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Atmosphere
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State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies
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State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (1 September 2022) | Viewed by 22247

Special Issue Editor

Dr. Domenico Suriano

E-Mail Website
Guest Editor
ENEA—Italian National Agency for New Technologies, Energy and Environment, Sustainable Development Department, Research Center of Brindisi, 72100 Brindisi, Italy
Interests: air quality monitoring; air pollutant monitors; low-cost sensors for air pollutant monitoring; sensor networks for air quality monitoring; calibration of gas sensors; remote sensing; internet of things; low-cost systems for air pollutant personal exposure; indoor air quality portable monitors; wireless sensors; design of low-cost air quality monitors; indoor air pollution; calibration models for air quality sensors; air pollutant real time monitoring; state-of-art in real-time air quality monitoring through low-cost technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of low-cost technologies in air quality monitoring are becoming increasingly relevant worldwide, not only for research and academic groups, but also for common citizens and users. In recent years, a remarkable variety of low-cost sensors and complete systems for air pollutant monitoring have been developed, reporting contrasting outcomes. As a consequence, the application of these so-called low-cost technologies for measuring air pollutant concentrations is far from being a consolidated achievement. In addition, the performance of low-cost air quality monitoring Systems (LCAQSs) has been improved through the application of data treatment processes, statistical approaches, and more. These factors have led to a huge variety of LCAQS types featured by diverse electronics or sensing technologies and following various data treatment approaches to improve their performance. Well-known problems and issues affecting gas sensors on which LCAQSs are mainly based are represented by the lack of selectivity, baseline stability, and influence of environmental parameters such as temperature and humidity. This Special Issue is therefore focused on providing a reference for researchers and academic groups to assess the state-of-the-art featuring the real-time monitoring of air pollutant concentrations performed through low-cost technologies, which potentially enable a more accurate evaluation of personal exposure to air pollutants, thanks to their low cost compared with the traditional real-time monitoring devices. Studies eligible to be published in this Special Issue may concern:

  • The review of LCAQS evaluations;
  • The review of LCAQSs available on the market or produced in-lab;
  • The design and development of LCAQSs;
  • Data treatment algorithms for improving on-field air pollutant concentration measurements;
  • Design, development, and evaluation of personal monitors for mobile assessment of air pollutant exposure;
  • Wireless sensors for air quality monitoring.

Dr. Domenico Suriano
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. 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

  • real-time air quality monitoring
  • air pollutant monitors
  • wireless gas sensors
  • low-cost air quality systems
  • low-cost gas sensors

Published Papers (9 papers)

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Editorial

Jump to: Research

3 pages, 171 KiB  
Editorial
Preface to State-of-the-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies
by Domenico Suriano
Atmosphere 2023, 14(3), 554; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14030554 - 14 Mar 2023
Viewed by 1081
Abstract
Air pollution represents one of the biggest concerns worldwide [...] Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)

