Modeling framework for simulating concentrations of solute chemicals, nanoparticles, and solids in surface waters and sediments: WASP8 Advanced Toxicant Module.

Toxicant concentrations in surface waters are of environmental concern due to their potential impacts to humans and wildlife. Numerical models provide system insight, support management decisions, and provide scenario testing on the impacts of toxicants. The Water Quality Analysis Simulation Program (WASP) is a widely used framework for developing site-specific models for simulating toxicant concentrations in surface waters and sediments over a range of complexities and temporal and spatial scales. WASP8, with the Advanced Toxicant module, has been recently released, incorporating a complete architecture redesign for an increased number of state variables and different state variable types. WASP8 incorporates a new structure for simulating light intensity and photoreactions in the water column, including the distinction of 10 different wavelength bands, and nanoparticle heteroaggregation to solids. We present a hypothetical case study, using the Cape Fear River, North Carolina as a representative example for simulating solute chemicals, nanoparticles, and solids to demonstrate the new and updated capabilities of WASP8.

WASP 8: The Next Generation in the 50-year Evolution of USEPA's Water Quality Model.

The Water Quality Analysis Simulation Program (WASP) helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2 and 3 dimensional systems and a variety of pollutant types-including both conventional pollutants (e.g., dissolved oxygen, nutrients, phytoplankton, etc.) and toxic materials. WASP has capabilities of linking with hydrodynamic and watershed models which allows for multi-year analyses under varying meteorological and environmental conditions. WASP was originally developed by HydroScience, Inc. in 1970 and was later adapted by the US Environmental Protection Agency's Large Lakes Research Station (LLRS) for applications to the Great Lakes. The LLRS first publicly released the model in 1981. WASP has undergone continuous development since that time and this year will mark its 50th anniversary. This paper follows the development of WASP from its origin to the latest release of the model in 2020, documenting its evolution and present structure and capabilities.

Application of ecosystem-scale fate and bioaccumulation models to predict fish mercury response times to changes in atmospheric deposition.

Management strategies for controlling anthropogenic mercury emissions require understanding how ecosystems will respond to changes in atmospheric mercury deposition. Process-based mathematical models are valuable tools for informing such decisions, because measurement data often are sparse and cannot be extrapolated to investigate the environmental impacts of different policy options. Here, we bring together previously developed and evaluated modeling frameworks for watersheds, water bodies, and food web bioaccumulation of mercury. We use these models to investigate the timescales required for mercury levels in predatory fish to change in response to altered mercury inputs. We model declines in water, sediment, and fish mercury concentrations across five ecosystems spanning a range of physical and biological conditions, including a farm pond, a seepage lake, a stratified lake, a drainage lake, and a coastal plain river. Results illustrate that temporal lags are longest for watershed-dominated systems (like the coastal plain river) and shortest for shallow water bodies (like the seepage lake) that receive most of their mercury from deposition directly to the water surface. All ecosystems showed responses in two phases: A relatively rapid initial decline in mercury concentrations (20-60% of steady-state values) over one to three decades, followed by a slower descent lasting for decades to centuries. Response times are variable across ecosystem types and are highly affected by sediment burial rates and active layer depths in systems not dominated by watershed inputs. Additional research concerning watershed processes driving mercury dynamics and empirical data regarding sediment dynamics in freshwater bodies are critical for improving the predictive capability of process-based mercury models used to inform regulatory decisions.

Evaluating regional predictive capacity of a process-based mercury exposure model, regional-mercury cycling model, applied to 91 Vermont and New Hampshire lakes and ponds, USA.

Regulatory agencies must develop fish consumption advisories for many lakes and rivers with limited resources. Process-based mathematical models are potentially valuable tools for developing regional fish advisories. The regional mercury cycling model (R-MCM) specifically was designed to model a series of lakes for a given region with site-specific data and parameterization for each application. In this paper, we explore the feasibility of R-MCM application to develop regional fish advisories from existing data by testing model performance across 91 Vermont ([VT], USA) and New Hampshire ([NH], USA) lakes. We use a progressive method of parameter refinement ranging from simple defaults specified by the model to site-specific parameterization to evaluate potential improvements in model prediction. Model applications and parameter refinement tiers are based on Regional Environmental Monitoring Assessment Program (REMAP) data. Results show that R-MCM generally underpredicts water column methylmercury and total mercury concentrations and overpredicts sediment methylmercury concentrations. Default level input parameterization produced the largest amount of random scatter in model forecasted values. Using site-specific values for the default level characteristics reduced this variability but did not improve overall model performance. By separating the observed and predicted data by lake characteristics, we identify some overall trends in bias and fit, but are unable to identify systematic biases in model performance by lake type. This analysis suggests that process-based models like R-MCM cannot be used for a priori predictive applications at the regional scale at this time. Further, this work reinforces the need for additional research on the transport and transformation of mercury to elucidate parameterization useable in a modeling framework to help refine predictive capabilities of process-based models.

Modeling mercury fluxes and concentrations in a Georgia watershed receiving atmospheric deposition load from direct and indirect sources.

This paper presents a modeling analysis of airborne mercury (Hg) deposited on the Ochlockonee River watershed located in Georgia. Atmospheric deposition monitoring and source attribution data were used along with simulation models to calculate Hg buildup in the subwatershed soils, its subsequent runoff loading and delivery through the tributaries, and its ultimate fate in the mainstem river. The terrestrial model calculated annual watershed yields for total Hg ranging from 0.7 to 1.1 microg/m2. Results suggest that approximately two-thirds of the atmospherically deposited Hg to the watershed is returned to the atmosphere, 10% is delivered to the river, and the rest is retained in the watershed. A check of the aquatic model results against survey data showed a reasonable agreement. Comparing observed and simulated total and methylmercury concentrations gave root mean square error values of 0.26 and 0.10 ng/L, respectively, in the water column, and 5.9 and 1 ng/g, respectively, in the upper sediment layer. Sensitivity analysis results imply that mercury in the Ochlockonee River is dominated by watershed runoff inputs and not by direct atmospheric deposition, and that methylmercury concentrations in the river are determined mainly by net methylation rates in the watershed, presumably in wetted soils and in the wetlands feeding the river.