Hello and welcome everyone. My name is Dane Menke. I am the digital marketing manager here at Regenesis and LandScience. Before we get started, I have just a few administrative items to cover. Since we’re trying to keep this under an hour, today’s presentation will be conducted with the audience audio settings on mute. This will minimize unwanted background noise from the large number of participants joining us today. If the webinar or audio quality degrades, please try refreshing your browser. If that does not fix the issue, please disconnect and repeat the original login steps to rejoin the webcast. If you have a question, we encourage you to ask it using the question feature located on the webinar panel. We’ll collect your questions and do our best to answer them at the end of the presentation. If we don’t address your question within the time permitting, we’ll make an effort to follow up with you after the webinar. We are recording this webinar, and a link to the recording will be emailed to you once it is available. In order to continue to sponsor events that are of value and worthy of your time, we will be sending out a brief survey following the webinar to get your feedback.

Today’s presentation will discuss PFAS criteria in groundwater. With that, I’d like to introduce our presenters for today. We are pleased to have with us Jeff Hale, Practice Leader Emerging Contaminants at Woodard and Curran. Jeff provides consultation, strategy, and direction for challenging environmental and resources issues with emphasis on PFAS, emerging contaminants, complex sites, remediation, liability management, and environmental forensics. He is a thought leader for emerging environmental issues, having served on expert panels and chaired multiple working groups on environmental issues ranging from hydraulic fracturing to emerging contaminants. Jeff’s core technical expertise includes contaminant hydrogeology, fate and transport, and bedrock characterization.

We’re also pleased to have with us today Dr. Kristin Thoreson, Vice President of Quality and Process Improvement at Regenesis. Dr. Thoreson manages and evaluates the company-wide quality processes and continuous improvement activities at Regenesis to ensure consistency and reliability in product quality, performance, and service. Prior to this role, Dr. Thoreson served as the Vice President of Research and Development for Regenesis and Land Science, where She led the development and implementation of numerous products and technologies. Dr. Thoreson is an inventor on several issued patents and is a frequent speaker at scientific industry events. All right, that concludes our introduction, so now I will hand things over to Jeff Hale to get us started.

Thanks for the intro, Dane. I’m pleased to be speaking to you today about the predictability, sensitivity, and practicability of attaining PFAS criteria in groundwater. So the presentation will have three main parts. First, I’ll talk about empirical PFAS fate and transport projections. Then we’ll use that to show estimating PFAS attainment distances and receptor vulnerability. And I’ll also cover statistical distribution of PFAS relative to regulatory criteria. And then we’ll wrap it up and pull it together with some conclusions. So the first item is empirical PFAS fate and transport projections. There’s been a lot of talk and research about what we know and what we don’t know about PFAS, particularly PFAS fate and transport. It’s, you know, some of the information out there is, you know, speculative about the unique characteristics of PFAS and how they might behave in the environment, but we’re also accumulating a lot of PFAS data as an industry that are being stored in publicly accessible databases.

So I think we’ve accumulated enough data that we can empirically determine what we might expect from a PFAS plume as far as its behavior and migration distances. And that’s what we’ll be talking about in this section of the presentation. So, as an example, here are a few diagrams of PFOS concentration in groundwater versus distance. On the left-hand side, we have an example from an Air Force base, and the left-hand graph is for PFOS, and we’re basically showing the concentration of PFOS versus distance. And on the right-hand side, similarly, this is a graph for PFOA, P-F-O-A, on the y-axis versus distance on the x-axis and on the right hand graph this is actually an amalgamation of data from four different sites and we’ve normalized the source concentration for each of those sites to one in order to put them on the same graph and we’ll be exploring that graph in further detail as we go but just as an illustration you can see that there’s a pretty fit to this function of concentration versus distance for some of these PFAS data from different sites.

