تجزیه و تحلیل حساسیت مدل های مکانیکی برای تخمین انتشار آمونیاک از حوضچه های ادرار گاو شیری

Ammonia (NH3) emission can cause acidification and eutrophication of the environment, is an indirect source of nitrous oxide, and is a precursor of fine dust. The current mechanistic NH3 emission base model for explaining and predicting NH3 emissions from dairy cow houses with cubicles, a floor and slurry pit is based on measured data from a limited number of studies. It requires input values for numerous variables, but the empirical equations for the model parameters in the literature vary. Furthermore, many of the input variables cannot be assessed accurately, and their actual influence on the prediction is unknown. We aimed to improve NH3 emission modelling, by assessing the contribution to the variation in NH3 emission of each input variable and each model parameter related to a single urine puddle. We did so for 27 candidate models, created by each possible combination of three equations per model parameter: the acid dissociation constant, Henry's law constant, and the mass transfer coefficient. After analysing each candidate model with a Global Sensitivity Analysis we found that at least 71% of the model variation in NH3 emission for each candidate model was explained by five puddle related input variables: pH, depth, area, initial urea concentration and temperature. NH3 emission was not sensitive to the other four variables: air temperature, air velocity, maximum rate of urea conversion and the Michaelis–Menten constant for urea conversion. Based on these results we recommend simplifying the model structurally and reducing the number of input variables.

Ammonia (NH3) emission can cause acidification and eutrophication of the environment. NH3 is also an indirect source of the greenhouse gas nitrous oxide (N2O) (IPCC, 1996) and is a precursor of fine dust particles. To lower NH3 emissions in the EU, member states are required to set a National Emission Ceiling (NEC) (EU, 2001 and UNECE, 1999). Twenty-five of the 27 EU member states complied with the 2010 NEC set by the European Commission. The total emission of ammonia in the Netherlands in that year was 122 kt, which was 4.9% below NEC 2010 (EEA, 2012). However, local and regional emission and deposition still cause a high overload in the Dutch Natura2000 areas (Planbureau voor de leefomgeving, 2012). If, as expected, the NECs set for 2020 are lower than those set for NEC 2010, further mitigation of NH3 emission will be necessary in the EU. In 2010, agriculture was responsible for 94% of all NH3 emission from the 27 EU member states (EEA, 2012). Of this, 80% was emitted from livestock production systems. Ammonia emission from cattle in the Netherlands fell from 184 kt in 1990 to 53 kt in 2009 (Van Bruggen et al., 2011), of which 34% originated from dairy cow houses and manure storage facilities. Monteny, Schulte, Elzing, and Lamaker (1998) considered a typical dairy cow house consisting of a living area with cubicles, plus walking and feeding-alleys, which together provide a total area of 3.5 m2 per cow. There is a slurry pit underneath the whole house, and a slatted concrete floor in the cow walking area. They estimated that one urine puddle occupies an area of 0.8 m2. In such a typical dairy cow house about 70% of NH3 emission is emitted from the slatted floor. Monteny et al. (1998) developed a conceptual mechanistic computer model in order to understand and predict NH3 emissions from dairy cow houses. Called the Monteny model, it describes the physical and chemical processes involved and quantitatively determines the NH3 emission according to model parameters, using input variables related to the characteristics of a urine puddle, air, floor and pit. Similar mechanistic NH3 emission models have been developed and validated against measurements in a limited number of studies for cows (Elzing and Monteny, 1997, Montes et al., 2009, Muck and Steenhuis, 1981, Vaddella et al., 2011 and Vaddella et al., 2013), and for pigs (Aarnink and Elzing, 1998, Arogo et al., 1999, Cortus et al., 2008, Liang et al., 2002 and Zhang et al., 1994). In this study we focus on the general mechanistic NH3 emission model theory. The Monteny model is currently used by the Dutch Ministry of Infrastructure and Environment to assess the NH3 emission from dairy cow houses that are applying new NH3 mitigation techniques, and also to obtain preliminary emission factors that are used when granting permits. This assessment is later followed by full-scale measurements in commercial houses in accordance with a prescribed protocol, with the aim of establishing definitive emission factors (Ogink, Mosquera, & Hol, 2011). Determining emission factors in commercial houses using full-scale measurements is costly and time-consuming. The accuracy of the results is also an issue, as emissions vary greatly between countries, animal houses and seasons (Groot Koerkamp et al., 1998). There is a need for an NH3 emission model that would support, simplify and standardise current measurement methods, and help elucidate and explain the emissions measured and their variations. This would benefit research in NH3 mitigation technology and the testing of new housing designs. The model should be as simple as possible, but still fit for purpose. Current mechanistic NH3 emission models require input values for numerous variables, but various empirical equations for model parameters have been reported in the literature and many of the input variables cannot be measured or assessed accurately in practice and their actual influence on the emission is unknown. When Monteny et al. performed a limited one-factor-at-a-time sensitivity analysis of their model (Monteny et al., 1998), they did not include all input variables. Because the values of input variables can vary widely in practice, the empirical equations for the model parameters vary (Ni, 1999) and variables can interact, it remains unclear to which variables the model is most sensitive. Our objective was to gain knowledge to improve NH3 emission models. To do so, our first step was to review the literature on emission models in order to ascertain the relevance of the input variables, model parameters, and model structure. In this study we assessed the contribution to the variation in NH3 emission of each input variable and each model parameter in the mechanistic NH3 emission model of a single urine puddle on a floor.

Our objective was to improve the NH3 emission model by gaining insight into the sensitivity to model parameters and input variables involved in the emission from urine puddles as a first step. We found that the NH3 emission from one urine puddle on a floor was sensitive for input variables pH, d p, [U0]liq[U0]liq, ap and Tliq. The sum of each first- and second-order sensitivity coefficient for these variables (sum-coefficient) ranged from 0.71 to 0.97. This means that 71–97% of the variation in NH3 emission can be explained by five input variables. These values hold for each candidate model; each such model contained varying model parameters and had both low and high input values. The four remaining variables Tair, v, Sm, and Km did not contribute substantially to the variation in the output. The sum-coefficient of these five variables to which the candidate models were most sensitive ranged from 0.71 to 0.88 when the model contained the version of the mass transfer coefficient that represents the lowest values. In each other candidate model the value for this sum-coefficient was at least 0.92. Based on our conclusions we recommend simplifying the model structurally and reducing the number of input variables. The variation in NH3 emission of individual urine puddles can be explained by five input variables. These variables need to be measured in practice to validate the model.