Global NEST Journal

ISSN:

Print ISSN: 1790-7632

Electronic ISSN: 2241-777X

Issue 2

Volume number:

Publication year:

2024

Publication month:

March

Number of publications: 30

Thematic

Water and wastewater
Solid waste management
Climate change and air pollution
Circular economy

Water and wastewater

Investigation of Key Technologies for Energy Saving and Consumption Reduction in Chongqing Municipal Wastewater Treatment Plants Based on Carbon Emission Reduction Contribution
Guanyu X, Qiyu Y, Hanyi Y, Yongheng W, Pages:1-12

DOI:
https://doi.org/10.30955/gnj.005642
Paper Topic:
Water and Wastewater Treatment

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Issue 2
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Abstract
In this paper, based on the factors of treatment process, treatment scale, as well as water quality, the level and distribution of electricity consumption, drug consumption, and indirect carbon emission of five typical wastewater treatment plants (WWTPs) were examined. The distribution of electricity consumption within the WWTPs were analyzed in terms of wastewater treatment units. The results uncovered that the biological treatment unit was the treatment unit with a high percentage of electricity consumption in the WWTPs. Carbon emissions of main units in the WWTPs presented that the aeration blower, sewage lifting pump, submersible pusher, phosphorus remover and return sludge pump of the biological treatment unit were the top 5 emission units of carbon emissions in the WWTPs, and the key influencing factors of the carbon emissions of the main carbon emission units had been analyzed. Combined with the current situation of sewage treatment energy consumption in Chongqing, the analysis put forward a library of energy saving and consumption reduction measures for Chongqing WWTPs, which applied them to the sewage treatment plant A energy saving and consumption reduction renovation project.
Activated rice husk biochar for azo dye removal: batch adsorption, kinetics and thermodynamic studies
Arivumani V, Vikash Singh, Geetha C and Senthilkumar C, Pages:1-14

DOI:
https://doi.org/10.30955/gnj.005498
Paper Topic:
Water and Wastewater Treatment

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Issue 2
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Abstract
The study aimed to analyze the adsorption behaviour of an activated rice husk biochar adsorbent in batch mode to expunge azo dye concentrations depletion from liquid solvents. The biochar adsorbent was formulated using different chemical synthesis processes, and its surface region was assessed using Nitrogen retention. The existence of the particular azo dyes was verified through SEM and EDX examinations. Additionally, XRD analysis was conducted to examine the intensity and crystalline structure of the activated biochar adsorbent. A batch mode study was conducted to ascertain the ideal adsorption parameters, including pH, contact time, biochar dose, and azo dye concentration. The adsorption kinetic studies supported the notion of a favourable adsorption interaction occurring through the mutual influence of the adsorbed matter and the adsorbing substrate. Most isotherm studies were well-fitted with regression values of isotherm models, indicating an adsorption process with non-uniform monolayer formation. Thermodynamic investigations validated that the binding forces between the adsorptive and the adsorbing phase are endothermic, and a significant amount of spent adsorbent was successfully recovered using a strong hydrochloric acid desorption process.
Predictive Modeling for Solar Desalination Using Artificial Neural Network Techniques: A Review
Vairavel Madeshwaren, Kathiresan Govindaraju ,Pragya Pandey, Suresh Thangavel ,Rajpradeesh Thangaraj , Pages:1-17

DOI:
https://doi.org/10.30955/gnj.005481
Paper Topic:
Desalination

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Issue 2
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Abstract
Due to the limitations of fossil fuels and the environmental problems associated with their usage, renewable energy sources have been exploited for desalination through the employment of various technologies and mediums. One of the most useful renewable energy sources for solar desalination, both directly and indirectly, is solar energy. The effectiveness of solar desalination is influenced by a variety of parameters, making it challenging to forecast their performance in particular circumstances. Artificial neural networks (ANNs), PSO, ANFIS, RO, and genetic algorithms would all be suitable techniques for their modeling and output predictions in this context. In the current research, multiple data-driven approaches are used in-depth to perform modeling of solar-based desalination facilities. By utilizing these methods with the proper inputs and structures, it can be deduced that the results of the solar desalination units can be consistently and correctly projected. Additionally, several suggestions are offered for future research in the relevant areas of the study.
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

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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 aqueous phase adsorption of Acid Brown on mesoporous activated carbon prepared from Azolla Pinnate seaweed
Hariharan T, Gopi Raghunadh P V S, Sivaramakrishnan S and Lakshmana Phaneendra Maguluri, Pages:1-8

DOI:
https://doi.org/10.30955/gnj.005560
Paper Topic:
Water and Wastewater Treatment

