Forthcoming and Online First Articles

Interdisciplinary Environmental Review

Interdisciplinary Environmental Review (IER)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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Interdisciplinary Environmental Review (2 papers in press)

Regular Issues

  • Advanced AQI Interpretation using deep convolutional neural networks - a case study for Delhi   Order a copy of this article
    by Kavita Pabreja, Gauri Banga, Gaurav Kumar Sharma, Shivansh Batra 
    Abstract: Air pollution has a wide range of harmful impacts on public health, economy of country, and the environment. Delhi, one of the most highly polluted places globally, consistently experiences alarmingly high levels of pollution, accompanied by associated health hazards, severe economic losses due to decreased productivity, and environmental degradation. Hence, there is a need for real-time, accurate Air Quality Index monitoring. Traditional methods, which often rely on location-based sensors, are effective but they lack broader coverage as they may not be installed at all locations or may malfunction. This research presents an advanced Convolutional Neural Network model for AQI interpretation based on image to enable continuous and accurate air quality monitoring. An accuracy of 85.71% has been obtained for classifying AQI in six categories. To ensure practical application, a web-based platform and a chatbot has been developed, allowing users to obtain real-time AQI information through an easy-to-use interface.
    Keywords: air quality index; AQI; visual geometry group; VGG16 model; deep convolutional neural networks; flask API.
    DOI: 10.1504/IER.2025.10071110
     
  • EVCS site selection and alternative station proposal with GIS-based multi-criteria decision-making method   Order a copy of this article
    by Ümmühan Nida Erol , Mustafa Göçken 
    Abstract: Electric vehicles (EVs) are environmentally friendly as they do not use fuel and save gas. Although they have many advantages, such as almost zero emissions and lighter vehicle weight, their main disadvantage is the problems with charging. One of the solutions produced to deal with this problem is the approach to building new electric vehicle charging stations (EVCSs). In order to increase the use and development of EVs, it is necessary to choose the most suitable location for the battery stations and to establish the stations. Hence, this article employs a geographic information system (GIS)-based multi-criteria decision making (MCDM) approach to tackle the task of selecting EVCS locations. A four-stage solution approach was developed: 1) criteria research and decision on 3 main and 9 sub-criteria, 2) determining the weights of criteria using the analytical hierarchy process (AHP), 3) creating a suitability map for site selection, 4) identifying and ranking alternative stations.
    Keywords: electric vehicle; site selection; battery station.
    DOI: 10.1504/IER.2025.10071802
     

 

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