Forever Chemicals in the Groundwater
A military base used AFFF firefighting foam for 30 years. PFOS seeped into the aquifer. The municipal well field is 800 meters downgradient. We asked seven questions — starting with when contamination reaches the wells and ending with which remedy the base should choose. The screening-level answer and the full analysis give opposite recommendations — and the contaminant everyone is watching for arrives second.
Source: EPA UCMR5 (Jan 2026, 10,299 systems, 1.9M samples). We downloaded and analyzed the raw data.
Same Site. Same Data. Different Answers.
We asked the same question — when does the plume reach the well field? — at three levels of model fidelity. The answer changed every time. So did the recommended action.
Plume arrival time at the municipal well field (800m downgradient).
A Contamination Crisis in Slow Motion
PFAS are synthetic chemicals with carbon-fluorine bonds — the strongest in organic chemistry — making them nearly impossible to break down. In April 2024, EPA finalized maximum contaminant levels of 4 parts per trillion for PFOS and PFOA. We downloaded the EPA's UCMR5 monitoring dataset — 1.9 million sample results from 10,299 public water systems. 59.7% have detectable PFAS. Among systems where PFOS was detected, 97% exceed the new 4 ppt MCL.
Our scenario: a composite military base fire training area, parameterized from published USGS data for Joint Base Cape Cod — one of the best-characterized PFAS sites in the US, with 1,500 hydraulic conductivity measurements and a 1.2 km PFOS plume tracked since the 1970s. It's happening at 700+ DoD installations right now.
Data: EPA UCMR5 (1.9M samples), USGS Water Quality Portal (62 monitoring wells at Cape Cod), published aquifer parameters. Full sources listed in the Sources section below.
Three Models. Two Dimensions of Fidelity.
This study uses three distinct models. But model complexity is only one dimension of fidelity. The other — and for this study, the more important one — is how honestly you treat uncertainty in the inputs.
The Models
Domenico Analytical
A closed-form equation for contaminant transport in a uniform aquifer. The standard EPA screening tool. One line of math, one answer, milliseconds.
MODFLOW 6 GWF+GWT
USGS MODFLOW 6 solves groundwater flow on a 200×100 grid with spatially varying hydraulic conductivity. The industry-standard tool for contaminant transport. ~70s/sim.
200-Realization Ensemble
Runs Model B 200 times with different randomly sampled parameters. Instead of one answer, you get a probability distribution.
Deterministic vs. Stochastic
The first two models are deterministic: one set of inputs, one answer. The Monte Carlo is stochastic: it samples hydraulic conductivity (K), sorption coefficient (Kd), and source concentration from realistic distributions — because we don't know these parameters exactly, and pretending we do produces false confidence.
The biggest fidelity gap in this study isn't between models. It's between deterministic and stochastic analysis of the same transport model. MODFLOW 6 didn't change. What changed is whether we pretended we knew the sorption coefficient to two decimal places. That single assumption — known Kd — is what makes the plume look distant when it isn't.
Seven Questions. Seven Deep Dives.
Each question was answered at the fidelity it required. Click any card to see the full analysis, charts, and methodology.
When Does the Plume Reach the Well Field?
Screening says 96 years. MODFLOW 6 says 71 years. Monte Carlo P5 says 5 years. And PFOA arrives before PFOS.
Where Is the Plume Right Now?
Homogeneous model says “drill on the centerline.” Heterogeneous model shows the plume bifurcated — centerline wells miss it.
P&T vs. PRB: Which Remedy Wins?
Deterministic says P&T is cheaper. Monte Carlo reverses the decision — PRB wins on risk-adjusted cost because P&T tail scenarios are devastating.
Is Better Site Characterization Worth It?
$500K in targeted measurements cuts the planning window from 32 to 22 years, saving $15M+. ROI: 30x. K matters more than Kd.
What If the MCL Changes?
4→2 ppt doesn't double the cost — it triples it. Regulatory uncertainty dominates geological uncertainty.
Where Does Fidelity Change the Decision?
For “is there a problem?” — screening suffices. For remedy selection — only Monte Carlo gets it right.