Issue:
Issue 2
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Abstract
A mesoporous activated carbon was produced from the Azolla Pinnate (AP) seaweed by two-step chemical activation technique using sulphuric acid as activating agent. The adsorption of Acid Brown (AB) from aqueous solutions is examined using the produced carbon (AP). The produced activated carbon renders a homogeneous porous structure, predominantly mesoporous with 686.5 m2/g of BET surface area. The infra-red spectrum revealed AB affinity by multiple functional groups. The point of zero charge and the pH studies evidenced that the surface charge responsible for electronic affinity favours adsorption at higher pH. The SEM and FTIR analysis of AP before and after adsorption of acid brown shows multiple interactions, which is further substantiated by equilibrium, kinetic and thermodynamic models. Equilibrium adsorption data matched best with Langmuir isotherm model, thus primarily follows chemical interaction. However, physical affinity and heterogeneity of surface and species interaction also do exist nearly equally. Pseudo-second order kinetics provided the best explanation of the adsorption kinetics. The temperature variation studies revealed that acid brown adsorption is endothermic with high surface affinity.
Analysis model of flood spatiotemporal change characteristics based on k-means clustering algorithm
Qianyi Chen Wei Zhang Weixiang Huang Peng Liu Shan Li, Pages:1-9

DOI:
https://doi.org/10.30955/gnj.005464
Paper Topic:
Floods, Droughts and Water scarcity

Issue:
Issue 2
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Abstract
In order to analyze the spatiotemporal change characteristics of regional flood disaster, analyze the main change characteristics and hydrological response of precipitation distribution in Guangxi, and improve the comprehensive utilization efficiency of water resources, this paper introduces the k-means clustering algorithm to design an analysis model of spatiotemporal change characteristics of flood disaster. First of all, obtain the flood data in Guangxi and the observation data of the national meteorological station within 5 km, complete the collection of the basic data of mountain flood disasters. Based on the collected data, an analysis was conducted on the spatial and temporal distribution of flash floods in the Guangxi region. Secondly, the Kriging spatial interpolation method was used to analyze the spatial distribution of precipitation data in Guangxi. The Mann-Kendall trend test was then employed to examine the trend of precipitation-related statistical parameters over time. Additionally, wavelet theory was applied to analyze the time series of annual precipitation and precipitation with different durations in Guangxi. Subsequently, the k-means clustering algorithm was introduced to construct a model for analyzing the spatiotemporal characteristics of flood changes, determining the concentration and duration of precipitation in different years in the region. Finally, analyze the spatiotemporal change characteristics of flood events in different seasons under various indicators, and realize the analysis of flood spatiotemporal change characteristics. The research results indicate that the Frank Copula function fits the best correlation between annual precipitation and temperature, and can better characterize the correlation between the two. The Frank Copula function has the best fitting effect on the correlation between precipitation and temperature in autumn in Guilin, summer in Nanning, and summer and winter in Beihai. In Guangxi Zhuang Autonomous Region, the annual precipitation shows a gradually decreasing trend, especially at R and P stations. In summary, the Frank Copula function can effectively characterize the correlation and trend of precipitation and temperature in different seasons and regions of Guangxi.
Cyanide removal from aqueous solution by oxidation with hydrogen peroxide in the presence of activated alumina-supported copper catalyst
Halet F. Chergui S. Hamdache F. Boutrif A. Chergui A. Ould-Dris A. Boudriche L. Guénin E. Nadjemi B. Yeddou A. R., Pages:1-7

DOI:
https://doi.org/10.30955/gnj.005639
Paper Topic:
Water and Wastewater Treatment

Issue:
Issue 2
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Abstract
Cyanide compounds are widely used in some electroplating, chemical and metallurgical industries. They are often found in their liquid discharges. This work highlights the performance of an activated alumina-supported copper catalyst in the removal of cyanide by oxidation with hydrogen peroxide in aqueous solution. The influence of catalyst dose, initial molar ratio of hydrogen peroxide/cyanides, temperature, and catalyst reuse was studied. The activated alumina-supported copper significantly enhanced the reaction rate showing a good catalytic activity. The efficiency of cyanide elimination was increased after 30 minutes of oxidation from 48% to 98% by increasing the catalyst dose from 1 to 10 g/L. Rising the temperature from 30°C to 40°C promoted cyanide removal. The catalyst can be recycled four times and show good stability. The kinetics of cyanide oxidation was revealed to be pseudo-first order with regarding cyanides. The rate constants as well as the activation energy were determined.
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

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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.
In-situ Construction of TiO2/Ag Heterojunction Coating for Forming a Photocatalytic Self-cleaning Surface of Concrete
Feng J, Zhang K, Huang K, Zhang J, Huo B, Cao X, Cong P*, Pages:1-7

DOI:
https://doi.org/10.30955/gnj.005686
Paper Topic:
Advanced Oxidation Processes

Issue:
Issue 2
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Abstract
As a widely used construction material, concrete is an ideal substrate for environmental functional materials. However, ordinary concrete lacks pollution resistance. When exposed to the natural environment, it is easily susceptible to microbial growth or organic contamination. Therefore, it is necessary to enhance its surface cleaning ability through modification methods. The existing anti-pollution materials are cumbersome to prepare and have a short lifespan. In this study, we employed in-situ synthesis to construct a TiO2/Ag coating on the surface of concrete. When illuminated, the catalyst coating generates electron-hole pairs, and with the synergistic acceleration of the heterojunction interface and oxygen defects, they separate and react with water to produce hydroxyl radicals and superoxide radicals. Under simulated sunlight with an intensity of 50mW/cm2, this material can achieve an 86.4% organic matter decomposition rate and a 96.7% microbial inactivation rate, and it can remain stable over ten cycles. This study presents a viable technical solution for the design of sustainable self-cleaning building materials.

