Research Output

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Now showing 1 - 10 of 15
  • Publication
    Carbon footprint assessment from purchased electricity consumption and campus commute in Universiti Malaysia Perlis (UniMAP): Pre- and during COVID-19 pandemic
    Most institutions and organizations nowadays have been taking responsibility in reducing their carbon footprint (CF) to curtail the global warming impact to at least 20–25% reduction by 2030. Universities and higher learning institutions are starting to invest in becoming greener and carbon-free. Current COVID19 communicable disease has swayed the routine and concurrently influenced regular trends of greenhouse gases (GHG) emissions throughout the world. This study explored the possible GHG emissions (calculated as CO2e) from internal campus commute and purchased electricity consumption from the year 2018–2020 at Universiti Malaysia Perlis main campus to analyze the influence of COVID19 pandemic on its CO2e emission. The average amount of CO2e emitted during pre-COVID19 period (n = 26) was 1,518.8 tCO2e/year while during COVID19 period, it was 1,071.5 tCO2e/year (n = 10), marked as 29.5% reduction. Due to completeness and quality of data for contracted bus (monitoring period of years 2018, 2019 and 2020 as 12 months, 12 months, and 2 months, respectively), year 2019 was determined as the appropriate baseline year for setting the CO2e reduction target due to COVID19 pandemic precedented year. In comparison to pre-COVID19 pandemic, almost 95%/year and 7%/year reductions of CO2e were recorded for both Scope 1 and Scope 2, respectively. Comparing Scope 1 and 2, it was obviously observed that the purchased electricity consumption (Scope 2) was the predominant contributor to GHG emission at UniMAP campus by 78% despite of current pandemic influence and its reduction was indistinct (7%/year reduction). Thus, the reduction target in future should be venturing in energy savings and energy auditing in addition to carbon offsetting.
      1
  • Publication
    Isu pemanasan global
    Panasnya hari ini! Pernahkan anda mendengar rungutan atau keluhan seperti ini keluar daripada mulut masyarakat sekitar anda? Atau anda sendiri pernah mengalami hal ini. Anda tidak salah, data-data yang ada memang menunjukkan bumi kita mengalami peningkatan suhu yang amat merisaukan sejak akhir-akhir ini. Hal ini berkait langsung dengan isu global yang kebelakangan ini makin hangat diperkatakan oleh masyarakat dunia iaitu pemanasan global. Ironinya, kesedaran mengenai hal ini dalam kalangan masyarakat amat kurang dan tidak hairan ada dalam kalangan masyarakat, langsung tidak tahu mengenai pemanasan global. Buku ini direalisasikan untuk memberi pengetahuan kepada pembacanya mengenai pemanasan global, punca pemanasan global, kesan-kesannya serta langkah-langkah pencegahan yang perlu dimainkan oleh semua pihak. Buku ini turut membincangkan pelaksanaan Protokol Montreal dan Protokol Kyoto dalam menangani masalah pemanasan global
      46  644
  • Publication
    Exploring the potential of agricultural waste as natural resource-based adsorbents for methylene blue removal
    ( 2024-01-01) ;
    Muhamad Farid Idham Sulaiman
    ;
    Ain Nihla Kamarudzaman
    ;
    ; ;
    Syakirahafiza Mohammed
    ;
    ;
    Deák G.
    ;
    Excessive agricultural waste in the agricultural industry leads to various forms of pollution, including water pollution. To address this issue, there's a growing interest in finding alternative methods. One approach is to utilize agricultural waste as natural resource-based adsorbents to eliminate contaminants, such as the case of methylene blue (MB) in this study. The study specifically focuses on using rice husk (RH) from a local rice mill in Perlis, Malaysia, to absorb methylene blue. The structure of rice husk, characterized by scanning electron microscopy (SEM), reveals a coarser and more compact outer area, contributing to its absorption capacity for methylene blue. The study on rice husk involves three main aspects: contact time, adsorbent dosage, and dye concentration. The removal percentage of MB increased as the three studied adsorption parameters increased. The adsorption data were analyzed using Langmuir and Freundlich adsorption isotherms, with the the Freundlich Isotherms were found to be more suitable based on higher coefficient of correlation (R2) values compared to Langmuir. The pseudo-second-order kinetics model demonstrated a higher R2value (1.00) compared to the pseudo-first-order model (0.747). The results indicate promising potential for addressing pollution through sustainable means and provide insights into the adsorption process under varying conditions.
      2
  • Publication
    Optimization of copper adsorption from synthetic wastewater by oil palm-based adsorbent using Central Composite Design
    ( 2020-06-10)
    Wong H.W.
