Abstract
Air pollution is a severe problem today and is a global threat impacting the health of populations and the environment severely. Many health impacts are associated with pollution, including premature deaths as a result of exposure to high levels of pollutants. Governments employ methods to measure levels of pollutants and implement prevention strategies to reduce their deleterious impact on populations. Traditional methods of air pollution measurements lack the capabilities of identifying the sources, the spread of pollution and their spatio-temporal nature, in addition to being expensive. Hence, the use of technologies such as Internet of Things (IoTs), Artificial Intelligence (AI) and Big Data Analytics are now on the rise that can monitor air pollution in real time. These technologies prove to be a solution that can help right now providing a comprehensive picture of the levels of air quality in near real time aiding faster decision-making and resulting in mitigation actions. The exponential digital transformations in every sector have led to platform technologies and in this chapter, we explore the network approaches platform ecosystems to adopt as opposed to a linear approach to tackling pollution. A multi-level socio-technical system of platform ecosystems and the technologies to mitigate pollution is discussed.
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0 |
| Place of Publication | Singapore |
| Publisher | Springer |
| Chapter | 1 |
| Pages | 1-22 |
| Number of pages | 22 |
| ISBN (Electronic) | 978-981-19-1434-8 |
| ISBN (Print) | 978-981-19-1433-1 |
| DOIs | |
| Publication status | Published - May 2022 |