Integrated Human Health and Ecological Risk Assessment for the South River, Virginia: a Bayesian Approach

Regional scale risk assessments can be used to determine the likelihood of effects from multiple stressors on ecological or human endpoints at multiple scales. The Relative Risk Model framework can incorporate ecosystem services as endpoints in this multiple stressor- multiple endpoint approach. Through this research, I aimed to demonstrate an approach to integrating ERA and HHRA that could be applied to assess risk to human health and ecosystem services using the South River, VA as a case study. I applied the Relative Risk Model with Bayesian networks (BN-RRM) to an integrated assessment of four ecosystem services of the South River, Virginia: Human health, Water quality, Recreation, and the Recreational fishery. The BN-RRM approach allowed for the calculation of relative risk to 14 human, biotic, and water quality endpoints from chemical and ecological stressors in the South River. Three separate conceptual models were developed for assessing risk to overall Ecosystem services, Human Health, and Recreation. The services at highest risk in the South River were Water quality and the Recreational fishery. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to Recreation in the South River was moderate with little spatial variation among the five risk regions. The sensitivity and uncertainty analysis of the BNs identified the parameters that influence risk for each endpoint in each risk region. Mercury concentrations in floodplain soils and river water influence human health risk. River temperature and E. coli bacteria were the main contributors of risk to water quality and recreational river uses. Lack of public access contributed risk to recreation and ecosystem services endpoints. This research is not meant to be a definitive assessment of human health risk to fulfill the regulatory requirements for the site. Rather, it is part of a larger effort to synthesize regional scientific research and better understand the effects of mercury contamination and other stressors in the South River watershed

Watershed assessment modelling to identify critical sources of pollution and evaluate effectiveness of conservation management practices

Critical watershed assessments allow land managers to create strategic plans and prioritize funding and technical assistance when resources are limited. The Natural Resources Conservation Service (NRCS) National Water Quality Initiative (NWQI) provides a framework for watershed assessment to support long-term, strategic watershed planning and prioritize resources. The Tenmile Watershed in the Nooksack Basin in Whatcom County was selected as a pilot watershed for the NWQI assessment for Washington State in 2017. The primary objective of this assessment was to identify critical source areas (CSAs) within the watershed that were most susceptible to nutrient, sediment and bacteria export based on physical (terrain) features and land use. Secondary objectives were to model the effectiveness of conservation practices within CSAs and create an outreach plan for maximum engagement of landowners in the watershed improvement process. NOAA’s open-source Nonpoint Source Pollution and Erosion Comparison Tool (OpenNSPECT) was used to identify CSAs. Spatial data representing terrain features (soils, elevation and hydrology), precipitation, and land use cover within the watershed were collected, aggregated and input into OpenNSPECT. The model identified CSAs for nitrogen, phosphorus, pathogens, and sediment, as well as a combined ranking of all contaminants for the watershed. OpenNSPECT was then used to model the effects of implementation of different conservation practices on pollutant reduction. In one example, the model showed that implementation of winter cover crops on agricultural fields reduced the export area of nitrogen and phosphorus into the watershed by 92% and 79%, respectively, thus improving water quality in the Tenmile Watershed and Nooksack Basin. This assessment process can be used in any watershed to help understand where CSAs are located and how land conservation practices reduce pollutants, thus helping NRCS and local partners prioritize location and land use type (crop, farm, residential, etc.) for conservation practice implementation, including cost-share and technical assistance

Confocal Microscopy and Nuclear Segmentation Algorithm for Quantitative Imaging of Epithelial Tissue

