Critical success factors of sustainable manufacturing practices in Malaysian automotive industry

The purpose of this study is to examine the critical success factors of sustainable manufacturing practices (SMPs) in Malaysian automotive industry. The questionnaires were designed and distributed to the 50 respondents. The pilot study sample consists of 26 respondents from 50 questionnaires, with an average response rate of 52%. The results of reliability analysis show that social responsibility is critical factor influencing the direct SMPs implementation success. By implementing SMPs in Malaysian automotive industry, it can assist company to improve the quality of management. Thus, this study recommends that the future research explore the influence of SMPs on sustainable performance in Malaysian automotive industry.     

Alterations in liver marker enzymes and recovery ability of cyprinids as indicators of aquatic pollution

Information on the detection of pesticides in fresh water Lakes of Kashmir (Dal and Mansbal) through GC–MS technique is scarce, and also the recovery in biochemical parameters (AST, ALT and ALP) of fish after transferring them to clean media has not been reported yet. The water samples were collected from three sites and analyzed for their pesticide profile by dispersive liquid–liquid micro extraction (DLLME) followed by GC–MS. Influence of pesticides on liver marker enzymes of Cyprinus carpio and Carassius carassius was also investigated. The results obtained showed the presence of three main pesticides viz. chlorpyrifros, dimethoate and dichlorvos in waters of Dal Lake whereas no pesticide was detected in waters of Mansbal. The higher values for AST, ALT and ALP activities and decrease in protein content were obtained in the samples from the Dal Lake compared with those from Mansbal Lake (p < 0.05). These data when compared with the values found in C. carpio and C. carassius from both the Dal Lake and Mansbal Lake kept under laboratory conditions after 15, 30, 45 and 60 days of maintenance in clean media found that during depuration, all the enzyme activities came down significantly (p < 0.05) indicating the compensatory response by the fish against the pesticide stress. Therefore, these parameters could be used as indicators of pesticide pollution in aquatic organisms and were recommended for environmental monitoring for investigating the mechanism involved in the recovery pattern.

     

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.     

Assessment of Groundwater Quality in a Structurally Deformed Granitic Terrain in Hyderabad, India

Geochemical study of groundwater from a structurally deformed granitic terrain near Hyderabad (India) was carried out to understand and evaluate the hydrogeochemical processes and quality of groundwater. Several trace elements (Fe, Mn, Be, Al, V, Cr, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Sb, Ba, Pb, U) along with major ions and minor elements were precisely estimated in shallow and drilled wells to know the suitability of water for drinking and irrigation purposes. Analytical data shows that pH and major ion chemistry in dug wells and bore wells do not vary significantly, while some trace elements (Fe, Mn, Al, Be, Co, Pb, U and Zn) vary in dug wells and bore wells, which can be attributed to differential mineral weathering and dissolution/precipitation reactions along fractures/joints. Although the water is not potable, it was found to be suitable for irrigation with little danger in the development of harmful level of exchangeable sodium. It is inferred that the chemical composition of the groundwater in this region is likely to have its origin from silicate weathering reactions and dissolution/precipitation processes supported by rainfall and groundwater flow.     

Environmental Kuznets Curve: the case of Bangladesh for waste emission and suspended particulate matter

Economists have shown the empirical relationships between the economic growth of a nation and its environmental quality using the hypothesis of the Environmental Kuznets Curve (EKC). To understand the EKC phenomena, a study of the literature was undertaken. As waste, waste emission, and suspended particulate matter (SPM) are the active agents of environmental degradation, the study focused on those agents for EKC consideration. Through understanding the different EKC trajectories, an attempt was made to relate the economic development of Bangladesh to the EKC. It is shown that the EKCs for waste, emissions from waste, and SPM follow the conventional EKC trajectory with a turning point related to higher income per capita in most cases. The type of economic policy that Bangladesh should follow to deal with the pollutants and their sources is also shown. The study recommends creating a tunnel in the EKC to lower the turning point in Bangladesh. The study is likely to help stimulate policy development in Bangladesh.     

Indicator-Based Approach for Assessing the Vulnerability of Freshwater Resources in the Bagmati River Basin,Nepal

To assess the vulnerability of water resources in the Bagmati River Basin in Nepal, this paper adopts an indicator-based approach wherein vulnerability is expressed as a function of water stress and adaptive capacity. Water stress encompasses indicators of water resources variation, scarcity, and exploitation and water pollution, whereas adaptive capacity covers indicators of natural, physical, human resource, and economic capacities. Based on the evaluation of eleven indicators, which were aggregated into eight vulnerability parameters, an increasingly stressful situation and lack of adaptive capacity became evident. Considerable spatial variation in indicator values suggests differential policy options. While the northern parts need attention to reduce pollution loading and conserve vegetation cover, the southern parts need improvements in physical capacity, i.e. water infrastructures. The comprehensive and easily interpretable findings of the study are expected to help decision makers reach sound solutions to reduce freshwater resources vulnerability in the Bagmati River Basin, Nepal. With its inherent flexibility, the approach has demonstrated its potential for application in different times and areas for monitoring and comparison purposes.     

Prediction of total bed material load for rivers in Malaysia: A case study of Langat,Muda and Kurau Rivers

A soft computational technique is applied to predict sediment loads in three Malaysian rivers. The feed forward-back propagated (schemes) artificial neural network (ANNs) architecture is employed without any restriction to an extensive database compiled from measurements in Langat, Muda, Kurau different rivers. The ANN method demonstrated a superior performance compared to other traditional sediment-load methods. The coefficient of determination, 0.958 and the mean square error 0.0698 of the ANN method are higher than those of the traditional method. The performance of the ANN method demonstrates its predictive capability and the possibility of generalization of the modeling to nonlinear problems for river engineering applications.     

Investigation of arsenic removal in batch wise water treatments by means of sequential hydride generation flow injection analysis

Arsenic water pollution is a big issue worldwide. Determination of inorganic arsenic in each oxidation state is important because As(III) is much more toxic than As(V). An automated arsenic measurement system was developed based on complete vaporization of As by a sequential procedure and collection/preconcentration of the vaporized AsH(3), which was subsequently measured by a flow analysis. The automated sensitive method was applied to monitoring As(III) and As(V) concentrations in contaminated water standing overnight. Behaviors of arsenics were investigated in different conditions, and unique time dependence profiles were obtained. For example, in the standing of anaerobic water samples, the As(III) concentration immediately began decreasing whereas dead time was observed in the removal of As(V). In normal groundwater conditions, most arsenic was removed from the water simply by standing overnight. To obtain more effective removal, the addition of oxidants and use of steel wools were investigated. Simple batch wise treatments of arsenic contaminated water were demonstrated, and detail of the transitional changes in As(III) and As(V) were investigated.