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Applied Sciences
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Smart City Environmental Monitoring Systems
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Smart City Environmental Monitoring Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3486

Special Issue Editors

Dr. Yun Fat LAM

E-Mail Website
Guest Editor
Department of Geography, The University of Hong Kong (HKU), Hong Kong, China
Interests: air quality modelling and pollution; climate change monitoring; microsensor development; emission data preparation and processing
Dr. Hugo Wai Leung Mak

E-Mail Website
Guest Editor
1. Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
2. Department of Mathematics, The Chinese University of Hong Kong, Hong Kong, China
Interests: satellite remote sensing; machine learning algorithms and data assimilation; land use retrieval; geospatial and urban analytics; environmental data science; smart city and sustainable development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental sustainability and smart city development have become hot topics in recent years, and these missions have been explicitly campaigned in the United Nation’s Sustainable Development Goals (e.g., SDG3, SDG7, SDG11, and SDG13). The SDGs aim to promote sustainable and healthy living environments and alleviate environmental risks induced by climate change, urban air pollution, and associated human factors. These aims will improve the health conditions of our living environment and steer cities forward through the use of digitalized approaches. To emphasize the interaction between humans and nature, real-time environmental conditions must be systematically monitored and delivered to the public, so that citizens can identify sources of environmental pollution and receive potential alerts of environmental hazards; such real-time monitoring and subsequent actions can minimize the devastating impacts of associated and undesired events, thus enhancing long-term quality of life.

To continuously monitor environmental changes at various spatial scales, in recent years, different kinds of monitoring systems and field experiments have been established and applied in various areas, including meteorological investigation, air and water pollution, and traffic and mobility studies. These studies cover a wide range of science topics and knowledge, including discussions of low-cost sensors and microsensor technology, community-based/citizen-science-based environmental monitoring network, national and/or international environmental campaigns, and satellite missions. To capture, characterize, and analyze these environmental datasets, different big-data-driven approaches and mathematical algorithms have been established by researchers and scientists, including the following: cloud computing and clusters, Internet of Things (IoT), machine learning (ML) and data assimilation approaches, wireless data transmission, and remote sensing (RS). Furthermore, the combination of modern geospatial and data analytical tools such as ArcGIS, programming techniques, protocols, and deep learning has also enhanced the reliability of retrieved results. This provides possibilities for environmental data sharing and cooperation between central government and local communities, or even between cities and continents.

This Special Issue, titled “Smart City Environmental Monitoring Systems”, aims to publish visionary papers on the latest advancement of technologies in environmental monitoring, environmental data analysis, and delivery of associated information to the general public. Related discussions might cover the question of how cutting-edge approaches have been adopted in promoting smart and sustainable living environments, or review the outlook of smart cities from environmental perspectives. It is our great pleasure to cordially invite you to submit original research papers, relevant critical literature reviews, case reports on relevant topics, and opinions and technical notes on applying relevant smart technologies in continuous environmental monitoring, both currently and in the future.

Submissions from worldwide experts in both academia and industry are welcomed and encouraged. We are looking forward to receiving your high-quality submissions.

Dr. Yun Fat LAM
Dr. Hugo Wai Leung Mak 
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • smart and sustainable community
  • community-based environmental monitoring network
  • citizen science
  • microsensor technology and development
  • field experiments in environmental discipline
  • Internet of Things (IoT)
  • machine learning and data assimilation
  • artificial intelligence and big data analytics
  • environmental remote sensing
  • environmental informatics and modelling
  • environmental data openness and coordination
  • data-driven solutions for environmental sustainability
  • transportation monitoring
  • city mobility

Published Papers (2 papers)

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Research

19 pages, 3857 KiB  
Article
A Citizen-Sensing System for Measuring Urban Environmental Quality: A Case Study Carried out in Taiwan
by Chia-chun Chung and Tay-sheng Jeng
Appl. Sci. 2022, 12(24), 12691; https://doi.org/10.3390/app122412691 - 11 Dec 2022
Viewed by 1311
Abstract
Urban planning is usually dependent on urban analysis and tends to use data from sensor networks collected over a long period time. However, in recent years, due to increased urbanization and the rapid growth of transport, a gap has developed between urban environments [...] Read more.
Urban planning is usually dependent on urban analysis and tends to use data from sensor networks collected over a long period time. However, in recent years, due to increased urbanization and the rapid growth of transport, a gap has developed between urban environments and citizen feelings. Rebuilding urban infrastructure or making urban planning changes require a lot of time and resource costs. The hardware in a city cannot be easily changed, but citizen activities change all the time. Distributing city space according to a software-based recommendation, such as arranging different locations for citizen activities or traffic, is a method that can be implemented to improve city environments and to avoid resource waste. In this paper, citizens were used as sensors to extract environmental information collected using a social network service (SNS), and the information was analyzed to turn subjective feelings into objective environmental phenomena. The research focused on how to collect citizens’ feelings regarding urban environments and to develop a citizen-sensing system to bridge the gap between citizen feelings and sensor networks. The results prove that citizens who sense the city environment create small-sized data that are suitable for small-scaled, high-density cities. Full article
(This article belongs to the Special Issue Smart City Environmental Monitoring Systems)
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24 pages, 4466 KiB  
Article
Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
by Zhao Xu, Xiang Wang, Yumin Niu and Hua Zhang
Appl. Sci. 2022, 12(21), 10822; https://doi.org/10.3390/app122110822 - 25 Oct 2022
Cited by 1 | Viewed by 1167
Abstract
Environmental monitoring is a crucial part of environmental management on construction sites. With the increasing integration of environmental-monitoring systems and cyber-physical systems (CPS), the environmental-monitoring cyber-physical system (E-CPS) has been developed, but it still suffers from uncertainty problems and a lack of robustness. [...] Read more.
Environmental monitoring is a crucial part of environmental management on construction sites. With the increasing integration of environmental-monitoring systems and cyber-physical systems (CPS), the environmental-monitoring cyber-physical system (E-CPS) has been developed, but it still suffers from uncertainty problems and a lack of robustness. In this study, ontology is utilized to establish an E-CPS model that can realize the integration and interaction of physical space, cyberspace, and social space, and the E-CPS model contains perception, transportation, fusion, and decision-making layers. Three uncertainty scenarios are then identified in four layers of the E-CPS to address the current E-CPS shortcomings. The proposed E-CPS model is applied in a construction project, and simulation experiments are then conducted on construction sites. The results show that the abnormal-data-recognition algorithm based on spatiotemporal correlation, whose detection rate is stable around 96%, improves the system’s anti-interference ability against anomalous data entering the perception layer and the transportation layer. This algorithm ensures the accuracy of environmental monitoring for early warning. The sensory data-fusion results based on the belief function method vary from 52.16 to 52.50, with a decrease rate reduced to 0.65%. Finally, the decision-fusion algorithm based on the improved Dempster–Shafer (D-S) evidence theory achieves robust performance. This study could enhance the robustness of the E-CPS in uncertainty conditions and aid the project managers to make decisions and take targeted measures according to the environmental monitoring results and experts’ decisions. Full article
(This article belongs to the Special Issue Smart City Environmental Monitoring Systems)
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