Today’s webinar will focus on reducing investigation and remediation costs using high resolution tools and 3D technology.

With that, I’d like to introduce our presenters for today.

We are pleased to have with us Jim Depa, Principal Geologist at Swenson, Marzak, and Associates.

Jim Deppa has more than 18 years of experience in the environmental consulting field and specializes in creating 3D, statistically-based visualizations from subsurface data.

He has created data deliverables on over 400 environmental investigation projects in 44 states and seven countries, from routine 3D visuals to multimillion-dollar design remedies.

Specifically, he has helped design over a half dozen thermal remediation systems, calculated the in-situ contaminant mass in soil from one of the largest petroleum spills in the United States history, and provided exhibits for four separate environmental lawsuits.

We’re also pleased to have with us today Juan Rincón Rodríguez, project manager in the Research and Development Department at Regenesis.

Juan Rico Rodriguez leads the Flux Tracer program and oversees laboratory services supporting advanced environmental remediation technologies at Regenesis.

He previously worked in the Remediation Services Division, RRS, as an environmental staff scientist where he conducted in-situ remedial applications across the United States, applying technologies such as in-situ chemical reduction, in situ chemical oxidation, biological enhancement, and sorption to address a range of contaminants in soil and groundwater, including petroleum hydrocarbons, chlorinated solvents, and PFAS.

All right, that concludes our introduction.

So now I will hand things over to Jim Depa to get us started.

Thanks, Dane.

And as Dane mentioned, I’m gonna discuss both sort of how to reduce investigation or mediation costs using both high resolution tools and the 3D modeling technology associated with that. So first, the drilling tools.

This is for collecting the subsurface data.

There are multiple different types of tools, but these are the three tools that are sort of most used.

The membrane interface probe or MIP, which is used for collecting data on bottle contaminants in the subsurface.

The groundwater profiling tool, which is used to collect discrete groundwater samples from pretty any constituent in the groundwater, and the optical image profiler or OIP, which is for investigating free-phase LNAPL, typically petroleum or diesel fuel in the subsurface.

Importantly, all three of these tools also collect geologic data using the hydraulic profiling tool or HPT.

That’s why you see these referred to as sometimes the MIHPT or the OIHPT.

It’s a really kind of somewhat new technology that injects water into the subsurface as the tools being advanced and measures the amount of pressure required to inject that water.

So if you’re in clay soils the pressure is going to be higher and if you’re in sand soils it’s going to be lower.

So not only do you get data about the contaminants in the subsurface but also the high resolution picture of the geology.

But there are some limitations to these tools.

First and foremost the logs alone might not be enough information.

As you see on the right here is a log from the optical image profiler tool, or OIP.

And on that third graph in red, you see where we have response, and that’s where there is LNAPL, or free-phase petroleum in the subsurface at about 20 feet in depth.

And then you could see the HPT pressure, where we are seeing some spikes here, we’re probably hitting some clay seams, and where this LNAPL is in relation to, you know, what the geology is in the subsurface.

Interestingly, you could also calculate the estimated hydraulic conductivity of the subsurface using the HPT pressure, but it’s really just an estimation.

But these logs are great, but they might not be enough to sort of get a full picture.

Also, there’s no data in between the borings.

So you might see, you know, on a surface map where you have all these borings located, But when you model it in 3D, you can kind of connect the dots and see how all the contamination kind of relates to one another.

And also there might be some significant elevation differences at your site.

Depending on what depth the contamination is located, it really depends on obviously what the ground surface is.

So that needs to be taken into account when building these three-dimensional models.

Here’s an example 3D model of membrane interface probe data or MIP data at a site in Indianapolis.

Only 18 points done here, but it really shows the picture of where the spill occurred and where it’s migrating.

And when we kind of turn this in 3D, we can see where the plume is.

There was a tank in the subsurface there and how it migrated in the subsurface.

Now, all of these tools also collect electrical conductivity data, or EC, as you see on the chart there.

The electrical conductivity of the subsurface soils is typically lower in sandier soils and higher in clay soils.

So at this particular site in Indianapolis, we’re seeing a lot of clay soils here at the subsurface.

We see some fill in the first four to five feet, but it’s pretty much all clay.

