Arbets- och miljömedicin | Lund

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AMM

Generalizations and Accuracy in Community Noise Modelling

– A Case Study on Railway Noise in Burlöv Municipality

Rapport AMM 2011:2

Kristoffer Mattisson

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This report for the EUROHEIS WP 7, Kristoffer Mattisson presents - a background describing simplified and elaborated noise modelling, - a case study on railway noise performed in Burlöv municipality, Scania, Sweden - a comparison of how individual factors influence the modelling of railway noise using the Railway Traffic Noise – Nordic Prediction Method from 1996 -a comparison of the result and method from this case study with a previous large scale modelling of noise from railways Kristoffer Mattisson Regional and University Laboratories Department of Occupational and Environmental Medicine 221 85 Lund Sweden [email protected]

http://[email protected]/

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Summary When modeling noise it is important to consider the uncertainty in the method. There are a number of sources of error that influence the result, such as the choice of calculation method, software, data and user specific choices. The purpose of this case study from Burlöv municipality in Scania, Sweden, was to show the influence of such factors when modeling noise from railways with the Nordic calculation method (Nordic council of ministers 1996) implemented in the software SoundPLAN. The results were compared to a detailed modeling, and to results from a previous large scale national noise mapping. The results show differences in the area size exposed to noise levels over Lden 55, 65, and 75 dB(A), when using different resolution, search radius, elevation, ground softness and inclusion or exclusion of buildings. The difference in the number of persons exposed to different noise levels is also presented. The comparison of the detailed noise mapping in this study and the previous national mapping shows large differences. The same calculation method and software was used, but different input data and modeling options had been used. . The differences in results shows that it might be important to make more detailed mapping of the noise levels, if specific areas are to be evaluated. Modeling large areas, without consideration to factor that might have a large local influence can give misleading results on specific areas. However, the calculation time increases rapidly when noise is modeled at a detailed level, and simplifications are often used in large scale investigations. The results from this case study underscore the need for standardized noise modelling methods for comparisons between different areas and different time periods.

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Table of Contents

SIMPLIFIED OR ELABORATED MODELING? ............................................................................... 1

Uncertainty in noise modeling ............................................................................................ 1

Calculation type ................................................................................................................... 2

MODELING NOISE FROM RAILWAYS IN BURLÖV MUNICIPALITY ............................................. 3

INFLUENCE OF INDIVIDUAL FACTORS........................................................................................ 4

COMPARISON BETWEEN OUR CALCULATIONS AND A LARGE SCALE INVESTIGATION OF NOISE

EXPOSURE FROM RAILWAYS .................................................................................................... 16

DISCUSSION .............................................................................................................................. 17

REFERENCE LIST ...................................................................................................................... 18

APPENDIX 1 .............................................................................................................................. 19

APPENDIX 2 .............................................................................................................................. 20

APPENDIX 3 .............................................................................................................................. 21

APPENDIX 4 .............................................................................................................................. 22

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Simplified or elaborated modeling?

Investigation of community noise exposure is of importance on different scales, from national (WSP Akustik 2009) to regional (ÅF-Ingemansson AB 2007) to local (Ramböll 2009). Large scale investigations of community noise on national or regional level are important for urban planning and noise reduction strategies (IMAGINE 2004). Mapping of noise exposure over large area such as countries or regions creates a picture of the problem in large and enable the politicians to put pressure on for example car manufacturer to make less noisy cars (European Commission 1996). In large scale investigation the most important feature is not to determine the exposure for each individual person or building, but to give an overall estimation of the exposure. In other types of investigations it is crucial that the noise exposure assessment for each individual or building is correct. It is therefore important to be as detailed as possible. Such small scale investigations are for example used to make plans of where to put new buildings, or where to put noise barriers or walls to protect persons from noise exposure. In other investigations, as in epidemiological studies, it is important both to cover a large area, and have a high level of accuracy. It might be preferable to use a method as detailed as possible to model noise, but noise is strongly fluctuating spatially and temporally. Thus, detailed calculations of noise exposure gets heavy fast when all possible considerations are paid and large areas needs to be covered. Another problem with large scale investigations is that the required data needed for the calculations might even not be available, or is too costly. It is therefore always a trade off between the needed accuracy of the results and the time and cost to perform the investigation.

