In this study, a systematic procedure for establishing the relationship between particulate matter (PM) and microbial counts in four operating rooms (ORs) was developed. The ORs are located in a private hospital on the western coast of Peninsular Malaysia. The objective of developing the systematic procedure is to ensure that the correlation between the PMs and microbial counts are valid. Each of the procedures is conducted based on the ISO, IEST, and NEBB standards. The procedures involved verifying the operating parameters are air change rate, room differential pressure, relative humidity, and air temperature. Upon verifying that the OR parameters are in the recommended operating range, the measurements of the PMs and sampling of the microbes were conducted. The TSI 9510-02 particle counter was used to measure three different sizes of PMs: PM 0.5, PM 5, and PM 10. The MAS-100ECO air sampler was used to quantify the microbial counts. The present study confirms that PM 0.5 does not have an apparent positive correlation with the microbial count. However, the evident correlation of 7% and 15% were identified for both PM 5 and PM 10, respectively. Therefore, it is suggested that frequent monitoring of both PM 5 and PM 10 should be practised in an OR before each surgical procedure. This correlation approach could provide an instantaneous estimation of the microbial counts present in the OR.
In this modern era, the global warming issue has been on the front burner of almost all countries including Malaysia. This study utilizing time series data spanning from 1970 to 2018. To this end, a linear and nonlinear autoregressive distributed lag model was conducted to reveal the foreign direct investment-growth-environment nexus. The conclusion validates the existence of the pollution haven hypothesis in Malaysia. Specifically, the empirical results of the linear autoregressive distributed lag model indicate that foreign direct investment and real gross domestic product have a significant positive impact on CO2 emission while carbon damage cost and the interaction term of foreign direct investment and carbon damage cost have a negative impact in the long run and short run. To find the asymmetric behavior of the foreign direct investment our study employed a nonlinear autoregressive distributed lag model. The findings confirmed the asymmetry association of foreign direct investment with CO2 emission. Interestingly, our results of the interaction term in both models are significant with a negative sign that shows the mediating effect of carbon damage cost that converts the positive effect of foreign direct investment on CO2 emission to negative. Thus, it is vital to reinforce the use of significant regulation as the Malaysian economy opens up to attract more foreign direct investment.
A large amount of ammonia volatilization from the agricultural system causes environmental problems and increases production costs. Conservation agriculture has emerged as an alternate and sustainable crop production system. Therefore, in the present study, ammonia losses from different agricultural practices were evaluated for the wheat crop under different tillage practices. The results of the present study showed that the cumulative emission of ammonia flux from the wheat field varied from 6.23 to 24.00 kg ha-1 (P ≤ 0.05) in conservation tillage (CA) and 7.03 to 26.58 kg ha-1 (P ≤ 0.05) in conventional tillage (CT) among different treatments. Application of basal 80% nitrogen resulted in the highest ammonia flux in conventional and conservation tillage practices. The ammonia volatilization followed the following trend: urea super granules with band placement > neem-coated urea with band placement > neem-coated urea with broadcast before irrigation > neem-coated urea with broadcast after irrigation > slow-release N fertilizer (urea stabilized with DCD and N(n-butyl)thiophosphoric triamide) with band placement. The conservation agricultural practices involving conservation tillage appear to be a sustainable approach for minimizing ammonia volatilization and improving wheat productivity.
