Cleaning up the large number of groundwater contamination sites is a significant and complex environmental challenge. The environmental industry is continuously looking for remediation methods that are both effective and cost-efficient. Over the past 10 years there have been amazing, important developments in our understanding of key attenuation processes and technologies for evaluating natural attenuation processes, and a changing institutional perspective on when and where Monitored Natural Attenuation (MNA) may be applied. Despite these advances, restoring groundwater contaminated by anthropogenic sources to allow for unrestricted use continues to be a challenge. Because of a complex mix of physical, chemical, and biological constraints associated with active in-situ cleanup technologies, there has been a long standing focus on understanding natural processes that attenuate groundwater contaminant plumes. We will build upon basic environmental science and environmental engineering principles to discover how to best implement MNA as a viable treatment for groundwater contamination plumes. Additionally we will delve into the history behind and make predictions about the new directions for this technology. Any professional working in the environmental remediation industry will benefit from this in-depth study of MNA. We will use lectures, readings, and computational exercises to enhance our understanding and implementation of MNA. At the completion of this course, students will have updated understanding and practical tools that can be applied to all possible MNA sites.
Estimating Remediation Timeframes
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From the course by Rice University
Natural Attenuation of Groundwater Contaminants: New Paradigms, Technologies, and Applications
97 ratings
Rice University
97 ratings
From the lesson
Modeling Tools to Support MNA
In this series of lectures, we talk about models and how they can be used to understand MNA. We first start with two key MNA questions: How Long? (will the plume get) and How Far? (how long until the site is clean). Then a review of analytical computer models, remediation timeframe models, the more complex but more powerful numerical models, and even a discussion of the “Fourth Paradigm: Big Data”.
Meet the Instructors
Pedro AlvarezGeorge R. Brown Professor of Civil and Environmental Engineering
Department of Civil and Environmental Engineering
Charles NewellVice President
GSI Environmental Inc.
David AdamsonSenior Environmental Engineer
GSI Environmental Inc.
CHUCK NEWELL: OK, well today we're going to talk about time
and in particular, remediation time, in other words, how long you have to wait,
how long you have to go until our critical space is clean.
So I'm sort of-- remember, there was this great term for Rod Serling
or sort of the phrase.
He talks about there's this fifth dimension beyond which that
is known to man, a dimension as vast as space and timeless as infinity.
It's the middle ground between light and shadow,
between science and superstition, and it lies between the pit of a man's fears
and the summit of his knowledge.
And that's the dimension of imagination.
So in some ways, it describes a problem of estimating remediation frame.
There's a lot of uncertainty, but we keep asking the question.
It's a tough problem, a fearful problem, but we want, in some way,
to estimate this beyond relying solely on our imagination
and make up a number.
DAVID ADAMSON: That's quite an introduction.
I guess we can go ahead and get started.
I assume we don't have any more Twilight Zone references throughout here,
but there are Twilight Zone episodes, you
know, lots of different remediation frame models
that we can work with, right?
CHUCK NEWELL: That's right, so let's take a look at a couple.
We've got some that are shown here.
There's one called SourceDK that's sort of a clever name.
You see how it's sort of spelled in there.
DAVID ADAMSON: Yeah, yeah.
I know you like model names that are easy to decipher.
CHUCK NEWELL: Yeah.
And then there's the BIOSCREEN, BIOCHLORS, and then
there's the REMChlor model, REMFuel, Matrix Diffusion Toolkit, the NAS
software, specially developed natural attenuation software developed
by Frank Chappelle and Mark Widdowson.
But we sort of break these out and how do they get the number?
How do they get this remediation time frame?
At the very top, there is one that really
talks about logarithmic extrapolation and a lot of them
deal with these boxed models, right?
So we're going to really focus on those type of sort of expressions
to sort of tell you a little bit about the fundamental options you
have to go out there to estimate this remediation time frame.
OK, well let's go to the model with maybe the best
name out there, the SourceDK.
And so you can download this thing from the GSI website.
It was developed for the US Air Force that's out in here.
DAVID ADAMSON: Yeah, a little bit of an older version.
I see that our old company name, Groundwater Service, is on there, so.
CHUCK NEWELL: That's right, so new name, GSI Environmental, but, you know,
the equations are still correct.
