PFAS Visualization Case Studies Demonstrate How to Improve Conceptual Site Models

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.

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Today’s webinar will discuss PFAS visualization case studies that demonstrate how to improve conceptual site models. With that, I’d like to introduce our presenter for today. We are pleased to have with us Dr. Grant Carey, President of Poor Water Solutions. Dr. Carey has more than 30 years of experience focusing on the characterization and remediation of hundreds of impacted sites. He specializes in PFAS fate and remediation, environmental forensics, groundwater modeling, and visualization. He is involved with a number of SERTIP and ESTCP projects with a focus on PFAS in situ remediation modeling and visualization. Dr. Carey is also an adjunct research professor at Carleton University in Ottawa, Canada, and an adjunct professor at the University of Toronto.

All right, that concludes our introduction, so now I will hand things over to Grant Carey to get us started.

Thanks, Dane. Hello, everyone. Thanks for joining today. I really appreciate your time. So let’s get started with PFAS site characterization. So, in the old days, which in the PFAS world is like two years ago, we all saw these maps. We all made these maps where we really only had to worry about PFAS and PFOA as the main two regulated PFAS constituents. So a lot of maps had these data tables where the numbers are put in a table, they’re kind of linked to where the well locations are. So it’s an effective way to get the data out there. But when you actually step back and look at this visually, you can’t really see what’s You’ve got to go in and look at the numbers in detail.

So in terms of presentation, it’s not a visual way of showing the data. So the new days, which is now two years later, we’ve got more regulated PFAS constituents. We’ve got new analytical methods with up to 40 PFAS analytes per soil or groundwater sample. We’re looking more now at precursors and whether they’re going to transform to regulated PFAS. We’re looking at organic co-chemicals that might be present at sites and affecting PFAS transport. We need to look at redox indicators so we can get a better idea of whether conditions are suitable for precursor degradation to the regulated PFAS. So there’s a lot of data we’ve really got to be able to look at. Which raises the question, how can we effectively communicate all these PFAS results?

So one thing we did last summer, Poor Water Solutions, we partnered up with a few folks to really go into detail with a large site PFAS database. It’s the South Dakota Air Force Base. We worked with Rita Krebs from the Air Force Civil Engineering Center who’s also our PM for the site. We work with Jeremiah Duncan from GZA and we looked at a number of different ways of trying to visualize the data to get a better feel for what works, what didn’t work. The things that we looked at that we wanted to evaluate with the PFAS constituents are on the left is a long list of different trends that we’re looking for, different questions we might want to answer. And what we found was that definitely radial diagrams, and something I hadn’t thought of before, but stack bars, not in Excel, but actually plotted on a map where the well locations are located. We found those to be really effective visualization approaches for different reasons.

So I’m going to walk through today some examples of that. And I just wanted to point out radial diagrams for visualizing PFAS, that’s not new at all. People have been doing this for a while, certainly for a long time with chlorinated solvents or redox indicators. I created a program 25 years ago to do radio diagrams for looking at natural attenuation and enhanced attenuation for chlorinated solvents. But now we’ve got PFAS with more chemicals. Back when I was working on the ITRC PFAS guidance manual with a team that was 2017 to 2020, One of the contributions I made was using radial diagrams to actually visualize PFAS at different sites, just to show an example of how we might be able to start visualizing all this data. So it’s definitely not a new technique.

So today I’m going to spend most of the time on this one site, South Dakota Air Force Base, looking at what we learned from the different visualization methods, what the benefits were, limitations, what did the methods tell us. And then we’re also going to look A little bit more quickly at some other sites, case studies of PFAS visualization. At one site, it’s a Navy site where plume stop was used as a barrier. We’re gonna look at data both before, during and after remediation to really demonstrate how helpful rate of diagrams might be when you’ve got a lot of chemicals to look at multiple time periods. Then we’re gonna look at, she switched gears a little bit, look at a chlorinated solvent remediation site that Regenesis has worked on.

This is a site where radiodiagrams are also effective not just with PFAS, but with chlorinated solvents to look at parent species degrading to daughter products, looking at redox indicators. So we’re going to look at that site and see how we can use radiodiagrams to support that kind of analysis. And then we’ll look at redox zone delineation. That’s something a lot of people don’t think about with PFAS sites. But because precursors can actually degrade to the regulated PFAS, which we don’t want to get higher. We really need to know where conditions aerobic, which tends to be the most favorable redox condition for precursor biodegradation. Where is it strongly anaerobic? Where might it change? So that’s something where there’s a special twist on radial diagrams. I invented like 25 years ago. I’ll show you how that works.

It makes it really simple to delineate how redox zones are changing in groundwater, where biodegradation is occurring, where is it aerobic, et cetera. And then finally, I’ll talk just a really simple overview of a new software product we’ve developed called Visual PFAS, which is being released actually this month just for being able to plot rate of diagrams and stack bar maps. So first to talk about the South Dakota site, we do have a paper that’s in review right now with Remediation Journal. If anybody would like a copy of that, it’s got tons of figures that show different methods of what we looked at. feel free to send me an email, and I’m happy to send you a draft of the paper. So in terms of acknowledging the authors, I’ve mentioned Rita Krebs is the RPM for this site. Jeremiah Duncan from GZA was very helpful with creative ideas. And then Gabe Carey actually did the programming to develop the Visual PFAS tool and built it in a way that helped us to analyze that South Dakota site. And then Mia Gillian Akira from Poor Water Solutions, also very helpful with analyzing site conditions and helping us with trial and error, what visual methods worked and what didn’t work.

