Parameter estimation for modelling clogging of granular medium permeated with leachate

2008 ◽  
Vol 45 (6) ◽  
pp. 812-823 ◽  
Author(s):  
Jamie F. VanGulck ◽  
R. Kerry Rowe
Keyword(s):  
Water Quality ◽  
Volatile Fatty Acids ◽  
Transport Model ◽  
Kinetic Constants ◽  
Inverse Modelling ◽  
Mineral Matter ◽  
Column Experiments ◽  
Chemical Transport Model ◽  
Degrading Bacteria ◽  
Synthetic Leachate

A numerical model called BioClog is used to backcalculate biological activity rate constants using measured values of water quality and clog chemical characteristics from well-controlled laboratory column experiments that contained a granular-sized material permeated with synthetic and real leachates. BioClog is a multispecies, reactive chemical transport model capable of predicting clogging of a porous media caused by the accumulation of biofilms, chemical precipitates, and entrained particles. Monod kinetic constants for acetate- and butyrate-degrading bacteria were obtained through inverse modelling of granular-sized material permeated with synthetic leachate. The model predicted the changes in concentrations of volatile fatty acids and dissolved calcium and it predicted the changes in clog composition from a juvenile clog containing biofilm to a mature clog containing biofilm with mineral matter. The kinetic constants were then applied to predict spatial and temporal water quality and clog composition for a granular-sized material permeated with real leachate. The kinetic constants deduced through inverse modelling of the synthetic leachate column experiments provided reasonable predictions of the behaviour of the columns permeated with real leachate.

scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid scale physical parameterizations

2015 ◽  
Vol 15 (8) ◽  
pp. 11853-11888
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Deep Convection ◽  
Chemical Transport ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modelling face new challenges. Indeed, the chemical transport model used to link the flux space with the mixing ratio space must be able to represent these different types of constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid scale physical parameterization errors on the global methane budget inferred by inverse modelling using the same inversion set-up but different physical parameterizations within one chemical-transport model. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer are tested. Different atmospheric methane datasets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid scale parameterizations. Inversions using satellite total-column retrieved by GOSAT satellite are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradient in methane emissions. At regional scale, the use of different sub-grid scale parameterizations induces uncertainties ranging from 1.2 (2.7%) to 9.4% (14.2%) of methane emissions in Africa and Eurasia Boreal respectively when using only surface measurements from the background (extended) surface network. When using only satellite data, we show that the small biases found in inversions using GOSAT-CH4 data and a coarser version of the transport model were actually masking a poor representation of the stratosphere–troposphere gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT-CH4 satellite data, which largely amplifies inconsistencies between surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relatively to physical parameterizations included in chemical-transport models.

scholarly journals Mass balance inverse modelling of methane in the 1990s using a Chemistry Transport Model

2004 ◽  
Vol 4 (11/12) ◽  
pp. 2561-2580 ◽  
Author(s):  
T. M. Butler ◽  
I. Simmonds ◽  
P. J. Rayner
Keyword(s):  
Mass Balance ◽  
Transport Model ◽  
A Priori ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Cycle ◽  
Surface Distribution ◽  
Inverse Results ◽  
Methane Budget ◽  
Starting Point

Abstract. A mass balance inverse modelling procedure is applied with a time-dependent methane concentration boundary condition and a chemical transport model to relate observed changes in the surface distribution of methane mixing ratios during the 1990s to changes in its surface sources. The model reproduces essential features of the global methane cycle, such as the latitudinal distribution and seasonal cycle of fluxes, without using a priori knowledge of methane fluxes. A detailed description of the temporal and spatial variability of the fluxes diagnosed by the inverse procedure is presented, and compared with previously hypothesised changes in the methane budget, and previous inverse modelling studies. The sensitivity of the inverse results to the forcing data supplied by surface measurements of methane from the NOAA CMDL cooperative air sampling network is also examined. This work serves as an important starting point for future inverse modelling work examining changes in both the source and sink terms in the methane budget together.

scholarly journals Quantifying the UK’s Carbon Dioxide Flux: An atmospheric inverse modelling approach using a regional measurement network

2018 ◽  
Author(s):  
Emily D. White ◽  
Matthew Rigby ◽  
Mark F. Lunt ◽  
Anita L. Ganesan ◽  
Alistair J. Manning ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Transport Model ◽  
Ecosystem Respiration ◽  
Carbon Dioxide Flux ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
The Uk ◽  
The Impact

