Global NEST Journal

ISSN:

Print ISSN: 1790-7632

Electronic ISSN: 2241-777X

Volume 25 - 2023

Articles

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.

Removal of Phenol from Olive Mill Wastewater Using Activated Rice Husk

Wedian F. Al-Zboon K. Mohamad S. Tashtoush R. , Pages:153-161

DOI:

https://doi.org/10.30955/gnj.004583

Paper Topic:

Water and Wastewater Treatment

Issue:

Issue 5

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Abstract

Olive mill wastewater (OMWW) contains many phenolic compounds that if discharged without proper treatment, may lead to substantial risks to human health and deterioration of the environment and aquatic systems. In this study, an attempt was made to produce activated carbon from Rice Husk (RH). The RH was chemically activated by calcium chloride and was utilized for the removal of phenol from the aqueous solution. The removal efficiency using the Activated Rice Husk (ARH) was investigated at different variables such as pH, contact time, adsorbent dose, initial concentration, and carbonization temperature. Results showed that the optimum removal efficiency was 91.62% which was achieved at pH=6, carbonization temperature=850 ˚C, and after 4hrs contact time. Adsorption isotherm fitted well to Langmuir and Freundlich isotherm models while the kinetic data fitted well to the pseudo-second-order model, and thermodynamic data showed that the adsorption is endothermic in nature. Then the activated rice husk sample was used for phenol removal from real OMWW samples from different locations in the city of Irbid-Jordan providing average removal efficiency reached 94.6%. The study indicates that RH and ARH have good potential to be used for phenol removal.

The Influence of Transportation on Regional Tourism Economic Development under the Environment of Sustainable Development-Taking Guizhou Province in China as an Example

Wenjiang Luo Shoufeng Wu , Pages:106-112

DOI:

https://doi.org/10.30955/gnj.004558

Paper Topic:

Urban environment and health

Issue:

Issue 5

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Abstract

The regional tourism economy of Guizhou Province (Latitude 26° 38' 49.1964'' N and Longitude: 106° 37' 47.9892'' E) can be developed by improving the transportation situation. The influence of transportation on the regional tourism economy is the main research content here. A correlation model is established to quantify the relationship between the two. Finally, the model experiment results are analyzed and summarized. Growth rate of total tourism revenue has the largest impact after a positive shock in the initial stage and then drops sharply, turning into the largest negative response. There are obvious shocks in the next few times. The impact gradually decreases and finally tends to 0. The impact of the shock disappears after 20 hits. When it is impacted by the total length of transportation lines, the growth rate of total tourism revenue doesn’t respond at the initial stage. It then responds negatively. The results indicate that there is a coupling relationship between transportation and tourism. The improvement of transportation serves as a strong booster for the tourism economy and supports the development of tourist attractions. The results are significant for promoting transportation and tourism in Guizhou Province.

Internet of Things Enabled Smart Solid Waste Management System

J. Vijayalakshmi A.Mohanarathinam V.Nandini S. Gayathri Priya , Pages:98-105

DOI:

https://doi.org/10.30955/gnj.004744

Paper Topic:

Solid Waste Management

Issue:

Issue 5

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Abstract

The Internet of Things (IoT) paradigm roles a crucial play to enhance smart city applications by controlling and tracking city procedures in real-time. Among the most important problems connected to smart city application is solid waste management that is a negative impression on our people's health and environment. The standard garbage management procedure starts with waste generated by city populations and garbage removal bins at the source. Smart waste management utilizing IoT contains for instance analytics and group of data in sensors on smart garbage bins (SGBs), management of waste trucks and city structure, formation and optimization of garbage truck routes, and so on. This study introduces an Elitist Barnacles Mating Optimizer with Hybrid Deep Learning Model for waste classification (EBMOHDL-WC) in the IoT enabled sustainable environment. The presented EBMOHDL-WC system allows the IoT devices to proceed data collection process. Next, the EBMOHDL-WC technique uses MobileNetv2 model for extracting features and the hyperparameter adjustment of the MobileNetv2 technique was implemented by the EBMO technique, showing the novelty of the work. Finally, the waste classification procedure is performed using HDL classifier which integrates two DL models. The experimental evaluation of the EBMOHDL-WC technique is tested on garbage classification dataset from Kaggle repository. Experimentation outcomes of the EBMOHDL-WC technique exhibit

