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June 3, 2026Gases0 citationsOpen Access

Artificial Intelligence-Driven Failure Analysis of Smog Mitigation for Sustainable Indoor Air Quality

SZSadaf ZeeshanUniversity of Central Punjab MMMuhammad Ali Ijaz MalikUniversity of Technology Sydney

Key Points

This research aims to assess and predict the operational reliability of urban smog mitigation systems using a data-driven approach.
Developed a machine learning model using Random Forest and XGBoost algorithms.
Utilized integrated meteorological and air quality metrics from Lahore.
Introduced a threshold-controlled indoor air purification system based on real-time predictions.
Random Forest achieved 95.5% accuracy, surpassing XGBoost at 94.5%.
The purifier effectively reduced indoor AQI from dangerous to safe levels within 30–40 minutes.
The model identified specific environmental conditions leading to mitigation failure, enabling proactive responses.

Abstract

In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min.

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