Air pollution detection using MODIS data

Jan Harbula; Veronika Kopacková

doi:10.1117/12.898107

28 October 2011 Air pollution detection using MODIS data

Jan Harbula, Veronika Kopacková

Proceedings Volume 8181, Earth Resources and Environmental Remote Sensing/GIS Applications II; 81811E (2011) https://doi.org/10.1117/12.898107
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

The quality of the environment has a great impact on public health while air quality is a major factor that is especially relevant for respiratory diseases. PM10 (particulate matter below 10 μ) particles are among the most dangerous pollutants, which enter the lower respiratory tract and cause serious health problems. Obtaining reliable air pollution data is limited to a number of ground measuring stations and their spatial location. We used an alternative approach and created statistical models that employed remotely sensed imageries. To establish empirical relationships, we used multi-temporal (2006-2009) MODIS aerosol optical thickness data (product MOD04, Level 2) and the PM10 ground mass concentrations. The north-western part of the Czech Republic (namely the Karlovarský and the Ustecký regions) was chosen as a test site, as all the different types of cultural landscape (forest-economical, agricultural, mining, and urban) can be found within one MODIS scene. This study was focused on the various aspects as follows (i) analysis of MODIS AOT / stationary PM10 time-series trend between 2006-2009, (ii) establishing a linear relationship between PM10 and AOT values for each station and (iii) evaluation of a spatial relationship of the annual mean AE (Ångstrom Exponent) and PM10 values.

Citation Download Citation

Jan Harbula and Veronika Kopacková "Air pollution detection using MODIS data", Proc. SPIE 8181, Earth Resources and Environmental Remote Sensing/GIS Applications II, 81811E (28 October 2011); https://doi.org/10.1117/12.898107

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KEYWORDS

MODIS

Aerosols

Statistical analysis

Atmospheric modeling

Air contamination

Data modeling

Atmospheric particles

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