Scope: Three models (Domenico analytical, MODFLOW 6 GWF+GWT, 200-realization Monte Carlo), ~1,200 lines of Python, published USGS aquifer parameters, 1.9M EPA monitoring records. Limitations documented honestly: no unsaturated zone transport, no density-driven flow, no multi-species precursor reactions. Each question got the model it needed — nothing more.
Finding the Right Model for the Decision
Each question above was answered at the fidelity it required. But scattered across seven questions, it's easy to miss the pattern: which dimensions of reality actually change the answer? Here it is in one place — four decisions evaluated at three model fidelities, showing where the answer converges and where it breaks.
| Decision | Screening (A) | MODFLOW 6 (B) | Monte Carlo (C) |
|---|---|---|---|
| Is there a problem? | Yes — ~96 yr | Yes, faster with heterogeneity | 5% chance within 5 yr |
| Which remedy? | Can't evaluate | P&T appears cheaper | PRB wins risk-adjusted |
| How much will it cost? | Can't estimate | ~$80M (ignores tail) | $80M median, $120M+ at P95 |
| Which species arrives first? | Can't distinguish | PFOA at 20yr, PFOS at 25yr | Distribution per species |
Every model up to MODFLOW 6 said P&T is cheaper. Then we added parameter uncertainty: Monte Carlo reverses the decision to PRB. The gap isn't between transport models anymore — it's between deterministic planning and stochastic reality. Real aquifers have unknown Kd. Real plumes follow unknown preferential flow paths. A planning process that ignores both is planning for the best case, not the likely one.
Methodology note: Our composite scenario uses published USGS data from Joint Base Cape Cod — one of the most extensively studied PFAS sites in the US, with 1,500 hydraulic conductivity measurements and a 1.2 km PFOS plume tracked since the 1970s. Monte Carlo samples K from log-normal(10, σ=0.5), Kd from uniform(0.3–5.0) based on Cape Cod site data, and source concentration from normal(100, 25). 200 realizations per scenario. Note: the sensitivity table below shows the full literature range for Kd (0.5–20 L/kg); the MC uses the narrower site-informed range.
What Drives the Answer?
Across 200 Monte Carlo realizations, two parameters control 85% of the uncertainty in plume arrival time. Everything else is noise for this decision.
| Parameter | Range | Variance Share | Decision Impact |
|---|---|---|---|
| K (hydraulic conductivity) | 1–100 m/d | ~60% | Determines whether plume arrives in years or decades |
| Kd (sorption) | 0.5–20 L/kg | ~25% | Controls PFOS vs. PFOA differential arrival |
| Gradient (i) | 0.002–0.008 | <10% | Secondary; scales with K |
| Source concentration | 50–200 ppb | <5% | Doesn’t change arrival timing |
| Porosity / Dispersivity | 0.2–0.4 / 5–50 m | <5% | Affects plume width, not arrival |
K and Kd together drive 85% of the uncertainty. So if you're a site manager deciding where to spend your characterization budget, the answer is pump tests (K) and sorption testing (Kd) — in that order. In our scenario, $500K in targeted measurements on those two parameters cuts the planning window from 32 to 22 years, saving $15M+ in contingency. The other three parameters barely move the needle. You don't need to measure everything — just the stuff that actually changes your decision.
Sensitivity from Q1 tornado diagram (arrival time) and Q5 variance decomposition (cleanup time). 200-realization Monte Carlo, USGS-sourced parameter ranges.
Model Validation
Before projecting remediation scenarios, we ran our MODFLOW 6 model against real monitoring data from Joint Base Cape Cod — one of the most extensively studied PFAS sites in the country. 62 USGS monitoring well measurements, same model, same algorithm.
| Model Configuration | Predicted Plume Front | vs. Observed (2,700 m) | Explanation |
|---|---|---|---|
| Cape Cod params (K=95, Kd=0.4) | 2,990 m | +11% | Site-specific data matches observed plume |
| Generic literature params (K=10, Kd=1.5) | 780 m | -71% | 3.5x underprediction without site data |
| Full 3D model (10 layers, 300K cells) | 2,910 m | +8% | Vertical structure matches qualitatively |
The honest reading: With published USGS aquifer parameters (K=95 m/d, n=0.39, back-calculated Kd=0.4 L/kg), our model predicts the plume front within 11% of what USGS actually measured. Generic literature parameters miss by 3.5x. This is why site characterization matters — and why the Monte Carlo P5 (which samples low-Kd realizations) captures reality that the deterministic base case misses. The 3D model confirms vertical structure: PFOS peaks at 25–35m depth, consistent with recharge pushing the plume downward.
