Global NEST Journal

Editorial office:

COSMOS SA, Greece

Editor:

Themistokles Lekkas

Email:

gnest@gnest.org

Print ISSN: 1790-7632

Electronic ISSN: 2241-777X

Latest articles

Ni-Ce-OMS-2 material: preparation and preliminary catalytic-study on removing ethanol in the gas phase

Gia-Han Nguyen, Quoc-Long Dang-Hung, Trong-Phu Tran, Dung V. Nguyen, Long Q. Nguyen and Tuyet-Mai Tran-Thuy

DOI:

https://doi.org/10.30955/gnj.004949

Paper Topic:

Emerging pollutants

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Abstract

Cryptomelane type manganese oxide K-OMS-2 and Ni-Ce-OMS-2 co-doping materials were synthesized by one-step preparation with refluxing system. The crystalline structure, and morphology were characterized by X-ray diffraction analysis and SEM images. Ni (II) and Ce (III) were incorporated into cryptomelane, substituted for Mn (III) and Mn (IV) resulting an increase of Mn-average-oxidation-state from 3.6 to 3.8 for un-doped and co-doped samples, respectively. With different amount of precursor-cerium concentration, NiCe1.0 improved the ethanol conversion by a factor of 8 compared to unmodified K-OMS-2. Additionally, NiCe1.0 showed a significant increase of ethanol conversion with reaction temperature. Over above 90% of ethanol was total oxidized at 150 C suggesting a potential Ni-Ce-OMS-2 catalyst for removing volatile organic compounds at low temperature.

The Use of multiple-soil layer bioreactors to boost decomposition of fresh leachate

Syafrudin, Hadiwidodo M, Widodo I.W., Budihardjo M.A, Ramadan B.S., Puspita A.S., Wati H.R.

DOI:

https://doi.org/10.30955/gnj.004602

Paper Topic:

Water treatment

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Abstract

The amount of waste is increasing daily; however, the available land area is limited. This problem can be overcome by segregating waste and other alternatives by accelerating landfill stabilization using landfill bioreactors. In this study, anaerobic bioreactor A with daily use of water and the leachate, anaerobic bioreactor B with the addition of water and reuse of the leachate daily, and a control bioreactor with the addition of water were used. The parameters measured were the pH, temperature, humidity, chemical oxygen demand (COD), and biochemical oxygen demand (BOD) of the leachate. The reuse of the leachate and addition of water to the bioreactor accelerated waste degradation and caused high humidity. The average COD and BOD in each bioreactor on day 40 were stable. On day 45, the BOD (74.925 mg/L) was below 100 mg/L and the average COD (781 mg/L) was stable below 1000 mg/L. These results were for all bioreactors except for the control bioreactor, which only received water addition treatment; its COD value was below 1000 mg/L on day 50, at 989 mg/L. Waste decomposition was stabilized, with the BOD below 100 mg/L and the COD below 1000 mg/L.

Valorization of Annona reticulata biochar by chitosan for the adsorption of Azo dye from Textile effluent

Rajakumar. S, Ramakrishnan. S, Monica Nandini . G. K, El –marghany. A, Warad. I, Senthil Kumar. M

DOI:

https://doi.org/10.30955/gnj.004882

Paper Topic:

Water and Wastewater Treatment

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Abstract

Azo dye is a synthetic aromatic dye which are direct dye frequently used in textile industry to colour fibres. It also contributes to the damage of the environment. This study examines the adsorption biochar made from Annona reticulata to remove azo dye from textile wastewater. To improve the adsorption capacity, biochar valorized by chitosan molecules, Investigations were conducted on the use of chitosan-based materials that are acid-resistant for azo dye adsorption. In the batch experiment, basic operating parameters such as initial Azo dye concentrations, pH, contact time and bed height were assessed. The percentage removal efficiency for Azo dye was recorded as 67.18%, 75.88% and 94.37% for three dissimilar sized columns that were 6cm, 8cm, and 10cm. The UV–visible absorption spectroscopy was used to quantify the decolorization of dyes. The changed surface properties of the adsorbent produced from Annona reticulata were characterized by using SEM, XRD, and FTIR. The effectiveness of the column's adsorption was calculated mathematically by Yoon-Nelson, Thomas, and Adams-Bohart models. The model that best represents equilibrium isotherm data by adsorption had R2 values of 94.63, 86.67 and 96.35 respectively.

