Emerging pollutants known as endocrine-disrupting compounds (EDCs) are a contemporary global issue, especially in aquatic ecosystems. As aquaculture production through mariculture activities in Malaysia supports food production, the concentration and distribution of EDCs in estuarine water ecosystems may have changed. Therefore, this current study aims to prepare a suitable and reliable method for application on environmental samples. Besides, this study also presented the occurrence of EDCs pollutant in Pulau Kukup, Johor, where the biggest and most active mariculture site in Malaysia takes place. Analytical methods based on a combination of solid-phase extraction with liquid chromatography tandem mass spectrometry (Solid-phase extraction (SPE)-LC-MS/MS) have been modified and optimised to examine the level of targeted EDCs contaminant. In the current study, this method displays high extraction recovery for targeted EDCs, ranging from 92.02% to 132.32%. The highest concentration detected is diclofenac (<0.47-79.89 ng/L) followed by 17β-estradiol (E2) (<5.28-31.43 ng/L) and 17α-ethynylestradiol (EE2) (<0.30-7.67 ng/L). The highest percentage distribution for the targeted EDCs in the current study is diclofenac, followed by EE2 and dexamethasone with the percentages of 99.44%, 89.53% and 73.23%, respectively. This current study can be a baseline assessment to understand the pollution profile of EDCs and their distribution in the estuarine water of the mariculture site throughout the world, especially in Malaysia. Owing to the significant concentration of targeted EDCs detected in water samples, the need for further monitoring in the future is required.
The basic aim of this work is (1) to review and present practically operational requirements for a sustainability assessment of marine environment, such as describing the monitoring process, research approaches, objectives, guidelines, and indicators and (2) to illustrate how physico-chemical and biological indicators can be practically applied, to assess water and sediment quality in marine and coastal environment. These indicators should meet defined criteria for practical usefulness, e.g. they should be simple to understand and apply to managers and scientists with different educational backgrounds. This review aimed to encapsulate that variability, recognizing that meaningful guidance should be flexible enough to accommodate the widely differing characteristics of marine ecosystems.
Threats to beaches have accelerated the coastal destruction. In recent decades, geotextile tubes were used around the world to prevent coastal erosion, to encourage beach nourishment, and to assist mangrove rehabilitation. However, the applications of geotextile tube in sandy and muddy coasts have different concerns as the geological settings are different. Applications of geotextile tubes in sandy beaches were mainly to prevent coastline from further erosion and to nourish the beach. However, for the muddy coasts, mangrove rehabilitation and conservation were additional concerns in coastal management schemes. The mangrove forests are natural barriers which can be found on the muddy coasts of many tropical countries. In this paper, the viability of geotextile tubes in sandy and muddy beaches was analysed. The advantages and disadvantages of the utilization of geotextile tubes in coastal management were discussed based on the experiences from the tropical countries such as Mexico, Malaysia, and Thailand. From the case studies, impressive improvements in coastal restoration after installation of geotextile tubes were shown. Based on the discussion, several recommendations to improve the application of geotextile tubes were suggested in this paper.
This study was conducted to determine the composition of surfactants in the sea-surface microlayer (SML) and atmospheric aerosol around the southern region of the Peninsular Malaysia. Surfactants in samples taken from the SML and atmospheric aerosol were determined using a colorimetric method, as either methylene blue active substances (MBAS) or disulphine blue active substances (DBAS). Principal component analysis with multiple linear regressions (PCA-MLR), using the anion and major element composition of the aerosol samples, was used to determine possible sources of surfactants in atmospheric aerosol. The results showed that the concentrations of surfactants in the SML and atmospheric aerosol were dominated by anionic surfactants and that surfactants in aerosol were not directly correlated (p>0.05) with surfactants in the SML. Further PCA-MLR from anion and major element concentrations showed that combustion of fossil fuel and sea spray were the major contributors to surfactants in aerosol in the study area.
Despite the growing demand of tourism in Malaysia, there are no resolute efforts to develop beaches as tourist destinations. With no incentives to monitor public beaches or to use them in a sustainable manner, they might eventually degenerate in quality as a result of influx of pollutants. This calls for concerted action plans with a view to promoting their sustainable use. The success of such plans is inevitably anchored on the availability of robust quality monitoring schemes. Although significant efforts have been channelled to collation and public disclosure of bacteriological quality data of rivers, beach water monitoring appears left out. This partly explains the dearth of published information related to beach water quality data. As part of an on-going nation-wide surveillance study on the bacteriological quality of recreational beaches, this paper draws on a situation analysis with a view to proffering recommendations that could be adapted for ensuring better beach water quality in Malaysia.
