Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

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.