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Atmosphere
Special Issues
The Future of Air Quality Monitoring
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The Future of Air Quality Monitoring

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 18983

Special Issue Editors

Prof. Dr. Kostas Karatzas

E-Mail Website
Guest Editor
Environmental Informatics Research Group, School of Mechanical Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: environmental informatics; computational intelligence oriented data analytics and modelling; urban air quality management and information systems; computational calibration and performance improvement of low-cost environmental sensors; quality of life information services; citizen science; mechanical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Nuria Castell

E-Mail Website
Guest Editor
Urban Environment and Industry Department, NILU – Norwegian Institution for Air Research, 2027 Kjeller, Norway
Interests: environmental monitoring; urban sustainability; citizen science; low-cost sensor technology; co-creation; urban living labs; transdisciplinary research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been substantial development in new ICT technologies, such as IoT sensor systems, artificial intelligence, webcrawling, crowdsourcing, social media data mining, etc., leading to new approaches to air quality monitoring, including the detection of allergens (pollen, mold) in the air. The synergies between technological development and other disciplines, such as social sciences and art, are revealing new ways of monitoring and visualizing the air we breathe and engaging citizens in doing so.

The emergence of low-cost electronics and sensors has favored the deployment of large sensor networks in cities, and has allowed citizens to start monitoring air quality by themselves. The combination of ubiquitous sensor technologies and citizen science opens up the opportunity to monitor air quality at spatial resolutions and locations not possible with traditional monitoring systems. However, due to the low accuracy of sensor systems and their degradation over time, there is the need for new research on post-processing techniques, including artificial intelligence algorithms, to improve sensor calibration and data quality control and to reduce data uncertainty. Data collected by ubiquitous sensor networks, sometimes managed by local communities, even when subjected to higher uncertainty than governmental reference stations, can enhance existing knowledge on air composition, provide personalized information to citizens, and contribute to knowledge-based policy-making.

The combination of data from many sources, in-situ and remote (e.g., satellite) sensing technologies, models, public data sources (e.g., social media, historical data), economics, health and social sciences (e.g., human behavior) using advanced computational intelligence-data mining-machine learning techniques can, for instance, provide better forecasting tools to protect human health or elaborate new real-time environmental awareness intelligent systems to improve behaviors towards more sustainable cities.

This issue welcomes papers on environmental intelligence, affordable sensor design and deployment, artificial intelligence techniques, citizen science, data assimilation, and other novel technologies, tools, and methods with a focus on improving air monitoring, increasing environmental awareness, and/or facilitating knowledge-based policy-making. Overall, developments towards future air quality monitoring methods may lead to a paradigm shift that engages a much broader part of the environmental monitoring spectrum than today, leading to new “soft” and “hard” services and products.

Prof. Dr. Kostas Karatzas
Dr. Nuria Castell
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. Atmosphere is an international peer-reviewed open access monthly 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 pollution (indoor and outdoor)
  • Aeroallergens – biological weather
  • Novel monitoring technologies
  • Low-cost sensor networks
  • IoT
  • Smart cities
  • Artificial intelligence
  • Environmental intelligence
  • Citizen participation - crowdsourcing
  • Soft sensors

Published Papers (5 papers)