Then, interestingly enough, in some locations when we get to 20 feet, we start seeing more lower EC readings and you’re getting into the sandy soils, which is extremely important if you’re designing an injection project, where if you were to put injection points too deep into the sand soils, it’s really just going to take off and you’re going to be injecting a lot of product where it doesn’t need to be.

So with just 18 points, you can kind of create a 3D visual like this to show where you have impacts, where it’s migrating and where you can best inject to get the most bang for your buck, which we’ll kind of talk about later in the presentation.

You could also model the data from the hydraulic profiling tool.

Here’s a site in Colorado, and I’ll just kind of play this animation here.

We’re showing the electro-conductivity of the subsurface soil, the flow rate of the injected water by the HPT tool, and then the HPT pressure of that injected water.

And you could kind of see how each of these compare to each other, and we’ll kind of zoom in there and kind of show each of these layers once this kind of comes back to the beginning here.

At the top, we had some sand fill, which is a little easier to see in the HPT pressure, and then silty clay, a sand layer, and then claystone.

I guess you really could call it bedrock here at this site.

So you can kind of see how these different parameters kind of relate to each other just from this HBT tool.

And kind of as an aside here, you can also create 3D models from laboratory analytical data.

This is another site in Indianapolis where there was a spill from some ASTs and interestingly it didn’t actually occur at the ASTs.

It occurred where they filled and unloaded the ASTs and that’s kind of the source there where they plugged in hoses, and when they were kind of filled, they unreleased the hoses and everything for probably dozens of years.

Everything in the hose just kind of spilled right at the source there.

But just kind of showing here that soil analytical data or groundwater analytical data for that matter can be modeled in 3D to kind of create these 3D visuals.

And in this case, this is mostly PCE in soil and we’re showing where the plume core is there in red.

The periphery of the plume in yellow, everything about one milligram per kilogram.

Kind of a foot there where that’s where the groundwater table is and the contamination is in the capillary fringe and it’s kind of migrating with groundwater. And then just kind of going back up to the to the surface there.

Just kind of showing that 3D modeling technology is not only useful for the direct image tools, the MIP, the OIP, but also soil or groundwater or really any kind of media. So a little bit of the how.

There’s some good data resources out there that can be used to help create these visuals.

The first, the USGS has something called the 3D Elevation Program or 3DEP.

This is free LiDAR data across almost the entire contiguous United States.

And this is really high res data, all free to download.

The caveat is it’s usually only updated every five to eight years in most locations.

So your data that you collect might be a little out of date, especially if there’s been some land development But it’s always good to check because sometimes there hasn’t been really any land development And the data is from five years ago is still good and free is always best Another data resource out there is something called near map This was an Australian company.

I think it just got bought out by a company here in the US But they have get really high-res aerial photos of pretty much everywhere in the continuous United States Not a free service, but it’s it’s fairly affordable Really high-res imagery and I’m talking as so good that you could actually see monitoring walls at your site You could zoom in and actually see where there might be monitoring walls before you actually go out there I think just about everybody knows about Google Earth great imagery, but this is even better and then finally You could also get, obviously, drone surveying done if you need high-resolution imagery at your site.

We provide this service, kind of something that we do for both high-res imagery and LIDAR data if these two services aren’t enough.

And then a little bit about the data processing.

So one of the main programs I use to process data, you guys probably know it, Excel, It’s written in Visual Basic, but you can write some code to basically create macros so that every data that comes in, you can play this macro and it basically allows you to compile data and build input files very efficiently that can be used in the modeling programs.

I’m not a coder by any stretch of the imagination, but there is a lot of good information out there you can find online to help you get 95% of the way there, and kind of get your way through to help create these macros to really make your processes really efficient.

And then once you have those input files, I use a program called Earth Volumetric Studio by CTECH.

It’s this modular-based program that creates these 3D visuals that you saw before.

There’s a lot of coding involved in this program, but you do not need no coding to learn the program.

Basically, you put data into these modules, and it’s passed into different modules, as you can see at the top there, that does very specific things than outputs to a viewer.

So this program basically allows you to process, model, and visualize datasets repeatedly.

I’ve got a dozen different applications to model different types of data so that I don’t have to reinvent the wheel each time I get a new project.

And finally, Python scripting, and just like how Excel is written in Visual Basic, EVS is written in Python.

and it actually writes some really short Python scripts to allow you to create data deliverables pretty much automatically.

