Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin

This study constructs downscaling statistical model in analyzing the hydrological modeling in the study area which faces the risk of flood occurrence as the impact of climate change. The combination of chemometric method and time series analysis in this study show that even during the monsoon season, rainfall and stream flow are not the major contribution towards the changing of water level in the study area. Based on Correlation Test, it shows that suspended solid and water level show high correlation with p-value < 0.05. Factor Analysis being carried out to determine the major contribution to the changes of Water Level and the result show that Suspended Solid shows a strong factor pattern with value 0.829. Based on Control Chat Builder for time series analysis, the Upper Control Limit for water level and suspended solid are 7.529 m and 1947.049 tons/day and the Lower Control Limit are 6.678 m and 178.135 tons/day. This shows that human development in the area gives high impact towards climate change and risk of flood in the study area which commonly faces flood during monsoon season.

Isolation,characterization and identification of plant growth-promoting rhizobacteria from the rhizosphere of Acacia mangium at sandy BRIS soil

This study has isolated,characterized,and identified potential plant growthpromoting rhizobacteria(PGPR)with multiple PGP characteristics(N_(2)-fixation,P-and K-solubilization,IAA,and siderophores production)from the rhizosphere BRIS soil of Acacia mangium.A total of 24 pure colonies were isolated and only 8 colonies were selected for further evaluation of the growth rate in 5%organic molasses medium supplemented with 2%KNO_(3).Based on the biochemical,potential PGP characteristics and growth performance,3 superior PGPR strains were selected and identified as Paraburkholderia unamae(UA1),Bacillus amyloliquefaciens(UA6),and Enterobacter asburiae(UAA2)by partial sequencing of the 16S rRNA gene.The selected bacterial strains either in single or mixed(UA1+UA6+UAA2)cultures have shown a significant biochemical estimation of the PGP characteristics.Each strain has its own PGPR traits superiority with UA1 showing the best PGP characteristic followed by UA6 and UAA2.The use of mixed bacterial strains was beneficial as it showed the best performance in N_(2)-fixation,siderophores production,and significant effect on corn phenology,growth and yield compared to using a single strain.These types of microbes showed potential to be used as biofertilizer and should be exploited more.

Faecal sterols as sewage markers in the Langat River,Malaysia:Integration of biomarker and multivariate statistical approaches

The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabitants of the Langat River Basin, were examined. Sediment samples from 22 stations （SL01-SL22） along the Langat River were collected, extracted and analysed by GC-MS. Six different sterols were identified and quantified. The highest sterol concentration was found at station SL02 （618.29 ng/g dry weight）, which situated in the Balak River whereas the other sediment samples ranged between 11.60 and 446.52 ng/g dry weight. Sterol ratios were used to identify sources, occurrence and partitioning of faecal matter in sediments and majority of the ratios clearly demonstrated that sewage contamination was occurring at most stations in the Langat River. A multivariate statistical analysis was used in conjunction with a combination of biomarkers to better understand the data that clearly separated the compounds. Most sediments of the Langat River were found to contain low to mid-range sewage contamination with some containing ＇significant＇ levels of contamination. This is the first report on sewage pollution in the Langat River based on a combination of biomarker and multivariate statistical approaches that will establish a new standard for sewage detection using faecal sterols.