Carcinomas, cancers that originate in the epithelium, account for more than 80% of all cancers. When detected early, the 5-year survival rate is greatly increased. Biopsy and histopathology is the current gold standard for diagnosis of epithelial carcinomas which is an invasive, time-intensive, and stressful procedure. In vivo confocal microscopy has the potential to non-invasively image epithelial tissue in near-real time. This dissertation describes the development of a confocal microscope for imaging epithelial tissues and an image processing algorithm for segmentation of epithelial nuclei. A rapid beam and stage scanning combination was used to acquire fluorescence confocal images of cellular and tissue features along the length of excised mouse colon. A single 1 × 60 mm2 field of view is acquired in 10 seconds. Disruption of crypt structure such as size, shape, and distribution is visualized in images of inflamed colon tissue, while the normal mouse colon exhibited uniform crypt structure and distribution. An automated pulse coupled neural network segmentation algorithm was developed for epithelial nuclei segmentation. An increase in nuclear size and the nuclear-to-cytoplasmic ratio is a potential precursor to pre-cancer development. The spiking cortical model algorithm was evaluated using a developed confocal image model of epithelial tissues with varying contrast. It was further validated on reflectance confocal images of porcine and human oral tissue from two separate confocal imaging systems. Biopsies of human oral mucosa are used to determine the tissue and system effects on measurements of nuclear-to-cytoplasmic ratio

Public outreach: growing and adapting with changing times

Drayton Harbor’s 2016 reopening of 810 acres of commercial, tribal, and recreational shellfish harvesting area marked a significant achievement in the efforts to improve water quality and allow year-round harvest of the productive shellfish growing area. Public outreach over the past 20 years played a critical role in engaging the local community and encouraging on-the-ground actions to reduce pollution throughout the watershed. Bacteria pollution is a complex issue requiring diverse solutions; no single fix exists. In the Drayton Harbor watershed, a variety of organizations, agencies, and community members participated in developing and carrying out a robust and diverse outreach strategy that was adapted over time. We will review the history of these key outreach players and their roles in Drayton Harbor water quality improvements. Successful outreach efforts from Whatcom County’s Pollution Identification and Correction (PIC) Program included the development of online water quality summaries, online interactive results map, community events, video shorts, and the septic system maintenance rebate program. Future outreach goals include the use of social marketing to normalize pollution prevention actions such as routine septic system maintenance. Ultimately, sustaining good water quality and safe, year-round shellfish harvest requires ongoing community engagement

Assessing the effects of chemical mixtures using a Bayesian network-relative risk model (BN-RRM) integrating adverse outcome pathways (AOPs) in three Puget Sound watersheds

Chemical mixtures are difficult to assess at the individual level, but more challenging at the population level. There is still little insight of the molecular pathway for numerous chemical mixtures. We have conducted a regional-scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a Bayesian Network- Relative Risk Model (BN-RRM) incorporating a molecular pathway. We used this BN-RRM framework in a case study with organophosphate pesticide (OP) mixtures (diazinon, chlorpyrifos, and malathion) in three watersheds (Lower Skagit, Nooksack, Cedar) in the state of Washington (USA). Puget Sound Chinook salmon (Oncorhynchus tshawytscha) Evolutionary Significant Units (ESU) were chosen as population endpoints. These populations are a valuable ecosystem service in the Pacific Northwest because they benefit the region as a species that provide protection of biodiversity and are spiritually and culturally treasured by the local tribes. Laetz et al. (2009, 2013) indicated that organophosphate pesticide mixtures act synergistically to salmon and impair neurological molecular activity which leads to a change in swimming behavior and mortality, which then leads to changes in population productivity. Exposure response curves were generated for OP mixtures to connect the molecular pathway. Ecological stressors from dissolved oxygen and temperature were also included in our risk analysis. Synergism within the mixtures as well as increasing temperature and decreasing dissolve oxygen content lead to increasing risk to Puget Sound Chinook salmon populations. This research demonstrates a probabilistic approach with a multiple stressor framework to estimate the effects of mixtures through a molecular pathway and predict impacts to these valuable ecosystem services

Using metapopulation models to estimate the effects of pesticides and environmental stressors to Spring Chinook salmon in the Yakima River Basin, WA

