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https://ptsldigital.ukm.my/jspui/handle/123456789/499597Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Mohd Talib Latif, Prof. Dr. | - |
| dc.contributor.author | Sahel Mohammad-Rateb Amer Al-Batayneh (P39714) | - |
| dc.date.accessioned | 2023-10-13T09:33:03Z | - |
| dc.date.available | 2023-10-13T09:33:03Z | - |
| dc.date.issued | 2015-02-26 | - |
| dc.identifier.other | ukmvital:80306 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/499597 | - |
| dc.description | Kajian ini bertujuan untuk memahami pengaruh cuaca kepada kepekatan PM10 di kawasan dengan berlatar belakang yang berbeza di Semenanjung Malaysia. Kajian ini juga telah menggunakan kriteria utama bahan pencemar udara seperti O3, NOx, SO2, CO dan komposisi unsur PM10 untuk menentukan pembahagian sumber bagi penentuan sumber PM10 di stesen-stesen dengan latar belakang yang berbeza. Data meteorologi dan kualiti udara (PM10, O3, NOx, SO2, CO) dari tahun 2004 hingga 2007 yang direkodkan mengikut jam di stesen pemantauan kualiti udara Jabatan Alam Sekitar iaitu di Kemaman (Terengganu), Kuantan (Pahang), Ipoh (Perak), Sungai Petani (Kedah), Gombak (Kuala Lumpur), Shah Alam (Selangor), Melaka (Melaka), Johor Bahru (Johor) dan Jerantut (Pahang) telah dipilih untuk kajian ini. Bagi kepekatan unsur PM10, sampel telah dikumpulkan secara manual menggunakan Pensampel Isipadu Tinggi (High Volume Sampler, HVS) di stesen pemantauan udara Jabatan Meteorologi Malaysia di Kota Bahru (Kelantan), Petaling Jaya (Selangor), Senai (Johor) dan Bayan Lepas (Pulau Pinang) antara Januari hingga Disember 2008. Spektrometri Jisim- Plasma Gandingan Aruhan - (Inductively Coupled Plasma-Mass Spec, ICP-MS) telah digunakan untuk menentukan kepekatan komposisi unsur PM10. Pengaruh faktor-faktor cuaca kepada PM10 yang direkodkan di kawasan kajian telah ditentukan melalui carta sinoptik, trajektori ke belakang dan pembolehubah cuaca tempatan. Hubungan antara PM10 dan parameter meteorologi telah dimodelkan dalam tiga cara: analisis komponen utama (PCA), regresi linear berganda (MLR) dan regresi komponen utama (PCR). PCA dan PCR telah digunakan untuk melihat kuantiti sumbangan pembahagian sumber kepada PM10 masing-masing. Keputusan PCA dikesan menunjukkan hubungan asas antara kepekatan PM10 dan pembolehubah meteorologi dapat dikesan. Faktor cuaca seperti suhu dan kelembapan memainkan peranan utama dalam model MLR (R2 = 0.53). Penggunaan PCR menunjukkan bahawa pembolehubah asal yang berkaitan dengan komponen-komponen utama yang sah adalah pembolehubah cuaca (R2 = 0.52). Secara amnya, kepekatan PM10 adalah lebih tinggi di kawasan yang terletak dalam zon perindustrian (54 ± 23 μg/m3) dan di stesen-stesen di kawasan bandar (48 ± 24 μg/m3), manakala di stesen latar belakang menunjukkan nilai terendah (44 ± 16 μg/m3). Hasil kajian juga menunjukkan bahawa nilai R2 untuk model MLR semasa monsun barat daya adalah 0.207 manakala ianya sebanyak 0.255 semasa monsun timur laut. Pengaruh faktor cuaca kepada kepekatan PM10 semasa monsun barat daya yang diperoleh daripada MLR, menunjukkan bahawa kelajuan angin adalah faktor meteorologi utama yang mempengaruhi PM10 pada 45%, diikuti oleh taburan hujan (33%) dan suhu (21%). Kelembapan mempengaruhi kepekatan PM10 pada peratusan yang tinggi (74%) semasa monsun timur laut diikuti dengan kelajuan angin pada 16%. Trajektori kebelakang yang diperolehi menggunakan model HYSPLIT menunjukkan bahawa kepekatan PM10 dipengaruhi oleh jisim udara dari kawasan-kawasan lain, seperti Indo-China dan Sumatera, Indonesia. Hasil daripada komposisi unsur PM10 menunjukkan bahawa pelepasan kenderaan adalah sumber penyumbang PM10 yang paling penting di kawasan bandar (63%), diikuti oleh bahankerak bumi (17%) dan semburan laut (9%). Semburan laut merupakan sumber utama aerosol di kawasan pantai (14%). Kajian ini menyarankan bahawa faktor cuaca bermusim memainkan peranan yang penting dalam mempengaruhi kepekatan PM10 di Semenanjung Malaysia.,This study attempts to understand the influence of meteorological factors to the concentration