Enhancing Air Quality Forecasting Using Machine Learning Techniques
Urbanization is rapidly shaping our world, with more people than ever residing in cities. While cities offer numerous opportunities and conveniences, they also face critical challenges, including air pollution. Addressing these challenges is vital for creating healthier and more liveable urban envir...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10798434/ |
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| author | Zeinab Shahbazi Zahra Shahbazi Slawomir Nowaczyk |
| author_facet | Zeinab Shahbazi Zahra Shahbazi Slawomir Nowaczyk |
| author_sort | Zeinab Shahbazi |
| collection | DOAJ |
| description | Urbanization is rapidly shaping our world, with more people than ever residing in cities. While cities offer numerous opportunities and conveniences, they also face critical challenges, including air pollution. Addressing these challenges is vital for creating healthier and more liveable urban environments. A transformative solution emerges, bridging the gap between sustainable urban mobility and air quality control through cutting-edge data-driven strategies. Finding a balance between efficient urban living and environmental stewardship is a pressing concern for cities worldwide. In envisioning a future where urban commuting becomes synonymous with eco-friendliness and air quality improvement, a comprehensive platform harnesses the power of data analytics and real-time information to empower commuters and city planners alike. Its intelligent algorithms continuously analyse air quality information, allowing it to predict and address poor air quality. This platform seamlessly integrates with existing urban infrastructure, making it accessible to commuters through user-friendly mobile applications and web interfaces. Commuters can receive personalized recommendations for eco-friendly commuting options. One standout feature is its ability to forecast air quality in urban areas, enabling users to make informed decisions that prioritize their health and environmental sustainability. Encouraging a sense of community among eco-conscious urban residents, it incentivizes sustainable behaviours and offers rewards for reducing emissions. By collecting data on commuting choices and air quality conditions, the platform contributes valuable insights to city authorities for urban planning and pollution control. Representing a paradigm shift in urban living, it aligns individual choices with broader sustainability and air quality goals. It is a testament to the power of technology, data, and community engagement in building smarter, greener, and healthier cities. The proposed approach presents the significance of the system as a transformative solution for sustainable urban living and air quality control, emphasizing the use of cutting-edge technology, data-driven insights, and community engagement to address pressing urban challenges. |
| format | Article |
| id | doaj-art-b44dfbf034c44f518ffd468cd2e26bff |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b44dfbf034c44f518ffd468cd2e26bff2025-08-20T02:39:51ZengIEEEIEEE Access2169-35362024-01-011219729019729910.1109/ACCESS.2024.351688310798434Enhancing Air Quality Forecasting Using Machine Learning TechniquesZeinab Shahbazi0https://orcid.org/0009-0008-4093-1156Zahra Shahbazi1Slawomir Nowaczyk2https://orcid.org/0000-0002-7796-5201Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, SwedenDepartment of Environmental Engineering, University of Padova, Padua, ItalyCenter for Applied Intelligent Systems Research, Halmstad University, Halmstad, SwedenUrbanization is rapidly shaping our world, with more people than ever residing in cities. While cities offer numerous opportunities and conveniences, they also face critical challenges, including air pollution. Addressing these challenges is vital for creating healthier and more liveable urban environments. A transformative solution emerges, bridging the gap between sustainable urban mobility and air quality control through cutting-edge data-driven strategies. Finding a balance between efficient urban living and environmental stewardship is a pressing concern for cities worldwide. In envisioning a future where urban commuting becomes synonymous with eco-friendliness and air quality improvement, a comprehensive platform harnesses the power of data analytics and real-time information to empower commuters and city planners alike. Its intelligent algorithms continuously analyse air quality information, allowing it to predict and address poor air quality. This platform seamlessly integrates with existing urban infrastructure, making it accessible to commuters through user-friendly mobile applications and web interfaces. Commuters can receive personalized recommendations for eco-friendly commuting options. One standout feature is its ability to forecast air quality in urban areas, enabling users to make informed decisions that prioritize their health and environmental sustainability. Encouraging a sense of community among eco-conscious urban residents, it incentivizes sustainable behaviours and offers rewards for reducing emissions. By collecting data on commuting choices and air quality conditions, the platform contributes valuable insights to city authorities for urban planning and pollution control. Representing a paradigm shift in urban living, it aligns individual choices with broader sustainability and air quality goals. It is a testament to the power of technology, data, and community engagement in building smarter, greener, and healthier cities. The proposed approach presents the significance of the system as a transformative solution for sustainable urban living and air quality control, emphasizing the use of cutting-edge technology, data-driven insights, and community engagement to address pressing urban challenges.https://ieeexplore.ieee.org/document/10798434/Air qualitymachine learningartificial intelligencesustainabilitysmart city |
| spellingShingle | Zeinab Shahbazi Zahra Shahbazi Slawomir Nowaczyk Enhancing Air Quality Forecasting Using Machine Learning Techniques IEEE Access Air quality machine learning artificial intelligence sustainability smart city |
| title | Enhancing Air Quality Forecasting Using Machine Learning Techniques |
| title_full | Enhancing Air Quality Forecasting Using Machine Learning Techniques |
| title_fullStr | Enhancing Air Quality Forecasting Using Machine Learning Techniques |
| title_full_unstemmed | Enhancing Air Quality Forecasting Using Machine Learning Techniques |
| title_short | Enhancing Air Quality Forecasting Using Machine Learning Techniques |
| title_sort | enhancing air quality forecasting using machine learning techniques |
| topic | Air quality machine learning artificial intelligence sustainability smart city |
| url | https://ieeexplore.ieee.org/document/10798434/ |
| work_keys_str_mv | AT zeinabshahbazi enhancingairqualityforecastingusingmachinelearningtechniques AT zahrashahbazi enhancingairqualityforecastingusingmachinelearningtechniques AT slawomirnowaczyk enhancingairqualityforecastingusingmachinelearningtechniques |