Modeling Biodegradation of Subsurface Oil in Sand Beaches Polluted with Oil

ABSTRACT In April 2010, the explosion of the Deepwater Horizon (DWH) drilling platform led to the release of nearly 4.9 million barrels of crude oil into the Gulf of Mexico. The oil was brought to the supratidal zone of beaches (landward of the high tide line) by waves during storms, and was buried during subsequent storms. The objective of this paper is to investigate the biodegradation of subsurface oil in a tidally influenced sand beach located at Bon Secour National Wildlife Refuge and polluted by the DWH oil spill. Two transects were installed perpendicular to the shoreline within the supratidal zone of the beach. One transect had four galvanized steel piezometer wells to measure the water level. The other transect had four stainless steel multiport sampling wells that were used to collect pore water samples below the beach surface. The samples were analyzed for dissolved oxygen (DO), nitrogen, and redox conditions. Sediment samples were also collected at different depths to measure residual oil concentrations and microbial biomass. As the biodegradation of hydrocarbons was of interest, a biological model based on Monod kinetics was developed and coupled to the transport model MARUN, which is a two dimensional (vertical slice) finite element model for water flow and solute transport in tidally influenced beaches. The resulting coupled model, BIOMARUN, was used to simulate the biodegradation of total n-alkanes and polycyclic aromatic hydrocarbons (PAHs) trapped as residual oil in the unsaturated zone. Model parameter estimates were constrained by published Monod kinetics parameters. The field measurements, such as the concentrations of the oil, microbial biomass, nitrogen, and DO, were used as inputs for the simulations. The biodegradation of alkanes and PAHs was predicted in the simulation, and sensitivity analyses were conducted to assess the effect of the model parameters on the modeling results. Simulation results indicated that n-alkanes and PAHs would be biodegraded by 80% after 2 ± 0.5 years and 3.5 ± 0.5 years, respectively.

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Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Hydrology and Earth System Sciences ◽

10.5194/hess-17-4995-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 4995-5011 ◽

Cited By ~ 19

Author(s):

Y. Sun ◽

Z. Hou ◽

M. Huang ◽

F. Tian ◽

L. Ruby Leung

Keyword(s):

Inverse Modeling ◽

Bayesian Model Averaging ◽

Surface Flux ◽

Sensitivity Analyses ◽

Soil Conditions ◽

Bayesian Inversion ◽

Parameter Estimates ◽

Model Parameters ◽

Posterior Distributions ◽

Community Land Model

Abstract. This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

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Seed Meals from Brassicaceae Oilseed Crops as Soil Amendments: Influence on Carrot Growth, Microbial Biomass Nitrogen, and Nitrogen Mineralization

HortScience ◽

10.21273/hortsci.44.2.354 ◽

2009 ◽

Vol 44 (2) ◽

pp. 354-361 ◽

Cited By ~ 28

Author(s):

André Snyder ◽

Matthew J. Morra ◽

Jodi Johnson-Maynard ◽

Donald C. Thill

Keyword(s):

Microbial Biomass ◽

Degradation Products ◽

N Uptake ◽

Biologically Active ◽

Microbial Biomass N ◽

Glucosinolate Content ◽

N Availability ◽

Fresh Market ◽

Microbial Biomass Nitrogen ◽

Total N

Brassicaceae seed meals (BSMs) average 6% nitrogen (N) by weight and contain glucosinolates (GLSs) that produce biologically active compounds. A two-season field study was initiated to determine how Brassica juncea L., Brassica napus L., and Sinapis alba L. seed meals, each with different glucosinolate profiles, alter carrot (Daucus carota L. subsp. sativus) growth, microbial biomass N (MBN), and soil N mineralization. BSM applications of 1 and 2 t·ha−1 36 days before planting did not influence carrot emergence, whereas carrot emergence decreased up to 40% in S. alba treatments seeded 15 days after BSM application. Crop quality was unaffected by BSM treatments and total fresh market yields were equal to or higher than the unamended controls in both years. At 4 and 8 days after seed meal application, MBN in the high-GLS B. juncea and S. alba treatments was 48% to 67% lower than in the low-GLS B. napus treatment. Seasonal apparent net N mineralized expressed as a percentage of the total N applied in the seed meals was unaffected by glucosinolate concentration and ranged from 30% to 81% across both years. BSMs can be used to increase soil inorganic N and carrot yields, but crop phytotoxicity is possible depending on the meal and its respective glucosinolate content. GLS degradation products inhibit microbial N uptake in the short term, but longer-term N availability is not compromised.

