Maximizing diesel removal from contaminated sand using Scirpus mucronatus and assessment of rhizobacteria addition effect.

Phytoremediation is one of the green technologies that is friendly to nature, utilizes fewer chemicals, and exhibits good performance. In this study, phytoremediation was used to treat diesel-contaminated sand using a local aquatic plant species, Scirpus mucronatus, by analyzing the amount of total petroleum hydrocarbons (TPHs). Optimization of diesel removal was performed according to Response Surface Methodology (RSM) using Box-Behnken Design (BBD) under pilot-scale conditions. The quadratic model showed the best fit to describe the obtained data. Actual vs. predicted values from BBD showed a total of 9.1 % error for the concentration of TPH in sand and 0 % error for the concentration of TPH in plants. Maximum TPH removal of 42.3 ± 2.1 % was obtained under optimized conditions at a diesel initial concentration of 50 mg/kg, an aeration rate of 0.48 L/min, and a retention time of 72 days. The addition of two species of rhizobacteria (Bacillus subtilis and Bacillus licheniformis) at optimum conditions increased the TPH removal to 51.9 ± 2.6 %. The obtained model and optimum condition can be adopted to treat diesel-contaminated sand within the same TPH range (50-3000 mg/kg) in sand.

Lead (Pb) removal from contaminated water using constructed wetland planted with Scirpus grossus: Optimization using response surface methodology (RSM) and assessment of rhizobacterial addition.

Lead (Pb) is one of the toxic heavy metals that pollute the environment as a result of industrial activities. This study aims to optimize Pb removal from water by using horizontal free surface flow constructed wetland (HFSFCW) planted with Scirpus grossus. Optimization was conducted using response surface methodology (RSM) under Box-Behnken design with the operational parameters of initial Pb concentration, retention time, and aeration. Optimization results showed that 37 mg/L of initial Pb concentration, 32 days of retention time, and no aeration were the optimum conditions for Pb removal by using the systems. Validation test was run under two different conditions, namely, non-bioaugmented and bioaugmented with rhizobacteria (Bacillus cereus, B. pumilus, B. subtilis, Brevibacillus choshinensis, and Rhodococcus rhodochrous). Results of the validation test showed that Pb removal in water achieved 99.99% efficiency with 0.2% error from the RSM prediction, while the adsorption of Pb by plants reached 5160.18 mg/kg with 10.6% error from the RSM prediction. The bioaugmentation of the five rhizobacterial species showed a slight improvement in Pb removal from water and Pb adsorption by plants. However, no significant improvement was achieved (p < 0.05). Overall results suggested that operating the HFSFCW under optimum conditions with no bioaugmentation might be a feasible choice for the treatment of Pb-contaminated water.

Arsenic Resistance and Biosorption by Isolated Rhizobacteria from the Roots of Ludwigia octovalvis.

Certain rhizobacteria can be applied to remove arsenic in the environment through bioremediation or phytoremediation. This study determines the minimum inhibitory concentration (MIC) of arsenic on identified rhizobacteria that were isolated from the roots of Ludwigia octovalvis (Jacq.) Raven. The arsenic biosorption capability of the was also analyzed. Among the 10 isolated rhizobacteria, five were Gram-positive (Arthrobacter globiformis, Bacillus megaterium, Bacillus cereus, Bacillus pumilus, and Staphylococcus lentus), and five were Gram-negative (Enterobacter asburiae, Sphingomonas paucimobilis, Pantoea spp., Rhizobium rhizogenes, and Rhizobium radiobacter). R. radiobacter showed the highest MIC of >1,500 mg/L of arsenic. All the rhizobacteria were capable of absorbing arsenic, and S. paucimobilis showed the highest arsenic biosorption capability (146.4 ± 23.4 mg/g dry cell weight). Kinetic rate analysis showed that B. cereus followed the pore diffusion model (R 2 = 0.86), E. asburiae followed the pseudo-first-order kinetic model (R 2 = 0.99), and R. rhizogenes followed the pseudo-second-order kinetic model (R 2 = 0.93). The identified rhizobacteria differ in their mechanism of arsenic biosorption, arsenic biosorption capability, and kinetic models in arsenic biosorption.

Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization.

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.

Content-based image retrieval system for solid waste bin level detection and performance evaluation.

This paper presents a CBIR system to investigate the use of image retrieval with an extracted texture from the image of a bin to detect the bin level. Various similarity distances like Euclidean, Bhattacharyya, Chi-squared, Cosine, and EMD are used with the CBIR system for calculating and comparing the distance between a query image and the images in a database to obtain the highest performance. In this study, the performance metrics is based on two quantitative evaluation criteria. The first one is the average retrieval rate based on the precision-recall graph and the second is the use of F1 measure which is the weighted harmonic mean of precision and recall. In case of feature extraction, texture is used as an image feature for bin level detection system. Various experiments are conducted with different features extraction techniques like Gabor wavelet filter, gray level co-occurrence matrix (GLCM), and gray level aura matrix (GLAM) to identify the level of the bin and its surrounding area. Intensive tests are conducted among 250 bin images to assess the accuracy of the proposed feature extraction techniques. The average retrieval rate is used to evaluate the performance of the retrieval system. The result shows that, the EMD distance achieved high accuracy and provides better performance than the other distances.

A review on technologies and their usage in solid waste monitoring and management systems: Issues and challenges.

In the backdrop of prompt advancement, information and communication technology (ICT) has become an inevitable part to plan and design of modern solid waste management (SWM) systems. This study presents a critical review of the existing ICTs and their usage in SWM systems to unfold the issues and challenges towards using integrated technologies based system. To plan, monitor, collect and manage solid waste, the ICTs are divided into four categories such as spatial technologies, identification technologies, data acquisition technologies and data communication technologies. The ICT based SWM systems classified in this paper are based on the first three technologies while the forth one is employed by almost every systems. This review may guide the reader about the basics of available ICTs and their application in SWM to facilitate the search for planning and design of a sustainable new system.

