|
Spatial Distribution and Source Apportionment of Air Pollutionin Bahrain using Multivariate Analysis Methods |
|---|---|
| รหัสดีโอไอ | |
| Creator | 1. Majeed S. Jassim 2. Gulnur Coskuner 3. Hussain Marzooq 4. Ahmed AlAsfoor 5. Ahmed Abu Taki |
| Title | Spatial Distribution and Source Apportionment of Air Pollutionin Bahrain using Multivariate Analysis Methods |
| Publisher | The Thai Society of Higher Education Institutes on Environment |
| Publication Year | 2561 |
| Journal Title | EnvironmentAsia |
| Journal Vol. | 11 |
| Journal No. | 2 |
| Page no. | ก.ย.-22 |
| Keyword | Air Quality Index, HACA, PCA, Multiple Linear Regression, Particulate matter |
| URL Website | http://www.tshe.org/ea/index.html |
| Website title | EnvironmentAsia |
| ISSN | 2586-8861 |
| Abstract | The objective of this study is to identify the most important air pollutants based on their individual contribution to Air Quality Index (AQI) and to determine the major air pollution sources in Bahrain. Data sets from seventeen air quality monitoring sites were evaluate using XLSTAT 2014 and Statistical Package for the Social Sciences (SPSS 22) over six-and-half-year between July 2006 and December 2012. Hierarchical Agglomerative Cluster Analysis (HACA) categorized the monitoring sites into three distinctive clusters based on similarities of air pollutants characteristics and meteorological parameters. Principal Component Analysis (PCA) identified major sources of air pollution in each cluster. Results demonstrated that dust storms, industrial activities, vehicular emissions, airport activities, power plants and filling stations were major air polluters. PCA analysis showed that temperature and wind speed have positive loading while relative humidity has negative loading. Multiple Linear Regression (MLR) analysis was applied to develop models for prediction of AQI for every cluster based on concentrations of key air pollutants. Results showed PM10 and PM2.5 highly contributed to AQI values. MLR models exhibited good fit with adjusted R2 value of 0.865, 0.794 and 0.842 for Clusters 1, 2 and 3 respectively. Standardized coefficient values for PM10 succeeded by PM2.5 were the highest in each cluster. |
