Smart Automation for Air Quality Monitoring using ML Techniques

Abstract

Ongoing industrial expansion presents persistent challenges in tracking and evaluating atmospheric quality in metropolitan regions. Assessing environmental air conditions is crucial for establishing contamination levels within specific areas. Atmospheric contamination continues to represent a major worldwide concern affecting both citizens and regulatory authorities. It results in substantial ecological harm, including acidic precipitation and climate change, while creating severe health hazards such as heart-related illnesses and dermatological malignancies. The primary goal of this framework is to utilize artificial intelligence techniques for computing the Air Quality Index (AQI). To guarantee reliable forecasting, the framework implements robust categorization methodologies. This suggested approach can assist metropolitan regions in controlling and minimizing contamination, providing advantages to both inhabitants and administrative organizations. It predicts the AQI employing parameters such as PM??, PM?.?, NO?, O?, CO, and SO?. Functioning as a live monitoring application, it aims to assist individuals in minimizing their contact with atmospheric pollutants.

Introduction

This study addresses the challenge of managing atmospheric pollution in urban environments, with a focus on Delhi, by leveraging Machine Learning (ML) techniques to predict Air Quality Index (AQI). While many studies have explored pollution forecasting, there is a research gap concerning Delhi's specific atmospheric conditions.

The project emphasizes the importance of ML and Artificial Intelligence (AI) in analyzing environmental data. It compares various ML algorithms, highlighting Naïve Bayes for its efficiency with small datasets and Artificial Neural Networks (ANNs) for handling large-scale AQI predictions. The investigation primarily uses Neural Networks and Support Vector Machines (SVM) for forecasting.

A literature survey reviews recent studies that applied different ML methods for air quality forecasting, noting limitations such as lack of real-time functionality, dependency on static datasets, and limited accuracy.

The methodology focuses on guided (supervised) learning, particularly using Bayesian classifiers and K-Nearest Neighbors (KNN), chosen for their reliability across diverse data types. The developed system predicts AQI based on pollutants like PM??, PM?.?, NO?, O?, CO, and SO?, aiming to provide a live monitoring tool to help mitigate pollution exposure in cities.

The study details Naïve Bayes and KNN algorithms, explaining their operation, and presents experimental results where the Naïve Bayes-based framework achieved a 94% accuracy with fast prediction times (~1.6 seconds), demonstrating its effectiveness for real-time air quality categorization.

Conclusion

Below are several approaches to reformulate the project summary, each highlighting different aspects. 1) Alternative 1 (Emphasis on the Challenge and Resolution) Atmospheric contamination represents a considerable danger to both community wellness and ecological systems, leading to severe complications such as cardiovascular conditions, dermatological malignancies, acidic precipitation, and climate change. To address this challenge, our framework employs an artificial intelligence methodology to forecast the Air Quality Index (AQI). The application delivers live information and practical guidance to assist regulatory authorities in monitoring and minimizing metropolitan contamination. 2) Alternative 2 (Emphasis on Framework Objective and Operation) To reduce the substantial hazards of atmospheric contamination, encompassing acidic precipitation, climate change, and additional health risks, our initiative created a live application for governmental implementation. This framework utilizes an artificial intelligence algorithm to precisely predict the Air Quality Index (AQI). Through monitoring and controlling contamination levels in urban areas, it delivers essential insights and proposes tactics to safeguard both ecological systems and human wellness. 3) Alternative 3 (More Streamlined and Straightforward) Acknowledging the serious consequences of atmospheric contamination on both wellness and ecological systems, we developed a live artificial intelligence application for governmental organizations. This framework predicts the Air Quality Index (AQI) and provides suggestions to assist in tracking and reducing contamination levels in urban areas, ultimately contributing to controlling environmental harm and enhancing community health.

References

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Copyright

Copyright © 2025 Krupa R K, Varadaraj R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.