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Air Quality Monitoring Sensors Network
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Air Quality Monitoring Sensors Network

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 2446

Special Issue Editors

Dr. Xufei Yang

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA
Interests: air quality; livestock environment; environmental engineering; waste management
Special Issues, Collections and Topics in MDPI journals
Dr. Jiayu Li

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Miami, Coral Gables, FL 33146, USA
Interests: air quality sensors; air quality

Special Issue Information

Dear Colleagues,

Alongside the advancements in sensor technologies, air quality monitoring has entered a new era featuring the dense deployment of air quality sensors. Low-cost gas and particulate matter (PM) sensors are being connected via wired or wireless communication protocols (e.g., WIFI and LoRaWAN) to create distributed or community air monitoring networks, providing an unprecedented capacity and opportunity to reveal the temporal–spatial distribution of air pollutants in indoor and outdoor environments. This, along with improved data sharing and accessibility over the Internet, not only benefits public health assessment and air pollution control at various geospatial scales but also promotes the engagement of communities in air quality management. This Special Issue aims to offer a platform for researchers to present recent advancements in air quality monitoring sensor networks. It welcomes the following topics:

  • The design, fabrication, calibration, and assessment of air quality sensors for establishing monitoring networks;
  • Internet-of-Things (IoT) technologies for integrating air quality sensors into a usable monitoring network;
  • Case studies of air quality monitoring sensor networks, focusing on sensor selection, network architecture, or system integration;
  • Novel technologies or case studies regarding the management (e.g., calibration) of a large-scale or dense sensor network.

Papers presenting monitoring data may be accepted. However, the environmental implications of the acquired data should not be the focus of a paper, given the scope of the journal.

Dr. Xufei Yang
Dr. Jiayu Li
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. Sensors 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 2600 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 monitoring
  • sensors
  • networks
  • Internet of Things
  • particulate matter
  • air pollutants

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Published Papers (1 paper)

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Research

17 pages, 16030 KiB  
Article
Multi-Sensor Platform for Predictive Air Quality Monitoring
by Gabriele Rescio, Andrea Manni, Andrea Caroppo, Anna Maria Carluccio, Pietro Siciliano and Alessandro Leone
Sensors 2023, 23(11), 5139; https://doi.org/10.3390/s23115139 - 28 May 2023
Cited by 8 | Viewed by 1872
Abstract
Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people’s health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden [...] Read more.
Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people’s health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden rise in CO2 levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people’s comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware–software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO2 trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants’ physical activity, temperature, humidity and CO2 in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days. Full article
(This article belongs to the Special Issue Air Quality Monitoring Sensors Network)
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