| Title |
Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring |
| ID_Doc |
42249 |
| Authors |
Krupnova, TG; Rakova, O; Bondarenko, KA; Tretyakova, VD |
| Title |
Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring |
| Year |
2022 |
| Published |
Big Data And Cognitive Computing, 6, 3 |
| DOI |
10.3390/bdcc6030075 |
| Abstract |
The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice. |
| Author Keywords |
environmental injustice; air pollution monitoring; artificial intelligence (AI); air pollution modelling; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000859359700001 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://www.mdpi.com/2504-2289/6/3/75/pdf?version=1657020048
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