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20 pages, 7306 KiB  
Article
Assessment of Low-Cost Particulate Matter Sensor Systems against Optical and Gravimetric Methods in a Field Co-Location in Norway
by Matthias Vogt, Philipp Schneider, Nuria Castell and Paul Hamer
Atmosphere 2021, 12(8), 961; https://doi.org/10.3390/atmos12080961 - 27 Jul 2021
Cited by 28 | Viewed by 4065
Abstract
The increased availability of commercially-available low-cost air quality sensors combined with increased interest in their use by citizen scientists, community groups, and professionals is resulting in rapid adoption, despite data quality concerns. We have characterized three out-the-box PM sensor systems under different environmental [...] Read more.
The increased availability of commercially-available low-cost air quality sensors combined with increased interest in their use by citizen scientists, community groups, and professionals is resulting in rapid adoption, despite data quality concerns. We have characterized three out-the-box PM sensor systems under different environmental conditions, using field colocation against reference equipment. The sensor systems integrate Plantower 5003, Sensirion SPS30 and Alphasense OCP-N3 PM sensors. The first two use photometry as a measuring technique, while the third one is an optical particle counter. For the performance evaluation, we co-located 3 units of each manufacturer and compared the results against optical (FIDAS) and gravimetric (KFG) methods for a period of 7 weeks (28 August to 19 October 2020). During the period from 2nd and 5th October, unusually high PM concentrations were observed due to a long-range transport episode. The results show that the highest correlations between the sensor systems and the optical reference are observed for PM1, with coefficients of determination above 0.9, followed by PM2.5. All the sensor units struggle to correctly measure PM10, and the coefficients of determination vary between 0.45 and 0.64. This behavior is also corroborated when using the gravimetric method, where correlations are significantly higher for PM2.5 than for PM10, especially for the sensor systems based on photometry. During the long range transport event the performance of the photometric sensors was heavily affected, and PM10 was largely underestimated. The sensor systems evaluated in this study had good agreement with the reference instrumentation for PM1 and PM2.5; however, they struggled to correctly measure PM10. The sensors also showed a decrease in accuracy when the ambient size distribution was different from the one for which the manufacturer had calibrated the sensor, and during weather conditions with high relative humidity. When interpreting and communicating air quality data measured using low-cost sensor systems, it is important to consider such limitations in order not to risk misinterpretation of the resulting data. Full article
(This article belongs to the Special Issue The Future of Air Quality Monitoring)
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15 pages, 1306 KiB  
Article
Transformative Potential and Learning Outcomes of Air Quality Citizen Science Projects in High Schools Using Low-Cost Sensors
by Sonja Grossberndt, Antonella Passani, Giulia Di Lisio, Annelli Janssen and Nuria Castell
Atmosphere 2021, 12(6), 736; https://doi.org/10.3390/atmos12060736 - 08 Jun 2021
Cited by 9 | Viewed by 3423
Abstract
The rise of advanced ICT technologies has made it possible to apply low-cost sensor systems for measuring air quality in citizen science projects, including education. High school students in Norway used these sensor systems in a citizen science project to design, carry out, [...] Read more.
The rise of advanced ICT technologies has made it possible to apply low-cost sensor systems for measuring air quality in citizen science projects, including education. High school students in Norway used these sensor systems in a citizen science project to design, carry out, and evaluate their own research projects on air quality. An impact assessment framework was designed to assess the impact of these activities, considering five areas of impact: scientific, social, economic, political, and environmental. In addition, the framework also considers the transformative potential of the citizen science pilot, i.e., the degree to which the pilot can help to change, alter, or replace current systems, and the business-as-usual in one or more fields such as knowledge production or environmental protection. Data for this assessment were gathered in the form of questionnaires that the students had to complete before starting and after finalizing the pilot activities. The results showed positive impacts on learning, a pro-environmental world view, and an increase in pro-science attitudes and interest in scientific and environmental-related topics at the end of the pilot activities. Only weak impacts were measured for behavioral change. The activities showed transformative potential, which makes the student activities an example of good practice for citizen science activities on air quality with low-cost sensors. Full article
(This article belongs to the Special Issue The Future of Air Quality Monitoring)
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22 pages, 13192 KiB  
Article
Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air
by Tobias Baur, Johannes Amann, Caroline Schultealbert and Andreas Schütze
Atmosphere 2021, 12(5), 647; https://doi.org/10.3390/atmos12050647 - 19 May 2021
Cited by 25 | Viewed by 4412
Abstract
More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard [...] Read more.
More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components. Full article
(This article belongs to the Special Issue The Future of Air Quality Monitoring)
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17 pages, 10145 KiB  
Article
High Resolution Mapping of PM2.5 Concentrations in Paris (France) Using Mobile Pollutrack Sensors Network in 2020
by Jean-Baptiste Renard and Christophe Marchand
Atmosphere 2021, 12(5), 529; https://doi.org/10.3390/atmos12050529 - 21 Apr 2021
Cited by 8 | Viewed by 2884
Abstract
There is a need for accurate monitoring of PM2.5 that adversely affects human health. Consequently, in addition to the monitoring performed by fixed microbalance instruments installed under legal obligation, we are proposing to deploy the Pollutrack network of mobile sensors within the city [...] Read more.
There is a need for accurate monitoring of PM2.5 that adversely affects human health. Consequently, in addition to the monitoring performed by fixed microbalance instruments installed under legal obligation, we are proposing to deploy the Pollutrack network of mobile sensors within the city of Paris (France). The measurements are performed by mobile aerosol counters mounted on the roof of cars, providing a constant series of readings in the 0.3–10 µm size range that are then aggregated to identify areas of mass concentrations of pollution. The performance of the Pollutrack sensors has been established in ambient air in comparison with the microbalance measurement devices and with the Light Optical Aerosols Counter (LOAC) aerosol counter. A measurement uncertainty of about 5 µg. m−3 is obtained with absolute values from the Pollutrack measurements made at a given location. Instead of the current modelizations based on very few PM2.5 values, maps built from real measurements with a spatial resolution down to 100 m can now be produced each day for Paris, and potentially for specific times of the day, thanks to the high number of measurements achievable with the Pollutrack system (over 70,000 on weekdays). Interestingly, the global trend of PM2.5 content shows several significant pollution events in 2020 despite the COVID-19 crisis and the lockdown. The Pollutrack pollution maps recorded during different PM2.5 pollution conditions in the city frequently identified a strong spatial heterogeneity where the North and the East of Paris were more polluted than the west. These “hot spots” could be due to the city topology and its sensitivity to wind direction and intensity. These high-resolution maps will be crucial in creating evidence for the relevant authorities to respond appropriately to local sources of pollution and to improve the understanding of transportation of urban PM. Full article
(This article belongs to the Special Issue The Future of Air Quality Monitoring)
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13 pages, 4487 KiB  
Article
Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device
by Evangelos Bagkis, Theodosios Kassandros, Marinos Karteris, Apostolos Karteris and Kostas Karatzas
Atmosphere 2021, 12(2), 251; https://doi.org/10.3390/atmos12020251 - 13 Feb 2021
Cited by 8 | Viewed by 3022
Abstract
Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a [...] Read more.
Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions. Full article
(This article belongs to the Special Issue The Future of Air Quality Monitoring)
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