Federated Learning for Air Quality Index Prediction: An Overview

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.

A significant challenge in air quality index (AQI) prediction is to accurately evaluate the potential outcomes after conducting interventions in pollutant factors such as industrial emissions for each enterprise. Existed methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. Specifically, we employ the front-door adjustment to explicitly eliminate unmeasured confounders by intervening in industrial emissions from the target enterprise. Meanwhile, we take industrial emissions of neighboring enterprises into account when intervening in the target enterprise and simulate the dispersion of industrial emissions through a Gaussian plume model based on meteorological factors. Experiments on two real-world datasets validate the superior performance of our model on AQI prediction compared to the state-of-the-art baselines.

Air Quality Index prediction using an effective hybrid deep learning model

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.

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.

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.

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.

One of the most burning environmental issues of the contemporary urban and industrial areas has turned out to be air pollution which poses serious threats to human health, ecological balance, and overall sustainability. The traditional monitoring stations are very precise, but expensive, sparsely distributed geographically and cannot give high spatial temporal information. To overcome these constraints, the presented study suggests to simulate and design an Internet of Things (IoT)-based air quality monitoring system with the use of low-cost and smart sensors and use MATLAB/Simulink as the main development environment. The suggested framework will incorporate particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and volatile organic compound (VOC) sensors. The nodes have a microcontroller unit (ESP32 or Raspberry Pi), wireless communication system (Wi-Fi, LoRa, or NB-IoT), and an energy management system powered by a solar to maintain sustainability. A variety of data pre-processing methods (calibration, temperature and humidity compensation, noise elimination with digital filters, and Kalman filtering) are performed at the edge level to improve measurement accuracy prior to transmission, followed by the simulation of the end-to-end system (data acquisition and transmission, cloud integration and visualization) with the help of MATLAB/Simulink. Moreover, more complex algorithms like sensor fusion and machine learning models (Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM) networks) are used to enhance air quality index (AQI) prediction and allow making predictions in the short term on pollution. The system is evaluated by use of performance metrics like: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), correlation coefficient (R2), energy consumption and network latency to determine the anticipated outcome of this research; a scalable, cost-effective, and energy-efficient IoT network that will be able to offer reliable real-time air quality monitoring and forecasting. The solution is a part of the smart city plans, aids policy-making regarding the environment, and contributes to the increase of the public awareness of their health through providing them with the accessible and high-quality air quality data.

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.

In recent times, the global population surge has led to a parallel increase in air pollution levels, posing significant threats to the economy, environment, and public well-being. Traditionally, air pollution is measured by placing sensors on buildings spaced a certain distance apart. Nevertheless, there are disadvantages to this strategy, such as higher power consumption for sensor operation in each house and restricted application in remote areas with inadequate infrastructure. Using satellite data from FEMTOSAT for autonomous air pollution monitoring, analysis and mitigation emerges as a ground-breaking solution to these problems. This innovative approach circumvents the limitations associated with traditional methods, offering a more comprehensive and versatile solution. FEMTOSAT leverages the capabilities of LoRa (Long Range) communication, a crucial component in the Internet of Things (IoT), enabling data transmission over extended distances while consuming minimal power. LoRa communication is essential to the FEMTOSAT installation because it makes information sharing between the satellite and ground stations easier. Data is transmitted and received using radio frequency (RF) impulses, which make for a dependable and effective form of communication. The Air Quality Index (AQI) for a particular place is then determined using the environmental data that was gathered by the satellite. An important metric for measuring air quality, the AQI provides information about the degree of pollution or cleanliness in a particular place. The AQI is determined by a number of factors, including the concentrations of pollutants including NO2, CO, O3, PM2.5, SO2, and PM10. Making use of machine learning (ML) techniques such as Support Vector Machines (SVM), direct regression, time series analysis and logistic regression to forecast and analyse AQI trends makes it very efficient. One of the distinctive features of this study is the development of AQI mappings, which are derived from the comprehensive AQI data collected at specific locations. It has been shown via thorough investigation that ML-based AQI prediction models are more consistent and dependable overall. Accuracy and precision have been ensured in the data collection process through the simplified integration of new technology and smart sensors. Only machine learning algorithms can manage the complex analysis needed to produce safe and reliable predictions from large datasets in the field of environmental monitoring. The main objective of this effort is exemplified by the integration of Integrated Sensors as the payload in the FEMTOSAT mission. The benefits of the system—such as its affordability, lightweight construction, resilience, redundancy and low power consumption—highlight FEMTOSAT's applicability and effectiveness in handling the intricate problems related to air pollution monitoring.

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.

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.

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.

Background The Internet of Things (IoT) is a swiftly advancing technology with wide-ranging applications in smart cities, where substantial data exchange and real-time services necessitate high standards of security, privacy, and operational efficiency. Current IoT frameworks frequently face difficulties in delivering strong protection against emerging post-quantum threats while also preserving system performance, especially in decentralized and data-heavy smart city settings. Methods To tackle these issues, this article introduces a Post-Quantum Ring Signature and ABC-based Effective IoT (PQRAEI) framework. This model combines blockchain technology, post-quantum cryptography, and hybrid swarm optimization methods. Initially, data preprocessing is conducted using the K-Nearest Neighbour (KNN) algorithm for data imputation and min–max scaling for normalization. Secure and privacy-preserving decentralized transactions are guaranteed through a Post-Quantum Linkable Ring Signature (PQLRS) scheme, which functions through a three-phase cryptographic process. Additionally, air quality forecasting is improved using an Artificial Bee Colony (ABC) optimization algorithm enhanced with Q-learning, facilitating efficient exploration and identification of optimal environmental parameters that affect the Air Quality Index (AQI). Results The effectiveness of the proposed PQRAEI framework is assessed against existing models, specifically FHPCC, ELDSE, and PQBFR, utilizing metrics such as signing time, verification time, Peak Signal-to-Noise Ratio (PSNR), latency, and throughput. Experimental findings indicate that PQRAEI achieves significantly reduced latency and increased throughput, while ensuring strong cryptographic efficiency and data integrity in comparison to the benchmark models. Conclusions The proposed PQRAEI framework significantly improves security, privacy, and performance in IoT-based smart city environments. By utilizing post-quantum cryptography, blockchain, and intelligent swarm optimization, the model offers a scalable and future-ready solution.

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.