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Effect of pipeline installation on crop yield and some biological properties of boreal soils

Canadian Journal of Soil Science ◽

10.4141/s99-096 ◽

2000 ◽

Vol 80 (3) ◽

pp. 483-488 ◽

Cited By ~ 3

Author(s):

Y. K. Soon ◽

W. A. Rice ◽

M. A. Arshad ◽

P. Mills

Keyword(s):

Microbial Biomass ◽

Crop Yield ◽

Crop Production ◽

Microbial Biomass Carbon ◽

Biological Properties ◽

Hordeum Vulgare L ◽

Microbial Biomass Nitrogen ◽

Total N ◽

Pipeline Construction ◽

Normal Ranges

Pipeline construction on the Grey soils of the Canadian boreal plains, which have a thin Ah horizon, could have considerable impact on their properties and productivity. This study was conducted because the effects of pipeline installation on crop yield and biological properties of these soils have not been well-documented. Soil was sampled from a Grey Luvisol and a Dark Grey Solod prior to pipeline construction in 1991, and in each of the following 3 yr. The right-of-way (RoW) was divided into three zones: a road (or work) area used for vehicular traffic; a trench where the pipeline was buried; and a pile (or spoil) area where soil was stockpiled during construction. The RoW was cropped to barley (Hordeum vulgare L.) in 1992 through 1994. Barley yield was low in 1992 (830–1120 kg ha−1), and near average (2050–3290 kg ha−1) in 1993 and 1994. Except for low shoot P concentration (1.1–1.3 mg g−1) in 1992, macronutrient concentrations (N, P and K) in barley tissues were within normal ranges. Soil organic carbon was reduced by 12–28% in all RoW areas in 1993 and 1994. Soil total N was reduced by 29–49% in all RoWs in 1992 and increased slightly from those levels in 1993 and 1994. Pipeline construction affected soil microbial biomass carbon (MBC) in the three RoW areas differently, and the effect was not consistent from year to year. However, the average level of MBC was not adversely impacted. In 1994, soil phosphatase activity in the RoW zones tended to be lower as compared to pre-pipeline installation, particularly in the pile area of the Dark Grey Solod. It is concluded that although some soil biological properties were degraded by pipeline construction, and barley yield was reduced in 1992, crop production in the following 2 yr was not significantly affected. Key words: Barley, carbon, microbial biomass, nitrogen, phosphatase, pipeline

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Sensitivity analysis of six soil organic matter models applied to the decomposition of animal manures and crop residues

Italian Journal of Agronomy ◽

10.4081/ija.2016.757 ◽

2016 ◽

Vol 11 ◽

Cited By ~ 1

Author(s):

Daniele Cavalli ◽

Pietro Marino Gallina ◽

Luca Bechini

Keyword(s):

Organic Matter ◽

Microbial Biomass ◽

Organic Matter Decomposition ◽

Model Parameters ◽

Monod Kinetics ◽

Decomposition Rates ◽

Mineral N ◽

First Order ◽

Inhibition Hypothesis ◽

C Decomposition

Two features distinguishing soil organic matter simulation models are the type of kinetics used to calculate pool decomposition rates, and the algorithm used to handle the effects of N shortage on C decomposition. Compared to widely used first-order kinetics, Monod kinetics more realistically represent organic matter decomposition, because they relate decomposition to both substrate and decomposer size. Most models impose a fixed C to N ratio for microbial biomass. When N required by microbial biomass to decompose a given amount of substrate- C is larger than soil available N, carbon decomposition rates are limited proportionally to N deficit (N inhibition hypothesis). Alternatively, C-overflow was proposed as a way of getting rid of excess C, by allocating it to a storage pool of polysaccharides. We built six models to compare the combinations of three decomposition kinetics (first-order, Monod, and reverse Monod), and two ways to simulate the effect of N shortage on C decomposition (N inhibition and C-overflow). We conducted sensitivity analysis to identify model parameters that mostly affected CO2 emissions and soil mineral N during a simulated 189-day laboratory incubation assuming constant water content and temperature. We evaluated model outputs sensitivity at different stages of organic matter decomposition in a soil amended with three inputs of increasing C to N ratio: liquid manure, solid manure, and low-N crop residue. Only few model parameters and their interactions were responsible for consistent variations of CO2 and soil mineral N. These parameters were mostly related to microbial biomass and to the partitioning of applied C among input pools, as well as their decomposition constants. In addition, in models with Monod kinetics, CO2 was also sensitive to a variation of the halfsaturation constants. C-overflow enhanced pool decomposition compared to N inhibition hypothesis when N shortage occurred. Accumulated C in the polysaccharides pool decomposed slowly; therefore model outputs were not sensitive to a variation of its decay constant. Six-month organic matter decomposition was generally higher for models implementing classical Monod kinetics, followed by models with first-order and reverse Monod kinetics, due to the effect of soil microbial biomass growth on decomposition rates. Moreover, models implementing Monod kinetics predicted positive priming effects of native organic matter after soil amendment, according to co-metabolism theory. Thus, priming was proportional to the increase of the microbial biomass and in turn to the decomposability of applied organic matter. We conclude that model calibration should focus only on the few important parameters.

