2.3  What are environmental noise models used for?

Environmental noise predictions are used in an increasing range of decision-making applications. The most common application is for assessments where a decision is to be made regarding some future change to an environmental noise field. However, given the practical and technical challenges to noise measurement strategies, there are an increasing number of situations in which predictions complement or substitute for measurement-based noise assessment techniques.

Common uses of predictions for practical noise assessment purposes are as follows:

• Forecasting the impacts or benefits of proposed changes to an environmental noise field such as introduction, change or removal of a commercial/industrial installation, or modification of significant features in the physical environment that affect noise propagation, such as the construction or removal of barriers or enclosures.
  
  
• Assessment of existing commercial/industrial installations where the effectiveness of different noise mitigation strategies needs to be evaluated. Predictions can be used to rank the relative contributions of individual component sources of an installation comprising multiple complex sources. These rankings can then be used to focus noise mitigation resources on to the component sources whose treatment will enable the greatest reduction in total noise levels.
  
  
• Investigating the results of a measurement study to better understand the causes of the measured levels. For example, predictions may be used to assist the investigation of observed but unexplained variability in measurement results. Alternatively, predictions may be used to provide an estimate of the extent to which a particular source, or group of sources, may have influenced the total noise level measured from all sources affecting the environment in question.
  
  
• Complementing the results of measurement studies to investigate a wider range of locations, time periods or noise sources than could be directly investigated with measurements.
  
  
• Assisting the design of measurement studies by using predictions to understand the possible criticality of the situation before committing to expensive measurement studies. The predictions can be used to identify situations that are most critical to the assessment outcome, such as locations where noise levels might be expected to be similar to some threshold value where the assessment outcome significantly differs. This knowledge can then be used to design the measurement study in a way that focuses the available resources on the most effective strategy. A further benefit of predictions used in this way is the reference it provides when conducting post measurement analysis to judge the validity of a set of measurements, and whether there are any aspects of the results that differ from original expectations and subsequently warrant specific explanation or further investigation.