So at this site, it’s a large Air Force base. They have known source areas where AFFF products were used. AFFF is aqueous film forming foam, containing PFAS up until recently. So there’s 14 AFFF source zones. One of them is actually a former fire training area called OU-1, the rest are all labeled with AFFF 1 through 12. And AFFF-1, which is in the bottom part of this map, your OU-1, that’s the current fire training area. And you can see on the right, it lists the AFFF areas, but also the locations, like why are these actual AFFF sources? You can see in some places there’s fire training, obviously with the current former areas, there’s buildings or distribution systems where PFAS were used. And then there’s crash sites where PFAS was used to actually help put out fires. So those all kind of make up these different AFFF source zones across the site. And just for your reference, groundwater flows from north to south.

So one thing we wanted to look at was, can we tell the difference between what AFFF products were used in the different source areas? This wasn’t for litigation. There was only one site here. Sometimes you might have multiple sites, different products used for AFFF or other PFAS constituents. So we want to look at what kind of forensic methods can we use to assess and can we actually tell the difference between the different products used predominantly in a source area. So to help with that, there’s a really good fact sheet, ITRC put this fact sheet together, the AFFF fact sheet, and it talks about three different AFFF products. There’s ECF just for background, ECF stands for electrochemical fluorination, and when Now, ECF is used to make AFFF. What you get in terms of impacts to soil and groundwater are high sulfonates like PFOS and PFHXS and lower concentrations of carboxylates like PFOA.

So there’s a legacy ECF product. That’s product number one. It’s called legacy because they don’t make that anymore. And you get the sulfonates mainly high concentrations with impacts to the subsurface. Then there’s two types of what we call FT products. FT stands for fluorotelomerization, which is another process used to make AFFF products. And that process, when that AFFF gets into the subsurface, what you’re left with is mainly carboxylates, like, for example, PFBA through to PFOA, PFNA, and the precursors that go with it. You’ve got lower concentrations of sulfonates when these FT AFFF products are used. And there’s two to have more, in terms of impacts, the impacts are predominantly long-chain PFAS, like PFOA. And then there’s the modern FTE product, which is more of a replacement product. They stopped using the long chains at some point in time. So impacts from the modern FTE AFFF tend to be more focused on short-chain carboxylates. So that gives us two clues of what we can look for if we want to tell what products were used in different AFFF areas.

We can at ratios of sulfonates to carboxylates, which tells us the difference between ECF and FT products. And we can look at long-chain versus short-chain carboxylates, which tells us is it an older product, legacy product, or is it a more modern product with short-chain compounds. So let’s go through the radial diagram process. I’m just going to go through slide by slide here. I do this in a certain way, so I just want to convey some of the things that I do in case it four-axis radiodiagram, obviously, four chemical species represented, one on each axis. This radiodiagram represents concentrations at one well. In this case, at this site, it’s called GW4. So on the left, we’ve got FHXSA, which is a precursor. And that precursor, under aerobic conditions, can, not always, but can biodegrade to PFHXS, which is a regulated PFAS compound. So, we’ve got on the right PFHXS, that’s the data product, if FHXSA is biodegrading.

We’ve also got PFBS on the top, PFOS on the bottom, so the regulated sulfonates, and a precursor that can degrade to one of those. So, that’s helpful because I do find plotting, if you want to look for evidence of our precursors actually transforming to the regulated PFAS constituents, putting the mono-radial diagram together precursors and the data products can really help looking at trends at individual wells and between wells on a flow path that can tell you whether transformation is occurring. And we’ll look at an example of that in a minute.

Another thing here is I use uniform axis ranges 0.01 microgram per litre up to a thousand on every axis, even though the actual ranges at the site concentrations differ for each chemical. The reason I use the same ranges for all chemicals if I can actually very quickly this way look at a radial diagram on a map and I can tell you which are the chemicals with the highest concentrations which are the lowest so it’s just a very quick way to see what’s high and low without thinking too much about it. Also the nice thing about radial diagrams with PFAS sites is you can make them log scale and the reason log scale is important is because PFAS sites at least some sites can have PFAS varying by many orders of magnitude. So in that case, log scale helps.

You can better see the transition along a flow path, for example. So the one thing to note here is each interval between a set of tick marks represents a change in concentration for that compound by a factor of 10 or by one order of magnitude, one ohm. So what that means is, if we have two data series plotted on a radial diagram and I see there’s two tick mark intervals on the PFHXS axis, that tells me there’s a two order of magnitude difference in concentrations. So just counting the numbers of tick marks between data series tells you what kind of changes you have, say between a source and a well or between different wells on a flow path. It’s a very quick way to get order of magnitude estimates of what’s changing. So in terms of data series, obviously we need the modern event data series.

