Is a measured value always more accurate than a modelled one?

Not necessarily. A measured value is accurate at the sensor; a model can estimate values where no sensor exists. The honest map shows both — measured points and modelled fields — with their respective uncertainties.

Are public AQI maps reliable?

Public maps vary widely. Maps built on validated reference data and indicative networks with co-location calibration can be reliable for trends and spatial gradients. Maps built on uncalibrated networks without quality control can mislead, especially during pollution episodes.

Spatial monitoring

Air quality mapping — spatial data, honest limits

Maps make air quality intuitive. They also make it easy to overstate confidence in places no sensor actually reached. Useful mapping is explicit about where the data ends and the interpolation begins.

Spatial air quality visualisation across a UK urban area

AirQualityTech.uk · Editorial

Approaches

The kinds of maps that actually exist

Fixed-network maps

Permanent sensor sites at known locations. Strong on temporal trends, limited by spatial density.

Mobile-survey maps

Vehicle, bicycle or pedestrian-mounted sensors mapping streets and routes. High spatial resolution, snapshot in time.

Hybrid measured + modelled

Sensor data combined with dispersion or land-use regression modelling for area-wide estimates.

Indoor building maps

Zone-level IAQ heat maps within a single building, overlaid on floor plans.

Method

From points to a continuous field

Every map is an interpolation between known points. Inverse-distance weighting, kriging and land-use regression each rest on assumptions about how the pollutant varies spatially — assumptions that hold better for some pollutants and geographies than others.

PM2.5 from regional episodes can interpolate reasonably across kilometres; NO₂ along a busy street can change by 50% over tens of metres. The same interpolation choice that works for one will mislead for the other. Map design should match pollutant behaviour, not a default template.

Honest cartography surfaces this. Confidence bands, sensor markers and an explanation of method belong on the map, not in a footnote.

Use cases

What air quality maps support

Public communication

Resident-facing dashboards that translate environmental data into action.

Estate prioritisation

Heat maps across a hospital, university or local-authority estate.

Episode response

Real-time spatial visualisation during high-pollution episodes.

Planning evidence

Site-specific air quality data for development assessments.

Limits

What mapping can hide

Risk	What goes wrong	Mitigation
Sparse sensor density	Map smooths over hotspots that no sensor recorded	Show confidence bands and sensor locations
Uncalibrated sensors	Spatial gradients reflect sensor drift, not pollution	Co-location and quality flags
Wrong interpolation method	Pollutant variability doesn't match the assumption	Choose method to match pollutant behaviour
Time mismatch	Stale data presented as live	Display timestamp and data age prominently
Measured vs modelled confusion	Modelled estimates read as measurements	Visually distinguish on the map

Suitable for

Who mapping serves well

Local authorities

Borough-wide spatial visibility for clean air zones, planning and community engagement.

Estate operators

Single-pane visibility across hospitals, campuses and commercial estates.

Environmental consultancies

Project-specific spatial data for assessments and reporting.

FAQ

Air quality mapping questions

Maps combine point measurements from fixed and sometimes mobile sensors, interpolated across the area of interest. The quality of a map depends on sensor density, sensor accuracy and the appropriateness of the interpolation method to the underlying pollutant behaviour.

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Indoor and outdoor air quality mapping, AQI dashboards and spatial sensor networks for UK clients.

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Outdoor monitoring

Systems

Data layer