Air Quality Prediction using Ensemble Voting based Deep Learning with Mud Ring Algorithm for Intelligent Transportation Systems

Sivanesh S G.Mani Venkatraman S R.Nandhini

DOI:

https://doi.org/10.30955/gnj.004810

Paper Topic:

Air Quality

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Abstract

In recent times, advanced technologies in transportation are developing like connected and automated vehicles and shared mobility services. A rapidly increasing number of vehicles in intelligent transportation system (ITS) and smart cities causes pollution and degrade the quality of air. Owing to the incredible effect of air quality on individual lives, it is indispensable to design a system by which air pollutants (PM2.5, NOx, COx, SOx) are predicted. But predicting air quality and its pollutants was complex since air quality relies on various elements like power plants, weather, and vehicular emissions. Deep learning (DL) and Machine learning (ML) techniques are leveraged for developing an air quality predictive method. This study develops an Air Quality Prediction utilizing Ensemble Voting based Deep Learning with Mud Ring Algorithm (AQP-EDLMRA) technique. The presented AQP-EDLMRA technique follows the ensemble voting model, which exploits three DL classification methods like long short-term memory (LSTM), deep belief network (DBN), and stacked autoencoder (SAE). Then, the new data can be classified by the weighted vote of their prediction outcomes. To adjust the hyperparameter values of the DL methods, the MRA was exploited, showing the novelty of the work. The simulation values of the AQP-EDLMRA approach are tested using a series of air quality data and the comprehensive comparative results demonstrated that the AQP-EDLMRA technique has reached improved forecasting performance.

A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

Salem Algarni Vineet Tirth Talal Alqahtani Pravin R. Kshirsagar , Pages:12-22

DOI:

https://doi.org/10.30955/gnj.004699

Paper Topic:

Environmental Health

Issue:

Issue 6 (In progress)

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Abstract

Toxicity poses a significant threat to the environment and human health. Heavy metals, such as cadmium, lead, copper, and zinc, have been shown to harm both agricultural ecosystems and human health. This paper aims to examine the effects of these heavy metals on the environment and human health, as well as discuss the problem of heavy metal deposition in soils. Additionally, the paper utilizes IoT and an SVM classifier to examine human disorders caused by heavy metal exposure. The reflectance spectra of soil samples were used to assess levels of heavy metals using an artificial intelligence algorithm. Arsenic, copper, lead, and cadmium concentrations were estimated using this method. Additionally, an artificial neural network and Naive Bayes model were developed to estimate heavy metal concentrations. The ANN model had R2 values of 0.82, 0.80, 0.76, and 0.70 for copper, cadmium, zinc, and lead, respectively, while the training data had R2 values of 0.70, 0.56, 0.62, and 0.59 for the same estimations.

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 (In progress)

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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.

Assessment of Groundwater Quality in and around the Solid Waste Dump Site using Multivariate Plot Analysis

Geethamani R, Kanmani S, Soundara B, Pages:162-173

DOI:

https://doi.org/10.30955/gnj.004766

Paper Topic:

Water Quality

Issue:

Issue 5

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Abstract

Groundwater sources play an important role in meeting the day today needs of the present generation. Frequent monitoring of the ground water source plays an important part. The aim of this study is to analyze the quality of the ground water in and around the solid waste dumpsite near Sathyamangalam town in Erode district and to identify the variation in concentration with respect to time. Groundwater samples were collected from twenty-one locations and analyzed for pH, Electrical conductivity, Total Alkalinity, Total Hardness, Total Dissolved solids, Calcium, Magnesium, Chlorides, Sodium, Potassium, Nitrates and Sulphates. The collected groundwater samples were analysed using “Weighted Arithmetic Index” and the water quality was indicated using WQI - a number used to identify the water quality easily by the general public. From the results obtained, it is inferred that Water Quality Index changes with respect to pre-monsoon and post monsoon seasons and different time periods for the same location. The changes in water quality are not fully influenced by the open dump site and it may be due to weathering of rocks, use of fertilizers in agricultural lands. Majority of the samples are of poor quality with respect to drinking water quality index. Gibb’s plot, Wilcox diagram and United States Salinity Laboratory diagram are used to assess the suitability of groundwater samples for irrigation purpose. Most of the samples are good for agricultural use. Hence frequent and continuous monitoring of the groundwater quality is necessary to identify its suitability for drinking and agricultural purpose.

