Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles
For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO<sub>2</sub>. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/24/7332 |
| _version_ | 1797544092334292992 |
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| author | Raphael Mawrence Sandra Munniks João Valente |
| author_facet | Raphael Mawrence Sandra Munniks João Valente |
| author_sort | Raphael Mawrence |
| collection | DOAJ |
| description | For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO<sub>2</sub>. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO<sub>2</sub> measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO<sub>2</sub> measurements in future outdoor experiments that include electrochemical sensor integration with UAV’s. |
| first_indexed | 2024-03-10T13:54:33Z |
| format | Article |
| id | doaj.art-bc2b0e788b9b4d30a9ddddf886dc4c7c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:54:33Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bc2b0e788b9b4d30a9ddddf886dc4c7c2023-11-21T01:46:58ZengMDPI AGSensors1424-82202020-12-012024733210.3390/s20247332Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial VehiclesRaphael Mawrence0Sandra Munniks1João Valente2Laboratory of Geo-Information Sciences and Remote Sensing at Wageningen University & Research (WUR), Droevendaalsesteeg 3, 6708 PB Wageningen, The NetherlandsWageningen Food Safety Research, Akkermaalsbos 2, 6708 WB Wageningen, The NetherlandsInformation Technology Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The NetherlandsFor years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO<sub>2</sub>. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO<sub>2</sub> measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO<sub>2</sub> measurements in future outdoor experiments that include electrochemical sensor integration with UAV’s.https://www.mdpi.com/1424-8220/20/24/7332air quality monitoring networkcalibrationelectrochemical sensornitrogen dioxidespatial resolutionUAV |
| spellingShingle | Raphael Mawrence Sandra Munniks João Valente Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles Sensors air quality monitoring network calibration electrochemical sensor nitrogen dioxide spatial resolution UAV |
| title | Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles |
| title_full | Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles |
| title_fullStr | Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles |
| title_full_unstemmed | Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles |
| title_short | Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles |
| title_sort | calibration of electrochemical sensors for nitrogen dioxide gas detection using unmanned aerial vehicles |
| topic | air quality monitoring network calibration electrochemical sensor nitrogen dioxide spatial resolution UAV |
| url | https://www.mdpi.com/1424-8220/20/24/7332 |
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