Fill gaps in air quality records.

Analyser maintenance, calibration windows and power cuts punch holes in PM and gas records, and completeness thresholds decide whether a whole year of monitoring is reportable.

How it works

Co-located pollutants, meteorology and the strong daily/weekly cycles in air quality data let Gaplad reconstruct missing spans — and its per-class validation shows exactly how much to trust each fill before it goes anywhere near a report.

• Quality scan first — error codes, spikes, frozen sensors and re-installed sensors are found before anything is modelled.
• Validated before filled — synthetic gaps are carved from your observed PM or gas analyser record, filled blind, and scored against held-out truth. The winning method per gap length fills the real gaps.
• Honest by construction — short gaps use interpolation because it wins there; unfillable gaps stay unfilled with the reason stated; every filled value is flagged, with uncertainty bands from measured residuals.

Your data never leaves your browser

Filled values are flagged in the export, and the report says outright that compliance exceedances must never be detected inside filled spans. The model trains in a background thread on your machine — there is no upload, no account, and nothing to get security clearance for.

Free tier runs the full pipeline on up to 20,000 time steps (about two years of hourly data) with a watermark line on exports. A one-time licence lifts the size limit and cleans the exports. Exported CSVs flag every filled value; the trust report is a single HTML file you can attach to a deliverable.