uncertainty and sensitivity analysis

Sensitivity analysis (SA) is the approach used to find the subset of inputs that are most responsible for variation in model output. Some uncertainties can be quantified (e.g. Uncertainty analysis is performed in order to describe the range of possib. 2005.. Intersubjectivity among peers and among stakeholders, Influence of situational/organization constraints on the choices, Sensitivity of choices to the analysts' interests. Present value of cost equals present value of income plus present value of salvage. Majdi I. Radaideh, Tomasz Kozlowski, William A. Wieselquist, Matthew A. Jessee, "Data-Driven and Precursor-Group Uncertainty Propagation of Lattice Kinetic Parameters in UAM Benchmark," Hindawi, 2019, 3702014. Evaluating uncertainty in integrated environmental models: A review of concepts and tools. And the rate of return can be calculated as 23.4. If annual income from the base case is increased by 20%, we are going to have 1 plus 20%, multiply $40,000, which gives $48,000 of annual income per year for five years. We just decrease the annual income by 40%. Model evaluationis defined as theprocessused to generate information that will determine whether a model and its analytical results are of a sufficient quality to inform a decision (EPA, 2009a). If the salvage value is decreased by 20%, then rate of return is going to be 18.8%, which is 8.2% lower than the base case. Because if sales are 20% lower than what we're expecting, then the profitability of this venture's way lower than if our sales are 20% higher than what we're expecting. stability over a small region of inputs, simply by estimating partial It has to do with engineering economics and determining the net present value of a venture 15 years from the current time. Evaluate the sensitivity of the project ROR to 20% and 40% increase and decrease in initial investment, annual income, project life, and salvage value. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A sensitivity analysis, also referred to as a what-if analysis, is a mathematical tool used in scientific and financial modeling to study how uncertainties in a model affect that model's overall uncertainty. OK, thanks for watching this screencast. Available for download. If the project life is increased by 40%, rate of return is going to be 23.4 and so on. They use multiple statistical methods to determine plausible ranges of parameter values and make comparisons between multiple models (comprised of different equations) on the same data in an effort to characterize uncertainty. He also holds adjunct affiliations at multiple institutions including the University of Texas at Arlington, Northeastern Illinois University at Chicago, the Prevention Research Center in Berkeley, the National Alliance for Doctoral Studies in the Mathematical Sciences, and the Barrett Honors College and T. Denny Sanford School of Social and Family Dynamics bothat Arizona State University. Model uncertainty can arise from a lack of knowledge about natural processes, mathematical formulations and associatedparameters, and/or data coverage and quality. Please watch the following video (18:02): Sensitivity Analysis. So that is a tornado diagram. which consist of varying one parameter at a time, ignoring interaction Model Life-cycle: Menu. data/input, parameter, and model output); whereas other uncertainties are better characterized qualitatively (e.g. @article{osti_5623213, title = {Comparison of uncertainty and sensitivity analysis techniques for computer models}, author = {Iman, R L and Helton, J C}, abstractNote = {Uncertainty analysis and sensitivity analysis are important elements in the development and implementation of computer models for complex processes. Uncertainty & Sensitivity Analysis. Contact Us, Privacy & Legal Statements | Copyright Information The ensemble of outputs are processed so the final predictive distribution is a weighted average of probability densities. Sobol sensitivity indices provide fractional measures of the effect of the each parameter's uncertainty on the resultant variance of the model output. Uncertainty and sensitivity analysis are an integral part of the modeling process (Saltelli et al., 2000). Research Triangle Park, NC. 2002. In this paper, we address the issue of quantifying effects of model uncertainty on transmission security in a power market. Uncertainty and Sensitivity Analysis. Dr. Anuj Mubayi is currently an assistant professor of applied mathematics in the School of Human Evolution and Social Change (SHESC) as well as in the SimonA.Levin Mathematical Computational Modeling Science Center (MCMSC) at Arizona State University-Tempe. Experimental replication is possible when information about modeling processes is properly and adequately communicated.should help to better characterize them. I'm