More than half of the world’s population is living in cities. According to the WHO air quality database 80% of people living in urban areas that monitor air pollution are exposed to air quality levels that exceed WHO limits. Narrowing down to cities in low and middle income countries with more than 100 000 inhabitants, this number increases to 98%. To resolve urban air pollution problems a clear understanding of the local situation is essential. Low-income cities, which are most impacted by unhealthy air, usually have less resources available for a good reference network. It is here where a combination of low-cost sensors and satellite data can make a difference.
So far, only very few studies aim at joining heterogeneous data sources of urban air quality, and to our knowledge no previous work has provided practical solutions which can be implemented in cities everywhere.
We therefore propose to develop and demonstrate a methodology that is capable of exploiting the various available data sources, to combine them in a mathematically objective and scientifically meaningful manner, and to provide value-added maps of urban air quality at high spatial resolution.