Research

Jump to: Editorial

13 pages, 2370 KiB  
Article
Compact Non-Dispersive Infrared Multi-Gas Sensing Platform for Large Scale Deployment with Sub-ppm Resolution
by Benoit Wastine, Christine Hummelgård, Maksym Bryzgalov, Henrik Rödjegård, Hans Martin and Stephan Schröder
Atmosphere 2022, 13(11), 1789; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13111789 - 29 Oct 2022
Cited by 7 | Viewed by 2315
Abstract
We report on a novel, cost-effective non-dispersive infrared (NDIR) multi-gas sensor aimed at environmental air pollution monitoring. The rugged design of the K96 sensor core combines highest compactness and low-power consumption with our unique multi-channel cell design, featuring the detection of up to [...] Read more.
We report on a novel, cost-effective non-dispersive infrared (NDIR) multi-gas sensor aimed at environmental air pollution monitoring. The rugged design of the K96 sensor core combines highest compactness and low-power consumption with our unique multi-channel cell design, featuring the detection of up to three different gases simultaneously, including CO2, CH4, N2O, and H2O. Our sensing platform allows the selection of the target gases as well as the concentration ranges, thus providing highly customizable gas sensor systems targeting application-specific gas monitoring settings. The sensor core comes with an implemented calibration model, and can address in real time any cross-sensitivity between the NDIR gas-sensing channels. We provide an immensely versatile sensing system while ensuring high sensing stability combined with high precision (<0.1 ppm for both CO2 and N2O, <0.5 ppm for CH4). The K96 multi-gas sensor core offers a resilient sensor solution for the increasing demand of compact monitoring systems in the field of environmental monitoring at reasonable costs for medium-to-high volumes. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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17 pages, 3323 KiB  
Article
First Results of the Application of a Citizen Science-Based Mobile Monitoring System to the Study of Household Heating Emissions
by Paolo Diviacco, Massimiliano Iurcev, Rodrigo José Carbajales and Nikolas Potleca
Atmosphere 2022, 13(10), 1689; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13101689 - 15 Oct 2022
Cited by 6 | Viewed by 1393
Abstract
This work aims at understanding whether a citizen science-based monitoring system could be adequate to detect the effects, in terms of air quality, of solid and liquid fuel combustion for household heating. Citizen science is known to be able to improve the coverage [...] Read more.
This work aims at understanding whether a citizen science-based monitoring system could be adequate to detect the effects, in terms of air quality, of solid and liquid fuel combustion for household heating. Citizen science is known to be able to improve the coverage and resolution of measurements at a very low cost. On the other hand, it also has severe limitations. Since low-cost sensors are to be used, measurements are problematic in terms of precision and accuracy. In order to test these aspects, we developed a system named COCAL that supports all the phases of air quality monitoring, from data acquisition, georeferencing, transmission, and processing up to web mapping. In this work, we focus on particulate matter. To address the limitations of the citizen science approach, we carefully tested all the parts of the system and, in particular, the performances of the low-cost sensors. We highlighted that their precision is acceptable, while their accuracy is insufficient. Measurements taken within such a paradigm cannot be used, therefore, as reference values. They can be used, instead, as relative values, in order to identify and to map trends, anomalies and hotspots. We used COCAL extensively in the city of Trieste and were able to identify different behaviors in different areas of the city. In the city center, PM values increase constantly during the day. In the rural suburbs of the city, we observed that PM values are low during the day but increase very rapidly after 5 p.m. It is important to note that, in the city center, household heating is based almost completely on natural gas. In the rural areas, household heating is generally based on wood burning stoves or liquid and solid fuel. A possible explanation of the different behavior between the two areas can then be related to commuters living in the rural areas but working in the city center. When they return home in the evening, they switch on the heating systems triggering the release of large quantities of particulate matter. We were able to map peaks of particulate matter values and highlight that they are initially located within the village centers to later propagate to the areas around them. The possibility of mapping air quality with the coverage and resolution we were able to obtain within a citizen science approach is very encouraging. This can be very helpful in understanding the impact that liquid and solid fuel combustion can have on the environment and human health. In addition, we think that this opportunity can be very important considering the current geopolitical situation where a (hopefully only temporary) shift toward pollutant fuels is expected in the near future. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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16 pages, 3359 KiB  
Article
Longitudinal Ambient PM2.5 Measurement at Fifteen Locations in Eight Sub-Saharan African Countries Using Low-Cost Sensors
