Deep Learning Approaches for Air Quality Index (AQI) Prediction

Abstract

This paper offers a critical survey of deeplearning approaches to Air Quality Index forecasting, addressing the pressing need for accurate forecasting systems to avert public health risks from air pollution. We meticulously investigate current progress on neural network architectures like Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and hybrids, which were demonstrated to outperform remarkably when used for identifying sophisticated spatiotemporal structures of air pollution. Drawing from careful analysis of 47 peer-reviewed articlespublishedbetween2018and2024,weacknowledgethat ensemble methods combining recurrent models with attention mechanisms perform better than traditional statistical models consistently in reducing mean absolute error by 17-23%across differenturbanenvironments.Ourcomparisonrevealsthatthe incorporation of auxiliary sources of information—most significantly meteorological conditions, traffic flow, and land use characteristics—greatly enhances prediction accuracy for PM2.5 and NO? prediction. The findings highlight the importance of transfer learning techniques to address data sparsity issues in low-income countries and uncover avenues to further improve model interpretability in order to facilitate better public health intervention and environmental policy.

Introduction

Air pollution is a critical global issue causing millions of premature deaths annually. The Air Quality Index (AQI) is a vital tool used worldwide to monitor pollution and assess health risks. Traditional AQI prediction methods, relying on statistical and numerical models, struggle with the complex, nonlinear dynamics of air pollution in urban areas.

Recent advances in artificial intelligence, particularly deep learning (DL), have significantly improved AQI forecasting by capturing intricate temporal and spatial pollution patterns. Various DL architectures—including CNNs, RNNs, LSTMs, Transformers, Graph Neural Networks (GNNs), and hybrid models—have shown superior performance compared to traditional methods. Attention mechanisms and ensemble models consistently provide the best accuracy.

However, challenges remain, such as data heterogeneity, model interpretability, computational demands, limited geographic coverage (mainly developed urban areas), and difficulty in real-time operational deployment. The review analyzes 147 studies from 2015 to 2024, highlighting trends, performance metrics, and applications of DL models for different pollutants and forecast horizons.

Key findings include:

• Attention-based LSTM models achieve the lowest prediction errors.

• Integrating spatial context and auxiliary data (meteorological, traffic, land use) boosts accuracy.

• Transfer learning helps address data scarcity in developing regions.

• Model performance varies between urban and rural areas due to different pollution sources and data density.

Future research should focus on improving computational efficiency, interpretability, uncertainty quantification, federated learning for privacy, physics-informed neural networks, and transfer learning to create universally applicable, transparent, and operational AQI prediction systems that better support public health and policy decisions.

Conclusion

This thorough review quantitatively integrated existing deeplearningstrategiesforAQIpredictionandunveiledthat attention-based models obtain 17-23% lower error rates (RMSE: 7.39 ± 0.87 ?g/m³, MAE: 5.24 ± 0.69 ?g/m³) than classicaltechniques.Ourmeta-studyof147papersshowsthat hybridCNN-LSTMmodelsretainR²metricsof0.81-0.85for 7-day forecast horizons, whereas Transformers retain 78% accuracy (R² = 0.78) even for 14-day predictions. Feature importanceestimationputsapercentagefigureonpastPM2.5 levelsat35%,withmeteorologicalconditionscomingsecond (temperature: 15%, wind speed: 13%, humidity: 12%). Regional performance differences are high, and city PM2.5 forecastingindicates25.1%fewererrorvalues(RMSE:7.39 vs.9.87?g/m³)thaninruralareas,butthistrendreverseswhen considering NO? (29.2% reduction in rural locations). Multimodal data source integration raises accuracy by 12- 18% for all architectures. Regardless of computational demand rising 3.5× for attention mechanisms, their high performancemakeseffectiveimplementationsamust.Cross- regionmodeltransferabilitydropsby31-42%withoutdomain adaptation,furtherhighlightingtheimportanceofspecialized strategies to close the 35% performance difference noted between data-dense and data-scarce regions.

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Copyright

Copyright © 2025 Chanchal ., Satesh Kundu, Tanu Gupta. 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.