Know more

About cookies

What is a "cookie"?

A "cookie" is a piece of information, usually small and identified by a name, which may be sent to your browser by a website you are visiting. Your web browser will store it for a period of time, and send it back to the web server each time you log on again.

Different types of cookies are placed on the sites:

  • Cookies strictly necessary for the proper functioning of the site
  • Cookies deposited by third party sites to improve the interactivity of the site, to collect statistics

Learn more about cookies and how they work

The different types of cookies used on this site

Cookies strictly necessary for the site to function

These cookies allow the main services of the site to function optimally. You can technically block them using your browser settings but your experience on the site may be degraded.

Furthermore, you have the possibility of opposing the use of audience measurement tracers strictly necessary for the functioning and current administration of the website in the cookie management window accessible via the link located in the footer of the site.

Technical cookies

Name of the cookie


Shelf life

CAS and PHP session cookies

Login credentials, session security



Saving your cookie consent choices

12 months

Audience measurement cookies (AT Internet)

Name of the cookie


Shelf life


Trace the visitor's route in order to establish visit statistics.

13 months


Store the anonymous ID of the visitor who starts the first time he visits the site

13 months


Identify the numbers (unique identifiers of a site) seen by the visitor and store the visitor's identifiers.

13 months

About the AT Internet audience measurement tool :

AT Internet's audience measurement tool Analytics is deployed on this site in order to obtain information on visitors' navigation and to improve its use.

The French data protection authority (CNIL) has granted an exemption to AT Internet's Web Analytics cookie. This tool is thus exempt from the collection of the Internet user's consent with regard to the deposit of analytics cookies. However, you can refuse the deposit of these cookies via the cookie management panel.

Good to know:

  • The data collected are not cross-checked with other processing operations
  • The deposited cookie is only used to produce anonymous statistics
  • The cookie does not allow the user's navigation on other sites to be tracked.

Third party cookies to improve the interactivity of the site

This site relies on certain services provided by third parties which allow :

  • to offer interactive content;
  • improve usability and facilitate the sharing of content on social networks;
  • view videos and animated presentations directly on our website;
  • protect form entries from robots;
  • monitor the performance of the site.

These third parties will collect and use your browsing data for their own purposes.

How to accept or reject cookies

When you start browsing an eZpublish site, the appearance of the "cookies" banner allows you to accept or refuse all the cookies we use. This banner will be displayed as long as you have not made a choice, even if you are browsing on another page of the site.

You can change your choices at any time by clicking on the "Cookie Management" link.

You can manage these cookies in your browser. Here are the procedures to follow: Firefox; Chrome; Explorer; Safari; Opera

For more information about the cookies we use, you can contact INRAE's Data Protection Officer by email at or by post at :


24, chemin de Borde Rouge -Auzeville - CS52627 31326 Castanet Tolosan cedex - France

Last update: May 2021

Menu Logo Principal AgroParisTech université Paris-Saclay

Welcome to ECOSYS

UMR ECOSYS - Ecologie fonctionnelle et écotoxicologie des agroécosystèmes

Poster 1 : Ammonia_inverse_modeling

Poster 1 : Ammonia_inverse_modeling
Benjamin Loubet et Marco Carozzi


Tropospheric ammonia (NH3) is a key player in atmospheric chemistry and its deposition is a threat for the environment (ecosystem eutrophication, soil acidification and reduction in species biodiversity). Most of the NH3 global emissions derive from agriculture, mainly from livestock manure (storage and field application) but also from nitrogen-based fertilisers. Inverse dispersion modelling has been widely used to infer emission sources from a homogeneous source of known geometry. When the emission derives from different sources inside of the measured footprint, the emission should be treated as multi-source problem. This work aims at estimating whether multi-source inverse dispersion modelling can be used to infer NH3 emissions from different agronomic treatment, composed of small fields (typically squares of 25 m side) located near to each other, using low-cost NH3 measurements (diffusion samplers). To do that, a numerical experiment was designed with a combination of 3 x 3 square field sources (625 m2), and a set of sensors placed at the centre of each field at several heights as well as at 200 m away from the sources in each cardinal directions. The concentration at each sensor location was simulated with a forward Lagrangian Stochastic (WindTrax) and a Gaussian-like (FIDES) dispersion model. The concentrations were averaged over various integration times (3 hours to 28 days), to mimic the diffusion sampler behaviour with several sampling strategy. The sources were then inferred by inverse modelling using the averaged concentration and the same models in backward mode. The sources patterns were evaluated using a soil-vegetation-atmosphere model (SurfAtm-NH3) that incorporates the response of the NH3 emissions to surface temperature. A combination emission patterns (constant, linear decreasing, exponential decreasing and Gaussian type) and strengths were used to evaluate the uncertainty of the inversion method. Each numerical experiment covered a period of 28 days. The meteorological dataset of the fluxnet FR-Gri site (Grignon, FR) in 2008 was employed. Several sensor heights were tested, from 0.25 m to 2 m. The multi-source inverse problem was solved based on several sampling and field trial strategies: considering 1 or 2 heights over each field, considering the background concentration as known or unknown, and considering block-repetitions in the field set-up (3 repetitions). The inverse modelling approach demonstrated to be adapted for discriminating large differences in NH3 emissions from small agronomic plots using integrating sensors. The method is sensitive to sensor heights. The uncertainties and systematic biases are evaluated and discussed.

Download documents