Uncertainty in noise modeling

When modeling noise there are a number of steps to consider. Four potential sources of error are presented below:

• Calculation method – There are a number of theoretical methods to calculate noise. Those theoretical methods of noise emission and dispersion of noise are models of reality and do not always model the correct noise level. The choice of calculation method is very important. Research has shown differences of up to 15 dB between different National European calculation methods (Nijland and van Wee 2005). It has been claimed that the Railway Traffic Noise – Nordic prediction Method (RTN; Nordic Council of Ministers 1996) has an accuracy of 3 dB at 300-500m distance to the track, based on comparison to measurements. But the accuracy at longer distance and in uneven terrain is uncertain.

• Software – Today’s calculation methods are so advanced that it is not possible to fully

apply them without computers. There is different software that can be used to implement the calculation methods. Some of them are specific for noise modeling such as SoundPLAN and CadnA, while others are general GIS-software such as ArcGIS. If detailed modeling should be applied it is recommended to use specific software to keep calculation time as short as possible. Implementation and performance of the same calculation method can vary between different software. SoundPLAN claims that the implementation of calculation methods are made with an accuracy of +-0,2 dB (SoundPLAN Help Manual 2010). Even so there are continually updates of the program. Implementations of the method in non noise specific software might have a much higher uncertainty.

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• Data – To be able to model noise there are some basic data that needs to be known.

Depending on the detail level of the noise mapping the type of data needed varies but also the quality of this data. Accurate and updated traffic intensity data are important.: How many trains pass each day? What speed do they have? What type of train is it? Other important data is ground softness, buildings, elevation and screens. The choice of the wrong ground softness can for example influence the results with up to 9 dB (Jonasson 2010)

• User’s application of calculation method – Even if the same calculation method,

software and data are used there are still a number of decisions that needs to be made by the noise modeler. It is possible to include or exclude a number of corrections in the calculation method and also software specific parameters. The calculation type is important, i.e., should noise be calculated in a grid or at the facades? The calculation method describes a number of factors, that can be considered or not, or modified, i.e. number of reflections, search radius, side diffraction, search angle. A study made with the Dutch national calculation method showed differences with up to 6 dB due to different interpretations made by three noise modelers (Nijland and van Wee 2005).

If the noise exposure for each persons is to be calculated a fifth source of potential error can be added - the linkage of noise mapping to population data. There are a number of ways to attribute that noise exposure to population data. Even if the noise is correctly modeled the result will not be good if the attribution is incorrect.

Calculation type

When modeling noise it is important to know what kind of exposure that is of interest. Are the numbers of persons, buildings or the area exposed the most interesting? There are different approaches that can be used for calculation of the number of persons or buildings exposed. Noise can for example be calculated for the facades of the buildings, or calculated in a grid. If the noise exposure of persons or buildings is of interest calculation of façade noise is a good method. Important questions to answers are how calculation points are distributed? One per façade? Evenly distributed along the facade depending on distance? Different calculation points for different floors? Will the most exposed façade be representative for the whole building? Is the requested information available? Façade noise calculations are a good way of limiting the number of calculation points and get a high accuracy. The exposure of persons or building can also be calculated from grid noise calculations, but then noise is not calculated for the specific façade and for example reflections in the own façade can not be excluded, as requested from the European Union (2002/EG/49). If the area distribution is of interest and calculation of façade noise is not possible, then grid noise calculation is an alternative choice. When using grid noise calculation the calculation points are evenly distributed in a grid. The size of the cell is very important for the accuracy in the results but it also greatly affects the calculation time. The grid size is depending on the requested accuracy. In urban areas the recommended cell size is 5-15 m (SoundPLAN Help manual 2010). The calculation time of grid noise exposure is long. However, noise is spatially auto correlated, and by calculating noise in areas with complex dispersion environment and interpolating in areas with a more uniform environment the calculation time can be reduced substantially.

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Modeling noise from railways in Burlöv Municipality

Burlöv municipality is a small county in south east Scania, situated between the cities Malmö and Lund (Figure 1). It is classified as a suburban municipality with 16 600 inhabitants and an area of about 19 2km . The reason that Burlöv is of interest in a noise perspective is that it has a heavy transport infrastructure, with an important part of the transportation of goods from Europe to Scandinavia passing through, and is adjacent to larger population centers. The self-reported noise disturbance from both roads and railways in the municipality is the highest in Scania (Albin and Bodin 2010), but previous studies modeling noise exposure have indicated low noise levels (WSP Akustik 2009, Soundcon 2009). Is it possible that people living close to roads and railways, and with a large proportion experiencing the noise as annoying, still have low exposure of noise? One possible explanation is that those previous studies were large scale investigations, not specially focusing on Burlöv. A more detailed investigation was therefore conducted in Burlövs municipality, with calculation of the noise level as detailed as possible for each person.

