CAP: Causal Air Quality Index Prediction Under Interference with Unmeasured Confounding

Air pollution remains a major global concern due to its significant impact on public health and environmental sustainability. This study aims to develop a reliable global Air Quality Index (AQI) prediction model by evaluating five regression-based machine learning algorithms, including Linear Regression, Support Vector Regression, Random Forest, XGBoost, and LightGBM. The dataset contains over twenty thousand pollutant concentration records from multiple countries. Since the dataset consists of independent pollutant observations without timestamps or temporal sequences, this research employs supervised regression techniques rather than time-series forecasting methods to ensure methodological consistency with the non-temporal structure of the data. The methodology includes data preprocessing, validation of geocoded country information for missing values, transformations to address skewed pollutant distributions, and feature selection based on established environmental standards. Sample weights were applied to account for uneven regional representation, and systematic hyperparameter tuning with cross-validation was conducted to optimize model parameters and reduce potential overfitting. Evaluation metrics are supported by correlation analysis to quantify relationships between pollutants and AQI. The results show that XGBoost delivers the highest and most stable performance, with a MAE of 0.0216, MSE of 0.0010, RMSE of 0.0318, R² of 0.9971, and MAPE of 0.5664. Feature importance analysis highlights PM2.5 as the most influential pollutant, followed by ozone, nitrogen dioxide, and carbon monoxide. The predicted AQI values closely align with observed measurements, demonstrating strong generalizability across regions. An interactive dashboard was developed to visualize AQI predictions and pollutant contributions across countries, improving practical usability for environmental monitoring. Overall, this study provides a comprehensive framework for global AQI prediction and demonstrates the potential of machine learning to support decision-making in environmental management and public health planning.

Air Quality Index prediction using an effective hybrid deep learning model

Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach.

Accurate air quality prediction is crucial for environmental monitoring and public health. This study explores a novel approach using machine learning algorithms and large language models to predict the Air Quality Index (AQI). While traditional AQI prediction relies on complex models and extensive data on pollutants and meteorological factors, this research investigates the use of readily available fuel consumption data as an alternative predictor, given its close link to emissions. The study employs supervised machine learning algorithms, including Random Forest and Gradient Boosting, utilizing fuel consumption data as input features to build predictive models. Additionally, a state-of-the-art large language model, GPT-3.5-turbo-instruct, is fine-tuned on historical AQI data and evaluated for its predictive capabilities. The performance of both machine learning models and the language model is compared using various metrics, and the results demonstrate that both approaches achieve high AQI prediction accuracy, outperforming traditional methods based on pollutant concentration data. Notably, the fine-tuned language model exhibits superior performance, potentially due to its ability to capture complex dependencies and contextual information from the training data. This work highlights the potential of leveraging readily available fuel consumption data and advanced language models for accurate and cost-effective AQI prediction. The findings have significant implications for developing scalable air quality monitoring systems, enabling timely interventions and informed decision-making to mitigate the adverse effects of air pollution.

Prediction of air quality index for certain locations in Nigeria has been carried out. The data used for this research was gotten from purple air data web site. The locations selected are; University of Ibadan, Edo Broadcasting station, Kogi, Nimet, Benin, Lekki phase1 in Lagos and Space center, Kebbi. Air quality Index was calculated based on PM2.5 data obtained from the purple air website. The other variables obtained are PM10, PM1, Temperature and Humidity. The machine learning algorithms used for modelling are linear regression, support vector machines (SVM), K-Nearest Neighbors (KNN), and regression trees. These algorithms were obtained using R programming language. The results show that for University of Ibadan station. AQI increased towards the end of 2021 up till the end of February 2022. There was a steady decrease in AQI towards the end of May 2022. For Edo Broadcasting station, AQI increased towards the end of 2021 up till the end of February 2022. There was a steady decrease in AQI towards the end of May 2022. There was an increase after May 2022. For Kogi station, AQI increased towards the end of October 2021 up till the end of February 2022. There was a steady decrease in AQI in April 2022. For Lekki phase 1, Lagos station, AQI increased from April 2021 towards the end of December 2021. There was a steady decrease in AQI towards the end of April 2022. For Nimet, Benin station, AQI increased steadily from October 2021 up till the end of February 2022. There was a steady decrease in AQI towards the end of April 2022 and a slight increase in May 2022. For Space center, Kebbi station, AQI decreased steadily from August 2021 up till the end of February 2022. There was a steady increase of AQI in March 2022 and a slight decrease in May 2022. AQI shows that the quality of air in Ibadan was classified as good, Edo broadcasting and Nimet, Benin were unhealthy for sensitive groups while Kogi, Space center Kebbi and Lagos were classified as moderate. Modelling result shows that of all the machine learning model selected for this research work, K-nearest neighbors was found to be the best model for the prediction of AQI across Nigeria. This is because it had the smallest value for the errors. Hence for the prediction of AQI in Nigeria, KNN is recommended.

