Send message to Authors

Short abstract:

In this presentation we explore how a citizen science approach to noise pollution research creates socially robust knowledge. Taking a closer look at a Dutch case study, we show how this approach adds to traditional methodologies for a more inclusive, locally grounded understanding of the issue.

Long abstract:

Noise pollution has emerged as a significant concern in Dutch communities near railroads, impacting health and well-being. As the EU plans to double rail freight traffic by 2050 for sustainable transportation, the issue is expected to become more severe. Therefore, accurately assessing noise pollution is crucial for effective policymaking and enforcement. Current scientific methods for noise monitoring predominantly rely on models, calculating mean sound levels over extended periods. However, these methods often diverge from local perceptions and experiences, such as insufficiently accounting for peak noise levels in the short term, leading to criticism and growing distrust.

In response, concerned citizens have embraced the role of citizen scientists, measuring noise levels themselves with affordable yet quality sound meters. Despite originating from dissatisfaction and distrust, this citizen science approach has the potential to bridge gaps between science, society, and policy. Our study focuses on a citizen science project concerning noise pollution from freight train traffic. In the village of America (North-Limburg, the Netherlands), we conducted an interdisciplinary citizen science case study, with scientists and citizens collaboratively measuring noise levels and acute noise hindrance. The study was a joint effort between scientists and citizens at every stage of the process. Through interviews and project documentation, we explored the transformative impact of citizen science on the social robustness of policy-relevant science. The results emphasize that acquiring "real-world knowledge" at a local level, employing a transdisciplinary approach, and using an iterative research process contribute to the social robustness of scientific knowledge in this field.