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Plastic pollution is a defining environmental contaminant and is considered to be one of the greatest environmental threats of the Anthropocene, with its presence documented across aquatic and terrestrial ecosystems. The majority of this plastic debris falls into the micro (1 lm–5 mm) or nano (1–1000 nm) size range and comes from primary and secondary sources. Its small size makes it cumbersome to isolate and analyze reproducibly, and its ubiquitous distribution creates numerous challenges when controlling for background contamination across matrices (e.g., sediment, tissue, water, air). Although research on microplastics represents a relatively nascent subfield, burgeoning interest in questions surrounding the fate and effects of these debris items creates a pressing need for harmonized sampling protocols and quality control approaches. For results across laboratories to be reproducible and comparable, it is imperative that guidelines based on vetted protocols be readily available to research groups, many of which are either new to plastics research or, as with any new subfield, have arrived at current approaches through a process of trial-and-error rather than in consultation with the greater scientific community. The goals of this manuscript are to (i) outline the steps necessary to conduct general as
well as matrix-specific quality assurance and quality control based on sample type and associated constraints, (ii) briefly review current findings across matrices, and (iii) provide guidance for the design of sampling regimes. Specific attention is paid to the source of microplastic pollution as well as the pathway by which contamination occurs, with details provided regarding each step in the process from generating appropriate questions to sampling design and collection.

This document was prepared with guidance gained through two RMP Sediment Workgroup workshops held in late 2022 and early 2023. Given the variety of participants involved, this Workplan encompasses interests beyond San Francisco Bay RMP funders. We thank the attendees for their contributions. 

In 2020, the Sediment Workgroup (SedWG) of the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP) completed a Sediment Monitoring and Modeling Strategy (SMMS) which laid out a conceptual level series of data and information gaps and generally recommended the use of both empirical data collection and modeling tools to answer initial high priority management questions (McKee et al., 2020). At the time, the SMMS promoted the use of surrogates such as time-continuous turbidity measurements for cross-section flux modeling within the Bay without an understanding of existing Bay hydrodynamic models, their strengths, weaknesses, and potential uses for understanding coupled Bay-mudflat-marsh processes. Since then, the Wetland Regional Monitoring Program (WRMP, www.wrmp.org) has generally promoted the use of coupling monitoring and modeling techniques to inform wetlands sediment management decisions. In addition, he completion of the Sediment for Survival report (a RMPEPA funded collaboration) and the further development of sediment conceptual models has also advanced the need for a coupled dynamic modeling and monitoring program that has the capacity to explore more complex management questions (Dusterhoff et al., 2021; SFEI, 2023). Such a program will take time to develop, but will be more cost-efficient and adaptable and allow for more timely answers to pressing questions. 

The San Francisco Bay total maximum daily loads (TMDLs) call for a 50% reduction in mercury (Hg) loads by 2028 and a 90% reduction in PCBs loads by 2030. In support of these TMDLs, the Municipal Regional Permit for Stormwater (MRP) (SFBRWQCB, 2009, SFBRWQCB, 2015, SFBRWQCB, 2022) called for the implementation of control measures to reduce PCBs and Hg loads from urbanized tributaries. In addition, the MRP has identified additional information needs associated with improving understanding of sources, pathways, loads, trends, and management opportunities of pollutants of concern (POCs). In response to the MRP requirements and information needs, the Small Tributary Loading Strategy (STLS) was developed, which outlined a set of management questions (MQs) that have been used as the
guiding principles for the region’s stormwater-related activities. In recognition of the need to evaluate changes in loads or concentrations of POCs from small tributaries on a decadal scale, the updated 2018 STLS Trends Strategy (Wu et al., 2018) prioritized the development of a new dynamic regional watershed model for POCs (PCBs and Hg focused) loads and trends. This regional modeling effort will provide updated estimates of POC concentrations and loads for all local watersheds that drain to the Bay. The Watershed Dynamic Model (WDM) will also provide
a mechanism for evaluating the impact of management actions on future trends of POC loads or concentrations.

As a multi-use modeling platform, the WDM is being developed to include other pollutants, such as contaminants of emerging concern (CECs), sediment, and nutrients and to be coupled with a Bay fate model to form an integrated watershed-Bay modeling framework to address Regional Monitoring Program (RMP) management questions. As this model is developed, flexibility to link with other models will be an important consideration.

The National Institute of Standards and Technology (NIST) has been collaborating with the San Francisco Bay Estuary Institute (SFEI) since 2009, providing biobanking services at the NIST Biorepository in Charleston, South Carolina in support of their ongoing water quality monitoring program, the Regional Monitoring Program for Water Quality in the San Francisco Bay (RMP). Specimens (bivalve tissue, bird egg contents, fish tissue and sediment) are collected and processed by SFEI-partnering institutions according to their established protocols and shipped to the NIST Biorepository for archival. This report outlines NIST's role in the project, describes collection and processing protocols developed by SFEI and their collaborators, details shipping and archival procedures employed by biorepository staff and provides an inventory of the collection maintained by NIST from 2009 to 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.