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: mjafri@usm.my, khirudd@usm.my 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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