Browsing by Author "Amalina Abu Mansor"

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    Air quality status during 2020 Malaysia Movement Control Order (MCO) due to 2019 novel coronavirus (2019-nCoV) pandemic
    (Elsevier, 2020) Samsuri Abdullah; Amalina Abu Mansor; Nur Nazmi Liyana Mohd Napi; Wan Nurdiyana Wan Mansor; Ali Najah Ahmed; Marzuki Ismail; Zamzam Tuah Ahmad Ramly
    An outbreak of respiratory illness which is proven to be infected by a 2019 novel coronavirus (2019-nCoV) officially named as Coronavirus Disease 2019 (COVID-19) was first detected in Wuhan, China and has spread rapidly in other parts of China as well as other countries around the world, including Malaysia. The first case in Malaysia was identified on 25 January 2020 and the number of cases continue to rise since March 2020. Therefore, 2020 Malaysia Movement Control Order (MCO) was implemented with the aim to isolate the source of the COVID-19 outbreak. As a result, there were fewer number of motor vehicles on the road and the operation of industries was suspended, ergo reducing emissions of hazardous air pollutants in the atmosphere. We had acquired the Air Pollutant Index (API) data from the Department of Environment Malaysia on hourly basis before and during the MCO with the aim to track the changes of fine particulate matter (PM2.5) at 68 air quality monitoring stations.
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    Indoor-Outdoor Air Quality Assessment in Nurseries
    (IOP Publishing, 2020) Amalina Abu Mansor; Ain Natasha Badrul Hisham; Samsuri Abdullah; Nur Nazmi Liyana Mohd Napi; Ali Najah Ahmed; Marzuki Ismail
    Poor Indoor Air Quality (IAQ) can adversely affect children's health, comfort and school performance. This study mainly aimed to assess indoor and outdoor air quality status in terms of physical and chemical aspects at nurseries of two higher public institutions. The monitored parameters are temperature (T), relative humidity (RH), air movement (AM), particulate matter (PM), carbon dioxide (CO2) and carbon monoxide (CO) by using Climomaster (RH, T, AM), Dust Trax™ (RSP) and Kanomax IAQ (CO, CO2). The measurement was taken continuously in a period of three days at two nurseries of public institutions.
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    Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for Ozone (O3) Concentrations Prediction
    (IOP Publishing, 2020) Nur Nazmi Liyana Mohd Napi; Mohammad Syazwan Noor Mohamed; Samsuri Abdullah; Amalina Abu Mansor; Ali Najah Ahmed
    Rapid economic growth has led to an increase in ozone (O3) concentration which significantly affecting human health and environment. The prediction of O3 is complicated due to the redundancy of influencing parameters which introduce the multicollinearity problem. The aim of this study is to assess the best prediction model for O3 concentration which is Multiple Linear Regression (MLR) and Principle Component Regression (PCR). Data from2012 to 2014 were used including O3, nitrogen dioxide (NO2), nitrogen oxide (O2), temperature, relative humidity and wind speed on hourly basis. Principle Component Analysis (PCA) was used in order to reduce multicollinearity problem, prior to the implementation of MLR. The hybrid model of PCR was selected as best -fitted models as it had higher correlation coefficient, R2 values compared with MLR model.