The Global NEST Journal is an open access journal that publishes original research articles, short papers and critical reviews on all aspects of Environmental Science and Technology. These comprise, but are not limited to, Pollution Control Technology, Global Environmental Change, Air Quality, Water Quality, Water and Waste Water Treatment, Solid Waste Management, Hazardous Substances and Risk Analysis, Emerging pollutants. Relevant topics incorporating the methodologies and state of the art of disciplines such as Environmental Management Policies, Ecosystems and Natural Resources Management, Hydrology and Water Resources Management, Clean Energy and Sustainability are included.
Submitted manuscripts are initially reviewed by the editor and selected after rigorous peer review by scientists in order to assess the significance, originality and appropriateness for publication.
Articles submitted to Global NEST Journal benefit from its broad scope and readership. We aim for a turnaround time of 4 weeks from submission to first decision.
Global NEST Journal is addressed to professionals in Academic, Consulting Offices, Government Agencies and Organisations, as well as others responsible for the investigations, evaluation of complicated environmental issues of global interest.
The 2024 Scopus CiteScore for the Global NEST Journal is 2.36. It is also ranked in the Q3 quartile for Environmental Science (miscellaneous).
Here's a breakdown of other key metrics:
- H-index: 41
- SJR: 0.250 (Q3)
- SNIP score: 0.48
Journal Impact Factor (JIF): The Journal Impact Factor (JIF) is not directly calculated in Scopus and is based on Web of Science data. Web of Science data. However, the Global NEST Journal does have a JIF, and the Journal Impact Factor (JIF) is 1.5-year Impact Factor is 1.1.
Scimago Journal and Country Rank (Scimago) provides information on the journal's H-index and SJR, while Researcher.Life lists the CiteScore and quartile. Web of Science Journal Info lists the JIF.
Prediction of adsorption efficiency for the removal malachite green and acid blue 161 dyes by marble sludge dust using ANN
Water and wastewater treatment and reuse
In the present study, batch adsorption studies were performed for the removal of malachite green and acid blue 161 dyes from aqueous solutions by varying parameters such as contact time, waste marble dust amount, initial dye concentration and temperature. The equilibrium adsorption data were...
Wind energy location prediction between meteorological stations using ANN
Sustainable Energy
Wind Energy is one of the important sources of renewable energy. There is a need to prepare the availability of wind energy in the area where there is no measured wind speed data. For this type of situation, it seems to be necessary to predict the wind energy potential using such as wind speed using...
Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time
General
Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration...
Artificial Neural Network and Weighted Arithmetic Indexing Approach for Surface Water Quality Assessment of the Brahmani River
Air pollution and health
An approach was used in this study to relate the predicted and calculated water quality index (WQI) of the Brahmani River. The WQI was predicted using an artificial neural network (ANN) tool, and the weighted Arithmetic Index technique was used to calculate the WQI (WAI). The WQI is calculated using...
A multi-stage methodology for selecting input variables in ANN forecasting of river flows
General
The scientific community has recognized the necessity for more efficiently selected inputs in artificial neural network models (ANNs) in river flows and has worked on this despite some shortcomings. Moreover, there is none or limited inclusion of ANN inputs coupled with atmospheric circulation under...
A Hybrid Stochastic-ANN Approach for Flow Partitioning in the Okavango Delta of Botswana
Hydrology and Water Resources Management
Since a spectrum of hydrological and geomorphological conditions produce flood pulse environment in a riverine or a deltaic system, it is essential to have the knowledge on spatial and temporal distributions of river flow and dependent processes for environmental flow requirements, ecosystem...
Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN
Environmental data analysis and modelling
Understanding the retention of organic compounds (OCs) is critical for membrane applications in water recycling. The objective of this study was to create an optimized model using Artificial Neural Networks for Quantitative Structure-Property Relationship (QSPR-ANN) to predict the effect of...
Investigation of Performance of Tropospheric Ozone Estimations in The Industrial Region Using Differential Artificial Neural Networks Methods
Environmental Sciences
The method of Levenberg-Marquardt learning algorithm was investigated for estimating tropospheric ozone concentration. The Levenberg-Marquardt learning algorithm has 12 input neurons (6 pollutants and 6 meteorological variables), 28 neurons in the hidden layer, and 1 output neuron for the Ozone (O3)...
Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City
Air pollution and health
In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological...
Neural Networks for Environmental Problems: Data Quality Control and Air Pollution Nowcasting
General
This work illustrates the use and some related results of Artificial Neural Networks (ANNs) for data quality control of environmental time series and for reconstruction of missing data. ANNs are applied to the following problems: i) short and medium-term predicting of air pollutant concentrations in...
Neural network estimation of the scour cone geometry around outlet in the pressure flushing
General
When flushing carry out as pressure condition, a scour cone is performed around the outlet. As the flow around the outlet in the pressure flushing is three dimensional, therefore that it is difficult to establish a general empirical model to provide accurate estimation for scour cone volume and...
Application of artificial neural networks for flood forecasting.
General
In hydrology, as in a number of diverse fields, there has been an increasing use of Artificial Neural Networks (ANN) as black-box simplified models. This is mainly justified by their ability to model complex non-linear patterns; in addition they can self-adjust and produce a consistent response when...
Comparison of a black-box model to a traditional numerical model for Hydraulic Head Prediction
General
Two different methodologies for hydraulic head simulation were compared in this study. The first methodology is a classic numerical groundwater flow simulation model, Princeton Transport Code (PTC), while the second one is a black-box approach that uses Artificial Neural Networks (ANNs). Both...
Measurement and prediction of karstic spring flow rates
Hydrology and Water Resources Management
This paper deals with prediction of the response of karstic springs by means of artificial neural networks (ANNs). A feed-forward back propagation ANN with three layers has been developed, to predict flow rates of two karstic springs, located at Rouvas area, Crete, Greece, using rainfall data as...
Assessment and prediction of benzene concentrations in a street canyon using a variety of models, in the means of environmental management purposes
General
Urban air quality nowadays is one of the major environmental issues, due to its impact to various health problems, caused by the daily exposure of the population in dangerous air pollutants. The highest levels of air pollutants are usually observed in street canyons, emitted by urban traffic. To...
Local scale simulation of air temperature by a two-step hybrid downscaling approach using regional climate modeling and artificial neural networks
General
The influence of microscale and mesoscale meteorology on the local scale variation of air temperature cannot be correctly simulated by the coarse resolution Global Climate Models. The scope of this work is to develop a hybrid dynamic-statistical downscaling procedure and quantify its predictive...
Estimation of microclimatic data in remote mountainous areas using an artificial neural network model-based approach
General
An artificial neural network (ANN) model-based approach was developed and applied to estimate values of air temperature and relative humidity in remote mountainous areas. The application site was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological stations were...
New Regression models for Estimation daily temperature of Karachi and its Neural Network analysis
Environmental data analysis and modelling
This study presents the determination of the average daily temperature distribution for Karachi city. Artificial Neural Network (ANN) has been used to predict the average daily temperature of 2018, 2019, and 2020. Two regression models (linear and non-linear) were also developed. These models are...
A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health
Air pollution and health
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...
Prognosis of maximum daily surface ozone concentration within the greater Athens urban area, Greece
Air pollution and health
In recent decades, there has been an increasing interest in the prognosis of maximum surface ozone concentrations due to the adverse effects on human health, animal population, agricultural productivity and forestry. The present study deals with the development and application of Artificial Neural...
Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data
General
Detailed meteorological data required for the equation of FAO-56 Penman-Monteith (P-M) method that was adopted by Food and Agriculture Organization (FAO) as a standard method in estimating reference evapotranspiration (ETo) are not often available, especially in developing countries. The Hargreaves...
Research on the design of green and low-carbon food packaging based on artificial intelligence technology
Hydrology and Water Resources Management
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...
Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy
Water and wastewater treatment and reuse
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...
Investigation on Tensile and Flexural Behaviour of Fibre Reinforced Concrete Using Artificial Neural Network
Solid Waste Management
The purpose of this study is to investigate the impact that using marble sludge powder as a partial replacement for cement in concrete can have. Experiments were conducted to investigate a variety of characteristics of fiber-reinforced concrete using both fresh concrete and concrete that had been...
Predictive Modeling for Solar Desalination Using Artificial Neural Network Techniques: A Review
Water and wastewater treatment and reuse
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...
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