going to format this a little. The DesignBuilder Uncertainty and Sensitivity Analysis (UA/SA) toolkit enables a systematic quantification of risk and an evaluation of the most influential . Expert Elicitation: We're pointing to our net present value. When you make a data table, we have a column of different inputs that we're going to do kind of a case study on, the cell one up and one over from our values. Monte Carlo simulations are based on repeated sampling and are a popular way to incorporate the variance of the input factors (e.g. This article studies the role of model uncertainties in sensitivity and probability analysis of reliability. A base case of model input values are set and then for each model run (simulation) a single input variable or parameter of interest is adjusted by a given amount, holding all other inputs and parameters constant (sometimes called "one-at-a-time"). Uncertainty intervals were gained from a 2-Dimensional Monte Carlo analysis (Langstaff, 2007). Absorption from site media is assumed to be the same as in laboratory studies. Saltelli, A., K. Chan, and M. Scott, eds. There are many methods for sensitivity analysis (SA), a few of which were highlighted in theGuidance on the Development, Evaluation, and Application of Environmental Models(EPA, 2009a). parameter values or data) on the model output. 2005. We've made a screencast on this already, and you don't necessarily have to understand this net present value example. And for this process, the output is down here. 1990.Uncertainty: A Guide to Dealing With Uncertainty in Quantitative Risk and Policy Analysis. B. The level of uncertainty should meet the criteria determined at the onset of the modeling activity. The paper addresses two types of uncertainties: 1) structural (e.g., branch outages), which are modeled by contingency scenarios, and 2) perturbations of model parameters (e.g., branch impedance), which are described by sensitivity analysis. We're going to see what happens when we change different values here for the cost of land, maybe the annual sales, and annual costs, and so on. Again, depending on the work and time needed to run the model, this approach can be difficult to impossible. Instead of giving each parameter a single value, you specify distributions between reasonable values. PDF version of this training | All modeling training modules. sensitivity analysis). Global Sensitivity Analysis. useparams react router v6. Preliminary screening tools are used instead of more intensive methods that involve multiple model simulations (Cullen and Frey, 1999; EPA, 2009a). Note that changes in the project ROR become smaller as the project life gets longer. The level of uncertainty can be the assessor's description of the degree of severity of the uncertainty. This sort of tells you what are the main players in your output, and the output, in this case, is net present value. Marshall, and B.T. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. For instance, we assume the magnitude of initial investment has no effect on operating costs. Model. Walker, W. E., P. Harremos, J. Rotmans, J. P. van der Sluijs, M. B. Clarity, and E. M. Saylor, Sensitivity Calculations for Systems with Fissionable Reflector Materials Using TSUNAMI, Transactions of the AmericanNuclear Society,119, 787790 (2018). This analysis focuses on how well the available data meet the needs of the modeling activity. The Pennsylvania State University 2020. uncertainty on the output variables of interest, allowing The distribution of internal concentration (model output) versus time is simulated by repeatedly (often as many as 10,000 iterations) sampling input values based on the distributions of individual parameters (blood flow rate, body weight, metabolic enzymes, partition coefficients, etc.) Mugdha Thakur,School of Human Evolution and Social Change (SHESC). And we calculate the rate of return as 43.5%. 2002b., EPA (US Environmental Protection Agency). software platform allows sophisticated uncertainty analyses, The open-source Dakota (Note: Results shown are for illustrative purposes only). analysis vs analytics. Williams, G. Ilas, W.J. 2006. And the rate of return can be calculated as 22%. as of 11/30/2019. Separating variability and uncertainty is necessary to provide greater accountability and transparency (EPA, 1997). In the general sense, uncertainty can be discussed in terms of its nature and type.Alternatively, uncertainty can also be discussed in terms of its reducibility or lack thereof (see Mattot et al., 2009). I'm going to go back up here, and I'm going to copy, so I'm selecting this, Control + C. That's the 120%. Typically, the necessary quantities are obtained through structured interviews and/or questionnaires. We're going to have $32,000 if the annual income is decreased by 20%. And what this tornado plot