Ezureen Ezani

Bio: Ezureen Ezani 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 1, co-authored 1 publications receiving 28 citations.

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.

High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review

Abstract: According to World Health Organization, 9 out of 10 people breathe polluted air and the ambient air pollution accounts for nearly 4.2 million early deaths worldwide. There is an urgent need for scientific management of urban air systems. Mathematical modeling of air quality helps the researchers and urban authorities in devising scientific management plans for mitigation of the associated impacts. We present an organized review of the broad aspects related to urban air quality modeling such as – urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models. The paper also discusses about the influence of urban land scape features on air quality, accuracy of emission inventory and model validation methods. The present review provides a vantage point to the researchers in the emerging field of high resolution urban air quality modeling for devising the location specific mitigation plans for the scientific management of the clean air.

The potential impacts of anthropogenic and climate changes factors on surface water ecosystem deterioration at Kenyir Lake, Malaysia

Abstract: Water ecosystem deterioration can be affected by various factors of either natural environment or physical changes in the river basin. Data observation were made during dry season (April 2017) and wet season (December 2017). 21 sampling stations were selected along Kenyir Lake Basin. Overall, the water quality status as stated in NWQS is categorized as Class I on dry season and Class II on wet sea-son. The major pollutants in Kenyir Lake are Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Dissolve Oxygen and pH which are contributed largely by untreated or partially treated sewage from tourism development and construction activities around the basin. The sedimentation problem level in the Kenyir Lake Basin is not in critically stage but the flow rate of water and land use ac-tivities (development around basin) will be contributed to the increasing levels of sedimentation. The good site management such as the implementation of proper site practice measures to control and treat run-off prior to discharge will ensure that the construction works will not affect the quality and quantity of the receiving waters or have significant impact upon the receiving waters.

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Abstract: The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

Characteristics of Respirable Particulate Metals Emitted by a Beehive Firework Display in YanShuei Area of Southern Taiwan

Abstract: This study investigates metals in the PM_(1.0) and PM_(2.5) collected using a micro-orifice uniform deposition impactor (MOUDI) sampler in the YanShuei area of southern Taiwan during a beehive firework display. The results of sample analyses indicate that during the beehive firework display, the ratios of metal concentrations in PM_(2.5) (D) to the background level (B) at leeward sampling site were 1,828 for Ba, 702 for K, 534 for Sr, 473 for Cu, 104 for Mg, 121 for Al, and 98 for Pb. The corresponding data for PM_(1.0) were 3036, 838, 550, 676, 594, 190, and 126, respectively. According to the results of metal composition ratio, Principal Component Analysis (PCA), and upper continental crust (UCC) analyses, the concentrations of particle-bound Al, Ba, Cu, K, Mg, Pb, and Sr increased during the beehive firework displays, suggesting that firework-display aerosols contained abundant metal elements of Al, Ba, Cu, K, Mg, Pb, and Sr. Before (background), trial, during, and after the beehive firework display, the Ba, K, Cu, Mg, Pb, and Sr (commonly regarded as firework display indicator elements) accounted for 0.520, 2.45, 26.4 and 0.849% mass of PM1, respectively, while for PM_(2.5) the corresponding data were 0.777, 2.32, 23.8, and 0.776%, respectively.