Smart City Gnosys
Smart city article details
| Title | Technological Advancements For Sustainable Clean Air |
|---|---|
| ID_Doc | 54566 |
| Authors | Izah S.C.; Amabie T.; Aigberua A.O. |
| Year | 2024 |
| Published | Handbook of Environmental Chemistry, 133 |
| DOI | http://dx.doi.org/10.1007/698_2024_1120 |
| Abstract | Technology advancements continue to have made a significant contribution to the search for sustainable clean air by offering a ground-breaking path toward environmental health and welfare. This chapter discusses key parts of the ongoing technological evolution for sustainable clean air with an emphasis on smart city initiatives, emission reduction approaches, predictive modeling for air quality forecasts, real-time air quality monitoring, and data analytics for pollution source identification. Real-time air quality monitoring enables the early identification of pollution sources because of sophisticated sensor technology. This information, when paired with data analytics, becomes a powerful tool for identifying sources and directing targeted mitigation efforts for air pollution. Predictive modeling improves air quality forecasts by providing communities and decision-makers with proactive information. Proposals to build “smart cities” make use of technology to incorporate environmental issues and urban planning into day-to-day life. The adoption of pollution reduction strategies backed by electric cars and renewable energy sources is necessary for a greener future. Public engagement and awareness campaigns reinforce the benefits of technology interventions and foster a sense of shared responsibility for environmental stewardship. International collaboration and data exchange provide a foundation for cross-national group action in the fight for clean air. The energy environment becomes more sustainable and the carbon footprint shrinks as renewable energy sources gain popularity. In conclusion, the cooperative use of technology is bringing us closer to a future where everyone will have access to clean air. Dedication to innovation, collaboration, and sustainable methods by researchers and government agencies may pave the way for a world that thrives in ecological balance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords | Data analytics; Emission reduction; Predictive modeling; Real-time monitoring; Renewable energy; Smart City initiatives; Sustainable clean air; Technology advancements |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
39228  | 0.907 | 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. | Next-Generation Air Quality Management: Unveiling Advanced Techniques For Monitoring And Controlling Pollution | Aerosol Science and Engineering (2025) |
| 22908 | 0.892 | Relvas H.; Lopes D.; Armengol J.M. | Empowering Communities: Advancements In Air Quality Monitoring And Citizen Engagement | Urban Climate, 60 (2025) |
| 23737 | 0.885 | Shahbazi Z.; Shahbazi Z.; Nowaczyk S. | Enhancing Air Quality Forecasting Using Machine Learning Techniques | IEEE Access, 12 (2024) |
| 31969 | 0.88 | Myeong, S; Shahzad, K | Integrating Data-Based Strategies And Advanced Technologies With Efficient Air Pollution Management In Smart Cities | SUSTAINABILITY, 13, 13 (2021) |
| 32270 | 0.864 | Mahideb H.; Kouah S.; Ahmim A. | Intelligent Air Quality Monitoring System: A Comprehensive Review | Lecture Notes in Networks and Systems, 783 LNNS (2023) |
| 36027 | 0.864 | Zareba M.; Cogiel S.; Danek T.; Weglinska E. | Machine Learning Techniques For Spatio-Temporal Air Pollution Prediction To Drive Sustainable Urban Development In The Era Of Energy And Data Transformation | Energies , 17, 11 (2024) |
| 1772 | 0.861 | Silva, D; Pinto, J; Varanda, J; Henriques, J; Caldeira, F; Wanzeller, C | A Framework For Monitoring Pollution Levels In Smart Cities | NEW TRENDS IN DISRUPTIVE TECHNOLOGIES, TECH ETHICS, AND ARTIFICIAL INTELLIGENCE, DITTET 2024, 1459 (2024) |
| 53645 | 0.857 | Essamlali I.; Nhaila H.; El Khaili M. | Supervised Machine Learning Approaches For Predicting Key Pollutants And For The Sustainable Enhancement Of Urban Air Quality: A Systematic Review | Sustainability (Switzerland), 16, 3 (2024) |
| 6584 | 0.855 | Sharma A.; Singh K.J.; Kapoor D.S.; Thakur K.; Mahajan S. | Advancements In 5G-Iot Technology-Based Healthcare: A Focus On Healthcare Monitoring For Smart Cities | Internet of Things, Part F4006 (2025) |
| 46496 | 0.854 | Kaginalkar A.; Kumar S.; Gargava P.; Niyogi D. | Review Of Urban Computing In Air Quality Management As Smart City Service: An Integrated Iot, Ai, And Cloud Technology Perspective | Urban Climate, 39 (2021) |