Calibration of Low-cost Gas Sensors for Air Quality Monitoring
Abstract Mobile monitoring devices equipped with low-cost gas sensors in fixed stations are an emerging solution to enhance the spatial coverage of air quality monitoring networks. We estimated the measurement accuracy of two AQMesh devices, evaluated their agreement, and examined the related calibr...
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| Format: | Article |
| Language: | English |
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Springer
2021-09-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.210073 |
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| author | Dimitris Margaritis Christos Keramydas Ioannis Papachristos Dimitra Lambropoulou |
| author_facet | Dimitris Margaritis Christos Keramydas Ioannis Papachristos Dimitra Lambropoulou |
| author_sort | Dimitris Margaritis |
| collection | DOAJ |
| description | Abstract Mobile monitoring devices equipped with low-cost gas sensors in fixed stations are an emerging solution to enhance the spatial coverage of air quality monitoring networks. We estimated the measurement accuracy of two AQMesh devices, evaluated their agreement, and examined the related calibration characteristics. Three widely used calibration approaches were investigated, namely uni- and multi-variate linear regression analysis and the random forest algorithm. Two identical commercial AQMesh platforms (monitoring NO, NO2, O3, and SO2) were installed on a fixed municipal station for 4 consecutive weeks. Widely used statistical indexes were employed to evaluate device performance and calibration outcomes. The devices exhibited favorable performance in following the pattern of the station’s reference time series in a 10-min average resolution. Nevertheless, their performance was lower, with respect to the reference values, in terms of the average error and overall bias. The calibration improved the agreement between the device and reference measurements. The emission time series of each device was consistent with the other (pre- and post-calibration) in terms of measurement patterns and point-by-point deviations. The three alternative methodologies had similar calibration performance overall. The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants. |
| format | Article |
| id | doaj-art-ffe8195462ff4692972601a9e51610ab |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-ffe8195462ff4692972601a9e51610ab2025-02-09T12:20:31ZengSpringerAerosol and Air Quality Research1680-85842071-14092021-09-01211111310.4209/aaqr.210073Calibration of Low-cost Gas Sensors for Air Quality MonitoringDimitris Margaritis0Christos Keramydas1Ioannis Papachristos2Dimitra Lambropoulou3Aristotle University of Thessaloniki / School of Chemistry, University CampusDepartment of Supply Chain Management, International Hellenic UniversityDepartment of Supply Chain Management, International Hellenic UniversityAristotle University of Thessaloniki / School of Chemistry, University CampusAbstract Mobile monitoring devices equipped with low-cost gas sensors in fixed stations are an emerging solution to enhance the spatial coverage of air quality monitoring networks. We estimated the measurement accuracy of two AQMesh devices, evaluated their agreement, and examined the related calibration characteristics. Three widely used calibration approaches were investigated, namely uni- and multi-variate linear regression analysis and the random forest algorithm. Two identical commercial AQMesh platforms (monitoring NO, NO2, O3, and SO2) were installed on a fixed municipal station for 4 consecutive weeks. Widely used statistical indexes were employed to evaluate device performance and calibration outcomes. The devices exhibited favorable performance in following the pattern of the station’s reference time series in a 10-min average resolution. Nevertheless, their performance was lower, with respect to the reference values, in terms of the average error and overall bias. The calibration improved the agreement between the device and reference measurements. The emission time series of each device was consistent with the other (pre- and post-calibration) in terms of measurement patterns and point-by-point deviations. The three alternative methodologies had similar calibration performance overall. The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants.https://doi.org/10.4209/aaqr.210073Electrochemical sensorsGas emissionsStatistical analysisCalibration models |
| spellingShingle | Dimitris Margaritis Christos Keramydas Ioannis Papachristos Dimitra Lambropoulou Calibration of Low-cost Gas Sensors for Air Quality Monitoring Aerosol and Air Quality Research Electrochemical sensors Gas emissions Statistical analysis Calibration models |
| title | Calibration of Low-cost Gas Sensors for Air Quality Monitoring |
| title_full | Calibration of Low-cost Gas Sensors for Air Quality Monitoring |
| title_fullStr | Calibration of Low-cost Gas Sensors for Air Quality Monitoring |
| title_full_unstemmed | Calibration of Low-cost Gas Sensors for Air Quality Monitoring |
| title_short | Calibration of Low-cost Gas Sensors for Air Quality Monitoring |
| title_sort | calibration of low cost gas sensors for air quality monitoring |
| topic | Electrochemical sensors Gas emissions Statistical analysis Calibration models |
| url | https://doi.org/10.4209/aaqr.210073 |
| work_keys_str_mv | AT dimitrismargaritis calibrationoflowcostgassensorsforairqualitymonitoring AT christoskeramydas calibrationoflowcostgassensorsforairqualitymonitoring AT ioannispapachristos calibrationoflowcostgassensorsforairqualitymonitoring AT dimitralambropoulou calibrationoflowcostgassensorsforairqualitymonitoring |