So the point here is PFAS data sets exhibit predictable concentrations versus distance. So let’s explore this a little further. As I said in the intro, you know, there’s a lot of PFAS data being collected, especially from groundwater, and a lot of cases that data is being stored and housed in various publicly accessible databases. So we’re going to look at data from the state of New Hampshire as an example today. And the New Hampshire DES PFAS map and database provides a rich data source. So in this example, we have a map from the New Hampshire PFAS database. And the red dots on that map represent the combined concentration of PFOA and PFOS, greater than 70 ppt, which is the current EPA health advisory level. Yellow dots on the map represent where individual PFAS may be above New Hampshire’s ambient groundwater quality standards for those individual PFAS. And the green dots, fortunately, there’s quite a few green dots, but that indicates where individual PFAS are less than respective ambient groundwater quality standards in New Hampshire.

So the database includes data for PFOA, PFOS, PFHXS, PFNA, as well as total PFAS. The sources to which these data are attributed include landfills, POTWs, industrial facilities, fire stations, airports, and a former Air Force base. And there are data for a variety of media including groundwater, surface water, and other. So I inspected this map, zooming in and out all across the state looking for distinct concentration gradients emanating from a distinct or particular source. And I was able to identify four examples where there was a distinct source and a distinct concentration gradient emanating from that source is an empirical basis to see what’s going on with PFAS migration in the state of New Hampshire. So of these facilities, we’ve got three landfills and a POTW, Publicly Owned Treatment Works. And I describe these as what I call end of PFAS life cycle waste facilities. And to the prior point, I think that’s important because there is some expectation that precursor transformation at these type of end of life cycle facilities may have already occurred. The other point I wanted to make is every one of these sites had relatively similar conditions or at least we could infer similar conditions such as similar physical setting, similar aquifer type, similar source type, and proximity to a water body where groundwater would be expected to discharge. So this is the select group of sites that’s going to be the basis for our example.

So let me give you a look at a couple of the sites that make up the data set that we will be evaluating. So this is a POTW, and I’ll animate what I consider to be the groundwater migration pathway. So we see here we have a source concentration of 838 nanograms per liter. This is for PFOA, and we’ll be using PFOA as an example for much of the presentation. But there is a pretty distinctive concentration gradient along an inferred groundwater flow path towards the river that’s nearby. Here’s an example of one of the landfills that I looked at, and again we have a source concentration near the landfill, inferred groundwater flow towards a surface water body, and a handful of monitoring wells between the two. So in In this instance, the monitoring wells do not fall as cleanly on the inferred groundwater flow path, but there is a discernible concentration gradient in that direction. But to verify and validate that we were looking in the right direction as far as contaminant transport and groundwater migration, I looked at the ratio of PFOA to PFOS here.

So PFOA and PFOS have different sorption coefficients. PFOS is more sorptive than PFOA, so as they migrate together in groundwater, their ratio is expected to change with an increase in PFOA relative to PFOS. So this graph shows the PFOA to PFOS ratio for the monitoring wells we just looked at on the map, and you can see there’s a linear relationship and a strong fit to changing PFOA to PFAS ratio that assures me that, you know, we were looking along the right groundwater flow direction. And just to further explain that, this is a graph that I like to show. It is the log KOC for a variety of PFAS, so an absorption coefficient for a variety of PFAS. This data derives from ITRC’s PFAS technical and regulatory guidance document, and I point out here PFOS has a higher sorption coefficient than PFOA, thus explaining why their ratio changes as they move through groundwater along the same path. So let’s get back into the basis for this example empirical model, and this is a graph I started to show towards the beginning, and again we’re looking at a normalized PFOA concentration for four sites versus distance from source. And again, since we’re putting multiple sites together with different source concentrations, we normalize those source concentrations to one.

