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Letter to physicians

Dear Doctor,

For more than 30 years, the promoters of wind turbines know that they are harmful for the health. The president of VESTAS himself admitted to the Danish State...
Nevertheless, the opponents are named foolish, legal actions against them are taken!

Claude Brasseur
Claude Brasseur, mathematician

A statistical, simple and little expensive analysis can demonstrate objectively what is now known by “subjective” inquiries in Denmark, in Portugal, in the USA, in Australia, in the Netherlands, etc...

The following text presents a working proposal for which I suggest you to ask an official authorisation. This authorisation is necessary to be able to gather the essential data. These data, I may, as a mathematician - statistician, treat.
No name of person or institution will be known or be useful. That means that a bailiff will be necessary to certify their authenticity.

With my thanks, please accept, dear Doctor, the expression of my very high consideration.

Claude Brasseur (

Claude Brasseur April 12, 2015 BelgiumBelgique

Infrasonic vibrations of wind turbines: a statistical test of sanitary harmfulness

This method excludes - for the first time - any subjectivity.
The subjectivity is systematically exploited by the wind lobby for 30 years!

Personne agée et éolienne
Infrasonic vibrations seem to be particularly harmful to the elderly.

By Claude Brasseur (contact:, mathematician, researcher, and founder of a research centre for renewable energy

Perturbed children who work badly on the school, the deformed feet of horses, minks who have a miscarriage or the dysfunction of human menstruations... all the people whose health degrades (1) in the environment of huge wind turbines, still prove nothing as for the direct link of cause to effect. It is necessary and it is possible to make an objective statistical study of the harmfulness for the health of the infrasonic vibrations of wind turbines by excluding any other sanitary risk factor. Here is a method proposed to compare the length of stay of the people of the 3rd age in institutions before the installation of wind turbines and later. This method excludes - for the first time - any subjectivity. The subjectivity is systematically exploited by the wind lobby for 30 years!


The more we shall gather measures of stay ended with death, the more the results will be significant. We shall watch to make sure that the study is not biased, that there are no other changes in the environment or the management of old people's homes concerned. We shall avoid the biases in particular thanks to institutions of the 3rd age not concerned by the wind industrial estates which have to be the object of similar statistical studies. These tests will show if these institutions have to have a distance of 5, 10 or 15 km to prevent the effects of the industrial wind turbines (IWT for Industrial Wind Turbine).

It is a hunch that if the average of length of stay before the installation of the industrial wind turbines is clearly longer than the average of the stays after the installation of the IWT, the demonstration will be clear. We suppose - but it must be verified - that the average lengths of stay in homes shielded from wind turbines do not vary statistically in time.

The IWT will obviously not much influence on the length of stay of old people of which the death will be noticed just after the starting up of wind turbines. Therefore, the average of lengths of stay observed after the starting up of the IWT will not reveal completely their role on the health of the concerned people. If it is possible to have - by making observations for numerous institutions - a large number of deaths every year after the installation of IWT, it will be possible to see if the rhythm of the deaths accelerates, that means that the stays in the institutions of the 3rd age shorten statistically...

In practice

  1. We note the length of stay of the elderly in one or several institutions before the installation of wind turbines (in a 4 km circle following the NASA).
    We go back up to 10 years behind for the deaths if the institution already existed in past.
  2. We note lengths of stay before deaths after the installation of the IWT.
  3. We do the same for several institutions far from the IWT (5, 10 or 15 km?).
    We can separate the samples of measures relative to the deaths before and after the installation of the IWT of the cases 1. and 2.
  4. The samples of measures which we have are supposed extracted statistically from a “normal” population. We calculate the diverse arithmetic means and the corresponding standard deviations.
  5. We return these values in reduced centered units which allow to compare samples between them.
  6. We remind the central theorem of the limit which demonstrates that the validity of the approximation increases here as the number of measures of available deaths.
  7. We compare the means and their standard deviations by the method of “unilateral no worthless test of hypothesis” because it is a question of considering if the life expectancy of people decreased after the installation of the IWT.

Statistical treatment of the data

The purpose of what follows is to show - through an example of imaginary measures- a procedure to be followed to process statistically the collected data.

Data: 10 lengths of stay before the installation of wind turbines: 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 year and 10 durations after their installation: 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 year.
We suppose that we introduced all the available data. The stays “later” often began before the installation of wind turbines.