Effects of dimethoate, an organophosphate insecticide, on photosynthesis of five selected phytoplankton species

Mavrogenis M. Lepetit B. Kroth P. G, Tsirtsis G., Pages:23-34

DOI:

https://doi.org/10.30955/gnj.004565

Paper Topic:

Biodiversity conservation

Issue:

Issue 6 (In progress)

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Abstract

Dimethoate is an organophosphate (OP) insecticide used in agriculture to kill insects. However, information on the effect of the insecticide on non-target organisms like phytoplankton is sparse. When dimethoate enters water ecosystems, it affects the photosynthesis of the phytoplankton cells. The effect of dimethoate on the photosynthesis mechanism of three Chlorophyceaen and two Bacillariophyceaen species was studied measuring oxygen production, chlorophyll fluorescence and xanthophyll pigments. Oxygen evolution rates of the five phytoplankton species decreased with increasing dimethoate concentrations. OJIP fluorescence characteristics in presence of dimethoate was similar to those of DCMU inhibition. DCMU is a PSII inhibitor used as a positive control. Non-photochemical quenching (NPQ) development and the concentration of specific xanthophyll pigments decreased with increasing insecticide concentration, giving another evidence for the target of dimethoate in the selected phytoplankton species. Dark relaxation kinetics did not show a photoinhibition of the cells in presence of dimethoate. Perfekthion, the commercial formulation of dimethoate, inhibited the photosynthesis of the tested phytoplankton species stronger than dimethoate, leading probably to photoinhibition.

Analysis and Evaluation of Heavy Metal Pollution in Kosasthalaiyar River near Ennore Creek Using Geo Statistical Approach

Sanghita Sen Karuppasamy Sudalaimuthu , Pages:113-125

DOI:

https://doi.org/10.30955/gnj.004733

Paper Topic:

Water Resources Management

Issue:

Issue 5

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Abstract

The increasing industrialization and urbanization in recent times has led to a significant increase in heavy metal pollution in soil and sediment. The present study aims to evaluate the heavy metal contamination (HmC) in surface sediments collected from the Kosasthalaiyar River located between latitudes 13°12′03″N and 13°16′34″N and longitudes 80°18′6″E and 80°19′56″E. Ten sampling locations were selected in the study area and samples of surface sediments were collected and analyzed for 16 geochemical and heavy metal parameters, including Silver (Ag), Aluminium (Al), Calcium (Ca), Cadmium (Cd), Chloride, Cobalt (Co), Total Chromium (Cr), Copper (Cu), Mercury (Hg), Manganese (Mn), Molybdenum (Mo), Nickel (Ni), Lead (Pb), Silica (Si), Titanium (Ti), and Zinc (Zn). The study aims to determine the potential environmental hazards of heavy metals in the sediment through calculation of the potential environmental hazard index (RI). Assess the impact of human activities on HmC in the surface sediments using four pollution indices: the Geo-accumulation index (Igeo), the Contaminant factor (CF), the degree of contamination (DC), the potential contamination index (PCI), and the pollution load index (PLI) and Conduct a human health risk assessment (HHRA) of heavy metals in the sediment. The study results will provide insight into the contamination levels and help inform measures for controlling pollution and promoting sustainability in the area.

A STUDY OF ENVIRONMENTAL IMPACT OF AIR POLLUTION ON HUMAN HEALTH: PM DEPOSITION MODELLING

Gandhimathi A Manju R, Ramadevi K, Rajalakshmi S, Mayilvahanan I

DOI:

https://doi.org/10.30955/gnj.004613

Paper Topic:

Air Quality

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Abstract

Earth is consisting of land, air and water. Air contains oxygen, nitrogen, carbon dioxide, water vapor, other gases are present above the permissible limit, and it creates effects on human health. It is one of the most important public health issues due to development of urbanization, industrialization and globalization in large cities. Air pollution levels in developed countries have been increased in recent years. In developing countries air pollution levels are still high. In recent years, the health studies have become visible showing harmful health effects in lungs and respirable system. Air pollution is responsible for 7million deaths annually. It affects the health of humans and other living beings in our world. The government has developed the guidelines for air quality and rule to restrict emissions in effort to control air pollution. These updated AQGs provide exact guidelines for PM 10 and pm 2.5. The primary objective of this paper is to study the ambient air quality and its impact on lungs. Particulate matters are the particles suspended in air/atmosphere. Mainly PM10and PM 2.5Ultra fine particles (<1µm) are mainly responsible for causing interruption for human health.PM10 and PM2.5 are characterized by their presence only within the upper respiratory tract. Some PM 2.5 and Ultra fine particles are characterized by their presence deeper regions of alveoli. Ultrafine particles (PM0.1), which are present in the air in large numbers, pose a health risk. They generally enter the body through the lungs but translocation to essential organs. Compared to fine particles (PM2.5) ,they cause more pulmonary inflammation and are retained longer in the lung. Their toxicity is increased with smaller size, larger surface area, adsorbed surface material, and the physical characteristics of the particles. Exposure to PM0.1 induces cough and worsens asthma. Metal fume fever is a systemic disease of lung inflammation most likely caused by PM0.1. The disease is manifested by systemic symptoms hours after severe exposure to PM0.1 could cause systemic inflammation, endothelial dysfunction, and coagulation changes that predispose individuals to cardiovascular disease and hypertension.

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