Che Noraini Che Hasnam

Bio: Che Noraini Che Hasnam is an academic researcher from Universiti Sultan Zainal Abidin. The author has contributed to research in topics: Air quality index & Pollution. The author has an hindex of 8, co-authored 13 publications receiving 275 citations.

Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia

Abstract: This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH4, NmHC, THC, O3, and PM10 are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R 2 and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.

Source Apportionment of Air Pollution: A Case Study In Malaysia

Abstract: Air pollution is becoming a major environmental issue in Malaysia. This study focused on the identification of potential sources of variations in air quality around the study area based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations for seven years (2006 – 2012) were gathered. The Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area. The PCA method has identified methane (CH 4 ), non-methane hydrocarbon (NmHC), total hydrocarbon (THC), ozone (O 3 ) and particulate matter under 10 microns (PM 10 ) are the most significant parameters around the study area. From the study, it can be concluded that the application of the PCA method in chemometric techniques can be applied for the source apportionment purpose. Hence, this study indicated that for the future and effective management of the Malaysian air quality, an effort should be placed as a priority in controlling point and non-point pollution sources.

Heavy metal in fish: analysis and human health-a review

Abstract: Living organisms require trace amounts of heavy metals, including cobalt, copper, manganese and zinc to survive. However, the excessive levels of the metal can be detrimental to the organism. Other heavy metals such as mercury, lead and cadmium have no vital on organisms, and their accumulation in long time period in the bodies can cause serious illness or death. The consumption of fish is recommended because fish is a basic and good nutritious food that has omega-3 fatty acids due to its cardio-protective effects. This present mini-review accounts for the description of heavy metal in fish and the effect of toxic metals on the human health. Besides, the acid digestion method was also discussed in order to identify the best method for applying in the laboratory analysis. The best method used can reduce the contamination error in the results.

Identification source of variation on regional impact of air quality pattern using chemometric

Abstract: This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric 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 novel design of air quality monitoring network for better management of air pollution can be achieved.

Coastal Erosion Measurement Along Tanjung Lumpur to Cherok Paloh, Pahang During the Northeast Monsoon Season

Abstract: The 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 steeper; meanwhile 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.

Using Multivariate Statistics

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Statistical Methods For Environmental Pollution Monitoring

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Bioaccumulation of heavy metals in some commercially important fishes from a tropical river estuary suggests higher potential health risk in children than adults.

Abstract: The Karnaphuli River estuary, located in southeast coast of Bangladesh, is largely exposed to heavy metal contamination as it receives a huge amount of untreated industrial effluents from the Chottagram City. This study aimed to assess the concentrations of five heavy metals (As, Pb, Cd, Cr and Cu) and their bioaccumulation status in six commercially important fishes, and also to evaluate the potential human health risk for local consumers. The hierarchy of the measured concentration level (mg/kg) of the metals was as follows: Pb (13.88) > Cu (12.10) > As (4.89) > Cr (3.36) > Cd (0.39). The Fulton’s condition factor denoted that fishes were in better ‘condition’ and most of the species were in positive allometric growth. The bioaccumulation factors (BAFs) of the contaminants observed in the species were in the following orders: Cu (1971.42) > As (1042.93) > Pb (913.66) > Cr (864.99) > Cd (252.03), and among the specimens, demersal fish, Apocryptes bato appeared to be the most bioaccumulative organism. Estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI) and carcinogenic risk (CR) assessed for potential human health risk implications suggest that the values were within the acceptable threshold for both adults and children. However, calculated CR values indicated that both age groups were not far from the risk, and HI values demonstrated that children were nearly 6 times more susceptible to non-carcinogenic and carcinogenic health effects than adults.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

Abstract: With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.