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Effective Deep Learning based Prediction Model for Groundwater Quality Assessment using Physio-chemical Parameters

Tamilchelvan P. Muralimohan N. Harikaran M. Kulanthaivel P. , Pages:1-11

DOI:

https://doi.org/10.30955/gnj.005474

Paper Topic:

Water Quality

Issue:

Issue 4 (In progress)

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Abstract

The purpose of this study is to anticipate and investigate the groundwater quality in the regions of the Cauvery basin. In the post-monsoon season of 2020, 800 samples were collected from 200 different places for this paper. The AlexNet model is used in this book to forecast the water quality. In order to examine the groundwater quality, the anticipated groundwater samples are analyzed using main cations (K+, Mg2+, Ca2+, and Na+), major anions (NO2- +NO3-, CO3-, F-, HCO3-, Cl-, and SO42-), hardness, pH, and Electrical Conductivity (EC). The accuracy, specificity, and sensitivity of the AlexNet model are used in this publication to validate its efficacy. Validating the following metrics determines if water is suitable for drinking and irrigation: sodium percentage, magnesium ratio, Kelly's ratio, sodium adsorption ratio, water quality index (WQI), and residual sodium carbonate (RSC). According to the research, the AlexNet model predicted water quality with 93%, 94.57%, and 91.47% accuracy, specificity, and sensitivity, respectively. According to the experimental results, the WQI value shows that 22% of the collected samples are acceptable for drinking, 63% are suitable for drinking, and the other 15% are only fit for irrigation and should not be consumed.

CO2 sorption from a mixture of N2/CO2 using an activated carbon and silica gel: equilibrium, breakthrough and mass transfer zone

Al Mesfer M.K. Danish M. Al Alwan B.A. Awwad N.S., Pages:1-12

DOI:

https://doi.org/10.30955/gnj.003166

Paper Topic:

Air Quality

Issue:

Issue 1

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Abstract

The temperature, feed rate, length of mass transfer zone, utilization factor and partial pressure are the parameters considered for fixed bed sorption of CO2 from N2/CO2 mixture. The breakthrough time relies strongly on the temperature and feed rate. The prolonged breakthrough and saturation times have been realized for AC. The response curves of AC are vastly steep signifying the maximal utilization of bed capacity at the breakpoint. In general, the length of MTZ increases with raised temperature and feed flow rate. The capacity utilization factor reduces with raised temperature and feed flow rate. A utilization factor of 0.919 was determined for AC. The maximal capacity for CO2 reduces significantly with an increased temperature. The maximal capacities of 32.99 gm CO2/Kg was determined at a temperature of 298 K for AC. The capacity improves considerably with CO2 partial pressure and AC exhibited higher adsorption capacity compared to SG. The capacity improves considerably with increased feed rates and maximal capacity of 39.14 g CO2/Kg adsorbent was determined for AC at the feed rate of 8.33 x10-3 m3/sec. Owing to higher sorption capacity and utilization factor, the AC may be used for economical separation of CO2 from N2/CO2 mixture

Nanoparticales in cosmetics, What we don’t know about safety and hidden risks . A mini-review

Lubna Abdulazeem, Fuad Ghazi Hassan, Mustafa Jawad Abed, Waleed S Torki, Ahmed Samir Naje, Pages:1-5

DOI:

https://doi.org/10.30955/gnj.004304

Paper Topic:

Environmental Health

Issue:

Issue 1

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Abstract

Nanotechnology exemplifies advancement in the field of research and development by enhancing product efficacy through the introduction of novel solutions. In the field of cosmeceuticals, the use of nanotechnology is increasing to solve some of the limitations associated with conventional goods. Cosmeceuticals are the fastest-growing section of the personal care market, and their use has skyrocketed in recent years. Nanocosmeceuticals are a type of skin, hair, nail, and lip care product that is used to cure wrinkles, photoaging, hyperpigmentation, dandruff, and hair damage. However, nanotoxicological studies have raised concerns about the impact of expanding nanoparticle use in cosmeceuticals, as nanoparticles have the potential to permeate skin and create health problems. The research on nanotechnology in cosmeceuticals focuses on the multiple unique carriers utilized for cosmeceutical delivery, their good and negative aspects, marketed formulations, toxicity, and nanocosmeceutical restrictions. The primary goal of this editorial is to highlight the advantages and disadvantages of nanoparticles used in cosmetics.