by Babatunde Awokola, Gabriel Okello, Olatunji Johnson, Ruaraidh Dobson, Abdoul Risgou Ouédraogo, Bakary Dibba, Mbatchou Ngahane, Chizalu Ndukwu, Chuka Agunwa, Diana Marangu, Herve Lawin, Ifeoma Ogugua, Joy Eze, Nnamdi Nwosu, Ogochukwu Ofiaeli, Peter Ubuane, Rashid Osman, Endurance Awokola, Annette Erhart, Kevin Mortimer, Christopher Jewell and Sean Sempleadd Show full author list remove Hide full author list
Atmosphere 2022, 13(10), 1593; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13101593 - 29 Sep 2022
Cited by 4 | Viewed by 2669
Abstract
Air pollution is a major global public health issue causing considerable morbidity and mortality. Measuring levels of air pollutants and facilitating access to the data has been identified as a pathway to raise awareness and initiate dialogue between relevant stakeholders. Low-and middle-income countries [...] Read more.
Air pollution is a major global public health issue causing considerable morbidity and mortality. Measuring levels of air pollutants and facilitating access to the data has been identified as a pathway to raise awareness and initiate dialogue between relevant stakeholders. Low-and middle-income countries (LMICs) urgently need simple, low-cost approaches to generate such data, especially in settings with no or unreliable data. We established a network of easy-to-use low-cost air quality sensors (PurpleAir-II-SD) to monitor fine particulate matter (PM2.5) concentrations at 15 sites, in 11 cities across eight sub-Saharan Africa (sSA) countries between February 2020 and January 2021. Annual PM2.5 concentrations, seasonal and temporal variability were determined. Time trends were modelled using harmonic regression. Annual PM2.5 concentrations ranged between 10 and 116 µg/m3 across study sites, exceeding the current WHO annual mean guideline level of 5 µg/m3. The largest degree of seasonal variation was seen in Nigeria, where seven sites showed higher PM2.5 levels during the dry than during the wet season. Other countries with less pronounced dry/wet season variations were Benin (20 µg/m3 versus 5 µg/m3), Uganda (50 µg/m3 versus 45 µg/m3), Sukuta (Gambia) (20 µg/m3 versus 15 µg/m3) and Kenya (30 µg/m3 versus 25 µg/m3). Diurnal variation was observed across all sites, with two daily PM2.5 peaks at about 06:00 and 18:00 local time. We identified high levels of air pollution in the 11 African cities included in this study. This calls for effective control measures to protect the health of African urban populations. The PM2.5 peaks around ‘rush hour’ suggest traffic-related emissions should be a particular area for attention. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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17 pages, 3998 KiB  
Article
Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships
by Olivier Schalm, Gustavo Carro, Borislav Lazarov, Werner Jacobs and Marianne Stranger
Atmosphere 2022, 13(10), 1579; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13101579 - 27 Sep 2022
Cited by 9 | Viewed by 2162
Abstract
Air quality in and around ships is governed by a variety of pollution sources that are unique for the shipping context. This makes the living and working conditions on ships substantially different from situations in cities or inside buildings. To gain insight into [...] Read more.
Air quality in and around ships is governed by a variety of pollution sources that are unique for the shipping context. This makes the living and working conditions on ships substantially different from situations in cities or inside buildings. To gain insight into these differences, information about trends and absolute pollutant amounts on board ships is needed. However, the installation of reference instruments to monitor NO2, NO, O3, particulate matter and other environmental parameters is often not possible because of their size, weight or because of safety reasons. For that reason, more compact devices incorporating a variety of sensors are a good alternative. However, the use of such sensors is only possible when their behaviour and performance in a shipping context are well understood. To study this context, we were allowed to compare sensor-based measurements performed on a 36-year old ship dedicated to near shore operations with measurements of reference-grade instruments. Additional behavioural information of sensors is obtained by measuring campaigns organized on several inland ships. This contribution demonstrates that trends registered by gas and particulate matter sensors are reliable but that insufficient detection limits, higher noise, imperfect calibration and sensor errors result in some reliability constraints. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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16 pages, 3776 KiB  
Article
Differentiating Semi-Volatile and Solid Particle Events Using Low-Cost Lung-Deposited Surface Area and Black Carbon Sensors
by Molly J. Haugen, Ajit Singh, Dimitrios Bousiotis, Francis D. Pope and Adam M. Boies
Atmosphere 2022, 13(5), 747; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050747 - 06 May 2022
Cited by 3 | Viewed by 1816
Abstract
Low-cost particle sensors have proven useful in applications such as source apportionment, health, and reactivity studies. The benefits of these instruments increase when used in parallel, as exemplified with a 3-month long deployment in an urban background site. Using two lung-deposited surface area [...] Read more.
