A1424
Title: Nonparametric estimation and bootstrap inference on the recent trends in atmospheric ethane (C2H6) above Europe
Authors: Marina Friedrich - Maastricht University (Netherlands) [presenting]
Whitney Bader - University of Liege Institute of Astrophysics and Geophysics (Belgium)
Bruno Franco - University of Liege Institute of Astrophysics and Geophysics (Belgium)
Bernard Lejeune - University of Liege Institute of Astrophysics and Geophysics (Belgium)
Emmanuel Mahieu - University of Liege Institute of Astrophysics and Geophysics (Belgium)
Hanno Reuvers - Maastricht University (Netherlands)
Stephan Smeekes - Maastricht University (Netherlands)
Jean-Pierre Urbain - Maastricht University SBE (Netherlands)
Abstract: Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere and an important precursor of tropospheric ozone. Its monitoring is therefore crucial for the characterization of air quality and of the transport of tropospheric pollution. Ethane is also an indirect greenhouse gas, influencing the atmospheric lifetime of methane. The main sources of ethane are located in the northern hemisphere, and the dominating emissions are associated to production and transport of natural gas. A preliminary trend analysis was conducted using measurements performed in the Swiss Alps. Over the last two decades, the trend of ethane showed a decline of around 1$\%$ per year, thanks to a reduction of fugitive emissions of fossil fuel sources. However, a recent upturn potentially attributed to the massive exploitation of shale gas and tight oil reservoirs in North America was found. The goal is to investigate the presence and form of changes in trend functions using nonparametric techniques. The possible location of such changes is investigated. In addition, nonparametric estimation techniques are used to allow for nonlinear trend functions. Given the nonstandard nature of the measurements we rely on dependent wild bootstrap techniques to conduct inference on possible breaks in linear trends and on nonparametric trend functions.