So as I said, we normalize source concentrations to one, which is C0. The source concentrations range from 63.7 to 838 nanograms per liter, and the 838 nanograms per liter for the one wastewater treatment plant, or POTW. As I noted before, this is represented by three landfills and one POTW, and it gives us 15 samples. So a lot of data in the data set, like I said, there are four sites that I could identify that, you know, we have a really know what’s going on here, distinct source, concentration gradient, can infer groundwater flow path, so more samples would be better, but I think this 15 gives us a pretty good data set for this function. So just another layer of information with this data set. The red dots represent that POTW site, and you can see it’s a pretty good fit, and I would say the POTW data perhaps are the backbone of this regression. It’s not a perfect fit.

Some of the data points do fall off the best fit function. So I did want to look at what is the variation in error in the data set we’re looking at. So the double arrows are instances where the best fit function underestimates an actual data point. So it would be less protective in reality, and the average of those underestimates is 1.9 times, so almost two times the measured value. So, you know, this, again, is an empirical model, I think is useful, but it’s not perfect, and we need to bear in mind, you know, the amount of error we may be dealing with. So, what are the sources of that error, or these points falling off the line? Analytical variability and error, different facility age or history, precursor transformation to PFOA, aquifer characteristics, and geochemistry. But I’d like to assert that for the examples that we selected, we know or infer that they have similar age, they’re similar types of facilities with a similar history. As I said, there’s an expectation that precursor transformation would have already occurred, and I think we’ve got very similar aquifer characteristics.

So although those are sources of error, I think these sites are fairly similar, allowing us to basically plot them on the same graph. And I just wanted to look at the worst-case scenario. In this one instance, looking at the double arrow, that’s the instance where we had a 3.3 times under estimate of the actual data point, and that occurred, you know, 9% out of all the data. So next I would, we’ll talk about estimating PFAS attainment distances and receptor vulnerability. So looking at that graph of concentration versus distance and amalgamated for a number of sites, I think it’s interesting, but how do we turn it into something that’s useful and practical? So what I did was I took that function and I developed a matrix of outcomes based on a range of distances and a range of potential source concentrations to develop this nomogram, which allows us to determine PFOA concentration at select distances from known source concentrations.

So as a matter of illustration, the red line here represents a 500 nanogram per liter source concentration. If we back up a step, you note that all of the colored lines represent different source concentrations. So starting in the bottom left with the blue line and blue squares represents a source concentration of 50 nanograms per liter, and the green line with green squares towards the top right represents a source concentration of a hundred, I’m sorry, of a thousand nanograms per liter, and again we’re dealing with PFOA as an example, and the lines in between represent intermediate source concentrations. Perhaps these source concentrations are known or perhaps they’re inferred. But in any case, as I started to show before, here’s an example. So the red line represents a 500 nanogram per liter source concentration for PFOA. And if we wanted to estimate what type of PFOA concentration, we could expect 800 feet from that source of 500 nanograms per liter, we could pick off 100 nanograms per liter PFOA concentration. So backing up with 500 nanogram per liter source concentration, we’re interested in what we could expect the concentration to be 800 feet from the source.

Perhaps we don’t have a monitoring well network. Perhaps we have a supply well or a receptor at that distance, and we want to have some idea of what the PFOA concentration could be. So, if our empirical relationship holds, we would expect a PFOA concentration of about 100 nanograms per liter, 800 feet from a source, with a 500 nanogram per liter PFOA source concentration. So, we can recast that nomogram in a different way, shown here, and this will show distance to the criterion from known source concentration. So, in this instance, you may want to know at what distance from a source concentration would I achieve a groundwater standard. So here, the blue line with the blue dots represents New Hampshire’s 12 nanogram per liter ambient groundwater quality standard for PFOA. And if I, again, had a 500 nanogram per liter source concentration of PFOA, or assumed that had one and I wanted to know at what distance would I, from the source, would I attain or achieve that 12 nanogram per liter ambient groundwater quality standard. I could pick it off the graph and it would be approximately 1800 feet from the source. So just rewind and go through that again. So the blue line represents the 12 nanogram per liter ambient groundwater quality standard and let’s say I have a 500 nanogram per liter source concentration. I want to you know, at what distance could I roughly expect to attain that standard and it would be about 1 ,800 feet.