Abstract. We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes at the national scale, demonstrated using data from a network of surface tall tower sites across the UK and Ireland over the period 2013–2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net primary productivity. Two different models, CARDAMOM and JULES, provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of −8 ± 79 Tg CO2 yr−1 (CARDAMOM prior) and −64 ± 85 Tg CO2 yr−1 (JULES prior), where -ve values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates are consistently higher than the prior estimates, which show much more pronounced uptake in the summer months.

scholarly journals Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network

2019 ◽  
Vol 19 (7) ◽  
pp. 4345-4365 ◽  
Author(s):  
Emily D. White ◽  
Matthew Rigby ◽  
Mark F. Lunt ◽  
T. Luke Smallman ◽  
Edward Comyn-Platt ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Transport Model ◽  
Ecosystem Respiration ◽  
Atmospheric Dispersion ◽  
Terrestrial Ecosystem ◽  
Carbon Dioxide Flux ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
The Uk ◽  
The Impact

Abstract. We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes at the national scale, demonstrated using data from a network of surface tall-tower sites across the UK and Ireland over the period 2013–2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net ecosystem exchange of CO2. Two different models, Data Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES), provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of 8±79 Tg CO2 yr−1 (DALEC prior) and 64±85 Tg CO2 yr−1 (JULES prior), where negative values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO2 than the prior estimates, which show much more pronounced uptake in summer months.

scholarly journals Atmospheric methane source and sink sensitivity analysis using Gaussian process emulation

2020 ◽  
Author(s):  
Angharad C. Stell ◽  
Luke M. Western ◽  
Matthew Rigby
Keyword(s):  
Gaussian Process ◽  
Mole Fraction ◽  
Transport Model ◽  
Chemical Transport ◽  
Inverse Modelling ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Gaussian Process Emulation ◽  
Source And Sink ◽  
Inputs And Outputs

Abstract. We present a method to efficiently approximate the response of atmospheric methane mole fraction and δ13C-CH4 to changes in uncertain emission and loss parameters in a three-dimensional global chemical transport model. Our approach, based on Gaussian process emulation, allows relationships between inputs and outputs in the model to be efficiently explored. The presented emulator successfully reproduces the chemical transport model output with a root-mean-square error of 1.2 ppb and 0.06 ‰ for hemispheric methane mole fraction and δ13C-CH4, respectively, for 28 uncertain model inputs. The method is shown to outperform multiple linear regression, because it captures non-linear relationships between inputs and outputs, as well as the interaction between model input parameters. The emulator was used to determine how sensitive methane mole fraction and δ13C-CH4 are to the major source and sink components of the atmospheric budget, given current estimates of their uncertainty. We find that our current knowledge of the methane budget, as inferred through hemispheric mole fraction observations, is limited primarily by uncertainty in the global mean hydroxyl radical concentration and emissions from fresh water. Our work quantitatively determines the added value of measurements of δ13C-CH4, which are sensitive to some uncertain parameters that mole fraction observations on their own are not. However, we demonstrate the critical importance of constraining isotopic initial conditions and isotopic source signatures, small uncertainties in which strongly influence long-term δ13C-CH4 trends, because of the long timescales over which transient perturbations propagate through the atmosphere. Our results also demonstrate that the magnitude and trend of methane mole fraction and δ13C-CH4 can be strongly influenced by the combined uncertainty of more minor components of the atmospheric budget, which are often fixed and assumed to be well-known in inverse modelling studies (e.g. emissions from termites, hydrates, and oceans). Overall, our work provides an overview of the sensitivity of atmospheric observations to budget uncertainties and outlines a method which could be employed to account for these uncertainties in future inverse modelling systems.

scholarly journals Mass balance inverse modelling of methane in the 1990s using a Chemistry Transport Model

2004 ◽  
Vol 4 (3) ◽  
pp. 3419-3483
Author(s):  
T. M. Butler ◽  
I. Simmonds ◽  
P. J. Rayner
Keyword(s):  
Mass Balance ◽  
Transport Model ◽  
A Priori ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Cycle ◽  
Surface Distribution ◽  
Inverse Results ◽  
Methane Budget ◽  
Starting Point