Figure 1: Overview of Burlöv municipality and its geographical position. Shaded areas inside Burlöv is buildings and black lines are railway. Method The noise modelling was performed with software, which is specially created to model noise exposure (SoundPLAN 2010). The calculation method used in the study was Railway Traffic Noise – Nordic Prediction Method (Nordic council of ministers 1996), which is implemented in SoundPLAN. This is a commonly used calculation method in Sweden and the Nordic countries. Railway and traffic intensity data were obtained from the Swedish transport administration.

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Noise was calculated at the middle of each façade of every residential building in Burlöv, and the highest exposed façade was used to represent the noise level in the whole building. The noise exposure of the buildings was connected through euclidean distance to a point layer where each person in Burlöv was represented by a point in the centroid of the real estate. Calculations where made for the measures LAeq24, LAFmax, Lden and Lnight at both 2m and 4m. A yearly average diurnal noise exposure was calculated based on a weekday and a weekend day, which were weighted to get a yearly average day. Laser scanned data was used as elevation with one measure point at each square meter. This very detailed elevation made it possible to consider very small changes in height. The laser data was also used to calculate the height of the buildings. All buildings in Burlöv municipality were included as screens but noise levels were only calculated at residential buildings. Land cover was obtained from the European Space Agency project Corine (Corine 2010). In Corine the land cover is classified into different classes based on satellite data. Those classes were divided into soft, medium hard and hard ground, based on report from the Swedish National Space Board describing how to connect land cover classes to ground softness (Sohlman et al. 2004). Information about screens was collected by Burlöv municipality. Only screens higher then 2 m where included. A summary of the data and method is presented in appendix 1 and 2. Area distribution of the noise exposure in Burlöv was also calculated for a grid. Considerations were almost the same as the façade noise model and a grid size of 10*10m was used. A summary of the grid noise parameters is presented in appendix 2. Results Results from the case study are presented in appendix 3 for façade noise and in appendix 4 for grid noise and will be discussed under “Comparison to a large scale investigation of noise exposure from railways”.

Influence of individual factors

Many options exist, when noise is modeled. We here illustrate how different choices affect the result, in a series of calculations of railroad noise in Burlöv, with different adjustments. To illustrate the influence and show how different choices affect the result a number of calculations with different adjustments, where made over Burlöv municipality. In the detailed calculation (see above) the façade noise was calculated for all residential buildings, but to be able to visually show the difference in exposure, grid noise calculations where made. Façade noise is also only possible to calculate when buildings are included. The calculated noise levels where connected trough a simple overlay to population points, to also show the number of persons exposed. The position of the population points where in the middle of the real estate. Resolution When calculating grid noise the selected resolution strongly influences the result. It is preferable to use as high resolution as possible, especially in complex areas. A grid size between 2-10m is recommended inside cities (Jonasson 2010). The effect of the grid size is much smaller in more homogenous areas. In some of the specific noise software it is possible for the program to identify areas with complex dispersion of noise and have smaller grid cells in those areas than in other areas. Figure 2-4 illustrate the effect of using different cell size in a very simple model. The area with noise exposure to levels over 55, 65 and 75 dB(A), respectively, are presented in the grey box in the top left corner of each picture.

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Figure 2: Grid noise exposure from railways over Burlöv municipality using distance as only dispersion factor, a grid size of 2*2m and a search radius of 2000m. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN.

Figure 3: Grid noise exposure from railways over Burlöv municipality using distance as only dispersion factor, a grid size of 10*10m and a search radius of 2000m. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN.

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Figure 4: Grid noise exposure from railways over Burlöv municipality using distance as only dispersion factor, a grid size of 100*100m and a search radius of 2000m. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN. There are some differences in the areas exposed to levels over 55 dB(A) between using 2m and 10m cell size, but small differences in area exposed over 65 or 75 dB(A). Differences in exposure between 10 and 100m are larger for all three noise level categories. Notice that the area exposed to very high levels (>75 dB(A)) is almost doubled when using 100m cell size. Calculation time is strongly influenced by the selected resolution, increasing from seconds to hours in our example (Table 1). Table 1: Calculation time using different resolution Resolution (Cell size in m) Calculation time

2 1h 47min 43s

10 3min 54s

100 3s

Distance The distance between the source and the calculation point is an important factor when modeling noise. Increasing distance means decreasing noise level. As noise decreases with distance, sources close to the calculation point are more likely to have a greater influence at the exposure level then sources far away. It is possible to decide the search radius i.e. the maximum distance between the source and the calculation point. The influence of the sources on noise exposure is also dependent on the strength of the emitted noise from the source. It is therefore important not only to consider sources close to the calculation point, but also sources further away. However, the increased calculation time is a problem when using long search radius. Using 1000m search radius instead of 500m increased the calculation time with 50%, while using 5000m search radius instead of 500m increased the calculation time with more then 250% (Table 2).

