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Sundheds- og Forebyggelsesudvalget 2013-14
SUU Alm.del Bilag 268
Offentligt

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There is a clear association between wind velocity and the noise emitted from a wind turbine which is characteristic and

available for each type of wind turbine (25). We will use these data to develop a model for noise immission from all types

of wind turbines at all wind velocities. Based on these modeled noise levels for each turbine we will identify all dwellings

within the immission area of wind turbine noise using geographical information system (GIS), and the noise level at each

dwelling will then be calculated using the distance between the wind turbine and the dwelling. It will in many cases be

necessary to calculate the noise contributions from several wind turbines, which will then be summed for each dwelling.

Nord2000 is a highly accurate method for calculation of noise, verified by controlled measurements including

measurements of wind turbine noise (26). As calculations with Nord2000 are very time consuming and costly, we will use

a simplified version of the method

described in details in “Vindmøllebekendtgørelsen”

from the Danish EPA (27). We

consider this method to be well fitted for the considerable number of calculations needed for this project. In addition, we

will correct all modeled values for the attenuation that occurs when sound propagates in headwind (see appendix 1).

Simulated wind speed and direction time series at each wind turbine location in Denmark back to 1980 will be produced

based on the mesoscale wind analysis method developed by DTU Wind Energy and used in many studies (28-30).

These data will be provided at a spatial resolution of approximately 5 km and downscaled to each required location at a

1-hour or 3-hour resolution and at different levels, including 10 m above ground and hub height. The wind analysis

method makes use of the advanced Weather Research and Forecasting (WRF) model (31) and a dynamic downscaling

atmospheric reanalysis technique (32).

Sub-study 1: Day-to-day variation in wind turbine noise and risk for cardiovascular disease

Previous studies on traffic noise and risk of cardiovascular disease have focused on long-term noise exposure. One

reason is that there is little day-to-day variation in exposure to traffic noise. In contrast, exposure to noise from wind

turbines is associated with considerable day-to-day variation, which gives us a unique opportunity to investigate whether

noise exposure can lead to MI or stroke immediately after exposure.

Study population and design

Based on modeled noise immission from all types of wind turbines (see exposure section) we will identify all residential

addresses within the immission area using GIS and the official Danish address database (Den Offentlige

Informationsserver (OIS),

www.ois.dk).

Subsequently, we will identify all persons who have lived at those addresses in

the period 1980-2012 by linking the addresses with the Danish civil registration system, thereby retrieving their unique

personal identification number (33). We will include all persons above 25 years of age at start of exposure (set up of new

turbine or moving into a noise immission area) who were exposed to noise from wind turbines in more than one year in

the period from 1980 to 2012. We estimate that this will sum up to approximately 10,000–15,000 persons. This estimate

is based on the following: 1) 7,500 wind turbines in Denmark (total of turbines in operation and cancelled), 2) for most

single wind turbines there will be one dwelling just below the noise limit value, as this determines the dimensions of the

turbine and the required distance to dwellings, 3) for smaller groups of wind turbines (typically 3-5) we estimate that 2-3

dwellings will be exposed just below the noise limit value, 4) for large wind farms with up to 100 turbines we estimate that

3-10 dwellings will be exposed just below the noise limit value, and 5) for both single and multiple wind turbine areas we

expect that in many cases there will also be dwellings within the immission area but at lower exposure than the limit

value.

We will identify the persons among this exposed population that have been hospitalized at least once with stroke and/or

MI during the maximum of 32 years we follow them. We estimate that this will sum up to approximately 2,000 relevant

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hospitalizations during the study period based on information on number of yearly events of the two diseases

(www.hjerteforeningen.dk). We use stroke and MI as endpoints as we have previously found road traffic noise to

increase the risk for these diseases (3, 4). Information on hospitalization for stroke (International Classification of

Disease (ICD) 10: I61, I63 and I64) and MI (ICD10: I21.0-I21.9) in the period 1980-2012 for all exposed persons will then

be collected from the Danish National Patient Registry (34), and information on death will be collected from the Danish

calculate equivalent continuous A-weighted wind turbine sound pressure level (L

Aeq

) at their residence for the each day

; 07:00–19:00 h), evening (L

; 19:00–22:00 h) and night (L

; 22:00–07:00 h) as described in the exposure section. The

low time resolution enables us to investigate health effects of wind turbine noise at different times of the day, which is of

great interest as one hypothesis is that exposure during night is particular hazardous (5).