Facile Prepared Fe3O4 Nanoparticles as a Nano-Catalyst on photo-Fenton process to remediation of methylene blue dye from water: Characterisation and Optimization

Ahmed Halfadji Mohamed Naous Khaldia nadia Kharroubi Fatima el zahraà Belmehdi Hanane Aoudia , Pages:1-6

DOI:

https://doi.org/10.30955/gnj.005312

Paper Topic:

Nanomaterials in the environment: applications and effects

Issue:

Issue 1

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Abstract

In this research, a simple method for the preparation of Fe3O4 nanoparticles (Fe3O4NPs) with an average size of 38.05 nm via co-precipitation was investigated. X-ray diffraction (DRX), scanning electron microscopy (SEM), and infrared spectroscopy (FT-IR) were used to characterize obtained Fe3O4 nanoparticles. These Fe3O4NPs were then used as nano-catalysts to degrade Methylene Blue (MB) in an aqueous solution via the Photo Fenton-like process. Also, under room solar light and low temperature, the photocatalytic activity of Fe3O4NPs for degrading MB was optimized through various experimental factors such as pH (ranging from 2 to 8), H2O2 concentration (from 10-2 to 5 × 10-1), catalyst amount (20 to 60 mg), and target organic compound concentrations (10 to 40 mg/L). The optimal experimental conditions were found to be a pH of 3, a dye concentration of 40 mg/L, and 40 mg of Fe3O4NPs as nano-catalyst. These conditions led to a high degree of removal (>86%) of MB dye from water. The pseudo-second-order kinetic model was the suitable model to describe the degradation of MB dye with a coefficient value of 0.969. From this, it was concluded that Fe3O4NPs could act as an effective nano-catalyst for a sustainable and environmentally friendly way to eliminate organic pollutants in water and wastewater. Keywords: Fe3O4Nps, structural characterization, BM degradation, photo-Fenton Oxidation process, pseudo-first-order Model, Nanocatalyst.

Evaluation of the response of dynamic changes in the natural attenuation of chloride-polluted groundwater via high-throughput sequencing: Degradation characteristics and environmental changes

Bo Wang Zhenning Liu Yongjun Huang Qing Jiang Dongdong Yan Conghai Xu Kunhua Wang Ke Shi Jianliang Xue, Pages:1-9

DOI:

https://doi.org/10.30955/gnj.005124

Paper Topic:

Soil and groundwater contamination and remediation

Issue:

Issue 1

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Abstract

Due to its cost-effectiveness and absence of secondary pollution, the biological approach for eradicating chloride pollution has attracted widespread interest. However, numerous obstacles, including the limited presence of degrading microbes and a substantial impact from environmental variables arises during its practical application. The purpose of this study was to investigate the impact of ambient environmental factors during field surveys on the degradation of microorganism performance. The findings of this study demonstrate a negative correlation between the chlorinated hydrocarbons (CHCs) concentrations and pH, oxidation-reduction potential (ORP). These results suggest that redox conditions would improve the efficiency of chloride degradation. In addition, the degradation of CHCs was found to be primarily influenced by Dechloromonas, which accounted for > 1% of the total 16S rRNA sequences. In the absence of dechlorinating bacteria, the degradation efficiencies for CCl4, C2Cl4, and C2H2Cl4 were 91.7%, 87.8%, and 86.0% lower than the efficiency in the presence of these microorganism, respectively. Therefore, this study provides a comprehensive understanding of the role of biological degradation in controlling chloride pollution, along with substantial evidence supporting the extensive implementation of biological degradation technology.

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.

