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Research on Performance Assessment of High Quality Development in Urban Green and Low Carbon Transformation

Yannan Luo Yu Zhang Xiuyan Han, Pages:1-14

DOI:

https://doi.org/10.30955/gnj.005356

Paper Topic:

Ecological effects of Environmental change

Issue:

Issue 1

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Abstract

In order to study the effective methods of green and low-carbon transformation in Chinese cities, this paper takes Rizhao City, Shandong Province, China, as the object of study. Based on the analysis of the research background and the literature review, and taking into account the characteristics of Rizhao City, the following five categories with a total of 25 indicators were selected to construct the assessment indicator system: the environmental quality of green and low-carbon transformation, the quality of the planning and construction of green and low-carbon transformation, the quality of the life and work in the green and low-carbon transformation, the quality of the process of the green transformation empowering the development of high-quality, and the quality of the results of the green transformation empowering high-quality development. Based on this, the two-level fuzzy comprehensive assessment model is reconstructed, and the relevant statistical data provided by the government is used to comprehensively assess the high-quality development performance of the green transformation and empowerment cities in Rizhao City from 2012 to 2022. It is found that the performance of high-quality development empowered by green transformation in Rizhao City, China, has shown a continuous upward trend, having risen from Level Ⅳ in 2012, to Level II by 2022, and remained at Level II during 2018-2022, with its assessment results and showing an upward trend. Finally, based on the specific research results, the policy suggestions to improve the high-quality development performance of cities empowered by the green and low-carbon transformation of Rizhao City, China are discussed.

Investigation on Tensile and Flexural Behaviour of Fibre Reinforced Concrete Using Artificial Neural Network

S.Nagamani, Dr.P.Muthupriya, C.Rajendra Prasad, A.Saradha Devi, Pages:1-6

DOI:

https://doi.org/10.30955/gnj.005350

Paper Topic:

Solid Waste Management

Issue:

Issue 1

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Abstract

The purpose of this study is to investigate the impact that using marble sludge powder as a partial replacement for cement in concrete can have. Experiments were conducted to investigate a variety of characteristics of fiber-reinforced concrete using both fresh concrete and concrete that had been allowed to solidify. In order to determine the split tensile and flexural property of FRC, two water binder ratios, such as 0.35 and 0.40, as well as percentage re-placements of 0%, 5%, 10%, 15%, 20%, and 25% of marble sludge powder and 0.5% of polypropylene 3S fibre were used. After curing for 7, 14, 28, and 56 days, the samples were put through a battery of mechanical tests to evaluate their qualities. The flexural strength and split tensile strength of the material were evaluated over the course of this investigation. In the end, an artificial neural network, also known as an ANN, was utilised in order to create a prediction model for split tensile and flexural strength. We displayed the experimentally obtained Split Tensile Strength and Flexural Strength against the regression analysis strength after 56 days for ANN. This was done so that we could compare the two. According to the findings of the experiments, using powder made from marble waste might lessen the damage that concrete causes to the environment while also providing economic benefits. In this study, dependable mechanical strength was developed by the use of a feed-forward back-propagation neural network, which consisted of eight input neurons, two hidden neurons, and one output neuron. According to the findings, it was discovered that the mechanical properties of concrete might be improved by using dry marble sludge powder as a substitute for up to 15% of the normal aggregate.

Utilizing Manilkara zapota seed oil for biodiesel production and conducting an investigation into its properties and characteristics

N.Kanthavelkumaran S.Mathu Kumar S.Iyyappan N.N.Suthan Raja Prasad, Pages:1-8

DOI:

https://doi.org/10.30955/gnj.005387

Paper Topic:

Biowaste

Issue:

