SATELLITE-BASED APPROACH FOR MULTI-TEMPORAL PM10 ANALYSIS OVER MALAYSIA.

SATELLITE-BASED APPROACH FOR MULTI-TEMPORAL PM10 ANALYSIS OVER MALAYSIA

ASMALA AHMAD, MAZLAN HASHIM, M. NIZAM AYOF, AGUS SETYO BUDI

Dept. of Science and Mathematics, Centre of Academic Services (CAS), National Technical University, College of Malaysia (KUTKM), Locked Bag 1200, Ayer Keroh, 75450 Malacca, Malaysia

asmala@kutkm.edu.my

Dept. of Remote Sensing, Faculty of Geoinformation Science and Engineering, Universiti Teknologi Malaysia (UTM), Locked Bag 791, 80990 Johor Bahru,Malaysia.

mazlan@fksg.utm.my

Dept. of Science and Mathematics, Centre of Academic Services (CAS), National Technical University, College of Malaysia (KUTKM), Locked Bag 1200, Ayer Keroh, 75450 Malacca, Malaysia

Dept. of Science and Mathematics, Centre of Academic Services (CAS), National Technical University, College of Malaysia (KUTKM), Locked Bag 1200, Ayer Keroh, 75450 Malacca, Malaysia

Abstract: This study aims to analyse one of the haze major constituent, particulate matter sizing less than 10 micron (PM 10) from space-borne platform. Seven dates of NOAA-14 AVHRR satellite were successfully recorded for this purpose. These remotely sensed data represented the seven days during the September 1997 thick haze episode in Malaysia. Five locations of air pollution station were selected where PM10 has been measured. Visible (0.58 - 0.68 m) and Near Infrared (0.725 - 1.00 m) of AVHRR band was utilised for this purpose. Relationship between the satellite reflectance and the corresponding PM 10 ground measured API was determined using regression analysis. Variation of PM 10 concentration for seven days was then analysed using the obtained model. Finally, accuracy of the result was assessed using RMSE technique. The result proven that remote sensing technique using NOAA-14 AVHRR data was capable of quantifying PM 10 concentration spatially and continuously. Complementarily, DMSP and Earth Probe TOMS satellite data were also used as comparison.

Keywords: Haze, PM 10, API, NOAA-14 AVHRR, regression

1. Introduction

In recent times, reduced visibility has become a recurrent phenomenon across areas of Southeast Asia, resulting from biomass burning in Kalimantan and Sumatra. Haze events have been recorded in 1983, 1990, 1991, 1994, 1997 and 1998 (Radojevic and Hassan 1999, Muraleedharan et al. 2000). Koe et al. (2001) noted that reduced visibility in Malaysia during the 1997 haze episode was the result of long range transport of smoke from Sumatra. Haze from biomass burning contains large amount of trace gases (e.g., CO, NO2, SO2) and particles matter (e.g., organic matter, graphitic carbon), which are

hazardous to health. Lung and eye deceases have been identified as the common immediate effect of haze. In long term, haze is capable of increasing the atmospheric greenhouse effects besides affecting the tropospheric chemistry. In big cities, emission from industrial and vehicle have acted as local contributors that worsen the situation.

Main constrains over conventional monitoring instruments are that they could not detect an early sign of haze and is impractical for large areas or for continuous monitoring. Besides that they are also logistically expensive and time consuming. The 1997 haze events prompt the need for searching of an alternative method for determining and monitoring haze in Malaysia.

Remote sensing offers a cheap, fast and more reliable alternative to monitor haze continuously over any given areas and at any time. The current study is an extension of previous work that earlier discussed on techniques for determination and mapping of haze from NOAA-14 AVHRR scene (Asmala and Mazlan, 1997, 2000, 2002).

2. Materials

Materials used in this study are as follows:

2.1 Ground-truth data

Figure 1 : (a) Study area, and (b) Combination of band 1 (0.58 - 0.68 m), 2 (0.725 - 1.00 m) and 4 (10.30 - 11.30 m) of NOAA-14 AVHRR data dated 22 September 1997, were used to differentiate between haze (orange), low clouds (yellow) and high clouds (white).

2.2 Satellite data

Seven sets of NOAA-14 AVHRR data dated 22, 23, 25, 2, 28, 29 and 30 September 1997 acquired from SEAFDEC (Southeast Asia Fishery Development Centre) receiving station were used. NOAA-14 AVHRR was suitable for haze study as it offers high spectral and temporal resolution with a minimum cost. Some useful characteristics of NOAA-14 AVHRR satellite are shown in Table 1.

2.3 Ancillary data

Meteorological information over study area, including visibility, air temperature, pressure, relative humidity, wind, etc were obtained from MMS (Malaysian Meteorological Service).

Station

A. Kuala Lumpur, Wilayah Persekutuan

B

.

Prai, Penang

C

.

Pasir Gudang

,

Johor

D. _BukitRambai

,

Melaka

Table 1: NOAA-14 AVHRR sensor and spectral characteristics

AVHRR Sensor characteristics

Swath width 2399km

Resolution at nadir 1.1km approx.

Altitude 833km

Quantisation 10 bit

Orbit type Sun synchronous

No. of orbits per day 14.1 (approx.)