And that script up there is actually takes a 3D model and slices it every foot, actually here it says the interval is a half a foot, and it’ll generate a map every half a foot.

So once the model is built, I could press a button and get hundreds of maps pretty much in an instant.

And then if there’s a change, basically do it again and not have to kind of painfully create those maps one by one.

So this is just a little bit of the how of these models are created.

Now the why.

First and foremost, it’s to improve communication of the subsurface data.

And I hope those 3D visuals really helped kind of show that initially.

But something that’s really been improved lately is this 3D scene viewer by CTECH.

And you could actually go to that website right now that’s in the top bar up there.

and you’ll get to this viewer right here.

And whenever I make a 3D visual, I deliver it to the client as a CTEK web scene or.ctws file.

You basically just drag and drop this file into this scene viewer.

And you basically can manipulate the model, zoom in and out, turn it on and off various parameters.

You don’t need to install any software.

This is a huge change from five or 10 years ago where it’s a view of 3D model.

You need to download software And people either couldn’t or wouldn’t do that.

And a lot of 3D models I know I created never even saw the light of day.

And that’s kind of changed now with the advent of this.

And I’ll kind of show a demo of what that looks like.

Essentially, if you just drag and drop one of these C-TECH web scenes into this viewer, here’s what you’ll get.

Here’s one from some OIP data.

And I’ll just play it.

I’m just basically recording myself operating the 3D visual.

I’m rotating and I’ll play back up to one, and just rotating the viewer back up to a top view where we can see the footprint of the 3D model.

And this viewer also allows you to create really detailed and useful deliverables.

So not just the 3D model, maps, cross-sections, and other useful things.

Here’s basically a snapshot of that 3D visual with where the OIP footprint is at, so we could kind of delineate where those L-mapple impacts are in relation to the tanks and the borings.

You can also create cross sections.

Basically, draw any transect through that 3D model, look at it from a side view, and see both where there’s fluorescence in the borings and the HPT pressure at each one of those locations.

And we could see where the HPT pressure is lower in blue or likely in sandier soils, and where it’s higher in gray where we’ll likely have clayey soils, and see how that L-NAPL fluorescence relates to that.

And you can kind of connect the dots and see where that L-NAPL is located.

You can also do this with MIP data from the membrane interface probe.

On the left, we have the PID screening data from the membrane interface probe, where we put about 25 or so MIP points.

Then we actually went back to the site after we collected this screening data to collect analytical data in the subsurface.

the contaminant here was TCE.

And you could really see how the PID really aligns to where we found the TCE in the subsurface soils.

And in fact we were able to kind of correlate this at this site where the PID measures in microvolts, kind of electrical voltage, where about a 10 ,000 microvolts equaled one ppm of TCE.

Now this can’t be done all the time, especially if you have commingled plumes with different contaminants, if you’ve got daughter products, or if you have really complex geologies, sometimes the PID will see things in the clay a little stronger than they do in the sand.

This site was a little unique that it was pretty much straight clay and all TCE, so we were able to really correlate it well.

Number three, reduce investigation costs by eliminating monitoring wells.

And I have an example site to show that here where we had a gas station with 30 monitoring wells.

Now, we did not put in these 30 monitoring wells.

This is obviously overkill.

I’m not sure who the consultant was that did this, but this is way too many monitoring wells.

And even after 30 monitoring wells, they didn’t have a good idea of kind of what the plume looked like, how it was migrating offsite to that river.

So we actually did six direct imaging points, six MIHPT borings in a north-south line along that Eastern property boundary.

And you could see the maximum PID at each of those locations.

And right away that tells you kind of where it’s migrating offsite.

And you could see that fourth boring down is sort of ground zero.

But obviously we need to look at it in a cross section view from North to South.

And we could really see how that contamination is migrating, where it is, what depth and in what geology it is.

So we can pinpoint where we want to put a monitoring well or injection wells for that matter if we wanted to treat this plume.

But the point that I’m trying to make here is the costs for a typical monitoring well are not just the installation and materials for the monitoring well, which is probably a little low there at $1 ,500 now with the inflation the way it is.

But just from quarterly sampling with the labor, analytical sampling, you’re talking $2 ,700 to $3 ,000 a year for the install and sampling.