Issue 2

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Abstract

Biodiesel can be derived from the oils of fruit seeds for powering diesel engines, and this study focuses on extracting biodiesel from Manilkara Zapota seeds, which are readily available and not intended for human consumption. The primary aim is to underscore the use of non-edible oils as a cost-effective raw material for biodiesel production. The research indicates a successful production yield of 92.45% using a 0.76% catalyst, an excess of 6 methanol equivalents in comparison to the oil, a temperature of 62°C, and a reaction time of 90 minutes. The biodiesel obtained from Manilkara Zapota seed oil predominantly comprises methyl esters of oleic, stearic, and palmitic acids, presenting a viable alternative to fossil diesel for unmodified diesel engines. Optimal performance variables for maximum conversion efficiency were determined as a 40°C reaction temperature, 6:1 alcohol-to-oil ratio, 120 minutes of experimental duration, and a 0.5wt.% NaOH catalyst. Among these variables, the alcohol-to-oil ratio was identified as the most influential, contributing 84.34% to the overall performance. The thermal profile of Manilkara Zapota Seeds biomass exhibited multistage decomposition behavior. The synthesized Manilkara Zapota Methyl Ester (MZME) derived from seed oil, using the optimized performance variables, complies with the ASME D 6751 and EN14214 Biodiesel Standards.

Evaluating the potential capability of Selenium nanoparticles on degradation of hazardess textile dye using environmental friendly approach

Haewon Byeon, V. Puvana Devi, A. Murugan, Anu Tonk, K. Haribabu, Harshal Patil and V.G. Anisha Gnana Vincy , Pages:1-9

DOI:

https://doi.org/10.30955/gnj.005633

Paper Topic:

Ecosystems and Natural Resources Management

Issue:

Issue 2

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Abstract

This study synthesizes selenium nanoparticles (Se NPs) using a green approach, utilizing Tectona grandis leaf extract in a hydrothermal method. The X-ray diffraction (XRD) analysis provided highly crystalline particles with a trigonal crystal structure, indicative of the well-defined nature of the synthesized Se NPs. Particle size analysis (PSA) results demonstrated a average particle size of 45 nm, further supporting the nano-sized characteristics of the particles. Field emission scanning electron microscopy (FESEM) results exhibited a hexagonal close-packed structure, providing valuable insights into the morphology of the Se NPs. Optical band gap calculations using the Tauc plot method yielded a value of 1.85 eV, highlighting the semiconductor nature of the synthesized Se NPs. The photocatalytic application of these nanoparticles was investigated against various cationic and anionic dyes, including Alizarin Red, Crystal Violet, Reactive Black, and Rhodamine B, under sunlight irradiation. Notably, the Se NPs demonstrated enhanced degradation, particularly for cationic dyes, attributed to electrostatic surface charge interactions. Rhodamine B exhibited the highest degradation efficiency among the dyes, reaching 96%. Furthermore, the stability of the Se NPs was evaluated through five consecutive cycles of photocatalytic degradation. Remarkably, the nanoparticles maintained stability, with only a marginal loss in degradation efficiency (after 5 cycles), affirming their potential as a stable and efficient nano photocatalyst for environmental remediation applications. This comprehensive investigation underscores the significance of green-synthesized Se NPs in advancing sustainable and effective photocatalytic processes.

Water Quality Prediction using Statistical, Ensemble and Hybrid models

Venkata Vara Prasad D, Lokeswari Y Venkataramana, Vikram V, Vyshali S and Shriya B, Pages:1-9

DOI:

https://doi.org/10.30955/gnj.005492

Paper Topic:

Water Quality

Issue:

Issue 2

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Abstract

Water is an essential elixir for several living organisms to function and survive. But it gets contaminated through several sources such as industrial wastes, oil spills, marine dumping, etc. With a growing population, availability of good quality water is of grave importance. This has become the motivation to probe into analysis of water quality from the outcomes of Statistical and Ensemble methods and to find the best working models from both methods. Research has been done to predict water quality analysis using standalone statistical and ensemble models. So, this research focuses on obtaining the best Statistical and Ensemble model separately among the models tried. The statistical models implemented for comparison are Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA). The Ensemble models used are Bagging, Boosting and Stacking. The models are then combined to build a Hybrid model to observe the comparisons between the three. The performance metrics used are Confusion Matrix, Accuracy, Precision, Recall, F1-score and ROC curve. While comparing the models, it is observed that Hybrid model produces the most accurate results, hence proving that the combination of Statistical and Ensemble model is efficient.

