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Atmosphere
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Emerging Technologies for Observation of Air Pollution (2nd Edition)
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Emerging Technologies for Observation of Air Pollution (2nd Edition)

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 (15 April 2026) | Viewed by 9450

Special Issue Editors

Prof. Dr. Klaus Schäfer

E-Mail Website
Guest Editor
Atmospheric Physics Consultant, 82467 Garmisch-Partenkirchen, Germany
Interests: air quality; air pollutants; measurement techniques; meteorological influences; atmospheric data analyses
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
Dr. Georgios Tsegas

E-Mail Website
Guest Editor
Sustainability Engineering Laboratory, Aristotle University Thessaloniki, 541 24 Thessaloniki, Greece
Interests: air quality; atmospheric pollution modelling; urban meteorology; data assimilation; numerical methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume in a series of publications dedicated to “Emerging Technologies for Observation of Air Pollution” (https://www.mdpi.com/journal/atmosphere/special_issues/EQ5K6Z2085).

The problem of poor air quality still influences inhabitant’s life in all cities of the globe. During growing urbanization scientific research shows origin of air pollution from local scales and from regional and global scales including interactions with climate protection measures. Additionally, the public awareness is growing to improve management and assessment strategies and effective control policies for reducing the health impact of air pollution.

The focus of this Special Issue is on new research contributions on developments in observation techniques and data operation algorithms which enable personal air pollution exposure determination, as well as new conclusions about sources of air pollutants and emission reduction measures.  New research results about spatially complete information on air pollutants, about urban air quality observations by smart air quality networks, as well as corresponding near-real time numerical simulations at the small scale are ideal contributions to this Special Issue.

We can offer substantial discounts for high-quality papers.

Prof. Dr. Klaus Schäfer
Dr. Nuria Castell
Dr. Georgios Tsegas
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 250 words) can be sent to the Editorial Office for assessment.

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

  • atmospheric observations
  • urban air quality
  • sensors and measurements
  • crowd sourcing
  • numerical simulations and modeling

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Published Papers (6 papers)

