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Urban Science
Special Issues
Urban Resources and Environment
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Urban Resources and Environment

A special issue of Urban Science (ISSN 2413-8851).

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 9965

Special Issue Editors

Dr. Parveen Sihag

E-Mail Website
Guest Editor
Department of Civil Engineering, Chandigarh University, Mohali 140413, India
Interests: water resources; climate change; infiltration; vortex tube; silt ejectors; streamflow; road accident analysis; mechanical properties of concrete-based materials; artificial neural network; fuzzy logic; adaptive neurofuzzy inference system; random forest; M5P; random tree; bagging; stochastic; support vector machine; Gaussian process; regression; generalized neural network; multivariate adaptive regression splines; group method of data handling
Dr. Saurabh Rana

E-Mail Website
Guest Editor
Department of Mathematics, Chandigarh University, Mohali 140413, India
Interests: artificial intelligence techniques; big data; data analysis; access control; IoT; digital management system; cloud computing; authentication
Dr. Kulwinder Singh

E-Mail Website
Guest Editor
University Centre for Research and Development, Chandigarh University, Mohali 140413, India
Interests: synthesis of nanomaterials; materials modeling; theoretical calculations; thin films; device-based applications of nanomaterials

Special Issue Information

Dear Colleagues,

The Special Issue of Urban Science invites you to contribute to original research papers on “Urban Resources and Environment”.  Cities around the world are facing challenges linked to population growth; consumption of resources has increased exponentially due to rapid urbanization. Similar to living organisms, cities have always required resources and energy to survive. However, technological development and population growth have consequently led to increasing urban inflows, thus deeply changing urban relations with the environment. For the expansion of cities land, water, energy, fuel for vehicles, etc., are required. In a planet with limited resources, the challenge is to find new resources as well as improve the way we use them and the lifestyles that they support, or in other words, to plan strategies to generate more value and higher quality of life with lesser input. It is well known that cities depend on imports of external resources; however, they also benefit from internal resources and ecosystem services. Based on this framework, urgent efforts are needed that explore crucial urban issues that have not yet been adequately investigated. Systematic resources management is required to actually move toward the goal of sustainable cities. The development of new and sustainable materials is crucial for urban cities. In the last few decades, machine learning and theoretical simulation techniques have been successfully used to solve complex problems. We are thus looking toward machine learning techniques and theoretical simulation techniques in urban resources. The Guest Editors of this Special Issue encourage submissions on sustainable materials, resources, environment, and applications of machine learning techniques in the field of urban resources, materials, and the environment.

Dr. Parveen Sihag
Dr. Saurabh Rana
Dr. Kulwinder Singh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Urban Science is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water quality
  • air quality
  • construction materials
  • infrastructure
  • vehicle analysis
  • urban planning and management
  • energy
  • food
  • sustainable management of non-renewable urban resources
  • machine learning techniques
  • theoretical calculations
  • big data
  • cloud computing
  • Internet of Things (IOT)
  • characterization and applications of materials

Published Papers (5 papers)