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Results of the calculation over the noise exposure from railways with 500m, 2000m and 5000m search radius are shown in figure 5-7. When the search radius is increased the area affected is increasing, depending on distance and source emission. However, in our example, areas exposed to very high values >75 dB(A) were almost exactly the same between 500m and 2000m search radius. This is due to that noise rapidly loses energy with increasing distance. The difference at high exposure >65 dB(A) was also negligible, about 0,18 2km . In contrast, the difference between areas exposed to levels >55 dB(A) was much higher. In this case the exposure from the railway seems to affect areas longer then 500 m from the railway. As the noise energy decreases with distance there is a point where a consideration of sources at longer distance no longer affects the results. The area exposed to levels >55 dB(A) was almost the same as for 2000m and 5000m search radius.

Figure 5: Grid noise exposure from railway over Burlöv municipality using distance as only dispersion factor, a grid size of 10m and a search radius of 500m. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN.

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The number of persons exposed to different noise levels is strongly affected by the search radius used. Table 2 show the difference in number of exposed in Burlöv with distance as the only influencing factor and various search radius. Thus, if a too short search radius is used and the sources in the model are emitting strong noise the number of persons exposed will be strongly underestimated. Table 2. Number of exposed and calculation time using different search radius

Number of Persons exposed to different search radius >55 dB(A) >65 dB(A) >75 dB(A) Calculation time

500m 7132 2704 228 2min

1000m 13885 3109 228 3min 1s

2000m 14803 3162 228 3 min 54s

5000m 14841 3162 228 7min 16s

Ground softness Next to distance ground softness is a very important factor affecting the noise exposure. The softness of the ground decides how much of the noise that is absorbed and how much that is reflected. Thus, hard ground is a factor that increases the noise levels. The increase affects all levels of noise exposure, from low exposure to high exposure areas. Figure 8 show the noise exposure in Burlöv municipality when including ground softness in the model.

Figure 8: Grid noise exposure over Burlöv municipality using distance and ground softness as dispersion factors. A grid size of 10m and a search radius of 2000m were used. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN. The number of persons exposed in Burlöv was strongly increased when including hard and medium hard ground in the model. The number of persons exposed to high and very high levels is especially affected (Table 3). Ground softness has little influence on the calculation time.

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Table 3. Number of exposed and calculation time including or excluding ground softness

Number of Persons exposed to different ground softness >55 dB(A) >65 dB(A) >75 dB(A) Calculation time

Without hard ground 14803 3162 228 3min 54s

With hard ground 16053 8678 560 5min 7s

Elevation Is a complex factor influencing the dispersion of noise. The relationship in height between the calculation point and the source is important, as is the terrain between them. Barriers in the terrain reflect noise and reduce the exposure. Detailed information about the elevation makes it possible to consider smaller changes in the landscape such as noise barriers. Figure 9 shows the noise exposure in Burlöv when consideration has been paid to distance and laser scanned elevation. This is very detailed data where the elevation is measured with one point every square meter. The arrow indicates where a noise barrier is situated.

Figure 9: Grid noise exposure over Burlöv municipality using distance and laser scanned elevation with a resolution of 1m as dispersion factors. A grid size of 10m and a search radius of 2000m were used. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN.

Figure 10 shows the noise exposure using satellite data with a resolution of 30*30m in the model. When using the resolution of 30*30m the elevation is smoothened out compared to the true elevation.

