This research’s novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization, highlighting the importance of using standard scalers to achieve optimal model performance.
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 (NO2), ozone (O3), and sulfur dioxide (SO2), 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 (R2). 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.
Air pollution remains one of the major concerns of health crisis in most developing countries with Africa facing a silent health crisis as air pollution worsens, yet predictive tools remain scarce. Pollutants such as PM2.5, NO₂, CO, and O₃ increase the risk of respiratory and cardiovascular diseases. This study develops a Machine Learning (ML) model to predict the Air Quality Index (AQI) and assess health risks across urbanized, industrialized, and rural regions using climatic parameters. A quantitative approach was applied to 23,463 AQI datasets obtained from Kaggle World AQI database. The data was pre-processed and feature engineering was used to remove null values and outliners then splitted into ratio 70:30 for training and testing. Four algorithms namely; Linear Regression, k-Nearest Neighbours, Decision Tree and Random Forest were evaluated using metrics such as R-Squared (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The result shows that Random forest model (R2 = 0.997316, RMSE = 2.865823, MAE = 0.2955499) demonstrated superior predictive performance followed by KNN (R2 = 0.996820, RMSE = 3.119500, MAE = 0.588252) while Decision Trees (R2 = 0.995046, RMSE = 3.893819, MAE = 0.302845) produced high accuracy with slightly higher error. SVR (R2 = 0.980160, RMSE = 7.792268, MAE = 1.301302) and Linear Regression (R2 = 0.975279, RMSE = 8.968122, MAE = 4.831951) showed moderate accuracy. This research confirms that Machine Learning models are valuable tools for predicting Air quality thus offering a powerful tool for mitigating the impact of deteriorating Air Quality in Africa.
O. A. Oduah, Oluseyi E. Ogunsola, Oludele Adeleke· Nigerian Journal of Physics· 0 citations
Air pollution has become a major environmental and public health concern due to rapid urbanization, industrial growth, and increasing vehicular emissions. High concentrations of pollutants such as PM2.5, PM10, NO₂, SO₂, CO, and O₃ can significantly impact human health and environmental sustainability. Accurate monitoring and prediction of air quality are therefore essential for effective environmental management and public safety. This paper presents AirAware, a machine learning–based system designed to predict and monitor Air Quality Index (AQI) levels using historical air pollution data and real-time environmental information. The system utilizes the XGBoost algorithm to analyze pollutant parameters and generate accurate AQI predictions and classifications. Data preprocessing techniques such as cleaning, normalization, and SMOTE-based class balancing are applied to improve model performance and ensure reliable predictions across different AQI categories. In addition, the system integrates real-time air pollution data through the OpenWeather API, enabling continuous monitoring of current environmental conditions. The predicted AQI values and pollution trends are displayed through a web-based dashboard, allowing users to visualize air quality patterns and compare real-time data with machine learning predictions. By combining machine learning techniques with real-time data integration, the proposed system provides an effective solution for air quality prediction, monitoring, and environmental awareness.
Neethu Roy, Jeeson Justin· International Journal of Lat...· 0 citations
The core objective of this research is to construct a machine learning-based air quality prediction model. This model aims to forecast the Air Quality Index (AQI) for the next 72 hours and classify its corresponding levels (e.g., Good, Moderate, Polluted), providing a robust scientific basis for environmental protection departments and related decision-making. For feature selection, we analyzed multiple key factors affecting air quality. While meteorological data, spatiotemporal features, and external pollution sources are important, this study focuses on the historical concentrations of six critical pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) as model inputs to establish a baseline model, acknowledging the need for incorporating broader influencing factors in future work. In the model construction phase, we performed extensive preprocessing on the collected historical air quality data, including standardization and normalization, to extract effective information. We then employed and compared several advanced machine learning algorithms, selecting the optimal combination to build the final prediction model. The experiments were conducted using the Python language. By continuously optimizing model parameters and feature combinations, we achieved predictions for both the numerical AQI values and their corresponding quality levels for the subsequent 72 hours. Experimental results demonstrate that the constructed model possesses high prediction accuracy and stability for the predominant “Excellent” and “Good” categories. However, the lack of severe pollution events in the dataset limits the evaluation of its predictive capability for pollution peak events.
