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Sustainability
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Real-Time Air Quality Monitoring Systems
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Real-Time Air Quality Monitoring Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 7729

Special Issue Editors

Dr. Catarina Silva

E-Mail Website
Guest Editor
Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-790 Coimbra, Portugal
Interests: social networks; machine learning; text classification; dynamic environments; crowdsourcing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Filipe Araujo

E-Mail Website
Guest Editor
Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: distributed systems; edge and cloud computing; wireless ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The World Health Organization (WHO) estimates that air pollution kills seven million people worldwide every year and that 9 out of 10 people breathe air with a concentration of pollutants exceeding its guideline limits. Providing timely, detailed, and accurate information to citizens and government officials about the air they breathe can, therefore, improve health quality. This is an interdisciplinary challenge requiring the joint effort of researchers from computer science, engineering, environment science, health, and other scientific areas.

Real-time air quality monitoring systems are a booming research and investment area, and we have witnessed an increasing interest in topics like low-cost sensor networks, development of wireless communication, crowdsourcing, intelligent prognosis, distributed storage systems, together with a growing public awareness about air quality.

We are organizing this Special Issue to advance the field of real-time air quality monitoring systems. We invite researchers to submit their work on theoretical and practical aspects of such systems, addressing open problems in sensors, data collection, fusion, and storage systems, privacy, intelligent forecasting and alarming as well as web and mobile applications directed at individuals and organizations for decision support.

Prof. Dr. Catarina Silva
Prof. Dr. Filipe Araujo
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. Sustainability 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

  • air quality and air quality assessment
  • air quality sensors
  • sensor data collection and fusion
  • mobile computing technologies
  • innovative real-time monitoring approaches
  • intelligent methods for air quality monitoring
  • calibration and prognostic systems in air quality monitoring
  • cloud-based approaches to air quality systems
  • high availability of real-time air quality systems
  • security in air quality systems
  • applications of air quality monitoring data
  • Air quality in smart cities
  • quality issues with air monitoring data
  • data storage and privacy
  • IoT

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Published Papers (2 papers)

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Research

34 pages, 11878 KiB  
Article
Attention-Based Distributed Deep Learning Model for Air Quality Forecasting
by Axel Gedeon Mengara Mengara, Eunyoung Park, Jinho Jang and Younghwan Yoo
Sustainability 2022, 14(6), 3269; https://doi.org/10.3390/su14063269 - 10 Mar 2022
Cited by 18 | Viewed by 3999
Abstract
Air quality forecasting has become an essential factor in facilitating sustainable development worldwide. Several countries have implemented monitoring stations to collect air pollution particle data and meteorological information using parameters such as hourly timespans. This research focuses on unravelling a new framework for [...] Read more.
Air quality forecasting has become an essential factor in facilitating sustainable development worldwide. Several countries have implemented monitoring stations to collect air pollution particle data and meteorological information using parameters such as hourly timespans. This research focuses on unravelling a new framework for air quality prediction worldwide and features Busan, South Korea as its model city. The paper proposes the application of an attention-based convolutional BiLSTM autoencoder model. The proposed deep learning model has been trained on a distributed framework, referred to data parallelism, to forecast the intensity of particle pollution (PM2.5 and PM10). The algorithm automatically learns the intrinsic correlation among the particle pollution in different locations. Each location’s meteorological and traffic data is extensively exploited to improve the model’s performance. The model has been trained using air quality particle data and car traffic information. The traffic information is obtained by a device which counts cars passing a specific area through the YOLO algorithm, and then sends the data to a stacked deep autoencoder to be encoded alongside the meteorological data before the final prediction. In addition, multiple one-dimensional CNN layers are used to obtain the local spatial features jointly with a stacked attention-based BiLSTM layer to figure out how air quality particles are correlated in space and time. The evaluation of the new attention-based convolutional BiLSTM autoencoder model was derived from data collected and retrieved from comprehensive experiments conducted in South Korea. The results not only show that the framework outperforms the previous models both on short- and long-term predictions but also indicate that traffic information can improve the accuracy of air quality forecasting. For instance, during PM2.5 prediction, the proposed attention-based model obtained the lowest MAE (5.02 and 22.59, respectively, for short-term and long-term prediction), RMSE (7.48 and 28.02) and SMAPE (17.98 and 39.81) among all the models, which indicates strong accuracy between observed and predicted values. It was also found that the newly proposed model had the lowest average training time compared to the baseline algorithms. Furthermore, the proposed framework was successfully deployed in a cloud server in order to provide future air quality information in real time and when needed. Full article
(This article belongs to the Special Issue Real-Time Air Quality Monitoring Systems)
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16 pages, 2380 KiB  
Article
Comparison of H2S Gas Sensors: A Sensor Management Procedure for Sewer Monitoring
by Micaela Pacheco Fernández, Daneish Despot and Matthias Barjenbruch
Sustainability 2021, 13(19), 10779; https://doi.org/10.3390/su131910779 - 28 Sep 2021
Cited by 6 | Viewed by 2311
Abstract
Hydrogen sulphide (H2S) emissions are one of the major problems associated with sewer networks. This gas, with its characteristic smell of rotten eggs is highly toxic and leads to the corrosion of sewer infrastructures. To protect cities and ensure the safety [...] Read more.
Hydrogen sulphide (H2S) emissions are one of the major problems associated with sewer networks. This gas, with its characteristic smell of rotten eggs is highly toxic and leads to the corrosion of sewer infrastructures. To protect cities and ensure the safety of sewer workers, sewers are commonly monitored using H2S gas sensors. In this work, three commercial H2S gas sensors for air quality monitoring were compared at two different sites in Berlin, Germany. Two of the sensors provide online access to data, while the other one is a data logger. Moreover, based on statistical measures (RMSE, MAE, MB, and a graphical analysis), we evaluated whether a rotation/exchange between data logger (reference) and online sensors is possible without significant differences in the gas measurements. Experimental evaluation revealed that measurement differences are dependent on the H2S concentration range. The deviation between sensors increases as the H2S concentration rises. Therefore, the interchange between reference and online sensors depends on the application site and the H2S levels. At lower ranges (0–10 ppm) there were no observed problems. Finally, to support practitioners on-site, a management procedure in the form of a decision-making tool is proposed for assessing whether gas sensors should be exchanged/rotated. Full article
(This article belongs to the Special Issue Real-Time Air Quality Monitoring Systems)
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