Performance Metrics for Software-Based Emissions Monitoring

How can we define the performance of a software-based emissions monitoring solution?

For hardware-based monitoring, that question is relatively straightforward. A camera has a tested emissions detection threshold. A sensor has a spec sheet. The performance characteristics are fixed, lab-validated, and well understood. Probability of Detection (POD) was designed for this world, where the detection capability of a physical device can be measured under controlled conditions and expressed as a single number.

Software-based Parametric Emission Monitoring (PEM) works fundamentally differently. It reads operational signatures from process data (SCADA data), including pressures, temperatures, flow rates, and valve states, to detect emissions events. There is no single sensor measuring methane. There is no fixed detection threshold. And that means the traditional POD framework, while valuable for hardware, doesn’t fully capture how software-based detection should be evaluated.

Why POD Alone Isn’t Enough for PEM

With hardware, you often recieve a binary answer: did the system detect the emission event, or didn’t it? For a camera pointed at a wellhead, that’s a meaningful measure. But for a software platform analyzing hundreds of SCADA signals simultaneously, detection confidence isn’t fixed—it’s shaped by the quality, frequency, and completeness of the underlying data.

Consider two facilities running the same PEM model. At one, SCADA data reports every second with 99.8% availability. At the other, data arrives every ten minutes with frequent gaps. The detection capability of the software is fundamentally different at each site—not because the model changed, but because the underlying process data conditions changed.

This is why AroIQ approaches confidence scoring as a dynamic, transparent, and multi-dimensional metric—not a single static number.

Data Quality Is the Foundation

In AroIQ, every confidence score is shaped by the quality of the underlying SCADA data. Three factors drive this assessment:

• Sensor Reporting Frequency. Does data arrive every second, every minute, or every ten minutes? Higher frequency gives the model more resolution to identify the precise onset and cessation of anomalous operating conditions, and that directly increases detection confidence.
• Data Availability. Was the sensor actually reporting during the event window? Outages, communication failures, and gaps reduce the model’s ability to characterize what happened. If 30% of the data is missing during an event, the confidence score reflects that.
• Imputation Required. When data is missing, the model reconstructs values to maintain continuity. But imputed data carries less certainty than observed data. The more imputation required, the lower the confidence, and AroIQ quantifies this explicitly.

In AroIQ, data quality isn’t an afterthought. It’s the foundation of every confidence score the platform produces.

Transparent Confidence for Every Event

Every emission event flagged by AroIQ includes a structured confidence profile across three dimensions:

• Detection Confidence. Was the event detected from clean, high-frequency data? Or was the detection based on sparse, partially imputed signals? This score tells the operator how reliable the detection itself is.
• Duration Confidence. How precisely can the system identify the start and stop boundaries of the event? A system with one-second SCADA resolution can pinpoint boundaries far more precisely than one reporting every ten minutes.
• Volume Confidence. How reliable is the estimated emission volume? This depends on the measurement pathway, available flow data, and how much uncertainty propagates through the quantification chain.

These three scores combine into an overall emission event confidence score. No black boxes. Operators see exactly what the system knows—and where uncertainty exists.

AI Governance That Holds Up to Scrutiny

Building trust in software-based emissions monitoring requires more than a good model. It requires governance—the processes, controls, and validation frameworks that ensure the machine learning models perform as claimed, adapt when conditions change, and remain auditable over time.

AroIQ’s AI governance framework includes:

• Validated ML frameworks with dedicated data science oversight and rigorous quality controls.
• Facility-specific model training on each facility’s unique SCADA data, not generic benchmarks. Every facility operates differently, and the model must reflect that.
• Blind testing protocols where known events are withheld during training and used to independently measure detection accuracy.
• Automatic confidence discounting when data conditions degrade. If sensor frequency drops or availability declines, confidence scores adjust in real time.
• Full traceability so that every confidence score traces back to the specific data conditions that produced it, essential for regulatory audits and internal reviews.

Why This Matters for the Industry

The shift toward continuous, software-based emissions monitoring is accelerating. But for operators, regulators, and stakeholders to trust these systems, the industry needs more than detection rates. It needs transparency about what the system knows, where the uncertainty lies, and how confidence is maintained as conditions change.

For operators, this means confidence scores that inform real dispatch decisions—not opaque alerts from a black box. For regulators, it means auditable systems with traceable methodology. And for the industry as a whole, it means building long-term trust in software-based monitoring as a credible, scalable category.

Learn More

To learn more about how AroIQ approaches confidence scoring and emissions intelligence, visit arolytics.com or contact our team directly.