AbstractAtmospheric Δ¹⁴CO₂measurements are useful to investigate the regional signals of anthropogenic CO₂emissions, despite the currently scarce observational network for Δ¹⁴CO₂. Plant samples are an easily attainable alternative, which have been shown to work well as a qualitative measure of the atmospheric Δ¹⁴CO₂signals integrated over the time a plant has grown. Here, we present the¹⁴C 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 Δ¹⁴CO₂that would be accumulated in each plant over a growing season. Our model simulates the Δ¹⁴CO₂signatures 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 Δ¹⁴CO₂values 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 Δ¹⁴CO₂gradients, 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, the¹⁴CO₂enrichment 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 Δ¹⁴CO₂, and we suggest that future fossil fuel monitoring efforts should prioritize other strategies such as direct atmospheric sampling of CO₂and Δ¹⁴CO₂.