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Figure 10: Grid noise exposure over Burlöv municipality using distance and satellite elevation with a resolution of 30m as dispersion factors. A grid size of 10m and a search radius of 2000m were used. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN. The number of persons that were exposed to different noise levels, when using different elevations, is presented in Table 4. Including detailed elevation data increased the calculation time substantially. Table 4: Number of exposed and calculation time using different elevation data

Number of Persons exposed to different elevation >55 dB(A) >65 dB(A) >75 dB(A) Calculation time

No elevation 14803 3162 228 3min 54s

ASTER elevation 14705 2876 184 9min 44s

Laser elevation 13999 2324 173 2h 26min 20s

Buildings Buildings are acting as screens and are reducing the noise exposure. Noise exposure levels are strongly influenced when including buildings as screens, as can be seen in figure 11.

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Figure 11: Grid noise exposure over Burlöv municipality using distance and buildings as dispersion factors. A grid size of 10m and a search radius of 2000m were used. Calculations of Lden at 4m height were made with Nordic prediction method from 1996 in the software SoundPLAN. As no noise exposure is calculated for areas where there are buildings it is not possible to estimate noise exposure from a simple direct overlay. Calculation time is very strongly influenced when including buildings. When calculating the noise exposure at 4m height including buildings the calculation time was as long as 1d 2h 30min. Height of calculation The height of the calculation is influencing the noise exposure. The relationship in height between source and calculation point is changed, as is the influence of screens Thresholds and recommendations in Sweden relate to 2m height, while noise exposure should be reported at 4m height according to the European Union. Modelling noise at different heights gives different results. However, when calculating a grid noise map using only distance and buildings, the difference was small between using 2 m and 4 m calculation height. Noise modelled at 2m height is shown in figure 12. A slightly larger area was exposed to levels over 55 dB(A) at 4 m height.

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Figure 12: Grid noise exposure over Burlöv municipality using distance and buildings as dispersion factors. A grid size of 10m and a search radius of 2000m were used. Calculations of Lden at 2m height were made with Nordic prediction method from 1996 in the software SoundPLAN. In a more detailed noise model the differences in results, depending on the chosen height, can be larger. In the detailed study with facade noise in Burlöv municipality, the noise was calculated for both 2 and 4m heights. Depending on which noise measure that was calculated and noise level the difference varied. Lnight was the measure that had the largest difference between calculations. 58,8% of the population in Burlöv were exposed to levels over the WHO threshold value of 45 dB(A) outside bedrooms at night time (WHO 1999) using 2 m height, in contrast to 72,6% when using 4m height. Connecting exposure to persons, buildings or area There are different methods to connect the exposure to persons, buildings or area. When using façade noise exposure the best way of connecting the noise exposure to buildings or persons, is to use the exact address. If the address is known for both the buildings and population points it is possible to connect the exact right building to the right person. Even if the address is known there are remaining problems, for example on which floor or on with side of the building the bedrooms are situated. There is an ongoing work in Sweden to register the floor and direction of the apartment for every person in Sweden. With this kind of data it will be possible to calculate the noise for different floors and different sides of the house, and to connect this information to each person. Doing so increases the accuracy of the exposure assessment, but it will also increase the calculation time considerably. If the geographical position is known for all persons and all buildings it is possible to use this information to connect the exposure to the population data via Euclidean distance. This is also a rather accurate method, but not as exact as using the address (Jonasson 2010). Using this method all residential buildings needs to be identified so that population data is connected to the right buildings. One possible source of error is the geographical positioning of the

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population. In the investigation in Burlöv the geographical coordinates where known in the centroid of the real estates. If there are more then one residential building on real estate or if the residential building is not situated close to the centroid of the real estate there is a possibility that the population points will be connected to the wrong residential building (Figure 13).

Figure 13: If the geographical position of the population data is known in the centroid of each real estate polygon sometimes the point is connected to the wrong building. In case 1 there is no problem. In case 2 there are two buildings on the real estate and a problem with connecting the right building to the right population point occurs. In case 3 two real estates are situated close to each other. One of the real estates has the building in the middle and on in the upper right corner. Population points from the right real estate are then linked to the building at the left real estate. A third way of connecting noise exposure exists when the population data is available in a grid. Each grid contains the number of persons living in that grid. This makes it possible to calculate the number of person per 2m in each cell of the grid. If noise exposure is calculated in a grid it is possible to overlay the grid noise map and the population grid to get the number of persons exposed. If the geographical position of each person is known it is possible to connect this position to the grid noise exposure as well. As noise in grid noise calculation is not calculated if the cell is completely covered by a building it is not possible to make a direct overlay if the population data is known for the centroid of the real estate (figure 14). But it is possible to first connect the population point to the right residential building, and then connect the noise level to the residential buildings. One problem that arises is the connection of noise grid cell values and the building polygon. When the highest cell value is selected to represent the whole building it is enough if only a corner of that cell crosses the building polygon to make that cell value representative for the whole building.

