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- Title
Analysing spatio-temporal patterns of the global NO<sub>2</sub>-distribution retrieved from GOME satellite observations using a generalized additive model.
- Authors
Hayn, M.; Beirle, S.; Hamprecht, F. A.; Platt, U.; Menze, B. H.; Wagner, T.
- Abstract
With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis - oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand - an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult - if not impossible. So, rather than following a model-based analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by wind-dominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport "path- ways". General temporal trends which were also part of our model - weekly, seasonal and linear changes - showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.
- Subjects
NITROGEN compounds &; the environment; EMISSIONS (Air pollution); WIND measurement; ARTIFICIAL satellites; ATMOSPHERIC research
- Publication
Atmospheric Chemistry & Physics, 2009, Vol 9, Issue 17, p6459
- ISSN
1680-7316
- Publication type
Article
- DOI
10.5194/acp-9-6459-2009