Evaluation of Toxic Heavy Metal Concentration in Aquifer System for Groundwater System Development Using Multivariate Statistical Techniques

Akhtar N. Flafel H. M. Ezhani A. A. A. Giuma A. A. Febriana A. Wijaya D. Anees M. T. Hussain R. S. R. Ahmed S. , Pages:1-8

DOI:

https://doi.org/10.30955/gnj.005788

Paper Topic:

Water Quality

Issue:

Issue 3

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Abstract

Groundwater is a vital resource for human consumption. The study aimed to evaluate toxic metal concentrations in groundwater systems and determine pollutant sources using multivariate methods including cluster analysis (CA), principal factor analysis (PCA), and Pearson correlation coefficient (r). The results were compared with World Health Organization (WHO 2017) and Bureau of Indian Standard (BIS 2012) standards, indicating that Al concentration observed within prescribed values and other Cd, As, Zn, Pb, and Cu were less than the acceptable values, as well as the rest of Fe, Mn, and Ni levels in groundwater were mostly within acceptable values. The PCA results showed three factors (F1, F2, and F3) were responsible for the data structure, which was specified as 37.954%, 23.331%, and 16.132%, as well as total variance of dataset associated with 77.416%, respectively. Factor 1 showed strong positive loading (Cu, Pb, Zn), 2 (Al, Mn), and 3 (As, Ni), which demonstrated the contaminants source from natural and agricultural activities. Moreover, CA results revealed three clusters indicating low to high water pollution due to rock weathering and anthropogenic activities. Overall, results showed that 50% of groundwater samples were acceptable for potable and agricultural uses. Therefore, groundwater treatment is necessary before any use.

Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

Jansi Rani S V Ramakrishnan A M Rishivardhan K, Pages:1-8

DOI:

https://doi.org/10.30955/gnj.003923

Paper Topic:

Water Quality

Issue:

Issue 1

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Abstract

Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.

Prediction of Land Use and Landcover Changes in Tiruppur Tamilnadu Using Hybrid Convolutional Neural Network

K.G. Akshaya R. Sathyanarayan Sridhar , Pages:1-11

DOI:

https://doi.org/10.30955/gnj.004716

Paper Topic:

Environmental data analysis and modelling

Issue:

Issue 6

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Abstract

Land use and landcover change (LU/LC) are important in global change studies because they can transform the local and global environment by developing the biochemical, biochemical and biogeographic properties of the Earth's structure. This paper is intended to develop Hybrid Convolutional Neural Network (HCNN) for land use and land cover changes prediction in Tiruppur Tamilnadu. Initially, the databases are collected from the open-source system. And the image dataset has been pre-processed using the image augmentation technique. Through which the image has been resized and processed for training it with the proposed mode. The resized images are sent to the HCNN for prediction of land cover and land use changes in Tiruppur Tamilnadu. The proposed classifier is a combination of Convolutional Neural Network (CNN) and Remora Optimization Algorithm (ROA). In the CNN, the ROA is utilized to select the hyper parameters to enable efficient prediction in land use and land cover changes. The proposed classifier is implemented in MATLAB and performances is evaluated by performance metrices such as accuracy, precision, recall, sensitivity, F_Measure and Kappa. The proposed methodology is compared with the conventional techniques such as CNN, Markov chain model and Recurrent Neural Network (RNN) respectively.

