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The Use of Remote Sensing and GIS to Estimate Air Quality Index (AQI) Over Peninsular Malaysia

The Use of Remote Sensing and GIS to Estimate Air Quality Index (AQI) Over Peninsular Malaysia

Asmala Ahmad
Jabatan Sains & Matematik
Pusat Perkhidmatan Akademik
Kolej Universiti Teknikal Kebangsaan Malaysia (KUTKM)
Locked Bag 1200, Ayer Keroh, 75450 Malacca, MALAYSIA
Phone: 606-233 3034 Fax: 606-233 3144
[email protected]

Mazlan Hashim
Dept. of Remote Sensing
Faculty of Geoinformation Science and Engineering
Universiti Teknologi Malaysia (UTM),
Locked Bag 791, 80990 Johor Bahru, MALAYSIA
Phone: 07-550 2873 Fax: 07-556 6163
[email protected]

Noorazuan Md Hashim
Pusat Pengajian Sosial, Pembangunan dan Persekitaran
Universiti Kebangsaan Malaysia (UKM), Bangi
Phone: 03-8926 6213

[email protected]

Mohamad Nizam Ayof
Jabatan Sains & Matematik
Pusat Perkhidmatan Akademik
Kolej Universiti Teknikal Kebangsaan Malaysia (KUTKM)
Locked Bag 1200, Ayer Keroh, 75450 Malacca, MALAYSIA
Phone: 606-233 3034 Fax: 606-233 3144

Agus Setyo Budi
Jabatan Sains & Matematik
Pusat Perkhidmatan Akademik
Kolej Universiti Teknikal Kebangsaan Malaysia (KUTKM)
Locked Bag 1200, Ayer Keroh, 75450 Malacca, MALAYSIA
Phone: 606-233 3034 Fax: 606-233 3144


The recent August 2005 haze episode was not a new experience for Malaysia as this phenomenon has been occurring almost every year. History revealed that the worst haze episode took place during May-November 1997. On the 23rd September 1997, the Sarawak capital, Kuching was declared in the state of emergency as its Air pollution Index (API) reached 839. This was the highest API ever been recorded in Malaysia. This paper reports result of a study in order to compute API using satellite-based method. Seven dates of NOAA-14 AVHRR satellite recorded data were used, representing seven days during the September 1997 thick haze episode in Malaysia. Five locations of air pollution station were selected where major pollutants have been measured conventionally. Haze information was extracted from the satellite data using ‘sky-light’ model. Relationship between the satellite recorded reflectance and the corresponding pollutant measurement was determined using regression analysis. Finally, accuracy of the result was assessed using RMSE technique. The result proven that satellite-based method using space-borne remote sensing data was capable of computing API spatially and continuously.

1 Introduction
Haze is said to be a partially opaque condition of the atmosphere caused by very tiny suspended solid or liquid particles in the air (Morris, 1975). Haze (originating from open burning or forest fire) usually contains large amount of particulate matter (e.g., organic matter, graphitic carbon). This particulate matter is hazardous to health, especially associated with lung and eye deceases. Besides that it is capable of reducing visibility, increasing the atmospheric greenhouse effects and affecting the tropospheric chemistry.

Coventionally, PM 10 can be measured from ground instruments such as air sampler, sun photometer and optical particle counter, however these instruments is impractical if measurement are to be made over relatively large areas or for continuous monitoring.

The haze episode which occured during mid-May to November 1997 is considered the worst since 1980 ( five similar haze episodes had occured in April 1983, August 1990, June 1991, October 1991 and August 1994). On 19th September 1997 Malaysian government had declared that Kuching (capital of Sarawak) was in the state of emergency when the PM10 API (Air Pollution Index) exceeded 650 (hazardous level). By 23rd September 1997 the condition worsened as Kuching’s PM10 API reached 839, the highest ever been recorded by the country.

This paper reports results of a study to determine PM 10 from NOAA-14 AVHRR satellite data. Their concentration and spatial distribution will be quantified based on updated measurement system, AQI. This current study is an extension of previous work by Ahmad and Hashim (1997, 2000, 2002), and mazlan et. al (2004) that produced models to quantify haze in API.

Figure. 1. Raw NOAA AVHRR data dated 22 September 1997. Location of the selected air pollution stations are damarcated as letter A,B,C,D and E designated for Kuala Lumpur, Prai, Pasir Gudang, Bukit Rambai, and Bukit Kuang respectively. Combination of band 1, 2 and 4 are used to visually differentiate between haze (orange), low clouds (yellow) and high clouds (white).