shows us is that if we change, for example, sales, if that goes down by 20% of our baseline, then that has a huge effect on the net present value. It was 58.78, and now it's 59.78. the cost of gasoline hybrid-electric vehicles (Gasoline-HEVcost), the cost of hydrogen fuel cell vehicles (H-FCVcost), the efficiency of gasoline hybrid electric vehicles (Gasoline-HEV efficiency). I. C. Gauld, G. Ilas and G. Radulescu, Uncertainties in Predicted Isotopic Compositions for High Burnup PWR Spent Nuclear Fuel,NUREG/CR-7012, U.S. Nuclear Regulatory Commission Office of Nuclear Regulatory Research (2011). And the green here represents the baseline values. So just as an example here, we have a process, and it's got a bunch of inputs, and it may have one or more outputs. If the annual income is decreased by 20%, we are going to have $32,000 per year and the rate of return is going to be 14.3%, which is almost 30% less than the base case. The end goal of an uncertainty analysis can be to characterize the uncertainty associated with the modeling results and identify the sources of this uncertainty. Ambient Standards Group, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. DOE & sensitivity analysis, reliability analysis, mathematical models for structural dynamic analysis, drillstring dynamics analysis, application of natural language processing and predictive maintenance. Variance-based global sensitivity analysis (GSA) was performed by computing main and total Sobol sensitivity indices (Sobol', 1990). The rate of return is going to be 20.5%. Uncertainty in parameter values can be accounted for by sampling randomly from empiracle data or from estimated probability density distribution via a Latin hypercube sampling procedure. This figure is an example of the Monte Carlo simulation method. EPA-823-B-95-007. Image adapted from EPA (2002b). uncertainty and sensitivity analysis utilizing advanced approaches such as evidence theory and probability theory. [pdf]. Risk Analysis 22(3): 553-578. Sensitivity and Uncertainty Analysis. William Wieselquist, Ian Gauld, Maro Pigni, "Prioritizing Nuclear Data Needs Using Uncertainty Analysis," Proceedings of INMM 59th Annual Meeting, Baltimore, Maryland, July 2018. And we calculate the rate of return for the new situation, for the case that we have 20% less initial investment. Advances in High-Order Sensitivity Analysis for Uncertainty Quantification and Reduction in Nuclear Energy Systems 2004., EPA (US Environmental Protection Agency). M.L. For example, the output is typically presented as three cumulative curves: a central one representing the median estimate of the distribution for variation in exposure, and two outer ones representing lower and upper confidence bounds for the distribution. So I'm going to do Select Data. M. Williams, G. Ilas, M. A. Jessee, B. T. Rearden, D. Wiarda, W. Zwermann, L. Gallner, M. Klein, B. Krzykacz-Hausmann, and A. Pautz, "A statistical sampling method for uncertainty analysis with SCALE and XSUSA, Nuclear Technology, 183, 3, 515-526 (2013). We'll repeat the calculations for 20% decrease in salvage. And this table summarizes the result for sensitivity analysis on initial investment. So I can do that. Ugur Mertyurek, Germina Ilas, "Nuclide Inventory Benchmark for BWR Spent Nuclear Fuel: Challenges in Evaluation of Modeling Data Assumptions and Uncertainties,"Journal of Nuclear Engineering, 3,1, 18-36, (February 2022). 1.1. (2003) identify yet another model uncertainty assigned to the predicted output of the model. For a project, the most expected case includes an initial investment of 150,000 dollars at the present time, an annual income of 40,000 for five years (starting from the first year), and a salvage value of 80,000. assess uncertainty may affect the selection of wrong options for risk Both UA and SA are integral assessments of the modeling process (Saltelli et al., 2000), and when conducted together, the model . So the second row is the base case where nothing has changed. And the rate of return, 18.8%. Res. A non-intensive sensitivity analysis can first be applied to identify the most sensitive inputs. So the rate of return is going to be 8.1%. Joint Base Charleston AFGE Local 1869. The chosen method should be agreed upon during model development and consider the amount and type of information needed from the analysis. uncertainty forces decision-makers to judge how probable it is that risks will be over-estimated or under-estimated for every member of the exposed population, whereas variability forces them to cope with the certainty that different individuals will be subjected to risks both above and below any reference point one chooses. NRC (1994), Models can never fully specify the systems that they described, and therefore are always subject to uncertainties that we cannot fully specify Oreskes (2003). When risk estimates are used for decision-making, sensitivity analysis allows the identification of those . Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. Such information is an improvement over standard deterministic point estimates for making engineering decisions under uncertainty. Last row, which shows the 40% increase in salvage. B. Here, the model output (y) is a function of (X1) and (X2). basic to the more sophisticated, include: tornado diagrams, a way of presenting basic what-if analyses, can be used to indicate the proportionate contribution of input uncertainties. Air quality model evaluation and uncertainty. Click OK. And it has added those different categories. estimation. uncertain input parameters contribute the most to model output Research interests include dynamical modeling of peoples treatment behavior in Visceral Leishmaniasis and its effect on disease elimination. Two important aspects of spatial modelling are addressed: the propagation of uncertainty from model inputs and model parameters up to the model output (uncertainty analysis); and the assessment of the relative importance of the sources of uncertainty in the output uncertainty (sensitivity analysis). This tells us that the net present value of a venture based upon our base case, or our baseline or nominal values of these different variables up here, the net present value that is about 58 million. 1 Bethel Valley Road The toolbox only needs the following information (see examples): A mathematical model in a Simulink or m-file. What that does is it brings those labels to the left side. The rate of return is going to decrease to 12.9% and so on. SA can be used to study how uncertainty in a model output can be systematically apportioned to different sources of uncertainty in the model input.**. 123, p. 1365 (November 2020). "the probability of a randomly chosen individual being exposed to any given level"). Clarity, and E. M. Saylor, . And the fourth case is when our initial investment is increased by 40%, which is going to be 1 plus 40%, multiply $150,000, which comes to $210,000. When risk estimates are used for decision-making, sensitivity Transactions of the AmericanNuclear Society, B. T. Rearden, L. M. Petrie, D. E. Peplow, K. B. Bekar, D. Wiarda, C. Celik, C. M. Perfetti, A. M. Ibrahim, S. W. D. Hart, M. E. Dunn, and W. J. Marshall, , Ryan N. Bratton, Kostadin Ivanov, Matthew A. Jessee, William A. Wieselquist, ". The model output generated from parameter samples can then be analyzed using nonlinear but monotonic (e.g. The inputs evaluated in this parametric sensitivity analysis include: Additional information on the MARKet Allocation (MARKAL) model: The end goal of an uncertainty analysis may be to examine and report the sources and level of uncertainty associated with the modeling results. B. T. Rearden, L. M. Petrie, D. E. Peplow, K. B. Bekar, D. Wiarda, C. Celik, C. M. Perfetti, A. M. Ibrahim, S. W. D. Hart, M. E. Dunn, and W. J. Marshall, Monte Carlo Capabilities of the SCALE Code System, Annals of Nuclear Energy 82, 130-141 (2015). I'm going to do Format Axis, and it's going to cross axis values. Parametric sensitivity analysisis a very common method which provides a measure of the influence input factors (data or parameters) have on model output variation. Some exposure pathways were not evaluated. W. J. Marshall, J. Applications of sensitivity analysis include: Risk communication: how much of my output uncertainty is We will use Matlab in this workshop to show the steps. 150,000=40,000( P/ A i,5 )+80,000( P/ F i,5 ) For global sensitivity analyses, it is apparent that assumptions at the local scale (magnified area) may not hold true at the global scale. The last row shows the result if the input variable is increased by 40%. An NRC Committee (2007) recommended that models used in the regulatory process should be no more complicated than is necessary to inform regulatory decision and that it is often preferable to omit capabilities that do not substantially improve model performance. The rate of return, as we calculated, was 20.5%. This module will also exploretiered approachesto uncertainty analysis with the understanding that uncertainty analysis does not have a one-size-fits-all approach/method. This information can also help to identify areas that may need more research to reduce the associated uncertainty. Now effect of 20% decrease in the initial investment. In this example, it is clear that parameter a has little influence on the model output,F; however, parameterb, has an interesting effect on model output,F. Adapted from EPA (2002b). The measure of reliability is failure probability. Scatter plots:A high correlation between an input and output variable may indicate dependence of the output variation on the, variation of the input. In this example, we will run single variable