Most developing countries, particularly Indonesia, will be facing problems of sludge pressure in the next decades due to the increase in practices of legal and illegal logging as well as land and water demands. Consequently, they will also be facing the challenges of soil erosion and sludge management due to increased quantities of sludge coming from several potential sources, such as activated sludge, chemical sludge, fecal sludge and solid wastes as well as erosion and sedimentation. Although the government of Indonesia has enacted laws and policies to speed up the implementation of the programs and activities related to sludge management, the detailed practice concepts in implementing the programs need to be identified. Discussion of role-sharing amongst the related government agencies, private institutions and other stakeholders is urgent for clarifying the participation of each party in the next years to come. This paper proposes a management approach and level of responsibilities in sludge management. Implementation of zero DeltaQ, zero DeltaS and zero DeltaP policies needs to be adopted by local and central governments. Application of sludge on the agricultural lands and other uses will promote sustainable development.
Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.
The geochemistry and distribution of major, trace and rare earth elements (REE's) was studied in the surface sediments of the Lower Baram River during two seasons: the Monsoon (MON) and Post - monsoon (POM). The major geochemical processes controlling the distribution and mobility of major, trace and REE's in the Lower Baram River surface sediments was revealed through factor analysis. The risk assessment of major and trace element levels was studied at three specific levels; i.e. the enrichment level [Contamination Factor (Cf), with the geo-accumulation index (Igeo)], the availability level [metals bound to different fractions, risk assessment code (RAC)], and the biological toxicity level [effect range low (ERL) and effect range medium (ERM)]. The results of all the indices indicate that Cu is the element of concern in the Lower Baram River sediments. The geochemical fractionation of major and trace elements were studied through sequential extraction and the results indicated a higher concentration of Mn in the exchangeable fraction. The element of concern, Cu, was found to be highly associated in the organic bound (F4) fraction during both seasons and a change in the redox, possibly due to storms or dredging activities may stimulate the release of Cu into the overlying waters of the Lower Baram River.
Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants' comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
The understanding of how the sediment deposit thickness influences the incipient motion characteristic is still lacking in the literature. Hence, the current study aims to determine the effect of sediment deposition thickness on the critical velocity for incipient motion. An incipient motion experiment was conducted in a rigid boundary rectangular flume of 0.6 m width with varying sediment deposition thickness. Findings from the experiment revealed that the densimetric Froude number has a logarithmic relationship with both the thickness ratios ts/d and ts/y0 (ts: sediment deposit thickness; d: grain size; y0: normal flow depth). Multiple linear regression analysis was performed using the data from the current study to develop a new critical velocity equation by incorporating thickness ratios into the equation. The new equation can be used to predict critical velocity for incipient motion for both loose and rigid boundary conditions. The new critical velocity equation is an attempt toward unifying the equations for both rigid and loose boundary conditions.
Volatile Organic Compounds (VOCs) in indoor air were investigated at 39 private residences in Selangor State, Malaysia to characterize the indoor air quality and to identify pollution sources. Twenty-two VOCs including isomers (14 aldehydes, 5 aromatic hydrocarbons, acetone, trichloroethylene and tetrachloroethylene) were collected by 2 passive samplers for 24h and quantitated using high performance liquid chromatography and gas chromatography mass spectrometry. Source profiling based on benzene/toluene ratio as well as statistical analysis (cluster analysis, bivariate correlation analysis and principal component analysis) was performed to identify pollution sources of the detected VOCs. The VOCs concentrations were compared with regulatory limits of air quality guidelines in WHO/EU, the US, Canada and Japan to clarify the potential health risks to the residents. The 39 residences were classified into 2 groups and 2 ungrouped residences based on the dendrogram in the cluster analysis. Group 1 (n=30) had mainly toluene (6.87±2.19μg/m3), formaldehyde (16.0±10.1μg/m3), acetaldehyde (5.35±4.57μg/m3) and acetone (11.1±5.95μg/m3) at background levels. Group 2 (n=7) had significantly high values of formaldehyde (99.3±10.7μg/m3) and acetone (35.8±12.6μg/m3), and a tendency to have higher values of acetaldehyde (23.7±13.5μg/m3), butyraldehyde (3.35±0.41μg/m3) and isovaleraldehyde (2.30±0.39μg/m3). The 2 ungrouped residences showed particularly high concentrations of BTX (benzene, toluene and xylene: 235μg/m3 in total) or acetone (133μg/m3). The geometric mean value of formaldehyde (19.2μg/m3) exceeded an 8-hour regulatory limit in Canada (9μg/m3), while those in other compounds did not exceed any regulatory limits, although a few residences exceeded at least one regulatory limit of benzene or acetaldehyde. Thus, the VOCs in the private residences were effectively characterized from the limited number of monitoring, and the potential health risks of the VOCs exposure, particularly formaldehyde, should be considered in the study area.
