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Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model

Title data

Nabavi, Seyed Omid ; Nölscher, Anke ; Samimi, Cyrus ; Thomas, Christoph ; Haimberger, Leopold ; Lüers, Johannes ; Held, Andreas:
Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model.
In: Environmental Pollution. Vol. 268 (2021) Issue Part A . - No. 115736.
ISSN 1873-6424
DOI: https://doi.org/10.1016/j.envpol.2020.115736

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Project information

Project title:
Project's official titleProject's id
MiSKOR: Minderung Städtischer Klima- und OzonrisikenK3-8503-PN 18-17

Project financing: Bayerischen Staatsministerium für Umwelt und Verbraucherschutz (StMUV) Bayerishces Staatsministerium für Gesundheit und Pflege (StMGP)

Abstract in another language

Ozone (O3) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O3 concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O3 concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O3 observations on a site scale with much higher accuracy than existing O3 models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O3 reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O3 events and the 1- and 24-h forecasts of hourly O3 were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O3, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME).
With SHAP, we showed an increasing effect of temperature on O3 concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O3 concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O3 concentrations during cold and wet periods.
While recently developed MLAs for the spatial estimation of hourly O3 concentrations had a station-based root-mean-square error (RMSE) above 27 μg/m3, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 μg/m3. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O3 concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 μg/m3, respectively).

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Air quality; Bavaria; Bayreuth; Germany; Modeling; Ozone; Downscaling; Surface ozone; Ensemble learning; Simulation interpretability
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Cultural Geography
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professor Micrometeorology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professor Micrometeorology > Professor Micrometeorology - Univ.-Prof. Dr. Christoph K. Thomas
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professor Climatology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professor Climatology > Professor Climatology - Univ.-Prof. Dr. Cyrus Samimi
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Junior Professor Atmospheric Chemistry
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Junior Professor Atmospheric Chemistry > Junior Professor Atmospheric Chemistry - Juniorprof. Dr. Anke Nölscher
Profile Fields > Advanced Fields > Ecology and the Environmental Sciences
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Faculties
Profile Fields
Profile Fields > Advanced Fields
Research Institutions
Research Institutions > Research Centres
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 500 Natural sciences
500 Science > 550 Earth sciences, geology
Date Deposited: 12 Nov 2020 09:01
Last Modified: 19 Nov 2020 07:19
URI: https://eref.uni-bayreuth.de/id/eprint/59604