Herein, we report a detailed study on creating heterojunction between graphitic carbon nitride (g-C3N4) and bismuth phosphate (BiPO4), enhancing the unpaired free electron mobility. This leads to an accelerated photocatalysis of 2,4-dichlorophenols (2,4-DCPs) under sunlight irradiation. The heterojunction formation was efficaciously conducted via a modest thermal deposition technique. The function of g-C3N4 plays a significant role in generating free electrons under sunlight irradiation. Together, the generated electrons at the g-C3N4 conduction band (CB) are transferred and trapped by the BiPO4 to form active superoxide anion radicals (•O2-). These active radicals will be accountable for the photodegradation of 2,4-DCPs. The synthesized composite characteristics were methodically examined through several chemical and physical studies. Due to the inimitable features of both g-C3N4 and BiPO4, its heterojunction formation, 2.5wt% BiPO4/g-C3N4 achieved complete 2,4-DCP removal (100%) in 90 min under sunlight irradiation. This is due to the presence of g-C3N4 that enhanced electron mobility through the formation of heterojunctions that lengthens the electron-hole pairs' lifetime and maximizes the entire solar spectrum absorption to generate active electrons at the g-C3N4 conduction band. Thus, this formation significantly draws the attention for future environmental remediation, especially in enhancing the entire solar spectrum's harvesting.
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.
Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.
Against the backdrop of current global collaboration on mitigating carbon emissions, how to reduce the energy uses in the Belt and Road Initiative area becomes an urgent and big challenge facing the global community. Using the Eora input-output database, this paper accounts the embodied energy trade between Belt and Road countries in 2015, followed by an investigation of the factors influencing the embodied energy trade through a panel gravity model. Global value chain participation and position are two newly considered factors in analyzing the determinants of embodied energy flow. We find that the main bilateral embodied flow paths are from South Korea to China, China to South Korea, Singapore to China, Ukraine to Russia, and Malaysia to Singapore. Five percent embodied energy flow paths account for 80% of the total bilateral embodied energy flow volume between Belt and Road countries. The gravity model results indicate that gross domestic product (GDP) per capita, population, global value chain participation are the key drivers of bilateral embodied energy trade, while the industrial share of GDP and global value chain position are negatively related to the trade. Energy intensity plays a crucial role in reducing the bilateral embodied energy flow. These results are useful in the policymaking of sustainable development for the Belt and Road Initiative.
The performance of local plants was tested using synthetic turbid water resembling real wastewater by measuring their ability to remove turbidity. The selected plants were A. indica, S. palustris, D. linearis, S. polyanthum, M. esculenta, P. sarmentosum, and M. malabathricum which can easily be found locally. The experiment was run based on coagulant dosages varied from 0 to 10 g/L for each plant with a rapid mixing speed at 180 rpm for 3 min, slow mixing speed at 10 rpm for 20 min, and settling time for 30 min. The results demonstrated that each plant has been capable of reducing turbidity by different amounts, with an increase in the coagulant dosage. The optimum coagulant dosages achieved for A. indica, S. palustris, S. polyanthum, and D. linearis were 10 g/L with turbidity removal at 26.9%, 24.9%, 24.9%, and 17.5%, respectively. P. sarmentosum and M. esculenta attained optimum coagulant dosages at 5 g/L with turbidity removal at 24.2% and 22.2%, and lastly M. malabathricum at 0.1 g/L (12.2%). P. sarmentosum was suggested to the best natural coagulant which achieved the highest removal of turbidity with a low dosage used.
Rapid urbanization and 'concretization' have increased the use of concrete as the preferred building material. However, the production of cement and other concrete-related activities, contribute significantly to both the carbon dioxide emissions and climate change. Agro-industrial wastes such as Palm Oil Fuel Ash (POFA) and Eggshell Powder (ESP) have been utilized in concrete as supplementary cementitious materials, to reduce the cement content, in order to minimize the carbon footprint and the environmental pollution associated with the dumping of waste. Both POFA and ESP have been utilized in ternary binder foamed concrete; however, higher content of cement replacement tends to reduce the concrete's strength significantly. Therefore, this research was conducted to study the influence of ternary binder foamed concrete, incorporating 30% POFA and 5-15% ESP by weight of the total binder, when reinforced with polypropylene (PP) fibres. Based on the results, the ternary binder foamed concrete showed better strength than the control foamed concrete due to the pozzolanic reaction and the addition of PP fibres slightly improved the strength. Furthermore, ternary binder foamed concrete can reduce up to 33.79% of the total CO2 emissions. In terms of cost, all ternary binder foamed concrete mixes reduced the overall cost of the mix. The lowest cost per 1 MPa was achieved by ternary binder foamed concrete mix which incorporated 30% POFA, 5% ESP and 0.20% PP fibres. However, the optimum S5 ternary binder foamed concrete mix, which incorporated 30% POFA, 10% ESP and 0.20% PP fibres, exhibited a cost of $3.74 per 1 MPa strength, which was $1.1 lower than the control foamed concrete. PP reinforced ternary binder foamed concrete is an eco-efficient and cost-effective concrete that can be used in numerous civil engineering applications, mitigating the environmental and the emissions generated by agro-industrial waste.