And as we all know, once you install a monitoring well, A lot of times, state agencies want you to sample it into perpetuity until the site’s clean.

So if you’re talking 10 years of sampling, the cost can easily run you $16 ,000.

So it’s best to be thought that if this high-resolution investigation helps you install two or less monitoring wells, it’s going to pay for itself over time.

And I think that’s a really important point to make to clients to kind of try to sell the services.

Obviously, it’s not free to do these MIP investigations, the mode time, the data collection, the model.

But if it helps you to minimize the amount of monitoring wells that you need, you’re going to save money.

Fourth is to assist in remedial technology selection, because the investigation costs are obviously a huge piece.

But anyone who’s done a lot of remediation projects knows that probably 80 to 90 plus percent of the money spent cleaning a site is in the remediation and not in the investigation.

So any kind of technology that could help you select and design a good remediation system is worth its weight in gold.

So one of the first tools you could do is you could very quickly with one of those macros that we have is essentially show the response curve with how much volume of impacted really quickly.

And so you see on the x-axis this is the XSD response from a MIP investigation of a 3D model.

The XSD is essentially the halogen specific detector, not sure what’s called XSD but it stands for halogen specific detector, basically just chlorinated solvents.

It’s not going to respond to petroleum but it will respond to anything that’s halogenated with a chlorine fluorine atom in it.

And on the y-axis is a volume of impacted soil.

So right away you could see how much volume of soil is in each of these locations and this is kind of what the 3d model looks like at 30 ,000 what that looks like at I think that’s about 15,000 how much soil that is and at that’s probably about 5 ,000 with what that is.

So this gives you a really good sense of sort of how big of a problem we talking when we model this in 3D how much soil is impacted at each of these levels and this is something that can be created literally in kind of 15 or 20 minutes.

We can also see how the contamination is distributed geologically by taking that 3D model and dissecting it into bins of the HPT injection pressure.

So what you see on the x-axis there is injection pressures at 10 psi intervals where on the left you’re in your soils and if you were to add up all of these numbers here you’ll get to a hundred percent.

So it’s essentially showing how your contamination is distributed geologically and in this particular example the majority of the PID response is in coarser grain sand units.

This is typically good news because it’s typically easier and more cost effective to remediate contamination in sand units and those types of methods could be a soil vapor extraction system, a pump and treat system, a permeable reactive barrier, or lower pressure injections.

Conversely, this is from another site where the contamination is clearly distributed more, a little bit to the finer grain section, and shows that there’s more response in the clayier units.

This is typically not as good a news when there’s more contamination in the clay units, because it’s harder to get out of the clay.

There’s probably a spill that was older, that had time to get into those fine grain units.

So it’s harder to extract it.

But there are methods for that, like soil blending or in situ solidification or thermal technologies or higher pressure injections.

The point is to have the best technology, you kind of need to know how your contamination is distributed geologically.

And this helps that, it gives you a really good idea.

And then finally, to optimize remedial design.

Because once again, the money is spent in remediation and not in the investigation.

And I’ll give a kind of three specific examples here for L-NAPL extraction, for Permanent Reactive Barriers or PRBs, and for targeted injection plans.

First we’ll go back to that L-NAPL model where we had our optical image profiler which showed where we had free-phase L-NAPL in the subsurface.

And this just gives us a good idea of where we could target extraction wells, not only locations but what depths we could install them in to sort of get the most bang for our buck.

Because if you were to install an extraction well too shallow or too deep, you’re likely not going to get any LNAPL from it.

So obviously, once you get the OIP data and model it in 3D, this could help you design where you want to place extraction wells.

We could improve the design of permeable reactive barriers.

This LNAPL site that I’ve kind of showed a few times, we actually did do a PID investigation, a MIP investigation following the OIP, and you could see how the plume is migrating off-site.

And at this particular site, we kind of passed a cross-section through A to A prime, and it shows there from MIP 17, 18, and 19, and it shows a fairly small transect of how this plume is migrating off-site in a fairly small area, only about 85 feet by 8 feet.

So if the site allowed, if you didn’t have to kind of remediate anything at the site, if you’re just trying to protect off-site resources, you know, it’s a pretty small area where you’d have to install this PRB to sort of get the most for your money if you didn’t have to treat it on-site.