Those are the results from GW4, from a sample collected at GW4. We’re looking at an event, this is published in a paper by McGuiretel, a great study early on from 2014 of what a PFAS investigation looks like, what they had found. I’ll show a screenshot of that paper in a minute. So we’re looking at the 2011 monitoring event. So every radial diagram for every well is gonna have different results for the monitoring event. Then there’s a second data series. I actually like to plot usually at least two data series on a radial diagram. And the other data series here is called a reference series, which means the results are the same for every well. In this case, what I’m plotting is the maximum source concentrations across the site plotted as this reference series. So every well has the same purple series, and every well has a different blue series.

And what we can see, if I’m just counting tick marks now, on the PFAS axis between the purple data series and the blue data series, the source concentrations and the actual well concentrations, some distance downgradient from the source. We’ve got between a one and a half and two order of magnitude decline in PFOS between the source and this well GW4. And I can tell that just by counting the tick mark intervals between the two data series. So one reason why I plot two data series is I actually don’t look so much at the blue data series inside. And I actually look at the gap between the two data series and how that changes along a flow path. Looking at the gap between two data series makes it much easier to see how concentrations change along the flow path downgraded from the source area. And then another thing we can do, it doesn’t cost extra time, it doesn’t take up any space on the diagram, is we can plot symbols for where chemicals are exceeding cleanup criteria at the site.

Let’s say, in this case, I’m using the EPA MCLs from last year, and I’m using the health-based water concentration for PFPS. So I plot symbols where the monitoring event data actually exceed those MCLs in the health-based water concentration. And that helps to see, well, which wells actually have exceedances, and which chemicals at a site actually don’t really exceed the MCLs anywhere. So it really helps to get a quick view, and there’s no extra time or cost to putting that It’s another layer of information that adds value to the maps that we’re going to plot. And same with non-detects. I actually like that just so you can see, okay, we know PFAS aren’t over there. I can tell the concentration is low, but when it’s non-detect, it’s just another piece of information that can help in our interpretation of what’s happening.

So I mentioned this paper by McGuire-Atel, a great reference for a lot of things that happen at PFAS sites, including how remediation of chemicals besides PFAS can actually cause harm, can actually increase concentrations of regulated PFAS. So at this particular site, so the paper from 2014 by McGuiretel, that’s the same site that we actually worked with last summer with Rita Krebs and Jeremiah Duncan. So there is a dissolved oxygen infusion well, actually a series them installed around the source area and down gradient and those were put into biodegrade hydrocarbons. They wanted to do aerobic biodegradation of hydrocarbons. That was before we really knew much about PFAS and what happened was they’re injecting oxygen and if you remember precursors, a lot of precursors will degrade most favourably under aerobic conditions.

So if you’re injecting oxygen, you’re making aerobic conditions, you might get precursors degrading to chemicals, we don’t want to see increase in the concentration. And that’s actually what happened. So when we look at a downgradient well, MW89105, you can see, okay, we’ve got the non-detect symbol now for that precursor. So that’s gone. So along that flow path, there was oxygen injected, now the precursor is completely gone, non-detect. We can see, if you look at the gap here between the event data for PFHXS and the source, it’s small but in order of magnitude, we’ve got almost no gap at the next one. So I can see we’ve got an increase in PFHXS, we’ve got an increase in PFBS, we’ve got an increase, a little bit of increase in PFOS. So all these chemicals increased because dissolved oxygen was injected along the flow path, which saying there were probably precursors there degrading to all three of those chemicals during that dissolved oxygen infusion remedy.

So let’s switch gears and actually look site-wide now back to where we saw those AFFF source zones and going to look at a radial diagram. This is one thing I do. So we can set up a radial diagram with nine axes to represent PFBA to PFNA, the carboxylates, and then PFPS, PFHXS and PFOS, that’s nine axes, all the short chain and long chain compounds that you typically hear about. Or we can set up a radial diagram that’s simpler and only show the chemicals that are regulated, which I call the PFAS of concern or POCs. So you can do either. I tend to internally do the more comprehensive radial diagrams to look at all the data. And at the end of the day, I kind of figure out, okay, what’s the simplified story I need to put together so people really understand what I’m showing.

If you put too much data on a figure, it makes it harder for people to really understand what the figure is showing. So just keep in mind that sometimes it’s better to simplify, but internally when you’re doing your evaluation, maybe you wanna put more things on and simplify afterward. So this radial diagram we’re gonna show on the map, there’s two data series again, this purple, that’s the reference series. So now we’re looking at site-wide, we’re looking at the site maximum concentration And from a site inspection that was done I think in around 2018-2019 the report was published. So it’s not a remedial investigation yet. This is early on. This is a site inspection data set. So the maximum concentrations out of all the AFFF source areas is the purple line. That’s going to be the same on every radial diagram we show on the map. And then we’ve got in one AFFF source area I had selected which wells had the highest PFOS and PFOA combined. And that’s really the well that has the highest concentrations in each of those 14 source areas that we saw earlier.