2 Materials
This study involved the usage of three types of data namely; ground-truth data, satellite data and ancillary data.

2.1 Ground-truth data
Conventional measurements of haze were complementarily used throughout performing data processing for extraction of PM 10 information. PM 10 measurements in micrograms per meter cube (?gm-3) from 1st to 30th September 1997 were carried out by ASMA (Alam Sekitar Malaysia Sdn. Bhd.) to represent the actual haze intensity over the study area. For the purpose of this study, the measurement was later converted to AQI.

Table 1. Air Quality Index (AQI) for Particulate Matter up to 10 micrometers in diameter (PM 10)

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 2.

Table 2. NOAA-14 AVHRR sensor and spectral characteristics
(Source: Kidwell et al., 1995)

2.3 Ancillary data
Meteorological information over study area, including visibility (Figure 2), air temperature, pressure, relative humidity, wind, etc were obtained from MMS (Malaysian Meteorological Service).

Fig.ure2. Reducing visibility of Petronas Twin Towers resulted from the appearance of haze

3 Method
Three modules incorporated in this study are (1) Derivation of haze model, (2) Regression analysis, and (3) Accuracy Assessment.

3.1 Derivation of haze model
Prior to further data processing, post launch calibration of visible Band 1 NOAA-14 AVHRR 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 3 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.

Figure 3. Model used in this study is based on the skylight parameter
(Source : Modified after Siegenthaler and Baumgartner, 1996)

This model can be described by:

σ – R = L – V (1)
σ : 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 Regression analysis
Calibration pixels of NOAA-14 AVHRR data were sampled within a radius of 2.5 km from each of the air pollution stations. The relationship between PM 10 AQI and satellite-recorded reflectance of band 1 AVHRR, were analysed using linear regression.

3.3 Accuracy Assessment
In order to verify the accuracy of the regression model, RMSE (Root-mean-squared Error) was implemented to the AQI values obtained by the model.

4 Results
The scatter plot for PM 10 versus satellite reflectance of band 1 NOAA AVHRR with its linear regression trend is shown in Figure 4 where the coefficient of determination, R2 is 0.5563. The linear regression model can be expressed as:

PM10_Concentration (AQI) = (5.174 x Satellite_Reflectance) – 77.877 (3)

Figure 4. PM 10 in AQI versus satellite reflectance in percentage. Linear regression trend is shown in black line

The RMSE varies accordingly for all the five PM 10 ground stations ranging from 7 to 62 and with the average of 33 (Table 3). It is believed that the relatively high RMSE was due to limited number of air pollution stations used. Future study will consider of using more air pollution stations as well as other value-added ancillary data in order gain better and reliable accuracy.

Table 3. Average RMSE for respective haze components at Penang and Johor Bahru

The spatial distribution of PM 10 can be shown in a colourful map (Figure 5) consisting of regions in green (good), yellow (moderate), orange (unhealthy for sensitive groups), red (unhealthy), purple (very unhealthy) and maroon (hazardous). Cautionary Statements for every region are given in detail in Table 1.

Figure 5. PM 10 concentration in AQI for 22nd September 1997. The PM 10 level in most area was good and moderate.

5 Discussion and Suggestion
Integration of remote sensing and GIS (global positioning system) technology has been widely used in the field of atmospheric science related to air pollution. Our current and future studies on this field will focus on such integration. Besides that, the usage of 36 air pollution stations (Fig. 6) will increase the accuracy of the result (previously only five stations were used).

Figure 6. Air pollutions stations used in current study

The use of GIS interpolation to map haze based on iso-lines seems to be successful. Some of the outcomes of using GIS to determine spatial distribution of PM 10 have been obtained as shown in Figure 7, 8 and 9.

Figure 7. Iso-lines of PM 10 for 10 and 11 August 2005.

Figure 8. Iso-lines of PM 10 for 12 and 13 August 2005.

Figure 9. Iso-lines of PM 10 for 14 and 15 August 2005.

6 Conclusion

The study shows that remote sensing technique is capable of determining PM 10 concentration spatially and 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. The result can further be improved by using integration of both remote sensing and GIS technology. Current progress of using this integration technique has show a promising outcomes.


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