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Figure 14: Grid noise calculation including buildings and population points. Outdoor noise is calculated and noise is therefore not calculated for cells completely covered by buildings. If population points are connected directly to the grid noise exposure points inside those areas get a noise exposure of 0 dB(A). This problem is solved if noise levels first are connected to buildings and population points then connected to the buildings.

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Comparison between our calculations and a large scale investigation of noise exposure from railways

In 2006 Banverket (now The Swedish transport administration) modelled the noise from railways with more then 60000 trains per year in Sweden 2006 according to 2002/EG/49. (Banverket 2007a). As this included railways from all of Sweden, several modelers were engaged, using instructions from Banverket (Banverket 2006a, Banverket 2006b). These instructions were general and gave the modelers a possibility to a number of choices in the method. These results where then assembled and reported (Banverket 2007a). “Södra stambanan”, the railway running trough Burlöv municipality, was included in the investigation (Banverket 2007b). In comparison to the detailed study made over Burlöv (described under Modeling noise from railways in Burlöv municipality) there are both differences and similarities. The same calculation method (NMT) and the same software (SoundPLAN) were used. The calculation type used in Banverkets study was grid noise both for person and area exposure. In contrast, the detailed study used the results from the façade noise as results for the number of persons and buildings exposed and the grid noise calculation to describe the area exposed, as recommended (Jonasson 2010). Banverket used elevation data with 5m equidistance, in comparison to laser scanned data with and uncertainty in height of a few centimetres in the detailed study. Water and industrial areas was selected as hard ground, while the detailed Burlöv study used satellite data. In the Burlöv study there where three possible classes of ground softness, which meant that consideration could be paid to residential areas where part of the area is asphalt (hard). Another difference is that Banverket used population data in a 100*100m grid which was connected to the noise exposure. There where also other differences in method, like the number of reflections and the search radius. Banverket used 1500m and 2 reflections, in contrast to 2000m search radius and 3 reflexions in the Burlöv study. Also, the railway distance “Lommabanan” was included in the detailed study. This is a railway with only 11 trains per day in comparison to Södra stambanan with more then 300 train per day. The number of persons that were exposed to different noise levels in the different studies is presented in Table 5. The differences are substantial at all noise levels. Table 5: Comparison of number of persons exposed between a national and a regional study

Lden dB(A), 4m above ground, on most exposed facade

55-59 60-64 65-69 70-74 >75

Banverket* 2500 800 300 100 0

Detailed study over Burlöv 4943 2217 696 229 270

*rounded to nearest 100

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Discussion

This study has clearly demonstrated that different methods for noise modelling, and the choice between options within models yield results which may vary substantially. Presently the EU countries use different calculation methods, which in comparison to each other can vary with up to 15 dB(A) for the same calculation point (Nijland and van Wee 2005). Even if the same calculation method is used, as for example in Sweden where most studies are made based on NMT, results may vary substantially. It is important to consider the uncertainty in the results from noise modeling. What if the noise levels are underestimated with for example 3 dB(A)? In the case study over persons exposed to noise from railways in Burlöv municipality a large percentage of the population are exposed to levels between 50 and 60 dB(A). In Sweden the threshold values for railways for the measure LAeq24 is 55 dB(A). This means that an under- or overestimation could result in a big difference in the number of persons exposed over threshold values. Calculation time can be the limiting factor for noise exposure calculations. In the detailed façade calculation the total calculation time was 5h and 48 min for the small municipality of Burlöv. If the same method would be applied on Scania, with about 75 times more inhabitants then Burlöv, the calculation time would be roughly 18 days. SoundPLAN makes it possible to connect a number of computers in a network that performs the calculations. In this study three fairly new computer where used, but even so the calculation time could be very long, and errors occurred due to computational overload. However, as shown in the Burlöv study, some simplifications can be made that substantially reduces calculation time, without any large effect on the results. The cost for data acquisition and preparation of data can be substantial when noise exposure is to be modelled over a large area, for example collecting information about train traffic. In Sweden this kind of information can be obtained from the Swedish Transport Administration, but the data still needs to be processed before it can be used in noise modelling. Moreover, some data might not be available. One such example is laser scanned elevation data, which in Sweden only exist over some parts of the country. Other data is free of charge like satellite elevation from ASTER, but using this data instead of laser scanned data reduces the accuracy in the results. In conclusion, there is a need for careful reporting of how noise has been modeled, and a need for standardization. The European Union has released guidelines to reduce differences due to different methods (European Commission Working Group - Assessment of exposure to noise 2006). In Sweden a complement to these guidelines is on its way (Jonasson and Gustafson 2010).