So that’s what the blue series represent. It’s like a monitoring event for the site inspection, and it’s representing the well with highest concentrations in each source area. Showing the symbols again. So what we can see is this one source area with the blue line and the orange fill, that has much lower concentrations than the site maximum. Orders of magnitude like PFAS, counting the tick marks here. one, two, three, four tick mark intervals. That’s four orders of magnitude lower concentrations at the well in this source area compared to the site maximum. So that’s one thing you get used to is just counting the tick marks. How many orders of magnitude difference do you see for each chemical along a flow path? So just overlaying these on the map, we can see the source area reference numbers here, AFFF one through 12. I’m not showing the former fire training area for now. we’re going to look at that area in a little bit in a more zoomed in section.

So what you can see is the current fire training area, AFFF1, that has by far the highest concentrations. That really, the concentrations at the well match up with the site maximum. So all chemicals are kind of at their maximum in that AFFF area, the current fire training area, which makes sense if you have fire training. That could be because the current fire training area is right next to the former fire training area, maybe there’s some overlap there, maybe just fire training is done regularly, maybe something happened, I’m not sure. But it just shows, okay, in terms of magnitude, you can see orders of magnitude by counting the tick marks where you have the highest concentrations at the site, just with a simple map. And we’re looking at this for all the PFAS of concern. And then just circling a couple here, so then looking at AFFF12, we can see all chemicals, all five chemicals, these PFAS of concern, all have red symbols, meaning they all exceed their cleanup criteria. Even PFBS. PFBS is on this axis on the right here, and you can tell by looking at the legend over here.

And you can see there are no other wells, no other source areas, other than the current fire training area, where PFBS actually exceeds the health-based water concentration of two microgram per litre. So just by plotting the symbols, we can see, yeah, PFBS doesn’t really exceed much. It does exceed here, and actually is basically at the site maximum as well. So there’s more short chains here. And we can also see just looking at magnitudes, this is two tick mark intervals for PFOS. It’s close to PFOA, a little bit bigger than PFOA, actually an order of magnitude higher than PFOA. PFNA is pretty low compared to PFOA. So just looking at magnitudes, we can see the sulfonates are higher than the long chain carboxylates. So it tells us, well, maybe the ECF was the dominant product out here.

But that, you know, we have to look at the short chain curve box lathes as well, and maybe it was a modern FTE product that turned out to be higher than the sulfonates. So let’s keep that in mind, and we’ll kind of move on to the next map. So in addition to radial diagrams, I found I hadn’t done this before last summer, but I did find taking these stack bars, which we all do in Excel, sometimes we put way too many chemical intervals, but if you keep them simple, they can be really effective for looking at proportions of chemicals or groups, but Excel doesn’t put them on a map. So we’re actually gonna take these bars and actually overlay them on a map, see what that tells us about the different source areas. So just to let you know, the orange represents total sulfonates. And remember, sulfonates are associated with the ECF, AFFF product. And the blue, that’s total carboxylates.

Those are typically associated, at least the high concentrations are associated with the FT process used to make AFFF products. So the blue is kind of the FT products, the orange is the ECF. So we can see at this one well that we’re looking at, this one source area, that sulfonates make up about 70% of the total of carboxylates and sulfonates. And carboxylates are 30%. So that’s telling us, I’m not sure that ECF was the only product used there, but it looks like it was the predominant product, which is not unusual. A lot of A-triple sites have seen, I’ve looked at a database of 100 of them, and typically the sulfonates have higher concentrations than the carboxylates, and this is kind of what you see at a lot of sites, but not all, and we’ll see that in a minute. So one thing to note, these tick marks basically are just a way of estimating what’s the percent of sulfonates versus carboxylates when you add the two up. That’s all this is.

So if you had a concentration total of, let’s say a thousand microgram per liter for the two in the source area, 70% or 700 would be sulfonates and 300 would be carboxylates. So this is just a proportion map. So looking at that on a map, we can see the current fire training area, they have a little over 60% sulfonates, little under 40% carboxylates. And that’s pretty common. A lot of these have that. And now I’m not even looking at the percents. Now I’m just looking at where is orange kind of like the main one, maybe three quarters. A lot of these just visually are in that range. This one’s higher, the sulfonates. This one, the blue are noticeably, the carboxylates are higher, the orange, the sulfonates are lower. This one down at the bottom, HFFF8, that was a crash site from 2006, way more carboxylates than sulfonates.

So this is a very quick way and a very simple way to visualize what’s the relative proportion of these different chemicals and maybe the different products used in each of the AFFF source areas in terms of ECF versus FT based. It doesn’t tell us whether FT was modern or legacy though. And that’s what this map might tell us. Remember how there’s a legacy FT AFFF product? When it has impacts, they’re mainly long chain.And that was used historically. The modern FT product, the long chains are swapped out for short chain. so you tend to see more short chains and subsurface impacts. So in this stack bar, we’ve got the six carboxylates from PFBA all the way up in order of chain length to PFNA. And if you’ll note, I’ve got one color for all the short chains, green, and I just use different shades of green to represent smaller to longer chain length. And then I’ve got really a couple but it’s kind of the same yellow slash red for the long chain.

