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PublicationUnravelling the deposition of indoor microplastics at various heights across rooms(EDP Sciences, 2023)Microplastics (MP) are widely present in both outdoor and indoor environments. Extensive research has thoroughly documented the potential negative impacts of MPs on human health. This study utilized a deposited sample method for 3 weeks, with eight-hour daily exposures, using funnels and bottles to investigate the properties of MPs in the office and laboratory settings of the Faculty of Civil Engineering & Technology (FCET), Universiti Malaysia Perlis. The characteristics examined included the deposition rate, size, form, and colour of the microplastics. Samples were collected at three different heights. The samples underwent pre-treatment procedures, such as physical counting and categorization (size, colour and shape). Micro-Raman analysis was performed to determine the primary polymer types. The deposition rate in the office was found to be 4,960 counts/(m2.h), while the rate in the laboratory was 6,940 counts/(m2.h). Human activities and the appearance of synthetic materials, especially from textiles, play a big role in the deposition rate of MPs in the environment. During the day, the rates were higher than at night. The results of the study showed that indoor MPs come in many different colours, with transparent and black being the most common. About 42% of the size range of fibrous MPs was between 200 μm and 2000 μm, and more than 15% of the particles were between 20 μm and 200 μm. Most of the time, fragments were smaller than strands. The most abundance polymers detected in both rooms were polycarbonate (PC), pigments and polymethyl methacrylate (PMMA).
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PublicationA critical study of the existing issues in circular economy practices during movement control order: can BIM fill the gap?(Emerald, 2022)The improper evaluation and information management of circular economy (CE) (i.e. design, planning, supply chain, waste pile and material hazard) is critical for public health and is a major problem in the waste management of precast concrete (PC) building manufacture and construction and demolition wastes industry. The CE model is particularly problematic for PC building construction projects where the standard practices for the total number of waste building materials are not appropriate and do not match the safe disposal design specification, such as the recent number increase in the Malaysian illegal construction waste pile during the Movement Control Order (11 March 2021, about 5 out of 29 landfills related to states enforcing Act 672). The study aims to develop a framework application (i.e. Building Information Modelling [BIM]) that supports intelligent waste recycling management and sophisticated CE model system solutions. Design/methodology/approach: Thus, the development of a new BIM-based programming algorithm approach is proposed for optimising CE in accordance with the needs of the current PC building construction schemes. As a precursor to this study, the concepts of CE practices are reviewed and the main features of BIM tools and techniques currently being employed on such projects are presented. Findings: Sophisticated CE system solutions are described as an essential component of this optimisation to reduce the amount of waste generated at the end of the life cycle of PC building construction projects and to better manage the resources used throughout it. Originality/value: Finally, the potential for a research framework for developing such a system in the future is presented.
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PublicationImproving the warehouse operation by implementing lean warehousing(AIP Publishing Ltd., 2023)The growing interest of customers towards online shopping have given a good impact to the e-commerce platform nowadays especially during this pandemic which have a strict limitation of movement among citizen in Malaysia. However, some issue regarding the courier services have arisen regarding the poor service quality that customers received recently and some issue related to warehouse operation arises in certain situation such as product delay, product defects, and short space for inventory placement which create lower productivity and service performance. In order to overcome this situation, this study identified the implementation of lean warehousing in improving service productivity performance in courier company. This research will be focusing on the courier services company that available in Malaysia Delivery Express X is selected as it is one of the courier services that frequently used by customers as a platform to delivers their parcels. This research been conducted using qualitative method which the data been collected by interviewing the respondent and the data gathered is analyzed using Arena Simulation Software. The finding shows what is the best alternative used to improve the service productivity performance of courier service. From this research, the courier service industry can make use of the finding for future purposes and future researcher can explore more about the lean warehousing in courier service industry. Its shows the reduction of time from the research is 8.2% improvement.
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PublicationPrediction of particulate matter (PM₁₀) during high particulate event in peninsular Malaysia using Novel Hybrid Model(EDP Sciences, 2023)High Particulate Events (HPE) contributes to the deterioration of air quality, as the fine particles present can be inhaled, leading to respiratory diseases and other health problem. Knowing the adverse effects of air pollution episodes to human health, it is crucial to create suitable models that can effectively and accurately predict air pollution concentration. This study proposed a hybrid model for forecasting the next day PM₁₀ concentration in peninsular Malaysia namely Shah Alam, Nilai, Bukit Rambai and Larkin. Hourly air pollutant concentration (PM₁₀, NOx, NO₂, SO₂, CO, O₃) and meteorological parameters (RH, T, WS) during the HPE events in 1997, 2005, 2013 and 2015 were used. Support Vector Machine (SVM) and Quantile Regression (QR) was combined to construct a hybrid models (SVM-QR) to reduce the number of input variables. Performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Index of Agreement (d2) were used to evaluate the performance of the predictive models. SVM-QR model resulted good performance in all areas. SVM-3 was selected as the best model at Bukit Rambai (MAE=5.72, RMSE=9.71) and Shah Alam (MAE=11.89, RMSE=22.66), while SVM-1 as the best model at Larkin and Nilai with the value (MAE=7.22, RMSE=13.38) and (MAE=6.88, RMSE=11.84), respectively. This strategy was proven to help reducing the complexity of the model and enhance the predictive capacity of the model.
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PublicationEnhancing ecosystem biodiversity through air pollution concentrations prediction using support vector regression approaches(Universitatea Gheorghe Asachi din Iasi, 2023)Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems. © 2023 Universitatea "Alexandru Ioan Cuza" din Iasi. All rights reserved.