So we can replicate this diagram for a variety of groundwater criteria or drinking water criteria. So in this instance we basically show the ambient groundwater quality standard in red of 12 nanograms per liter, the yellow line and triangle is the EPA’s health advisory level of 70 nanograms per liter, and the green line represents 10 times the ambient groundwater quality standard of 120. So we can see, sticking with an example source concentration of 500 nanograms per liter PFOA, if our groundwater standard was 120 nanograms per liter, or 10 times what’s been promulgated in New Hampshire, we would attain that standard at 713 feet. So looking at the health advisory level, again, 500 nanogram per liter source concentration, we would expect to attain the health advisory level at 983 feet. And lastly, for the actual groundwater quality standard, if we had a 500 nanogram per liter source concentration, we would expect to attain that standard at about 1 ,800 feet.

So again, this is based on the empirical function of 15 samples for four sites that I think holds up pretty well, but it is relying on that relationship. But even if it’s not perfect, I think it does give you a good sense of the distances to attainment from a particular source concentration, and it also helps illustrate the sensitivity of that distance in groundwater to attain a standard, especially as regulatory criteria change and as they get more and more stringent. Even if you don’t know the exact distance, it still puts you in the ballpark to understand some of the implications of these changing and more stringent regulatory criteria. So we can take that last example and transfer it to a real world situation. So here I have an example flow net and a source and animating our flow line here. And what pops up on the screen are the distances from that source on a real flow net where we expect to attain those criteria. So in a practical way, this is important, especially if there are supply wells and they area or we want to know where to intelligently place our monitoring wells.

So we may have a supply well downgradient from our source and we would want to know, we may want to assess or predict where we would attain a regulatory criterion before reaching that supply well. And, you know, in the absence of any groundwater data or monitoring well network, we could also use this type of analysis to intelligently place our monitoring well locations and to do that more efficiently as opposed to what I call the poke and hope approach. So let’s cover the last segment of the presentation and look at the statistical distribution of PFAS relative to regulatory criteria. So what this graph shows is the PFOA concentrations for the entire New Hampshire database for PFOA concentrations of one nanogram per liter and higher. And these data are organized from the highest concentration in the data set on the left-hand side, 52 ,000 nanograms per liter, to one per liter on the far end of the diagram to the right.

So this basically is the PFOA concentration for every sample in the New Hampshire database for PFOA concentrations one anagram per liter and higher. And you know what it shows is there’s a pretty large spike on the left-hand side and a relative in a long tail of relatively low concentration but still above the criteria. So when you look at and inspect this data distribution, only 13.7 percent of the samples correspond to 99.8 percent of the upper concentration range. So 99.8 percent of the concentration range is represented by only 13.7 percent of the samples. However, a fairly large percentage, 42.1 percent of the samples represents only 0.2 percent of the concentration range and all those samples are above the regulatory criterion of 12. So all of that 42 percent of samples occurs between 12 nanograms per liter and 10 times 12 nanograms per liter or 120 nanograms per liter. So again a long tail of relatively low concentration that are still above standard. We could look at this data on a log scale, which helps you see it better. And that’s what we’re presenting here.

So the concentrations are presented on the log scale. But flipping back, I think this is really the more relevant way to look at it. Because again, it shows that most of your concentration range corresponds to a small of your samples. So let’s look at this on a mass basis. So the intent of this graph is to show that most PFAS mass, most of the PFAS mass corresponds to a small volume of water. So I think this graph requires a little explanation. So from right to left on the graph, the graph progressively sums the concentrations of samples from highest to lowest. And if we assume that each sample, if we assume a one liter aliquot per sample, this graph represents the cumulative mass of PFOA for all 7401 samples in the database, meaning that 100% on the left-hand side of the graph corresponds to the sum of PFOA for all 7401 samples, again assuming a one liter aliquot per sample. And it is used to represent the mass distribution of PFOA in New Hampshire groundwater with bias towards sampling sites or we could use this to represent the mass distribution of PFOA at any individual site.

