Assessing the potentials of artificial intelligence to facilitate citizen science in their effort to improve air quality data in North-Holland The Netherlands

Globally, Artificial Intelligence (AI) is gaining momentum. Developments in technologies have empowered citizens to make extensive contributions. Citizen Science (CS) has attributed to both social and technological benefits. In the fields of citizen science and urban development, implementation of new technologies offers some enormous opportunities. CS can channelize rampant technologies and systems that are commonly grown such as sensor technology for air quality (AQ) monitoring. Machine Learning, deep learning and big data analysis are generating deep insights in addressing environmental and urban issues. With advances in technology, significant questions are emphasized concerning human ethics. And, with the involvement of citizens, especially, technologies impact and role in society are broadly questioned.

In this study, the case study Hollandse Luchten Pilot is analysed in North Holland. The core objective of the research is to explore the opportunities and risks of AI to enhance the quality of CS AQ data, to explain the factors influencing the integration of AI tools and CS initiatives for AQ assessment in the case of the Hollandse Luchten pilot project and to draw conclusions that can enhance the implementation of the project, especially the AQ data collected by citizens.

Case Study was selected as the strategy, regarding the research methodology. Eleven semi- structured interviews were conducted with experts from the field of AI, experts from the organizations involved with the Hollandse Luchten Project and the Community Leaders. Additionally, secondary data was analysed to support the primary data collection.

The research revealed that AI methods can be integrated with the different aspects in the process of CS AQ monitoring, namely, data analysis (especially calibrations), optimization of location and increasing resolutions (using satellite imageries). The socio-technical factors play a vital role in mediating the relationship between the AI and the quality of CS AQ data. AI offers a remarkable opportunity in overcoming data gaps, which indirectly affects citizens perception and participation cycle. These factors are crucial in fulfilling society’s goals of enhancing their living environment.

To conclude, appropriate management of data, especially, regarding data reliability and accuracy, that can fulfil their roles of empowering communities, must be critically considered and analysed. To maximise the impact of CS data, CS projects should adopt AI methods and tools that enable the use of large volumes of data from known or unknown sources, across platforms and stakeholders to increase data quality and accuracy. Consequently, CS will be able to intensify and reach its remarkable potential for progressing environmental research for a project like Hollandse Luchten.