So I can very quickly see, well, all the greens add up to 80%. And the long chains here are about 20%. That’s one simple way to look at this. You can look at each chemical individually. PFBA here is probably 68%, maybe 6%. But I look at it more for what’s the difference between short and long chain? Where do you see dominant long chains? Where do you see dominant short chains? So, putting that on the map, we see we’ve got the carboxylates in the current fire training area, 80% of them are short-chain, which makes sense. We’ve got a lot of 6-2 FTS, which degrades to the short-chain carboxylates. We’ve got very low 8-2 FTS, which would degrade to the long-chain carboxylates. So, that matches what we have for the FTS distribution. And up in this AFFF12 area, we have almost, I’d say, 95% short-chain carboxylates. And then we’ve got PFHPA. So that’s telling me, whoa, that’s not a sulfonate thing.

Remember how the sulfonates were about 40% at this location, carboxylates were a bit more, and the short chains are by far higher in concentration than the long chain. So that’s saying the predominant product may have been modern AFFF. So, we do still have 40% sulfonates here from the last stack bar map we looked at. So, it’s starting to tell us what’s the relative proportion. It’s not exact. It’s just line of evidence, really approximate way of how to come up with methods to try and distinguish what products were used in different areas. And something else we did, because I know like the stack bars, that’s really just like pie charts. All we’re doing is showing proportion of different chemicals, adding them up, and the size of And the pi in this case is the same as the size of the interval in the stack bar.

So it’s a similar approach, but I do find the stack bars are actually more efficient for looking at. I can see things more quickly and more easily. And part of the reason is on the maps, you can see we do put tick marks on the map. So I can very quickly get an estimate of proportion of short chain to long chain, or if there’s one chemical, I can see what the actual percent is in the total by scaling it off visually with the tick marks. So that makes it easy. Whereas pie charts, at least the pie charts I’ve seen, you typically don’t have those tick marks. And I find that because we’re looking at short chain to long chain, a linear scale, just vertical, bottom to top, I find that just more intuitive to deal with. I do know that the short chains start up here and they kind of go clockwise. it’s just for me a little bit less intuitive. But pie charts have their place.

So anyway, yeah, it’s a personal preference thing, but I do find stack bars, when you can put them on a map, can actually be really effective for forensics. And another thing we looked at was ratios. So we’ve talked about plotting concentration so far. There has been some work, you know, looking at, when do you plot ratios of chemicals? So one thing we looked at, this is looking now at the former fire training area, which is kind of zooming in on that map we saw. And this is the OE1 area. And one thing we looked at was ratios of 6 to FTES, that’s the precursor, that’s the parent species, for these three daughter products, PFHXA, PEA, and BA. So I’ve only got three axes on the radial diagram, nice and simple. And the ratio of the parent to the first daughter product is here. The second data product ratio is here and the third data product on the bottom.

Now when plotting ratios, one thing, because I really like plotting two series at least on these radial diagrams, as I mentioned, we actually plot a reference ratio where the ratio is equal to one. It’s the same for every chemical, it’s just joining lines up where the ratio is one. And the reason I do that is you can see, well, is the ratio increasing or decreasing relative to this reference series. It’s very quick to see where it’s getting bigger, where it’s getting smaller. If I didn’t have a reference series, it’s really hard to tell actually how things change with just one data series. So one thing to keep in mind, this is a ratio of the parent over the daughter. So if the parent species starts to decline in concentration because it’s biodegrading and the data products going up, then the ratio is getting smaller. So these ratios will actually decrease as you go along a flow path if the precursor is biodegrading to these data products.

So that’s one thing we can look for is changes in the ratio where the ratio gets smaller along a flow path. So different map now, we’ve zoomed into the former fire training area, this is the former burn pit area here. And you can see in the north part of the source area that the blue line is the actual data for the well. The black dash line, that’s the reference series, ratio equal to one, it’s the same everywhere. So in the north part of the source area, the ratio is bigger than one, which means 62 FTS is higher in concentration than all these data products at these wells here. As you go down gradient, that ratio decreases a little bit. Now it’s at one.

When we get to the bottom of the OU1 area, kind of still in some source material possibly, but we see the ratio decreasing, meaning now maybe some precursor degradations occurring and the data products are increasing a little bit. And then you go down gradient further, water flows north to south, and the plus sign is a dissolved oxygen infusion well that was operating before 2011, and right next to this well, MW89105, and you can see a big decrease in the ratio by an order of magnitude once you get to this well near the dissolved oxygen infusion injection well. So what that’s telling us is that oxygen was causing 6 ,2 FTS to decrease because it was biodegrading, and the data products were increasing to some extent. So this actually, this kind of ratio is really helpful for looking at transformation along a flow path. And you do need that reference ratio so you can see how things change more easily, but it does help to look at transformations if you’re plotting these ratios. And another area, it’s in our draft paper.

I don’t have it on a slide. For the bigger map, the HFF source areas, I took all the PFAA axes and took a ratio to PFOS to try and get an idea of, okay, what’s the ratio of PFOA to PFOS and PFNA and PFHXA. So that can help you to look a little bit more at sulfonate versus carboxylate concentration comparisons. So one more thing with this site, another thing that I found really useful actually with rate of diagrams was looking at top assay results. So we had a great data set for this from the McGuire et al. paper 2014. And in this one, we have two data series plotted, None of them are reference series, meaning the two series are different for each well. So the one data series is the pre-top assay carboxylate concentrations, what was measured before the top assay was done from the groundwater sample. And then we added the original to the carboxylates that were produced during the top assay, this delta-top, to get a total, and that’s the second data series. And what we can see is, just looking at PFBA, so before the top assay concentrations were about 45 microgram per liter.

