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Smart city article details
| Title | Next-Generation Air Quality Management: Unveiling Advanced Techniques For Monitoring And Controlling Pollution |
|---|---|
| ID_Doc | 39228 |
| Authors | Kumari S.; Choudhury A.; Karki P.; Simon M.; Chowdhry J.; Nandra A.; Sharma P.; Sengupta A.; Yadav A.; Raju M.P.; Gupta J.; Garg M.C. |
| Year | 2025 |
| Published | Aerosol Science and Engineering |
| DOI | http://dx.doi.org/10.1007/s41810-024-00281-1 |
| Abstract | Air quality management has become critical due to the growing concerns about air pollution resulting from industrialization, urbanization, and anthropogenic activities. This paper explores advanced techniques for monitoring and controlling pollution, with a focus on next-generation technologies that can address dynamic environmental challenges. We examine the evolution of air quality monitoring, from basic visual methods to sophisticated technological advancements like remote sensing and the Internet of Things (IoT). Innovations in sensor technologies, such as nanosensors, offer compact, affordable, and highly responsive solutions, making them suitable for widespread deployment in air quality monitoring networks. The paper delves into innovative sensor technologies and optical sensors for real-time air quality analysis. These technologies enable accurate detection and measurement of particulate matter (PM) and volatile organic compounds (VOCs), providing crucial data for pollution management. We discuss emerging applications, such as nanosensors, optical sensors, and chemical sensors, and highlight their advantages in capturing detailed air quality data. Big data analytics play a central role in air quality management, allowing for comprehensive analysis and prediction of pollution levels. Machine learning models, including artificial neural networks (ANN), support vector machines (SVM), and decision trees, are used to forecast air quality and identify pollution patterns. Data fusion strategies, combining sensor networks with chemistry-transport models (CTMs), offer enhanced air quality assessments and forecasting capabilities. The paper also discusses smart control systems for emission reduction, emphasizing adaptive control algorithms for real-time emission management. The integration of IoT-enabled smart city components provides real-time monitoring and adaptive responses to pollution events, leading to more effective pollution control. Renewable energy sources, such as wind, solar, and biomass, are explored for their potential to reduce air pollution and support sustainable energy transition. Challenges and future directions are addressed, with a focus on regulatory frameworks and executive actions to support pollution control measures. © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2025. |
| Author Keywords | Air pollution; Internet of things; Machine learning; Particulate matter; Renewable energy |
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