Air Quality Index (AQI) Prediction Using Machine Learning and Deep Learning Approaches

Abstract

Precise prediction of the Air Quality Index (AQI) is vital for the prevention of public health hazards and policymaking. In this research, we introduce an extensive assessment of machine learning (ML) and deep learning (DL) models for AQI prediction on India’s Central Pollution Control Board (CPCB) 2023 data with pollutant levels (PM2.5, PM10, NO2, SO2, CO, O3) and meteorological features. We pre-process the data using mean imputation, one-hot encoding, and standardization and classify the AQI value into six categories of pollution according to CPCB guidelines. Three models, K-Nearest Neighbors (KNN), XGBoost, and a Neural Network (NN), are utilized and compared. For improved performance, we use hyperparameter optimization for the Neural Network using Keras Tuner, adjusting the number of layers, units, dropout rates, and learning rates. The hyperparameter-optimized Neural Network attains 98.17% accuracy, outperforming conventional models (KNN: 85.39%, XGBoost: 72.91%) and attaining improved precision (98.32%), recall (98.17%), and F1-score (98.18%). Results show the superiority of deep learning in identifying intricate air quality patterns and the importance of hyperparameter optimization. This framework offers a scalable approach for real-time AQI monitoring systems to facilitate timely public alerts and datadriven policymaking. The research introduces the capability of hyperparameter-optimized Neural Networks in environmental informatics and recommends future integration with temporal models (e.g., LSTM) for dynamic forecasting

Introduction

???? Overview

Air pollution is a major global health crisis, causing approximately 7 million premature deaths annually (WHO). In India, urbanization and industrialization have significantly degraded air quality, with cities like Delhi frequently showing hazardous AQI levels.

To combat this, accurate AQI prediction models are essential for early warnings, policy enforcement, and empowering public health decisions.

???? Research Aim

This study introduces a regression-to-classification approach for AQI prediction using the CPCB 2023 dataset, comparing:

• K-Nearest Neighbors (KNN)

• XGBoost

• Neural Networks (NNs) (with and without hyperparameter tuning)

It also emphasizes hyperparameter tuning to improve deep learning performance and applicability in real-world air quality monitoring.

???? Key Contributions

1. Benchmarking classical ML vs. deep learning approaches

2. Hyperparameter Tuning (layers, dropout, learning rate), improving NN accuracy from 94.67% to 98.17%

3. Practical Relevance: Tuned NN outperforms all models and is suitable for real-time AQI monitoring

?? Methodology Summary

• Dataset: CPCB 2023 — includes hourly pollutant and meteorological data

• Preprocessing: Missing values handled, outliers capped, one-hot and label encoding used, data standardized

• AQI Categorization: Transformed into 6 classes (Good to Hazardous) based on CPCB standards

???? Models Evaluated:

• KNN: Simple, but weak on high-dimensional and temporal data

• XGBoost: Strong with missing values, but lacks temporal modeling

• Neural Network (NN):

  • Baseline: 3-layer FFNN

  • Optimized: Tuned with Keras Tuner (256-128-64 units, dropout: 0.2, LR: 0.001)

???? Results

The tuned neural network achieved state-of-the-art results, accurately detecting extreme pollution levels and enabling real-time AQI prediction.

???? Limitations in Prior Models

• KNN: High-dimensional inefficiencies and local-only analysis

• XGBoost: Fails to capture long-term temporal patterns

• Others (e.g., CNN-LSTM): Lack global pollutant dispersion modeling and interpretability

• Sensor & latency issues: IoT sensors show 20% error; wireless delays hinder real-time prediction

???? Identified Research Gaps

1. Global Spatiotemporal Modeling missing (e.g., pollutant spread by wind)

2. Lack of Explainability (only 15% of models use SHAP or similar tools)

3. Geographical Bias: 92% of monitoring stations are urban, causing rural prediction errors

4. Climate Adaptation is overlooked (e.g., ozone rise with temperature)

5. Computational Barriers: Many DL models are too heavy for deployment on low-resource devices

6. Ethical Concerns: Little attention to model fairness and exposure disparities

7. Metric Inconsistency: 78% of studies lack standardized evaluation metrics

???? Future Directions

• Edge Optimization: Use quantization/pruning to reduce model size for IoT deployment

• Federated Learning: Train decentralized rural models without data centralization

• Explainable AI (XAI): Use SHAP, counterfactuals for policy-relevant insights

• Climate-Aware Forecasting: Integrate IPCC scenarios and reinforcement learning

• Ethics & Equity: Ensure fair exposure modeling and inclusive sensor deployment

• Standardization: Push for IEEE/ISO standards for data and evaluation

Conclusion

This work showcases the greater accuracy of deep learning models, especially hyperparameter-adjusted Neural Networks (NNs), to predict the Air Quality Index (AQI) with India’s CPCB 2023 data. The adjusted NN had an accuracy of 98.17% and an F1-score of 98.18%, superior to common machine learning algorithms such as KNN (85.39%) and XGBoost (72.91%) and the baseline NN (94.67%). These findings emphasize the pivotal importance of architectural optimization in retrieving intricate spatiotemporal correlations between pollutants (e.g., PM2.5, NO2) and meteorological variables (e.g., wind speed, humidity). The high accuracy of the model in classifying ”Hazardous” AQI levels (99.2% recall) emphasizes its value for timely public health interventions during extreme pollution events. Yet, in practical deployment, challenges remain, such as computational expense (8.2 GFLOPS), geographical bias (urban-rural accuracy difference: 9.2%), and black-box properties of deep learning. Lightweight edge device architectures, explainable AI paradigms for policy decision-making, and federated learning for mitigating data scarcity in rural areas must be the focus of future work. By merging climate projections with ethical AI methods, these models can become scalable, fair, tools for managing global air quality. This study not only moves forward the field of environmental informatics but also maps a model for turning AI innovation into public health solutions.

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Copyright

Copyright © 2025 Agrim Verma, Vidushi Sharma. 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.