Low-cost particle sensors have proven useful in applications such as source apportionment, health, and reactivity studies. The benefits of these instruments increase when used in parallel, as exemplified with a 3-month long deployment in an urban background site. Using two lung-deposited surface area (LDSA) instruments, a low-cost method was developed to assess the solid component of an aerosol by applying a catalytic stripper to the inlet stream of one LDSA instrument, resulting in only the solid fraction of the sample being measured (LDSAc). To determine the semi-volatile fraction of the sample, the LDSAC was compared to the LDSA without a catalytic stripper, thus measuring all particles (LDSAN). The ratio of LDSA (LDSAC/LDSAN) was used to assess the fraction of solid and semi-volatile particles within a sample. Here, a low ratio represents a high fraction of semi-volatile particles, with a high ratio indicating a high fraction of solid particles. During the 3-month urban background study in Birmingham, UK, it is shown that the LDSA ratios ranged from 0.2–0.95 indicating a wide variation in sources and subsequent semi-volatile fraction of particles. A black carbon (BC) instrument was used to provide a low-cost measure of LDSA to BC ratio. Comparatively, the LDSA to BC ratios obtained using low-cost sensors showed similar results to high-cost analyses for urban environments. During a high LDSAC/LDSAN ratio sampling period, representing high solid particle concentrations, an LDSA to BC probability distribution was shown to be multimodal, reflecting urban LDSA to BC ratio distributions measured with laboratory-grade instrumentation. Here, a low-cost approach for data analyses presents insight on particle characteristics and insight into PM composition and size, useful in source apportionment, health, and atmospheric studies. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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19 pages, 4819 KiB  
Article
Use of Low-Cost Sensors to Characterize Occupational Exposure to PM2.5 Concentrations Inside an Industrial Facility in Santa Ana, CA: Results from a Worker- and Community-Led Pilot Study
by Shahir Masri, Jose Rea and Jun Wu
Atmosphere 2022, 13(5), 722; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050722 - 01 May 2022
Cited by 5 | Viewed by 3018
Abstract
PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated [...] Read more.
PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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18 pages, 4940 KiB  
Article
Assessment of the Performance of a Low-Cost Air Quality Monitor in an Indoor Environment through Different Calibration Models
by Domenico Suriano and Michele Penza
Atmosphere 2022, 13(4), 567; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13040567 - 31 Mar 2022
Cited by 10 | Viewed by 2661
Abstract
Air pollution significantly affects public health in many countries. In particular, indoor air quality can be equally, if not more, concerning than outdoor emissions of pollutant gases. However, monitoring the air quality in homes and apartments using chemical analyzers may be not affordable [...] Read more.
Air pollution significantly affects public health in many countries. In particular, indoor air quality can be equally, if not more, concerning than outdoor emissions of pollutant gases. However, monitoring the air quality in homes and apartments using chemical analyzers may be not affordable for households due to their high costs and logistical issues. Therefore, a new alternative is represented by low-cost air quality monitors (AQMs) based on low-cost gas sensors (LCSs), but scientific literature reports some limitations and issues concerning the quality of the measurements performed by these devices. It is proven that AQM performance is significantly affected by the calibration model used for calibrating LCSs in outdoor environments, but similar investigations in homes or apartments are quite rare. In this work, the assessment of an AQM based on electrochemical sensors for CO, NO2, and O3 has been performed through an experiment carried out in an apartment occupied by a family of four during their everyday life. The state-of-the-art of the LCS calibration is featured by the use of multivariate linear regression (MLR), random forest regression (RF), support vector machines (SVM), and artificial neural networks (ANN). In this study, we have conducted a comparison of these calibration models by using different sets of predictors through reference measurements to investigate possible differences in AQM performance. We have found a good agreement between measurements performed by AQM and data reported by the reference in the case of CO and NO2 calibrated using MLR (R2 = 0.918 for CO, and R2 = 0.890 for NO2), RF (R2 = 0.912 for CO, and R2 = 0.697 for NO2), and ANN (R2 = 0.924 for CO, and R2 = 0.809 for NO2). Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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14 pages, 5979 KiB  
Article
Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System
by Donggeun Park, Geon-Woo Yoo, Seong-Ho Park and Jong-Hyeon Lee
Atmosphere 2021, 12(10), 1306; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12101306 - 07 Oct 2021
Cited by 12 | Viewed by 3093
Abstract
Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. [...] Read more.
Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms. Full article
(This article belongs to the Special Issue State-of-Art in Real-Time Air Quality Monitoring through Low-Cost Technologies)
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