Our noise modelling using INM. May 2015
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1 Our noise modelling using INM May 2015
2 INM model developed to reflect operating characteristics of Heathrow. Our process adopts a similar approach to CAA using ANCON. Our INM model is set up to reflect specific operational characteristics for Heathrow including use of actual flight track data from Heathrow NTK system and best-fit climb profiles are adopted of aircraft at Heathrow not the standard city-pair profiles traditionally adopted in INM. Actual flight tracks Standard noise contour and grid outputs are generated for specific operations eg westerly departures (shown). Noise contours GIS tools used to derive differences and community specific results with population. GIS MAPPING Best fit departure climb profiles Noise level grid Average modeled noise level data verified with measured noise data at specific locations. INPUTS/SETUP Additional metrics (N65) OUTPUTS Average noise level, db Location Period Modelled Measured Englefield Green: 13/ 10/ / 11/ Englefield Green: post 13/ 11/ / 12/ Ascot 24/ 09/ / 11/ Cumberland Lodge 25/ 08/ / 11/ NPL, Teddington 28/ 07/ / 11/ Feltham 28/ 07/ / 11/ Note: Only the monitor in Englefield Green was installed for periods that included the and outside the. VERIFICATION
3 Actual flight tracks from Heathrow s NTK system are used rather than standard backbone tracks.
4 Height, ft Actual departure climb profiles were reviewed best fit INM profiles, rather than standard city-pair profiles adopted for each aircraft type. Example shown using A380 Dubai Standard city pair process would use Stage Length 5 profile Standard INM approach uses city-pair distance to derive climb profile and noise characteristics. This can lead to an underestimate of noise levels. Evening departures more closely resemble ICAO A Stage Length 8 Daytime departures more closely resemble ICAO B Stage Length 8 Track distance, ft
5 Modelled average noise level results verified using measured noise data close agreement. Location Period Average noise level, db Modelled Measured Englefield Green: 13/ 10/ / 11/ Englefield Green: post 13/ 11/ / 12/ Ascot 24/ 09/ / 11/ Cumberland Lodge 25/ 08/ / 11/ NPL, Teddington 28/ 07/ / 11/ Feltham 28/ 07/ / 11/ Note: Only the monitor in Englefield Green was installed for periods that included the and outside the. Noise measurements were obtained during the at 6 locations. The results of the noise modelling have been compared with the measured noise level data. Average noise levels have been found to be in close agreement at the noise measurement locations. It is therefore considered that the modelling as currently developed is a reliable way in which to predict noise exposure over a wider area for this than the installation of noise monitors alone could achieve.
6 Noise level data is generated for specific operating patterns. eg 16hr day average noise levels for westerly departures. Standard noise contour outputs.
7 INM produces a noise level grid as well as noise contours. This enables us to look at noise levels & differences over a wide geographic area. difference
8 Noise level data can be generated for specific operating cases. eg noise levels specifically associated with the routes. difference
9 We can also generate multiple metrics from the model. For example N65 the number of events with a maximum noise level > 65 dba Overall Route specific
10 Using GIS tools we can map the noise data and can derive noise level data at postcodes, ie community specific results inc population. Overall Route specific
11 . and then differences at communities Overall Route specific
12 .and also, for different metrics (again showing the N65). Overall Route specific
13 .and also, N65 differences at postcode locations. Overall Route specific
14 So, noise modelling is a reliable and versatile way to derive noise levels across a wide geographic area. Our model: uses INM set-up to reflect operating characteristics of Heathrow. Our model has been verified using measured data at a number of locations. Uses GIS to generate noise level and event data and differences specific to communities. Modeling: enables us to predict noise levels at any location, not just those with monitors. can predict multiple metrics (noise level, number of events etc). can also be used to predict noise level changes ahead of operational changes. However, modeling is complementary to monitoring and should not substitute. Both should be adopted.
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