Three Years of Δ14CO2 Observations from Maize Leaves in the Netherlands and Western Europe. Issue 3 (7th April 2016)

• Record Type: Journal Article
• Authors: Bozhinova, D; Palstra, S W L; van der Molen, M K; Krol, M C; Meijer, H A J; Peters, W
• Is Part Of: Radiocarbon. Volume 58:Issue 3(2016)
• Page Start: 459
• Page End: 478
• Publication Date: 2016-04-07
• Subjects: radiocarbon in plants, -- modern atmosphere, -- experimental, -- modeling, -- anthropogenic CO emissions; Radiocarbon dating -- Periodicals; 930.1028505
• DOI: 10.1017/RDC.2016.20
• Languages: English
• ISSNs: 0033-8222

Abstract

Abstract: Atmospheric Δ 14 CO2 measurements are useful to investigate the regional signals of anthropogenic CO2 emissions, despite the currently scarce observational network for Δ 14 CO2 . Plant samples are an easily attainable alternative, which have been shown to work well as a qualitative measure of the atmospheric Δ 14 CO2 signals integrated over the time a plant has grown. Here, we present the 14 C analysis results for 89 individual maize ( Zea mays ) plant samples from 51 different locations that were gathered in the Netherlands in the years 2010 to 2012, and from western Germany and France in 2012. We describe our sampling strategy and results, and include a comparison to a model simulation of the Δ 14 CO2 that would be accumulated in each plant over a growing season. Our model simulates the Δ 14 CO2 signatures in good agreement with observed plant samples, resulting in a root-mean-square deviation (RMSD) of 3.30‰. This value is comparable to the measurement uncertainty, but still relatively large (20–50%) compared to the total signal. It is also comparable to the spread in Δ 14 CO2 values found across multiple plants from a single site, and to the spread found when averaging across larger regions. We nevertheless find that both measurements and model capture the large-scale (>100 km) regional Δ 14 CO2 gradients, with significant observation-model correlations in all three countries in which we collected samples. The modeled plant results suggest that the largest gradients found in the Netherlands and Germany are associated with emissions from energy production and road traffic, while in France, the 14 CO2 enrichment from nuclear sources dominates in many samples. Overall, the required model-based interpretation of plant samples adds additional uncertainty to the already relatively large measurement uncertainty in Δ 14 CO2, and we suggest that future fossil fuel monitoring efforts should prioritize other strategies such as direct atmospheric sampling of CO2 and Δ 14 CO2 .