scholarly journals Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots

2018 ◽  
Vol 15 (11) ◽  
pp. 3439-3460 ◽  
Author(s):  
Benjamin Loubet ◽  
Marco Carozzi ◽  
Polina Voylokov ◽  
Jean-Pierre Cohan ◽  
Robert Trochard ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Agricultural Practices ◽  
Surface Fluxes ◽  
Control Treatment ◽  
Background Concentrations ◽  
Inference Method ◽  
Surface Exchange ◽  
Novel Method ◽  
Ammonia Volatilisation

Abstract. Tropospheric ammonia (NH3) is a threat to the environment and human health and is mainly emitted by agriculture. Ammonia volatilisation following application of nitrogen in the field accounts for more than 40 % of the total NH3 emissions in France. This represents a major loss of nitrogen use efficiency which needs to be reduced by appropriate agricultural practices. In this study we evaluate a novel method to infer NH3 volatilisation from small agronomic plots consisting of multiple treatments with repetition. The method is based on the combination of a set of NH3 diffusion sensors exposed for durations of 3 h to 1 week and a short-range atmospheric dispersion model, used to retrieve the emissions from each plot. The method is evaluated by mimicking NH3 emissions from an ensemble of nine plots with a resistance analogue–compensation point–surface exchange scheme over a yearly meteorological database separated into 28-day periods. A multifactorial simulation scheme is used to test the effects of sensor numbers and heights, plot dimensions, source strengths, and background concentrations on the quality of the inference method. We further demonstrate by theoretical considerations in the case of an isolated plot that inferring emissions with diffusion sensors integrating over daily periods will always lead to underestimations due to correlations between emissions and atmospheric transfer. We evaluated these underestimations as −8 % ± 6 % of the emissions for a typical western European climate. For multiple plots, we find that this method would lead to median underestimations of −16 % with an interquartile [−8–22 %] for two treatments differing by a factor of up to 20 and a control treatment with no emissions. We further evaluate the methodology for varying background concentrations and NH3 emissions patterns and demonstrate the low sensitivity of the method to these factors. The method was also tested in a real case and proved to provide sound evaluations of NH3 losses from surface applied and incorporated slurry. We hence showed that this novel method should be robust and suitable for estimating NH3 emissions from agronomic plots. We believe that the method could be further improved by using Bayesian inference and inferring surface concentrations rather than surface fluxes. Validating against controlled source is also a remaining challenge.

scholarly journals Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots

2017 ◽  
Author(s):  
Benjamin Loubet ◽  
Marco Carozzi ◽  
Polina Voylokov ◽  
Jean-Pierre Cohan ◽  
Robert Trochard ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Agricultural Practices ◽  
Control Treatment ◽  
Ammonia Emission ◽  
Ammonia Emissions ◽  
Background Concentrations ◽  
Inference Method ◽  
Surface Exchange ◽  
Novel Method ◽  
Ammonia Volatilisation

Abstract. Tropospheric ammonia (NH3) is a threat to the environment and human health and is mainly emitted by agriculture. Ammonia volatilisation following application of nitrogen in the field accounts for more than 40 % of the total ammonia emissions in France. This hence represents a major loss of nitrogen use efficiency which needs to be reduced by appropriate agricultural practices. In this study we evaluate a novel method to infer ammonia volatilisation from small agronomic plots made of multiple treatments with repetition. The method is based on the combination of a set of ammonia diffusion sensors exposed for durations of 3 hours to 1 week, and a short-range atmospheric dispersion model, used to retrieve the emissions from each plot. The method is evaluated by mimicking ammonia emissions from an ensemble of 9 plots with a resistance-analogue-compensation-point surface exchange scheme over a yearly meteorological database separated into 28-days periods. A multi-factorial simulation scheme is used to test the effects of sensor number and heights, plot dimensions, source strengths and background concentrations, on the quality of the inference method. We further demonstrate by theoretical considerations in the case of an isolated plot that inferring emissions with diffusion sensors integrating over daily periods will always lead to underestimations due to correlations between emissions and atmospheric transfer. We evaluated these underestimations as −8 % ± 6 % of the emissions for a typical western European climate. For multiple plots, we find that this method would lead to median underestimations of −16 % with an interquartile [−8 % −22 %] for two treatments differing by a factor of up to 20 and a control treatment with no emissions. We further evaluate the methodology for varying background concentrations and ammonia emission patterns and demonstrate the low sensitivity of the method to these factors. The method was also tested in a real case and proved to provide sound evaluations of ammonia losses from surface applied and incorporated slurry. We hence showed that this novel method should be robust and suitable for estimating ammonia emissions from agronomic plots. Further work should anyway be produced for validating this method in real conditions.