Vigna stipulacea mediated Fe nanoparticles synthesis: A greener approach for sequestration of Pb2+ from aqueous environment

R. Mafaz Ahamed R. Saraswathi, Pages:1-11

DOI:

https://doi.org/10.30955/gnj.005132

Paper Topic:

Water and Wastewater Treatment

Issue:

Issue 1

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Abstract

The greener approach offers a viable, sustainable and eco-friendly way to synthesize nanoparticles. This study used the seed extract of Vigna stipulacea (VS) as a bioreducing agent to synthesize iron nanoparticles (VS-Fe). The VS seed extract contains polyphenols and lignin content that acted as a bioreducing agent during VS-Fe formation. The Vigna stipulacea-mediated Fe nanoparticles were characterized using UV, XRD, FTIR, EDAX and BET surface analysis. The as-synthesized VS-Fe, comprised of Fe0 phase and Fe hydroxides, had an average crystallite size of 30.65 nm. It possessed a surface area of 199.189 m2/g and magnetic saturation of 11.21 m emu. The VS-Fe exhibited excellent adsorptive behavior during the sequestration of Pb2+ ions from an aqueous environment. The Pb2+ uptake was maximum (96.7%) under the optimal conditions of 60 min contact time, 0.01 g/ 100 mL VS-Fe dosage and pH 6. The equilibrium data of Pb2+ adsorption was more appropriate with pseudo-second-order kinetics (R2 = 0.9903) and Langmuir isotherm (R2 = 0.9941) with qmax of 1020.50 mg/g. Thus, the dominance of chemisorption in Pb2+ removal was revealed. It was further confirmed with the SEM micrograph of Pb-loaded VS-Fe nanoparticles. Overall, this study demonstrated the inexpensive and non-toxic way of synthesizing Fe nanoparticles and their utilization in effectively removing Pb2+ ions from water.

Spatiotemporal evolution pattern of carbon emission performance in Asian countries

Na Liu, Pages:1-10

DOI:

https://doi.org/10.30955/gnj.005445

Paper Topic:

Climate change mitigation and adaptation

Issue:

Issue 1

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Abstract

Due to extensive energy consumption and industrial production activities, Asian countries have become the largest sources of global carbon emissions, with emerging economies like China and India contributing significantly. Against the backdrop of increasing global climate change, carbon emissions have emerged as a focal concern within the international community. To mitigate these emissions, enhancing carbon performance is identified as a crucial endeavor. This study employs the Super-efficiency Slack-Based Measure (SBM) model to comprehensively evaluate the carbon performance of major Asian countries and delves into its influencing factors through factor analysis. Additionally, we utilize geographical methodologies to explore the spatiotemporal evolution characteristics of carbon performance in these countries. Preliminary results indicate that spatial centroid has gradually shifted towards the northeast, which could potentially reflect a geographic concentration change in some form of economic or industrial activity. Moreover, the elliptical parameters for carbon emission efficiency did not exhibit any discernible trend, maintaining a relatively stable overall pattern and distribution. The findings of this study hold significant implications for understanding the spatial heterogeneity and dynamic characteristics of carbon emission efficiency in Asia, and offer valuable insights for future policy-making.

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Research on Performance Assessment of High Quality Development in Urban Green and Low Carbon Transformation

Investigation on Tensile and Flexural Behaviour of Fibre Reinforced Concrete Using Artificial Neural Network

Utilizing Manilkara zapota seed oil for biodiesel production and conducting an investigation into its properties and characteristics

Evaluating the potential capability of Selenium nanoparticles on degradation of hazardess textile dye using environmental friendly approach

Water Quality Prediction using Statistical, Ensemble and Hybrid models

Evaluation of Toxic Heavy Metal Concentration in Aquifer System for Groundwater System Development Using Multivariate Statistical Techniques

Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

Prediction of Land Use and Landcover Changes in Tiruppur Tamilnadu Using Hybrid Convolutional Neural Network

Vigna stipulacea mediated Fe nanoparticles synthesis: A greener approach for sequestration of Pb2+ from aqueous environment

Spatiotemporal evolution pattern of carbon emission performance in Asian countries

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