So when we look at it we realize that 90 percent of the mass corresponds to samples that are 120 nanograms per liter or greater. So, 90% of the PFOA mass in all of these samples corresponds to samples that are 10 times the regulatory criterion or greater. Only 8.3% of the mass corresponds to the tail here, which is between and still above the regulatory criterion of 12, but less than 10 times that regulatory criterion. So I think it begs the question, if you could get 90% of the PFOA mass out of the ground at any particular site or out of all of the groundwater in New Hampshire, would it be worth it if you still left behind concentrations over a significant volume of water that were up to 10 times the regulatory standard? So the point is, you know, all the mass, most of the mass is concentrated in a small volume of water, but there’s a large volume of water that still has PFOA above the regulatory criterion. So I think this presents some practicability considerations.

So with that, let’s take it to the conclusions. So PFAS, in this case represented by perfluoroalkyl acids, and we relied heavily on PFOA in our example, may exhibit predictable concentration versus distance function. This can be useful for estimating attainment distance in groundwater or receptor vulnerability. As I said, it might not be perfect. There’s some variation in that empirical function, but if you want to get in the and in the absence of data, you know, understand where you might want to place your monitoring wells or at a high level determine whether a receptor might be at risk. I think the data that are available to us can be really useful for that. The attainment distance is sensitive to different regulatory criteria. So we showed that a 10 times more stringent regulatory criterion results in a 2.6 greater distance, 2.6 times greater distance to attainment. So few samples, 13.7, corresponded to most of the upper concentration range, and many samples, 42.1%, comprised the low end of the concentration range. So again, a relatively long tail, relatively low concentration, but still above standard.

And we showed that most PFAS mass occurs in a small volume of groundwater. And even if you remove 90% of the mass, which I think would be, you know, very beneficial and in itself might have some practicability limitations, you would still leave a large volume of water with PFAS concentrations above criteria, as high as 10 times above criteria. And, again, a large volume of groundwater includes PFAS concentrations up to 10 times regulatory criteria, the point I just made. And these distributions present practicability limitations for attainment or remediation of distal portions of PFAS plumes in groundwater. So thank you for your attention to that presentation. And at this point, I will turn it over to Kristen Thorson of Regenesis.

Great. Well, thanks so much, Jeff, for presenting here today and for showing that data. I think it’s a really great visual of, as you mentioned, this large data set that is out there. But I like how it’s coming together to really, I think, help make decisions. As you mentioned, that there are some practicality issues here, and I think that segues nicely into the last portion of this webinar today, which we’ll talk about. And I think, again, that data could probably help go about thinking through this type of approach or where it’s most applicable. And that is using colloidal activated carbon for integer PFAS remediation. So I’m going to describe this approach and answer a couple of common questions that come across with it as well. So Jeff did a good job, I think, of really describing this challenging problem that’s associated with PFAS in general, the vast nature of these plumes that can be created in the sense that they often can expand over thousands of feet and still be above the regulatory limits.

Obviously the concentrations change greatly across that range, but generally they are quite low levels, and we’re going after low target levels, and so it does bring up the question of what can be done right now. Obviously, if we do nothing, the problem continues and we may already have places where there is a risk being posed to these sensitive receptors, but our toolbox is rather empty. We don’t have a lot to reach for due to the recalcitrant nature of these compounds. Pump and treat is certainly an option and can be implemented, but often it doesn’t align well with this type of a plume and it can be rather cost-ineffective and not very when we think about the nature of these sites. So the remedial approach that we have been implementing over the last few years and what I’ll discuss today is this flu management solution of enhanced attenuation. The simple overview of this approach includes installing an injectable permeable reactive barrier of colloidal activated carbon in the path of this contaminated water and again using Jeff’s data it might be useful to think about where you put this barrier whether it’s closer to the source or farther down to, again, be most cost effective.