Observed data: 49 PFOS detections (1.3–610 ng/L) at 62 monitoring wells, USGS Water Quality Portal (2019–2020 sampling). Predicted: MODFLOW 6, 300×100 grid, 55-year simulation.
What We Modeled and What We Didn't
Transport modeling uses USGS MODFLOW 6 (v6.6.3). Aquifer properties from Joint Base Cape Cod USGS studies (K=60–110 m/d, n=0.39, αL=0.96m from 1,500 borehole flowmeter tests). National context from EPA UCMR5 (1.9M samples). Every parameter sourced.
| Parameter | Base Value | Range | Source |
|---|---|---|---|
| Hydraulic conductivity (K) | 10 m/d | 1–100 m/d | USGS, Gelhar (1992) |
| Effective porosity (n) | 0.30 | 0.20–0.40 | Freeze & Cherry (1979) |
| PFOS Kd | 1.5 L/kg | 0.5–20 L/kg | Anderson et al. (2019) |
| Source concentration | 100 ppb | 50–200 ppb | DoD FTA data |
| Decay rate | ≈0 | — | “Forever chemicals” |
What we didn't model: Unsaturated zone transport, density-driven flow, multi-species precursor reactions, air-water interface sorption, co-contaminant interactions. Each would increase fidelity — and each would increase computation 10–100x. The ADM question is: which of these actually changes the remedy selection decision? That's the follow-up study.
What's Actually Happening
The analysis above answers “what if.” This section answers “what is.” Real contamination has been measured. Real regulations have been finalized. Real remediation is underway. Here's how reality maps onto our model.
What's Coming: The Scale of the Problem
We downloaded the EPA's UCMR5 monitoring dataset — 1.9 million sample results from 10,299 public water systems. The numbers are stark.
| Metric | Value | Context |
|---|---|---|
| Systems with detectable PFAS | 59.7% | 6,148 of 10,299 systems |
| PFOS detections exceeding 4 ppt MCL | 97% | Nearly every detection is an exceedance |
| Median detected PFOS concentration | 6.8 ppt | Nearly twice the legal limit |
| Systems currently exceeding MCL | 1,693 | Each must remediate or find alternative supply |
| DoD installations requiring investigation | 700+ | More sites than Superfund has ever managed simultaneously |
The Regulatory Landscape
| Event | Date | Impact |
|---|---|---|
| EPA finalizes 4 ppt MCL for PFOS/PFOA | Apr 2024 | 700+ DoD sites need remediation |
| Trump EPA rolls back GenX/PFHxS/PFNA limits | May 2025 | Compliance extended to 2031; uncertainty increases |
| Several states propose 2 ppt or lower | Ongoing | Our Q6 shows this triples remediation cost |
| UCMR5 monitoring ~95% complete | Jan 2026 | 59.7% of systems have PFAS; data is now public |
Specific Sites in the Pipeline
| Site | Status | Estimated Cost | Key Challenge |
|---|---|---|---|
| Joint Base Cape Cod, MA | Active remediation since 2015 | $100M+ (ongoing) | 1.2 km plume in sand/gravel; 6,200-acre zone |
| Pease AFB, NH | RI/FS underway, report mid-2026 | Estimate pending final RI/FS | Municipal water supply contaminated |
| Luke AFB, AZ | Preliminary assessment complete | Estimate pending final RI/FS | Arid climate reduces recharge but concentrates plume |
| 700+ other DoD installations | Various stages of investigation | $30–100B total | Scale exceeds anything Superfund has managed |
The range tells the story. The DoD PFAS Task Force estimated total remediation costs at $30–100 billion — a range that reflects exactly the uncertainty our Monte Carlo analysis quantifies. The low end assumes deterministic cleanup timelines. The high end accounts for the P95 tail scenarios our analysis shows are 2–3x more expensive than the median.
