1.  The sources patterns were evaluated using a soil-vegetation-atmosphere model (SurfAtm-NH3) that incorporates the response of the NH 3 emissions to surface temperature (Personne et al. 2009).  To explore the variability of the NH 3 source, four patterns of surface emission potentials (  ) are generated over a period of 28 days (t 0 ): (1) a constant  =  0, (2) a linearly decreasing  =  0 × ( 1 – t /  0 ), (3) an exponentially decreasing  =  0 exp( ‑ 4.6 t /  0 ), (4) a Gaussian  = N(  0,   ).  The multi-source inverse problem was solved based on several samplings and field trial strategies: considering 4 heights over each field, considering the background concentration as known or unknown, and considering block-repetitions in the field set-up (3 repetitions).  The meteorological dataset of the fluxnet FR-Gri site (Grignon, FR) in 2008 was employed.  13 application periods of 28 days each throughout the year  The WindTrax and the FIDES models are used to generate the transfer time matrix between the sources and the receptors D(x i,t,S i ). The concentration at each receptor C(x i,t) is then estimated given the source strengths S j (t) and the background C bgd (t) (eq. 1).  The average NH 3 emission (S j unknown (t)) over the whole application period is estimated from these averaged concentrations (C mod (x i,t)) (eq. 3).  The NH 3 emission (S j unknown (t)) (eq. 3), is obtained from a linear regression. The objective is to find the conditions where S j unknown (t) is close to S j (t), or rather, when C(x i,t) is closer to C mod (x i,t). E VALUATION OF THE INVERSE DISPERSION MODELLING METHOD FOR ESTIMATING AMMONIA MULTI - SOURCE EMISSIONS USING LOW - COST LONG TIME AVERAGING SENSORS Loubet B. (1), Carozzi M. (1) (1) INRA, UMR 1402 INRA-AgroParisTech ECOSYS, 78850 Thiverval-Grignon, FR (loubet@grignon.inra.fr) Context Objectives Methods  Perspectives: study of the minimum detection limit by exploring different emission potentials; Individuate the error committed in the estimation of the source by different integration times, heights, source dimensions and patterns, and background concentration values; This study will be continued to analyse further the differences between WindTrax and FIDES dispersion models, although preliminary comparison shows a general tendency of this latter to give larger transfer time. References: ₋ Flesch, T.K., et al., 2004. Deducing ground-to-air emissions from observed trace gas concentrations: A field trial. J. of App. Met., 43(3): 487-502. ₋ Loubet, B., et al., 2010. An inverse model to estimate ammonia emissions from fields. European J. of Soil Science, 61: 793-805. ₋ Personne, E., et al., 2009. SURFATM-NH3: a model combining the surface energy balance and bi-directional exchanges of ammonia applied at the field scale. Biogeosciences, 6(8): 1371-1388. ₋ Sutton, M.A., et al., 2001. A new diffusion denuder system for long-term regional monitoring of atmospheric ammonia and ammonium. Wat. Air Soil Poll.: Focus(1): 145-156. The NH 3 emission estimation over the integration times results more accurate when surface temperature are low (i.e. during winter) and when high friction velocities (u * ) occur. Sampling integration times of 3 to 6 hours are much more efficient in the estimation of the source with the “integrated inverse modelling method” compared to longer integration times. This efficiency does not vary sensibly from 12 to 48 h; the integration at 168 h took the worst score (Tab. 1). The size of the field is also relevant and it must be related to the measurement heights. For small fields, the error can become high due to the undetermination of the source, as D(x i,t,S i ) tends to zero. The uncertainty, then, can assumes much larger values. Tropospheric ammonia (NH 3 ) 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 NH 3 global emissions derive from agriculture, mainly from livestock manure (storage and field application) but also from nitrogen-based fertilisers. There is still a need for a method easy to deploy under real field conditions, to better characterize the variability of NH 3 emissions with respect to concomitant agronomic practices. This implies many repetitions and the use of the micrometeorological methods is therefore difficult as it requires large fields. One option is the use of the inverse modelling approach such as WindTrax (Flesch et al. 2004) or the FIDES model of Loubet et al. (2010). However such an approach requires to measure NH 3 concentrations at a hourly time step. Unfortunately there are no low cost NH 3 analysers to deploy over several fields. The use of passive diffusion sensors (ALPHA badges, Sutton et al. 2001) is a very easy method to deploy over a large number of locations. However these sensors need a longer time integration to allow sufficient quantities of NH 3 to be captured on the acid coated filters, as a function of the source strength. This work aims at estimating in silico whether multi-source inverse dispersion modelling can be used to infer NH 3 emissions from different agronomic treatment, composed of small fields (squares of 25 m side) located near to each other, using low-cost NH 3 measurements (passive diffusion samplers). This method is thereafter called “integrated inverse modelling method”. Four main parameters are tested : (1) The strength of the sources (2) The height of the sampler (0.25 m to 2 m) (3) The sampling periods (3 hours to 1 week) (4) The application date (13 dates throughout the year) Fig. 3. a) relative root mean squared error (RRMSE; eq. 5) from NH 3 emission generated (S j ) and estimated (S j unknown ) as a function of the friction velocity (u * ), over 13 periods of 28 days and integration times; b) modelling efficiency (EF, see eq. 4) from NH 3 emission generated (S j ) and estimated (S j unknown ) as a function of the surface temperature, over 13 periods of 28 days and integration times. Tab. 1. Mean of the modelling efficiency (EF) from the estimated emission (S j unknown ) and the source (S j ) over the 13 application periods in function of the integration time and the emission potentials (  ). Data at 1 m height. 2 4 (4) (5) a) b) Acknowledgement. This study was funded by Èclaire (EU) and CASDAR-NH 3 (FR). (1)  These concentrations are then averaged (overbars) over a sampling period t ranging from 3 hours to 1 week (eq. 2). (2) Relative error (3) Results The modelled NH 3 emission patterns were variable according to the  pattern (Fig. 1) and the application period. The emission estimated using Eq. (3) is very sensitive to the strength of the emission potentials (  ) from the blocks, especially for low values:  and  * 10² (Fig. 2). For the given field size, the main factor influencing the error in the “integrated inverse modelling method” is the daily NH 3 emission pattern which is mainly governed by the friction velocity (u * ) and the surface temperature pattern (Fig. 3). NH 3 emission potential  constant  linearly decreasing  exponential decreasing  gaussian (a) Time (28 days) Fig. 1. NH 3 emission pattern modelled with Surfatm-NH 3 over a 28 days period (here 1 st March) Fig. 2. NH 3 emission generated (S j ) and estimated (S j unknown ) over different emission potential (  ) in the blocks. Data reported are at 0.25 m height, 3 hours of integration period, in the 28 days period over March. S j (µg/m²:s)  10 2  10 4 