Year: 2025
Type: article
Abstract: Rapid urbanization, industrialization, and vehicle emissions all contribute to the widespread environmental and public health problem of urban air pollution. Because of their low spatial-temporal resolution and dependence on static sensor networks, conventional air quality monitoring systems frequently fall short in providing the high-resolution, real-time data required for environmental management and well-informed decisions. In order to achieve scalable, economical, and high-resolution environmental monitoring, this dissertation offers a thorough framework for an Internet of Things-enabled urban air quality monitoring system that combines multi-layer sensor architectures, sophisticated communication protocols, and predictive analytics. This study shows that the suggested framework is effective in attaining notable improvements in spatial-temporal resolution and prediction accuracy when compared to current systems through thorough testing and implementation in urban settings. The framework's ability to pinpoint pollution hotspots, predict air quality indices, and offer useful information to legislators and urban planners is demonstrated by case studies carried out in urban regions. The results also emphasize how important user accessibility, system interoperability, and data security are to the expansion of IoT-based monitoring systems for widespread usage. This study presents a cloud-native architecture for real-time noise and air pollution analytics, leveraging IoT technology and cloud computing to enhance urban environmental monitoring. The system collects and analyses data on various pollutants and noise levels, providing timely insights for effective decision-making and improved public health. By integrating AI-driven analytics and IoT sensors, this innovative approach enables scalable, secure, and data-driven urban planning strategies. This study advances the state-of-the-art in IoT-enabled air quality monitoring systems, adding to the expanding corpus of information on sustainable urban development. In addition to addressing important technological issues, the suggested strategy offers a way to combine environmental monitoring with smart city projects, promoting more sustainable and healthy urban settings. To improve the system's scalability and usefulness even more, future research topics include integrating policy-driven frameworks, cross-sector IoT applications, and adaptive artificial intelligence algorithms.
DOI: 10.14419/r3hw5f22
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416466372