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Retrieval of Air Quality using a newly Simulated Algorithm from Aerosol Optical Depth

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Retrieval of Air Quality using a newly Simulated Algorithm from Aerosol Optical Depth

H. S. Lim, M. Z. MatJafri and K. Abdullah
School of Physics,
University of Science Malaysia,
11800 Penang, Malaysia
Tel: +604-6533888, Fax: +604-6579150
E-mail: [email protected], [email protected]

Introduction
Aerosols are tiny particles suspended in the air (mostly in the troposphere). Some come from natural sources, such as volcanic eruptions, dust storms, forest and grassland fires, living vegetation and sea spray. About 11 % of the total emitted aerosols in our atmosphere come from human activities, such as the burning of vegetation and fossil fuels and changing the natural land surface cover, which again leads to windblown dust. Yet the human-produced aerosols account for about half of the total effect of all aerosols on incoming sunlight. From a satellite’s perspective, aerosols raise the Earth’s albedo, or make it appear brighter, by scattering and reflecting sunlight back to a space. The overall effect of these tiny particles is to cool the surface by absorbing and reflecting incoming solar radiation. Aerosol optical thickness is a measure of how much sunlight airborne particles prevent from traveling through a column of atmosphere (King and Herring, 2003). Airborne particulate matter or aerosols, whether anthropogenic or have natural origin constitutes a major environmental issue: At regional level, aerosols are contributors to visibility degradation (haze) and to acid deposition; at global level that they play a role in climate change (Sifakis and Soulakellis). The direct effect of aerosols is that aerosols directly scatter and absorb the radiation, while the indirect effect is caused by aerosols acting as cloud condensation nuclei (CCN) to change the cloud lifetime (Nakajima, et al., 2001). Air pollution in Asian cities has grown with the progressing industrialization and urbanization. This recent experience in Asia is predated by similar problems in the western countries at early stages of their economic development (UNEP Assessment Report).

The objective of this study is to estimate the concentrations of the air pollutant in time and space. We use a normal digital camera, Kodak DC290 to capture digital images of a selected target. This study gives an economical way for estimation air quality at University Sains Malaysia campus, Penang, in local scale. An algorithm was generated based on the aerosol optical depth theory. The algorithm was use to estimate the PM10 measurements. A normalization technique was used in this study for correction of multitemporal data for algorithm calibration.

Remote sensing technique has been widely used for environment pollutant application such as water quality [Dekker, et al., (2002), Tassan, (1993) and Doxaran, et al., (2002)] and air pollutant (Ung, et al., 2001b). Several studies have shown possible relationships between satellite data and air pollution [Weber, et al., (2001) and Ung, et al, (2001a)]. Other researchers used satellite data in such environment atmospheric studies such as NOAA-14 AVHRR (Ahmad and Hashim, 1997) and TM Landsat (Ung, et al., 2001b).

Study Area
The selected air quality station is located in USM campus at longitude of 100° 17.864′ and latitude of 5° 21.528′ (Figure 1). The site consists mainly of undulating land and has many assets that make it an ideal University campus. University Sains Malaysia is situated in the northeast district of Penang island (Figure 1).


Figure 1. Study area and Air Quality Station


Algorithm Model
The atmospheric reflectance due to molecule, Rr, is given by (Liu, et al., 1996) as



where

tr = aerosol optical thickness (Molecule)
Pr(q) = Rayleigh scattering phase function
mv = cosine of viewing angle
ms = cosine of solar zenith angle

We assume that the atmospheric reflectance due to particle, Ra, was also linear with the ta of a factor, K0. This assumption was reasonable because Liu, et al., (1996) also found the linear relationship between both aerosol and molecule scattering.


Atmospheric reflectance was the sum of particle reflectance and molecule reflectance, Ratm, (Vermote, et al., 1997).

Ratm = Ra+Rr (3)

where
Ratm = atmospheric reflectance
Rp = particle reflectance
Rr = molecule reflectance

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Retrieval of Air Quality using a newly Simulated Algorithm from Aerosol Optical Depth



The optical depth was given by Camagni and Sandroni, (1983), as equation (5). From the equation, we rewrite the optical depth for particle and molecule as equation (6)



where
t = optical depth
s = absorption
s = finite path



Equations (6) are substituted into equation (4). The result was extended to a three-band algorithm as equation (7)

Form the equation; we found that PM10 was linearly related to the reflectance for band 1 and band 2. This algorithm was generated based on the linear relationship between t and reflectance. Retalis et al. also found that the PM10 was linearly related to the t and the correlation coefficient for linear was better that exponential in their study (overall). This means that reflectance was linear with the PM10.



Where
A = particle concentration (PM10)
G = molecule concentration
Ratmi = atmospheric reflectance, i = 1, 2 and 3 are the number of the band
ej = algorithm coefficient, j = 0, 1, 2 and 3 are then empirically determined.

Data Analysis and Results
All the image-processing tasks were carried out using PCI EASI/PACE version 6.2 digital image processing software at the School Of Physics, University of Science Malaysia (USM). The digital images were separated into three bands (red, green and blue) for multispectral algorithm analysis. The average DN for each digital image captured at near and far distance from the reference targets were extracted. Digital images captured near to the reference target were corrected using normalization technique. Presumption made in this study was that the digital imagery captured from near to the reference target was not affected by atmosphere scattering.

A red colour paper was stick on the wall of a building as a reference target. The digital image capture near to the reference target at 9.00 a.m on 5 December 2003 was used as reference image. The difference from the DN value was used to correct for each image captured from near to the target. All the DN values of the digital imageries captured from far to the reference were adjusted according to their correspo0nding difference in the DN values based on the digital images captured from near to the reference target. This normalization technique forced the digital images to have the same illumination condition and the effect due to different solar angle was removed.

All the DN values were then converted into irradiance (equation 1, 2 and 3) using the digital camera coefficients calibrated previously for each bands (Lim, 2003). The irradiances were then converted to reflectance using equation 11 for each band. The solar angles and Earth-Sun distance were calculated corresponding to the acquisition times of the digital images. The mean solar exoatmospheric irradiance values used in this study were 1555 W/m2/mm, 1843 W/m2/mm and 1970 W/m2/mm for the red, green and blue bands respectively.

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Retrieval of Air Quality using a newly Simulated Algorithm from Aerosol Optical Depth

The calibrated digital camera coefficients are



Finally, the data were separated into two group, one consisted of 17 data points for algorithm calibration and the other one consisted of 16 data point for verification analysis. Table 1 shows the algorithm calibration coefficient obtained using the generated algorithm. Figure 2 shown the correlation coefficient and RMS error of the measured and estimated PM10 values for calibration analysis. Figure 3 shows the correlation coefficient and RMS error of the measured and estimated PM10 values for verification analysis.

Table 1: The coefficients for the calibrated algorithm
Algorithm Coefficients Values
e0 -27.1313
e1 -8.9957
e2 -140.4144
e3 873.4354



Figure 2: Correlation coefficient and RMS error of the measured and estimated PM10 values for calibration analysis


Figure 3: Correlation coefficient and RMS error of the measured and estimated PM10 values for verification analysis


Conclusion
We are quite confident with the result produced by this algorithm after verification analysis. This first application of the generated algorithm illustrates the potential use of digital camera imagery for the PM0 estimation. The high correlation was found between the retrieval reflectance and PM10 values gave an indication of the reliability of the generated algorithm. This study gives an economical way for air quality detection. Further work will be carried out to improve the normalization technique and more data are required for verification analysis of the generated algorithm.

Reference

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