Population-level endpoints provide ecological relevance to Ecological Risk Assessments (ERAs), because this is the level at which environmental management decisions are made. However, many population-level risk assessments do not reflect the spatial and temporal heterogeneity of the populations they represent, and thus preclude an understanding of how population dynamics and viability are affected by toxicants on a regional scale. We have developed a probabilistic ERA (specifically, a Bayesian Network-Relative Risk Model (BN-RRM)) that integrates an Adverse Outcome Pathway (AOP) framework, to quantify the sub-lethal and lethal effects of toxicants and environmental stressors on the metapopulation dynamics of salmonids. As a case study for developing this model, we have examined the impacts of organophosphate (OP) insecticides, water temperature, and dissolved oxygen on the Spring Chinook (Oncorhynchus tshawytscha) salmon metapopulation in the Yakima River Basin (YRB), Washington. A stochastic Matrix Metapopulation Model was developed using demographic data for three Spring Chinook salmon populations and one supplemental hatchery population in the YRB. Site specific data on OP contaminated habitats utilized by various salmonid life stages were incorporated into the metapopulation model by incrementally reducing survival parameters based on levels of exposure. Exposure scenarios were simulated for 200 replications of 50-year population projections using RAMAS Metapop©, and the results were incorporated into the BN-RRM. The results of this modeling effort indicated that small, wild Spring Chinook populations in the YRB have a greater probability of altered population dynamics when exposed to stressors than larger, supplemented populations. Additionally, the results indicated a seasonal effect of the stressors, with summer conditions posing a greater risk to salmon populations than winter conditions. This probabilistic ERA framework shows promise for estimating the spatiotemporal impacts of stressors on ESA-listed species (i.e., Pacific salmon) at the metapopulation level, where population dynamics and spatial structure create complex risk dynamics

Assessing the Effects of Chemical Mixtures using a Bayesian Network-Relative Risk Model (BN-RRM) Integrating Adverse Outcome Pathways (AOPs)

There are long-standing uncertainties about toxicity of chemical mixtures to populations. Laboratory toxicity tests have confirmed synergistic and antagonistic effects to individuals, but not to populations.We will conduct a regional scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a new Bayesian Network- Relative Risk Model (BN-RRM) incorporating an Adverse Outcome Pathway (AOP). We started applying this new BN-RRM framework in a case study with organophosphate pesticide mixtures (diazinon, chlorpyrifos, and malathion). Acetylcholinesterase inhibition (AChE) was chosen the molecular initiating event and the Puget Sound Chinook salmon (Oncorhynchus tshawytscha) and Coho salmon (Oncorhynchus kisutch) Evolutionary Significant Units (ESU) were chosen as population endpoints. Dose-response equations will be generated from the mixtures, integrated into the new BN-RRM framework and then overall risk will be calculated for the populations. Preliminary results indicate that organophosphate pesticide mixtures act synergistically and impair olfactory function that lead to loss of antipredator, homing and reproductive behavior which lead to changes in population age structure and patch dynamics. Assessing mixtures through this new BN-RRM framework is an innovative method of predicting effects to populations. This research will demonstrate a probabilistic approach to synthesize the effects of mixtures and predict impacts to populations

Dataset for the Environmental Risk Assessment of Chlorpyrifos to Chinook Salmon in four Rivers of Washington State, United States

Data files available below. This data set is in support of Landis et al (in press) The integration of chlorpyrifos acetylcholinesterase inhibition, water temperature and dissolved oxygen concentration into a regional scale multiple stressor risk assessment estimating risk to Chinook salmon in four rivers in Washington State, USA. DOI: 10.1002/ieam.4199. In this research We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT) and dissolved oxygen concentrations (DO) in four watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle -Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an AChE inhibition pathway parameterized using data from chlorpyrifos toxicity datasets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling run up to 50 year timeframes. There were 4 primary conclusions drawn from the model building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65 to 85 percent. Environmental conditions contributed to a larger proportion of the risk compared to chlorpyrifos. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential that CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management-goal, we calculated the conditions that would increase in juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. This research was supported by USEPA STAR Grant RD-83579501. Excel spreadsheet, model in Netica

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle–Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN‐RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN‐RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50‐y timeframes. There were 3 primary conclusions drawn from the model‐ building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2020;16:28–42. © 2019 SETA