The impact of air pollution on health is measured by the Air Quality Index (AQI). Accurate AQI prediction is essential for pollution reduction and public health recommendations. Traditional methods of monitoring air quality are inaccurate and time-consuming. This study uses IoT-based air quality data from Kampung Kalipaten, Tangerang to build an AQI prediction model with machine learning, specifically an ensemble model. Ensemble techniques such as bagging and boosting, which increase the reliability of predictions by reducing model bias and inconsistency, improve AQI prediction. Four ensemble models used in this study, they are Random Forest Regressor, Gradient Boosting Regressor, Adaboosting Regressor, and Bagging Regressor. As the evaluation, RMSE and R2 metrics used. Random Forest Regressor perform the best with RMSE value of 0.6054 and R2 value of 0.6271, although no significant differences of RMSE and R2 value of the rest models.

Accurate Air Quality Index (AQI) prediction is essential for timely health risk management in urban environments, yet challenges such as missing data and complex pollutant interactions limit the performance of traditional approaches. This study investigates AQI prediction for three years from six stations in Chennai, a South Indian coastal city, by coupling fourteen imputation techniques with five machine learning (ML) models to identify the most effective framework. Among the tested combinations, the Multi-Layer Perceptron (MLP) model with k-Nearest Neighbor imputation (kNNI-MLP) achieved the best performance, with a coefficient of determination of 0.9999, a root mean squared error of 0.4920, a mean absolute error of 0.2723, a symmetric mean absolute percentage error of 0.4522%, and a mean absolute scaled error of 0.0069%. Residual and calibration analyses confirmed unbiased and well-calibrated predictions, while trend analysis showed strong alignment between actual and predicted AQI values. Seasonal evaluation revealed consistent fluctuations, with AQI peaking in winter and post-monsoon and stabilizing during summer and monsoon. Station-wise patterns further highlighted site-specific pollution drivers such as traffic density, industrial activity, and waste burning. The findings establish kNNI-MLP as a robust AQI prediction framework and provide evidence for targeted interventions, including improved traffic regulation, waste management, and emission controls. Future research will focus on external validation to confirm the model's generalizability across diverse urban contexts, as well as exploring interpretability techniques such as SHAP or variable importance analysis to enhance understanding of predictor contributions.

A new hybrid prediction model of air quality index based on secondary decomposition and improved kernel extreme learning machine

The air quality index forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. We have examined over 70 carefully selected papers in this scope and discovered that multi-model federated learning is the most effective technique that could enhance the air quality index prediction result. Therefore, this mechanism needs to be considered by science community in the coming years.

In smart cities, air pollution has harmful impacts on human physical health and the quality of living environment. correctly predicting air quality is important for developing effective strategies to reduce air pollution and promote healthier, more sustainable environments. Tracking and predicting air pollution is essential for enabling individuals to make wellinformed choices that safeguard their health. Predicting air quality is vital for public health, environmental management, and the development of effective policies. This research focuses on predicting the Air Quality Index (AQI) using machine learning techniques, with an emphasis on improving model efficiency and prediction accuracy. This study compares the performance of two regression algorithms: Linear Regression and Principal Component Regression (PCR) with Decision Tree Regression, across several key evaluation metrics. The performance is assessed using measures such as Mean Squared Error (MSE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Median Absolute Error (MedAE), Explained Variance, Adjusted R². The analysis reveals that the PCR with Decision Tree Regression model outperforms Linear Regression in terms of accuracy, as indicated by lower error values and higher explained variance. The superior model also demonstrates better generalization, with more robust metrics like MedAE, which reduces sensitivity to outliers. Overall, the study highlights the advantages of combining principal component regression with decision tree regression for enhanced predictive accuracy.

Air quality index prediction using a new hybrid model considering multiple influencing factors: A case study in China

Together with water and land, air is a fundamental necessity of life. Nevertheless, the ambient air quality is deteriorating around the world because of rapid urbanization and industrialization. The problem of air pollution has become a prominent issue for the public and academia. In fact, the public is more interested in being informed about the possibility of occurrence of air pollution episodes than the accurate forecasting of a specific pollutant. Therefore, this study proposes a process based upon discrete-time Markov chains (DTMC), to predict the air quality index (AQI) and identify the prime air pollutants in a specific area. This study utilizes online air quality monitoring data retrieved from the Taiwan Environment Protection Administration, to demonstrate the application of the process. The findings of the study revealed that there are three prime air pollutants, namely ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM10), which frequently contaminate the ambient air in Taipei city. Furthermore, this study used data for three time periods to verify the proposed process and found that the performance of the process in predicting the AQI values for 7 days is better than the prediction for 30 days and 62 days.

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Air pollution is a serious environmental issue that affects public health and climate change. Accurate forecasting of the Air Quality Index (AQI) is essential to mitigate its adverse effects and implement effective pollution control measures. This review paper evaluates various time series modelling approaches used for AQI forecasting, including traditional statistical models such as ARIMA and SARIMA, hybrid models that combine statistical and machine learning techniques and deep learning-based approaches such as LSTM and fuzzy time series models. The findings suggest that while ARIMA and SARIMA are effective for short-term forecasting, hybrid models and deep learning techniques provide better accuracy by capturing complex temporal patterns. However, challenges such as data quality issues, computational cost, and regional variations affect the reliability of these models. Future research should focus on developing efficient hybrid approaches to integrate real-time data sources, enhance model interpretability, and improve AQI forecast accuracy. This study provides insights into the strengths and limitations of different forecasting techniques, providing a basis for future advancements in air quality forecasting.