We will use a case-crossover design, where we estimate the relationship between day-to-day variability in wind turbine

noise at each address and day-to-day variation in hospitalizations for cardiovascular disease (stroke and MI) among

people exposed to wind turbines noise. In a case-crossover study the noise exposure of a case person on the day of

hospitalization (and a few days before) is compared with noise exposure during a control period. The advantage of the

design is that each person is their own control and differences in socio-economic status and lifestyle factors are

accounted for via the study design.

Daily variations in air pollution and outdoor temperature could potentially confound the results. The majority of all Danish

wind turbines are placed in rural settings where air pollution levels are mainly determined by the regional background

contribution. Department of Environmental Science at Aarhus University has historical daily measurements on regional

air pollution background levels as well as temperature which will be included in this study.

Statistical analyses

We will estimate risk using conditional logistic regression and compare the noise exposure on the day a person is

hospitalized with stroke/MI (lag 0) with the noise level on the same weekday within the same month (3 control days), with

adjustment for regional background air pollution and temperature. In addition, we will analyze various other time-

windows of wind turbine noise: on the previous day and up to 4 days (lag 4) before stroke/MI hospitalization and the

accumulated exposure over 5 days (lag 0

–

4) as well as exposure at daytime (07:00–19:00 h), evening (19:00–22:00 h)

and night (22:00–07:00 h).

Sub-study 2: Long-term exposure to wind turbine noise and risk for cardiovascular disease

We will use the unique Danish registers on wind turbines, residential addresses, health and socioeconomic status to

conduct the first prospective study ever on effect of long-term exposure of noise from wind turbines on risk for

cardiovascular disease.

Study population and design

The study population will consist of the 10,000-15,000 people (above 25 years of age) in Denmark who lived in dwellings

within a noise immission area of one or more wind turbines in 1980-2012 (described in sub-study 1) as well as

additionally 30,000 unexposed persons living just outside the noise immission areas of wind turbines, giving us a total

study population of 40,000-45,000 persons.

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Statistical analyses

Statistical analyses will be based on a Cox proportional hazards model with age as the underlying time, which ensures

comparison of individuals of the same age (38). We will use left truncation at age of enrolment into the population

(January 1st 1985 (to allow for calculation of 5 year exposure preceding event), moving into an area close to a wind

turbine (figure 3) or setting up of new wind turbine), so that people will be considered at risk from enrolment into the

cohort, and end of follow-up at the age at diagnosis of a cardiovascular disease (event), death or December 31th 2012,

whichever came first.

Exposure to wind turbine noise will be modeled as time-weighted averages the preceding 1- and 5-years at a given age.

These exposures (1- and 5-years) will be entered as time-dependent variables into the statistical risk model, thus for

each incident cardiovascular event recalculating exposure for all population members at exactly the same age as the

case and at risk at the time of the hospitalization for cardiovascular disease. Time-weighted averages will be calculated

based on 1) whole day exposure and 2) only nighttime exposure.

Estimates will be calculated crude and adjusted for a priori defined potential confounders: sex, level of education,

calendar year, individual and household income, affiliation to the work market and cohabiting status. We will also adjust

for distance to major road and traffic load within 200 m and 500 m of the residence as proxies for air pollution and road

traffic noise.

We have calculated the power of this study based on a population of 40,000 where one third of the population is

exposed. We have a statistical power of >90 % to detect associations of 1.10, 1.15 and 1.20 for all cardiovascular

diseases (9,000 cases), atrial fibrillation and flutter (3,000 cases) and MI (1,500 cases), respectively. Proc power, SAS

version 9.2, was used for these calculations.