After the top assay, they went up to about 75 microgram per liter. So not double, but went up pretty high. So that means the top assay had a lot of short chain precursors that were oxidized during the top assay. Same with PFPEA, there was an increase. PFHXA almost tripled, and there was no increase for the long chains PFHPA and PFOA. So that’s telling us yeah there’s a lot of precursors there that when oxidized will go to the short chain compounds but there were no precursors oxidized that went to the long chain, at least not at this well. And then when we overlay this on a site map you can see the long chain this is PFOA, PFHPA, none of these wells, small increase here for There was almost no increase for the long chains. Where we saw increases in the top assay, it was for the short chains, which is consistent. There is a fair bit of 6 ,2-FTS in this water that could biodegrade, probably be oxidized to the short chains. There might be other compounds there as well.

So this is just another way of representing this. And one thing to note, when you’re looking at top assays, then you want to use arithmetic scale. You’re just looking at, does it double, does it triple? It’s typically not an order of magnitude change, so arithmetic scale might be better for top assays. Okay, so I’m going to go through the last three sites a little bit more quickly now. This is a site, really interesting site actually, I’m working on a CERTA project with data from AV research project that was conducted by APTEM. Really cool results. So what they did at the site, it’s a pilot test, they injected plume stop, which is colloidal activated carbon. really small particles of carbon, you inject it in the ground and that carbon attaches to soil and the PFAS in the groundwater filters out of the groundwater because it absorbs so strongly to that activated carbon in the barrier. So the water coming out of the barrier is going to be low concentration PFAS for a long time.

Longevity depends on how much carbon you put in and other things but it is a good remedy. It’s a question of how long it works and do you have to re-inject down the road. So this was a study where they set this up. So I’ll just go through the results here. These are my co-authors, Dr. Paul Hatzinger and Greg and David from Paul’s office at Aptum. They did great field work, really high resolution sampling to see what’s happening throughout the two-year monitoring period. Dr. Tony Danko from NatVac Exwick and Dr. Brent Sleeps been helping me with modeling. I’m actually modeling the site data, but I needed a way to visualize what’s happening. So that’s why I’m showing this here. So, we’ve got this 12-foot long barrier parallel to the direction of groundwater flow.

There were two monitoring wells installed in the barrier. One is five foot into the barrier, the second well is 10 feet into the barrier, then you’ve got two downgrading wells downgrading to the PRB, so five and 10 feet downgrading to the PRB. So, first I’m showing two data series on the first well in the barrier, five feet in. So the outer data series, that’s a reference series. It’s gonna be the same for all the wells. These are the pre-injection PFAS concentrations before plume stop was injected. And the yellow data series, that’s three months after the plume stop injection. So you can see at this first well, all the sulfonates, this is PFBS going clockwise with increasing chain length right up to PFAS. And we can see all sulfonates decrease substantially and concentration in the barrier because carbon was absorbing the PFAS. So you’re not seeing the PFAS in groundwater five feet into the barrier, and that’s three months in. So now I’m gonna add a data series for two years.

I could add all the data series, but it gets really unwieldy. So I’m just gonna show really before three months, which is the yellow data series, and then the blue is two years after plume stop injection. And what we’re seeing is even after two years, PFAS, the sulfonate concentrations are all low. There has been a slight increase in PFBS and PFOS, but I can tell you looking at the data leading up to this, it was a stable concentration. It wasn’t breakthrough. It’s just a small number, which you expect to see in these barriers. There’s a lot of things going on. But when you look downgrading at the further well in the barrier, a lot of triangles, meaning non-detects, which is great. That’s what we expect to see. So, you can definitely see, yeah, nothing’s getting through the barrier after two years. Really effective at removing PFAS for this period.

And then looking at downgradients, it’s a little different because downgradient, remember, you put the barrier in, it goes right into the middle of a plume. And downgradient, the plume’s still there when the barrier gets implemented. It takes time for PFAS to desorb from the soil, the natural organic matter, and basically get flushed out after the barrier’s been implemented. So one thing we’re doing is we’re modeling different desorption processes, kinetics, to see what it takes to match that. So what we’re seeing is downgradient at this further downgradient well, three months in, you have an order of magnitude decrease, but it’s lower than I would have thought, Ashley. It’s not automatic, it’s not immediate, 10 feet downgradient. And then another two years later, another order of magnitude or two decrease, because things are taking time.

So it takes time for things to flush out, but the barrier is working, the plume is flushing out down gradient. And just by using three data series, we can look at short chain and long chain compounds, see where we have exceedances, different times where it’s non-detect, just with these radial diagrams. So it’s a very simple way of showing a lot of data visually instead of at the table. And the carboxylates, we see something pretty similar. The only difference is PFBA and PFPEA, which we know don’t absorb as strongly to carbon as longer chain carboxylates and the sulfonates. We do see PFBA came down a little bit, but that came, if we look at two years, we see at the five foot well, we do see breakthrough of PFBA and PFPEA after two years. And if I go 10 feet down gradient, looking at this well, the PFPEA hasn’t broken through yet. So, it’s still absorbing, but it is starting to break through.

