At the project level, where options involve specific interventions, it is likely that a spatially detailed transport model will be available. The output from this type of model will enable an understanding to be gained of differences in road traffic flows on a link by link basis, which in turn will allow differences in noise for specific communities to be predicted. At this level, a detailed understanding of rail movements is also likely to be available.
Having generated data on road and rail traffic flows, the following four steps are required to calculate the noise impacts of different options. The results should be recorded using Worksheet 7.1 shown below, which can be downloaded from Section 17.5.
i) Noise contours, using 3dB(A) and 5dB(A) increments, should be generated along transport alignments using simplified standard prediction methodologies, such as the Calculation of Road Traffic Noise and the Calculation of Railway Noise. Contours are required for the do-minimum scenario and the do-something scenarios for each transport option. More detailed data on properties can provide more accurate estimates of noise levels in given situations. Many factors, such as the type of ground cover, the presence and degree of screening, wind direction and strength, can all influence noise levels and the extent of the noise footprint. Professional judgement is needed to assess the significance of ignoring specific factors.
ii) Populations within these contours should be estimated using the latest available census information. Explicit assumptions may need to be made about population densities in order to estimate population exposure, although, where available, building occupancy databases and other sources can be used.
iii) For each noise contour in the do-minimum and do-something scenarios, the relevant annoyance response relationship shown in Table 7.1 should be applied to the numbers of people exposed to estimate the total population annoyed.
iv) The incremental impact of each option, expressed in terms of difference in population annoyed, can be derived by subtracting, for each noise contour, the population annoyed in the do-minimum from the population annoyed in the do-something and summing over all noise contours.
Worksheet 7.1 - Calculation of estimated population annoyed by noise
|
Option Name: |
Year: | |||
| Noise Level | Estimated Population exposed – do-minimum | Estimated Population exposed – do-something | Annoyance Response Function - % highly bothered by noise | Estimated Population Annoyed |
| Road Traffic NoiseLA10,18 hour (dB) | ||||
| <57 | ||||
| 57-59 | ||||
| 60-64 | ||||
| 65-69 | ||||
| 70-74 | ||||
| >75 | ||||
| Estimated Population Annoyed by road traffic | ||||
| Road Traffic NoiseLAeq,18 hour (dB) | ||||
| <57 | ||||
| 57-59 | ||||
| 60-64 | ||||
| 65-69 | ||||
| 70-74 | ||||
| >75 | ||||
| Estimated Population Annoyed by railway noise | ||||
| Total Estimated Population Annoyed | ||||
Care is needed where there appears to be the potential for double counting populations exposed to multiple sources of transport noise. As noted above, little is known about annoyance from multiple sources and expert judgement is important in these situations. In some cases, "double counting" could give the correct answer. For example, those disturbed by railway noise may be different from those who would be disturbed by road traffic noise, or, where noise sources are transient in nature, noise from one source might "fill the gaps" in the varying noise levels arising from another. Furthermore, multiple sources may impact different facades of exposed buildings. For example, a road might affect the front of a property, while a railway line might be to the rear of the same property. Even if the façade noise levels generated by the two were similar, there is no reason to assume that the annoyance caused would be identical. If the two sources were dissimilar, the problem is compounded.
Where the levels of noise from different sources are dissimilar, it may be reasonable to make a simplifying assumption and ignore annoyance from the source giving lower annoyance. However, where there is uncertainty, it is more difficult to make such a simplifying assumption and professional judgement will be needed to reduce the risk of double counting populations.
The entries in the Quantitative column of the AST should show the estimated numbers of people who are likely to be annoyed in the longer term in the do-minimum scenario and the do-something scenario in the fifteenth (or worst) year.
The entry in the Overall Assessment column should show the net difference in the estimated population who are likely to be annoyed in the longer term as a result of the option compared to the do-minimum scenario in the fifteenth year.
A qualitative entry in the AST should be used to highlight any factors which cannot be readily understood from the numbers in the Quantitative and Overall Assessment columns. An indication can be given of the main factors causing any change in conditions.