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Microbial and enzymatic activity as influenced by existing cropping pattern in the soils of Ganges floodplain

Plant Science Today ◽

10.14719/pst.2019.6.3.545 ◽

2019 ◽

Vol 6 (3) ◽

pp. 309-314 ◽

Cited By ~ 2

Author(s):

Mohammad Zaber Hossain ◽

Md. Rezaul Karim ◽

Bina Rani Majumder ◽

Falguni Akter

Keyword(s):

Microbial Biomass ◽

Organic Carbon ◽

Enzymatic Activity ◽

Respiration Rate ◽

Microbial Biomass Carbon ◽

Cropping Systems ◽

Cropping Pattern ◽

Microbial Biomass Nitrogen ◽

Total N ◽

Floodplain Soil

Effect of multi cropping (Potato-Jute-Sweetgourd-T.Aman, Sweet gourd-Brinjal-Jute, Cauliflower-Radish-Lentil-Basil, Jute-Lentil-Mustard-Wheat and Sweetgourd-Turnip, designated as P-J-S-T, S-B-J, C-R-L-B, J-L-M-W and S-T, respectively) and mono cropping systems (orchard of Lychee, Teak, Turmeric and Banana) on microbial and enzymatic activity of Ganges floodplain soil was investigated. Organic carbon, microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), soil respiration, total nitrogen and urease activity (UA) of the soils were examined. Upon examination it was observed that soils under mono cropping pattern (Lychee, Teak, and Banana) showed significantly (p?0.05) higher MBC, MBN and UA than those under multi cropping pattern. Highest values of MBC and UA found in teak plant were 95.44 milligram/kilogram (mgkg-1) and 6.51µg N released g-1day-1 respectively while for multi cropping pattern the respective values were 37.52 mgkg-1 and 2.23 µg N released g-1day-1 found in S-T and J-L-M-W cropping pattern. The highest MBN (12.70 mgkg-1) was obtained in soil where lychee was practiced. Multi cropping soil showed significantly (p?0.05) higher respiration rate than mono cropping soil and the highest rate was found 508.75 mg CO2 g-1day-1 in J-M-L-W cropping pattern. Turmeric showed the lowest respiration rate (120.75 mg CO2 g-1day-1) among the cropping pattern studied. Both MBC and UA showed positively significant relation with soil organic carbon, and total N at 0.01 % level. High microbial and enzymatic activity of mono cropping soil represent combined effect of vegetation and low tillage practices in soil.

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A Kinetic Model for Blood Biomarker Levels After Mild Traumatic Brain Injury

Frontiers in Neurology ◽

10.3389/fneur.2021.668606 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sima Azizi ◽

Daniel B. Hier ◽

Blaine Allen ◽

Tayo Obafemi-Ajayi ◽

Gayla R. Olbricht ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Kinetic Model ◽

Sensitivity Analyses ◽

Oral Drug ◽

Parameter Estimates ◽

Model Parameters ◽

Mild Tbi ◽

Social Burden ◽

Blood Biomarker

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as to the trajectories of blood biomarker levels over time after mild TBI. Modeling the kinetic behavior of these biomarkers could be informative. We propose a one-compartment kinetic model for S100B, UCH-L1, NF-L, GFAP, and tau biomarker levels after mild TBI based on accepted pharmacokinetic models for oral drug absorption. We approximated model parameters using previously published studies. Since parameter estimates were approximate, we did uncertainty and sensitivity analyses. Using estimated kinetic parameters for each biomarker, we applied the model to an available post-concussion biomarker dataset of UCH-L1, GFAP, tau, and NF-L biomarkers levels. We have demonstrated the feasibility of modeling blood biomarker levels after mild TBI with a one compartment kinetic model. More work is needed to better establish model parameters and to understand the implications of the model for diagnostic use of these blood biomarkers for mild TBI.

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Measurement of microbial biomass N:C by chloroform fumigation-incubation

Canadian Journal of Soil Science ◽

10.4141/s96-064 ◽

1997 ◽

Vol 77 (4) ◽

pp. 507-514 ◽

Cited By ~ 27

Author(s):

D. Harris ◽

R. P. Voroney ◽

E. A. Paul

Keyword(s):

