Jasim Mohammed Rajab

Regression analysis in modeling of air surface temperature and factors affecting its value in Peninsular Malaysia

Abstract. This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2Ovapor) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R=0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R=0.845 to 0.918), indicating the model’s efficiency and accuracy. Statistical analysis in terms of β showed that H2Ovapor (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.

Algorithm for TSS Mapping Using Satellite Data for Penang Island, Malaysia

The traditional sampling method for environmental monitoring of aerosols is time consuming and expensive. Remote sensing data have been widely used in environmental studies like land cover change, flood observation, environmental pollution monitoring. This study is dealing with obtaining water pollution using Landsat TM data over Penang Strait, Malaysia. With the availability of remotely sensed and in situ data sets the derivable geophysical parameters is sediment (suspended matter) concentration. The proposed algorithm is based on the reflectance model that is a function of the inherent optical properties of water, which can be related to its constituents concentrations. Regression and accuracy analysis is performed using SPSS analysis software. Water samples locations were determined using a handheld GPS. The digital numbers were extracted corresponding to the ground-truth locations for each band was converted into radiance and reflectance and later used for the calibration of the developed algorithm. The efficiency of the present proposed algorithm, in comparison to other forms of algorithm, was also investigated. Based on the values of the correlation coefficient (R) and root-mean-square deviation (RMS), the proposed algorithm is considered superior. The proposed algorithm is considered superior to other tested algorithms based on the values of the correlation coefficient, R=0.94 and root-mean-square error, RMS=5 mg/l. The calibrated TSS algorithm was used to generate the water quality map. The TSS map was color-coded and geometrically corrected for visual interpretation.

Analysis of Ozone column burden in Peninsular Malaysia retrieved from Atmosphere Infrared Sounder (AIRS) data: 2003–2009

Ozone (O3) is a radiatively active trace gas that plies important role in atmosphere heating rates because of its good ability to absorb the infrared radiation and occur both at the ground level and naturally in the earths upper atmosphere. Results from the analysis of the retrieved monthly (AIRX3STM) 1°×1° spatial resolution Atmosphere infrared sounder (AIRS) data were utilized to analyze the distribution of O3 column burden in Peninsular Malaysia for the period 2003–2009. AIRS is one of the several instruments onboard the Earth Observing System (EOS), onboard NASAs Aqua Satellite, launched on May 4, 2002. The analysis of O3 above five dispersed stations in study area shows the seasonal variation in the O3 values fluctuated considerably between wet and dry periods, and O3 values strongly correlated with weather conditions. The lowest O3 observed during rainy months, low temperature and low sunshine hours, vice versa. The highest O3 values occurred over Industrial and congested urban zones. The monthly O3 maps were generated, to study O3 distribution over peninsular Malaysia for 2009, using Kriging Interpolation technique. The AIRS data and the Satellite measurements are able to measure the increase of the atmosphere O3 concentrations over different regions.

Interannual variability of Atmosphere Methane from (AIRS) data over Peninsular Malaysia: 2003-2009

Methane (CH4) is a potent greenhouse gas and its second in importance only to CO2 with relative global warming ability 23 times that of CO2 over a time horizon of 100 years. It has a much shorter atmospheric lifetime, about 12 years and released to the atmosphere by biological processes occurring in anaerobic environments. Interannual variations of the atmosphere CH4 have been studied utilising Atmosphere Infrared Sounder (AIRS), onboard NASAs Aqua Satellite, and data of CH4 emission in Peninsular Malaysia during the period 2003 - 2009. AIRS is one of several instruments onboard the Earth Observing System (EOS) launched on May 4, 2002. The analysis of CH4 above five dispersed stations in study area shows the seasonal variation in the CH4 fluctuated considerably between wet and dry period and high CH4 growth rates observed at the end of each year were attributed to the reduced hydroxyl (OH) sink and the increased emissions from wetlands and biomass burning. The CH4 value in the north regions, up of the latitude 4º, higher than its value in the rest of regions throughout the year. The CH4 values in dry season higher than wet season. The monthly CH4 maps were generated, to study CH4 distribution over peninsular Malaysia for 2009, using Kriging Interpolation technique. The AIRS data and the Satellite measurements are able to measure the increase of the atmosphere CH4 concentrations over different regions.

Algorithm for PM2.5 Mapping over Penang Island, Malaysia, Using SPOT Satellite Data

Air pollution is an important issue being monitored and regulated in industrial and developing cities. In this study, we explored the relationship between particulate matters of size less than 2.5 micron (PM 2.5) derived from the SPOT using regression technique. The aim of this study was to evaluate the high spatial resolution satellite data for air quality mapping by using FLAASH software. The corresponding PM 2.5 data were measured simultaneously with the acquired satellite scene and their locations were determined using a handheld Global Positioning System (GPS). Due to the fact that the current commercial aerosol retrieval method (FLAASH) was designed to work reasonably well over land, but is not accurate for water applications, adjustments had to be made to the software to optimize it for reflectance retrieval over water. In-situ reflectance spectra will be plot against spectra derived from the atmospherically corrected images. In this study, we ensure that all the atmospherically corrected spectra match reasonably with the in-situ spectra. We have developed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from remotely sensed imagery under general atmospheric and surface conditions. The algorithm was developed base on the aerosol characteristics in the atmosphere. The efficiency of the developed algorithm, in comparison to other forms of algorithm, will be investigated in this study. Results indicate that there is a good correlation between the satellites derived PM 2.5 and the measured PM 2.5. This study shows the potential of using the thermal infrared data for air quality mapping. The finding obtained by this study indicates that the FLAASH can be used to retrieve air quality information for remotely sensed data.