What question did this study set out to answer?

This research aims to predict air quality index values and assess the performance of various machine learning models.

July 10, 2026PLoS ONE0 citationsOpen Access

Air quality index prediction using machine learning regression models: A comparative analysis

FMFiaz MajeedUniversity of Gujrat SSSana SalehaInformation Technology University LALaraib AbbasInformation Technology University

Key Points

This research aims to predict air quality index values and assess the performance of various machine learning models.
Compared four machine learning algorithms: Random Forest, Gradient Boosting, Linear Regression, and Ridge Regression.
Used AQI datasets from Karachi and Peshawar.
Applied standard scaling for AQI normalization and evaluated using metrics like MAE and RMSE.
Machine learning models effectively predicted AQI values with improved accuracy measures.
Mean Absolute Error and Root Mean Square Error yielded significant reduction in prediction error.
Evaluation metrics indicated optimal performance across different cities using standard scaling.

Abstract

Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people’s health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), and sulfur dioxide (SO 2 ), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). This research’s novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.

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