Seasonal and spatial PM₁₀ concentration in selected regions in Klang Valley, Malaysia / Siti Norhayati Mohamad Tarmizi
Meteorological parameters are one of the factors that will affect the concentration and distribution of particulate matter but the strength of the relationship is different depends on the certain parameters such as geographical location; land use, rapid growth of industrialization and transportation...
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | https://ir.uitm.edu.my/id/eprint/18590/1/TM_SITI%20NORHAYATI%20MOHAMAD%20TARMIZI%20AS%2016_5.pdf https://ir.uitm.edu.my/id/eprint/18590/ |
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| Institution: | Universiti Teknologi Mara |
| Language: | English |
| Summary: | Meteorological parameters are one of the factors that will affect the concentration and distribution of particulate matter but the strength of the relationship is different depends on the certain parameters such as geographical location; land use, rapid growth of industrialization and transportation activity in the study area. The main objective in this study is to establish the relationship between PM₁₀ concentration and meteorological parameters in seasonal pattern for these three years (2004-2006). The highest value of correlation coefficient for PM₁₀ and wind speed was measured at - 0.18 during spring intermonsoon in 2004 at Kuala Selangor station. It shows that that PM₁₀ was increased as wind speed decreased. Relationship between PM₁₀ and temperature has showed positive relationship PM₁₀ where PM₁₀ increases as temperature increases. The r-square value ranged from 0.14 to 0.36. Klang and Petaling Jaya station has recorded the higher r-square value with 0.36 and 0.34 as compared with other stations. The highest correlation between PM₁₀ and humidity was observed at Klang station during autumn intermonsoon in 2005. The second objective is to analyze spatial distribution of PM₁₀ concentration distribution in seasonal pattern and the third objective is to compare the spatial interpolation technique for distribution of PM₁₀ concentration. In the spatial analysis of PM₁₀ concentration, Ordinary Kriging was chosen as best interpolator as less Root mean Square Error achieved by this technique. Another set of PM₁₀ data in 2010 was being tested with 2006 data by using the similar technique and 81.5% similarity result was obtained. |
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