Globally, concrete is widely implemented as a construction material and is progressively being utilized because of growth in urbanization. However, limited resources and gradual depravity of the environment are forcing the research community to obtain alternative materials from large amounts of agro-industrial wastes as a partial replacement for ordinary cement. Cement is a main binding resource in concrete production. To reduce environmental problems associated with waste, this study considered the recycling of agro-industrial wastes, such as sugarcane bagasse ash (SCBA), rice husk ash (RHA), and others, into cement, and to finally bring sustainable and environmental-friendly concrete. This study considered 5%, 10%, and 15% of SBCA and RHA individually to replace ordinary Portland cement (OPC) by weight method then combined both ashes as 10%, 20%, and 30% to replace OPC to produce sustainable concrete. It was experimentally declared that the strength performance of concrete was reduced while utilizing SCBA and RHA individually and combined as supplementary cementitious material (SCM) at 7, 28, 56, and 90 days, respectively. Moreover, the initial and final setting time is increased as the quantity of replacement level of OPC with SCBA and RHA separates and together as SCM in the mixture. Based on experimental findings, it was concluded that the use of 5% of SCBA and 5% of RHA as cement replacement material individually or combined in concrete could provide appropriate results for structural applications in concrete.
Plastics are synthetic polymers known for their outstanding durability and versatility, and have replaced traditional materials in many applications. Unfortunately, their unique traits ensure that they pose a major threat to the environment. While literature on freshwater microplastic contamination has grown over the recent years, research undertaken in rapidly developing countries, where plastic production and use are increasing dramatically, has lagged behind that in other parts of the world. In the South East Asia (SEA) region, basic information on levels of contamination is very limited and, as a consequence, the risk to human and ecological health remains hard to assess. This review synthesises what is currently known about microplastic contamination of freshwater ecosystems in SEA, with a particular focus on Malaysia. The review 1) summarises published studies that have assessed levels of contamination in freshwater systems in SEA, 2) discusses key sources and transport pathways of microplastic in freshwaters, 3) outlines what is known of the impacts of microplastic on freshwater organisms, and 4) identifies key knowledge gaps related to our understanding of the transport, fate and effects of microplastic.
The Belt and Road Initiative (BRI) is an ambitious development project initiated by the Chinese government to foster economic progress worldwide. In this regard, this study aims to investigate the dynamics of energy, economy, and environment among 42 BRI developing countries using an annual frequency panel dataset from 1995 to 2019. The major findings from the econometric analyses revealed that higher levels of energy consumption, economic growth, population growth rate, and FDI inflows exhibit adverse environmental consequences by boosting the CO2 emission figures of the selected developing BRI member nations. However, it is interesting to observe that exploiting renewable energy sources, which are relatively cleaner compared to the traditionally-consumed fossil fuels, and fostering agricultural sector development can significantly improve environmental well-being by curbing the emission levels further. On the other hand, financial development is found to be ineffective in explaining the variations in the CO2 emission figures of the selected countries. Besides, the causality analysis shows that higher energy consumption, FDI inflows, and agricultural development cause environmental pollution by boosting CO2 emissions. However, economic growth, technology development, financial progress, and renewable energy consumption are evidenced to exhibit bidirectional causal associations with CO2 emissions. In line with these findings, several relevant policies can be recommended for the BRI to be environmentally sustainable.