SIGNIFICANCE AND DISSEMINATION

Exposure to wind turbine noise is suspected of affecting health and concern for this is increasing among people living

close to wind turbines. However, very limited scientific data exists on effect of noise from wind turbines and risk for

cardiovascular disease or other major diseases. This study will therefore be among the first contributing to answer the

question of whether noise from wind turbines affects the risk of a major disease and has the potential to contribute in

providing guidance on regulations for human habitation close to wind turbines.

Results will be published in international peer-reviewed journals and presented to the general population through

national media and popular science articles.

FEASIBILITY

The Danish Cancer Society research group members have many years of experience in conducting environmental

epidemiology research, and have during the last years specialized in investigating health effects of noise. We expect no

problems in collecting and generating data and results for this study. Information on all Danish wind turbines is readily

available from The Danish Energy Agency and Energinet.dk. We have extensive experience in collecting, managing and

analyzing data from the national registers to be used in this study and expect no difficulties in gaining access to these

registers or in obtaining permission from the Danish Data Protection Agency. We also have extensive experience in wind

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speed and traffic modelling and the applicant has already been in contact with two acoustical consultant agencies

interested in modelling the wind turbine noise for the project. Many of the research group members have successfully

collaborated in several projects.

TIMELINES

The project runs for 24 months:

Month 1:

The Danish Cancer Society and DTU Wind Energy will meet with at least two acoustical consultant

agencies to discuss and plan the wind turbine noise modeling. Based on this one consultant will be

selected and a contract written and signed.

The Danish Cancer Society will apply the Danish Data Protection Agency and national registers (OIS,

Statistic Denmark and ‘Statens Serum Institut’)

for permissions for use of data for the study.

The consultant agency will generate a model for noise immission from all types of wind turbines to be

used for identifying the exposed Danish dwellings.

DTU Wind Energy will model wind speed and direction time series at each wind turbine location.

Based on the modeled noise immission data the Danish Cancer Society will use the Danish address

database (OIS) to identify addresses of all Danish dwellings exposed to wind turbine noise as well as

unexposed dwellings for sub-study 2. Subsequently, these addresses will be linked to the Danish civil

registration system, followed by linkage to Danish National Patient Registry, Danish register of causes of

death and Statistic Denmark.

Based on wind modeling data and addresses of all exposed Danish dwellings the consultant agency will

model wind turbine noise for all exposed dwellings.

Department of Environmental Science, Aarhus University, will generate traffic proxies and obtain

information on historical background air pollution and temperature measurements.

The Danish Cancer Society will conduct the statistical analyses of sub-study 1.

The Danish Cancer Society will conduct the statistical analyses of sub-study 2.

The Danish Cancer Society will write scientific papers with input from all other research group members.

Month 2-3:

Month 2-6:

Month 4-5:

Month 7-9:

Month 10-15:

Month 16-20:

Month 16-24:

THE RESEARCH GROUP

The highly interdisciplinary research team will be headed by the applicant Mette Sørensen. The expertise of the team

includes noise exposure, wind energy, register-based health research, use of GIS and biostatistics:

Mette Sørensen,

PhD, senior researcher in the environment and cancer research group at the Danish Cancer Society,

has many years of experience in environmental epidemiology. Since 2008 her research has focused on health effects of

traffic noise, which among others have resulted in publications that as the first ever showed exposure to road traffic

noise to be associated with risk for stroke and diabetes (3, 39). In March 2012 she received a 5-year starting grant from

the European Research Council to investigate health consequences of noise exposure from road traffic (QUIET).

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Ole Raaschou-Nielsen,

PhD, head of the environment and cancer research group at the Danish Cancer Society, has

many years of experience in health effects of traffic pollution, and participates in designing the study, choosing

consultant agency, dialog with consultant agency etc.

Rikke Nordsborg,

PhD student in the environment and cancer research group at the Danish Cancer Society. Rikke

mastered in geography and has extensive experience in the use of GIS. She will be responsible for identification of

dwellings in the vicinity of wind turbines using GIS, and for identifying persons living in the dwellings during the study

period and link them to national registry data.

