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.
Kalman filter based prediction system for wintertime PM10 concentrations in Macau
General
In the present study, the Kalman filter algorithm was applied to forecast the wintertime PM10 concentrations of Macau. The algorithm was implemented on an AR(2) model and an AREX model, respectively. The AR(2) model is essentially an autoregressive model of order 2, i.e., the daily averaged PM10...
Short-Term Prediction of PM2.5 Pollution with Deep Learning Methods
Air pollution and health
Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short...
Air Pollution Prediction using Attention Module With CNN -OptBiLSTM
Air pollution and health
Predicting air pollution using environmental data assessment parameters becomes increasingly significant amid growing fears about climate change and the sustainability of urban areas. The use of sophisticated deep learning (DL) methods to model the intricate relation among these variables represents...
Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach
Solid Waste Management
Fuzzy Inference System (FIS) based prediction models for the Municipal Solid Waste (MSW) generation has been developed in the present work to study the influences of total population, percapita annual income, literacy rate, age group and monthly consumer expenditure on temporal variability of MSW...
Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS models
General
In this study, a new model for biomass higher heating value (HHV) prediction based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was proposed. Proximate analysis (volatile matter, fixed carbon and ash content) data for a wide range of various biomass types from the literature were...
Graph-based neural network model for predicting urban environmental air quality using spatio-temporal data optimization
Air pollution and health
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...
Observational Uncertainty in Hydrological Modelling using Data Depth
Hydrology and Water Resources Management
For any river basin management, one needs tools to predict runoff at different time and spatial resolutions. Hydrological models are tools which account for the storage, flow of water and water balance in a watershed, which include exchanges of water and energy within the earth, atmosphere and...
An Efficient Flood Forecasting Model Using an Optimal Deep Belief Network
Hydrology and Water Resources Management
Floods inflict significant damage globally each year, underscoring the importance of accurate and timely flood prediction to mitigate property loss and life. Precise flood prediction provides governments with crucial preemptive alerts regarding potential flood disasters, enabling timely evacuations...
Air Pollution Monitoring Approach using Atomic Orbital Search Algorithm with Deep Learning Driven
Air pollution and health
Air pollution is a major reason for health-related issues and weather changes, one of humanity's most dangerous problems. It is the most crucial environmental issue in the 21st century and has attracted global attention. These challenges are exacerbated by an overabundance of automobiles, industrial...
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...
A Hybrid CNN-LSTM Predictive Model Deployed Federated Learning Model for Advanced Flood Prediction Systems to Forecast Coastal Region of Smart Cities
Climate change
Flooding in coastal regions of smart cities poses significant challenges, including infrastructure damage, economic losses, and threats to public safety. Traditional flood prediction models often suffer from data privacy concerns, limited spatial-temporal generalisation, and computational...
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...
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...
Machine Learning based Random Forest Algorithm for Prediction of Soil Organic Carbon Decompositions
Environmental management and policy
The Kanyakumari District was selected as a study area due to its diverse flora and favorable climate and soil conditions. Various remote sensing parameters were derived from Landsat 8 and 9 satellite data, including NDVI, BSI, NDMI, NDWI, and SAVI. Moreover, data on land use and land cover (LULC)...
An Intelligent Weather Prediction Model Using Optimized 1D CNN With Attention GRU
Climate change
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...
Deep Learning and Machine Learning based Air Pollution Prediction Model for Smart Environment Design Planning
Air pollution and health
For the past few decades, owing to human activities, urbanization, and industrialization, air pollution is becoming severe across several countries. Deep Learning (DL) and Machine Learning (ML) techniques had great contribution to the development of methods in various aspects of prediction, planning...
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...
Air Quality Prediction using Ensemble Voting based Deep Learning with Mud Ring Algorithm for Intelligent Transportation Systems
Air pollution and health
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...
An IoT-aware Air Quality Prediction System utilising Hybrid Optimization and Fuzzy Temporal Rules Enabled Auto Encoded Bi-LSTM
Air pollution and health
Recently, the environmental pollution is becoming a challenging issue due to the lacking of awareness about the importance of the environment and the growth of transportation facility and the various factories. The Internet of Things (IoT) technology is useful today for collecting and updating the...
Water level prediction by artificial neural network in a flashy transboundary river of Bangladesh
Hydrology and Water Resources Management
This paper presents the sensitivity analysis results of feed forward multilayer perceptron based Artificial Neural Network model for water level prediction in a data constraint transnational Surma River of Bangladesh. Catchment characteristics, hydro-geomorphological, meteorological and headwater...
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...
Prediction of Land Use and Landcover Changes in Tiruppur Tamilnadu Using Hybrid Convolutional Neural Network
Environmental data analysis and modelling
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...
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...
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...
Theoretical prediction of odour determining parameters in dairy effluent using adaptive neuro fuzzy inference system
Water and wastewater treatment and reuse
People are prompted to complain about air pollution by offensive odours. They can produce psychological consequences, such as nausea, headaches, lack of appetite, breathing difficulties, and various adverse reactions in some circumstances. Among various industries, the dairy industry emits the most...
Climate change
16-05-2025
Water and wastewater treatment and reuse
16-05-2025
Environmental management and policy
13-05-2025
Solid Waste Management
12-05-2025
Circular Economy and Bioeconomy
12-05-2025
Environmental management and policy
09-05-2025
Sustainable Energy
05-05-2025
Water and wastewater treatment and reuse
05-05-2025
Circular Economy and Bioeconomy
12-06-2025
Hydrology and Water Resources Management
12-06-2025
Climate change
04-06-2025
Solid Waste Management
04-06-2025
Sustainable Energy
04-06-2025
Environmental management and policy
04-06-2025
Water and wastewater treatment and reuse
04-06-2025
Hydrology and Water Resources Management
02-06-2025
Hydrology and Water Resources Management
28-05-2025
