Cloud Sense: Real-Time Emission Monitoring for Urban Environments

Urmila Burde
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Urmila Burde: Department of Electronics and Telecommunication, Ajeenkya DY Patil School of Engineering, Lohegaon, Pune, Maharashtra, India

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 1671-1678

Abstract: Escalating air pollution as a result of urbanization and industrialization is one of the most problematic issues affecting the environment and public health. Urban living requires stringent controls on emissions and effective management of growth trends. In this paper, we propose an emission control system for metropolitan cities called Green Guard, which is a cloud-based emission monitoring system. This pollution emission control system incorporates IoT (Internet of Things) sensors, cloud computing, and data analytics to enable real-time air quality monitoring along with predictive insights for pollution control. As part of Green Guard, IoT-empowered sensing devices are deployed throughout the city to monitor critical pollutants, including PM2.5, and PM10, CO, NO₂, and SO₂. The aforementioned sensors relay data to the cloud where sophisticated machine learning algorithms monitor air quality indicators and identify pollution hotspots. Environmental agencies, politicians, and citizens receive information and alerts through dashboards. Thus, giving them the ability to make decisions in realtime. Green Guard’s predictive analytics component is one of the most remarkable features of the project. It provides the ability to predict the level of pollution based on analyzing historical data along with meteorological conditions. Through this, authorities are able to take preventive measures by restricting traffic, controlling industrial emissions, and issuing public health warnings. It achieves all necessities for metropolitan cities like scalability, enhanced security, and improved accessibility using a cloud based platform. Therefore ,making it an ideal solution for larger metropolitan cities. To evaluate the efficiency of the system, GreenGuard was implemented in a metro area through a pilot study. Findings showed successful integration with the environmental systems already in place and a strong ability to accurately calculate pollution levels. The ease of use of the systems interface along with the pollution report automation made pollution management strategies much more sophisticated. GreenGuard’s predictive emission monitoring capabilities enable sustainable development within metropolitan areas, which leads to better public health. Addition of AI-powered analytics and wider geographic coverage would improve the results of the proposed system even further.

Date: 2025
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