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Correlation between satellite observation and PM10 concentration
H. S. Lim
School of Physics
Universiti Sains Malaysia
Minden 11800 Penang, Malaysia.
Tel: +604-6577888, Fax: +604-6579150
hslim111@yahoo.com.sg
M. Z. MatJafri
School of Physics
Universiti Sains Malaysia
Minden 11800 Penang, Malaysia.
Tel: +604-6577888, Fax: +604-6579150
mjafri@usm.my
K. Abdullah
School of Physics
Universiti Sains Malaysia
Minden 11800 Penang, Malaysia.
Tel: +604-6577888, Fax: +604-6579150
khirudd@usm.my
N. M. Saleh
School of Physics
Universiti Sains Malaysia
Minden 11800 Penang, Malaysia.
Tel: +604-6577888, Fax: +604-6579150
C. J. Wong
School of Physics
Universiti Sains Malaysia
Minden 11800 Penang, Malaysia.
Tel: +604-6577888, Fax: +604-6579150
ABSTRACT
This study investigates the feasibility of satellite thermal observation and ground measurement of particulate matter having a diameter less than 10- micron meter (PM10) for air quality mapping over Penang Island, Malaysia. The objective of this study was to test the linear relationship between the satellite thermal and ground measurements observation. The use of temperature and raw digital number (DN) is discussed in this paper. The corresponding PM10 data were measured simultaneously with the acquisition satellite scene for algorithm regression analysis. The locations of the corresponding PM10 locations were determined using a handheld GPS. The DNs were extracted corresponding to the PM10 locations. The extracted data were then used for algorithm regression analysis. This study produced a high spatial distribution map of PM10 using Landsat TM thermal data.
INTRODUCTION
Atmospheric aerosol particles directly affect the Earth’s radiation balance by backscattering and absorbing short wavelength solar radiation. However, there is considerable uncertainty over the ‘‘direct effect’’ of aerosols due to their spatial and temporal heterogeneity (Slater, et al., 2004). Atmospheric pollution in cities is receiving more and more attention (Wald and Baleynaud, 1999). Air pollution has long been a problem in the industrialized nations of the West. It has now become an increasing source of environmental degradation in the developing nations of East Asia.
Recent studies have shown the relationship between Landsat TM thermal infrared satellite data and ground-based measurements [Weber, et al., (2001) and Ung, et al, (2001)]. This study considered the assumption that the particulate matter (PM) was strongly correlated with the thermal infrared image of Langsat TM (Weber, et al., 2001). These satellite data are radiances observed by the sensor in the thermal infrared band. These radiances are the function of the temperature and emissivity of the surface and also of the optical properties of the atmospheric column above the pixel and its surroundings. Most of the bodies have emissivity values larger than 0.8. In that case, for the spectral band of interest, the emission by the surface is the most important phenomenon. The radiance emitted by the atmosphere towards the sensor after reflection on the surface accounts for approximately 10% of the total radiance and may be neglected (Ung, et al., 2003). Accordingly, the temperature, Tsat observed by the space-borne sensor can be written approximately:
Tsat=teTsur+Tatm
where Tsur is the surface temperature, e the surface emissivity, t the transmission factor of the atmosphere and Tatm a weighted-average temperature modelling the emission of the atmosphere itself. More detail information is give by Ung, et al. (2003).
The objective of this study is test the feasibility of Landsat TM for PM10 mapping using the proposed developed algorithm. The aim of air quality monitoring is to get an estimate of pollutant concentrations in time and space.
Routine observation made by the environmental satellite is certainly a valuable aid in improving the actual methods of mapping. Mapping capability is strongly required because of the major following benefits:
- it provides a complete survey of the city.
- it shows the major sources of pollution together with their distribution
- it indicates where efforts should be preferably made to decrease the level of pollution
- it helps further analysis in showing relationships that might exist between city features (taken in the broad sense) and air pollution distribution
- it serves as a basis for re-enforcing the sampling strategy by moving some stations to appropriate locations, or adding some or possibly removing some (Wald and Baleynaud, 1999).
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