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eLichens — A modelled 5-metre street-level air-quality surface for 300+ cities, plus the sparse-input/fine-output pair that trains it: real-time eLos station measurements (NO2, O3, CO, CO2, PM1/PM2.5/PM10, temperature, humidity, pressure and an onboard microphone) fused with regulatory reference-station data, topography, traffic and meteorology into hourly and 24-hour-forecast grids. The licensable byproducts are (a) the station-versus-reference-station fusion pairs, which are ready-made supervised training data for hyperlocal downscaling and land-use-regression models; (b) forecast-versus-ob…
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A modelled 5-metre street-level air-quality surface for 300+ cities, plus the sparse-input/fine-output pair that trains it: real-time eLos station measurements (NO2, O3, CO, CO2, PM1/PM2.5/PM10, temperature, humidity, pressure and an onboard microphone) fused with regulatory reference-station data, topography, traffic and meteorology into hourly and 24-hour-forecast grids. The licensable byproducts are (a) the station-versus-reference-station fusion pairs, which are ready-made supervised training data for hyperlocal downscaling and land-use-regression models; (b) forecast-versus-observed residual pairs at 5 m resolution and 24-hour horizon — scoring data for any urban dispersion model; (c) a cross-jurisdiction AQI harmonisation layer, the same grid scored against three baselines (proprietary eLichens index, US EPA, EU Citeair), effectively a mapping table between regulatory regimes; (d) urban acoustic/noise readings from the eLos microphone, a byproduct no air-quality peer advertises; (e) a European pollen index; and (f) street-level CO2, which regulatory networks almost never measure at that density.
From 11 yearsCoverage Information Technology · IndustrialsAsset class Equities
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eLichens — A modelled 5-metre street-level air-quality surface for 300+ cities, plus the sparse-input/fine-output pair that trains it: real-time eLos station measurements (NO2, O3, CO, CO2, PM1/PM2.5/PM10, temperature, humidity, pressure and an onboard microphone) fused with regulatory reference-station data, topography, traffic and meteorology into hourly and 24-hour-forecast grids. The licensable byproducts are (a) the station-versus-reference-station fusion pairs, which are ready-made supervised training data for hyperlocal downscaling and land-use-regression models; (b) forecast-versus-ob…
eLichens offers (Alternative, Geospatial, Esg, Sentiment) — Modelled surface dominates: 300+ cities at 5 m resolution implies order 1e10 grid cells if naively gridded, so the delivered product is per-street summaries plus queried rasters rather than a flat dump. Underlying observations: order 1e8 to 1e9 station-hours across the eLos fleet plus ingested reference-station series since ~2015. Hourly forecasts for 300 cities add a comparable forecast stream.
Training and validation of deep-learning air-quality downscaling and land-use-regression models — the sparse-station-plus-covariates to 5 m ground-truth pair is exactly the supervision that is otherwise impossible to assemble, and 300 cities makes it cross-climate and cross-urban-form; scoring and bias-correction harnesses for any dispersion model or satellite-derived surface, with the multi-baseline AQI layer as the harmonisation reference; hyperlocal exposure assignment for cohort epidemiology where monitoring stations are kilometres apart; personalised health-advisory and asthma/COPD alerting engines; outdoor-advertising and out-of-home media audience scheduling against clean-air time windows (a fit given the CMO-led commercial motion); mobility, routing and micro-mobility services avoiding exposure peaks; urban-design and pedestrianisation impact simulation; real-estate and office-siting environmental scoring; ESG and Scope-3-adjacent air-impact disclosure; pollen-and-air composite allergy-risk products; urban noise-mapping products that piggyback on an existing deployed sensor fleet.
The data is with 11 years of history.
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