Plant (vegetable) oil has been evaluated as a substitute for mineral oil-based lubricants because of its natural and environmentally friendly characteristics. Availability of vegetable oil makes it a renewable source of bio-oils. Additionally, vegetable oil-based lubricants have shown potential for reducing hydrocarbon and carbon dioxide (CO2) emissions when utilized in internal combustion (IC) engines and industrial operations. In this study, sunflower oil was investigated to study its lubricant characteristics under different loads using the four-ball tribometer and the exhaust emissions were tested using a four-stroke, single-cylinder diesel engine. All experimental works conformed to American Society for Testing and Materials standard (ASTM D4172-B). Under low loads, sunflower oil showed adequate tribological characteristics (antifriction and antiwear) compared with petroleum oil samples. The results also demonstrated that the sunflower oil-based lubricant was more effective in reducing the emission levels of carbon monoxide (CO), CO2, and hydrocarbons under different test conditions. Therefore, sunflower oil has the potential to be used as lubricant of mating components.Implications: An experimental investigation of the characteristics of nonedible sunflower oil tribological behaviors and potential as a renewable source for biofluids alternative to the petroleum oils was carried out. The level of emissions of a four-stroke, single-cylinder diesel engine using sunflower oil as a biolubricant was evaluated.
Nitrogen is essential for seagrass productivity but excesses in nitrogen exposure contribute to declines in meadow health. This study reports baseline data of bulk nitrogen loadings and contents in surficial sediments and seagrass tissues to determine the extent of nitrogen inputs in meadows of Sungai Pulai estuary (Johor, Malaysia). The sediment contained relatively low nitrogen loadings (mean range of 91-94 g N m-2) with likely origins from land-based sources. At the meadow-level, Enhalus acoroides, Cymodocea serrulata and Thalassia hemprichii are the most important species as nitrogen sinks. The highest δ15N values of seagrass tissues were recorded for T. hemprichii (10.7 ± 0.4‰), which indicated an elevated capacity for internal recycling of nitrogen. The data demonstrates the provision of ecosystem services by the meadows in mitigating excess nitrogen imported into the estuary. Seagrasses health, however, needs to be at optimum levels for the effectiveness of the meadow as a nutrient sink.
This study investigates the presence of microplastics in surface seawater and zooplankton at five different locations off the Terengganu coast in Malaysia, southern South China Sea. A total of 983 microplastic particles, with an average abundance of 3.3 particles L-1 were found in surface seawater. An average of one plastic particle was detected in 130 individuals from 6 groups of zooplankton. These groups include fish larvae, cyclopoid, shrimps, polychaete, calanoid and chaetognath where they ingested 0.14, 0.13, 0.01, 0.007, 0.005 and 0.003 particle per individual, respectively. Microplastics in the form of fragments are the most common type of ingested microplastics that ranged between 0.02 mm (cyclopoid) - 1.68 mm (shrimp and zoea). Contrastingly, fibers, which are identified as polyamide are the main type of microplastics that dominate in seawater.
Environmental monitoring is important to determine the extent of eco-system pollution and degradation so that effective remedial strategies can be formulated. In this study, an environmentally friendly and cost-effective sensor made up of novel carbon electrode modified with cellulose and hydroxyapatite was developed for the detection of trace lead ions in aqueous system and palm oil mill effluent. Zinc, cadmium, and copper with lead were simultaneously detected using this method. The electrode exhibited high tolerance towards twelve common metal ions and three model surface active substances - sodium dodecyl sulfate, Triton X-100, and cetyltrimethylammonium bromide. Under optimum conditions, the sensor detected lead ions in palm oil mill effluent in the concentration range of 10-50 μg/L with 0.11 ± 0.37 μg/L limit of detection and 0.37 ± 0.37 μg/L limit of quantification. The validation using tap water, blood serum and palm oil mill effluent samples and compared with Atomic Absorption Spectroscopy, suggested excellent sensitivity of the sensor to detect lead ions in simple and complex matrices. The cellulose produced based on "green" techniques from agro-lignocellulosic wastes, in combination with hydroxyapatite, were proven effective as components in the carbon electrode composite. It has great potential in both clinical and environmental use.