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Research

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18 pages, 4677 KiB  
Article
Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development
by Sachin Gowda, Vaishakh Kunjar, Aakash Gupta, Govindaswamy Kavitha, Bishnu Kant Shukla and Parveen Sihag
Urban Sci. 2024, 8(1), 4; https://doi.org/10.3390/urbansci8010004 - 02 Jan 2024
Viewed by 2201
Abstract
In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming [...] Read more.
In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R2 value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management. Full article
(This article belongs to the Special Issue Urban Resources and Environment)
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14 pages, 5670 KiB  
Article
Spatial and Temporal Analysis of Drought Forecasting on Rivers of South India
by Ayub Shaikh, Kul Vaibhav Sharma, Vijendra Kumar and Karan Singh
Urban Sci. 2023, 7(3), 88; https://doi.org/10.3390/urbansci7030088 - 17 Aug 2023
Viewed by 1249
Abstract
Extreme weather events such as droughts are catastrophic and can have serious consequences for people and the environment. Drought may be managed if measures are taken in advance. The success of this endeavor depends on a number of factors, not the least of [...] Read more.
Extreme weather events such as droughts are catastrophic and can have serious consequences for people and the environment. Drought may be managed if measures are taken in advance. The success of this endeavor depends on a number of factors, not the least of which is accurate descriptions and measurements of drought conditions. Reducing the negative consequences of droughts requires an early forecast of drought conditions. The primary objective of this research is, hence, to establish a process for the assessment and prediction of drought. The drought evaluation was carried out using the standards established by the SPI and the Indian Meteorological Department. Maps of drought severity were generated using severe drought data. Thirty years’ worth of SPI readings was analyzed. Fuzzy-based drought forecasting model parameters were determined during a 25-year period, and the model was validated throughout the remaining years. The findings of this study can be used by the community to help combat the drought. Before the drought worsens, the local government can implement lifesaving mitigating measures. Full article
(This article belongs to the Special Issue Urban Resources and Environment)
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10 pages, 1462 KiB  
Article
Particulate Matter Accumulation and Elemental Composition of Eight Roadside Plant Species
by Huong-Thi Bui, Jihye Park, Eunyoung Lee, Moonsun Jeong and Bong-Ju Park
Urban Sci. 2023, 7(2), 51; https://doi.org/10.3390/urbansci7020051 - 10 May 2023
Cited by 1 | Viewed by 1511
Abstract
Particulate matter (PM) is the most dangerous air pollutant that adversely affects health. Increasing PM in urban areas is a big problem that must be solved. This study analyzed the amount of PM that accumulated on plant leaves, as well as the leaf [...] Read more.
Particulate matter (PM) is the most dangerous air pollutant that adversely affects health. Increasing PM in urban areas is a big problem that must be solved. This study analyzed the amount of PM that accumulated on plant leaves, as well as the leaf traits that contribute to PM accumulation, to determine the plant’s ability to accumulate PM and the impact of PM on the plants. Scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analysis were used to quantitatively assess metal concentrations in the particles that had accumulated on the leaf samples. Eight common plant species that grow on the roadside were used to analyze leaf traits using leaf samples. Specific leaf areas (SLA), leaf extract pH (pH), relative leaf water content (RWC), chlorophyll (Chl), and carotenoids were analyzed. PM accumulation and leaf traits varied among plant species, and Parthenocissus tricuspidata showed the highest PM accumulation on its leaf surface. The leaf’s elemental composition included C, O, Ca, K, Mg, S, P, Al, Si, Na, Cl, and Fe. Among these elements, Ca, K, and Cl made up a relatively large percentage. Fe was only detected in the leaves of Pachysandra terminalis and P. tricuspidata, while C and O were excluded as they are not relevant in determining PM metal content. Plants not only accumulate PM but also heavy metals from the atmosphere. This study found that plants with highly effective PM accumulation, such as P. tricuspidate, should be considered for optimizing the benefits of plants in improving air quality. Full article
(This article belongs to the Special Issue Urban Resources and Environment)
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11 pages, 3079 KiB  
Article
Waste Removal Efficiencies of Floating Macrophytes for Restoration of Polluted Stream: An Experimental Analysis
by Bharati Mahajan, Sameer Shastri and Shreenivas Londhe
Urban Sci. 2023, 7(1), 27; https://doi.org/10.3390/urbansci7010027 - 16 Feb 2023
Cited by 2 | Viewed by 1597
Abstract
Freshwater sources are affected by a diverse range of pollutants, which increases the demand for effective remediation. Aquatic phytoremediation is a nature-based solution. It has the potential to provide efficient, adaptable, and multi-targeted treatment of polluted waters. The aim of this research is [...] Read more.
Freshwater sources are affected by a diverse range of pollutants, which increases the demand for effective remediation. Aquatic phytoremediation is a nature-based solution. It has the potential to provide efficient, adaptable, and multi-targeted treatment of polluted waters. The aim of this research is to evaluate non-mechanized, low-cost onsite treatment of waste water intrusions. It includes an experimental set up with three replicates. Each consists of a modified flow pattern under outdoor conditions. Experimental set up A and B were provided with macrophytes, water lettuce and duckweed, respectively, with plant coverage at 50% and 90%. Experimental set up C was a controlled set up without macrophytes. The highest removal of BOD, COD and Total solids by using water lettuce were observed to be 89%, 77% and 38.5%, respectively. By using duckweed, the highest removal of BOD, COD and Total solids were observed at 88%, 66% and 27.59%, respectively. Removal was also observed in Set up C for BOD, COD and Total solids; its efficiency was 48%, 47% and 25%, respectively. Set up A can be recommended for treating wastewater intrusion, so that wastewater will purify to a to satisfactory to disposal standard level before mixing in river water. The area available in the stream itself can be used as a treatment zone. Full article
(This article belongs to the Special Issue Urban Resources and Environment)
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Review

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26 pages, 3304 KiB  
Review
Indoor Environmental Quality (IEQ) and Sustainable Development Goals (SDGs): Technological Advances, Impacts and Challenges in the Management of Healthy and Sustainable Environments
by Iasmin Lourenço Niza, Ana Maria Bueno and Evandro Eduardo Broday
Urban Sci. 2023, 7(3), 96; https://doi.org/10.3390/urbansci7030096 - 20 Sep 2023
Cited by 1 | Viewed by 1971
Abstract
The growing concern for sustainability is evident, given the importance of guaranteeing resources for the next generations, especially in the face of increasing energy consumption in buildings. Regardless of the context, people seek comfort, which makes investigating Indoor Environmental Quality crucial. This covers [...] Read more.
The growing concern for sustainability is evident, given the importance of guaranteeing resources for the next generations, especially in the face of increasing energy consumption in buildings. Regardless of the context, people seek comfort, which makes investigating Indoor Environmental Quality crucial. This covers aspects such as indoor air, temperature, noise and lighting, positively impacting quality of life, reducing stress, saving energy and promoting health, well-being and productivity. A literature review was conducted using the Scopus and PubMed databases to analyze technological advances and challenges in managing healthy and sustainable environments, focusing on the relationship between Indoor Environmental Quality and the Sustainable Development Goals. Initially, 855 articles were identified, of which 123 were selected based on established criteria. Three research questions (RQs) were formulated, leading to the following conclusions. (i) The assessment of sustainability in buildings is crucial, encompassing economic, social and environmental aspects. Furthermore, the COVID-19 pandemic has underscored the importance of adapting energy strategies, thereby contributing to the achievement of the Sustainable Development Goals through the utilization of advanced technologies that promote healthy and efficient environments. (ii) Evaluations have evolved, ranging from energy savings to human well-being and mental health, including disease prevention strategies. (iii) Challenges in managing the promotion of Indoor Environmental Quality include excessive resource consumption, emissions and economic–environmental balance. Full article
(This article belongs to the Special Issue Urban Resources and Environment)
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