Shiting Wu, Xiaohua Qian, Xiaodong Zhou et al.· 2026 3rd World Conference on...· 0 citations
Accurate forecasting of air pollution is important for monitoring the environment and protecting human health. The Air Quality Index (AQI) is commonly used to measure pollution levels, but forecasting it proves to be challenging because of the complex interactions that occur between contaminants, weather conditions, and changes that happen over time. This study utilizes machine learning (ML) techniques to predict the Air Quality Index (AQI) based on pollution and environmental data. The method uses feature analysis, data pre-processing, and an ensemble voting regression model. Chaotic-Guided Crested Porcupine Optimizer (CG-CPO) is used to find the best values for the parameters. The model achieves low error values with a Mean Absolute Error of 5.72 and a Root Mean Square Error of 8.71, alongside a high R2 score of 0.981, indicating strong results. To facilitate understanding of the data, AQI values are categorized into standard air quality classifications. This approach offers a useful and reliable method for monitoring air quality and making decisions based on that information.
Meshram Lalitha, Dr. V. Jaiganesh· 2026 6th International Confe...· 0 citations
: Air quality monitoring is widely implemented worldwide; however, most existing approaches focus on measuring pollutant concentrations rather than identifying the minimal set of variables that most strongly influence air quality dynamics. This limitation increases system complexity and operational costs. Therefore, this study aims to identify the most influential environmental variables affecting air quality, represented by fine particulate matter (PM₂.₅ ) levels and classified into three states: Good, Moderate, and Poor. To do this, a database from the Science Data Bank was used, which includes: a) atmospheric pollutants (carbon monoxide (CO), sulfur dioxide (SO₂), nitric oxide (NO), nitrogen oxides (NOₓ) and tropospheric ozone (O₃)) and b) meteorological conditions (air temperature (TA), relative humidity (RH), atmospheric pressure (PA), dew point (DP), net radiation (NR), solar radiation (SR), direction (DIR) and average wind speed (VEL)). Using the J48 algorithm on the WEKA platform, complemented with a machine learning model developed in Python, a selection and prediction process of the dependent variable was carried out. The results show, with an accuracy of 94%, that the most influential variables in air quality are: a) pollutants: CO, Nox/TA, NOx, O₃ and b) meteorological: Air temperature (TA). These findings simplify environmental modeling and provide a replicable methodological basis for predictive air quality systems in different urban contexts.
Sofia Alvarez, O. Gómez, Juan Alvarez· Proceedings of the 16th Inte...· 0 citations
Air pollution has become a major environmental and public-health concern worldwide, and understanding its behaviour is essential for effective monitoring and management. This study investigates air-quality patterns across four regions in the Sultanate of Oman—Al Khuwair, Salalah, Al Khoud, and Bediya—using a combination of statistical modelling and machine-learning techniques. Hourly data for 2023, including pollutant concentrations and key meteorological variables, were obtained from the Environment Authority of Oman, cleaned, and pre-processed to construct region-specific datasets. Air Quality Index (AQI) values were calculated for each pollutant and classified into three categories (Good, Moderate, and Unhealthy). Kernel Support Vector Machine (KSVM) and Gaussian Process Regression and models were trained using a 70/30 temporal split to classify AQI levels. Results showed that KSVM achieved the highest accuracy in Salalah (96.97%), Al Khoud (94.33%), and Bediya (93.37%), while Gaussian Process Regression performed best in Al Khuwair (70.32%). In conclusion, this research demonstrates that advanced kernel-based classifiers can effectively model non-linear environmental data, providing a scalable solution for regional environmental management.
Shamssa Abdullah Al-Rahbi, M. Alodat· SISTEMASI· 0 citations