    ;
    ;
    Muhammad Adli Hanif
    ;
    ; ;
    Oil palm empty fruit bunch (EFB) was chemically activated by phosphoric acid and heat treatment to produce porous activated carbon (AC) for adsorption of copper ions from synthetic wastewater using static batch test. Copper adsorption process was optimized using Response Surface Method (RSM) by varying four operating parameters i.e. pH (A), initial concentration (B), adsorbent dosage (C) and contact time (D) through a quadratic model developed based on Central Composite Design (CCD) approach. Within the tested parameter range, copper adsorption was found to be at optimum condition at pH 5, initial concentration of 200 mg/L, adsorbent dosage of 0.55 g per 200 mL copper solution and contact time of 2.5 hours, yielding 52.5% of copper removal. A good agreement was achieved by comparing the predicted model with experimental data (R2=0.9618). All four operating parameters tested are significant in affecting the adsorption process, with pH being the most significant with an F-value of 171.70. The interaction between pH and initial concentration (AB) has the most significant interacting effects (F-value of 18.30), while quadratic effects of pH (A2) and adsorbent dosage (C2) are most significant with F-values of 62.80 and 42.58 respectively.
      3  3
  • Publication
    The replacement of missing values of continous air pollution monitoring data using Mean Top Bottom Imputation technique
    ( 2006) ;
    Ahmad Shukri Yahaya
    ;
    Nor Azam Ramli
    ;
    Air pollutants data such as PM10 carbon monoxide, sulphur dioxide and ozone concentration were obtained from automated monitoring stations. These data usually contain missing values that can cause bias due to systematic differents between observed and unobserved data. Therefore, it is impirtant to find the best way to estimate these missing values to ensure that the data analyzed are of high precision. This paper focuses on the usage of mean top bottom imputation technique to replace the missing values. Three performance indicators were calculated in order to describe the goodness of fit of this technique. In order to test the efficiency of the method applied, PM10 monitoring dataset for Kuala Lumpur was used as case study. Three distributions that are Weibull, gamma and lognormal were fitted to the datasets after replacement of missing values using mean top bottom method and performance indicators were calculated to describe the qualities of the distributions. The results show that mean top bottom method gives very good performances at low percentage of missing data but the performances slightly decreased at higher degree of complexity. It was found that gamma distribution is the most appropriate distribution representing PM10 emissions in Kuala Lumpur.
      2  13
  • Publication
    Temporal and spatial variability of PM10 in daycare centres in Perlis
    ( 2020-06-10) ;
    Marianne M.A.
    ;
    Abdullah L.C.
    ;
    ;
    A good indoor air quality (IAQ) is preferred for a healthy and safe indoor environment especially for children since they are more susceptible to the effects from indoor pollutants. Most of indoor air pollution researches focus on the health effect on the children but they eliminate the possibility of how the environmental factors and daycare characteristics could contribute to this problem. This study investigates the concentration level of PM10 and its relationship with environmental factors and daycare centers characteristics and two different sampling sites, representing residential and near roadside. Gravimetrical method was used in order to present spatiotemporal analysis utilizing descriptive analysis, Pearson Correlation and Coefficient of Divergence (COD) treatments of data. The average indoor concentration in Taska Penyayang 1 Malaysia (TP1M, representing residential setting) were 105.97 ° 40.06 μg/m3 indoor and 50.77 ° 30.85 μg/m3 outdoor. Taska Penyayang Permata (TPP), represented near roadside setting were 59.88 ° 18.53 μg/m3 and 69.09 ° 23.54 μg/m3 indoor and outdoor, respectively. PM10 variations at TP1M was observed to be originated from indoor/local strong sources and was minimally influenced by weather parameters and outdoor infiltration. Infiltration of pollutants occurred at TP1M, showed by large IOR (above unity) while exfiltration of pollutants governed at TPP, indicated by low IOR and insignificant COD values between all of its micro-location. Natural ventilation as practiced by TPP may also be the reason of very much lower levels of PM10 concentration, evidenced by strong positive correlation between number of occupants and inverse correlation between number of activities. Lower frequency of activities accumulates PM10, contributing to its higher level. In contrast, persistent closed-windows and doors may contribute to inadequate ventilation and accumulated air pollutants, as observed at TP1M. This has been evident by higher COD correlation, indicating similar sources of PM10 at micro-environments with outdoor air.
      1
  • Publication
    Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: domestic or solely transboundary factor?
    (Elsevier, 2023)
    Nur Alis Addiena A Rahim
    ;
    ;
    Izzati Amani Mohd Jafri
    ;
    Ahmad Zia Ul-Saufie
    ;
    ;
    Ain Nihla Kamarudzaman
    ;
    ;
    Mohd Remy Rozainy Mohd Arif Zainol
    ;
    Sandu Andrei Victor
    ;
    Gyorgy Deak
    Haze has become a seasonal phenomenon affecting Southeast Asia, including Malaysia, and has occurred almost every year within the last few decades. Air pollutants, specifically particulate matter, have drawn a lot of attention due to their adverse impact on human health. In this study, the spatial and temporal variability of the PM10 concentration at Kelang, Melaka, Pasir Gudang, and Petaling Jaya during historic haze events were analysed. An hourly dataset consisting of PM10, gaseous pollutants and weather parameters were obtained from Department of Environment Malaysia. The mean PM10 concentrations exceeded the stipulated Recommended Malaysia Ambient Air Quality Guideline for the yearly average of 150 μg/m3 except for Pasir Gudang in 1997 and 2005, and Petaling Jaya in 2013. The PM10 concentrations exhibit greater variability in the southwest monsoon and inter-monsoon periods at the studied year. The air masses are found to be originating from the region of Sumatra during the haze episodes. Strong to moderate correlation of PM10 concentrations was found between CO during the years that recorded episodic haze, meanwhile, the relationship of PM10 level with SO2 was found to be significant in 2013 with significant negatively correlated relative humidity. Weak correlation of PM10-NOx was measured in all study areas probably due to less contribution of domestic anthropogenic sources towards haze events in Malaysia.
