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Environmental Modeling and Analysis of Urban Greening and Air Pollutants

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 6090

Special Issue Editors


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Guest Editor
Center for Ecological Noosphere Studies of National Academy of Sciences, RA, Yerevan 0025, Armenia
Interests: urban geochemistry; urban greening; mining geochemistry; media-specific environmental pollution assessment; geochemical mapping and modeling

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Assistant Guest Editor
Central Economics and Mathematics Institute of Russian Academy of Sciences, National Research University Higher School of Economics, Moscow 101000, Russian
Interests: environmental modeling; agent-based and individual-based modeling; urban greening; evolutionary computations; ecological economics

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Assistant Guest Editor
1. Department of Ecological and Biological Sciences (DEB), University of Tuscia, 01100 Viterbo, Italy
2. Institute of Research on Terrestrial Ecosystem (IRET), National Research Council (CNR), 05010 Porano, Italy
Interests: impact of nature-based solutions on environment and society; development of new techniques to assess the air quality mitigation by plants; use of urban forest for source apportionment
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Special Issue Information

Dear Colleagues,

This Special Issue will be devoted to the modeling of urban environment issues, placing a special emphasis on relationships between urban green spaces and atmospheric pollution. 

Both environmental modeling and urban greening analysis enable a multidisciplinary and interdisciplinary approach for urban environment pollution issues, acting as a tool to be best effectively used in proper and effective urban environment management, selection of economically beneficial solutions, improvement of the potential of urban ecosystem services, and urban health.   

By offering this Special Issue, we expect participation in a single scientific platform of broad specialists, including model developers, environmentalists, and urban policy makers. 

We look forward to receiving your innovative work.

Dr. Chiara Baldacchini
Prof. Dr. Andranik S. Akopov
Dr. Lilit Sahakyan
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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

  • urban greening
  • air pollution
  • urban environment modeling
  • nature-based solutions
  • urban green infrastructure

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Published Papers (2 papers)

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Research

17 pages, 2790 KiB  
Article
Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events
by Nurulkamal Masseran and Muhammad Aslam Mohd Safari
Int. J. Environ. Res. Public Health 2021, 18(13), 6754; https://doi.org/10.3390/ijerph18136754 - 23 Jun 2021
Cited by 7 | Viewed by 2130
Abstract
This article proposes a novel data selection technique called the mixed peak-over-threshold–block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular [...] Read more.
This article proposes a novel data selection technique called the mixed peak-over-threshold–block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia. Full article
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33 pages, 3482 KiB  
Article
Dynamic Optimization and Coordination of Cooperative Emission Reduction in a Dual-Channel Supply Chain Considering Reference Low-Carbon Effect and Low-Carbon Goodwill
by Ziyuan Zhang and Liying Yu
Int. J. Environ. Res. Public Health 2021, 18(2), 539; https://doi.org/10.3390/ijerph18020539 - 11 Jan 2021
Cited by 32 | Viewed by 3388
Abstract
Although the issue of cooperative emission reduction in supply chains has been extensively studied, there is little literature that considers the impact of consumers’ reference low-carbon effect and product low-carbon goodwill on their purchasing behavior in the issue of dual-channel supply chain cooperative [...] Read more.
Although the issue of cooperative emission reduction in supply chains has been extensively studied, there is little literature that considers the impact of consumers’ reference low-carbon effect and product low-carbon goodwill on their purchasing behavior in the issue of dual-channel supply chain cooperative emission reduction. In order to explore the impact of consumers’ reference low-carbon effect and product low-carbon goodwill on the balanced emission reduction decisions and profit of dual-channel supply chain members, we establish a dual-channel supply chain emission reduction dynamic optimization model, use differential game theory to solve the manufacturer’s optimal emission reduction investment and the retailer’s optimal low-carbon publicity investment strategies under four different decision scenarios, and analyze them in detail. In addition, we also design an effective low-carbon publicity cost-sharing contract to achieve coordination of the supply chain. The research results show that the equilibrium strategies of the manufacturer and retailer and the overall profit of the supply chain under the centralized decision scenario are better than those of decentralized decision scenario. When the initial reference low-carbon level is low, the online and offline reference low-carbon effects are beneficial to the manufacturer and retailer. When the initial low-carbon goodwill is high, it is beneficial for both the manufacturer and retailer to increase consumer recognition of low-carbon goodwill. When the ratio of low-carbon publicity cost sharing provided by the manufacturer to the retailer is within a reasonable range, the cost-sharing contract can reduce the double marginal effect and achieve supply chain coordination. Full article
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