169 research outputs found
Determination of selected heavy metals in airborne particles in industrial area: a baseline study
No full textSpatial air quality modelling using chemometrics techniques: a case study in peninsular malaysia
No full textAbstract
This study shows the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and multiple linear regressions (MLR) for assessment of air quality data and recognition of air pollution sources. 12 months data (January-December 2007) consisting of 14 stations in Peninsular Malaysia with 14 parameters were applied. Three significant clusters - low pollution source (LPS), moderate pollution source (MPS), and slightly high pollution source (SHPS) were generated via HACA. Forward stepwise of DA managed to discriminate eight variables, whereas backward stepwise of DA managed to discriminate nine variables out of fourteen variables. The PCA and FA results show the main contributor of air pollution in Peninsular Malaysia is the combustion of fossil fuel from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 account as the most and the highest pollution contributor to Malaysian air quality. From the study, it can be stipulated that the application of chemometrics techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novelty design of air quality monitoring network for better management of air pollution can be achieved via these methods
Air quality pattern assessment in Malaysia using multivariate techniques
No full textThis study aims to investigate the spatial characteristics in the pattern of air quality monitoring sites, identify the most discriminating parameters contributing to air pollution, and predict the level of air pollution index (API) in Malaysia using multivariate techniques. Five parameters observed for five years (2000-2004) were used. Hierarchical agglomerative cluster analysis classified the five air quality monitoring sites into two independent groups based on the characteristics of activities in the monitoring stations. Discriminate analysis for standard, backward stepwise and forward stepwise mode gave a correct assignation of more than 87% in the confusion matrix. This result indicates that only three parameters (PM10, SO2 and NO2) with a p<0.0001 discriminate best in polluting the air. The major possible sources of air pollution were identified using principal component analysis that account for more than 58% and 60% in the total variance. Based on the findings, anthropogenic activities (vehicular emission, industrial activities, construction sites, bush burning) have a strong influence in the source of air pollution. Furthermore, artificial neural network (ANN) was used to predict the level of air pollution index at R2 = 0.8493 and RMSE = 5.9184. This indicates that ANN can predict more than 84% of the API
Assessment on bacteria in the heavy metal bioremediation
No full textThe aim of this study was to identify and verify the potential bacteria as the bioremediation agent. It involved bacteria isolation, identification through Gram staining, analytical profile index (API) test and determine bioremediation activities by using inductively coupled plasma mass spectrometry (ICPMS). The soil and water sample were collected from downstream of Galing River, Kuantan Malaysia. Based on phenotypic identification and biochemical analysis, the bacteria present at the vicinity area are possibility of Myroides spp. and Micrococcus spp. These bacteria were proven as bioremediation agent based on the ICPMS result. The result 1 ppm of Zink (Zn), Lead (Pb), Arsenic (As), Selenium (Se), Cadmium (Cd), Manganese (Mn), and Indium (In) dwindled after the bacteria inoculated and incubated for seven days in mixture of base salt media (BSM) with the heavy metal elements. Therefore, this proves that the bacteria which are present at downstream of Galing River, Kuantan Malaysia are significant to help us in the bioremediation activity to decrease the heavy metal pollution in the environment
Coastal erosion measurement along Tanjung Lumpur to Cherok Paloh, Pahang during the northeast monsoon season
No full textThe map of Tanjung Lumpur to Cherok Paloh from 1996 to 2004 revealed that there were significant changes on coastal profiles. If the problem remains unsolved within 5 to 10 years, the beaches in the area might be fully eroded. The main objective of this study is to measure erosion of the coastline along Tanjung Lumpur to Cherok Paloh, Pahang during the northeast monsoon (December 2013 to February 2014). Transit set and dry sieving method were used for beach profile and grain size characteristics measurement. GRADISTAT v8 program is used for sedimentological analysis. Cluster analysis was used to show the group of higher eroded, medium eroded and lower eroded. The study found that almost all of the beach profiles had increased in length and the beach slopes were steepermeanwhile the sedimentological analysis indicated that all the stations were dominated by sandy type during the period of study. The action of higher waves, tides and currents were the biggest contribution to erosion during northeast monsoon. From this study, it can be concluded that almost all stations have undergone erosion during the northeast season
Air quality pattern assessment in Malaysia using multivariate techniques