But the longer chain compounds are fully absorbed, which is good in the barrier, not breaking through. And the key is the PFBA is not regulated. So we’re looking at chemicals that are regulated and not. We’ve only got one chemical five feet in that has a small low-level exceedance, no exceedances for PFOA or PFNA for the downgradient in the barrier. So, that’s where the exceeding symbols can be helpful as well. Okay, so switching gears now to chlorinated solvents, because this is another example where we can look at parent species and daughter products on the same map at multiple time periods to assess remediation effectiveness. So, this is a Regenesis site that was worked on, I think, five or six years ago. It was a great design.

They had to very quickly knock down a plume because it was in a residential area in a groundwater aquifer. We’re plotting two data series. 2010 is pre-remediation before anything was done and post-remediation is the second data series. These are both events so they both change for every well and we’re showing the symbols. So I was not a co-author on this I’m just visualizing the data so Douglas Davis, Paul Erickson from Regenesis and Dora Taggart from Microbial Insights because it was bio-augmentation as part of the remedy so there was a lot of bacterial counts going on. So just to show you quickly, the area of interest is where most of the plume was. The green lines and the black lines are transects, wherein solutions were injected. The green is where 3D microemulsion was injected, a long-lived electron donor, and the black is where plume stop was injected with an electron donor, hydrogen release compound, and the biodichlor inoculum plus, which is basically bio-augmentation.

So they did a of work here to quickly knock that plume down. We’ve talked about the data series that are on here and just the advantage of adding the symbols. Literally no extra time, doesn’t take up space on the map, but you get a lot more information from this. You like to see is it non-detect, you like to see where the exceedances are, and how many rooms, just counting the tick mark intervals, how much decline from before and after remediation, that’s easy to do. And then you just overlay this the site map groundwater flows south to north and one thing we do some sites we have to offset radial diagrams which is uh we have software that helps with that just so we don’t have overlap too much overlap with wells that are close together but what we’re seeing is when you look down gradient all exceedances for chlorinated solvents before remediation after remediation chlorinated solvents are all non-detected in the standard gradient area exactly what you want to see okay and then Finally, I’m going to finish up, I’ve got a few minutes here, I’m going to talk about redox zones and how we can actually delineate changes in redox zones spatially and temporally and why we actually want to do that.

So this is literally going back a couple of decades, but I’m sure, and I’m sure a lot of you are familiar with this, where we have biodegradation of natural organic matter that can cause, or hydrocarbons, which we have in a lot of fire training areas, the biodegradation electron donors is paired up with transformations of electron acceptors. And those electron acceptors end up providing energy for cell metabolism. But they go in sequence. So the most preferentially utilized electron acceptor when it’s present is oxygen. So bacteria that use oxygen when it’s available during, say, hydrocarbon biodegradation in a fire training area. And that can actually cause depletion of the oxygen and slightly anaerobic conditions.

And then the next electronic septic that gets utilized in the list is nitrate. Then down on the list, solid manganese and iron get used up as electronic receptors and we see metabolic byproducts. We see reduction in the solution of the manganese and iron, which causes increased dissolved manganese and iron in groundwater. Then the next one down on the list with manganese and iron are largely depleted in soil. we have sulfate depletion as an electron acceptor, and eventually when a lot of that’s used up, we have methanogenesis where methane’s produced. So we have electron acceptors that decrease during biodegradation, and we have metabolic byproducts that increase during biodegradation. And the reason it’s important to delineate what’s the redox zone in a certain area, this is just one example, but TCE has very different degradation rates, slow or non-existent over here when it’s aerobic or nitrate reducing, or it gets quicker as you get more anaerobic conditions.

And that’s why we’re injecting electron donors at chlorinated solvent sites to speed up the degradation of chlorinated solvents. So even just looking at simple redox zones can really help us figure out how much degradation of solvents we’re getting in different parts of the site. So this is an example actually from almost 30 years ago, 1996, I published a paper with a group of folks at Conestoga Rovers at the time, now GHD, but this is still classic work. It actually, I’ve used this over and over again on many sites and it makes a big difference in how we can visualize where degradation’s occurring and what redox zones we have. So I’m not even gonna show you what’s represented on the axes. All I’m gonna tell you is that where you’ve got the big orange shapes, like in the background groundwater, the big shape, that’s aerobic conditions.

And as you transition from aerobic to moderately anaerobic, the orange shapes get smaller. And then the really smaller in shapes, those are strongly anaerobic. So all I have to tell you is that the size of these orange shapes tells us whether we have aerobic, moderately anaerobic or strongly anaerobic conditions. And I can tell you that we have a drum area where chemicals may have been getting into the ground. We have really strongly anaerobic conditions down gradient of that drum area. So there were probably electron donors that were getting into groundwater biodegrading and causing the strong anaerobic conditions. So visually it’s obvious when you know what the shapes represent, and small and big. So just to show you what’s on this diagram, the redox indicators, so oxygen, nitrate, electron acceptors, manganese and iron, those are metabolic byproducts.

