In order to reduce air pollution, we need to know what is causing it. Increased confidence in the ability to link specific sources of air pollution to localized hotspots can boost support for specific policy measures, especially where there may be political or social push-back to change or control. Source apportionment methods define relationships between emissions from sources and air pollution concentrations at locations of interest, and thus facilitate the design of the most cost effective and efficient control strategy for air pollution reductions. Increasingly we are aware of large spatial variability in air pollution at resolution too fine for typical source apportionment tools. City-block scale (hyper-local) variability in air pollution is often caused by sources that aren’t characterized well in “official” emissions inventories. Often, there are sparse or no emissions inventories in many developing countries. Linking emissions sources to areas of high pollution (air pollution “hotspots”) is sometimes as “simple” as looking upwind from those hotspots. Especially with hyper-local hotspots, the air pollution sources that cause them are often nearby. We are presenting a real-time and retrospective website that will make the relationship between high levels of air pollution and nearby emissions sources visible to grass-roots advocates, community-scientists, regulators, and anyone interested in learning more about sources of air pollution.

Using examples in partner cities as proof of concept, Air Tracker integrates a map of real time air pollution measurements from regulatory monitors and air sensors. It uses a weather model to develop and overlay “source areas” showing the area of influence upwind of those measurements, ie: the area where the air likely traveled over before being measured. By integrating these tools within a single, user-friendly platform, we are helping the public visualize the relationship between air pollution, weather and emissions sources, making science-heavy information accessible. We will also identify areas where scientific, data or tool advancements would be the most beneficial.