Rethinking Field Screening with AISCT: A New Standard for Environmental Confidence

August 7, 2025
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This article explores the limitations of conventional field screening in environmental site assessments, particularly in heterogeneous soil conditions. It introduces the US EPA’s Triad Approach as a more dynamic and data-driven framework—emphasizing systematic planning, adaptive sampling, and real-time decision-making. AISCT (Artificial Intelligence Site Characterization Technology) is highlighted as a tool that enhances confidence in field data through real-time QA/QC metrics like %RPD and %COV. A field case study demonstrates how AISCT was used to identify inconsistencies in stockpile mixing, leading to improved treatment accuracy and cost savings. The article challenges outdated expectations of lab-to-field equivalence and advocates for a smarter, more transparent approach to site characterization.

Part 1 : Confidence in a Heterogeneous World

In contaminated site assessment, the stakes are high. Decisions made on partial or misinterpreted data can result in unnecessary soil removal, regulatory setbacks, or—worse—remediation failures that risk human and environmental health. Despite decades of guidance, field screening remains either undervalued or misused, often judged unfairly by its lack of direct numerical equivalence with laboratory results. This must change.

The Triad Approach: A Smarter Characterization Framework.

The US EPA’s Triad Approach is built on three pillars:

  • Systematic Planning
  • Dynamic Work Strategies
  • Real-Time Measurement Technologies.

Rather than seeing field screening as a compromise, Triad places it at the heart of dynamic decision-making—helping teams understand site variability, manage uncertainty, and make real-time adjustments. Field screening doesn’t replace labs—  when used properly, they can reveal contaminant distributions and heterogeneity patterns that lab-only sampling strategies often miss.. [Source: US EPA, “The Triad Approach: A New Paradigm for Environmental Project Management,” EPA 542-R-01-016].

The Real Problem: Heterogeneity, Not Technology

Soils are naturally variable. Texture, moisture, and contaminant partitioning differ across short distances. Yet, the industry still expects field screening to mirror lab results, disregarding the inherent uncertainties of subsurface sampling. Take Relative Percent Difference (%RPD). Elevated RPDs in duplicates often surface after fieldwork, wrongly blamed on lab or handling errors. More often, they signal natural heterogeneity—something that can't be corrected after the fact.

AISCT: Measuring Confidence On-Site

AISCT (Artificial Intelligence Site Characterization Technology) tackles these challenges head-on. By embedding QA/QC routines into high-density field screening, AISCT allows teams to:

  • Evaluate %RPD: Checks precision between duplicates
  • Monitor %COV: Measures consistency across triplicates

Crucially, these metrics are measured in real-time, during fieldwork—not weeks later in a lab.

Case Study: Stockpile Screening in Action

At a Northern mining site, AISCT was deployed across five stockpiles (R0–R5). Key findings:

  • R1–R4 exhibited low %COVs (0.05–0.11), confirming even contaminant distribution.
  • R0 and R5 had high %COVs (>0.30, >0.56), signaling the need for further remixing.
  • Average duplicate %RPD was 34.7%, with variances up to 58%.

This proactive field assessment delayed chemical dosing on R0 and R5 until blending achieved homogeneity, reducing amendment volumes and optimizing treatment accuracy.

Why Is This Shift Urgent?

EPA case studies show Triad-based field screening can reduce project costs by 30–60%, slash timelines by half, and enhance decision defensibility. When AISCT is integrated, these benefits are magnified through precision and repeatability.

Field screening should not be a compliance checkbox—it must be a core strategy for confident, cost-effective, and sustainable site decisions.

In Part 2, we’ll explore how to design and implement a Triad-informed field program using AISCT—covering statistical planning, data validation, and real-time visualization tools that bring site data to life. We will learn how TRIUM is ;

  • Designing Triad-informed AISCT field programs
  • Optimizing real-time visualization tools
  • Standardizing practical steps to transition from uncertainty to data-driven confidence during site assessments.

Stay tuned.

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