Machine Learning Approaches for PM2.5 Prediction: A Comparative Study

Air pollution poses a serious environmental and public health challenge, particularly due to fine particulate matter (PM₂. ₅), which can penetrate deep into the human respiratory system. Accurate forecasting of PM₂. ₅ concentrations is therefore essential for early warning systems and mitigation planning. This study presents a comparative evaluation of five predictive models—Linear Regression, Random Forest, XGBoost, CatBoost, and Long Short-Term Memory (LSTM) using a multi-year hourly (PM₂. ₅) dataset from India. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that all models achieve strong predictive performance, with LSTM yielding the lowest MAE and RMSE, while CatBoost attains the highest R². Visual analyses, including time-series comparisons and observed-versus-predicted plots, further validate model robustness. The findings demonstrate that machine learning and deep learning approaches can provide accurate and interpretable PM₂. ₅ forecasts, supporting effective air quality management and decision-making air quality forecasts to facilitate prompt decision-making.

Air Quality Prediction, PM2.5 Forecasting, Machine Learning, Linear Regression, Random Forest XGBoost, CatBoost

1. Krzyżewska, “Breathing Cities: Air Quality, Population Exposure, and Sustainability Implications in 33 European Capitals,” Sustainability (Switzerland), vol. 17, no. 16, Aug. 2025, doi: 10.3390/su17167476. [Google Scholar] [Crossref]

2. Y. Liu, K. Zhang, B. Yu, B. Liao, F. Song, and C. Tang, “A Symmetry-Driven Hybrid Framework Integrating ITTAO and sLSTM-Attention for Air Quality Prediction,” Symmetry (Basel), vol. 17, no. 8, Aug. 2025, doi: 10.3390/sym17081369. [Google Scholar] [Crossref]

3. L. Dronjak, S. Kanan, T. Ali, R. Assim, and F. Samara, “A Multi-Faceted Approach to Air Quality: Visibility Prediction and Public Health Risk Assessment Using Machine Learning and Dust Monitoring Data,” Sustainability (Switzerland), vol. 17, no. 14, Jul. 2025, doi: 10.3390/su17146581. [Google Scholar] [Crossref]

4. M. Kozłowski, A. Asenov, V. Pencheva, S. A. Bęczkowska, A. Czerepicki, and Z. Zysk, “Autonomous System for Air Quality Monitoring on the Campus of the University of Ruse: Implementation and Statistical Analysis,” Sustainability (Switzerland), vol. 17, no. 14, Jul. 2025, doi: 10.3390/su17146260. [Google Scholar] [Crossref]

5. N. G. Rezk, S. Alshathri, A. Sayed, E. El-Din Hemdan, and H. El-Behery, “Sustainable Air Quality Detection Using Sequential Forward Selection-Based ML Algorithms,” Sustainability (Switzerland), vol. 16, no. 24, Dec. 2024, doi: 10.3390/su162410835. [Google Scholar] [Crossref]

6. Y. El Mghouchi, M. T. Udristioiu, and H. Yildizhan, “Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania,” Sensors, vol. 24, no. 5, Mar. 2024, doi: 10.3390/s24051532. [Google Scholar] [Crossref]

7. J. Ma, Y. Ding, V. J. L. Gan, C. Lin, and Z. Wan, “Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM,” IEEE Access, vol. 7, pp. 107897–107907, 2019, doi: 10.1109/ACCESS.2019.2932445. [Google Scholar] [Crossref]

8. Y. Zhou, S. De, G. Ewa, C. Perera, and K. Moessner, “Data-driven air quality characterization for urban environments: A case study,” IEEE Access, vol. 6, pp. 77996–78006, 2018, doi: 10.1109/ACCESS.2018.2884647. [Google Scholar] [Crossref]

9. I. Stergiou, N. Traka, D. Melas, E. Tagaris, and R. E. P. Sotiropoulou, “A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment,” Atmosphere (Basel), vol. 16, no. 6, Jun. 2025, doi: 10.3390/atmos16060739. [Google Scholar] [Crossref]

10. M. Zareba, S. Cogiel, and T. Danek, “Spatio-Temporal PM2.5 Forecasting Using Machine Learning and Low-Cost Sensors: An Urban Perspective,” MDPI AG, Jul. 2025, p. 6. doi: 10.3390/engproc2025101006. [Google Scholar] [Crossref]

11. M. Andrade et al., “On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms,” Sustainability (Switzerland), vol. 16, no. 2, Jan. 2024, doi: 10.3390/su16020708. [Google Scholar] [Crossref]

12. I. E. Agbehadji and I. C. Obagbuwa, “Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction,” Nov. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/atmos15111352. [Google Scholar] [Crossref]

• Students' Perception Towards Artificial Intelligence in Higher Education in India
• Strategic Leadership and Cybersecurity Readiness in Digitally Transforming Organisations
• Spatial Distribution of Tourism Infrastructure in Awka, Onitsha and Nnewi Urban Areas of Anambra State.
• Emerging Technologies, Education and Skill Development for A Sustainable Blue Economy in Nigeria.
• Quantum Dot–Based Solar Cells: Advancements, Challenges, and Future Prospects