Abstract
Statistical learning models have been applied to study the spatial patterns of ambient Nitrogen Dioxide (NO2), which is a highly dynamic, traffic-related air pollutant. Commonly, the validation process in most studies is based on bootstrapped split-sampling of training and test sets from fixed ground station measurements. As the ground stations distribute mostly sparsely over a region or country, this kind of cross-validation validation method does not consider how well models are capable of representing spatial variations in air pollution mostly occurring over distances shorter than the ground station sampling spacing. This may lead to inadequate hyperparameter optimisation and bias when comparing different statistical models. External mobile measurements are therefore needed for more reliable model evaluations as these provide detailed and spatially continuous information on air pollution patterns. However, most current designs of mobile NO2 sensing instruments suffer from the trade-off between flexibility and measurement accuracy, as high-end sensors are commonly too heavy to be carried by a person or on a bike. In addition, sufficient repetitions over time are needed so that the measurements are representative to concentrations over a relatively long-term period. In this study, we installed a mobile air quality station onboard a cargo-bike to collect a dataset suitable for external validation. With the external validation dataset the model hyperparameter setting and statistical model comparison results alter. Our model comparison results also differ from previous studies relying only on ground stations for cross-validation.
Original language | English |
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Article number | 101205 |
Pages (from-to) | 1-11 |
Journal | Atmospheric Pollution Research |
Volume | 12 |
Issue number | 11 |
DOIs | |
Publication status | Published - Nov 2021 |
Bibliographical note
Funding Information:This research is funded by the Global Geo Health Data Centre (Utrecht University) and the Startimpulsprogramma Meten en Detecteren van Gezond Gedrag (Dutch Science Foundation) . We are thankful to Evert Duistermaat, who cycled the cargo-bike and Sieger Henke, who contributed with practical, coordination and some scientific works. The authors appreciate Ton Markus for his advises and contributions on improving the figures. The authors are grateful to the editors and reviewers for their contributions.
Funding Information:
This research is funded by the Global Geo Health Data Centre (Utrecht University) and the Startimpulsprogramma Meten en Detecteren van Gezond Gedrag (Dutch Science Foundation). We are thankful to Evert Duistermaat, who cycled the cargo-bike and Sieger Henke, who contributed with practical, coordination and some scientific works. The authors appreciate Ton Markus for his advises and contributions on improving the figures. The authors are grateful to the editors and reviewers for their contributions.
Publisher Copyright:
© 2021 Elsevier Ltd
Funding
This research is funded by the Global Geo Health Data Centre (Utrecht University) and the Startimpulsprogramma Meten en Detecteren van Gezond Gedrag (Dutch Science Foundation) . We are thankful to Evert Duistermaat, who cycled the cargo-bike and Sieger Henke, who contributed with practical, coordination and some scientific works. The authors appreciate Ton Markus for his advises and contributions on improving the figures. The authors are grateful to the editors and reviewers for their contributions. This research is funded by the Global Geo Health Data Centre (Utrecht University) and the Startimpulsprogramma Meten en Detecteren van Gezond Gedrag (Dutch Science Foundation). We are thankful to Evert Duistermaat, who cycled the cargo-bike and Sieger Henke, who contributed with practical, coordination and some scientific works. The authors appreciate Ton Markus for his advises and contributions on improving the figures. The authors are grateful to the editors and reviewers for their contributions.
Keywords
- High-end mobile sensing instruments
- Hyperparameter optimisation
- Model validation
- Nitrogen Dioxide
- Spatial prediction
- Statistical modelling