Alfredo Peña,

PhD, senior scientist at DTU Wind Energy has extensive experience in wind resource assessment, wind

power meteorology and meso-scale modeling. He will be responsible for providing the simulated wind speed and

direction time series at the wind turbine locations.

All statistical analyses will be conducted by a

statistician/epidemiologist

who will be employed at the Danish Cancer

Society.

Responsible for the modeling of wind turbine noise will be a

consultant agency

with extensive expertise within the area

of wind turbine noise. To ensure the best value for money at least two consultant agencies will be approached.

Matthias Ketzel,

PhD Senior Scientist, Department of Environmental Science, Aarhus University has extensive

experience in air pollution modeling and will be responsible for generating traffic proxies and historical background

concentrations of air pollution.

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REFERENCES

1. Babisch W. Transportation noise and cardiovascular risk: updated review and synthesis of epidemiological studies

indicate that the evidence has increased, 2006. Noise Health 8:1-29

2. Babisch W. Road traffic noise and cardiovascular risk, 2008. Noise.Health 10:27-33

3. Sorensen M, Hvidberg M, Andersen ZJ, et al. Road traffic noise and stroke: a prospective cohort study, 2011. Eur

Heart J 32:737-744

4. Sorensen M, Andersen ZJ, Nordsborg RB, et al. Road traffic noise and incident myocardial infarction: a

prospective cohort study, 2012. PlosONE 7:e39283

5. World Health Organization Regional Office for Europe. 2009. Night noise guidelines for Europe

6. Lusk SL, Gillespie B, Hagerty BM, Ziemba RA. Acute effects of noise on blood pressure and heart rate, 2004.

Arch.Environ.Health 59:392-399

7. Wagner J, Cik M, Marth E, et al. Feasibility of testing three salivary stress biomarkers in relation to naturalistic

traffic noise exposure, 2010. Int.J Hyg.Environ Health 213:153-155

8. Steptoe A, Hamer M, Chida Y. The effects of acute psychological stress on circulating inflammatory factors in

humans: a review and meta-analysis, 2007. Brain Behav.Immun. 21:901-912

9. Haralabidis AS, Dimakopoulou K, Velonaki V, et al. Can exposure to noise affect the 24 h blood pressure profile?

Results from the HYENA study, 2010. J Epidemiol.Community Health

10. Griefahn B, Brode P, Marks A, Basner M. Autonomic arousals related to traffic noise during sleep, 2008. Sleep

31:569-577

11. Miedema HM, Vos H. Associations between self-reported sleep disturbance and environmental noise based on

reanalyses of pooled data from 24 studies, 2007. Behav.Sleep Med. 5:1-20

12. Ekstedt M, Akerstedt T, Soderstrom M. Microarousals during sleep are associated with increased levels of lipids,

cortisol, and blood pressure, 2004. Psychosom.Med. 66:925-931

13. Stamatakis KA, Punjabi NM. Effects of sleep fragmentation on glucose metabolism in normal subjects, 2010.

Chest 137:95-101

14. Wright CE, Erblich J, Valdimarsdottir HB, Bovbjerg DH. Poor sleep the night before an experimental stressor

predicts reduced NK cell mobilization and slowed recovery in healthy women, 2007. Brain Behav.Immun. 21:358-

363

15. Hui L, Hua F, Diandong H, Hong Y. Effects of sleep and sleep deprivation on immunoglobulins and complement in

humans, 2007. Brain Behav.Immun. 21:308-310

16. Janssen SA, Vos H, Eisses AR, Pedersen E. A comparison between exposure-response relationships for wind

turbine annoyance and annoyance due to other noise sources, 2011. J.Acoust.Soc.Am. 130:3746-3753

