Nurul Latiffah Abd Rani

Bio: Nurul Latiffah Abd Rani is an academic researcher from Universiti Sultan Zainal Abidin. The author has contributed to research in topics: Air quality index & Air pollution. The author has an hindex of 3, co-authored 8 publications receiving 45 citations.

Air Pollution Index Trend Analysisin Malaysia, 2010-15

Abstract: Air pollution index (API) is used in Malaysia to determine the level of air quality. API is based on the calculation consist of pollutants PM10, O3, CO2, SO2, and NO2. Unhealthy air quality can harm human health and the environment as well as property. In view of this fact, a study of air pollution trend analysis in Malaysia from 2010 to 2015 was performed with the objective of determining the API trend in Malaysia from 2010 to 2015. A dataset of API value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). In this study, 19,872 datasets for all Malaysian air quality monitoring stations that had API value greater than 100 and a total of 52,584 datasets for Muar District in Johor were used. XLSTAT add-in 2014 was used to analyze the API hourly reading. Analysis shows that the air monitoring station at Sekolah Menengah Teknik Muar in Johor shows the highest value of API reading with 663 on 23 June 2013 (emergency level), where on that day Malaysia faced its worst air quality due to haze episodes. Other locations also show the worst air quality with API registering at unhealthy, very unhealthy, and hazardous levels.

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.

Assessing Indoor Air Quality UsingChemometric Models

Abstract: The objectives of this study are to identify the significant variables and to verify the best statistical method for determining the effect of indoor air quality (IAQ) at 7 different locations in Universiti Sultan Zainal Abidin, Terengganu, Malaysia. The IAQ data were collected using in-situ measurement. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discrimination analysis (LDA), and agglomerative hierarchical clustering (AHC) were used to classify the significant variables as well as to compare the best method for determining IAQ levels. PCA verifies only 4 out of 9 parameters (PM10, PM2.5, PM1.0, and O3) and is the significant variable in IAQ. The PLS-DA model classifies 89.05% correct of the IAQ variables in each station compared to LDA with only 66.67% correct. AHC identifies three cluster groups, which are highly polluted concentration (HPC), moderately polluted concentration (MPC), and low-polluted concentration (LPC) area. PLS-DA verifies the groups produced by AHC by identifying the variables that affect the quality at each station without being affected by redundancy. In conclusion, PLS-DA is a promising procedure for differentiating the group classes and determining the correct percentage of variables for IAQ.

Development of missing data prediction model for carbon monoxide

Abstract: Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM 10 ) and ozone (O 3 ) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R 2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R 2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.

Determination of selected heavy metals in airborne particles in industrial area: A baseline study

Abstract: This study focuses on airborne heavy metal pollution in the industrial area. Eight points from Paka and Gebeng Industrial Area respectively were selected for this study within two monsoon seasons. The samples were analysed for heavy metals (Cd, As, Cu, Fe, Ni, Pb, and Zn) by using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that the mean concentration value of As, Pb and Cd for Paka were 5.0 ng/L ± 1.0, 107.0 ng/L ± 88.2, and 10.0 ng/L ± 7.5, respectively and Gebeng were 3.5 ng/L ± 1.5, 69.3 ng/L ± 59.3 and 5.1 ng/L ± 3.8, respectively in the southwest monsoon - much higher than the target value by European Commission in Directive 2004/107/EC and Directive 2008/50/EC. It could be concluded that the industrial and transportation emission were the major source of heavy metals in the atmosphere along the Paka and Gebeng Industrial Area.

Using Multivariate Statistics

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Air pollution and its health impacts in Malaysia: a review

Abstract: Air pollution is strongly tied to climate change. Industrialization and fossil fuel combustion are the main contributors leading to climate change, also being significant sources of air pollution. Malaysia is a developing country with a focus on industrialization. The preference of using private cars is a common practice in Malaysia, resulting in the after-effects of haze and transboundary air pollution. Hence, air pollution has become a severe issue in Malaysia in recent times. Exposure to air pollutants such as ozone and airborne particles is associated with increases in hospital admissions and mortality. For the past few years, the focus of the research is moving towards air quality and the impacts of air pollution on health in Malaysia. In this study, we establish the definition of air pollution, the motivation to study it, and its impacts and sources of air pollution and climate change. We discuss the air quality monitoring system in Malaysia and compare Malaysian ambient air quality standards with global standards. We also look comprehensively on the health impacts of air pollution globally and in the Malaysian context. We discuss where the health impact studies in Malaysia are lacking and what are the gaps in the research. The role of the Malaysian government concerning air pollution and its impacts is discussed. Lastly, we look into the future work and research opportunities with a focus on engineering, estimation, predictive models and lack of research projects.

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Abstract: Nowadays air quality is the main issue in urban areas that have been affecting human health, the environment, and the ecosystem. So, governmental authorities, environmental and health agencies usually need the prediction of daily air pollutants. This prediction is often based on statistical relations between various conditions and air pollution. This study aims to compare the performance of Multiple Linear Regression (MLR) and Multi-layer perceptron (MLP) for predicting SO2 concentration in the air of the Tehran. Different parameters namely meteorological parameters, urban traffic data, urban green space information, and time parameters were chosen for the prediction of SO2 daily concentration. Considering result, the correlation coefficient (R2), and root means square error (RMSE) of the MLR model are 0.708, and 6.025, respectively while these values for the MLP equal 0.9 and 0.42. According to the result of sensitivity analysis, the value of the one-day time delay, park indicator, season/year, and the total area parks were the main factors influencing SO2 concentration. MLP model suggested in this research could be applied to support, analysis, and improve predicting air pollution and air quality management. This study shows the importance of modeling and application of ANN in presenting management strategies to reduce urban pollution.

Impact of Extreme Drought Climate on Water Security in North Borneo: Case Study of Sabah

Abstract: For countries in Southeast Asia that mainly rely on surface water as their water resource, changes in weather patterns and hydrological systems due to climate change will cause severely decreased water resource availability. Warm weather triggers more water use and exacerbates the extraction of water resources, which will change the operation patterns of water usage and increase demand, resulting in water scarcity. The occurrence of prolonged drought upsets the balance between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts. The objectives of this study are to identify trends and determine the impacts of extreme drought events on water levels for the major important water dams in the northern part of Borneo, and to assess the risk of water insecurity for the dams. In this context, remote sensing images are used to determine the degree of risk of water insecurity in the regions. Statistical methods are used in the analysis of daily water levels and rainfall data. The findings show that water levels in dams on the North and Northeast Coasts of Borneo are greatly affected by the extreme drought climate caused by the Northeast Monsoon, with mild to the high risk recorded in terms of water insecurity, with only two of the water dams being water-secure. This study shows how climate change has affected water availability throughout the regions.