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Fine particles are critical to stream ecosystem functioning, influencing in‐stream processes from pathogen transmission to carbon cycling, all of which depend on particle immobilization. However, our ability to predict particle immobilization is limited by (1) availability of combined solute and particle tracer data and (2) identifying parameters that appropriately represent fine particle immobilization, due to the myriad of objective functions and model formulations. We found that improved predictions of the full distribution of possible fine particle residence times requires using an objective function that assesses both the peak and tailing of breakthrough curves together with solute tracers to constrain in‐stream transport processes. The representation of immobilization processes was significantly improved when solute tracer data were combined with a particle model, starkly contrasting the common assumption that fine particles transport as washload. We develop a clear strategy for improving fine particle transport predictions, reshaping the potential role of fine particles in water quality management. Fine particles, a general term that can be used to describe inorganic material like clays, particulate organic carbon, and harmful bacteria (i.e., pathogens), are important to stream functioning and water quality. The time it takes fine particles to move through a stream is difficult to predict primarily because there is no general guidance regarding the required data types and modeling approaches. Through comprehensive computational experiments and data analysis, we found that fine particles remain in streams much longer than commonly assumed, reshaping understanding of the role of fine particles in stream functioning and how waterborne pathogens are transmitted. Applying a balanced objective function and two tracers improves accuracy of particle retention up to 57% versus applying a washload assumptionImproved parameter identifiability found with an objective function that considers both peak and tailing of breakthrough curvesParticle immobilization can be predicted directly from solute data by adding particle transport terms to a model with hyporheic exchange

Novel observation techniques (e.g., smart tracers) for characterizing coupled hydrological and biogeochemical processes are improving understanding of stream network transport and transformation dynamics. In turn, these observations are thought to enable increasingly sophisticated representations within transient storage models (TSMs). However, TSM parameter estimation is prone to issues with insensitivity and equifinality, which grow as parameters are added to model formulations. Currently, it is unclear whether (or not) observations from different tracers may lead to greater process inference and reduced parameter uncertainty in the context of TSM. Herein, we aim to unravel the role of in‐stream processes alongside metabolically active (MATS) and inactive storage zones (MITS) using variable TSM formulations. Models with one (1SZ) and two storage zones (2SZ) and with and without reactivity were applied to simulate conservative and smart tracer observations obtained experimentally for two reaches with differing morphologies. As we show, smart tracers are unsurprisingly superior to conservative tracers when it comes to partitioning MITS and MATS. However, when transient storage is lumped within a 1SZ formulation, little improvement in parameter uncertainty is gained by using a smart tracer, suggesting the addition of observations should scale with model complexity. Importantly, our work identifies several inconsistencies and open questions related to reconciling time scales of tracer observation with conceptual processes (parameters) estimated within TSM. Approaching TSM with multiple models and tracer observations may be key to gaining improved insight into transient storage simulation as well as advancing feedback loops between models and observations within hydrologic science. Solute experiments and transport models, called commonly tracer experiments, are used to understand the relative importance of different stream processes, especially those that influence water, solutes, and nutrients as they move through a stream network. Within these tracer experiments, there are processes that exchange mass beyond the main stream channel to other parts of the river valley bottom environment. Sometimes, there are single or multiple types of tracers used and modeled to try to understand this exchange. There are also multiple models with different equations and structures to simulate these tracers. This study shows that what you can learn about these stream processes depends on experiment choices and which model you use. Hence, refining future multiple tracer experiments and models is needed to determine how we best obtain consistent measurements of key stream processes. TSM interpretation improved with analysis of multiple tracers but results in increased parameter uncertainty for a more complex modelNonconservative tracers enabled interpretation of parameters that were highly uncertain when estimated by conservative tracers aloneAchieving reliable parameter estimates depends on choice of tracers and model framework and should be coupled with uncertainty assessment

Organic micropollutants (OMPs) represent an anthropogenic stressor on stream ecosystems. In this work, we combined passive sampling with suspect and nontarget screening enabled by liquid chromatography–high-resolution mass spectrometry to characterize complex mixtures of OMPs in streams draining mixed-use watersheds. Suspect screening identified 122 unique OMPs for target quantification in polar organic chemical integrative samplers (POCIS) and grab samples collected from 20 stream sites in upstate New York over two sampling seasons. Hierarchical clustering established the co-occurrence profiles of OMPs in connection with watershed attributes indicative of anthropogenic influences. Nontarget screening leveraging the time-integrative nature of POCIS and the cross-site variability in watershed attributes prioritized and confirmed 11 additional compounds that were ubiquitously present in monitored streams. Field sampling rates for 37 OMPs that simultaneously occurred in POCIS and grab samples spanned the range of 0.02 to 0.22 L/d with a median value of 0.07 L/d. Comparative analyses of the daily average loads, cumulative exposure–activity ratios, and multi-substance potentially affected fractions supported the feasibility of complementing grab sampling with POCIS for OMP load estimation and screening-level risk assessments. Overall, this work demonstrated a multi-watershed sampling and screening approach that can be adapted to assess OMP contamination in streams across landscapes.