The average of the stays before wind turbines:
M1 = 5.5 = (10 + 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1) / 10
The average of the stays after the putting into service of wind turbines:
M2 = 4.5 = (9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 + 0) / 10
Standard deviation concerning M1: S1
S1² = {(10² + 9² + 8² + 7² + 6² + 5² + 4² + 3² + 2² + 1²) / 10} – {M1²} = 8.25
S1 = 2.9

Standard deviation concerning M2: S2
S2² = {(9² + 8² + 7² + 6² + 5² + 4² + 3² + 2² + 1² + 0²) / 10} – {M2²} = 8.25
S2 = 2.9

Then we suppose that 2 samples go out of the same population, that means that wind turbines have no effect on the health of the senior citizens. In statistics it is called the null hypothesis.

Standard deviation of the difference of average M1 and M2:

S of M1–M2 = {(8.25/10) + (8.25/10)}1/2 = 1.3

To be able to compare distributions of data between them, we calculate the reduced centered variable: z = (5.5 – 4.5) / 1.3 = 0.77

With an unilateral level test of meaning 0.05 (5% of chances to make a mistake), we adopt the worthless hypothesis because z is smaller than 1.645 supplied by the normal law).
Both samples can come from the same population. No observable effect of wind turbines with our data.
Let us suppose that our 2 samples multiply tenfold as for the number of measures while having the same averages and the standard deviations of the same values.
What changes, is the value of S:

S of M1–M2 = {(8.25/100) + (8.25/100)}1/2 = 0.4

Then z = (5.5 – 4.5) / 0.4 = 1 / 0.4 = 2.5

Even with an unilateral level test of meaning 0.01 (1 chance on 100 to make a mistake) we see that z is more tall than 2.33. The hypothesis is refused, 2 samples are not extracted from the same population. Wind turbines are harmful and we can then be interested to know which hurts would create the exposure in the infrasonic vibrations chopped by the IWT named “Wind Turbine Signatures”.

(1) Some references:

1. NASA Technical Memorandum 83288, Guide to the evaluation of human exposure to noise from large wind turbines, March 1982

2. NASA Contractor Report 172482 Response measurements for two building structures excited by noise from a large horizontal axis wind turbine generator, November 1984

3. D.S.Nussbaum, S.REINIS, Some individual differences in human response to infrasound, Insitute for Aerospace Studies, University of Toronto, January 1985

4. Acoustic Noise Associated with the MOD-1 Wind Turbine : its Source, Impact and Control, Prepared for the U.S. Department of Energy, February 1985

5. J.Chatillon, Limites d'exposition aux infrasons et aux ultrasons, INRS, 2006

6. Nina Pierpont, MD, PhD, Le Syndrome Eolien : un rapport sur une expérimentation naturelle, décembre 2009 (traduction autorisée et approuvée par l'auteur)

7. Shepherd Daniel & alter. Evaluating the impact of wind turbine noise on health related quality of life – Noise & Health - 7-10-2011

8. Carl V. Phillips, Properly Interpreting the Epidemiologic Evidence About the Health Effects of Industrial Wind Turbines on Nearby Residents, Bulletin of Science, Technology & Society, 2011

9. Nissenbaum Michael A & alter, Effects of industrial wind turbine noise on sleep and health – noise & health. 7-10-2012, vol.14, p.243

10. Rand Acoustics, Brunswick, ME, A Cooperative Measurement Survey and Analysis of Low Frequency and Infrasound at the Shirley Wind Farm in Brown County, Wisconsin, decembre, 2012

11. Steven Cooper, Cape Bridgewater Wind Farm Acoustic Study, january, 2014

12. Steltenrich Nate. Wind Turbines. A different Breed of Noise ? Environmental Health Perspectives, vol. 122 – number 1, 1-2014

13. Dr.Mariana Alves Pereira, How to test for the effects of low-frequency turbine noise, Lusofona University, Portugal, February 2014

14. Robert Y McMurtry, Carmen ME Krogh, Diagnostic criteria for adverse health effects in the environs of wind turbines, JRSM Open, October 2014

15. Denise Wolfe, Review of the Health Canada Wind Turbine Noise and Health Study, November 2014

By Claude Brasseur | April 12, 2015