17. Pedersen E, Waye KP. Perception and annoyance due to wind turbine noise--a dose-response relationship, 2004.

J.Acoust.Soc.Am. 116:3460-3470

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18. Pedersen E, Persson WK. Wind turbine noise, annoyance and self-reported health and well-being in different

living environments, 2007. Occup.Environ.Med. 64:480-486

19. Pedersen E. Health aspects associated with wind turbine noise - Results from three field studies, 2011. Noise

Control Engineering Journal 59:47-53

20. Shepherd D, McBride D, Welch D, Dirks KN, Hill EM. Evaluating the impact of wind turbine noise on health-

related quality of life, 2011. Noise.Health 13:333-339

21. Bakker RH, Pedersen E, van den Berg GP, Stewart RE, Lok W, Bouma J. Impact of wind turbine sound on

annoyance, self-reported sleep disturbance and psychological distress, 2012. Sci.Total Environ. 425:42-51

22. Nissenbaum MA, Aramini JJ, Hanning CD. Effects of industrial wind turbine noise on sleep and health, 2012.

Noise.Health 14:237-243

23. McColl BW, Allan SM, Rothwell NJ. Systemic infection, inflammation and acute ischemic stroke, 2009.

Neuroscience 158:1049-1061

24. Biasucci LM, Leo M, De Maria GL. Local and systemic mechanisms of plaque rupture, 2008. Angiology 59:73S-

76S

25. Søndergaard B, Henningsen P. Generelle data om støjen fra ældre vindmøller, 2011. Miljøstyrelsen Miljøprojekt

Nr. 1398:

26. Søndergaard B, Plovsing B, Sørensen T. Validation of the Nord2000 propagation model for use on wind turbine

noise, 2009. PSO-07 F&U project no.7389 Final report:1-53

27. Miljøministeriet. Bekendtgørelse om støj fra vindmøller, 2012.

https://www.retsinformation.dk/Forms/R0710.aspx?id=139658

28. Hahmann AN, Lange J, Pena A, Hasager CB. The NORSEWInD numerical wind atlas for the South Baltic, 2012.

DTU Wind Energy.DTU Wind Energy E, no 0011(EN).

29. Pena A, Gryning S-E, Hahmann AN. Observations of the atmospheric boundary layer height under marine

upstream flow conditions at a coastal site, 2013. J.Geophysical Research 118:1924-1940

30. Marinelli M, Maule P, Hahmann AN, Isleifsson FR, Nørgård P, Cutululis.N.A. Wind and photovoltaic large scale

regional models for hourly production evaluation, IEEE transaction on sustainable energy, 2013. In preparation

31. Skamarock WC, Klemp JB, Dudhia J, et al. A description of the advanced research WRF version 3, 2008.

NCAR/TN-475+STR Mesoscale and microscale meteorology division, National Center for Atmospheric Research,

Boulder, Colorado, USA, 113pp:

32. Hahmann AN, Rostkier-Edelstein D, Warner TT, et al. A reanalysis system for the generation of mesoscale

climatographies, 2010. J Applied Meteorol Climatol 49:954-972

33. Pedersen CB. The Danish Civil Registration System, 2011. Scand.J.Public Health 39:22-25

34. Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register, 2011. Scand.J.Public Health 39:30-33

35. Helweg-Larsen K. The Danish Register of Causes of Death, 2011. Scand.J.Public Health 39:26-29

36. Thygesen LC, Daasnes C, Thaulow I, Bronnum-Hansen H. Introduction to Danish (nationwide) registers on health

and social issues: structure, access, legislation, and archiving, 2011. Scand.J.Public Health 39:12-16

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37. Jensen SS, Hvidberg M, Pedersen J, et al. GIS-based national street and traffic data base 1960-2005 (In Danish

with English summary), 2009. National Environmental Research Institute, Aarhus University, Roskilde NERI

Technical Report No. 678:

38. Thiebaut AC, Benichou J. Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation

study, 2004. Stat.Med. 23:3803-3820

39. Sorensen M, Andersen ZJ, Nordsborg RB, et al. Long-term exposure to road traffic noise and incident diabetes: a

cohort study, 2013. Environ.Health Perspect. 121:217-222