Quantifying Fossil Fuel CO₂ Enhancements Along an Urban‐Rural Gradient With Radiocarbon Analysis of Turfgrasses

Atmospheric measurements are needed to verify progress in reducing fossil fuel carbon dioxide (ffCO₂) emissions, especially in cities where most ffCO₂ is emitted. However, measurements of CO₂ enhancements alone cannot identify ffCO₂ signals due to complexities in atmospheric dynamics and large natural CO₂ fluxes. Analysis of the radiocarbon (¹⁴C) content of urban annual plants can reveal ffCO₂ patterns and is more cost-effective than air ¹⁴CO₂ sampling, but its use has been limited because of uncertainty in the temporal integration period and because it has not been quantitatively evaluated against other approaches. Here, we analyze the ¹⁴C content of managed perennial turfgrasses collected along an urban to rural gradient in the Greater Los Angeles area. We compare the turfgrass ¹⁴C to measurements of surface CO₂ and total column CO₂ (XCO₂). We find that turfgrass ¹⁴C is highly sensitive to local ffCO₂ emissions at the intra-city scale and captures pronounced differences between urban to rural sites. Despite their different atmospheric footprints, we observe significant correlations between fossil fuel enhancements (C_ff) derived from turfgrass ¹⁴C and total CO₂ enhancements from atmospheric CO₂ measurements. Furthermore, we combine the turfgrass ¹⁴C and surface CO₂ measurements to quantify the portion of excess CO₂ attributable to biospheric fluxes (C_bio). We find that the turfgrass ¹⁴C is dominated by a fossil fuel signal and shows minimal influence of biogenic CO₂ fluxes. We show that turfgrass ¹⁴C analysis can become a useful tool for quantifying ffCO₂ trends in cities that lack permanent surface CO₂ and XCO₂ measurement infrastructure.

The authors wish to thank volunteers who assisted with collecting turfgrass samples and XCO₂ measurements, including I. Frausto-Vicencio, V. Carranza, Y. Liu, T. Mackey, A. Lesaca, U. Tran, A. Ocampo, Y. Miao, M. Haddad, A. Welch, J. Kim, M. Arcilla, A. Odwuor, H. Vasquez, F. Sunga, S. Syed, E. Mayfield, A. Nunez, J. Villatoroa, and E. Mou. We are thankful to A. Karion for providing the LA Megacities network data and sharing helpful insights. C.C. Yañez received funding from the University of California's Lab Fees Research Program In-Residence Graduate Fellowship. Samples analyses were supported by NSF EAR-MRI 2117634 (to CIC and FMH).