Advection of NH3 over a pasture field and its effect on gradient flux measurements

Abstract

Deposition of atmospheric ammonia (NH3) to semi-natural ecosystems leads to serious adverse effects, such as acidification and eutrophication. A step in quantifying such effects is the measurement of NH 3 fluxes over seminatural and agricultural land. However, measurement of NH3 fluxes over vegetation in the vicinity of strong NH3 sources is challenging, since NH3 emissions are highly heterogeneous. Indeed, under such conditions, local advection errors may alter the measured fluxes. In this study, local advection errors (δFz, adv) were estimated over a 14 ha grassland field, which was successively cut and fertilised, as part of the GRAMINAE integrated Braunschweig experiment. The magnitude of δFz, adv was determined up to 810m downwind from farm buildings emitting between 6.2 and 9.9 kgNH3 day-1. The GRAMINAE experiment provided a unique opportunity to compare two methods of estimating δFz, adv: one inference method based on measurements of horizontal concentration gradients, and one based on inverse dispersion modeling with a two-dimensional model. Two sources of local advection were clearly identified: the farm NH3 emissions leading to positive δFz, adv ("bias towards emissions") and field NH3 emissions, which led to a negative δFz, adv ("bias towards deposition"). The local advection flux from the farm was in the range 0 to 27 ngNH3 m-2 s-1 at 610m from the farm, whereas δFz, adv due to field emission was proportional to the local flux, and ranged between-209 and 13 ngNH3 m-2 s -1. The local advection flux δFz, adv was either positive or negative depending on the magnitude of these two contributions. The modeled and inferred advection errors agreed well. The inferred advection errors, relative to the vertical flux at 1m height, were 52% on average, before the field was cut, and less than 2.1% when the field was fertilised. The variability of the advection errors in response to changes in micrometeorological conditions is also studied. The limits of the 2-D modeling approach are discussed. © Author(s) 2009.