Solid waste management

Anaerobic co-digestion of low Chemical oxygen demand concentration sludge mixture by different pretreatment methods
Li Q. Yin Y. Chen Q. Xiang DD. Shi L. Lin WH. , Pages:1-6

DOI:
https://doi.org/10.30955/gnj.005241
Paper Topic:
Biowaste

Issue:
Issue 2
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Abstract
Three laboratory scale systems were operated to study the anaerobic co-digestion of sewage sludge with the excess activated sludge and cattle manure which contained low COD concentration. The experiments were performed to evaluate the effects of different methods of pretreatment. Since the poor organic substrate, the organic loading rate(OLR) was designed about 1.6kg total solids(TS)(l *d)-1 and the hydraulic retention time was controlled from 78days to 53days throughout the experiment to investigate the effects of the input on overall stability. When the digesters got stabilized, the COD removal efficiencies were 61%, 89% and 54%, respectively, and the pH value fluctuated little in the three digesters. The results indicated that the treatment of 1g volatile solids(VS) of SSEASCM on semi-continuous co-digestion generated more than 200ml of biogas on average. Thus, anaerobic co-digestion of the mixed sludge provided a means for low COD waste treatment and produced renewable energy.

Climate change and air pollution

Impact of ultraviolet radiation on human skin owing to ozone depletion
Sivasankari B Nagarajan P Jasmine Gnanamalar A Ahilan A, Pages:1-7

DOI:
https://doi.org/10.30955/gnj.004775
Paper Topic:
Advanced Oxidation Processes

Issue:
Issue 2
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Abstract
Depletion of ozone molecules in the stratospheric layer surges the flow of ultraviolet (UV) rays on the surface of the earth. Generally, UV rays are the hazardous energy waves emitted from the sun that can cause acute and chronic effects on bio species. Exposure to UV rays in humans causes skin problems such as wrinkles, leathery skin, liver spots, actinic keratosis, and solar elastosis. It is also stated to be a basic premise for skin melanoma. Raising environmental pollution and natural disasters triggered the ozone depletion crisis into global criteria. The proposed work is developed with the intent to analyse the harmful effects of UV radiation on human skin. It detects the skin diseases raised due to UV rays and categorizes them based on chronic intensity using the convolution neural network. The deep learning network Multires U-Net is developed to classify acute and chronic UV skin lesions based on intensity. The proposed technique achieved 95% model accuracy in the testing and training process. Hence the proposed model has better efficiency in the detection of UV lesions.
Predicting Ozone Depletion using BIn2O3 With RH based Solid Conductometric Sensor
Sivasankari B Ahilan A Rajakumar S Raja Kumar R, Pages:1-7

DOI:
https://doi.org/10.30955/gnj.005457
Paper Topic:
Advanced Oxidation Processes

Issue:
Issue 2
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Abstract
Ozone depletion arises due to the presence of gases like chlorofluorocarbons and halons which are liberated into the atmosphere. The region of earth’s atmosphere named as ozone shield, in which absorbs the ultra violet radiations of the sun. Although, its thickness varies periodically, this layer is primarily located in the lower stratosphere between 15 to 35 kms above the earth. Due to this, the ultraviolet rays directly enter into the earth causes harmful effects little by little on both living and non-living organisms. In this paper, BIn2O3 with rh sensor in co precipitation method is used to detect the ozone depletion and it is based on the solid-state gas sensors. To achieve the excellent performance of an ozone depletion detecting branch like BIn2O3 with rh, this was designed using a simple precipitation method. The coprecipitation method had deposited in BIn2O3 with a thickness of 45 to 50 nanometer. The detection limit of ozone reduced as 30 parts per billion. Further, the gas detector demonstrated high sensitivity and selectivity of the device. The rhodium covers the surface areas ranges from 0 to 0.1. It reaches the accuracy of 96%. According to the degree of rhodium coverage, the sensors will be response to the ozone gas. The oxygen chemisorbed ability and a coverage of a great surface area of BIn2O3 with rhodium highly related to the excellent sensing behavior. This study paves the way for an exact sensing and ppb level detection ozone gas.
An Intelligent Weather Prediction Model Using Optimized 1D CNN With Attention GRU
Hemamalini S, Geetha Rani K, Rajasekar B, Sadish Sendil M, Pages:1-9

DOI:
https://doi.org/10.30955/gnj.005408
Paper Topic:
Climate change mitigation and adaptation

