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Assessment of air quality sensor system performance after relocation
Open accessAbstract
Sensor based monitoring systems have been indicated as a promising tool to increase information on spatio-temporal distribution of air pollution but several issues have been raised about the accuracy of such monitors when used in the field. The study aimed at assessing the performance of sensor based systems after multiple relocation in different seasons and sites. The systems included electrochemical sensors to measure NO2 and O3 concentrations. The approach consisted in two-week field calibration of each device at a reference monitoring station and the test of the calibrated device at different reference station sites. The main specific goal was a comparison of sensor performance considering site-specific (SS) and no site-specific (no_SS) calibration, i.e. calibration and testing carried out or not at sites with similar characteristics. Calibration was performed by season using random forest (RF) models. Very good performance was found for calibrated O3 sensors with R2 ≥ 0.82 regardless of seasons and sites. Mean normalized root mean square error (nRMSE) was around 7% and 6% in winter and summer tests, respectively. Very good performance of sensor systems was observed also for NO2 during winter (R2 ≥ 0.84) with much better accuracy for SS compared to no_SS calibration (nRMSE equal to 6% and 17%, respectively). A marked decrease of performance was observed for NO2 sensors during summer. Our results show a good potential of sensor based systems after SS field calibration in increasing information on the distribution of air pollution at high spatial and temporal resolution.
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Current approaches to improve the accuracy of sensor readings majorly involve the use of statistical or probabilistic models and point calibrations.
“Meanwhile, current approaches to improve the accuracy of sensor readings majorly involve the use of statistical or probabilistic models [5] and point calibrations [6].”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [6]
Traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of IoT data streams.
“This is because the traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of Internet of Things (IoT) data streams.”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [6]
Section 2.4 discusses calibration models and uses cross-validation to evaluate them; it does not assess traditional data-validation methods for IoT data streams. The Introduction discusses limitations of laboratory calibration for air-quality sensors, not whether validation methods can handle dynamic, heterogeneous IoT data. Searches for those terms and their paraphrases found no passage addressing the claim. (LLM, conf=0.74)
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Metadata
- Authors
- Zauli-Sajani, S., Marchesi, S., Pironi, C., Barbieri, C., Poluzzi, V., & Colacci, A.
- Affiliations
- Agenzia Regionale Prevenzione e Ambiente della Regione Emilia-Romagna (IT)
- Venue
- Atmospheric Pollution Research
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 12
- Issue
- 2
- Pages
- 282-291
- ISSN
- 1309-1042 (ISSN-L)
- OpenAlex
- W3108208851
- Cited by
- 30
- References
- 38
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 6 · 2023: 8 · 2022: 4 · 2021: 2
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Atmospheric Pollution Research (publishedVersion, cc-by-nc-nd)
- Funders
- European Commission
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Atmospheric chemistry and aerosols
- Keywords
- low-cost air quality sensors, sensor relocation, field calibration, site-specific calibration, electrochemical sensor, nitrogen dioxide, ozone, sensor accuracy, seasonal performance variation
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