The Remediation Market
The PFAS remediation market is growing 20%+ annually. Clean Harbors, Arcadis, Tetra Tech, Geosyntec — the companies doing this work face a fundamental challenge: cost estimates based on deterministic models systematically undercount duration and cost. The tail risk on P&T cleanup time means contracts priced to the median are underfunded half the time. This isn't a modeling curiosity — it's a balance-sheet risk for every company in the remediation supply chain.
What We'd Recommend
Seven questions, three model fidelities, 200 Monte Carlo realizations — and now grounded in what's actually being measured, regulated, and remediated.
For Remediation Contractors
If your cost estimates are based on deterministic models, you're underpricing long-term contracts. Our Monte Carlo shows P95 cleanup time is 2–3x the deterministic estimate. Build contingency into bids, or propose fixed-scope PRBs instead of open-ended P&T.
For Site Owners
The most valuable investment may not be infrastructure — it's characterization. $500K in targeted pump tests and sorption measurements can save $15M+ by cutting the planning window from 32 years to 22 years. K drives 51% of variance — pump tests matter more than lab sorption tests.
For Monitoring Programs
PFOA travels faster than PFOS (lower Kd). At sites with mixed AFFF contamination, PFOA arrives at the well field 5 years earlier. If your monitoring plan only tests for PFOS, you're missing the leading edge. Test for the full PFAS suite — PFOA and PFHxS are your early warning system.
Compliance Deadlines
The original EPA compliance deadline was 2029. The May 2025 rollback extended it to 2031 for the remaining PFAS compounds, but PFOS/PFOA limits stand. With 1,693 water systems currently exceeding MCLs, the remediation pipeline is already backlogged. States with stricter standards — New Jersey (58% exceedance), Massachusetts (40%), North Carolina (39%) — face the earliest and most expensive compliance requirements.
Analysis current as of March 2026. EPA regulations, state MCL proposals, and UCMR5 monitoring data are evolving. This analysis will be updated as material changes occur.
Explore the Data
See for yourself — adjust the parameters and watch the plume move.
3D Plume Replay
Rotate, zoom, and scrub through 55 years of PFAS migration. Watch PFOA race ahead of PFOS through 10 aquifer layers.
Remediation Decision Tool
Adjust sorption, conductivity, and regulatory assumptions. Watch the winning remedy — and its cost — change in real time.
Sources
Regulatory & Monitoring Data
EPA (2024). PFAS National Primary Drinking Water Regulation, 89 FR 32532.
EPA (2025). MCL confirmation for PFOS/PFOA, rollback for GenX/PFHxS/PFNA.
EPA UCMR5 Occurrence Data (Jan 2026, 10,299 systems).
EWG PFAS Contamination Map (9,728 sites).
Transport & Sorption
Domenico (1987). Analytical transport model. J. Hydrology 91:49–58.
Anderson et al. (2019). PFAS Kd values. J. Contaminant Hydrology 220:59–65.
Brusseau (2018). Air-water interface sorption. Sci. Total Environ. 613–614:176–185.
Gelhar et al. (1992). Field-scale dispersivity. Water Resources Res. 28(7):1955–1974.
Hydrogeology
Bear (1972). Dynamics of Fluids in Porous Media.
Freeze & Cherry (1979). Groundwater.
Walter et al. (2018). USGS SIR 2018-5139. Cape Cod MODFLOW model.
LeBlanc et al. (1991). Cape Cod tracer test. Water Resources Res. 27(5):895–910.
Remediation Costs
EPA (2021). PFAS Treatment Technologies. EPA/600/R-21/164.
ITRC (2023). PFAS Technical and Regulatory Guidance.
DoD PFAS Task Force (2020). 700+ military installations.
Validation Data
USGS via Water Quality Portal.
62 monitoring well measurements at Joint Base Cape Cod (2019–2020).
49 PFOS detections, 1.3–610 ng/L, plume front ~2,700 m after 55 years.
Modeling Tools
Langevin et al. (2024). MODFLOW 6. Groundwater.
USGS MODFLOW 6 v6.6.3 with GWT transport model.