Issue:
Issue 2
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Abstract
One of the main factors affecting human livelihoods is weather events. High weather disasters with forest fires, high air temperature, and global warming that cause drought. An efficient and accurate weather forecasting approach is required to take measures against climate disasters. Therefore, it is important to design an approach that makes better weather prediction. This work presents an optimized deep learning model, 1D convolutional neural network (CNN), with an attention gated recurrent unit (GRU) model for reliable weather forecasting. That is, to capture the local features of weather data, 1D CNN is used, and to capture the temporal features of the weather data, an optimized GRU is used. The attention mechanism is used for improving the performance, and the hyperparameter of GRU are optimized by the adaptive wild horse algorithm (AWHA). This work considered the Jena meteorological database which has 14 parameters, and the comparative analysis is carried out for different prediction measures. The proposed weather prediction model achieved better mean square error (MSE) and root mean square (RMSE) values.
Spatial variation of air pollutants by using GIS modelling
Sidharthan K. Gandhimathi A. Akshayaa M., Pages:1-6

DOI:
https://doi.org/10.30955/gnj.005230
Paper Topic:
Air Quality

Issue:
Issue 2
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Abstract
Air pollution in Tamil Nadu has become a pressing issue, as various pollutants have surpassed the limits established by the pollution control board. The major air pollutants in the region, namely sulphur dioxide, nitrogen dioxide, and suspended particulate matter, have been identified. This study aims to monitor and analyse the levels of air pollution in Tamil Nadu, with a specific focus on key parameters such as SO2, NO2, PM2.5, PM10, and ozone. By examining the atmospheric levels of these pollutants and their correlation with temperature fluctuations, the impact on human health and respiratory problems can be evaluated. Moreover, the study will assess the contribution of traffic emissions and vehicular pollution to the concentration of PM10 in different areas. Additionally, GIS technology will be utilized to spatially and temporally investigate pollution concentrations and evaluate their effect on the air quality index. By comparing the pollution concentration from thermal power stations and vehicular pollution, the study will identify the sources of pollutants, with a particular emphasis on SO2 and NOx emissions from vehicles and higher levels of suspended particulate matter from thermal stations. The analysis of these pollutants and their sources will provide valuable insights for the implementation of effective pollution control measures and the management of environmental pollution in Tamil Nadu. Given the concerns regarding pollutants exceeding the prescribed limits set by the pollution control board, monitoring air pollution in Tamil Nadu is of utmost importance.
An Automated Graph-Based Neural Network Model for Predicting Urban Environmental Air Quality Using Spatio-Temporal Data Optimization
Yogapriya J Deepa S Radha N Madhumitha E , Pages:1-10

DOI:
https://doi.org/10.30955/gnj.005598
Paper Topic:
Air Quality

Issue:
Issue 2
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Abstract
Environmental protection and the need for accurate pollutant forecasting have become increasingly important as worries about environmental issues and the harmful effects of pollution have grown. Predictive accuracy of air pollutants is generally unsatisfactory due to the fact that conventional methodologies prioritise time series analysis over the important spatial transmission dynamics among neighbouring locations. To address this inherent limitation, our proposed solution introduces an innovative Time Series Prediction Network, augmented by the auto-optimization capabilities of a Spatio-Temporal Graph-based Neural Network. This groundbreaking network comprises distinct spatial and temporal modules. The spatial module harnesses a Graph Sampling and Aggregation Network to extract essential spatial information from the data. Simultaneously, the temporal module integrates a Bayesian approach with a Complex Valued Graph Gated Recurrent Unit (BCV-GRU), seamlessly incorporating a graph network into the Gated Recurrent Unit (GRU) to capture temporal intricacies. Moreover, to manage the challenge of model inaccuracy stemming from inappropriate hyperparameters, Bayesian optimization was employed. The efficacy of our proposed method was validated using real PM2.5 data from the USGS website, showcasing a significant enhancement in prediction accuracy. This study puts forth a robust and effective approach for forecasting PM2.5 concentrations, bridging gaps in existing methodologies and contributing substantially to the evolution of environmental prediction models.
Prediction of Ozone Depletion Levels using Intelligent CNN-SVM Classification System
Arun V Gnana Sophia S Ahilan A Jayapriya, Pages:1-8

DOI:
https://doi.org/10.30955/gnj.005461
Paper Topic:
Environmental data analysis and modelling

Issue:
Issue 2
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Abstract
The concentration of ozone in the earth atmosphere has been steadily falling by 4% in the total amount since late 1970. With the widespread usage of modern industry chlorofluorocarbons, the rate at which ozone content decreases is escalating, resulting in an ozone hole. The depletion permits harmful UV into the earth surface which brings harmful hazards to earth living organisms. Increased UV radiation exposure can lead to skin cancer, cataracts, and ecological disruptions. The machine learning models face difficulties in accurately accounting for unpredictable events, such as sudden changes in emission patterns or unforeseen interactions, which limits their capacity to provide precise and reliable forecasts for future ozone depletion scenarios. To overcome this issue, a novel hybridization of Convolution Neural Network (CNN) and Support Vector Machine (SVM) is proposed to detect the variation in the ozone depletion around earth surfaces. The input images are collected from the thermosphere meteorological satellite and transformed into clean data in preprocessing. Then, the images are annotated and fed to the learning model for training. Followed by SVM classifier taken the CNN feature as an input and show the exact level of the ozone. The experimental findings show that the proposed CNN-SVM framework accomplishes satisfactory prediction accuracy of 99.44%. The overall accuracy range improves by 0.21%, 6.74%, and 4.44% with the CNN, SVM-IF, and Faster RCNN test outcomes, and by 2.59%, 3.52%, and 4.13% with the proposed CNN model, respectively. The proposed SVM model increases the total f1-Score by 2.3%, 3.19%, and 0.7%, respectively. The proposed CNN-SVM model obtains high accuracy rate than other existing models.
Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception
Khristodas P. M. (Corresponding Author) Palanivelu K. Ramachandran A Prusty B. A. K., Pages:1-11