No full textThis study aims to investigate the spatial characteristics in the pattern of air quality monitoring sites, identify the most discriminating parameters contributing to air pollution, and predict the level of air pollution index (API) in Malaysia using multivariate techniques. Five parameters observed for five years (2000-2004) were used. Hierarchical agglomerative cluster analysis classified the five air quality monitoring sites into two independent groups based on the characteristics of activities in the monitoring stations. Discriminate analysis for standard, backward stepwise and forward stepwise mode gave a correct assignation of more than 87% in the confusion matrix. This result indicates that only three parameters (PM10, SO2 and NO2) with a p<0.0001 discriminate best in polluting the air. The major possible sources of air pollution were identified using principal component analysis that account for more than 58% and 60% in the total variance. Based on the findings, anthropogenic activities (vehicular emission, industrial activities, construction sites, bush burning) have a strong influence in the source of air pollution. Furthermore, artificial neural network (ANN) was used to predict the level of air pollution index at R = 0.8493 and RMSE = 5.9184. This indicates that ANN can predict more than 84% of the API
Flood risk pattern recognition by using environmetric technique: A case study in langat river basin
No full textThis study looks into the downscaling of statistical model to produce and predict hydrological modelling in the study area based on secondary data derived from the Department of Drainage and Irrigation (DID) since 1982-2012. The combination of chemometric method and time series analysis in this study showed that the monsoon season and rainfall didnot affect the water level, but the suspended solid, stream flow and water level that revealed high correlation in correlation test with p-value < 0.0001, which affected the water level. The Factor analysis for the variables of the stream flow, suspended solid and water level showed strong factor pattern with coefficient more than 0.7, and 0.987, 1.000 and 1.000, respectively. Based on the Statistical Process Control (SPC), the Upper Control Limit for water level, suspended solid and stream flow were 21.110 m3/s, 4624.553 tonnes/day, and 8.224 m/s, while the Lower Control Limit were 20.711 m, 2538.92 tonnes/day and 2.040 m/s. This shows that human development in the area gives high impact towards climate change and flood risk, and not the monsoon season. Prediction was carried out using the Artificial Neural Network (ANN) to classify risks into their own classes, and the rate of accuracy for the prediction was 97.1%. This meant that the points in the time series analysis which were located beyond Upper Control Limit were considered as High Risk class, and the probability for flood occurrence is very high. The other classes classified in this prediction are Caution Zone, Low Risk and No risk. This is important to set a trigger for warning system in the case of emergency response plan during flood
Assessment of surface water quality using multivariate statistical techniques in the terengganu river basin [Penilaian kualiti air permukaan menggunakan teknik statistik multivariat bagi lembangan sungai Terengganu]
No full textMultivariate Statistical techniques including cluster analysis, discriminant analysis, and principal component analysis/factor
analysis were applied to investigate the spatial variation and pollution sources in the Terengganu river basin during 5 years of
monitoring 13 water quality parameters at thirteen different stations. Cluster analysis (CA) classified 13 stations into 2 clusters
low polluted (LP) and moderate polluted (MP) based on similar water quality characteristics. Discriminant analysis (DA)
rendered significant data reduction with 4 parameters (pH, NH3
-NL, PO4 and EC) and correct assignation of 95.80%. The
PCA/FA applied to the data sets, yielded in five latent factors accounting 72.42% of the total variance in the water quality data.
The obtained varifactors indicate that parameters in charge for water quality variations are mainly related to domestic waste,
industrial, runoff and agricultural (anthropogenic activities). Therefore, multivariate techniques are important in environmental
management
A study of microbe air levels in selected rooms of a hospital cultivated on two culture medias [Kajian tahap mikrob dalam udara yang di kultur pada dua media kultur di dalam bilik hospital terpilih]
No full textThe levels of airborne microbe in hospital are unknown previously in Terengganu. Typically, fungi and bacteria are usually presented in indoor environments and cause of human health effects. The aim of this descriptive study was to investigate the level of airborne microbial pollution in the indoor air of the selected hospital rooms. A total of 8 rooms were investigated for this study. Sampling was conducted with an Eco-Mas 100 Single-Stage Microbial Air Impactor and counting plates containing two selective media, Rose Bengal Chloramphenicol Agar (RBCA) and Sabouraud Dextrose Agar (SDA). Air sampling was taken for 5 min at an airflow rate of 28.3 L/min. A medium-low level of bacterial and fungal concentrations (8 to 38 CFU/m3 for RBCA and 2 to 149 CFU/m3 for SDA), respectively were found in indoor air quality of the hospital. The highest microbe air levels for RBCA was measured in the washing room, while the highest microbe air levels for SDA was measured in the reprocessing room of hemodialysis unit. The culturable airborne bacterial and fungal concentrations on SDA agar were higher than those on RBCA. The most common culturable airborne microbes were Penicillium and Cladosporium. In addition, the use of RBCA rather than SDA significantly improved the fungal yield. The study also revealed that no indoor atmosphere in the hospital is completely free from microorganisms
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