AI-powered methane monitoring, emissions quantification, and environmental evidence for oil and gas, landfill, and industrial applications in Canada and globally.
Zolena Lab translates environmental engineering and atmospheric science research into practical monitoring workflows for methane emissions data support, predictive emissions monitoring, oil sands energy analysis, pollution source attribution, and satellite-supported evidence packages.
A measurement and evidence company, not a generic ESG writer.
Zolena Lab works closer to the environmental data layer: satellite remote sensing, sensor calibration, machine-learning analysis, emissions quantification, source attribution, and technical evidence documentation. ESG, EUDR, land-risk, and property-related reports are customer-facing applications of that evidence layer, not additional core technical capabilities.
AI-driven methane monitoring and emissions quantification
A tiered monitoring framework combining satellite screening, continuous low-cost sensor networks, and machine-learning calibration to support methane hotspot identification, anomaly detection, trend analysis, emissions quantification, and report-ready data support.
For landfills, wastewater treatment facilities, oil and gas sites, industrial parks, and carbon-market teams.
Designed to move from one-time estimates toward traceable monitoring evidence.
Zolena Lab provides monitoring data and analytical evidence. Statutory compliance reporting, legal verification, regulatory approval, carbon-credit certification, or formal assurance must be handled by the relevant qualified parties and review processes.
Oil sands energy efficiency analysis and decision support
KDD-based industrial data analysis using Petrinex operating records, k-means clustering, Association Rules, and Chi-square testing to identify high-efficiency operating patterns and factors associated with Steam-Oil Ratio.
Built around real operating data rather than simulation-only analysis.
Supports discovery of operating patterns and optimization opportunities.
Reported finding: Gas/NCG co-injection showed a statistically significant association with lower SOR (Chi-square, p < 0.005), indicating potential for energy-efficiency improvement and reduced steam demand.
This is decision support and opportunity identification, not a real-time optimization control system or a promise of direct optimal operating parameters.
Atmospheric pollution source attribution and regional transport identification
PMF and other receptor models can resolve major pollution sources and relative contributions when monitoring data are suitable. CMB may be used when reliable source profiles exist. CALIPSO aerosol extinction data and MERRA-2 reanalysis support regional particulate transport identification.
Supports environmental management and decision-making.
OFP analysis can support ozone-formation-contribution-oriented priorities.
Technical source attribution supports decisions; legal responsibility requires separate regulatory, permitting, enforcement, inventory, and evidentiary processes.
Predictive Emission Monitoring System for industrial facilities
AI-powered PEMS uses existing process parameters and transparent Keras/TensorFlow model architectures to predict combustion-related emissions, especially NOx, with site-specific model training and validation.
Reference case: 28 months of continuous industrial field validation.
Reported metrics include MAE = 0.5982, r = 0.9451, and 0.14% total-emissions difference in the test set.
The reference case was evaluated against US EPA PS16, CEN/TS 17198:2018, and AER/RATA-related requirements.
The reference case reported 99.93% data availability; this is not prediction accuracy. Models are site- and equipment-specific.
Application scenarios
Customer-facing reports and evidence packages
These scenarios translate Zolena Lab's monitoring and evidence capabilities into customer-facing reports, screening workflows, and partnership support.
Trust comes from saying what the technology does not do.
Compliance
Reports provide data and evidence support for compliance workflows, but Zolena Lab does not provide statutory compliance determinations, legal verification, regulatory approval, carbon-credit certification, or formal assurance opinions.
Satellite evidence
Satellite data supports screening and evidence preparation; it cannot directly observe subsurface contamination or replace all field investigation.
Model transferability
PEMS and other site-specific models require appropriate data collection, training, calibration, and validation before use in a new setting.
Next step
Discuss a scoped environmental evidence workflow.
Zolena Lab can review project goals, available data, and the evidence boundaries before recommending a monitoring or analysis path.
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