DOI:
https://doi.org/10.30955/gnj.004859
Paper Topic:
Global Environmental Change

Issue:
Issue 2
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Abstract
Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change.
Potential risk assessment of respiratory exposure to heavy metals in the air dust for the metropolitans of Khuzestan Province, Iran
Payandeh K Rahimi M M Mohammadi Rouzbahani Nazarpour A Panahpour E, Pages:1-12

DOI:
https://doi.org/10.30955/gnj.005208
Paper Topic:
Air Quality

Issue:
Issue 2
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Abstract
The present study aimed to compare the concentration of 14 metals in the air of 4 cities of Khuzestan province (Ahvaz, Dezful, Mahshahr, and Abadan) in dusty and non-dustynon-dustyy conditions, and determine risk and non-carcinogenic risk in a six-month period from 2017 to 2018 (autumn, winter, and spring). A total of 72 samples were collected in dusty and 72 samples in non-dustydusty conditions at different locations and times using a high volume sampling pump with 110 lit/s flow rate and a fiberglass filter with the pure size of 1μm for 6 hours. Additionally, the concentration of metals was measured using the Spectro Acros Model (Germany) device. Risk assessment and probable ways of entering the body of these metals were determined using USEPA indicators. The average concentration of metals (except for Ni, Mg, and Mn) was higher in dusty conditions non-dustythan in non-dustyy conditions (p < 0.05).The concentration order of heavy metals in dusty and non-dustyy condictions was Zn>Mn>Pb>Ni>Cr>Fe>Cu, and Mg >Mn>Pb>Ni>Cr>Fe, respectively, showing the origin of Zn in the polluted air caused by dust and pollution transfer from outside into cities by this phenomenon. In children and adults, every four cities of Ding (inhalation of dust particles through direct dust consumption) were the main way to attract more metals. Chromium and nickel, chromium and magnesium, and nickel and magnesium were observed among the children of Abadan, Ahvaz, and Dezful and Mahshahr, respectively. The maximum HI indicator was observed for magnesium in Abadan, copper, and magnesium in Ahvaz, chromium, zinc, nickel, and ferrous in Mahshahr in dusty and non-dusty conditions. Moreover, lead had the maximum HI indicator in Abadan and Ahvaz in the non-dustydusty condition. Based on the HI<1 standard, in dusty and clean air conditions, all of the four cities had no concerning issues of non-carcinogenic effects for children and adults.
A review of using deep learning from an ecology perspective to address climate change and air pollution
R. Murugadoss, S.Leena Nesamani, A.Banushri, K M Monica, M. Vairavel, S.Nirmal Sugirtha Rajini, Gopi.P , Pages:1-18

DOI:
https://doi.org/10.30955/gnj.005697
Paper Topic:
Ecological effects of Environmental change

Issue:
Issue 2
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Abstract
Artificial intelligence techniques that can shatter pattern recognition accuracy records, has recently attracted a lot of attention. With its flexibility and capacity to handle massive and complicated datasets, deep learning has transformed numerous academic domains, including bioinformatics and medicine, in a few of years. We think ecologists can also benefit from these methods, since ecological datasets are getting bigger and more complicated. This review examined current implementations and demonstrates how deep learning has been effectively applied to classify animal activity, identify species, and estimate biodiversity in big datasets such as audio recordings, videos, and camera-trap photos. This review paper show that most ecological disciplines, including applied contexts like management and conservation, can benefit from deep learning. This review also identify frequent problems concerning the application of deep learning, like what is the process for building a deep learning network, what resources are available, and what kind of data and processing power are needed. To assist ecologists in utilising deep learning, it offer guidelines, suggestions, and helpful materials, such as a reference flowchart. This contend that deep learning has the potential to become an extremely useful reference tool for ecologists at a time when automated population and ecological monitoring creates massive amounts of data that humans are no longer able to interpret efficiently.
An evaluation of the Ganga river’s carbon flow and its impact on India’s nutritional and climate change
S.JAYARAMAN N.Brindha , Pages:1-7

