Advanced AI/ML Analysis of AQI to Study the Crowd Density Effect

Abstract

Public environments can experience rapid changes in person density, particularly during festivals, weekends, and special occasions. These changes may influence carbon dioxide concentration, particulate matter, temperature, humidity, and the resulting air-quality condition. This paper presents an integrated framework that combines camera-based person counting with environmental sensing and artificial intelligence and machine learning analysis. A vision module estimates the number of people and crowd density from video frames, while environmental observations are collected with synchronized timestamps. The combined dataset is preprocessed, aligned, and analyzed to identify relationships between crowd density and air-quality indicators. The framework also supports dashboard-based visualization and alert generation for practical monitoring. The proposed approach is intended to provide a common analytical view of occupancy and environmental conditions in public premises. Since experimental measurements are not included in the supplied project material, numerical performance results are not claimed in this paper; the evaluation structure is defined for completion after data collection

Introduction

The study proposes an AI-based Integrated Crowd Density and Air Quality Monitoring System for public spaces, particularly religious sites where crowd levels can change rapidly during festivals, weekends, and special events. Increased crowd density may influence ventilation requirements and indoor environmental conditions, including carbon dioxide (CO?), particulate matter, temperature, and humidity. Monitoring these factors independently makes it difficult to understand their relationship over time.

The main objective of the proposed system is to integrate computer vision, environmental sensors, and machine learning into a unified platform. It aims to estimate the number of people and crowd density from camera footage, collect corresponding environmental measurements, synchronise both data streams using timestamps, and analyse their relationships to support better crowd and environmental management.

The methodology consists of several stages. First, video frames are processed using YOLO and OpenCV to detect and count people. Crowd density is estimated by dividing the number of detected individuals by the monitored area. Second, environmental sensors collect measurements such as CO? concentration, PM2.5, PM10, temperature, and humidity. An air quality index (AQI) can also be calculated using the relevant official pollutant concentration standards. The collected data is then synchronised, cleaned, and processed to handle missing values, duplicate records, and outliers.

Machine learning techniques, including Random Forest and XGBoost, are proposed for analysing relationships between crowd density and environmental conditions and for conducting AQI prediction experiments. Statistical methods such as Pearson and Spearman correlation can help identify associations between crowd levels and pollutant concentrations. The study also recommends time-aware training and testing procedures to ensure reliable predictive evaluation.

A web-based dashboard is proposed to display real-time crowd counts, crowd density, environmental measurements, historical trends, and threshold-based alerts. These alerts can indicate when crowd density or pollutant levels exceed predefined limits, helping authorities identify conditions that may require attention. The system is designed to support both real-time monitoring and historical data analysis.

The key advantages include integrated crowd and environmental monitoring, timestamp-based data synchronisation, flexible deployment across different public spaces, machine learning-based analysis, and configurable alerts. The framework may be particularly useful in religious gatherings, transportation hubs, and other crowded indoor or outdoor environments.

Conclusion

Here\'s a rewritten version of the input paragraph in a more human-like tone and style, inspired by the reference human samples: When we think about crowded places like bus stops and railway stations, we often wonder how they affect the air we breathe. This study aimed to explore the connection between the number of people in these areas and the quality of the air around them. To do this, researchers used a combination of computer vision, environmental sensors, and artificial intelligence to analyse the data. The goal was to create a single dataset that brings together information about crowd density and environmental conditions, making it easier to study their relationship. While the approach shows promise, its effectiveness still needs to be proven through real-world measurements and testing. Future research should focus on collecting more data, comparing different models, and interpreting the results in a way that\'s based on evidence. By doing so, we can gain a better understanding of how crowded places impact air quality and take steps to improve the situation.

References

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Copyright

Copyright © 2026 Dr. Ashish Jirapure, Komal Mahalle, Swejal Tembhare , Aniket Tayde, Shruti Sarode . 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.