The interaction and the interplay of climate change with oil palm production in the Southeast Asia region are of serious concern. This particularly applies in Malaysia due to its rank as the second largest palm oil producer in the world. The anthropogenic activities and the agroecological practices in oil palm plantation, including excessive use of fertilisers, bush fire due to land clearing, and cultivation on peatland, have exacerbated the effects of climate change featuring extreme events, drought, flooding, heatwave, as well as infestation of pest and diseases. These adverse impacts on oil palm production highlight the significance of deploying effective adaptation strategies. The study aims to examine the impact of climate change on oil palm production and identify the farmers' adaptation strategies to the impacts of climate change in Malaysia. This study was conducted a comprehensive review of the articles published from 2000 to 2021 in the contexts of climate change and oil palm production in Malaysia. The review shows that climate change has a range of impacts on the oil palm production in Malaysia. As a result, several adaptation options were identified, such as breeding of hybrid varieties that are tolerant and resistant to heat; sustainable management of soil; pit and tranches to enhance water management in plantation areas; minimal use of fertilisers, herbicides, and pesticides; zero burning; and minimum tillage. The reviewed studies recommended the following to mitigate the adverse impacts of climate change: sustainable national policy on climate change, conservation of the existing carbon stock, effective management of tropical rainforest biodiversity, afforestation for carbon sequestration, and reduction in greenhouse gas (GHG) emission.
The present study investigates the impact of climate change on biodiversity loss using global data consisting of 115 countries. In this study, we measure biodiversity loss using data on the total number of threatened species of amphibians, birds, fishes, mammals, mollusks, plants, and reptiles. The data were compiled from the Red List published by the International Union for Conservation of Nature (IUCN). For climate change variables, we have included temperature, precipitation, and the number of natural disaster occurrences. As for the control variable, we have considered governance indicator and the level of economic development. By employing ordinary least square with robust standard error and robust regression (M-estimation), our results suggest that all three climate change variables - temperature, precipitation, and the number of natural disasters occurrences - increase biodiversity loss. Higher economic development also impacted biodiversity loss positively. On the other hand, good governance such as the control of corruption, regulatory quality, and rule of law reduces biodiversity loss. Thus, practicing good governance, promoting conservation of the environment, and the control of greenhouse gasses would able to mitigate biodiversity loss.
The world faces the challenge to produce ultra-low sulfur diesel with low-cost technology. Therefore, this research emphasised on production of low sulfur fuel utilising nanoparticle catalyst under mild condition. A small amount of cobalt oxide (10-30 wt%) was introduced into the Fe/Al2O3 catalyst through the wet impregnation method. Cobalt modification induces a positive effect on the performance of the iron catalyst. Hence, the insertion of cobalt species into Fe/Al2O3 led to the formation of lattice fringes in all directions which resulted in the formation of Co3O4 and Fe3O4 species. The optimised catalyst, Co/Fe-Al2O3, calcined at 400 °C with a dopant ratio of 10:90 indicating the highest desulfurisation activity by removing 96% of thiophene, 100% of dibenzothiophene (DBT) and 92% of 4,6-dimethyl dibenzothiophene (4,6-DMDBT). Based on the density functional theory (DFT) on Co/Fe-Al2O3, two pathways with the overall energy of -40.78 eV were suggested for the complete oxidation of DBT.
