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Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

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Abstract:The predominantly hilly terrain of Penang Island combined with average maximum daily temperatures ranging between 27-35 o C and peak rainfall as high as 647 cm makes the overall area potentially susceptible to landslips. Over the recent past construction industry has shown a rapid growth mainly due to increase in the inflow of international tourists and other economic reasons. Eventually, the magnitude of disaster associated with landslides has also increased and that is one of the major concerns of engineering geologists and geotechnical engineers. With this background this paper attempts to characterize the largely granitic residual soils of Penang Island by discussing the nature, structural features, engineering behavior and field properties of soil samples extracted from 8 sites. These sites are distinctly chosen from a database of 31 sites for they are located over different prime geological formations. The mean values of various design properties at different depths are calculated and plotted to identify the property trend with depth and important behavioral features relevant to landslides are discussed. Similarly, compression index values are plotted against initial void ratio and liquid limit separately and resulting correlations are compared with the established ones. Correlations given by Azzous are found to hold good. Lastly, in the light of the lessons learnt from the past landslides and the current characterization results some improvements regarding slope instability problem are discussed.

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Data collected in air pollution monitoring such as PM10, sulphur dioxide, ozone and carbon monoxide are obtained from automated monitoring stations. These data usually contained missing values due to machine failure, routine maintenance, changes in the siting of monitors and human error. Incomplete datasets can cause bias due to systematic differences between observed and unobserved data. Therefore, it is important to find the best way to estimate these missing values to ensure the quality of data analysed are of high quality. Incomplete data matrices are problematic: incomplete datasets may lead to results that are different from those that would have been obtained from a complete dataset (Hawthorne and Elliott, 2004). There are three major problems that may arise when dealing with incomplete data. First, there is a loss of information and, as a consequence, a loss of efficiency. Second, there are several complications related to data handling, computation and analysis, due to the irregulaties in data structure and the impossibility of using standard software. Third, and more important, there maybe bias due to systematic differences between observed and unobserved data. One approach to solve incomplete data problems is the adoption of imputation techniques (Junninen et al., 2004). Thus, this study compared the performance between linear interpolation method (imputation technique) and substitution of mean value for replacement of missing values in environmental data set.

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