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Reference list

Albin Maria and Bodin Theo (2010) ,Vägtrafikbuller – Projekt inom miljösamverkan Skåne Trafikbullerstörning i Skåne, Rapport, Miljösamverkan Skåne och Universitetssjukhuset i Lund Arbets- och miljömedicinska kliniken Banverket (2006a), Förordning om omgivningsbuller – Indata och utdata vid bullerberäkningar, Editor Nicolas Renard Banverket (2006b), Beräkning av buller och antal boende, Andreas Gustafsson, Sveriges provnings- och forskningsinstitut, Rapport 2006:2 Banverket (2007a), Bullerkartläggning enligt förordning om omgivninjgsbuller – Resultat från bullerkartläggning 2007, Karin Blidberg Banverket (2007b), Södra stambanan och kontinentalbanan, Malmö stad samt Södra Stambanan Arlöv – Örtofta – Beräkning av antal utsatta boende m m enligt förordning 2004:675 Corine (2004), Collected from: http://www.eea.europa.eu/publications/COR0-landcover, Collected: 2010-01-06 European Commission (1996), Future Noise Policies – European commission Green paper, Commission of the European Communities, Brussels European Comission Working Group - Assesment of exposure to noise (2006), Good Practice Guide for Strategic Noise Mapping and the Production of Associated Data on Noise Exposure, Version 2 IMAGINE (2004), IMAGINE – State of the art, Document identity: IMA10TR-040423-AEATNL32 Jonasson H. (2010), Känslighetsanalys av bullerkartläggning, SP Rapport 2009:27, ISBN 978-91-86319-14-4 Jonasson and Gustafsson (2010), Anvisningar för kartläggning av buller enligt 2002/29/EG, SP Rapport 2010:XX UTKAST Nijland H. A. and Van WEE G. P. (2005), Traffic Noise in Europe: A Comparison of Calculation Methods, Noise Indices and Noise Standards for Road and Railroad Traffic in Europe, Transport Reviews, Vol. 25, No. 5, 591-612 Nordic Council of Ministers (1996), Railway Traffic Noise – Nordic Prediciton Method, TemaNord 1996:524 Ramböll (2009), Burlövs kommun Trafikutredning Kronotorp, Rapport för Burlöv kommun Sohlman M., Jonasson H. and Gustafsson A. (2004), Using Satellite data for the determination of acoustic impedance of ground, Report to the Swedish National Space Board Soundcon (2009), Inventering av vägtrafikbuller I Skånes kommuner – Sammanställning av enkätsvar, Rapport åt Miljösamverkan Skåne och Vägverket SoundPLAN (2010), Collected from: www.soundplan.com, Collected: 2011-01-05 SoundPLAN Help Manual (2010) Searchword: Physics versus standard WSP Akustik (2009), Uppskattning av antalet exponerade för väg-, tåg- och flygtrafik-buller överstigande ekvivalent ljudnivå 55 dBA, Rapport till Naturvårdsverket ÅF-Ingemansson AB (2007), Malmö stad Strategisk bullerkartlägging, Rapport 60-02855-07020600 för Malmö stad WHO (1999), Guidelines for community noise, Guidelines from The World Health Organisation 12hTRHOa0r

http://www.eea.europa.eu/publications/COR0-landcover

http://www.soundplan.com/

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Appendix 1

Summary of the method used to model facade noise railways in Burlöv municipality. Railway façade noise Data used: Railways Elevation Ground Softness Buildings Noise Screens Parameters Reflection order 3 Maximal reflection distance to receiver 200 m Maximal reflection distance to source 50 m Search radius 2000 m Weighting: dB(A) Tolerance: 0,001 dB Standards: Rail: Nordic Pred. Method For Train Noise (NMT); 1996 Emission according to: NMT 1996 Limitation of screening loss: single/multiple 20 dB /40 dB

Meteo. Corr. C0(6-18h)[dB]=0,0; C0(18-22h)[dB]=0,0; C0(22- 6h)[dB]=0,0;

Lmax = LmaxF for electrically driven trains (LmaxM+3-(3dc/100)dB) Assessment: Lden (SE) - rail Facade Noise Map: One receiver in center of facade Calculating in fixed height above ground: 4,00 m and 2,00 Reflection of "own" facade is suppressed