Renewable energy investments possess great potential for reducing the consumption of fossil fuels influenced by various determinants. This study investigates the individual investors' renewable energy investments' intention within the framework of the theory of planned behaviour (TPB) based on a survey conducted in 3 major states in Malaysia. The results indicate that one's intention to invest in renewable energy investments is influenced by attitude, subjective norm, perceived behavioural control and evaluation of regulatory framework. Risk aversion on the other hand was found to have no effect on investors' intention towards such investments. The findings also reveal that the evaluation of regulatory framework is the most important determinant. This outcome contradicts the outcomes arrived at by the previous studies that focus on investment behaviours or other types of pro-environmentally intention or behaviours. This research also investigates the indirect effects of TPB on explaining investor's intention towards renewable energy investments through the evaluation of regulatory framework. The results indicate that the investors' intention towards renewable energy investments is indirectly influenced by attitude and perceived behavioural control. Subjective norm does not have an indirect effect on investors' intention towards renewable energy investments. This study provides policymakers' important practical implications to improve renewable energy investments.
The rise of urbanisation in Belt and Road Initiative (BRI) countries that contribute to the disruption of the ecosystem, which would affect global sustainability, is a pressing concern. This study provides new evidence of the impact of urbanisation and institutional quality on greenhouse gas (GHG) emissions in the selected 48 BRI countries from the years 1984 to 2017. The models of this study are inferred by using panel regression model and panel quantile regression model to meet the objectives of our study as it contemplates unobserved country heterogeneity. From the panel regression model, the findings indicate that although urbanisation in BRI supports the 'life effect' hypothesis that could dampen the environment quality, this effect could be reduced through better institutional quality. Using the quantile regression method, this study concludes that one-size-fits-all strategies to reduce GHG emissions in countries with different GHG emissions levels are improbable to achieve success for all. Hence, GHG emissions control procedures should be adjusted differently across high-emission, middle-emission and low-emission countries. Based on these results, this study provides novel intuitions for policymakers to wisely plan the urbanisation blueprints to eradicate unplanned urbanisation and improve institutional quality in meeting pollution mitigation goals.
The reduction in oil prices might make crude oil a cheaper alternative to renewable energy (RE). Given this, the present paper examines the effect of fluctuation of oil prices on the use of RE in the United States (US) during the period 1970 to 2018. We constructed two nonlinear autoregressive distributed lag (NARDL) models to examine the effect of the positive and negative oil price shocks on the use of RE in the US. The RE consumption is taken as the dependent variable and the gross domestic product (GDP), Brent crude prices, population density, trade openness, and price index as independent variables. The result revealed that the rise in crude oil price, GDP, and population density will increase RE use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease RE use in the short run and its effect will eventually diminish in the long run. On the policy front, it is suggested that US should raise its energy security by reducing its dependency on imported crude oil and increase the role of RE through the imposition of taxes on oil and increase the base of production and consumption through a series of measures.
The production of cement releases an enormous amount of CO2 into the environment. Besides, industrial wastes like silica fume and fly ash need effective utilization to reduce their impacts on the environment. This research aims to explore the influence of silica fume (SF) and fly ash (FA) individually and combine them as binary cementitious material (BCM) on the hardened properties and embodied carbon of roller compacted concrete (RCC). A total of ten mixes were prepared with 1:2:4 mix ratio at the different water-cement ratios to keep the zero slump of roller compacted concrete. However, the replacement proportions for SF were 5%-15%, and FA were 5%-15% by the weight of cement individually and combine in roller compacted concrete for determining the hardened properties and embodied carbon. In this regard, several numbers of concrete specimens (cubes and cylinders) were cast and cured for 7 and 28 days correspondingly. It was observed that the compressive strength of RCC is boosted by 33.6 MPa and 30.6 MPa while using 10% of cement replaced with SF and FA individually at 28 days, respectively. Similarly, the splitting tensile strength of RCC is enhanced by 3.5 MPa at 10% cement replaced with SF and FA on 28 days, respectively. The compressive and splitting tensile strength of RCC is increased by 34.2 MPa and 3.8 MPa at SF7.5FA7.5 as BCM after 28 days consistently. In addition, the water absorption of RCC decreased while using SF and FA as cementitious material individually and together at 28 days. Besides, the embodied carbon of RCC decreased with increasing the replacement level of SF and FA by the mass of cement individually and combined.
Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of β showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.