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Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application
| Content Provider | Scilit |
|---|---|
| Author | Bigi, Alessandro Mueller, Michael Grange, Stuart K. Ghermandi, Grazia Hueglin, Christoph |
| Copyright Year | 2018 |
| Description | Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices is being delivered to the public notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using 3 different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4–month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, precision and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE |
| DOI | 10.5194/amt-2018-26 |
| Volume Number | 2018 |
| Language | English |
| Publisher | Copernicus GmbH |
| Publisher Date | 2018-03-12 |
| Access Restriction | Open |
| Subject Keyword | Real World Urban Background Field Calibration Co Located Switzerland Using Pollution Gradients |
| Content Type | Text |
| Resource Type | Article |