So I can see that data being very useful here. But the goal is to get that activated carbon in the path of these contaminants and significantly increase the retardation of those contaminants. So really remove them from groundwater and slow the migration greatly. And we’ve got data, it depends on the site, of course, and the exact contaminants that are present and how much carbon you’re putting in. But this single application can typically last for years, decades. At a time, so it’s definitely a long-term slowing of the migration over time, and that sends for attenuating the plume. The definition of enhanced attenuation coming from ITRC is shown here, and again, that describes this process really well. It’s sustainable and that a single application with no ongoing operation maintenance costs can be implemented, and it’s manipulating the natural attenuation process. So, there is already natural absorption occurring between these contaminants and the FOC present.

But by adding activated carbon, we’re essentially supercharging that FOC and really increasing that sort of property. What the result is that we greatly reduce the mass flux of contaminants with the goal of preventing further migration. Now, sometimes we call it sequestering, but it’s really containment strategies to keep it in place and, again, prevent the enlargement of the problem. So, a number of common questions come up with this approach. The first one often is the idea that there is no destruction happening here, it’s a sort of property only, so how does this fit with the scope of remediation? I think it’s helpful to look at the definition of remediation to answer this question, where we see that, you know, it really comes down to having a process that can eliminate the risk that comes from the exposure to these harmful chemicals.

So another way to write that is in the form of an equation. We can write it out as the risk is equal to the product of the hazard times the exposure. So when you write it this way, you can really see that if you can address the hazard or address the exposure, drive either of those down to zero, you have a good way of addressing and decreasing the risk greatly. And this is in line with guidance that has been put out by the EPA over the years, where they have described a couple of different processes or methods to reduce the risk. And those first two listed there really are about addressing the hazard, trying to eliminate or remove the hazard to address the risk. And then the third point, you know, reducing the contaminant mobility and bioavailability is really going after that exposure. And typically that’s done through adsorptive or stabilizing type approach. And that is exactly what this approach of using colloidal activated carbon is doing here. By absorbing PFAS to the activated carbon, we’re removing it from the groundwater, which reduces the mobility and the exposure pathway. So really not something that’s new.

Perhaps it’s a new way of thinking about it as it pertains to PFAS, but it’s something that has been done over the years for other recalcitrant compounds, metals, things that we just really can’t degrade and address the hazard very easily. And more recently, I think this is, you know, coming into light, there’s some precedent for this. A paper came out at the end of 2021 that sort of described, it’s really kind of a nice guidance on how to think through a site if, you know, monitored natural attenuation is appropriate, and if there’s potentially a need to enhance monitored natural attenuation possibly with the use of adsorptive compounds like a activated carbon. So, you know, this aligns well and I think is a good kind of background or guidance for this strategy.

A second question that comes up is, can you effectively implement a colloidal activated carbon barrier? Obviously, this is a really important aspect, you know, we’re essentially trying to build a fence of activated carbon in order to capture those contaminants from moving farther down gradient. And if that is not implemented properly, it’s not going to be a very effective strategy. So to answer this question, it really comes down to first having a technology that allows us to get it into the ground. You know, PlumeStop is the technology we’re using. We developed it about 10 years ago, and it was really with that goal in mind. We wanted to be able to inject a form of activated carbon into the ground, and so that’s what we set out to do here. In its simplest form, it is a form of activated carbon and that it’s a good absorbent, the key to it being that its particle size is one to two microns, which is what allows it to fit through the poor throats and distribute readily.

There’s some other advantages to having small particle size, but really that’s the essential part for the distribution aspect of it. There’s some other additives in there too that keep it from exonerating and clumping together and help it move within the soil subsurface. But the goal is really to have it distribute under low pressures to ensure that we can get really good contact with the water and the contaminants that are fluxing through the zone. I think this key attribute of distribution is really best described in these gravity columns. So there’s no pressure being applied here. These are a mixture of sand, silt, and clay, which is typically a little bit harder to get solid particles through. And just to give you a sense here, it’s compared to a commodity powder activated carbon, which is just generally too large to fit through, so it’s filtered out readily on the right side versus the plume stop which readily passes through the soil and distributes nicely without again pressure having to be applied.

