Towards Fine-Grained Sensor-Based Probabilistic Individual Air Pollution Exposure Prediction using Wind Information
| dc.contributor.author | Tremper, Paul | |
| dc.contributor.author | Riedel, Till | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.contributor.editor | Kranzlmüller, Dieter | |
| dc.contributor.editor | Höb, Maximilian | |
| dc.date.accessioned | 2023-12-15T09:22:24Z | |
| dc.date.available | 2023-12-15T09:22:24Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The estimation of pollutant exposure is highly dependent on the spatial and temporal resolution of the underlying model. This work presents a street-level Gaussian Process Regression model for urban air quality that uses a novel covariance kernel based on physical considerations to process wind information. This model can be driven by information from observations from low-cost sensor networks. We present the model, including the construction of the wind angle kernel, and discuss the inconclusive evaluation to date, the current challenges, and the way forward. | en |
| dc.identifier.doi | 10.18420/env2023-020 | |
| dc.identifier.isbn | 978-3-88579-736-4 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43341 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | EnviroInfo 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-342 | |
| dc.subject | Gaussian Process Regression; kriging; air quality; interpolation; wind direction; prediction | |
| dc.title | Towards Fine-Grained Sensor-Based Probabilistic Individual Air Pollution Exposure Prediction using Wind Information | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 229 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 219 | |
| gi.conference.date | 11.-13. Oktober 2023 | |
| gi.conference.location | Garching, Germany | |
| gi.conference.review | full | |
| gi.conference.sessiontitle | Environmental Impact Assessment and Optimization |
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