Map PFAS patterns

PSIS is being developed to support contamination assessment, uncertainty-aware site decisions, targeted sampling, routing support, and audit-ready reporting across soil workflows.

CAPABILITIES

What PSIS helps teams do

Decision-support software for PFAS site investigation and remediation planning.

Plan better

Generate contamination and uncertainty surfaces, delineate hotspots with confidence, and receive targeted next-sample recommendations.

Execute faster

Get confidence-aware provisional routing before full lab turnaround. Refine decisions when final chemistry arrives.

Report more defensibly

Clearly separate measured from inferred values. Produce reproducible, audit-ready outputs for regulatory review.

THE CHALLENGE

Why this matters

PFAS decisions are made under difficult conditions: sparse data, threshold-sensitive outcomes, heterogeneous site conditions, and increasing pressure for transparency, defensibility, and cost control.

Traditional investigation approaches were not designed for contaminants with this complexity. Teams need better tools to characterize uncertainty, prioritize sampling, and support defensible decision-making.

Built on
Data from real contaminated sites
Research basis
UvA, NUS
INVESTIGATION LIFECYCLE

Built around the workflow

PSIS supports iterative site investigation, from initial characterization through remediation planning.

01

Data intake

Import existing sample data, site boundaries, and relevant contextual layers. PSIS normalizes formats and flags quality issues.

02

Spatial modeling

Spatial modeling generates contamination surfaces, uncertainty estimates, and candidate hotspot zones for technical review.

03

Sample optimization

Identify high-value follow-up sampling locations that can reduce uncertainty more efficiently. Prioritize based on decision relevance.

04

Iterative refinement

As new data arrives, models update. Uncertainty surfaces sharpen. Decisions become more defensible over time.

TECHNICAL FOUNDATION

How it works

PSIS is being developed to combine spatial statistics, machine learning, and decision-support logic for PFAS site characterization.

Spatial modeling under development

PSIS is being developed around kriging, ensemble machine learning, and uncertainty-aware decision logic for PFAS site characterization.

Uncertainty quantification

The target workflow is designed to expose prediction uncertainty so teams can see where model support is stronger and where confirmatory sampling is still needed.

Adaptive sampling logic

The target workflow includes adaptive sampling logic to help prioritize locations where additional data is expected to be most decision-relevant.

Audit-ready outputs

Clear separation between measured and inferred values, reproducible methods, and documentation suitable for technical review.

WHO IT'S FOR

Built for technical teams

PSIS is being developed for professionals making decisions about contaminated sites.

Environmental consultants

Can support more efficient site investigation, reduce re-mobilization, and deliver defensible characterization reports.

Site owners and RPOs

Gain clarity on contamination extent, remediation scope, and long-term monitoring requirements before committing resources.

Regulators and reviewers

Evaluate submissions with clearer uncertainty documentation, reproducible spatial analysis methods, and clear distinction between measured and inferred outputs.

Built on research from leading institutions

University of Amsterdam/ Research
National University of Singapore/ Research
University of Amsterdam/ Research
National University of Singapore/ Research
Get started

Ready to improve site decisions?

We work with consultants, site owners, and research partners on the next stage of PFAS decision-support workflows.