Aug 2026· Air quality, atmosphere and health· Vol 19· 0 citations· 25 references
TL;DR
GreenAirOps is a production-ready MLOps system that ingests multithsource environmental data, automatizes preprocessing and feature extraction, and combines Random For- est and XGBoost with ensemble learning for prediction of low-latency, near real-time AQI.
Abstract
Pollution is a growing problem with dire effects on public health; therefore, accurate prediction of Air Quality Index (AQI) is paramount for both urban development and public health strategy. Here, we present GreenAirOps, a production-ready MLOps system that ingests multithsource environmental data, automatizes preprocessing and feature extraction, and combines Random For- est and XGBoost with ensemble learning for prediction of low-latency, near real-time AQI. This production pipeline has full MLOps pipeline capabilities: Data versioning with DVC; experiment tracking and model registration with MLflow; automated retraining and deployment using GitHub Actions. The deployment infrastructure utilizes Docker containers run on an AWS environ- ment and is designed for production grade. The specific contributions of this work are: an optimized ensemble learning system for low-latency AQI prediction; an end-to-end MLOps system which guarantee reproducibility and operational capability; and a set of production grade functions for auto-retraining, model health monitoring and auto-rollback.
There are significant gaps that remain in terms of model interpretability and the ability to generalize across climate variations, so this article provides a relatively comprehensive overview of the application of federated learning in air quality forecasting and monitoring.
Yuhao Wu· Mathematical Modeling and Al...· 0 citations
Air quality monitoring datasets are inherently temporal, multi-pollutant, and long-horizon in character, making them well suited to scalable distributed computing frameworks that can handle the volume, velocity, and variety of multiyear daily observations while supporting transparent, SQL accessible analytical workflows. This paper presents a reproducible PySpark–SparkSQL analytical pipeline applied to four years (2021–2024) of daily urban air quality monitoring data, addressing five structured research questions about seasonal and temporal variability, pollutant drivers of the Air Quality Index (AQI), human-activity effects on ambient concentrations, and compliance with the revised WHO 2021 Air Quality Guidelines. Our findings document: consistently higher AQI values in winter months, with November peaking at a mean AQI of 342.13 across the four-year period; PM2.5 and PM10 as the dominant AQI drivers with Pearson correlations of 0.87 and 0.91 respectively; marginal but consistent reductions in pollutant levels during holiday and weekend periods, with the strongest effects for trafficrelated NO2; and a chronic, near-daily exceedance of WHO 24-hour mean thresholds for PM2.5 (343–364 exceedance days per year) and PM10 (347–359 days). A previously unreported SO2 exceedance spike in 2024—110 exceedance days compared to fewer than 30 in each prior year—is identified as an anomaly of policy significance indicating an emerging industrial emission source. The pipeline, implemented in Python (PySpark, Pandas, Matplotlib, Seaborn) on Google Colab, provides a reproducible, computationally scalable framework adaptable to other urban air quality monitoring contexts.
Prince Nweke Onyeka, Chinoso Job., Onwe, Festus Chijioke· International journal of re...· 0 citations
A Temporal-Aware Multi-Task Learning (TMTL-AQI) framework to assess urban air quality via structured data that outperforms single-task and baseline multi-task models with an accuracy of 0.7605 and an F1-score of 0.7422.
Iman Youssif Ibrahim, D. M. Ahmed· Dasinya Journal for Engineer...· 0 citations
Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and control the mechanisms that determine air quality, cities deploy networks of measurement stations, and launch initiatives for collecting denser data about the concentration of pollutants in the atmosphere. Extracting insights from the stations relies on their reliable and uninterrupted operation. However, hardware is susceptible to faults and black- outs that may result in data unavailability, which affects the overall quality of analyses. In this paper, we present a deep-learning model, called SATADL, which can infer complex relations and output multiple-hour-ahead air-quality forecasts. The goal of the model is to simulate the mea- surements of an unresponsive station until its operation is restored. The architecture of the model, which allows it to extract information from different aspects of the data, is described in detail and a careful examination of all of its components is provided. We demonstrate the performance of SATADL on four sets of air quality stations from around the world, by using it to simulate the concentration of PM10 for periods of hypothetical failures of one of the measurement stations, lasting for as long as 48 hours. A selection of baseline and published deep learning models were trained and used as a benchmark. The results show that SATADL per- forms better across different prediction windows, for both coefficient of determination and root mean squared error, demonstrating its suitability as a virtual proxy station.
Alexander Kostadinov, P. O. Hristov, D. Petrova-Antonova· 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
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