This contribution presents an approach to map urban air quality using passive-sensor satellite data.
Air pollution is inherently a spatial phenomenon: airborne transport of substances yields a multi-source mixture
of pollutants at any given location, at and beyond source regions like cities. Thus, satellite data may provide infor-
mation on the distribution of pollutants relative to geophysical parameters pertaining to surface and atmospheric
states. We develop a method to estimate spatial patterns of street-level particulate matter (PM) concentrations
based on satellite-retrieved aerosol optical depth (AOD). AOD data is obtained based on the Multi-Angle
Implementation of Atmospheric Correction (MAIAC) algorithm applied on data from the Moderate Resolution
Imaging Spectroradiometer (MODIS) instrument.
The relationship between vertically integrated AOD and PM at street level is modulated by variability in
geophysical conditions. Hence, we focus on the detection of sets of conditions that allow a reliable analysis of
street-level air pollution based on satellite-derived AOD. AOD and ground-based (PM) measurements are paired
with explicit consideration of geophysical parameters such as boundary layer height, relative humidity, wind
speed, wind direction, temperature and land surface cover. AOD and PM are linked via a normalization approach,
in which the parameters for each month per year are ranked individually from highest to lowest value. This allows
us to easily compare associated AOD and PM data pairs and to investigate the governing factors of the relationship
between these parameters.
First results indicate that planetary boundary layer height and relative humidity exert the strongest influence on
the correlation of AOD and PM, the magnitude and type of the influence changing with season. We conclude that
conditions often associated with high air pollution, e.g. stable inversion situations not perturbed by high wind
speeds may be particularly suited for satellite-based analysis. Based on our spatial estimates of street-level air
quality, we ultimately aim to obtain a better understanding of pollution event patterns and their development in
time and space, relative to urban topography and surface cover.