Selenium Concentrations in Water and Clams in North San Francisco Bay, 2019-2020

The Regional Monitoring Program for Water Quality in San Francisco Bay (RMP) started implementing a new design for North Bay selenium (Se) sampling in 2019 after a long-term USGS program was terminated in 2017. To determine if the RMP sampling and analysis methods are producing similar results to the USGS study, we compared Se concentrations and stable isotope values in clams at two stations in Suisun Bay, California, that were sampled by the USGS long-term monitoring program from 1995-2010 and the RMP in 2019-2020. We also compared Se concentrations in water (dissolved and particulate) from the RMP Status and Trends sampling in Suisun Bay (1993-2019) to the samples collected as part of this study. 

Spatial patterns in clam Se concentrations between the USGS and RMP studies were similar, with lower concentrations at Station 4.1 (Suisun Bay) than at Station 8.1 (Carquinez Strait). Se concentrations at both stations were consistently lower for the RMP samples than those reported in the long-term USGS dataset. Stable isotope values for δ13C and δ15N were similar for the USGS and RMP samples with δ13C and δ15N more enriched at Station 8.1 than 4.1. However, there was not close alignment of the RMP samples to the USGS long-term average, particularly at Station 8.1. Average dissolved Se concentrations in water were consistently lower than the long-term average at Station 4.1 and similar to or above the long-term RMP Status and Trends (S&T) average at Station 8.1. Particulate Se concentrations at Stations 4.1 and 8.1 were nearly one-third of the long-term S&T average in Suisun Bay, likely due to a change in methods for calculating particulate Se. 

Additional information is needed to evaluate whether the lower Se concentrations measured in clams as part of the RMP study could be suggestive of declining Se concentrations in North Bay clams between 2010 and 2019 or an artifact of the new analytical lab. Additional data from samples collected by the USGS from 2011-2017 should be examined to fill the gap between the datasets. Continued RMP monitoring will also be valuable in evaluating long-term trends.