- Understanding aviation noise
Understanding aviation noise is the first and the broadest chapter of the ANIMA Noise Platform. Throughout the project's years, during the evaluation of interventions and case studies of airport noise management, ANIMA has noticed that there are still many knowledge gaps among relevant stakeholders.
In other words, as the dialogue between different stakeholders takes place, not all people are equipped with the same knowledge, particularly when it comes to such complex subject as aviation noise management, which includes several areas of expertise (airport management, avionics, noise and its exposure, knowledge of health and human behaviour, legislation, policymaking, etc.) Therefore, this chapter aims to provide as much basic knowledge as possible per each expertise area in the most understandable way to facilitate the dialogue between different stakeholders and communities' representatives in the long run.
The key areas are structured as follows:
- Glossary – throughout the platform, we cover different areas of expertise, including rather complex terminology and acronyms. The glossary aims to facilitate users' experience by providing the main terms and definitions for better understanding;
- Airport environment – this subchapter is essential for the basic understanding of how the airports work, the key aspects of aviation noise, the main stakeholders involved and how airport operations contribute to noise;
- Noise concepts – it is impossible to use an arithmetical scale to measure the aviation noise level. Measuring the noise level and all its indicators is possible with a logarithmic scale (decibel, dB), which is often difficult to understand. This subsection presents the underlying factors of noise and provides examples to facilitate understanding;
- Noise mapping – this subsection explores the concepts contributing to the official aircraft noise exposure representation to understand better the effects of several factors on the airport's noise footprints. To this end, ANIMA has developed a helpful tool – the public noise toolset;
- Regulation and mitigation strategies – the regulation of aviation noise is complex. There are several areas of regulation that must be considered in order to have an overall vision of the legislation surrounding aviation noise;
- Health impacts of noise – aviation noise impacts on people's health are studied by researchers globally. ANIMA aims to summarise the latest research and provide an overview of the current status of research on this matter. Additionally, ANIMA highlights the importance of continuing this research;
- Noise annoyance management – ANIMA is a project that strives to disseminate and enrich best practices in airport noise management, as well as to develop new methodologies, approaches and tools to manage and mitigate noise annoyance and effects based on a novel approach. ANIMA looks for new solutions while assessing the measures that reduce the annoyance specifically and not necessarily the actual decibels;
- Improving impact management – a chapter of the Noise Platform dedicated to communication and engagement between the airport and residents living in its close vicinity, quality of life interventions and understanding spatial variations (AnimApp and Dynamic population maps);
- ANIMA raw research data – to further use project's collected data and scientific knowledge acquired during the project, ANIMA participates in Open Data pilot action and provides access to the research data generated by the different research areas;
- Noise coordination research – the aviation noise reduction roadmap is a highly important ANIMA task. It has gathered researchers working on noise reduction in order to develop a joint roadmap, which would take into account not only technical improvements in aviation, but also consider aviation noise impacts;
- ANIMA related scientific publications and deliverables – this subsection serves as a library of articles, posters, presentations and other material presented by ANIMA over four years of the project. The information is categorised into main topics.