Dynamic Correcting Dispersion Parameters of Lagrangian Puff Model in Atmospheric Tracer Experiments

2014 ◽  
Author(s):  
Yuanwei Ma ◽  
Dezhong Wang ◽  
Zhilong Ji ◽  
Nan Qian
Keyword(s):  
Numerical Simulation ◽  
Dispersion Model ◽  
Fitness Function ◽  
Atmospheric Dispersion ◽  
Correction Procedure ◽  
Model Errors ◽  
Dispersion Parameters ◽  
Fitness Functions ◽  
Atmospheric Dispersion Models ◽  
Atmospheric Tracer

In atmospheric dispersion models of nuclear accident, the empirical dispersion coefficients were obtained under certain experiment conditions, which is different from actual conditions. This deviation brought in the great model errors. A better estimation of the radioactive nuclide’s distribution could be done by correcting coefficients with real-time observed value. This reverse problem is nonlinear and sensitive to initial value. Genetic Algorithm (GA) is an appropriate method for this correction procedure. Fitness function is a particular type of objective function to achieving the set goals. To analysis the fitness functions’ influence on the correction procedure and the dispersion model’s forecast ability, four fitness functions were designed and tested by a numerical simulation. In the numerical simulation, GA, coupled with Lagrange dispersion model, try to estimate the coefficients with model errors taken into consideration. Result shows that the fitness functions, in which station is weighted by observed value and by distance far from release point, perform better when it exists significant model error. After performing the correcting procedure on the Kincaid experiment data, a significant boost was seen in the dispersion model’s forecast ability.

A Research Reactor Source Term and Dose Results in the Event of a LOCA

2014 ◽  
Author(s):  
Charalampos Pappas ◽  
Andreas Ikonomopoulos ◽  
Athanasios Sfetsos ◽  
Spyros Andronopoulos ◽  
Melpomeni Varvayanni ◽  
...  
Keyword(s):  
Research Reactor ◽  
Source Term ◽  
Dispersion Model ◽  
Ventilation System ◽  
Atmospheric Dispersion ◽  
Reactor Core ◽  
Forecast Model ◽  
Scale Model ◽  
Continuous Reactor ◽  
Reactor Building

The present study discusses the source term derivation and dose result calculation for a hypothetical accident sequence in the Greek Research Reactor – 1 (GRR-1). A loss-of-coolant accident (LOCA) has been selected as a credible accident sequence. The source term derivation has been based on the GRR-1 confinement performance where the inventory has been computed assuming continuous reactor operation. A core damage fraction of 30% has been considered for the calculations while conservative core release fractions have been employed. The radionuclides released from the reactor core to the confinement atmosphere have been subjected to natural decay, deposition on and resuspension from various internal surfaces before being led to the release pathway. It has been assumed that an emergency shutdown is initiated immediately after the beginning of the accident sequence and the emergency ventilation system is also activated. Subsequently, the source term has been derived comprising of noble gases, iodine and aerosol. The JRODOS computational software for off-site nuclear emergency management has been utilized to estimate the dose results from the LOCA-initiated source term that is released in its entirety from the reactor stack at ambient temperature. The Local Scale Model Chain in conjunction with the DIPCOT atmospheric dispersion model that is embedded in JRODOS have been used with proper parameterization of the calculation settings. Five weather scenarios have been selected as representative of typical meteorological conditions at the reactor site. The scenarios have been assessed with the use of the Weather Research and Forecast model. Total effective, skin, thyroid, lung and inhalation doses downwind of the reactor building and up to a distance of 10 km have been calculated for each weather scenario and are presented. The total effective gamma dose rate at a fixed distance from the reactor building has been assessed. The radiological consequences of the dose results are discussed.

scholarly journals Modelling the dispersion of particle numbers in five European cities

2016 ◽  
Vol 9 (2) ◽  
pp. 451-478 ◽  
Author(s):  
J. Kukkonen ◽  
M. Karl ◽  
M. P. Keuken ◽  
H. A. C. Denier van der Gon ◽  
B. R. Denby ◽  
...  
Keyword(s):  
Particle Number ◽  
Atmospheric Dispersion ◽  
Emission Inventories ◽  
Vehicular Traffic ◽  
Background Concentrations ◽  
Reasonable Accuracy ◽  
Annual Average ◽  
Urban Background ◽  
European Cities ◽  
Regional Background

Abstract. We present an overview of the modelling of particle number concentrations (PNCs) in five major European cities, namely Helsinki, Oslo, London, Rotterdam, and Athens, in 2008. Novel emission inventories of particle numbers have been compiled both on urban and European scales. We used atmospheric dispersion modelling for PNCs in the five target cities and on a European scale, and evaluated the predicted results against available measured concentrations. In all the target cities, the concentrations of particle numbers (PNs) were mostly influenced by the emissions originating from local vehicular traffic. The influence of shipping and harbours was also significant for Helsinki, Oslo, Rotterdam, and Athens, but not for London. The influence of the aviation emissions in Athens was also notable. The regional background concentrations were clearly lower than the contributions originating from urban sources in Helsinki, Oslo, and Athens. The regional background was also lower than urban contributions in traffic environments in London, but higher or approximately equal to urban contributions in Rotterdam. It was numerically evaluated that the influence of coagulation and dry deposition on the predicted PNCs was substantial for the urban background in Oslo. The predicted and measured annual average PNCs in four cities agreed within approximately  ≤  26 % (measured as fractional biases), except for one traffic station in London. This study indicates that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remain in the evaluation of both the emissions and atmospheric transformation of PNCs.

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