So once again the MIP can allow that to, you know, target where you want to install these.

And the finally the creation of targeted injection plans.

This is some more membrane interface probe data.

This was at a parking lot, but it was a former gas station.

And we’ll just kind of play this animation here where we can see where we have our highest PID response in those dark purples and pinks.

When you model it in 3D, it starts to really kind of reveal itself.

There’s the plume core at 15 million microvolts, seven million, five, two and a half, and then down to one, which was determined as the extent of soil that was was going to be treated.

This also shows where there’s a hydraulic profiling data, the geologic data, where we have our clay soils in gray and the sandy soils in blue.

And now we’re going to show where our simulated injection points are targeting that 1 million PID plume at 10 foot centers and at half a foot increments in depth. And we could see how those points line up exactly with the plume.

And then we could also extract that HPT data to see what the hydraulic HPT pressure is at each one of those locations, too.

We could see there’s a lot of gray points in there where it’s a lot of clay soils that has that contamination in it.

And we could look at this from an overhead view to see where our injection points are.

If we wanted to target that one million plume at 10 foot centers, it would be 98 injection points.

or if we are limited if we you know sometimes there’s not an infinite amount of money to do everywhere we wanted if we wanted to just you know had 40 or 30 points this would be the most bang for your buck to kind of go where the targeted PID response is the highest and we you know to target that five million interval we’d need 40 points but these 3d visuals are nice and the maps are nice but when you get out to the field and do an injection you need that data and you can extract that data from the 3D model to have basically every injection location, the ID, the easting and northing, the coordinates of those points, what the ground surface is at that point, what depth you’re targeting, the elevation and the HVT pressure and PID at each one of those locations.

So you kind of have a plan that could be put into practice, you know, even before you’re out there and have a good idea of what you’re going to find, but even before you start injecting at every location.

So I’ve created a handful of these plans for other contractors, and they seem to have gone pretty well.

Finally, the cost, everybody always asks me about the cost of visualizing this data.

Now, this is the cost to do 10 days of field work.

If you’re out there doing an OIP investigation for up to 10 days, a 3D visual can be created for $1 ,900.

And this gives you the interactive 3D web scene, plan view maps, a couple sets of cross sections, the volume calculations of those plumes like you saw in that graph, and compiled Excel data from all of the logs.

This does not give you the injection plan or any other things that we’ve seen today.

Basically, just sort of what’s kind of listed here.

But people are usually surprised at the cost, but it’s because of the stuff that I discussed earlier, the programs and the code that really helps streamline and create these.

And this could be done for larger projects too, just each additional week of data out there collecting the data, it’s going to be an additional $500.

And turnaround time is usually pretty quick, I could usually get the data and put out a model in three days, sometimes quicker if I’m not backlogged.

And finally, there’s the last slide here, this is not something that we’ve done once or twice.

We’ve done 406 projects from all across the country in I think 44 states now. It’s kind of an old hat.

I love getting projects in and it’s been going pretty well. So yeah thank you very much for listening.

If you have any questions feel free to email or give me a call and at this point I’d like to pass it over to Juan for his presentation on flux tracers.

Thank you much. Thank you Jim and thank you everyone for joining this webinar.

I’m excited to share an analysis on mass flux data towards building better conceptual site models and at the end better remedial applications. So this presentation contains five sections.

We’ll first dive into mass flux, how to calculate it with available data, what are the advantages and utility of approach, but also what is the disadvantage while we explore measuring directly mass flux through passive sampling.

We’ll then utilize this data to build conceptual site models, and we want to understand first where is the groundwater moving, but also where is the contaminant mass moving.

Then we’ll look at data from various sites where we collected both Darcy velocity and mass flux data in a vertical profile, and we analyze correlations, if any.

We’ll then look at one specific site where we use mass flux data to first develop a targeted and effective remedial approach and some conclusions by the end.

To introduce indirect methods to estimate mass flux, we can first discuss active sampling.

So active sampling is a very common technique to take a sample, usually over a few hours in a certain discrete depth.

Once you take the sample, you send it to the lab, analyze concentrations.

These concentrations are pretty useful to determine a few things, and I’ll mention a couple here.

First, determine if your well is under compliance, how it relates to the MCLs, and second, to delineate your plume.