Now a fine point, a very very key fine point to this is that we want it to distribute but it can’t distribute forever. We don’t want it to continue flowing with the groundwater far down gradient and so after the activated carbon was applied here we actually flush these columns with clean water and you can see that there’s a of the soil. And so that is another aspect of plume stop is that it distributes, but while it’s distributing, it’s actually interacting and adhering to the soil surface. And so in that sense, it’s going to stay in place and it’s not going to distribute forever. It’s going to help us build that barrier and not continue to move down gradient. The goal with any kind of injection, which is usually through direct push or injection wells, is to target the flux zones. We to put the plume stop, put this chloro-activated carbon in the same zones where the water’s moving, where these contaminants are flowing, and how these plumes are being developed so that we get good contact and can intercept those contaminants.

So this is all fine, you know, kind of on papers looking at it, but obviously what it comes down to is being able to implement this in the field. And so we have a team who’s out implementing plumes up in the field, our remediation services team. And they have a number of quality control steps that they’re taking for every site because every site is a little bit different to ensure that the distribution is meeting the intended goals and getting a complete distribution to build that fence. And so for example, this is a site where the target treatment zone was from 15 to 27 feet. So in that case, you know, these two cores should be completely black as well as the top of that. And while we are seeing some in this area, it’s certainly not to the full extent that was targeted, and so they’re in constant contact with our technical team who designed this and making adjustments, real-time adjustments in the field, to get the distribution up to where it needed to be.

So after making those adjustments, they go back and take some cores and are now seeing that targeted zone being implemented. So this is a really important aspect to being able to successfully implement that barriers, having, you know, the tools and the team to be watching this and doing those quality control procedures to ensure that distribution is occurring as we need it to be. In addition to CORD, there’s also a number of water samples that can be taken to really do the same thing, just to ensure that we’re getting the type of distribution needed. These could be taken from monitoring walls or piezometers, again, just to be continually monitoring the distribution over time. And then the final question really comes on to how long will the treatment last? you know, these aren’t, it’s not degrading. And especially if there’s a infinite source, there is a limited number of sorption sites this barrier would fill up. And so our ability to predict the longevity and what all is gonna impact longevity is very important when designing this. And we do have the ability to predict longevity because we can calculate retardation factors for specific contaminants with PlumeStop.

And that’s something that we’ve done and created over the years to be able to do that. And that’s really because plume stop behaves like natural organic carbon, but on steroids, in that it is acting to ****** the movement of contaminants. And once we have that retardation factor, now we can start to use that in models and start to think about how that translates to time. And so we’ll look at that here on this next slide. At first, just as a reminder, this is what the retardation factors are telling us, you know, the groundwater is moving at some distance per time has its own velocity, and the retardation factors for contaminants is telling us how it’s moving relative to that groundwater velocity. So an R1 means it’s moving at the same velocity as the groundwater, and an R10 would mean it’s moving at a tenth of the velocity as the groundwater. And again, there’s always natural retardation factors with the natural FOC that’s present, and it depends on the exact species that are present, but typically those are in single digits, double digits, and can range, definitely.

But when we add activated carbon, again, it’s like a supercharged FOC. So for an example, if we have kind of a standard diffs of plume stop being applied, and in this case, we’ll look at TCE as the first example at 100 micrograms per liter, if you assume that we have about a 13 foot long barrier and it’s moving at about 250 feet per year, it would take that groundwater about three weeks to move across that 13 foot zone. But the retardation factor that the activated carbon creates on that TCE is a factor of about 600. So now the time it takes a TCE to move across that zone is about 30 years. Again, we have the retardation factors calculated for PFOA as it relates to plume stop as well. And if we drop it down to concentrations that are more typical of PFAS, which I think are in line with some of the ones that Jeff described, now that retardation factor increases even more. So again, we’re talking about many, many years for it to translate across that.