On this one I’m showing arsenic, different reason I won’t get into the details, showing sulfate, not showing methane, we didn’t have methane data at the time in 1996, literally this is before M &A had been coined as a phrase when we started looking at the site. But most radio diagrams for redox conditions will have dissolved oxygen, nitrate, manganese, iron, sulfate, and methane. The ones shown in blue, those are the electron acceptors. And in aerobic groundwater, electron acceptors are high in concentration. So, the arrows show that the electron acceptor axes increase as you move away from the origin of this radial diagram. They all increase as you move away from the origin.

Metabolic byproducts are the opposite. They actually, at aerobic aquifers, they’re low concentrations. So, if you plot symbols for an aerobic aquifer, they all plot on the outside of that radial diagram. Metabolic byproducts will increase towards the origin because they increase during biodegradation. So if I join the dots, this is what an aerobic aquifer looks like. Big shape, green instead of orange, but still a big shape. This is what a strongly anaerobic condition looks like. Low electron acceptors, high metabolic byproducts. You can see high methane here. So, very quickly, by reversing the axes of the electron acceptors in the metabolic byproducts, we get this really effective visualization of redox zones. And you can literally, I’ve seen sites where I can see the transition, either over time or spatially on a flow path, moving through the different redox zones.

So, this redox area associated with each well gets smaller and smaller as you move into more anaerobic conditions. So, we can look at a table, which is how a lot of sites we used to do this, just look at the table and try and figure it out, or we can just visually, these graphs are the exact same data just plotted with radial diagrams, much more effective way of seeing what’s happening. So, this shows temporal changes in redos conditions for that Regenesis site we just saw. In the area of interest, flow from south to north. The green is the pre-remediation 2011 redos conditions and the orange are the 2016 poster mediation. And you can see, like just taking this well here, for example, SB230, this is the methane axis, low concentrations here in 2011, much higher concentrations in 2016.

So we have an increase in methane, we have a decrease in sulfate, increases in manganese and iron, telling us things got a lot more anaerobic because of the bioremediation that was done with all these transects. So just very quickly, it shows us with, instead of looking at a table of data, shows us things got more anaerobic because of the bioremediation. And that’s exactly what was intended to happen. Different site, Weir Smith Air Force Base. This is 1996, a paper, a classic paper by Dave Chappelle. Great data set. This is a cross section ground surface up here.

And he looked at nested wells and he saw the shallow wells where this is a fire training area before they knew about PFAS, looking at hydrocarbons and chlorinated solvents, you can see the hydrocarbon degradation was causing strong anaerobic conditions in this area, the small green shapes, and we can literally start putting contours around the different redox zones by looking at these radial diagrams. So a nice simple way of visualizing how redox conditions change and becomes more aerobic as you go down in depth. I don’t think I’m gonna go through this just for sake of time.

Well, I’ll just go through quickly. This is Charleston Naval Weapons Station. There was a USGS report, excellent work on bioremediation looking at redox conditions and how they change with time. I actually came up with a method to just semi-quantify redox zones by looking at the limits on what the relative redox areas are for the different aerobic, nitrate reduction, and manganese reduction, et cetera, et cetera. And through that semi-quantification, I took this table of data and put it and made this chart with these two data series. Changes over time, looking at a couple of injection and monitoring wells, and what I can actually graph, a plot of the rate of diagrams, got the redox areas, and just calculated how the redox areas were getting smaller, so getting more anaerobic, as time went on during the bioremediation. And the orange wells are at moderating wells, the blue are at ejection wells, so more anaerobic at the ejection wells, still pretty anaerobic at the moderating wells nearby.

So it’s just a very simple way using radiodiagrams to semi-quantify things. So what we’ve seen is radiodiagrams can be helpful tools for visualizing chemical interrelationships at wells or between wells. So looking at parent versus daughter concentrations, looking at short versus long chains, sulfonase versus carboxylates. So one radial diagram can actually replace five to 10 different chemical maps if the chemical maps have one species plotted at a time. We can also look at, visualize how many orders of magnitude changes we have along a flow path or over time if remediation’s being done. And we can quickly see where chemicals exceed cleanup criteria, we can use that to delineate plumes, plotting all the PFAS of concern. And we’ve got this kind of cool trick for redox zones. is still worse today. It’s actually really effective for visualization and very simple to do.

So just to finish up, Portwater Solutions has developed this new software product I mentioned earlier called Visual PFAS to help with site characterization, remediation assessments, forensics. So we’re partnering with a company called Water Services and Technologies where they’re going to actually do the sales and distribution for the software. The people that run this company are actually the people that made Visual Modflow and used to sell Visual Modflow. So they really know what they’re doing in terms of software. There are two websites listed here. You can go to either one. If you want more information on Visual PFAS. And in terms of price, the price is 14.95 regular price and that’s for a site license. Any number of users in an office can use this without restriction. It’s not a per user license.

There’s no annual subscription. And we are offering a 20% discount for any purchases up to May 1st. So the price would be 11.95. So, on that note, I think I’ll end there and see if we have any questions.