guest ::
| Home > Publications database > Mapping and Interpolation of Tropospheric Ozone Data with Machine Learning Methods |
| Home > Publications database > Mapping and Interpolation of Tropospheric Ozone Data with Machine Learning Methods |
| Dissertation / PhD Thesis | FZJ-2023-05441 |
2023
Universitäts- und Landesbibliothek Bonn
Bonn
This record in other databases:
Please use a persistent id in citations: doi:10.48565/BONNDOC-179 doi:10.34734/FZJ-2023-05441
Abstract: Tropospheric ozone is a toxic trace gas in the atmosphere. It threatens human health, damages crops and vegetation, and it is a short-lived climate forcer. Ozone is a secondary air pollutant that undergoes multiple physical and chemical processes on a wide range of timescales. Therefore, as with many environmental variables, it is difficult to quantify ozone concentrations where measurements are not available. To solve this problem, the goal of this work is to develop spatio-temporal mapping and interpolation methods using machine learning techniques with the example application of ozone data. We train the machine learning models on a large number of ozone measurements available in the Tropospheric Ozone Assessment Report (TOAR) database. The most important contributions of this work are: • Mapping and interpolating ozone data, providing high-resolution, high-accuracy, spatiotemporal data products. The data products cover spatial domains from the regional to the global level, and their temporal resolution ranges from hourly data to multi-year statistics. We use large quantities of ozone measurements, combined with model data and geospatial data to generate the data products. • Adapting, developing, and explaining new state-of-the-art machine learning methods that we use to create these data products. The most relevant algorithms of this work are tree-based and graph-based methods. For example, we develop a multi-scale evaluation technique for spatial machine learning models and verify their physical consistency by using Shapley additive explanations. • Utilizing spatiotemporal patterns in geospatial data and ozone measurements in machine learning models. We use aggregated local to regional geospatial site conditions as input features for machine learning models. Furthermore, we adopt a graph machine learning algorithm to work on ozone measurements at irregularly placed air quality monitoring stations.With this work, we publish AQ-Bench, a benchmark dataset for machine learning on global long-term ozone metrics. We link explainable machine learning on AQ-Bench with uncertainty assessments to point out limits in the dataset and the applicability of the resulting machine learning models. With the trained models, we also create the first completely data-driven, global, high-resolution map of long-term ozone metrics (resolution 0.1°×0.1°, years 2010 - 2014). Finally, we develop a high-performance graph-based missing data interpolation method for ozone measurements. It has an index of agreement of 0.96 - 0.99 for hourly missing data interpolation in Germany. The synthesis of this work is that an interplay of physically sound data selection, uncertainty quantification, and explainability in machine learning can produce trustworthy environmental data products. We also found that the accuracy of the data products in a specific region is mainly dependent on good coverage with ozone measurements in that region. Therefore, this work contributes not only to the gapless quantification of ozone concentrations but also to trustworthy machine learning in the environmental sciences.
Keyword(s): air quality ; tropospheric ozone ; machine learning ; ddc:550
|
The record appears in these collections: |
PDF