scholarly journals The atmospheric chemistry box model CAABA/MECCA-3.0

2011 ◽  
Vol 4 (2) ◽  
pp. 373-380 ◽  
Author(s):  
R. Sander ◽  
A. Baumgaertner ◽  
S. Gromov ◽  
H. Harder ◽  
P. Jöckel ◽  
...  

Abstract. We present version 3.0 of the atmospheric chemistry box model CAABA/MECCA. In addition to a complete update of the rate coefficients to the most recent recommendations, a number of new features have been added: chemistry in multiple aerosol size bins; automatic multiple simulations reaching steady-state conditions; Monte-Carlo simulations with randomly varied rate coefficients within their experimental uncertainties; calculations along Lagrangian trajectories; mercury chemistry; more detailed isoprene chemistry; tagging of isotopically labeled species. Further changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-3.0 is a community model published under the GNU General Public License.

2011 ◽  
Vol 4 (1) ◽  
pp. 197-217 ◽  
Author(s):  
R. Sander ◽  
A. Baumgaertner ◽  
S. Gromov ◽  
H. Harder ◽  
P. Jöckel ◽  
...  

Abstract. We present version 3.0gmdd of the atmospheric chemistry box model CAABA/MECCA. In addition to a complete update of the rate coefficients to the most recent recommendations, a number of new features have been added: chemistry in multiple aerosol size bins; automatic multiple simulations reaching steady-state conditions; Monte-Carlo simulations with randomly varied rate coefficients within their experimental uncertainties; calculations along Lagrangian trajectories; mercury chemistry; more detailed isoprene chemistry; tagging of isotopically labeled species. Further changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-3.0gmdd is a community model published under the GNU General Public License (GPL).


2019 ◽  
Vol 12 (4) ◽  
pp. 1365-1385 ◽  
Author(s):  
Rolf Sander ◽  
Andreas Baumgaertner ◽  
David Cabrera-Perez ◽  
Franziska Frank ◽  
Sergey Gromov ◽  
...  

Abstract. We present version 4.0 of the atmospheric chemistry box model CAABA/MECCA that now includes a number of new features: (i) skeletal mechanism reduction, (ii) the Mainz Organic Mechanism (MOM) chemical mechanism for volatile organic compounds, (iii) an option to include reactions from the Master Chemical Mechanism (MCM) and other chemical mechanisms, (iv) updated isotope tagging, and (v) improved and new photolysis modules (JVAL, RADJIMT, DISSOC). Further, when MECCA is connected to a global model, the new feature of coexisting multiple chemistry mechanisms (PolyMECCA/CHEMGLUE) can be used. Additional changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-4.0 is a community model published under the GNU General Public License.


2018 ◽  
Author(s):  
Rolf Sander ◽  
Andreas Baumgaertner ◽  
David Cabrera ◽  
Franziska Frank ◽  
Jens-Uwe Grooß ◽  
...  

Abstract. We present version 4.0gmdd of the atmospheric chemistry box model CAABA/MECCA which now includes a number of new features: (i) skeletal mechanism reduction, (ii) the MOM chemical mechanism for volatile organic compounds, (iii) an option to include reactions from the Master Chemical Mechanism (MCM) and other chemical mechanisms, (iv) updated isotope tagging, and (v) improved and new photolysis modules (JVAL, RADJIMT, DISSOC). Further, when MECCA is connected to a global model, the new feature of coexisting multiple chemistry mechanisms (PolyMECCA/CHEMGLUE) can be used. Additional changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-4.0gmdd is a community model published under the GNU General Public License.


1998 ◽  
Vol 376 ◽  
pp. 149-182 ◽  
Author(s):  
MICHAEL B. MACKAPLOW ◽  
ERIC S. G. SHAQFEH

The sedimentation of fibre suspensions at low Reynolds number is studied using two different, but complementary, numerical simulation methods: (1) Monte Carlo simulations, which consider interparticle hydrodynamic interactions at all orders within the slender-body theory approximation (Mackaplow & Shaqfeh 1996), and (ii) dynamic simulations, which consider point–particle interactions and are accurate for suspension concentrations of nl3=1, where n and l are the number density and characteristic half-length of the fibres, respectively. For homogeneous, isotropic suspensions, the Monte Carlo simulations show that the hindrance of the mean sedimentation speed is linear in particle concentration up to at least nl3=7. The speed is well predicted by a new dilute theory that includes the effect of two-body interactions. Our dynamic simulations of dilute suspensions, however, show that interfibre hydrodynamic interactions cause the spatial and orientational distributions to become inhomogeneous and anisotropic. Most of the fibres migrate into narrow streamers aligned in the direction of gravity. This drives a downward convective flow within the streamers which serves to increase the mean fibre sedimentation speed. A steady-state orientation distribution develops which strongly favours fibre alignment with gravity. Although the distribution reaches a steady state, individual fibres continue to rotate in a manner that can be qualitatively described as a flipping between the two orientations aligned with gravity. The simulation results are in good agreement with published experimental data.


2015 ◽  
Vol 59 (10) ◽  
pp. 6344-6351 ◽  
Author(s):  
A. Smits ◽  
R. F. W. De Cock ◽  
K. Allegaert ◽  
S. Vanhaesebrouck ◽  
M. Danhof ◽  
...  

ABSTRACTBased on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization.


2017 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  

Abstract. We present a Monte-Carlo Genetic Algorithm (MCGA) for efficient, automated and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find the solution of an optimization problem and to explore the space of solutions with similar model output. It addresses a problem inherent to complex models whose extensive input parameter sets might not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools. The MCGA algorithm has been used successfully to constrain parameters such as reaction rate coefficients, diffusion coefficients and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere-biosphere interface. It should be portable to any numerical model with similar computational expense and extent of the fitting parameter space.


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