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Poster Session 2
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Mapping and measuring the troposphere pollutants originated from the 1997 forest fire in south east asia.
3. Results and Discussion
Table 3 shows the best model derived from multi regression analysis for each of the atmospheric pollutants. All the five pollutants reveal a good relationship where the r 2 is exceeding 0.7. However, SO 2 exhibits the strongest relationship between satellite reflectance and sulphur readings and this is followed by NO 2, O 3, CM and PM 10. These models were then applied on calibrated images to map the spread and the amount of these pollutants over Peninsular Malaysia.
The ppm values for CM, SO 2, NO 2 and O 3 and ug/cu.m value for PM10 observed over Peninsular Malaysia versus the regressed computed values using both bands 1 and 2 is shown in figure 4. Meanwhile, figure 5 shows the spread of the pollutants over Peninsular Malaysia. All the pollutants except for PM10 not been seen at unhealthy levels. When the measured level of each of the four pollutants were converted to a scale index (Air Pollution Index), they respectively reveal a value of less than 50 which is a good Malaysian air quality index. However, for PM10 156.84 ug/cu.m ( exceeds API value of 100) indicates this pollutant is an indicator of an unhealthy condition. PM 10 was identified as the main pollutant driving the atmospheric pollution during the fire in 1997 in Malaysia and is the concern of many people because it is respirable and can cause diseases like asthma attacks, chronic bronchitis, decreased lung function and etc.
4. Summary
Remote sensing data can be used for mapping air pollutants over large areas with minimum cost and time. The regression analysis using air pollutant readings obtained from observations stations and satellite reflectance shows good correlation for all the pollutants. Of all the five pollutants, PM10 was identified as the most hazardous pollutant present over Malaysian atmosphere during the 1997 forest fire scenario in South East Asia. The high level of this pollutant could also be contributed by construction activities and unpaved roads. The ongoing study using more sample points is anticipated to further improve the results of the study.
References
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Asmala Ahmad and Mazlan Hashim, 1997, Determination of Haze from remotely sensed data: some preliminary results, Proceedings of 18th Asian Conference on Remote Sensing 1997 in Kuala Lumpur, 20-24 October 1997, R-11-1- R-11-6, Asia Associate in Remote Sensing.
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Cahoon Jr., D.R., Stocks, B.J., Levine, J.S., Cofer III, W.R., dan Pierson, J.M., 1994, "Satellite Analysis of the Severe 1987 Forest Fires in northern China and Southeastern Siberia." Journal of Geophysical Research. 99(D9) ; 18,627 - 18, 638.
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Franca, G.B., and Cracknell, A.P., 1995, A simple cloud masking approach using NOAA AVHRR daytime data for tropical areas, IJRS, Vol.16, No.6, 1697-1705.
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Kaufman, Y.J., Tucker C.J.,dan Fung, I., 1990, "Remote Sensing of Biomass Burning in the Tropics." Journal of Geosphysical Research. 95 (D7) ; 9927-9939.
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Kaufman, Y.J, 1993, Aerosol Optical thickness and atmospheric path radiance, Journal of Geophysical Research, 98, 2677-2692.
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Lillesand, T M., and Kiefer R W., 1994, Remote Sensing and Image Interpretation, John Wiley & Sons, Inc.
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Rao, N.C.R and Chen, J., 1998, Calibration updates for the visible and near infrared channels of the Advanced Very High Resolution Radiometer on the NOAA- 14 spacecraft, http://140.90.207.25:8080/EBB/ml/niccal2.html
(date of access: 08 December 1999).
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UNEP (1999). Levine, J.S., Bobbe, T., Ray, N., Singh, A. and R.G. Witt., Wildland Fires and the Environment: a Global Synthesis. UNEP/DEIAEW/TR.99-1.
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Vadivale, M., 1997, http://www.geocities.com/HotSprings/
2188/hazemma.html
(date of access: 09 March 2000).
Table 3. Best model derived from multi regression analysis for each of the atmospheric pollutants.
| Atmospheric pollutants |
Best regression coefficients (r2) |
Model |
| Nitrogen dioxide (NO2) |
0.879 |
y = -1.614 x 10-2(DN1) + 1.511 x 10-2(DN2) + 4.148 x 10-3) + 0.005 |
| Carbon monoxide (CO3) |
0.806 |
y = -0.534(DN1) + 1.604(DN2) + 0.112) |
| Sulphur dioxide (SO2) |
0.991 |
y = 0.005352(DN1) - 0.006247(DN2) + 0.007604 |
| Ozone (O3) |
0.864 |
y = -0.005738(DN1) - 0.0005142(DN2) + 0.05224 |
| PM 10 |
0.778 |
y = 146.903(DN1) - 99.071(DN2) + 88.984 |
Figure 4. The relationship between observed value and regression adjusted computed values (channels 1 and 2) for the five pollutants originated from forest fire emission.
Figure 5. The spread and amount of PM 10 CM, SO2,O3 and NO2 over Peninsular Malaysia during September, 1997.
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