Now these are relatively simple examples without other factors being imparted on it. And obviously the longevity prediction is more complex at any given site when there are more than one species present and other site-specific factors are considered. So, to help with that, we developed a model that can take all those factors and roll them up and give us a predictive output on longevity. This will take into account, again, multiple species that may be present, what the actual contaminant mass flux is moving through that barrier. That’s really an important term. If there are any other non-target compounds present, as those can and will absorb to the activated carbon as well. And then this can all be taken into and calculated what the competitive absorption is going to look like. So we know how that those species will move through the zone. And then we can also start to take into consideration the carbon dose, what the barrier thickness is, what the amount of time that it’s going to translate to.

And this again, it gives us a predictive output of what a dose will last and how we might adjust that to meet the needs of the site. I also just mentioned that the longevity can also greatly depend on what is occurring at the source. So if there’s an infinite source, then there’s going to be an ongoing amount of contaminant flux coming into that zone, and that will certainly affect longevity. But if the source has been, if some kind of source control measure has been taken, so perhaps if a lot of the contaminant mass is in the veto zone and that’s been stabilized or removed or capped or some way, then we may actually have a decreasing source input. And that’s going to help extend the longevity of that barrier. And even in some cases, a single application may suffice and that you might not have to go back and put in another barrier down the road.

And there’s, I put a QR code here that takes you to our PFAS Resource Center. But there’s a great video, a technical video that kind of describes this process as it to how a barrier may last, getting into details of isotherms, and just a lot of more technical discussion on that. So I’d welcome you to go there if you want some more information, as well as just a lot of other information on case studies and whatnot related to how PLMSTEP is being used to address PFAS. And I’ll close here with just giving a sense of, you know, how this is being implemented. You know, at this point in time, there’s been close to 30 sites where PLMSTEP has actually been applied in pilot or full scale sites to address PFAS. And there’s a very large number that are in the design and review phase, getting ready to go and down the road or looking at the feasibility of how this might be applied. And this summarizes the types of sites where we have applied PlumeStop to address PFAS.

So nothing too surprising here, because most of these are sites that are commonly associated with PFAS, so between the airports, industrial sites, landfills, we’re really getting a large suite of different types of sites where this is being implemented and so we can kind of understand how each one has a little bit different nuance. Now, many of these have been implemented in the last six months to two years, so some of these don’t have a lot of long-term data, but I will highlight that there are a couple that have been in the ground for a little bit longer, which is typically a question we get on what kind of long-term data do we have. So the Michigan Army Airfield site, Camp Grayling, is one that we have data for over about three and a half years now, where concentrations have remained non-detect since first implementing. And then this Ontario industrial site is another one where we have five and a half years now, where again concentrations have remained non-detect since the initial application.

So while there are many new sites going in there, there are some term data that we’re continuing to collect to show how this can continue to work over time. So with that, I’ll close this out by hopefully showing that we have PlumeStop as a cost-effective, long-lasting strategy to manage PFAS. And a key part of that is having the ability to install these barriers with a lot of quality control steps that are performed to confirm the installation. And we didn’t have time to get into this, but we’d certainly be happy to talk to you if you have questions on this. But all of, you know, the experience we have with Plum Stop and how we can design these and have confidence in our ability to put together a good design, we actually are offering an extended warranty for these types of barriers where Plum Stop is being used to address PFAS. So in that sense, you know, we will assess a site and implement the barrier and, you know, keep monitoring it for 10 years. And if at any point the standards are not being achieved related to that site, we’ll go back out and fix it.

And so again, we’re happy to talk through that program with you, but we know that this one’s, you know, with PFAS and the inability to degrade, it’s essential to get that barrier installed. And again, we’re confident in that ability to do that and we’ll stand behind it with this extended warranty. So, with that, we’d be happy to take any questions that you have for myself or Jeff. And again, just want to thank Jeff for his presentation today and coming on here to talk about some really interesting data analysis.