DOI:
https://doi.org/10.30955/gnj.005216
Paper Topic:
Water Resources Management

Issue:
Issue 2
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Abstract
Changes in weather patterns and the global climate are a result of global warming, which has been linked to rising carbon dioxide concentrations. Understanding the mechanisms on earth that have an impact on atmospheric carbon concentrations and its cycling—collectively referred to as the "Carbon cycle"—is essential for estimating carbon budgets. Studies on carbon budgets have largely ignored the role of inland waterways in the carbon cycle, yet rivers play an active role by absorbing/releasing carbon into the atmosphere and transferring dissolved carbon into oceans. In tropical parts of the planet, it is anticipated that water bodies, like as rivers, would contribute to carbon emissions to a greater extent newline Although there are many studies on the impact of human activity on the Ganga River, there are very few that take an ecological viewpoint. In light of these facts, the following was designed as the study's hypothesis: newline The Ganga River has positive feedbacks as a result of human activity, which alters the environment at a level significantly greater than what has previously been projected.The current investigation was conducted in a significant tropical, non-perennial river of the Ganga. In order to calculate the carbon flow of the Ganga river newline, it is important to understand the interaction between geological processes and the concentration of different ions in groundwater and surface water.
Research on Air Pollution Loss Measurement of Harmful Substance Emissions from Tourism Energy Consumption in Jiangsu Province of China Based on Low Carbon Constraints
Luo K.M Yan X.L Shen Q.Q, Pages:1-11

DOI:
https://doi.org/10.30955/gnj.005311
Paper Topic:
Air Quality

Issue:
Issue 2
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Abstract
In order to explore the air pollution loss measurement of energy consumption pollutant emissions in low-carbon tourism in Jiangsu Province, this paper, based on background analysis, literature review and research design, selects 13 prefecture-level cities in Jiangsu Province as research objects, and investigates the Measurement of Air pollution Losses from Energy Consumption Pollutant Emissions in 13 tourism areas in Jiangsu Province by reconstructing the shadow price model of air pollution loss and using the statistical data from 2013 to 2021 provided by the government. It is found that: the air pollution loss caused by the emission of eight pollutants of low-carbon tourism energy consumption in Jiangsu province ranges from 142.92 million yuan to 253.93 million yuan, and the air pollution loss of low-carbon tourism energy consumption emission in 13 prefecture-level cities is the largest in Nanjing, which produces an air pollution loss of 387.69 million yuan in 2019, and the lowest air pollution loss year is 2020, with an air pollution loss of 2430.02 million yuan. Therefore, to reduce the air pollution loss of 13 prefecture-level cities as well as 8 energy consumption pollutants in Jiangsu Province, special attention should be paid to the maximum index as well as the minimum index, and the economic loss caused by the energy consumption pollutant emission of low-carbon tourism in the province should be controlled by strategies such as minimizing the maximum air pollution loss index as well as maintaining the minimum air pollution loss index.
Influence and its spatial verification of digital economy development on carbon emission intensity from urban energy consumption
Ge G.Y. Liu S.T. Liu Y.B. Han X.Y., Pages:1-9

DOI:
https://doi.org/10.30955/gnj.005660
Paper Topic:
Environmental Management and Policies

Issue:
Issue 2
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Abstract
In order to explore the influence of digital economy development on carbon emission intensity from urban energy consumption, digital economy development is incorporated into the reduction framework of carbon emission intensity from urban energy consumption in this paper. With carbon emission intensity from urban energy consumption in China taken as the research object, empirical tests and dynamic effect analyses were carried out on the factors influencing carbon emission intensity from urban energy consumption, using urban panel data for the period of 2012–2022. It is found that digital economy has a significant inhibitory effect on carbon emission intensity from urban energy consumption in China, with a coefficient of -0.348. Among the five selected control variables, PCG, UR, and IS show a positive correlation with carbon emission intensity from urban energy consumption, enhancing EI with corresponding influence coefficients of 0.472, 0.135, and 0.331 respectively; EGRI and TRL show an inverse correlation with EI, inhibiting the growth of EI with corresponding influence coefficients of -0.556 and -0.201, respectively. Policy suggestions were put forward to minimize EI on the basis of the test results combined with the actual situation of China's urban economic development.

Circular economy

Characterization and optimization studies of cellulose based bioplastics extracted from Musa paradisiaca L.
Devika R Ratnadip Saha , Pages:1-10

DOI:
https://doi.org/10.30955/gnj.005613
Paper Topic:
Ecosystems and Natural Resources Management

Issue:
Issue 2
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Abstract
In recent past, attentions are towards preserving and restoring the resources of environment in terms of development of new products of environmental safety, Therefore utilization of vegetable fibres, extracts from stem, leaves, fruits and roots for the production of biopolymers / improving mechanical properties/ recovery byproducts etc. are recently incorporated as reinforcing elements. Cellulose, a linear chain of molecular rings of glucose linked through covalent bond (C1 oxygen to glucose of a ring and ring adjacent C4) is an abundant polymer in nature from plants and it is advantageous in biodegradability, non toxicity, inexpensive, thermal stability etc. and has potential applications. Cellulose due to its poor solubility has a wide range of applications in packing, upholstery, coating, netting etc. and cellulose derivatives are tailored for specific industrial applications. The present investigation was concentrated to produced cellulose from banana pseudostem and characterized the nature of cellulose extracted and optimization studies for production of bioplastics from Musa paradisiaca L.
Characteristics and evolution of bioethanol from Manila Tamarind (Pithecellobium dulce) leaf through fermentation
Dr.N.Kanthavelkumaran Dr.S.Mathu Kumar S.Iyyappan N.N.Suthan Raja Prasad, Pages:1-7

