Correlation between satellite observation and PM10 concentration


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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