They're still good.
And they have these different tiers-- tier one, tier two,
tier three to do this.
So let's look at, Dave, let's look at tier one.
DAVID ADAMSON: Tier one's just concentration versus time data, right?
CHUCK NEWELL: Some empirical data and sort of this
is the way it works is you can download this spreadsheet
and then you have to do these three steps, and what are they?
DAVID ADAMSON: OK, well again, you've got concentration versus time data
that you're entering up into this table up in there.
You have to specify a cleanup level, so what you're trying to get to,
and then basically, your answer is going to come out of here.
CHUCK NEWELL: That's right.
So we have to put the dates in a date format.
You have your concentration mixture.
You've got your duplicates and your non-detects fixed up.
But you put them in there and then it draws this line
and it's a logarithmic extrapolation, a first order decay-type model.
In this case, they had these data and it says
that you're going to reach this clean up sort of goal in about four years,
with the data we're showing here.
It also shows you the 95% limits on this.
So if you have a lot of scatter in your data, that range may really increase.
But this first method, just these extrapolations
is the simplest way if you have a lot of data.
And we're actually going to talk a lot more about this
in some of the other lectures, that this is
how you can do this in terms of estimating remediation time frame,
collecting this particular model that's in there.
OK let's go back to SourceDK and look a little bit about the box models again.
And this type box model's used in BIOSCREEN
and a sort of a more powerful version of this is used in the REMChlor model,
but let's go through again some of the math and how this works.
So we talked about this in the last lecture,
but we're going to talk about mass balances with these source zones
to ask this question, how long do I have to wait to reach my cleanup goal?
So here is-- this idea here is this first order decay.
We've got this different mass that's in there.
We've got 10 kilograms in the source, which is 10,000 milligrams, right?
And what's the flow going through the source?
DAVID ADAMSON: Well, we got 500 liters per day going through that source.
CHUCK NEWELL: And then two milligrams per liter.
You just basically say, take the mass divided by the mass discharge,
in which case, how many years do you have to wait?
DAVID ADAMSON: Got a 10 year estimate here for the remediation time frame.
CHUCK NEWELL: That's right.
But I think as we talked last time, be careful.
This is not the way these models work, but if you're
going to sort of go through it, to sort of illustrate this,
we've sort of presenting this graphically right here.
DAVID ADAMSON: Yeah, and this looks pretty familiar.
I think we wrote a paper on this, right?
CHUCK NEWELL: That's right.
So sort of planning level models for source attenuation and MNA, but here's
this example.
I've got a certain source.
If I'm just not going to do any remediation to that source,
then it's going to be out there 40 years.
But then if I removed 70% of the mass so that the RF, the remaining fraction,
is 0.3.
DAVID ADAMSON: Yeah, well, I mean, we've assumed a step function,
so that changing that mass by 70% would change your remediation time
frame by 70%.
You go from 40 years to 12 years, that red line.
CHUCK NEWELL: So we sort of wrote this paper with this idea
that people are, in many cases, trying to compare
monitored natural attenuation.
Will it clean things up soon enough versus remediation?
And we want to really make people sure to understand the dynamics of that,
that if you do this remediation, in some cases,
you do not get something like this.
You do not get the 70% reduction in the remediation time frame.
And so here is just how the math works.
This RTF source depletion is in the top, the numerator.
RTF MNA is in here.
And for this simple step function, it's this remaining fraction and that's
what the ratios of these two frames.
But it's not like that in real life, Dave.
why not?
DAVID ADAMSON: Well, no, and again, we talked
about this in a previous lecture, that you
would expect to see more of a sort of a decay in that source term over time,
not a steep drop off.
So something more on the order of what you'd
see on that right hand panel, where you got that decay occurring at maybe
a first order rate that you can describe with that ks term that's showing there.
CHUCK NEWELL: OK, so more complicated, but more
realistic because it's sort of-- this is what we see out there in real life.
We don't see plumes that are clean, that are dirty for years and years
and then it gets to be a Wednesday, then on Thursday, pshh,
it completely cleans up.
It's this more slow decay and you need models like this
to sort of simulate this type stuff.
Now if you're doing this type of equation, here's that same curve.
It's mass discharge on the y-axis, time in years in the x-axis.
This is the mass discharge leaving that source.
