Development of multispectral algorithm and remote sensing technique for air quality measurements over Makkah, Mina and Arafah
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Date
2011
Authors
Othman, Nadzri
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Abstract
The air quality indicator approximated by satellite measurements is known as an
atmospheric particulate loading, which is evaluated in terms of the columnar optical
thickness of aerosol scattering. The effect brought by particulate pollution has gained
interest among researchers to study aerosol and particulate matter. This study
presents the potentiality of retrieving concentrations of particles with diameters less
than ten micrometre (PMIO) and aerosol optical thickness (AOT) in the atmosphere
using the Landsat 7 ETM+ satellite imageries over Makkah, Mina and Arafah. A
multispectral algorithm was developed by assuming that surface condition of the
study area was lambertian and homogeneous. It also neglected atmospheric effect
due to Rayleigh scattering. PMlO in situ measurements were colle\;ted using
DustTrak aerosol monitor 8520, while AOT data was measured using' FieldSpec
handheld spectroradiometer and their locations were determined by a handheld
global positioning system (GPS). The Beer Lambert law was used to calculate AOT
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from transmittance of atmospheric measured using the FieldSpec handheld /.t'
spectroradiometer. The digital number (DN) recorded by satellite imageries were' -
converted to top of the atmosphere (TOA) reflectance, which is the sum of the
ground reflectance and atmospheric reflectance. Then, the atmospheric correction
(A TCOR2) method was used to retrieve the surface reflectance. Atmospheric
reflectance is obtained by subtracting the reflectance at the top of the atmosphere
(TOA) with the surface reflectance. Measured PM 10 and AOT were correlated with
atmospheric reflectance value using regreSSlOJ1 k' ... h;)ique. Various types of
regression algorithms were then ex~ined by comparing the correlation coefficient
(R) values and the root-mean-square error (RMSE) values. Then, the three band
regression algorithm (Red, Green and Blue) with highest R value and the lowest
RMSE was selected to generate PMIO and AOT maps for the study areas. Various
types of filters and windows size were used, for example, average, median and mode,
were applied to Landsat 7 ETM+ satellite imageries in order to increase the accuracy
and to minimise the noise effect of the PMIO and AOT maps over the study area.
The multispectral algorithm model showed that PMlO and AOT were high during
Hajj season as compared to other season. The overall results for calculated values of
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PMlO had average accuracy of 0.897 ± 0.085 flg/m and 0.870 ± 0.095 flglm for
single day and combined three days respectively. While calculated values of AOT
gave average accuracy of 0.8775 ± 0.0676. The proposed AOT algorithm was also
validated using multi temporal data and aerosol product from the Terra Moderate
Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging
Spectro Radiometer (MISR), and were within ±5% of calculated values. These results
provide confidence that the multispectral algorithm AOT and PMIO models can
make accurate predictions of the concentrations of AOT and PMlO over the study
area.