Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free: https://www.ghostery.com/fr/products/

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site: http://www.youronlinechoices.com/fr/controler-ses-cookies/, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Realytics
Google Analytics
Spoteffects
Optimizely

Targeted advertising cookies

DoubleClick
Mediarithmics

The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at cil-dpo@inra.fr or by post at:

INRA
24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal RMT (Mixt Technological Network) Animal Farms and Environnement French Environment and Energy Management Agency logo "animal emissions" animal emissions

Animal Emissions

Guidelines for Gaussian dispersion technique for measuring ammonia emissions

figure_Gaussian

Introduction

Gaussian dispersion models are used since many years to evaluate the average concentrations supposed in industrial site environments. The emitted fluxes either known (because measured) or are thus arbitrarily fixed (in the case of nonexisting sites). The landscape roughness should not be too important to not disturb the wind flow around the emission source.

We seek to determine, based on concentration measurements in the environment, the emission flow through an evaluation of dispersion ratio (ßi) connecting the concentrations measured in the environment (Ci) to the emitted flux (E) :

Ci = ßi * E

Gaussien model is used here to determine the dispersion ratio ßi of each measuring point retained, as presented in folowing figure.

If the emitted flux E is arbitrarily set to 1, then the dispersion pattern results in the procurement of each dispersion ratio
ßi.

From the gas concentration measurements that we attempt to quantify Ci, one can then determine the emitted flux (E):

E = Ci / ßi

By multiplying the measuring points and by leading this method to the level of the latters, one can evaluate the results dispersion and thus determine uncertainties related to the method.

Equipment

Equipment should allow the measurement of meteorological data and gas concentration at time steps that are homogeneous from the meteorological point of view, i.e. if the climate change at a hourly time step, the observed

values should integrate the temporal variability during one hour.

In this last case, it is recommended to use:

  •  Sensors with a response time less than 1 minute: optical or photoacoustic analyzers, open path remote sensing optical analyzers (DOAS, FTIR, TDLAS, etc;)
  • Passive samplers with hourly analysis (if concentration levels are high enough),
  • Electronic sensors where the signal can be recorded giving either hourly averages or a collection of at least 5 values within one hour.

Meteorological sensors should include at least temperature, humidity, wind speed and direction.

Guidelines

File (PDF)