You want to know where your plume is, where are the boundaries, and how it’s moving over the years.

You can of course calculate mass flux with this data once you combine these with Darcy velocity.

So Darcy velocity is usually calculated through either a slug or pump tests plus the gradients.

Once you have these, you can calculate mass flux data.

Now, we can introduce also passive sampling for mass flux calculations.

And we are not the first ones introducing this term.

We are basing this on over 20 years of external research where the principle has been the same.

So you have an adsorbent media that has been preloaded with tracers.

This adsorbent media is displaced or deployed in existing monitoring wells, once the groundwater starts flowing through, first the mass is captured.

This is how we calculate mass flux, but at the same time the tracers are being displaced at different rates. Through this displacement you can calculate the Darcy velocity.

Here at Regenesis we adopted this principle, developed our own research, and developed flux tracers.

So flux tracers is an easy-to-deployed canister that are attached to each other.

We send them easy-to-deployed and ready to be deployed by the personnel in the field.

Once they are deployed for over two weeks, they are retrieved back to us where we break them down, analyze, and generate a report.

So there are two main advantages other than this is, of course, a direct method to estimate mass flux.

The first one being that we are generating data at every foot that improves the resolution of the data, and the second one is that this method is deployed for over two weeks.

Therefore, we are providing a cumulative average value.

That means that, of course, it’s not only a couple of hours, such as in active sampling, but here we are developing a better visualization and resolution of the mass flux.

We can also estimate flux derived concentration from this method, that is dividing the mass flux over the Darcy velocity But bear in mind that if you are trying to compare these flux derived concentrations to concentrations obtained through active sampling, there can be effects in the active sampling, such as, of course, you are taking only one sample at a single depth or you are undergoing dilution effects.

The way it has been tested in the past is performing discrete active sampling where we are taking samples at different depths.

And that has shown pretty good correlation with the passive sampling method.

Now, here at Regenesis, we use indirect methods to perform or do preliminary designs, but when we are jumping into a final design, we really want to see data with high quality.

That’s why we prefer direct methods.

Towards the conceptual side model, what you can see here is based on the ITRC guidance of 2010.

We want to understand, of course, where the groundwater is moving, and the groundwater is moving, of course, through the most permeable zone.

And we can see here three layers.

First, a low Darcy, medium, and high.

Of course, groundwater is moving mostly towards a high Darcy velocity.

But we also want to understand where the contaminants are moving.

And contaminants, we will tend to think, yes, they are moving towards where the water is moving, but they also undergo different interactions that can affect this behavior.

For example, here you can see that there is a secondary source zone at the bottom.

And the secondary is providing contaminant mass towards the middle zone and then towards the high zone.

So it is easy to understand these when you have these clearly defined zones.

But this can be a small layer that is difficult to identify, and that’s where analyzing field data becomes quite relevant.

So there are two considerations that we want to do in including conceptual site models.

First is the distribution.

We want to understand where the mass is moving, and this will help us to better use our resources, reduce cost, at the end.

And also we want to identify where is the maximum peaks, where are the flux maximum.

And this helps to first estimate the dosing, and the dosing will be critical to define the longevity of a given barrier.

This is an example where the contaminants of concern are PC, TCE, and C-DCE.

As you can see, there was an active sampling performed six months before, where the most predominant contaminant is PCE at a concentration of 4,500 micrograms per liter.

We performed the flux tracer study.

This is in one of the wells.

You can see in the y-axis, it’s depth, lower x-axis is mass flux in units of mass per area per time, and the top x-axis corresponds to the Darcy velocity in units of centimeters per day.

If you look at the red triangles, you’ll see the Darcy varies from one up to four, close to four.

There are two important spikes at the top and towards the 19 feet, and these are the only spikes that you see.

Other than that, the groundwater velocity or Darcy velocity seems to be pretty homogeneous.

Now looking at the mass flux, for TCE, cis-DCE, pretty low mass flux compared to the PCE.

and when you are looking at the PCE of course it’s evident this high spike that you observe at a depth of about 18 feet.

Now what we want to see is understand and analyze this peak, what this implies towards a remedial design, and for that purpose we look at what is the maximum value. So the maximum value of this peak is about 1200 milligrams per square meter per day.