We've got the two curves.
It's the same idea.
If I remove 70% of the mass, I get the red curve, but how does that effect
the remediation time frame?
DAVID ADAMSON: You no longer get sort of that equivalent
reduction in your remediation time frame,
so you look on the x-axis of where you're
going to get to-- without source depletion, 130 years,
with source depletion moving 70% of mass,
it'd still take you 103 years to get that concentration goal.
CHUCK NEWELL: So the key point is, don't think
that remediation is going to give you this complete benefit
that if you move x percent of the mass, it
reduces remediation time frame by this.
It's more complicated than that and these remediation time frame models
can really help you understand that and see what the relative benefit of some
of these remediations are.
OK so if you actually put this into an equation here, Dave,
it's the same idea.
What's the ratio of the remediation with source depletion on top?
What's this RTF, remediation time frame for MNA?
This is the reduction in the remediation time frame you get.
If you do that active remediation, what do those expressions look like?
DAVID ADAMSON: Well I mean we've got a ratio that's based on a first order
decay, so they've got these natural log terms in them,
but it's basically the ratio then of the concentration
goal over the original concentration on the top there divided by the remaining
fraction, and then you've got a denominator that
has sort of the natural log of the goal over the original concentration.
CHUCK NEWELL: OK, so it's the remaining fraction
is how much is left, how much you didn't get out from that active remediation.
The concentration goal is a CG over CO and then
I can just use this button right here to get the logarithm--
DAVID ADAMSON: OK.
CHUCK NEWELL: --and then, but wait-- there's no mass term in here.
Why's that?
DAVID ADAMSON: There's no mass term.
The mass just sort of cancels out in this case when you're doing it.
CHUCK NEWELL: Because in this case, you're
looking at the ratios of these two.
So you're sort of free from trying to do that dirty calculations with all
that uncertainty.
This just says if you got this first order decay-type process
and you remove that much of the mass and this
is the remaining fraction and this is how far you had to go, hey,
there's that ratio that's in there.
I think we've actually got this as a curve, right?
So here is sort of the graphics of the curve with three different things.
We talked about the step function in the first order model,
but let's just-- to go through example.
Y-axis is percent reduction of the remediation time
frame, how much benefit you do get from active remediation compared to MNA.
This is the reduction in the source mass, so one
minus this remaining fraction.
Give us an example, Dave.
DAVID ADAMSON: Well I mean if you're looking at first order model,
so that's that green line there, you could
imagine that maybe you reduce the source mass by 80%, something like that.
So where would you find yourself then on the percent reduction curve and time
frame?
CHUCK NEWELL: Looks like your mediation time frame
is reduced by about, what, 15%?
DAVID ADAMSON: Yeah, so maybe not that much really.
CHUCK NEWELL: From 100 years to 85 years.
But this is partly dependent on the C goal to the C original.
This says, in this case, you have four orders of magnitude to go.
This sort of tells you how much of that tail on the end you have to deal with,
but that's difficult to remove.
Now just one last thing is that the REMChlor model, the REMFuel model
have the same sort of box model, but they got an extra dial in here
that we talked about in the last lecture.
Remember what the name of that variable is?
DAVID ADAMSON: Well it's gamma, and it's written right there,
so I can remember that.
CHUCK NEWELL: OK.
And you can turn that dial so you get these sort of instead
of just a first order decay-type thing, you
can get these different characteristics.
And so there's these idea that if you have a site that's really old,
there's not much NAPL there, but you
think it's dominated by matrix diffusion,
maybe turn that dial so you got a gamma that's maybe closer to two
and you can then get a better simulation of this.
But REMChlor, REMFuel, powerful tools to look at remediation time frame for MNA
and then you go in there and reach into the heart of that dragon,
pull out some of the mass, and then see what that change in remediation
time frame is, what benefit you get.
OK, well shall we wrap up?
And I guess the key points, there are several of these remediation time frame
models that are available.
DAVID ADAMSON: And then that time frame reduction's
not going to necessarily be linear related to the amount of mass
that you removed.
CHUCK NEWELL: And a key important point that we've tried to bring out
and if you don't believe it, run the models
and you can sort of see how this works.
But one other note is there's considerable uncertainty
in these remediation time frame estimates, so
have to understand that going in.
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