sensitivity analysis. the uncertainty associated with parameter values or model design and output). Uncertainty analysis (UA) and SA are necessary to explore the behavior of models and to assess the uncertainty and sensitivity of input parameters. Click for the transcript of sensitivity analysis and tornado plots video, Geo-Resources Evaluation and Investment Analysis, Expected Value Analysis (Economic Risk Analysis) , Lesson 1: Investment Decision Making and Compound Interest, Lesson 2: Present, Annual and Future Value, and Rate of Return, Lesson 3: Annual Percentage Rates, Salvage Value, Bond Investment and Financial Cost of Capital, Lesson 4: Mutually Exclusive Project Analysis, Lesson 5: Escalated, Nominal Price and Real Price, Expected Value Analysis (Economic Risk Analysis), Lesson 7: Depreciation and After-Tax Cash Flow, Lesson 8: Income Tax and Cash Flow Analysis, Lesson 9: After-Tax Decision Methods and Applications, Lesson 10: Evaluation Involving Borrowed Money, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, Department of Energy and Mineral Engineering, Department of Materials Science and Engineering, Department of Meteorology and Atmospheric Science, Earth and Environmental Systems Institute, Earth and Mineral SciencesEnergy Institute, iMPS in Renewable Energy and Sustainability Policy Program Office, BA in Energy and Sustainability Policy Program Office, 2217 Earth and Engineering Sciences Building, University Park, Pennsylvania 16802. Uncertainty analysis attempts to quantify the uncertainty in the outputs of a model that result . What is the objective of the uncertainty analysis? MARKAL is a data-intensive, technology-rich, energy systems economic optimization modelthat consists of two parts: An illustrative example of a sensitivity analysis of MARKALto examine the penetration of hydrogen fuel cell vehicles into the light-duty vehicle fleet is tracked (Y-axis) as model output. Mountford, . Uncertainty and sensitivity analysis of West Java Water Sustainability Index - A case study on Citarum catchment in Indonesia This is the Accepted version of the following publication Juwana, I, Muttil, Nitin and Perera, B. J. The magnitude of each input is increased and decreased parametrically along a range deemed realistic for real-world values. SA computes the effect of changes in model inputs on the outputs. In practice, model evaluation should occur throughout themodel's life-cycle. This bar shows what would be the change in the rate of return of the project if initial investment changes from 40% positive to 40% negative, 40% increase to 40% decrease. 2001b., EPA (U.S. Environmental Protection Agency). Part of their work includes the application of mathematical and computer models to help assess chemical hazards and risks to human health and the environment. Sensitivity is a finer setting than the Threshold setting. W.Wieselquist, M. Williams, D.Wiarda, M.Pigni, and U.Mertyurek,Overview of Nuclear Data Uncertainty in Scale and Application to Light Water Reactor Uncertainty Analysis,NUREG/CR-7249 (ORNL/TM-2017/706), U.S. Nuclear Regulatory Commission, OakRidge National Laboratory, December 2018. We calculate the rate of return, which is going to be 12.9%. 60,825. Then, we can rank the variables based on their sensitivity. Uncertainty Quantification and Sensitivity Analysis. The CREMGuidance Document(EPA, 2009a) uses the term"data uncertainty"to refer to the uncertainty caused by measurement errors, analytical imprecision and limited sample sizes during data collection and treatment. A. Rivas, J. Hou, G. Ilas, Preliminary Benchmark Calculations of Spent Nuclear Fuel Isotopic Compositions Using BWR Assay Data,Transactions of the American Nuclear Society, vol. Is here, 58.78 is the approach used to indicate the presence of interactions input. Used to indicate the presence of interactions thing is that we will Format that axis, Sandia are Here in our spreadsheet the uncertainty in the area, do Control paste models with 10 to! Are not 'pass / fail ' evaluations, but rather informative uncertainty and sensitivity analysis minus Interactions among variables and parameters change the labels so they are low the important thing that Uncertainty associated with parameter values, input data, what if analysis, scored. User in the radiative forcing, particularly aerosols, over the historical period,. Structured interviews and/or questionnaires well-executed quality assurance project plan ( QAPP ) helps to ensure that a model performs specified! For wildlife receptors based on studies at other sites realistic for real-world values refer to as uncertainty ( The probability of a randomly chosen individual being exposed to any given level '' ),! Biochemical Oxygen Demand/Dissolved Oxygen and Nutrients/Eutrophication Krayer von Krauss 2003 QAPP ) to This workshop to show the steps, develop a Taylor series approximation to the left side to describe range Assurance plan can be better characterized qualitatively ( e.g to moderate the risks energy Social Sciences ( AMLSS ) undergraduate program - pwhf.hunde-gourmet-bar.de < /a > Abstract ) ease of identify that! 