Screening, Identification, and Optimization of Sulfur-Oxidizing Bacteria from Post-coal Mining Ponds of Different Ages in Samarinda, East Kalimantan, Indonesia

Kusumawati E. Retnaningdyah C. Mustafa I. Suharjono., Pages:1-10

DOI:

https://doi.org/10.30955/gnj.005513

Paper Topic:

Water Quality

Issue:

Issue 3

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Abstract

This research was conducted to isolation, screening, identification, and culture conditions optimization (pH, temperature, thiosulfate concentration, and incubation period) of potential sulfur-oxidizing bacterial (SOB) to reduce sulfur concentrations in post-coal mining ponds water of various ages located in Samarinda, East Kalimantan, Indonesia. The ponds used in this study were ponds < 5 years old and > 20 years old. The research obtained seventy-five bacteria isolates, among them nine isolates can increase the pH of thiosulfate broth media. Among the nine bacteria isolates, three isolates KT1.8, KT1.9, and KT1.13 have the highest potency to reduce sulfur concentration of media were 6%, 148%, and 101%. Based on the similarity of the 16S rDNA sequences, the KT1.8, KT1.9, and KT1.13 isolates were identified as Priestia qingshengii HLS-7 (98.9%), Sphingobacterium siyangensis DS48 (97.6%), and Pseudomonas putida CFBP 4966 (98.3%). The three potential SOB isolates grew better at pH 6 at 30° C temperature with the growth of KT1.8 = 146x1014 cell/mL; KT1.9 = 81x107 cell/mL; and KT1.13 = 33x107 cell/mL; decrease in sulfur concentration KT1.8 = 43.57%; KT1.9 = 43.57%; and KT1.13 = 42.48%. On media containing 10 mM and 20 mM thiosulfate on the sixth day of incubation, cell density isolates KT1.8 = 98x1012 cell/mL; KT1.9 = 25x107 cell/mL; and KT1.13 = 85x106 cell/mL; accompanied by a decrease in sulfur concentration of KT1.8 = 46.52%; KT1.9 = 40.40%; and KT1.13 = 43.71% were obtained. Those three bacteria isolate potential can be developed as bioremediation agents of post-coal mining pond water contaminated by sulfur.

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.

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.

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

Yuvaperiyasamy M Senthilkumar N Deepanraj B Gokilakrishnan G Premkumar R , Pages:1-11

DOI:

https://doi.org/10.30955/gnj.005773

Paper Topic:

Desalination

Issue:

Issue 4 (In progress)

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Abstract

Potable water is essential in various aspects of daily life. Converting brackish water to drinkable water using traditional methods is costly and has environmental impacts. Solar energy is preferred over fossil fuels and other energy sources due to its cost and environmental benefits. Solar stills are increasingly popular due to the growing need for drinkable water, but their performance requires improvement. The study examines the pyramid solar still (PSS) productivity using different operating parameters, such as solar intensity (350-950 W/m2), water inlet temperature (30-50°C), water depth (4-8 cm), and insulation thickness (0.05-0.15m). The response surface technique (RSM) was used to examine the performance of the still under different operating conditions. The RSM approach optimized the process for maximum output and reduced testing time and effort. To verify the optimum response derived from RSM, the artificial neural networks (ANN) model was implemented. The comparative studies of the ANN and RSM models demonstrates a strong correlation with the outcomes achieved in maximizing the productivity of PSS. It found that the ideal values for solar intensity, water intake temperature, water depth, and insulation thickness are 950 W/m2, 50°C, 4 cm, and 0.15 m, respectively. These parameters contribute to the production of 2.585 kg/m2 of distillate.

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Effective Deep Learning based Prediction Model for Groundwater Quality Assessment using Physio-chemical Parameters

CO2 sorption from a mixture of N2/CO2 using an activated carbon and silica gel: equilibrium, breakthrough and mass transfer zone

Nanoparticales in cosmetics, What we don’t know about safety and hidden risks . A mini-review

Facile Prepared Fe3O4 Nanoparticles as a Nano-Catalyst on photo-Fenton process to remediation of methylene blue dye from water: Characterisation and Optimization

Evaluation of the response of dynamic changes in the natural attenuation of chloride-polluted groundwater via high-throughput sequencing: Degradation characteristics and environmental changes

Prediction of Ozone Depletion Levels using Intelligent CNN-SVM Classification System

Screening, Identification, and Optimization of Sulfur-Oxidizing Bacteria from Post-coal Mining Ponds of Different Ages in Samarinda, East Kalimantan, Indonesia

In-situ Construction of TiO2/Ag Heterojunction Coating for Forming a Photocatalytic Self-cleaning Surface of Concrete

A review of using deep learning from an ecology perspective to address climate change and air pollution

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

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