METHODOLOGY: The data show that the status of atmospheric environment in Malaysia, in particular in highly industrialized areas such as Klang Valley, was determined both by local and transboundary emissions and could be described as haze and non-haze periods.
RESULTS: During the non-haze periods, vehicular emissions accounted for more than 70% of the total emissions in the urban areas and have demonstrated two peaks in the diurnal variations of the aforementioned air pollutants, except ozone. The morning 'rush-hour' peak was mainly due to vehicle emissions, while the late evening peak was mainly attributed to meteorological conditions, particularly atmospheric stability and wind speed. Total suspended particulate matter was the main pollutant with its concentrations at few sites often exceeding the Recommended Malaysia Air Quality Guidelines. The levels of other pollutants were generally within the guidelines. Since 1980, six major haze episodes were officially reported in Malaysia: April 1983, August 1990, June 1991, October 1991, August to October 1994, and July to October 1997. The 1997 haze episode was the worst ever experienced by the country. Short-term observations using continuous monitoring systems during the haze episodes during these periods clearly showed that suspended particulate matter (PM10) was the main cause of haze and was transboundary in nature. Large forest fires in parts of Sumatra and Kalimantan during the haze period, clearly evident in satellite images, were identified as the probable key sources of the widespread heavy haze that extended across Southeast Asia from Indonesia to Singapore, Malaysia and Brunei. The results of several studies have also provided strong evidence that biomass burning is the dominating source of particulate matter. The severity and extent of 1997's haze pollution was unprecedented, affecting some 300 million people across the region. The amount of economic costs suffered by Southeast Asian countries during this environmental disaster was enormous and is yet to be fully determined. Among the important sectors severely affected were air and land transport, shipping, construction, tourism and agro-based industries. The economic cost of the haze-related damage to Malaysia presented in this study include short-term health costs, production losses, tourism-related losses and the cost of avertive action. Although the cost reported here is likely to be underestimated, they are nevertheless significant (roughly RM1 billion).
CONCLUSIONS: The general air quality of Malaysia since 1970 has deteriorated. Studies have shown that should no effective countermeasures be introduced, the emissions of sulfur dioxide, nitrogen oxides, particulate matter, hydrocarbons and carbon monoxide in the year 2005 would increase by 1.4, 2.12, 1.47 and 2.27 times, respectively, from the 1992 levels.
METHODS: The pre- and post-operative CT images of 55 patients undergoing DC surgery were analyzed. The ICV was measured by segmenting every slice of the CT images, and compared with estimated ICV calculated using the 1-in-10 sampling strategy and processed using the SBI method. An independent t test was conducted to compare the ICV measurements between the two different methods. The calculation using this method was repeated three times for reliability analysis using the intraclass correlations coefficient (ICC). The Bland-Altman plot was used to measure agreement between the methods for both pre- and post-operative ICV measurements.
RESULTS: The mean ICV (±SD) were 1341.1±122.1ml (manual) and 1344.11±122.6ml (SBI) for the preoperative CT data. The mean ICV (±SD) were 1396.4±132.4ml (manual) and 1400.53±132.1ml (SBI) for the post-operative CT data. No significant difference was found in ICV measurements using the manual and the SBI methods (p=.983 for pre-op, and p=.960 for post-op). The intrarater ICC showed a significant correlation; ICC=1.00. The Bland-Altman plot showed good agreement between the manual and the SBI method.
CONCLUSION: The shape-based interpolation method with 1-in-10 sampling strategy gave comparable results in estimating ICV compared to manual segmentation. Thus, this method could be used in clinical settings for rapid, reliable and repeatable ICV estimations.