Abstract. A mass balance inverse modelling procedure is applied with a time-dependent methane concentration boundary condition and a chemical transport model to relate observed changes in the surface distribution of methane mixing ratios during the 1990s to changes in its surface sources. This work serves as an important starting point for future inverse modelling work examining changes in both the source and sink terms in the methane budget together. The model reproduces essential features of the global methane cycle, such as the latitudinal distribution and seasonal cycle of fluxes, without using a priori knowledge of methane fluxes. A detailed description of the temporal and spatial variability of the fluxes diagnosed by the inverse procedure is presented, and compared with previously hypothesised changes in the methane budget, and previous inverse modelling studies. The sensitivity of the inverse results to the forcing data supplied by surface measurements of methane from the NOAA CMDL cooperative air sampling network is also examined.

scholarly journals Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation

2021 ◽  
Vol 21 (3) ◽  
pp. 1717-1736
Author(s):  
Angharad C. Stell ◽  
Luke M. Western ◽  
Tomás Sherwen ◽  
Matthew Rigby
Keyword(s):  
Gaussian Process ◽  
Mole Fraction ◽  
Transport Model ◽  
Chemical Transport ◽  
Inverse Modelling ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Gaussian Process Emulation ◽  
Source And Sink ◽  
Inputs And Outputs

Abstract. We present a method to efficiently approximate the response of atmospheric-methane mole fraction and δ13C–CH4 to changes in uncertain emission and loss parameters in a three-dimensional global chemical transport model. Our approach, based on Gaussian process emulation, allows relationships between inputs and outputs in the model to be efficiently explored. The presented emulator successfully reproduces the chemical transport model output with a root-mean-square error of 1.0 ppb and 0.05 ‰ for hemispheric-methane mole fraction and δ13C–CH4, respectively, for 28 uncertain model inputs. The method is shown to outperform multiple linear regression because it captures non-linear relationships between inputs and outputs as well as the interaction between model input parameters. The emulator was used to determine how sensitive methane mole fraction and δ13C–CH4 are to the major source and sink components of the atmospheric budget given current estimates of their uncertainty. We find that our current knowledge of the methane budget, as inferred through hemispheric mole fraction observations, is limited primarily by uncertainty in the global mean hydroxyl radical concentration and freshwater emissions. Our work quantitatively determines the added value of measurements of δ13C–CH4, which are sensitive to some uncertain parameters to which mole fraction observations on their own are not. However, we demonstrate the critical importance of constraining isotopic initial conditions and isotopic source signatures, small uncertainties in which strongly influence long-term δ13C–CH4 trends because of the long timescales over which transient perturbations propagate through the atmosphere. Our results also demonstrate that the magnitude and trend of methane mole fraction and δ13C–CH4 can be strongly influenced by the combined uncertainty in more minor components of the atmospheric budget, which are often fixed and assumed to be well-known in inverse-modelling studies (e.g. emissions from termites, hydrates, and oceans). Overall, our work provides an overview of the sensitivity of atmospheric observations to budget uncertainties and outlines a method which could be employed to account for these uncertainties in future inverse-modelling systems.

Application of the BioClog model for landfill leachate clogging of gravel-packed columns

2005 ◽  
Vol 42 (6) ◽  
pp. 1600-1614 ◽  
Author(s):  
A J Cooke ◽  
R K Rowe ◽  
J VanGulck ◽  
B E Rittmann
Keyword(s):  
Landfill Leachate ◽  
Volatile Fatty Acids ◽  
Transport Model ◽  
Packed Columns ◽  
Chemical Transport Model ◽  
Inorganic Solids ◽  
Leachate Collection ◽  
Suspended Biomass ◽  
Reactive Chemical Transport ◽  
Multiple Species

A numerical, multiple-species, reactive chemical transport model (BioClog) developed to predict clogging in landfill leachate collection systems is used to interpret results from experiments conducted with gravel-packed columns permeated with landfill leachate. The model predicts changes to the microbial community and leachate chemistry, including the concentrations of volatile fatty acids, suspended biomass, dissolved calcium, and suspended inorganic solids. The calculated quantity and composition of the clog matter (biomass and mineral), along with the associated decrease in porosity, are compared to the measured values. The modelled clogging is in reasonable agreement with that observed in the gravel column experiments. By identifying and quantitatively linking many microbiological, chemical, and transport mechanisms, the model helps elucidate the phenomena controlling the rate and extent of clogging.Key words: clogging, landfills, leachate collection systems, biofilms, mineral precipitation.

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

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