AIR POLLUTION – MONITORING MODELLING AND HEALTH

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172 Air Pollution – Monitoring, Modelling and Health 3. Conclusion Malaysia air quality monitoring generally are focused on five air pollutants which are CO, O 3 , NO 2 , SO 2 and PM 10 . Ambient air quality monitoring in Malaysia was installed, operated and maintained by ASMA under concession by the DOE through a network of 51 CAQM. CAQM monitors a wide range of anthropogenic and natural emissions; there are SO 2 , NO 2 , O 3 , CO, and PM 10 . Similar to other ASEAN countries, the air quality in Malaysia is reported as the API. API system of Malaysia closely follows the PSI system of the United States. Four of the index's pollutant components (i.e., CO, O 3 , NO 2 , and SO 2 ) are reported in ppmv on the other hand PM 10 is reported in µg/m 3 . An individual score is assigned to the level of each pollutant and the final API is the highest of those 5 scores. To reflex the status of the air quality and its effects on human health, the ranges of index values is categorized as follows: good (0-50), moderate (51-100), unhealthy (101-200), very unhealthy (201-300) and hazardous (>300).Most of the time, API in Malaysia is always based on the concentration of PM 10 . EQR 2009 reported that, there was a slight decrease in the number of good air quality days recorded in 2009 (55.6 percent of the time) compared to that in 2008 (59 percent of the time) while remaining 43 percent at moderate level and only 1.4 percent at unhealthy level. It is estimated that, the combined air pollutant emission load was 1,621,264 metric tonnes of CO; 756,359 metric tonnes of NO 2 ; 171,916 metric tonnes of SO 2 and 27,727 metric tonnes of PM 10 . There have a change of presenting air pollutants data in spatial from the wearisome numerical or literal form. The dispersion PM 10 concentration, spatial PM 10 mapping, was drawn by the IDW method. The results reveal that decreasing of estimation PM 10 concentration with the increasing of the distance between the CAQM. The average of the dispersion of PM 10 from year 2004 until 2008 is in the range of 44 – 54 µg/m 3 . The maximum of the dispersion of PM 10 concentrations from year 2004 until 2008 is about 112 – 118 µg/m 3 . During the Northeast Monsoon and Southwest Monsoon, the mean are in the range 39 – 50 µg/m 3 and 52 – 60 µg/m 3 respectively. The maximum concentration of PM 10 during Southwest Monsoon is 75 µg/m 3 , whereas, during Northeast Monsoon the PM 10 concentration is 58 µg/m 3 . The intermediate monsoon period PM 10 concentration is the highest value contribution of the PM 10 concentrations to the maximum overall the year 2004 until 2008. The different between the interpolated and in-situ monitoring PM 10 concentration at the selected point have not differ by more than a factor of 2, thus, the IDW interpolation method can be used to describe the PM 10 dispersion in Kuala Lumpur. Double reciprocal relationship formed between in-situ monitoring data and estimation IDW data with the correlation coefficient 0.289. Therefore, the IDW interpolation method is suitable for determining the air pollution status in areas which are not covered by the monitoring stations. 4. Acknowledgement The authors would like to thank the Malaysian Department of Environment (DOE), for the air pollutions data provided. The authors also wish to express appreciation for the technical support provided by Dr. Razak Zakaria, Department of Marine Science, Faculty of Maritime Studies and Marine Science, Universiti Malaysia Terengganu.
Mapping the Spatial Distribution of Criteria Air Pollutants in Peninsular Malaysia Using Geographical Information System (GIS) 173 5. References Brimblecombe, P. & Maynard, R.L. (2001). The urban atmosphere and its effects, Imperial College Press, London. Bunnell, T., Barter, P. A., & Morshidi, S. (2002). City Profile Kuala Lumpur metropolitan area A globalizing city-region. Cities, Vol 19, No. 5, p. 357-370. Burrough, P.A. (1986). Principles of Geographic Information Systems (1 st ed), Oxford University Press, New York, 336pp. David Liu, H.F & Lipták, B.G. (2000). Air pollution, Lewis, U.S. Department of Statistics, Malaysia. (2009). Basic population characteristics by administrative districts, Department of Statistics, Malaysia. DOE, (1997). A guide to air pollutant index in Malaysia (API) (Third Edition), Department of Environment, Malaysia. DOE, (2008). Air Quality Monitoring, Feb 27, 2010, . DOE. (2006). Malaysia environmental quality report 2005, Department of Environment, Malaysia. DOE. (2009a). Air Pollutants Index Management System (APIMS), Sep 8, 2009, . DOE. (2009b). Malaysia environmental quality report 2009, Department of Environment, Malaysia. Ecographica Sdn. Bhd. (2010). Tropical Malaysia weather “Be prepared for dry, wet and wild”, March 26, 2010, . Godish, T. (2004). Air quality (4 th Edition), Lewis, U.S. Kapetsky, J.M. & Travaglia, C. (1995). Geographical information system and remote sensing: an overview of their present and potential applications in aquaculture. In: Nambiar, K.P.P. & Singh, T. (Eds), AquaTech ’94: Aquaculture Towards the 21 st Century. INFOFISH, Kuala Lumpur, pp. 187-208. Ledesma, C., Lewis, M., Lim, R. Martin, S., & Savage, P. (2006). The rough guide to Malaysia, Singapore & Brunei, Rough Guide, New York. Matějíček, L., Engst, P., & Jaňour, Z. (2006). A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague’s environment extended by LIDAR data. Ecological Modelling, 199 (2006), 261-277. McGranahan, G. & Murray, F. (2003). Air pollution & health in rapidly developing countries, Earthscan, UK. Mkoma, S.L., Wang, W., Maenhaut, W. & Tungaraza, C.T. (2010). Seasonal Variation of Atmospheric Composition of Water-Soluble Inorganic Species at Rural Background Site in Tanzania, East Africa. Ethiopian Journal of Environmental Studies and Management, Vol.3 (2). Moussiopoulos, N. (2003). Air Quality in Cities, Springer, Germany. Msnbc.com. (2010). Hazardous haze shrouds Kuala Lumpur, port schools closed as government encourages residents to wear masks, March 26, 2010, . Nath, S.S., Bolte, J.P., Ross, L.G., & Aguilar-Manjarrez, J. (2000). Applications of geographical information systems (GIS) for spatial decision support in aquaculture. Aquacultural Engineering, 23 (2000), 233-278.
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AIR POLLUTION - MONITORING, MODELLI
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Contents Preface IX Chapter 1 Urban
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Preface This book provides a compre
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