This is carrying about 70% of the PCE mass flux in only 20% of the screen section.

So once we know this data, we can implement this data into our design tools, such as PlumeForce, to estimate what is the best amendment, what is the best dosing, and of course, once we know this data, we also know where to inject it.

The other analysis that we did is to compare, okay, what is the increase in Darcy velocity, increasing here by a factor of 3, and what is the increase in PCE mass flux?

Increasing here by a factor of 20.

So what this is saying is that a small increase in Darcy velocity, in this case, in this site, created a big increase in PCE mass flux that will be pretty difficult to estimate unless you go out to the field and collect this data.

We also wanted to compare the indirect calculation of mass flux that was done with the help of the active sampling.

So The value for the active sampling was around 4 ,500 micrograms per liter of PCE, and we grabbed a Darcy velocity from this flux tracer study of about 4 centimeters per day.

With that, the final value is 180 milligrams per square meter per day.

If we compare that with our high peak of about 1 ,200, we can see the big difference.

And there are two hypotheses that we can draw here.

The first one is, of course, active sampling was done at a single depth.

The single depth could have been a depth of 20 feet, 21, 22.

If you sample at those depths, you can see the mass flux could be around the value that we are calculating.

The other possibility is that there are dilution effects.

There are movements in the well when it’s empty or while it was undergoing the active sampling.

And if you account for dilution from the 1 ,200 milligram per square meter per day in a 15-foot well screen, a 2-inch pipe, you will obtain a value similar to the 200 range.

Of course, I didn’t mention before, but the repercussion of utilizing either of the two values in the remedial design will be either underdosing or dosing the right amendment that you want to use for that.

These are other examples where for Site 1 and Site 2, we are also looking at CBOC sites.

These are screens, usually one screen or multiple screens in the case of Site 1.

What you see is how the variation of Darcy velocity.

We are seeing Darcy velocities from 1 up to 10 in the case of Site 1.

And the increase where you see these peaks is from a factor of 2 to a factor of 6.

But, still, there is a big peak of TCE in this case of a factor of 7 up to a factor of 20 higher in Site 1 and Site 2.

What I want you to see also is that it doesn’t mean that every time you see a high increase in Darcy velocity, you see a high increase in mass flux, but there are multiple factors and these will vary from site to site analyzing the data and accounting for these variations.

In either of these two sites, more than 60% of the TCE mass flux is moving in a pretty small zone that you have to account for.

Now, for CBOCs, there is important to account that we are considering destructive and disruptive technologies.

But for PFAS, it becomes even more important to look at this data and the details as we are only accounting for disruptive technologies.

And what you can see inside four is how this slow variation in Darcy velocity, less than a factor of two, created a significant peak that is 20 times higher for PFOS, carrying more than 70% of the mass flux.

In this case, we accounted for this peak, designed the right dose in an injected with a targeted approach.

Now, this is our case study. It’s the Mortos-Vinjar Airport.

Here we have an AFFF testing area where there was a pilot, you’ll see the red line corresponds to the pilot or the barrier.

And we have a monitoring well that is close to this.

This project was with Tetra Tech and we wanted to, of course, inject Plum Stop here.

The first design was calling for a 20 feet vertical thickness injection.

And what we did after that is to deploy flux tracers into monitoring wells.

So, the results of the flux tracers show that this is the top 10 feet of the flux tracer in one of the monitoring wells, and this is carrying more than 95% of one of the most predominant species in this case, 6-2-FDS.

What we did after this is analyzing what is the mass flux being carried in the bottom 10 feet, and after analyzing that most of the mass flux is being carried in the top 10 feet, we reduce the vertical thickness from 20 feet to 10 feet.

That means redistributing the excess carbon to the side and making the barrier longer.

This will allow for better monitoring.

As a result, after one year of performance, more than 99% of the concentration was reduced.

To wrap up the presentation, there are three take-home messages.

The first one is that veritable barriers, they don’t address concentrations.

they are always addressing mass flux, then significant variations in mass flux are not always aligned or correlated with significant variations in the RC velocity.

There can be homogeneous sites where you wouldn’t expect these high peaks in mass flux and you still see them.

That’s where the utility of this.

And second and probably the most important is that understanding what are the values for these high peaks will determine the dosing and that at the end will ensure the longevity of a barrier.