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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:

  1. it provides a complete survey of the city.
  2. it shows the major sources of pollution together with their distribution
  3. it indicates where efforts should be preferably made to decrease the level of pollution
  4. it helps further analysis in showing relationships that might exist between city features (taken in the broad sense) and air pollution distribution
  5. 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).
STUDY AREA
The study area is Penang Island, Malaysia, located within latitudes 5o 12’ N to 5o 30’ N and longitudes 100o 09’ E to 100o 26’ E (Figure 1). The corresponding satellite track for the TM scene is 128/56. The corresponding PM10 measurements were collected simultaneously during the satellite overpass.


Figure 1. The location of the study area.


DATA ANALYSIS AND RESULTS
Recently most the works on air quality using satellite data have been done using MODIS data. In this study, data from satellite Landsat TM7 was used because of its high spatial resolution of 60 m for thermal infrared band.

The Landsat TM satellite image was rectified using the second order polynomial coordinates transformation to relate groumd control points in the map to their equivalent row and column positions in the TM scences. A nearest neighbour geometric correction method was applied to the acquired satellite image to ensure that the digital numbers of the image remained the same.

The DN value for the thermal infrared of the corresponding PM10 measurements location were also extracted and converted into radiance and apparent temperature. The main focus in this study was to test the relationship between thermal infrared data and the air quality measurements at the ground.

The thermal signals computed from the thermal band were used as independent variables. The TM signals were then regressed against the PM10 concentrations using linear equation. This model produced high correlation coefficient and low root-mean-square deviation (RMS) (Figure 2). Raw DN was used in this study because of the use of radiance and apparent temperature in the regression analysis did not improve the accuracies. A good correlation agreement between measured PM10 and estimated PM10 (linear correlation coefficient, R, of 0.94) obtain in this study indicates that the accuracy of the proposed algorithm is high. A PM10 map over Penang, Malaysia was generated using the proposed algorithm and colour-coded for visual interpretation (Figure 3).


Figure 2. Relationship between measured and estimated PM10 (µg/m3)



Figure 2. Map of PM10 around Penang Island, Malaysia (Blue < 40 µg/m3, Green = (40-80) µg/m3, Yellow = (80-120)µg/m3, Orange = (120-160) µg/m3, red = (>160) µg/m3 and Black = Water and cloud area)


CONCLUSION
This study shows a strong correlation between the satellite observation of thermal band and the PM10 concentration at the ground. Further analysis will done to be verification of the PM10 measurements using the proposed linear equation.

ACKNOWLEDGEMENTS
This project was carried out using a short term research grants from Universiti Sains Malaysia. The data used in this study was supply with compliment from Malaysia Centre for Remote Sensing (MACRES). We would like to thank the technical staff who participated in this project. Thanks are extended to USM for support and encouragement.

REFERENCES
  • Slater, J. F., Dibba, J. E., Campbell, J. W. and Moore, T. S., 2004, Physical and chemical properties of surface and column aerosols at a rural New England site during MODIS overpass, Remote Sensing of Environment, 92 (2), 173 – 180.
  • Ung, A., Weber, C., Perron, G., Hirsch, J., Kleinpeter, J., Wald, L. and Ranchin, T., 2001, Air Pollution Mapping Over A City – Virtual Stations And Morphological Indicators. Proceedings of 10th International Symposium “Transport and Air Pollution” September 17 - 19, 2001 – Boulder, Colorado.
  • Ung, A., Wald, L., Ranchi, T., Weber, C., Hirsch, J., Perron, G. and Kleinpeter, J., 2003, Air pollution mapping: relationship between satellite-made observation and air quality parameters. Procceding of the 12th International Symposium , Avignon, 16-18 June 2003, France, p.p. 105-112, [Online] available: http://www-cenerg.cma.fr/Public/themes_de_recherche/teledetection/title_tele_air/title_tele_air_pub/air_pollution_mappin4043.
  • Wald, L. and Baleynaud, J. M., 1999, Observing Air Quality Over The City Of Nantes By Means Of Landsat Thermal Infrared Data, International Journal Of Remote Sensing, 20 (5), 947 – 959.
  • Weber, C., Hirsch, J., Perron, G., Kleinpeter, J., Ranchin, T., Ung, A. and Wald, L., 2001, Urban Morphology, Remote Sensing and Pollutants Distribution: An Application To The City of Strasbourg, France. International Union of Air Pollution Prevention and Environmental Protection Associations (IUAPPA) Symposium and Korean Society for Atmospheric Environment (KOSAE) Symposium, 12th World Clean Air & Environment Congress, Greening the New Millennium, 26 – 31 August 2001, Seoul, Korea. [Online] available: http://www-cenerg.cma.fr/Public/themes_de_recherche/teledetection/title_tele_air/title_tele_air_pub/paper_urban_morpho
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