Smart City Gnosys
Smart city article details
| Title | Smartairq: A Big Data Governance Framework For Urban Air Quality Management In Smart Cities |
|---|---|
| ID_Doc | 51813 |
| Authors | Kaginalkar A.; Kumar S.; Gargava P.; Kharkar N.; Niyogi D. |
| Year | 2022 |
| Published | Frontiers in Environmental Science, 10 |
| DOI | http://dx.doi.org/10.3389/fenvs.2022.785129 |
| Abstract | Rapid urbanization across the world has put an enormous burden on our environment. Cities from developing countries, in particular, are experiencing high air pollution levels. To address this challenge, the new WHO global air quality guidelines and various nations are mandating cities to implement clean air measures. However, these implementations are largely hindered by limited observations, siloed city operations, absence of standard processes, inadequate outreach, and absence of collaborative urban air quality management (UAQM) governance. The world is experiencing transformative changes in the way we live. The 4th industrial revolution technologies of artificial intelligence, Internet of Things, big data, and cloud computing bridge gaps between physical, natural, and personal entities. Globally, smart cities are being promulgated on the premise that technologies and data aid in improving urban services. However, in many instances, the smart city programs and UAQM services may not be aligned, thereby constraining the cumulative advantage in building urban resilience. Considering the potential of these technologies as enablers of environmental sustainability, a conceptual urban computing framework “SmartAirQ” for UAQM is designed. This interdisciplinary study outlines the SmartAirQ components: 1) data acquisition, 2) communication and aggregation, 3) data processing and management, 4) intelligence, 5) application service, 6) high-performance computing- (HPC-) cloud, and 7) security. The framework has integrated science cloud and urban services aiding in translating scientific data into operations. It is a step toward collaborative, data-driven, and sustainable smart cities. Copyright © 2022 Kaginalkar, Kumar, Gargava, Kharkar and Niyogi. |
| Author Keywords | AI; air pollution; cloud computing; data governance; frontier technologies; machine learning; smart cities; urban computing |
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