What question did this study set out to answer?

To evaluate various machine learning models for classifying air quality based on pollutants in urban settings.

February 25, 2026Sustainability2 citationsOpen Access

A Hybrid Machine Learning Framework for Multi-Pollutant Air Quality Assessment in Urban Environments

MMMuzzamil Mustafa MAMaaz Akhtar AAAshfaq AhmadInstitute of Mountain Hazards and Environment

Key Points

To evaluate various machine learning models for classifying air quality based on pollutants in urban settings.
Utilized Random Forest, Support Vector Machine, LSTM, and Bi-LSTM models.
Preprocessed multi-pollutant data from urban monitoring stations in India.
Applied class-balancing and validation protocols for accurate comparisons.
Random Forest achieved the highest accuracy at 0.9971.
Bi-LSTM, LSTM, and SVM followed with accuracies of 0.9615, 0.9495, and 0.9442 respectively.
Bi-LSTM demonstrated stable performance in boundary-sensitive classifications.

Abstract

Urban air quality assessment is central to environmental sustainability and public health management. This study presents a structured comparative evaluation of Random Forest (RF), Support Vector Machine (SVM), LSTM, and Bi-LSTM models for pollutant-driven air quality classification under the Indian National Air Quality Index (NAQI) framework defined by CPCB guidelines. To provide a fair comparison, multi-pollutant data of Indian urban monitoring stations were preprocessed, and the class-balancing protocol and validation protocol were combined. RF had highest total accuracy (0.9971) in the held-out set, with Bi-LSTM (0.9615), LSTM (0.9495), and SVM (0.9442) coming next. Although ensemble methods proved to be very separable in line with the threshold-based NAQI structure, Bi-LSTM was more stable when it came to boundary-sensitive switches among the adjacent severity classes. Calibration analysis (multiclass Brier score: 0.08) showed consistent probabilistic behavior and interpretation, and using SHAP showed physically significant pollutant driving factors. The results explain the appropriateness of comparative models in organized AQI classification and present a reproducible assessment framework for the NAQI framework.

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