  • Publication
    Assessment of the state of Ichthyofauna from Danube River – Caleia Branch, Romania: a sustainable development context
    (IOP Publiishing Ltd, 2020)
    Tiberius-Marcel Danalache
    ;
    György Deák
    ;
    Elena Holban
    ;
    Cosmin Parlog
    ;
    Carmen Georgeta Nicolae
    ;
    Stelian Matei
    ;
    Mihai-Alexandru Cristea
    ;
    Evaluating the state of ichthyofauna at both the Lower Danube level and at the national level contains knowledge gaps regarding species dynamics, with the most complex studies regarding species composition being undertaken more than 50 years ago. Over time, the Danube River - an important navigation route that connects Western Europe with Asia - has suffered a series of anthropogenic interventions that led to river discharge regularization, interruptions of longitudinal/latitudinal connectivity and reductions in floodplain area. These anthropogenic activities may negatively impact suitable fish habitats leading to demographical effects. The Danube is regarded as a river with high species richness that provides a source of income for the local population by the practice of commercial fishing. The area of interest for this study was selected taking into account the fact that, in the last decade, it was subject to hydrotechnical works that aim to redistribute the river discharge to improve navigation conditions. The ichthyofauna population dynamics is analyzed using an 8 year-long dataset that includes baseline data before the project started and a monitoring period after the project ended. The results indicate the presence of 38 fish species (excluding anadromous fish species – sturgeons and shads). The identified fish species are classified in two categories: 1) species of commercial interest and 2) species of Community interest. This study provides evidence that the high mobility capacity of the fish species is the main factor affecting species dynamics as support of the national efforts in action to stop the degradation of aquatic habitats and biodiversity, in response to goal 15 “Life Earth” of the UN 2030 AGENDA for sustainable development.
  • Publication
    General framework for ecosystem assessment for measures to adapt and mitigate the effects of climate change
    (IOP Publishing Ltd, 2020)
    Coman Valentina
    ;
    Voicu Madalina
    ;
    Laslo Lucian
    ;
    Rotaru Anda
    ;
    Matei Monica
    ;
    Bara Norbert
    ;
    Enache Natalia
    ;
    Boboc Madalina
    ;
    Deak György
    ;
    Tanciu Silvius
    ;
    The effects of climate change are becoming more intense in the last decades. Moreover, according to many official reports, climate changes are directly affecting ecosystems and their services. To assess the impact of climate change on ecosystems, various methods are being used in order to identify changes and interactions with other pressures such as degradation or fragmentation. Adaptation and mitigation measures on the effects of climate change generally include land use changes and land use practices. In order to assess the effectiveness of adaptation and mitigation measures, the services provided by ecosystems and their status are monitored. The paper presents the general framework for evaluating adaptation and mitigation measures and it is based on research from reference works that generally recommend how to evaluate adaptation and mitigation measures. A local adaptation of the mitigation and adaptation framework is presented, by identifying methods for assessing state indicators and ecosystem services. Depending on the availability and accuracy of the data, are proposed methods structured on different levels of detail such as: statistical data, field measurements, modeling software. The application of the proposed methods was verified in a case study: Divici Pojejena wetland, for which detailed methods of assessing the state and services of ecosystems were used.
  • Publication
    Prediction of missing data in rainfall dataset by using simple statistical method
    (IOP Publishing, 2020)
    Izzati Amani Mohd Jafri
    ;
    ;
    Ahmad Zia Ul-Saufie
    ;
    Annas Suwardi
    Almost all of the data obtained from hydrological station contains missing data. Usually, this problem occurs due to equipment failures, maintenance work and human error. Incomplete dataset will reduce the ability of a statistical analysis and can cause a bias estimation due to systematic differences between observed and unobserved data. In this study, four simple statistical method such as Series Mean, Average Mean Top Bottom, Linear Interpolation and Nearest Neighbour were applied to predict the missing values in a rainfall dataset. An annual daily data for rainfall from nine selected monitoring station (from 2009 until 2018) were described using descriptive statistic. Then, the dataset were randomly simulated into 4 percentages of missing (5%, 10%, 15% and 20%) by using statistical package for social sciences software. The performance of this imputation methods were evaluated by using four performance indicators namely Mean Absolute Error, Root Mean Squared Error, Prediction Accuracy, and Index of Agreement. Overall, Linear Interpolation method was selected as the best imputation method to predict the missing data in the rainfall dataset.