reducing air pollution are explored. The findings indicate that hybrid AI models, which combine deep learning with statistical techniques, demonstrate superior predictive capabilities, presenting a promising approach for real-time air quality monitoring and informed decision-making

Introduction

Air pollution is a critical global environmental issue with severe impacts on health, climate, and ecosystems, particularly due to particulate matter (PM). Accurate monitoring and forecasting are essential for effective mitigation and policy-making.

History:
Air pollution awareness dates back to ancient times but systematic monitoring began in the 20th century, with key events like the 1952 Great Smog of London prompting regulation. The establishment of agencies like the US EPA and the development of air quality indices (AQI) laid the foundation for air quality research.

Evolution of Prediction Models:
Early models used statistical methods (e.g., ARIMA, linear regression) that struggled with nonlinear data. The 2000s saw machine learning (SVM, ANN, random forests) improve predictions by capturing complex patterns. Recently, deep learning models (CNNs, LSTMs) leveraging big data and IoT sensors have advanced real-time forecasting, combining spatial and temporal analysis in hybrid CNN-LSTM architectures.

Challenges:
Air quality prediction faces sensor inaccuracies, data gaps, nonlinear pollution dynamics, and high computational demands of AI models. Additionally, AI models often lack interpretability, and inconsistent regulations complicate application.

Deep Learning Advantages:
Deep learning handles complex, nonlinear relationships in pollution data better than traditional models. LSTMs excel at temporal forecasting, CNNs at spatial analysis, and hybrid models combine both strengths for improved accuracy.

Literature Insights:

• AI and deep learning outperform conventional models in classifying and forecasting pollutants.

• Meteorological data integration enhances predictions.

• Urban greening significantly reduces pollution.

• Hybrid and ensemble models yield superior results.

• Multi-output models can forecast multiple pollutants simultaneously, improving efficiency.

Methodologies:
Air quality prediction involves data collection, preprocessing, feature extraction, model training, and evaluation. Traditional ML models like SVM, Random Forest, and XGBoost are widely used alongside deep learning (CNN, LSTM, Transformers). Hybrid models balance accuracy and interpretability.

Evaluation Metrics:
Models are evaluated by accuracy, precision, recall, and F1-score. Deep learning and hybrid models achieve higher scores (accuracy up to ~99%, F1 > 92%) compared to traditional ML (accuracy ~85–95%, F1 ~85–90%).

Conclusion

Air pollution remains a critical global issue, posing significant health and environmental risks. While traditional statistical models like regression and time-series forecasting have laid the groundwork for air quality assessment, their limitations in capturing complex, nonlinear pollution patterns have led to the growing adoption of AI-based techniques. Literature reveals that hybrid and ensemble models—such as CNN-LSTM combinations, SVM with advanced kernels, and ensemble methods like Random Forest and Gradient Boosting—offer superior accuracy by leveraging both spatial and temporal features of pollution data. Among these, LSTM models stand out for their strength in time-series forecasting, especially when multiple pollutants and meteorological parameters are involved. Additionally, studies emphasize the effectiveness of urban greening in reducing PM levels and improving air quality. Future advancements should focus on integrating state-of-the-art AI architectures like Transformers and federated learning for decentralized, real-time predictions, expanding IoT-based sensor networks, and combining AI with statistical and physical simulations. Personalized monitoring through wearables and AI-driven insights can further support policy-making and smart city planning, ultimately enhancing global air quality management.

References

[1] Hussain, et al. (2020). Machine learning classification of PM2.5 & PM10 using SVM, Decision Tree, and KNN. Environmental Monitoring and Assessment, 192(4), 1-15. [2] Akbal, & Unlu. (2023). Hybrid deep learning models for PM2.5 forecasting using CNN, RNN, and LSTM. Atmospheric Environment, 289, 119317. [3] Ceran, et al. (2023). AI-integrated meteorological models for PM10 concentration prediction. Journal of Air Quality and Climate Change, 45(2), 67-82. [4] Chen, et al. (2021). Time-series decomposition and hybrid AI models for short-term AQI forecasting. International Journal of Environmental Science, 58(3), 225-240. [5] Ali Shah, et al. (2019). Phase Space Reconstruction for nonlinear modeling of air pollution fluctuations. Environmental Data Science, 12(1), 78-90. [6] Liu, et al. (2022). Evaluating urban green infrastructure and its impact on PM10 reduction. Urban Sustainability Review, 33(5), 310-325. [7] Wang, et al. (2020). AI-based comparative analysis of AQI forecasting models: XGBoost, DBN, and Regression. Journal of Environmental Informatics, 27(4), 152-168. [8] Schwartz, J. (2019). The effect of PM2.5 exposure on respiratory diseases: A Poisson Generalized Additive Model (GAM) approach. Environmental Health Perspectives, 127(8), 82002. [9] Zhang, et al. (2021). Ensemble learning with Random Forest and Gradient Boosting for AQI prediction. Air Quality, Atmosphere & Health, 14(3), 129-144. [10] Navares, R., & Aznarte, J. L. (2020). Predicting air quality with deep learning LSTM: towards comprehensive models. Ecological Informatics, 55, 101019. [11] Rakholia, R., Le, Q., Ho, B. Q., Vu, K., & Carbajo, R. S. (2023). Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam. Environment International, 173, 107848.

Copyright

Copyright © 2025 Akshayaa M, T. Rajasenbagam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.