of PM10 at different background in Malaysian Peninsula. The study also used main criteria air pollutants such as O3, NOx, SO2, CO and PM10's elemental composition to determine the source apportionment of PM10 recorded at monitoring stations with different background. Meteorological and air quality data (PM10, O3, NOx, SO2, CO) from 2004 to 2007 recorded hourly at Department of Environment air quality monitoring stations namely Kemaman (Terengganu), Kuantan (Pahang), Ipoh (Perak), Sungai Petani (Kedah), Gombak (Kuala Lumpur), Shah Alam (Selangor), Melaka (Melaka), Johor Bahru (Johor) and Jerantut (Pahang) have been selected for this study. For elemental concentrations of PM10, samples were collected manually using High Volume Sampler (HVS) at Meteorological Department air monitoring stations at Kota Bahru (Kelantan), Petaling Jaya (Selangor), Senai (Johor) and Bayan Lepas (Pulau Pinang) between January-December 2008. Inductively coupled plasma spectrometry (ICP-MS) has been used to determine the concentrations of elemental composition of PM10. The influence of the meteorological factors on PM10 recorded at the study areas was determined by means of synoptic charts, back-trajectories and local meteorological variables. The relationships between PM10 and meteorological parameters were modelled in three ways: principal component analysis (PCA), multiple linear regression (MLR) and principal component regression (PCR). PCA and PCR were used to qualify and quantify the source apportionment contributions to PM10 respectively. The results of the PCA detected underlying relationships among PM10 concentrations and meteorological variables. The meteorological factors, temperature and humidity played a main role in the MLR model (R2=0.53). The application of the PCR shows that the original variables associated with the valid principal components was the meteorological variable (R2=0.52). Generally, the PM10 mean concentrations were higher in the areas located in industrial zone (54±23 µg/m3) and at the stations in urban areas (48±24 µg/m3), while in the background station shows the lowest value (44±16 µg/m3). The results showed that The R2 value for the MLR model during the southwest monsoon was 0.207 while it was 0.255 during the northeast monsoon. The influence of meteorological factors on PM10 concentrations during the southwest monsoon, as obtained from MLR, shows that the wind speed was the main meteorological factor influencing PM10 at 45%, followed by rainfall precipitation (33%) and temperature (21%). Relative humidity influenced PM10 concentrations with a very high percentage (74%) during the northeast monsoon followed by wind speed at 16%. The backward trajectory obtained using the HYSPLIT model showed that the PM10 concentrations influenced by air mass came from other regions, such as Indo-China and Sumatra, Indonesia. The results from elemental composition of PM10 showed that the vehicle emissions were the most important source of PM10 at the urban sites (63%), followed by crustal materials (17%), and marine spray (9%). Marine spray was found to be more significant in coastal area (14%). This study strongly suggests that seasonal meteorological factors play an important role in influencing the concentration of PM10 in the Malaysian Peninsula.,PhD | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Science and Technology / Fakulti Sains dan Teknologi | - |
| dc.rights | UKM | - |
| dc.subject | Meteorological factors | - |
| dc.subject | Particles - Environmental aspects | - |
| dc.title | The influence of meteorological factors on PM10 concentrations and composition in the Malaysian Peninsula | - |
| dc.type | Theses | - |
| dc.format.pages | 198 | - |
| dc.identifier.callno | TD884.5.B348 2015 tesis | - |
| dc.identifier.barcode | 001169 | - |
| Appears in Collections: | Faculty of Science and Technology / Fakulti Sains dan Teknologi | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_80306+SOURCE1+SOURCE1.0.PDF Restricted Access | 5.17 MB | Adobe PDF |  View/Open |
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