Research on the design of green and low-carbon food packaging based on artificial intelligence technology

Yanyan Dai, Pages:90-97

DOI:

https://doi.org/10.30955/gnj.004705

Paper Topic:

Water, Energy and/or Food Nexus

Issue:

Issue 5

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Abstract

An economic system focused on low energy usage, low pollution, and low emission is known as a "low-carbon economy". The concept of environmentally friendly and low-carbon food packaging architecture directs practice growth and reform in accordance with the customer's green psychology. As the primary benchmark for packaging concerned with designing practices, it uses the green conceptual design of contemporary eco-environmental preservation and other meaningful reforms. It is significant need to mix people's developed green psychology with this issue in order to discover a solution for green packaging design. The design and use of eco-friendly and low-carbon food packaging is covered in this research. With the implementation of wireless sensor network (WSN) and Artificial Intelligence (AI) technique, it accomplishes the fusion of market and ecologically responsible design, enhances production effectiveness and product usage. The confidence weight information choice technique is used to determine the type of smart energy-saving packaging, and the fundamental element is that the energy usage vector is formed by an Artificial Neural Network (ANN). Packaging makers must reevaluate how design and the environment interact, eliminate packaging design pollutants, make environmental protection their top concern, and develop a design idea that complies with ecological ethics for the long-term profit of human survival. Environment, politics, economy, and social factors all play a role in how the food system functions and are influenced by them. Compared to traditional techniques for monitoring the environment We use wireless sensor networks to keep an eye on the agricultural environment because they have three key benefits: (1) There is no need to lay wire; (2) the system is only implemented once; (3) the nodes are dense; (4) collected data has good precision; and (5) sensor nodes with a specific calculation and storage capacity, enabling collaboration among nodes, are perfect for unsupervised monitoring system. The incorporation and handling of product packaging data is made possible by smart packaging technology, which is essential in the creation of low-carbon food packaging and helps

Electrokinetic Remediation for Textile Wastewater Contaminated Soil

Chandra Devi R. Hemavathi R. Ashwinkumar S. Gowtham B. Selvam M. Vijay Vignesh A., Pages:83-89

DOI:

https://doi.org/10.30955/gnj.004691

Paper Topic:

Soil and groundwater contamination and remediation

Issue:

Issue 5

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Abstract

Soil contamination due to industrial activities is very common and discharging of partially treated or untreated textile wastewater into nearby environment in developing countries contaminated soils. The remediation of textile wastewater contaminated soils containing residual organic dyes and inorganic salts is risky. It is required to identify an economic and eco-friendly in-situ remediation technique for the textile wastewater contaminated soil. This paper investigates the effective removal of residual textile dyes and inorganic salts from the soil using electrokinetic remediation. Laboratory scale acrylic reactors were utilized and the soil was collected from the contaminated agricultural site. The experiment was optimized for different electrode combination (Iron-iron, Iron-stainless steel, Iron-aluminium, Iron-graphite), electrode distance (3 cm, 5.5 cm, and 10.5 cm) and electrical potential gradient (3V/cm, 4V/cm, and 5V/cm). Tap water was used as electrolytes in order to prevent further contamination during remediation process. An electroosmotic flow of 256 mL was observed at cathode reservoir for Iron-graphite electrodes and these electrodes removed 93% electrical conductivity and 82% of total organic carbon from soil. It is found that electroosmosis is the dominant mechanism by which residual textile dyes were removed. The proposed remediation technique is found efficient, economic and eco-friendly compared to the existing technologies.

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Global NEST Journal

Volume 25 - 2023

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