Lecture 4: Models Flashcards Preview

EHS 572: Environmental Impact Assessment > Lecture 4: Models > Flashcards

Flashcards in Lecture 4: Models Deck (31):

Model Jargon test: simulation, sensitivity analysis, optimization, simulation/gaming. What do each of these words indicate about the motivation to model (4)? What are other reasons to model (5)?

predict effects > simulation
system function > sensitivity analysis
alternatives > optimization
behavior > gaming

forecast, source apportionment, economy, complexity, required use


Common model applications (3 and 2 notes)

complex trade-offs, policy decisions, compromises. ]

important to determine non-inferior solutions / production possibility frontier

models provide a way to make a rational choice


What are boundaries in the modeling context? What boundaries need to be considered?

def: the delineation of processes included or excluded from the model

boundary of problem environment: determined by needs and constraints

boundary of model environment: what takes place in model (geographic scale, temporal scale, media pathways)


Def mass balance modeling and some key terms (8)

def: mass accounting of chemical transport and fate of contaminants in various media compartments

source, pathway (route), sink, load/input/flux, control volume, reactions, steady state, conservative/inert


What are the 5 key elements of a Mass Balance model? Most important thing?

Clearly defined control volume, inputs and outputs, transport characteristics, kinetics, recognition of assumptions

Make sure units match!


Model 1: Dilution Model. What are the assumptions?

uniform mixing, infinite accuracy, contaminant is conservative


Model 2: BOD Model. assumptions?

first order decay of contaminant, constant river velocity, uniform river geometry


Model 3: Chemical Fate and Transport in 3 directions. just describe

control volume is fixed in space, fluid moves through it, and control volume properties change


Model 4: Multimedia Fate and Transport.

shows major environmental compartments, travel along major pathways,

fugacity type model, equilibrium partitioning at steady state


Model 5: Growth Model,

growth rate and death rate, you can solve with numerical integration, or calculus with assumptions and boundary conditions,


Model 6: FSCCR

reactor volume is constant, balanced inflow and outflow. pollutant is conservative.

application: indoor air quality bioaerosol model and air exchange rate. problem separates into total concern, initial conditions, incoming air


Model 7: FSNCCR

similar to FSCCR but material is allowed to degrade. the concept of retention time (how long the particle spends i the reactor, or V/Q). Compare the results here to the FSCCR example: what is the same, what is different?


Governing Equations

differential equations representing a mass balance in a control volume. The solution requires initial conditions and boundary conditions, and analytical solutions require constant coefficients / parameters


Describe the Model Development process, overview and 6 step process

System analysis , model development, model, model evaluation, decision making

1) define objectives 2) outcome variables 3) develop conceptual model or flow sheet (mechanisms, parameters, constraints) 4) implement 5) verify 6) check feasibility


What does it mean for a model to be not feasible?

no solution, or no realistic solution can be obtained, meaning that the model is formulated incorrectly (constraints or problems boundaries are too restrictive or tight)


What does the model formulation process look like for statistical or empirical models?

1) determine a structure based on prior understanding 2) estimate numerical values for parameters
2) estimate parameters
3) test validity
+ revise and iterate


Define systems in the model formulation context.

A collection of components that are connected by some type of interaction to achieve a purpose (flows of mass, energy, etc)


Define unit operation and some characteristics

unit operation: a physical chemical or biological treatment process (typically one step in a process). Allows simple models to be linked to create complex systems. applies to black box processes,

unit operations must conserve: mass, stoichiometry, equilibrium, kinetics, energy


What are the 2 general rules to developing adequate models?

Need expert - modeling must be done by someone who understands the science
Need relevant data - the model must be able to be calibrate and analyzed


List some common questions that come up with modeling

What makes a good model (i.e., constitution)? formulation? selection? calibration? accuracy? errors? result interpretation? sensitivity? assumptions?


What makes a good model?

high explanatory power - validated with real data,
mechanistically consistent,
as simple as possible


5 types of lower-level model errors

round-off error, truncation error, coding errors, input data errors, parameterization errors (GIGO), structural errors,


Higher level model errors

variable selection, structure, calibration, validation, extrapolation, cross-level


How do you select the best model?

find simplest model which is adequate, easiest to validate, provides required outputs, incorporates necessary features


What is the biggest responsibility of a model user

understand the underlying assumptions and uncertainties

include a discussion of certainty and prediction confidence, discussion of errors


define verification, calibration, evaluation, validation, validity

verify - ensure it works as intended,
calibrate - process of finding suitable parameters
evaluate - comparison of model results with data
validation - formal recognition of a model
validity - ability of a model to predict outcomes with accuracy enough to be used in decision making


What is a margin of safety?

factor of 2, it should not be used because it is very costly


Define variability and uncertainty

variability - true heterogeneity in the population
uncertainty - represents lack of knowledge


What does the NRC say about model evaluation

It should continue throughout the life of the model, not just stop at release, to ensure that the model is not being used for purposes that they were not designed for


What does the NRC say about model development

model developers should strive to make simple is best models, findings are that often too many unnecessary variables are included


Common model deficiencies (6)

including irrelevant variables, excluding important variables, incorrect structure, calibration, inadequate validation, incorrect use,