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Appendix 2

Summary of the method used to model grid noise railways in Burlöv municipality. Railway grid noise Data used: Railways Elevation Ground Softness Buildings Noise Screens Reflection order 2 Maximal reflection distance to receiver 150 m Maximal reflection distance to source 50 m Search radius 1500 m Weighting: dB(A) Tolerance: 0,001 dB Standards:

Rail: Nordic Pred. Method For Train Noise (NMT); 1996 Emission according to: NMT 1996 Limitation of screening loss: single/multiple 20 dB /40 dB Calculation with side screening

Meteo. Corr. C0(6-18h)[dB]=0,0; C0(18-22h)[dB]=0,0; C0(22-6h)[dB]=0,0;

Lmax = LmaxF for electrically driven trains (LmaxM+3-(3dc/100)dB) Assessment: Lden (SE) - rail Grid Map: Grid space: 10,00 m Height above ground: 4,00 m Grid Interpolation: Field size = 3x3 Min/Max = 10,0 dB Difference = 0,5 dB

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Appendix 3

Facade noise exposure from railways Lden dB(A)

Number of persons at 4m (%)


< 35 392 (2,4%) 386 (2,3%) 35-40< 19 (0,1%) 112 (0,7%) 40-45< 404 (2,4%) 530 (3,2%) 45-50< 2559 (15,4%) 3811 (22,9%) 50-55< 4899 (29,5%) 5700 (34,3) 55-60< 4943 (29,7%) 3357 (20,2%) 60-65< 2217 (13,3%) 1720 (10,3%) 65-70< 696 (4,2%) 581 (3,5%) 70-75< 229 (1,4%) 227 (1,4%) >=75 270 (1,6%) 204 (1,2%)

Sum >=60 3412 (20,5%) 2732 (16,4 %)

Facade noise exposure from railways Lnatt dB(A)



< 35 423 (2,5%) 532 (3,2%) 35-40< 712 (4,3%) 1146 (6,9%) 40-45< 3424 (20,6%) 5148 (31,0%) 45-50< 5251 (31,6%) 5045 (30,3%) 50-55< 4525 (27,2%) 3072 (18,5%) 55-60< 1444 (8,7%) 970 (5,8%) 60-65< 461 (2,8%) 368 (2,2%) 65-70< 279 (1,7%) 312 (1,9%) >=70 109 (0,7%) 35 (0,2%)

Sum >=45 12069 (72,6%) 9802 (58,9%)

Facade noise exposure from railways LAeq24 dB(A)



< 35 423 (2,5%) 532 (3,2%) 35-40< 721 (4,3%) 1139 (6,8%) 40-45< 3665 (22%) 5160 (31%) 45-50< 4998 (30,1%) 5039 (30,3%) 50-55< 4275 (25,7%) 3068 (18,5%) 55-60< 1686 (10,1%) 964 (5,8%) 60-65< 468 (2,8%) 381 (2,3%) 65-70< 283 (1,7%) 310 (1,9%) >=70 109 (0,7%) 35 (0,2%)

Sum >=60 860 (5,2%) 726 (4,4%)

Facade noise exposure from railways LAFmax dB(A)



40-45< 392 (2,3%) 394 (2,3%) 45-50< 181 (1,1%) 239 (1,4%) 50-55< 644 (3,9%) 1116 (6,7%) 55-60< 4077 (24,5%) 4940 (29,7%) 60-65< 3023 (18,2%) 4054 (24,4%) 65-70< 4569 (27,5%) 2822 (17,0%) 70-75< 1668 (10,0%) 1171 (7,0%) 75-80< 840 (5,1%) 734 (4,4%) 80-85< 852 (5,1%) 836 (5,0%) 85-90< 296 (1,8%) 285 (1,7%) >=90 86 (0,5%) 37 (0,2%)

Sum >=70 3742 (22,5%) 3063 (18,4%)

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Appendix 4

When modeling noise it is important to be aware of the uncertainty in the

method. There are a number of factors that influence the result, such as the

choice of calculation method, software, data and user specific choices.

This report presents a number of railway noise calculations in Burlöv municipality

in Scania, Sweden, and the resulting estimations of area size and population size

that are exposed to different noise levels. The differences between models are

substantial.

The results from this case study underscore the need for standardized noise

modelling for comparisons between different areas and different time periods.

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