Noise Modelling

Environmental noise modelling describes the process of theoretically estimating noise levels within a region of interest under a specific set of conditions.

The specific set of conditions for which the noise is being estimated will be a fixed representation or 'snapshot' of a physical environment of interest. However, in practice the physical environment will usually not be fixed, but will be characterised by constantly varying conditions.

These variations in real world conditions will subsequently cause the actual sound field to vary in time and space. Thus it is important to recognise that the output of an environmental noise model will only represent an estimate for a ‘snapshot’ of the range of actual environmental noise levels that could occur in time and space.

Recognising that modelling is a means of estimating noise for a specific set of conditions, attention is now directed to defining what these conditions are.

The key conditions that a noise model relates to are:

• An approximation of the noise source, or sources, for which associated environmental noise levels are of interest
• An approximation of the physical environment through which noise will transmit from the noise source(s) to the location or region of interest. This includes the ground terrain, the built environment, and atmospheric conditions (e.g. wind, temperature, humidity)
• An approximation of the way in which sound will travel from the input noise source(s) via the input physical environment, to the receiver location or region of interest

Thus, producing an environmental noise model involves defining a series of noise sources to be investigated, describing acoustically significant features of the environment through which sound will propagate to the receiver, and then applying a calculation method that accounts for these descriptions to produce an estimated noise level at a location or region of interest.

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, a new road or modification of significant features in the physical environment that affect noise propagation, such as the construction or removal of buildings, 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.
• 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.