AVHRR Spectral characteristics

Channel No Wavelength (microns) Typical use

1 0.58 - 0.68 Daytime cloud and surface mapping

2 0.725 - 1.00 Land-water boundaries

3 3.55 - 3.93 Night cloud mapping, sea surface _temperature

3A N/A Snow and ice detection

3B N/A Night cloud mapping, sea surface _temperature

4 10.30 - 11.30 Night cloud mapping, sea surface _temperature

5 11.50 - 12.50 Sea surface temperature

(Source: Kidwell et al., 1995)

3. Method

Four modules incorporated in this study are (a) Derivation of haze model, (b) Sampling and Regression analysis, (c) Multi-temporal analysis, and (d) Visual analysis from other satellite data.

3.1 Derivation of haze model

Prior to further data processing, post launch calibration of visible and near infrared channel was earlier implemented in order to compensate data degradation due to extreme temperature change before and after launching of AVHRR sensor to space (Rao et al., 1996). Clouds and haze were successfully differentiated using thresholding technique (Baum et al., 1997). This to ensure both were not being misinterpreted between each other. Model used in this study is based on Siegenthaler and Baumgartner (1996), which make use of skylight to indicate the existence of haze. Skylight is an indirect radiation, which occurs when radiation from the sun being scattered by elements within the haze layer. It is not a direct radiation, which is dominated by pixels on the earth surface. Figure 2 shows electromagnetic radiation path propagating from the sun towards the NOAA-14 AVHRR satellite penetrating through a haze layer. Path number 1, 3 and 4 are skylight caused by direct radiation, whereas path 2 is indirect radiation.

This model can be described by:

 - R = L – V …(1)

where,  : reflectance recorded by satellite sensor,

R : reflectance from known object from earth surface ,

L : skylight, and

V : lost radiation caused by scattering and absorption.

3.2 Sampling and regression analysis

From the NOAA-14 AVHRR data, calibration pixels were sampled within a radius of 2.5 km from each of the air pollution stations in order to execute regression analysis. The best regression models for PM10 is shown below where the R2 _{(coefficient of determination) obtained is 0.5098 (Figure 3).}

Figure 3 : Regression model for PM 10

3.3 Multi-temporal analysis

Multi-temporal analysis was performed at two selected cities, Prai Station in Penang and Pasir Gudang Station (Figure 4) in Johor. Results of the analysis were shown as graphs of haze intensity variation (API vs time) for all the five major components throughout the seven NOAA-14 AVHRR acquisition dates (Figure 6). Root-mean-square error (RMSE) analysis was carried out where ground-truth measured API and model derived API were used as input. Obtained RMSE both cities are shown in Table 2.









 API_calculated API_measured 2 n

1

RMSE …

(2) where:

n : sample size,

APIcalculated : Air Pollution Index calculated by model

APImeasured : Air Pollution Index measured by air station

3.4 Visual analysis from other satellite data

Visually, the scenario was also observed from other satellite data such as DMSP (Defence Meteorological Satellite Program) satellite (Figure 7) and Earth Probe TOMS (Total Ozone Mapping Spectrometer) satellite (Figure 8).

4. Results and Discussion

Result of this study was presented as PM 10 concentration map for the whole Peninsular Malaysia. PM 10 condition for 26 September 1997, 7.29 GMT is shown in Figure 5. RMSE for selected big cities; Prai and Pasir Gudang are 31 and 27 respectively. It was believed that the relatively high gained RMSE was due to limited number of air pollution stations used. Future study will consider of using more air pollution stations as well as higher resolution satellite data in order to obtain better accuracy.

PM10 VS Band 1

PM10 = -0.2410*(Band 1)2_{+0.007*(Band 1)}3_+108.3328

R2 = 0.5098 0 50 100 150 200 250 300

0 5 10 15 20 25 30 35 40 45 50

Band 1 (Reflectance)

Table 2 : RMSE for PM 10 concentration at Penang and Johor Bahru

Location PM10 (API)

Prai, Penang 31

Pasir Gudang, Johor Bahru 27

5. Conclusion

The study shows that remote sensing technique is capable of monitoring and quantifying PM 10 concentration continuously with minimum cost and time. These are useful in order to provide haze early warnings, so that necessary measures could be taken effectively by both government authorised party as well as public.

(a)

(b)

Figure 4 : Multitemporal trend of calculated and measured PM 10 at (a) Prai, Penang, and

(b) Pasir Gudang, Johor.

PM10(API)

0 (Awan)

151-200

1-50

201-250

51-100

251-300

101-150

[image:8.595.57.529.58.228.2]

Figure 6 : Haze visual analysis from band 1, 2 and 4 of NOAA-14 AVHRR satellite data for March 1997 (a)22nd_,

(b)23rd_{, (c)25}th_{, (d)26}th_{, (e)28}th_{, (f)29}th _{and (g)30}th _.

Landcover Water Stable Fire Cloud Boundary N/A

[image:8.595.53.552.281.472.2]

(Source : National Geophysical Data Center, NOAA, USA)

Figure 7 : Haze visual analysis from DMSP satellite data for March 1997 (a)22nd_{, (b)23}rd_{, (c)25}th_{, (d)26}th_,

(e)28th_{, (f)29}th _{and (g)30}th _.

(Source: Goddard Space Flight Center, USA)

Figure 8 : Haze visual analysis from Earth Probe TOMS satellite data for March 1997 (a)22nd_{, (b) 23}rd_{, (c)26}th_,

(d)28th_{, (e)29}th _{and (f)30}th _.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(a)

(b)

(c)

(d)

(f)

[image:8.595.68.536.516.668.2]

References

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