Per and polyfluoroalkyl substances (PFAS) consist of a large group of compounds used to make products more resistant to stains, grease, and water and for fire suppression. They have been widely detected in the environment and exposure has been linked to adverse human health effects. Phytoremediation could be used to remediate PFAS-impacted sites, but there is little information on herbaceous and woody plant species uptake of PFAS compounds from soil. A greenhouse study evaluated the potential for eight herbaceous and seven woody plant species to absorb PFAS compounds. Six PFAS compounds: PFPeA, PFHxA, PFOA, PFBS, PFHxS, and PFOS were added weekly to irrigation water, and the plants grown for up to 14 weeks after an initial establishment period. Significant accumulation of all PFAS compounds occurred in at least one plant species. Mass recovery in above-ground tissue by the best performing plant ranged from a low of 3.8% for PFOS by Festuca rubra to a high of 42% for PFPeA by Schedonorus arundinaceus. Hyperaccumulation, defined as tissue/soil concentrations >10/1, was observed for all six PFAS compounds in at least one plant species. These results demonstrate the potential use of phytoremediation as a tool for remediating PFAS-contaminated sites.

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r² = 0.68 and 0.20, respectively, p < 2.2 × 10⁻¹⁶ for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.