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Research

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17 pages, 4020 KB  
Article
Indoor Air Filtration System Performance: Evidence from a Two-Week Office Study Within the EDIAQI Project
by Nikolina Račić, Valentino Petrić, Gordana Pehnec, Ivana Jakovljević, Marija Jelena Lovrić Štefiček, Goran Gajski, Francesco Mureddu and Mario Lovrić
Atmosphere 2026, 17(4), 393; https://doi.org/10.3390/atmos17040393 - 14 Apr 2026
Viewed by 307
Abstract
This two-week pilot study within the Horizon Europe EDIAQI project evaluated the real-life performance of portable air filtration units in two office environments (a small office and a shared kitchen) under continuous device operation and daily filter replacement. Indoor particle concentrations were monitored [...] Read more.
This two-week pilot study within the Horizon Europe EDIAQI project evaluated the real-life performance of portable air filtration units in two office environments (a small office and a shared kitchen) under continuous device operation and daily filter replacement. Indoor particle concentrations were monitored continuously using low-cost sensors (LCS) from three providers and supported by gravimetric measurements, while daily activity logs documented occupancy patterns, printing, cooking, and other source events together with purifier ON/OFF status. Particulate matter (PM) mass concentrations showed no systematic improvement during purifier ON periods; instead, temporal variability was dominated by indoor activities and episodic emissions, with occasional short-term peaks around filter replacement suggestive of minor resuspension. Chemical analysis provided a clearer picture: polycyclic aromatic hydrocarbons (PAHs) responded differently across fractions and compositions. Across monitored locations, high-molecular-weight PAHs in the PM1 fraction decreased during purifier ON periods (approximately 30% lower on average), whereas low-molecular-weight PAHs measured in total suspended particles (TSP) were higher during ON periods, indicating that semi-volatile fractions and activity/ventilation dynamics can outweigh simple filtration effects. Overall, the findings highlight a gap between laboratory-derived filtration performance metrics and outcomes in occupied, mixed-source indoor environments and emphasise the importance of device sizing, placement, airflow mixing, and complementary source control and ventilation strategies when deploying filtration-based IAQ interventions. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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25 pages, 5667 KB  
Article
Machine Learning Calibration Transfer for Low-Cost Air Quality Sensors: Distance-Based Uncertainty Quantification in a Hybrid Urban Monitoring Network
by Petar Zhivkov and Stefka Fidanova
Atmosphere 2026, 17(4), 335; https://doi.org/10.3390/atmos17040335 - 26 Mar 2026
Viewed by 485
Abstract
Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic [...] Read more.
Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved R2∈(0.53,0.75) across PM2.5, PM10, NO2, and O3, representing from 40% (for O3) to 408% (for PM2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates (α) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM10 and O3, p<0.05). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived α parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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23 pages, 10065 KB  
Article
Assessment of Sensor Data from an Air Quality Monitoring Network—The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events
by Valentino Petrić, Nikolina Račić, Ivana Hrga, Danijel Grgec, Marko Marić, Adela Krivohlavek, Zvonimir Anić, Mario Lovrić and Matijana Jergović
Atmosphere 2025, 16(12), 1358; https://doi.org/10.3390/atmos16121358 - 29 Nov 2025
Cited by 1 | Viewed by 1332
Abstract
Accurate, high-resolution air quality data are crucial for understanding environmental health risks; however, the cost and complexity of maintaining dense, reference-grade monitoring networks remain a significant barrier. This study presents the first city-wide evaluation of next-generation air quality sensors in Zagreb, Croatia, involving [...] Read more.
Accurate, high-resolution air quality data are crucial for understanding environmental health risks; however, the cost and complexity of maintaining dense, reference-grade monitoring networks remain a significant barrier. This study presents the first city-wide evaluation of next-generation air quality sensors in Zagreb, Croatia, involving 35 sensor locations, one local reference-grade station, and three national reference stations that measure PM10 and NO2. Sensor performance was evaluated against reference data under various meteorological and temporal conditions. To better understand sensor drift and measurement bias, we developed machine learning (ML) calibration models (XGBoost) using spatiotemporal features, ERA5 meteorological variables, and traffic proxy indicators. The models significantly improved accuracy, reducing the root mean squared error (RMSE) by up to 82%, with the greatest improvements observed during pollution peaks. A rolling Root Mean Square Error (RMSE) approach was introduced to track model degradation over time, revealing that recalibration was typically needed within 1–6 months. Our findings demonstrate that, with proper calibration and maintenance, sensor networks can serve as reliable and scalable tools for urban air quality monitoring, capable of supporting both public health assessments and informed decision-making. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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26 pages, 2178 KB  
Article
Air Sensor Network Analysis Tool: R-Shiny Application
by Karoline K. Barkjohn, Todd Plessel, Jiacheng Yang, Gavendra Pandey, Yadong Xu, Stephen Krabbe, Catherine Seppanen, Renée Bichler, Huy Nguyen Quang Tran, Saravanan Arunachalam and Andrea L. Clements
Atmosphere 2025, 16(11), 1270; https://doi.org/10.3390/atmos16111270 - 8 Nov 2025
Viewed by 1536
Abstract
Poor air quality can harm human health and the environment. Air quality data are needed to understand and reduce exposure to air pollution. Air sensor data can supplement national air monitoring data, allowing for a better understanding of localized air quality and trends. [...] Read more.
Poor air quality can harm human health and the environment. Air quality data are needed to understand and reduce exposure to air pollution. Air sensor data can supplement national air monitoring data, allowing for a better understanding of localized air quality and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality, and analyzing sensor data often requires extensive data analysis. To address these issues, an R-Shiny application has been developed to assist air quality professionals in (1) understanding air sensor data quality through comparison with nearby ambient air reference monitors, (2) applying basic quality assurance and quality control, and (3) understanding local air quality conditions. This tool provides agencies with the ability to more quickly analyze and utilize air sensor data for a variety of purposes while increasing the reproducibility of analyses. While more in-depth custom analysis may still be needed for some sensor types (e.g., advanced correction methods), this tool provides an easy starting place for analysis. This paper highlights two case studies using the tool to explore PM2.5 sensor performance under the conditions of wildfire smoke impacts in the Midwestern United States and the performance of O3 sensors for a year. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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17 pages, 7826 KB  
Article
Evaluating the Spatial Coverage of Air Quality Monitoring Stations Using Computational Fluid Dynamics
by Giannis Ioannidis, Paul Tremper, Chaofan Li, Till Riedel, Nikolaos Rapkos, Christos Boikos and Leonidas Ntziachristos
Atmosphere 2025, 16(3), 326; https://doi.org/10.3390/atmos16030326 - 12 Mar 2025
Cited by 5 | Viewed by 2508
Abstract
Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant [...] Read more.
Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant concentrations within urban areas is therefore crucial. This study developed a computational fluid dynamic (CFD) model designed to capture turbulence effects that influence pollutant dispersion in urban environments. The focus was on key pollutants commonly associated with vehicular emissions, such as carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter (PM). The model was applied to the city of Augsburg, Germany, to simulate pollutant behavior at a microscale level. The primary objectives were twofold: first, to accurately predict local pollutant concentrations and validate these predictions against measurement data; second, to evaluate the representativeness of air quality monitoring stations in reflecting the broader pollutant distribution in their vicinity. The approach presented here has demonstrated that when focusing on an area within a specific radius of an air quality station, the representativeness ranges between 10% and 16%. On the other hand, when assessing the representativeness across the street of deployment, the spatial coverage of the sensor ranges between 23% and 80%. This analysis highlights that air quality stations primarily capture pollution levels from high-activity areas directly across their deployment site, rather than reflecting conditions in nearby lower-activity zones. This approach ensures a more comprehensive understanding of urban air pollution dynamics and assesses the reliability of air quality (AQ) monitoring stations. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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Review

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23 pages, 348 KB  
Review
Machine Learning-Based Quality Control for Low-Cost Air Quality Monitoring: A Comprehensive Review of the Past Decade
by Yong-Hyuk Kim and Seung-Hyun Moon
Atmosphere 2025, 16(10), 1136; https://doi.org/10.3390/atmos16101136 - 27 Sep 2025
Cited by 2 | Viewed by 2507
Abstract
Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine [...] Read more.
Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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