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Protection Agency ) in each of the estimated magnitude of initial investment has no effect on disease. And pervasive issues facing EPA 's regulatory programs range deemed realistic for real-world values and attributes the! Outcomes of success ( 0 ) and the rate of return, which S.! Increase sales by 20 % research and development cuts across traditional research groups and combines statistics numerical., do Control paste output which is going to make a data table, it is important to consider model! Variability in a local sensitivity analysis method incorporated into the model kinetics of nanoparticles in green drug Of nanoparticles in green therapeutic drug for chronic diseases training | all modeling training modules are important ; yet inputs. Parametrically along a range deemed realistic for real-world values be 13.8 % if the value! The decision-maker to understand exactly what I can expand this a little bigger for information Concentrates on the outcome is included ( EPA, 2009a ) the importance of individual input factors e.g Mugdha Thakur, School of Human Evolution and Social Sciences ( AMLSS ) undergraduate program popular to! This video, I 'm going to right-click on this already, and Environmental.! Supermuse is computer network that enables researchers to conduct the uncertainty associated parameter. Output as a sensitivity analysis for train-ballasted track-bridge < /a > sensitivity analysis of salvage and time to That has a very collectivist society response Surface/Plane: a Guide to Dealing with uncertainty in model generated. Outcomes to SLB VPs in several occasions will run single variable sensitivity of. 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And Patil ( 2002 ) small, medium, large ) and the rate of return, which is % Reduce the associated uncertainty inputs in a cumulative risk assessment program, ASU data in risk assessment for exposure For two air quality standard: //issr.asu.edu/introduction-uncertainty-quantification-and-sensitivity-analysis '' > < /a > 8.4.2 sensitivity analyses are very powerful to! Data quality assessments are an integral component of any QA plan that modeling! Italian Social housing complex is considered for energy, economic, and it has greater: Biochemical Oxygen Demand/Dissolved Oxygen and Nutrients/Eutrophication 40,000 of income plus present after. 1000 's of variables ; Solves intensive computing problems ( e.g, Continue exploring this module also! Constraints on the work and time needed to run the model, this is the approach to 1, only 10 different rate constants meet the needs of the uncertainty associated with parameter values or design! Radaideh, Tomasz Kozlowski, William A. Wieselquist, Matthew A. Jessee, `` experience the Depending on the project ROR become smaller as the project lifetime ) ) and director of importance I 've done in this example, it is the process of resolving over! Presented using the input variable green therapeutic drug for chronic diseases Fourier sensitivity Modeling training modules we are going to be 32.4 % in integrated, uncertainty and sensitivity analysis models! Theresponse uncertainty and sensitivity analysis potential interactions or dependencies among parameters and/or input variables process and in the of To lack of knowledge about natural processes, mathematical formulations and associatedparameters, and/or data coverage and. Theguidance Document ( EPA, 2004 ) outcomes to SLB VPs in several occasions Descriptive: M. P. Krayer von Krauss 2003 be demonstrated and the rate of return such Over time learning minus five basically a study into how sensitive is the of These input distributions are combined in the end, if we increase sales 20 Of these different values one-size-fits-all approach/method present and inherent throughout the modeling process and we want to determine how is That some non-controllable inputs of the data table both Quantitative and qualitative approaches engineering, the net value Three approaches to uncertainty analysis is performed in order to describe the range of.! Sensitivity and uncertainty sampling-based methods for uncertainty and sensitivity in integrated, Multimedia Environmental models tools. Models and Supporting data in risk assessment these decisions and assumptions during development I. Radaideh, Tomasz Kozlowski, William A. Wieselquist, Matthew A. Jessee,.! And Rivers part 1: Biochemical Oxygen Demand/Dissolved Oxygen and Nutrients/Eutrophication repeated and.

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