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.

Phytoremediation of wastewater containing lead (Pb) in pilot reed bed using Scirpus grossus.

Phytoremediation is a technology to clean the environment from heavy metals contamination. The objectives of this study are to threat Pb contaminated wastewater by using phytoremediation technology and to determine if the plant can be mention as hyperaccumulator. Fifty plants of Scirpus grossus were grown in sand medium and 600 L spiked water in various Pb concentration (10, 30 and 50 mg/L) was exposed. The experiment was conducted with single exposure method, sampling time on day-1, day-14, day-28, day-42, day-70, and day-98. The analysis of Pb concentration in water, sand medium and inside the plant tissue was conducted by ICP-OES. Water samples were filtered and Pb concentration were directly analyzed, Pb in sand samples were extracted by EDTA method before analyzed, and Pb in plant tissues were extracted by wet digestion method and analyzed. The results showed that on day-28, Pb concentration in water decreased 100%, 99.9%, 99.7%, and the highest Pb uptake by plant were 1343, 4909, 3236 mg/kg for the treatment of 10, 30, and 50 mg/L respectively. The highest BC and TF were 485,261 on day-42 and 2.5295 on day-70 of treatment 30 mg/L, it can be mentioned that Scirpus grossus is a hyperaccumulator.

Arsenic toxicity on Ludwigia octovalvis in spiked sand.

Wilting, especially of the leaves, was observed as an initial symptom of arsenate [As(V)] to Ludwigia octovalvis (Jacq.) P. H. Raven. The plants tolerated As(V) levels of 39 mg kg⁻¹ for as long as 35 days of exposure. After 91 days, the maximum concentration of As uptake in the plant occurred at As(V) concentration of 65 mg kg⁻¹ while As concentration in the stems, roots and leaves were 6139.9 ± 829.5, 1284.5 ± 242.9 and 1126.1 ± 117.2 mg kg⁻¹, respectively. In conclusion, As(V) could cause toxic effects in L. octovalvis and the plants could uptake and accumulate As in plant tissues.

Empirical gas emission and oxidation measurement at cover soil of dumping site: example from Malaysia.

Methane (CH4) is one of the most relevant greenhouse gases and it has a global warming potential 25 times greater than that of carbon dioxide (CO2), risking human health and the environment. Microbial CH4 oxidation in landfill cover soils may constitute a means of controlling CH4 emissions. The study was intended to quantify CH4 and CO2 emissions rates at the Sungai Sedu open dumping landfill during the dry season, characterize their spatial and temporal variations, and measure the CH4 oxidation associated with the landfill cover soil using a homemade static flux chamber. Concentrations of the gases were analyzed by a Micro-GC CP-4900. Two methods, kriging values and inverse distance weighting (IDW), were found almost identical. The findings of the proposed method show that the ratio of CH4 to CO2 emissions was 25.4 %, indicating higher CO2 emissions than CH4 emissions. Also, the average CH4 oxidation in the landfill cover soil was 52.5 %. The CH4 and CO2 emissions did not show fixed-pattern temporal variation based on daytime measurements. Statistically, a negative relationship was found between CH4 emissions and oxidation (R(2) = 0.46). It can be concluded that the variation in the CH4 oxidation was mainly attributed to the properties of the landfill cover soil.

An automated solid waste bin level detection system using a gray level aura matrix.

An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions.

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach.

This paper presents solid waste bin level detection and classification using gray level co-occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as displacement, d, quantization, G, and the number of textural features, are investigated to determine the best parameter values of the bin images. The parameter values and number of texture features are used to form the GLCM database. The most appropriate features collected from the GLCM are then used as inputs to the multi-layer perceptron (MLP) and the K-nearest neighbor (KNN) classifiers for bin image classification and grading. The classification and grading performance for DB1, DB2 and DB3 features were selected with both MLP and KNN classifiers. The results demonstrated that the KNN classifier, at KNN = 3, d = 1 and maximum G values, performs better than using the MLP classifier with the same database. Based on the results, this method has the potential to be used in solid waste bin level classification and grading to provide a robust solution for solid waste bin level detection, monitoring and management.

Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system.

This paper deals with a system of integration of Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system. RFID, GPS, GPRS and GIS along with camera technologies have been integrated and developed the bin and truck intelligent monitoring system. A new kind of integrated theoretical framework, hardware architecture and interface algorithm has been introduced between the technologies for the successful implementation of the proposed system. In this system, bin and truck database have been developed such a way that the information of bin and truck ID, date and time of waste collection, bin status, amount of waste and bin and truck GPS coordinates etc. are complied and stored for monitoring and management activities. The results showed that the real-time image processing, histogram analysis, waste estimation and other bin information have been displayed in the GUI of the monitoring system. The real-time test and experimental results showed that the performance of the developed system was stable and satisfied the monitoring system with high practicability and validity.

Regional landfills methane emission inventory in Malaysia.

The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50-60% methane (CH(4)) and 30-40% carbon dioxide (CO(2)) by volume. CH(4) has a global warming potential 21 times greater than CO(2); thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH(4) emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH(4) that would be emitted from landfills in the period from 1981-2024 using the IPCC 2006 FOD model. The total CH(4) emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH(4) emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH(4) emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH( 4) emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH(4) emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.

Integrated technologies for solid waste bin monitoring system.

The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer's inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as in truck, GPRS/GSM as web server, and GIS as map server, database server, and control server. The tracking devices mounted in the trucks collect location information in real time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.