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Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring
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Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring

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

Deadline for manuscript submissions: 25 May 2024 | Viewed by 5874

Special Issue Editors

Prof. Dr. Itai Kloog

E-Mail Website
Guest Editor
The Department of Geography and Environmental Development,Ben-Gurion University of the Negev, Beer Sheva P.O.B. 653, Israel
Interests: GIS; exposure assessment; environmental epidemiology; geo-statistics; air pollution
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Itzhak Katra

E-Mail Website
Guest Editor
Department of Environmental, Geoinformatics, and Urban Planning Sciences, Ben-Gurion University of the Negev, Beer Sheba 8410501, Israel
Interests: geomorphology; soil erosion; aeolian processes; dust sources and emissions; arid soils under human activities; sand transport and land formation; boundary-layer wind tunnel experiments; dust storms and air pollution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, we have seen the rapid development, evaluation, and application of commercial, off-the-shelf, low-cost sensors (COTS). COTS sensors are increasingly used to understand air quality, climate conditions, and other environmental exposures and stressors in the modern built environment. These COTS devices, which are lower in cost, power, and easier to operate, can better help us to assess and characterize a variety of environmental exposures which up until recent years were impossible due to cost and complexity of such studies. The emergence of COTS technologies can also lower the technological and financial barriers for LIMC countries to monitor environmental exposures and can encourage more citizens to participate in collecting environmental data.

While COTS sensors have been thoroughly validated and used in human exposure and health studies, creating high density urban exposure grids (“meshes”), continuous urban surfaces have not been wildly explored. In addition, monitoring personal exposures of populations while mobile for commuting or leisure has not been explored in depth.  

For this Special Issue titled “Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring”, we aim to focus on research papers related to 1) the application of COTS sensors in mobile exposure assessment (such as cycling, public transport, etc.) and 2) the use of COTS sensors by researchers, communities or governments/municipalities to create real-time urban mesh networks to collect environmental exposures and stressors. 

Prof. Dr. Itai Kloog
Prof. Dr. Itzhak Katra
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

  • mobile monitoring
  • COTS
  • exposure assessment
  • satellite measurements
  • mesh
  • cycling
  • air pollution
  • temperature
  • noise
  • light
  • urban climate
  • indoor and outdoor
  • air quality

Published Papers (4 papers)

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Research

24 pages, 9258 KiB  
Article
Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania
by Youness El Mghouchi, Mihaela Tinca Udristioiu and Hasan Yildizhan
Sensors 2024, 24(5), 1532; https://doi.org/10.3390/s24051532 - 27 Feb 2024
Viewed by 590
Abstract
Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health [...] Read more.
Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021–17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified. Full article
(This article belongs to the Special Issue Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring)
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22 pages, 27840 KiB  
Article
Quantifying the Variability of Ground Light Sources and Their Relationships with Spaceborne Observations of Night Lights Using Multidirectional and Multispectral Measurements
by Noam Levin
Sensors 2023, 23(19), 8237; https://doi.org/10.3390/s23198237 - 03 Oct 2023
Viewed by 1879
Abstract
With the transition to LED lighting technology, multispectral night-time sensors are needed to quantify the changing nightscapes, given the limitations of the panchromatic sensors. Our objective was to quantify the contribution of lighting sources as measured on the ground and examine their correspondence [...] Read more.
With the transition to LED lighting technology, multispectral night-time sensors are needed to quantify the changing nightscapes, given the limitations of the panchromatic sensors. Our objective was to quantify the contribution of lighting sources as measured on the ground and examine their correspondence with night-time brightness and color as measured from space. We conducted ground-based measurements of night-time brightness using the multidirectional (top, rear, right, front, left) and multispectral LANcube v2, which was mounted on the roof of a car, over 458 km of roads in central Israel and in Brisbane, Australia. For spaceborne measurements, we used the SDGSAT-1 multispectral Glimmer sensor. We found that spaceborne measurements of apparent radiance were best explained when including all ground-based directional measurements, with greater explanatory power for highways (R2 = 0.725) than for urban roads (R2 = 0.556). Incoming light in the five directions varied between road classes and land use. In most cases, the variability in night-time brightness and color was greater for urban road sections than for highways. We conclude that due to the spectral mixture of lighting sources, at a medium spatial resolution, the impact of the transition to LED lighting may be more easily recognized from space over highways than in dense urban settings. Full article
(This article belongs to the Special Issue Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring)
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10 pages, 2164 KiB  
Article
Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers
by Callum E. Flowerday, Philip Lundrigan, Christopher Kitras, Tu Nguyen and Jaron C. Hansen
Sensors 2023, 23(18), 7721; https://doi.org/10.3390/s23187721 - 07 Sep 2023
Cited by 1 | Viewed by 1025
Abstract
Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors [...] Read more.
Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbataar, Mongolia to measure PM2.5 concentrations inside yurts or “gers”. Some of these gers were part of a non-government agency (NGO) initiative to improve insulating properties of these housing structures. The goal of the NGO was to decrease particulate emissions inside the gers; a secondary result was to lower the use of coal and other biomass material. LCSs were installed in gers heated primarily by coal, and interior air quality was measured. Gers that were modified by increasing their insulating capacities showed a 17.5% reduction in PM2.5 concentrations, but this is still higher than recommended by health organizations. Gers that were insulated and used a combination of both coal and electricity showed a 19.1% reduction in PM2.5 concentrations. Insulated gers that used electricity for both heating and cooking showed a 48% reduction in PM2.5 but still had higher concentrations of PM2.5 that were 6.4 times higher than recommended by the World Health Organization (WHO). Nighttime and daytime trends followed similar patterns and trends in PM2.5 concentrations with slight variations. It was found that at nighttime the outside PM2.5 concentrations were generally higher than the inside concentrations of the gers in this study, meaning that PM2.5 would flow into the ger whenever the doors were opened, causing spikes in PM2.5 concentrations. Full article
(This article belongs to the Special Issue Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring)
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19 pages, 5860 KiB  
Article
Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring
by Ivan Vajs, Dejan Drajic and Zoran Cica
Sensors 2023, 23(5), 2815; https://doi.org/10.3390/s23052815 - 04 Mar 2023
Cited by 3 | Viewed by 1517
Abstract
Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. [...] Read more.
Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. Being inexpensive and mobile, with wireless transfer support, such devices represent a very promising solution for hybrid sensor networks comprising public monitoring stations supported by many low-cost devices for complementary measurements. However, low-cost sensors can be influenced by weather and degradation, and considering that a spatially dense network would include them in large numbers, logistically adept solutions for low-cost device calibration are essential. In this paper, we investigate the possibility of a data-driven machine learning calibration propagation in a hybrid sensor network consisting of One public monitoring station and ten low-cost devices equipped with NO2, PM10, relative humidity, and temperature sensors. Our proposed solution relies on calibration propagation through a network of low-cost devices where a calibrated low-cost device is used to calibrate an uncalibrated device. This method has shown an improvement of up to 0.35/0.14 for the Pearson correlation coefficient and a reduction of 6.82 µg/m3/20.56 µg/m3 for the RMSE, for NO2 and PM10, respectively, showing promise for efficient and inexpensive hybrid sensor air quality monitoring deployments. Full article
(This article belongs to the Special Issue Low-Cost Sensor Applications for Mobile and Urban Environment Monitoring)
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