DOI:
https://doi.org/10.30955/gnj.005634
Paper Topic:
Sustainable Energy

Issue:
Issue 2
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Abstract
This study's two primary goals are to synthesize bioethanol from biowaste and examine its thermal characteristics. Bioethanol must first undergo a comprehensive assessment of its thermal characteristics in order to be approved for usage in spark-ignition engines. A multi-step procedure comprising extraction, pretreatment, enzymatic hydrolysis, and fermentation was used to manufacture the bioethanol. The raw material was put through a preliminary screening using thermogravimetric analysis to find the mass loss rate as a function of temperature before to starting this process. Remarkably, the Manila tamarind leaves exhibited the highest mass loss, up to 34%. Enzymatic cellulose conversion to fermentable sugars was a critical step in the production of cellulosic ethanol. Following hydrolysis, Saccharomyces cerevisiae was employed in the fermentation process, leading to the bioethanol synthesis phase. The Manila Tamarind leaves yielded the most, around 29% by weight, which is amazing. In order to assess the thermal properties of the extracted ethanol, a variety of parameters were carefully examined, including the viscosity, density, cetane number, and calorific value. In each of these areas, mixtures of tire oil, gasoline, and bioethanol consistently outperformed the others. Furthermore, Fourier Transform Infrared Spectrometry spectra were utilized to validate the concentrations of potential groups, including alcohol, aromatic, alkyne, amide, and carbonyl groups. Clear objectives guide the fermentation process, aiming for consistent quality, safety, and functionality in the end product.
Utilization of cement power plant beds for aerated concrete thermal blocks
Bhagavathi Pushpa T and Swaminathan S, Pages:1-10

DOI:
https://doi.org/10.30955/gnj.005432
Paper Topic:
Water treatment

Issue:
Issue 2
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Abstract
Nowadays cement industry power plant bed wastes can be used to create aerated concrete blocks for widespread usage in the construction sector instead of sand. The optimum materials for building enclosures for a variety of uses include aerated concretes. To enhance the physical and mechanical qualities of Non-Autoclaved Aerated Concrete (NAAC) blocks, bed material is introduced in this study as a superior alternative material. The non-autoclaved concrete blocks in this study are made with cement, bed materials, fly ash, gypsum, and a consistent amount of 0.65 grams of aluminium powder. The mix preparation and method employed for manufacturing NAAC blocks, the composition of mix specimens and the dosing and mixing processes have been expounded upon, shedding light on the critical steps in the production. According to the suggested method in IS 2185 (Part III) of 1984, the proportion of bed materials was taken by volume of compacted dry material for NAAC of Size 22cm x 10.5cm x 7cm. Experiments into the NAAC block's compressive strength plus water absorption of the bed materials were followed by comparisons of these characteristics with clay and fly ash bricks sold in the market. As a result, NAAC blocks met the 6 MPa strength criteria specified by Indian Standard code IS2185 (Part III): 1984. However, the strength of the aforementioned NAAC brick at 28 days was 7.28 MPa for Sample T5. A more in-depth presentation of the testing methods, focusing on the compressive strength tests was conducted at various intervals (7, 14, 21, and 28 days). The density values and water absorption rates for each test sample (T1 to T5) are now presented with additional insights into the observed trends. According to the research, blocks manufactured with NAAC bed materials tend to be stronger and lighter than those made with conventional clay bricks. They also produce non-autoclaved concrete blocks. Therefore, the creation of such inexpensive blocks can be employed for extensive production.
Transforming waste into sustainable building materials: Properties and environmental impacts of Geopolymer concrete with recycled concrete aggregates
Periyasamy L Nagarajan V, Pages:1-8

DOI:
https://doi.org/10.30955/gnj.005518
Paper Topic:
Solid Waste Management

Issue:
Issue 2
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Abstract
Industrial by-products such as cementitious materials or aggregates have the potential to mitigate the adverse environmental effects associated with traditional cement manufacture. Geopolymer Concrete (GPC) is more eco-friendly than conventional concrete because it does not require cement. GPC with Recycled concrete aggregates (RCA), Ground granulated blast furnace slag (GGBS), and Fly Ash (FA) reduce raw material use and create sustainable infrastructure. GPC compounds increase workability, slump value above standard concrete, and reduce the amount of water usage. The study examines GPC mechanical properties, durability, and environmental properties with different RCA content. The M30 concrete mix design is established by trial and error utilizing a 0.45 water/binder ratio. GPC with 0%, 10%, 20%, 30%, 40%, and 50% recycled coarse aggregate replaced natural aggregate(NA) by mass. GPC with 50% GGBS provides an early strength of 96% of normal compressive strength on day seven. The compressive, split tension, and flexural strengths exhibit significant improvement with up to a 40% substitution of NA with RA. These results highlight GPC's potential as a sustainable alternative in the construction sector.
Conversion of the styrofoam waste into a high-capacity and recoverable adsorbent in the removing the toxic Pb(II) from water media
Aprilita, N.H. Ofens, T.F.P. Nora, M. Nassir, T.A. Wahyuni , E.T., Pages:1-10

DOI:
https://doi.org/10.30955/gnj.005415
Paper Topic:
Water and Wastewater Treatment

Issue:
Issue 2
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Abstract
The sequential nitration and magnetization of waste styrofoam to form a novel adsorbent has been investigated. The novelty of this study assigned approach lies in the introduction of an anionic surface on the styrofoam, enabling it to adsorb cationic Pb2+ ion, combined with its magnetic property that facilitates rapid and practical recovery. The nitration was conducted using concentrated nitric acid in the presence of sulfuric acid, and the magnetization was performed by coprecipitation of Fe2+ and Fe3+ at high pH to form Fe3O4. The prepared adsorbents were characterized using Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), and Scanning Electron Microscope-Energy Dispersive X-ray (SEM-EDX) instruments. The characterization data clearly showed the evidence that nitrated-magnetic-styrofoam was successfully produced. The adsorption process of Pb2+ ion from aqueous media was evaluated via batch experiment. The contact time of the adsorption, adsorbent weight, solution pH, and initial concentration of Pb2+ were optimized to get the best adsorption condition. The adsorbent with 33.3 wt% of Fe3O4 fraction revealed a compromise of high adsorption effectivity around 90.0% and efficient separability with the optimum condition of 30 mg adsorbent dose at solution pH 6 within 60 minutes of adsorption time and initial concentration at 50 mg L-1 Pb2+. The parameters isotherm and kinetic of adsorptions were also determined. According to three simulated isotherm models of Langmuir, Freundlich, and Sips, Langmuir isotherm is well-fitted with an adsorption capacity of 9.81 mg g-1. The adsorption kinetic matches to a pseudo-second order giving an adsorption rate constant of 0.131 g mg-1 min-1 and is expected to give chemisorption mechanism.
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.
Sustainable Treatment Approach to Dumped Construction Waste with Acids and CO2 for its Effective Re-utilization in Concrete
Ravichandran P Rampradheep G S Jagan S , Pages:1-10

DOI:
https://doi.org/10.30955/gnj.005540
Paper Topic:
Solid Waste Management

Issue:
Issue 2
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Abstract
Dumping of construction wastes has become one of the serious concerns in the construction industries subsequent to the scarcity of construction materials, specifically aggregates. Extensive research works have been performed to utilize the dumped waste as aggregates, yet its substandard properties affect the concrete. Furthermore, several treatments were proposed to improve the quality of aggregates such as microbial, slurry etc, but the brittleness of concrete remains unavoidable. This research discusses the effect of acid treatment and carbonation treatment on recycled concrete aggregates (RCA) and the impact of hybrid fibres on concrete properties. The steel fibres (SF) and polypropylene fibres (PF) were optimized to 1.5% and 1.0% with 100% treated RCA to assess the concrete's hardened properties and water absorption. The study reports that the carbonation treatment to RCA performs better compared to acid treatment and the mix with the use of hybrid fibres shows enhanced properties than single fibres. The strength of the mix with 100% carbonated RCA 1.5% SF and 1.0% PF was 57% higher than that of recycled aggregate concrete (RAC) and the water absorption of similar mix was found to be lower than other mixes. Microstructural investigations through SEM and XRD were also performed to support the research findings.
Inhibition impact of major active ingredients of Jordanian Peganum harmala on the corrosion rate of Cu, Fe and Al metals
Ghassab M. Al-Mazaideh, Pages:1-6

DOI:
https://doi.org/10.30955/gnj.004606
Paper Topic:
General

Issue:
Issue 2
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Abstract
The active components of Jordanian Peganum harmala L. (P. harmala) were investigated using both GC-MS and DFT calculations. The study focused on the methanol extract of P. harmala leaves, which was found to contain 21 significant natural components, of which 9 compounds constituted more than 2% of the total composition. These 9 compounds, including α-D-Glucopyranose (GLUC), Phenobarbital (PHEN), Melibiose (MELI), Cyanuric acid (CYAN), D-Glucuronic acid (GLUCU), Ethylamine (ETHY), 3-Hydroxysebacic acid (HYSE), 3-Hydroxytetradecanedioic acid (HYTD), and α-Tocopherol (TOCO), were evaluated as eco-friendly corrosion inhibitors for Al (Aluminium), Fe (Iron), and Cu (Cooper) metals using DFT calculations. The results showed that TOCO had the highest anti-corrosion performance among the 9 compounds, with Fe exhibiting the most inhibitory activity compared to Al and Cu. The high electrophilicity and Gibbs free energy of adsorbate value on metal surfaces made TOCO a distinguished corrosion inhibitor. The HOMO and LUMO gap of the inhibitors decreased in the following order: ETHY > GLUC > MELI > HYTD > HYSE > CYAN > GLUCU > PHEN > TOCO. These findings suggest that these chemicals may serve as environmental-friendly anticorrosion inhibitors for metal surfaces, although more in vitro experiments are needed to confirm these results.

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Issue 2

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Water and wastewater

Solid waste management

Climate change and air pollution

Circular economy

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