Microbial Biomass ◽

Soil Microbial Biomass ◽

C:N Ratio ◽

Residual Fraction ◽

Model Parameters ◽

C Mineralization ◽

Microbial Biomass Nitrogen ◽

Soil Microbial ◽

Biomass N ◽

Chloroform Fumigation

We present a calculation for soil microbial biomass N:C ratio determined from a 10-d incubation following chloroform fumigation. The calculation is based on a mathematical model of the N content of the pre- and post-fumigation soil microbial biomass and the growth yield of the biomass that develops after fumigation. Biomass N is calculated from the N:C ratio and biomass C. The mineralization of bacteria and fungi, with different N contents, added to fumigated soils was used to establish the model parameters. The model was tested against an independent set of measurements and considers two assumptions: 1) The ratio of N:C mineralized from killed biomass is equal to the ratio of N:C mineralized from soil non-biomass constituents. 2) More realistically, the N and C mineralization in the fumigated soil, from sources other than killed biomass, is a residual fraction of the N and C mineralization in the unfumigated soil. Biomass C:N ratios calculated without a control correction (assumption 1) were, on average, 20% wider than corrected values (assumption 2). Biomass N calculated as the product of N:C and biomass C was compared with published values for several data sets. The new calculation method was robust even when net immobilization of N followed fumigation. Key words: Soil microbial biomass, nitrogen, chloroform fumigation, C:N ratio

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Effect of Nitrogen Sources on Microbial Biomass Nitrogen under Different Soil Types

ISRN Soil Science ◽

10.5402/2012/310727 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

Haytham M. El-Sharkawi

Keyword(s):

Microbial Biomass ◽

N Uptake ◽

Nitrogen Sources ◽

Ammonium Concentration ◽

Available Nitrogen ◽

Microbial Biomass Nitrogen ◽

Total N ◽

Plant Nitrogen ◽

Microbial Nitrogen ◽

Microbial N

Knowledge to increase the microbial biomass nitrogen (MBN) as a bulk of free-living microbes in paddy soil is limited. The potential benefit of these microorganisms was evaluated, in this study, under different nitrogen sources and two paddy soils. The results revealed that pots treated with organic matter recorded the maximum value of the total N uptake and MBN, followed by the Urea treated pots. Pots amended with sludge exhibited a higher microbial N forming ability than those amended with straw compost under both soils. But ammonium concentration in soil increased with straw compost application. Under fresh soil treatment, microbial N uptake rate and proportion of plant nitrogen derived from microbial nitrogen sources () were higher than autoclaved soil. A positive correlation was found between the and the total N in rice shoot in both soils. Finally, we can say that MBN was governed not only by the soil nitrogen content but also by the type of the nitrogen source. The addition of sludge to fresh soil increased total MBN and consequently could be indirectly beneficial to rice production especially in poor soils. Thus, soil microbes contribute to plant growth by serving the available nitrogen during the season.

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Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-5077-2013 ◽

2013 ◽

Vol 10 (4) ◽

pp. 5077-5119 ◽

Cited By ~ 2

Author(s):

Y. Sun ◽

Z. Hou ◽

M. Huang ◽

F. Tian ◽

L. R. Leung

Keyword(s):

Inverse Modeling ◽

Bayesian Model Averaging ◽

Surface Flux ◽

Least Square ◽

Sensitivity Analyses ◽

Bayesian Inversion ◽

Parameter Estimates ◽

Model Parameters ◽

Least Square Fitting ◽

Community Land Model

Abstract. This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC) Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent – as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

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Conducting Sensitivity Analyses to Identify and Buffer Power Vulnerabilities in Studies Examining Substance Use over Time

10.31234/osf.io/4evgk ◽

2018 ◽

Author(s):

Sean Patrick Lane ◽

Erin Hennes

Keyword(s):

Substance Use ◽

Individual Differences ◽

Sensitivity Analyses ◽

Large Individual ◽

Parameter Estimates ◽

Model Parameters ◽

Multiple Sources ◽

Base Rates ◽

The Impact ◽

Power Analyses

Introduction: A priori power analysis is increasingly being recognized as a useful tool for designing efficient research studies that improve the probability of robust and publishable results. However, power analyses for many empirical designs in the addiction sciences require consideration of numerous parameters. Identifying appropriate parameter estimates is challenging due to multiple sources of uncertainty, which can limit power analyses’ utility. Method: We demonstrate a sensitivity analysis approach for systematically investigating the impact of various model parameters on power. We illustrate this approach using three design aspects of importance for substance use researchers conducting longitudinal studies ─ base rates, individual differences (i.e., random slopes), and correlated predictors (e.g., co-use) ─ and examine how sensitivity analyses can illuminate strategies for controlling power vulnerabilities in such parameters.Results: Even large numbers of participants and/or repeated assessments can be insufficient to observe associations when substance use base rates are too low or too high. Large individual differences can adversely affect power, even with increased assessments. Collinear predictors are rarely detrimental unless the correlation is high.Conclusions: Increasing participants is usually more effective at buffering power than increasing assessments. Research designs can often enhance power by assessing participants twice as frequently as substance use occurs. Heterogeneity should be carefully estimated or empirically controlled, whereas collinearity infrequently impacts power significantly. Sensitivity analyses can identify regions of model parameter spaces that are vulnerable to bad guesses or sampling variability. These insights can be used to design robust studies that make optimal use of limited resources.

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