Benzene, toluene, ethylbenzene and xylenes (BTEX) are well known hazardous volatile organic compounds (VOCs) due to their human health risks and photochemical effects. The main objective of this study was to estimate BTEX levels and evaluate interspecies ratios and ozone formation potentials (OFP) in the ambient air of urban Kuala Lumpur (KL) based on a passive sampling method with a Tenax® GR adsorbent tube. Analysis of BTEX was performed using a thermal desorption (TD)-gas chromatography mass spectrometer (GCMS). OFP was calculated based on the Maximum Incremental Reactivity (MIR). Results from this study showed that the average total BTEX during the sampling period was 66.06 ± 2.39 μg/m3. Toluene (27.70 ± 0.97 μg/m3) was the highest, followed by m,p-xylene (13.87 ± 0.36 μg/m3), o-xylene (11.49 ± 0.39 μg/m3), ethylbenzene (8.46 ± 0.34 μg/m3) and benzene (3.86 ± 0.31 μg/m3). The ratio of toluene to benzene (T:B) is > 7, suggesting that VOCs in the Kuala Lumpur urban environment are influenced by vehicle emissions and other anthropogenic sources. The average of ozone formation potential (OFP) value from BTEX was 278.42 ± 74.64 μg/m3 with toluene and xylenes being the major contributors to OFP. This study also indicated that the average of benzene concentration in KL was slightly lower than the European Union (EU)-recommended health limit value for benzene of 5 μg/m3 annual exposure.
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
We have pragmatically but accurately evaluated the natural capital of a small northern town, Shimokawa, Hokkaido, Japan. The key industries are forestry, wood manufacturing, and agriculture. From an environmental perspective, Shimokawa was nominated as a Japanese FutureCity. Consequently, the total natural capital value (NCV) of the forest and agricultural lands was calculated to be 1.326 billion USD/year (or 24,161 USD/ha/year) and 44 million USD/year (or 19,692 USD/ha/year), respectively, in 2012. The sum of these NCVs was more than 7 times greater than the yearly gross production of the town, although the forest had a higher NCV because of the larger area (54,862 ha for forest area), compared with 2953 ha for agricultural area. This substantial NCV is mainly generated by sustainable forest management. The timber account showed that the annual tree growth was greater than the annual harvest of trees. The CO2 account derived from a one-year calculation showed that the town served as a CO2 sink at 107,249 t-CO2/year due to the large amount of annual tree growth and CO2 storage in the harvested wood products even if CO2 was emitted from industries and households. The forestry and wood manufacturing industries, as well as agriculture, created socioeconomic effects for the townspeople, ranging from job creation, study tours, and social welfare. This NCV accounting for Shimokawa town ensures the sustainable use of valuable environmental assets and will help other communities recognize their own NCV accounts.
The identification of spatio-temporal patterns of the urban growth phenomenon has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Therefore, spatio-temporal and quantitative techniques should be used hand in hand for a deeper understanding of various aspects of urban growth. The main purpose of this study is to monitor and assess the significant patterns of urban growth in Seremban using a spatio-temporal built-up area analysis. The concentric circles approach was used to measure the compactness and dispersion of built-up area by employing Shannon's Entropy method. The spatial directions approach was also utilised to measure the sustainability and speed of development, while the gradient approach was used to measure urban dynamics by employing landscape matrices. The overall results confirm that urban growth in Seremban is dispersed, unbalanced and unsustainable with a rapid speed of regional development. The main contribution of using existing methods with other methods is to provide several spatial and statistical dimensions that can help researchers, decision makers and local authorities understand the trend of growth and its patterns in order to take the appropriate decisions for future urban planning. For example, Shannon's Entropy findings indicate a high value of dispersion between the years 1990 and 2000 and from 2010 to 2016 with a growth rate of approximately 94 and 14%, respectively. Therefore, these results can help and support decision makers to implement alternative urban forms such as the compactness form to achieve an urban form that is more suitable and sustainable. The results of this study confirm the importance of using spatio-temporal built-up area and quantitative analysis to protect the sustainability of land use, as well as to improve the urban planning system via the effective monitoring and assessment of urban growth trends and patterns.
In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).