Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
IoT-Based Sensing Systems for Urban Air Quality Forecasting
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

IoT-Based Sensing Systems for Urban Air Quality Forecasting

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

Deadline for manuscript submissions: closed (20 June 2025) | Viewed by 692

Special Issue Editor

Prof. Dr. David J. Lary

E-Mail Website
Guest Editor
School of Natural Sciences and Mathematics, University of Texas at Dallas, Dallas, TX 75080, USA
Interests: artificial intelligence; machine learning; smart cities; remote sensing; hyper spectral imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on how scientific, societal, and public health advancements can be facilitated using diverse IoT systems in dense urban environments (outdoors and indoors), on autonomous robotic systems, and in wearable IoT devices. These IoT devices can be enhanced by machine learning, e.g., for sensor calibration, and provide a range of data products and/or the development of advanced forecasting methods. There is a natural synergy of data from IoT sensors with operational forecasting and data assimilation systems. There are many public health benefits of precise, localized air quality information facilitated by IoT devices and/or forecasts, especially for those with health vulnerabilities. We seek contributions that shed new light on how IoT technologies can elevate urban air quality monitoring and forecasting, enhancing the scientific understanding of microenvironments, the human exposome, public health, and environmental policies. There is significant value in exploring methodologies for assessing IoT data quality and uncertainty, studying cost-effective sensor calibration techniques, characterizing the optimum spatial and temporal scales required to capture the natural variability of micro-environments, and promoting transparency and reproducibility through open source approaches, open data, open data standards, and open-design sensor systems.

Prof. Dr. David J. Lary
Guest Editor

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

  • IoT
  • machine learning
  • sensor calibration
  • data products
  • forecasting methods
  • autonomous robotic systems
  • wearable devices
  • dense urban environments
  • operational forecasting
  • data assimilation
  • data quality
  • data uncertainty
  • calibration
  • open design
  • open source
  • open data
  • open data standards
  • spatial scales
  • temporal scales
  • public health
  • urban air quality
  • air quality forecasting
  • health vulnerabilities
  • environmental policies
  • microenvironments
  • human exposome
  • transparency
  • reproducibility
  • natural variability

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

31 pages, 4435 KiB  
Article
A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty
by Nurdaulet Tasmurzayev, Bibars Amangeldy, Gaukhar Smagulova, Zhanel Baigarayeva and Aigerim Imash
Sensors 2025, 25(14), 4521; https://doi.org/10.3390/s25144521 - 21 Jul 2025
Abstract
The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO2 and PM2.5 often accumulate, elevating the risk of respiratory and cardiovascular [...] Read more.
The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO2 and PM2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO2, PM2.5, and PM10, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article
(This article belongs to the Special Issue IoT-Based Sensing Systems for Urban Air Quality Forecasting)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop