Decoupling the influence of biological and physical processes on the dissolved oxygen in the Chesapeake Bay

It is instructive and essential to decouple the effects of biological and physical processes on the dissolved oxygen condition, in order to understand their contribution to the interannual variability of hypoxia in Chesapeake Bay since the 1980s. A conceptual bottom DO budget model is applied, using the vertical exchange time scale (VET) to quantify the physical condition and net oxygen consumption rate to quantify biological activities. By combining observed DO data and modeled VET values along the main stem of the Chesapeake Bay, the monthly net bottom DO consumption rate was estimated for 1985-2012. The DO budget model results show that the interannual variations of physical conditions accounts for 88.8% of the interannual variations of observed DO. The high similarity between the VET spatial pattern and the observed DO suggests that physical processes play a key role in regulating the DO condition. Model results also show that long-term VET has a slight increase in summer, but no statistically significant trend is found. Correlations among southerly wind strength, North Atlantic Oscillation index, and VET demonstrate that the physical condition in the Chesapeake Bay is highly controlled by the large-scale climate variation. The relationship is most significant during the summer, when the southerly wind dominates throughout the Chesapeake Bay. The seasonal pattern of the averaged net bottom DO consumption rate ( B20) along the main stem coincides with that of the chlorophyll-a concentration. A significant correlation between nutrient loading and B20 suggests that the biological processes in April-May are most sensitive to the nutrient loading

A Stakeholder-Engaged Economic Evaluation of Site-Specific Wastewater-Based Surveillance for COVID-19 Outbreak Control in Long-Term Care Facilities

Wastewater-based surveillance (WBS) gained prominence during the coronavirus disease 2019 (COVID-19) pandemic as a non-invasive infectious disease monitoring tool. Long-term care facilities (LTCF) have been disproportionately impacted during the pandemic. Effective surveillance methods are needed to provide early warnings of new infections to protect both residents and staff. This study aims to analyze the cost-effectiveness of incorporating site-specific WBS in monitoring COVID-19 in LTCFs. This thesis leveraged longitudinal data based on nine facilities in the city of Edmonton, Alberta, Canada participating in the WBS program between January 2021 and May 2023. The study comprises four chapters. The first study (Chapter 2) focuses on exploring stakeholders’ opinions in applying site-specific WBS in LTCFs and engaging them to develop the economic evaluation plan. Their feedback guided the cost-effectiveness analysis by proposing WBS-based actions and refining key assumptions, perspectives, and outcome measures, thereby enhancing the relevance of the evaluation. The second study (Chapter 3) involves a statistical analysis of wastewater data. Constrained distributed lag models were used to estimate the lead time of WBS to detect the existence of COVID-19 infections before clinical diagnosis and the accuracy of wastewater data in predicting mass testing outcomes. Results demonstrated that wastewater detection preceded clinical cases by up to five days, with a three-day lead time being the most common. Additionally, wastewater data predicted mass testing outcomes with 80% accuracy for negative results and 60% for positive results, better predicting resident cases than staff cases (74% vs. 33%, p = 0.02). No significant associations were found between predictive performance and WBS operational factors. The third study (Chapter 4) developed a Susceptible-Infected-Case-Recovered (SICR) model to compare WBS-informed interventions with the Standard of Care across pandemic phases between March 2020 to February 2023. The model estimated that WBS-informed prevalence testing in the early pandemic phase averted 122 cases (95% credible interval (C.I.): 103–143) across four outbreaks, while WBS-informed symptom screening in the endemic transition phase averted 132 cases (95% C.I.: 106-152) across 17 outbreaks. These results highlight the potential of WBS in outbreak control, particularly when vaccination or specific treatment is unavailable. The last study (Chapter 5) integrated the case-averted estimates into a cost-effectiveness analysis. WBS implementation cost at nine LTCFs over three years was $1.6 million, in 2024 Canadian dollars. WBS is shown to be the most cost-effective during the early pandemic, with an expected incremental cost-effectiveness ratio (ICER) of$2,065 per quality-adjusted life year (QALY). Across all three years, the expected ICER was $47,263/QALY, which is within the$50,000/QALY threshold. Sensitivity analyses revealed that implementation costs, the characteristics of the virus, and healthcare system responses significantly influenced cost-effectiveness. Overall, this thesis demonstrates the potential cost-effectiveness of WBS in preventing COVID-19 outbreaks in LTCFs. This thesis will inform various stakeholders about the value of WBS and provide insights into its future development and application for other infectious diseases

Simple relationships between residence time and annual nutrient retention, export, and loading for estuaries

Simple mathematical models are derived from mass balances for water and transported substance to provide insight into the relationships between import, export, transport, and internal removal for nonconservative sub-stances in an estuary. Extending previous work, our models explicitly include water and substance inputs from the ocean and are expressed in terms of timescales (i.e., mean residence time and the timescale for net removal). Steady-state, timescale-based expressions for ratios of export to import, retention to import, and net export to loading, as well as for loading and annually averaged concentration, are provided. The net export:loading model explains the underlying mechanisms for a well-known empirical relationship between fractional net export and residence time derived by other authors. Although our simplified models are first-order approximations, the relative importance of physical and biochemical processes influencing export or retention of a substance can be assessed using mean residence time and the timescale for net removal. Assumptions employed in deriving the simplified models(e.g., well-mixed, dynamic steady state) may not be met for real estuaries. However, model application to Chesapeake Bay for 1985–2012 demonstrates that interannual variations in total nitrogen (TN)net export:loading can be evaluated, and annual nutrient loadings can be well estimated using numerically modeled time-varying mean residence time, observation-based mean concentration, freshwater inflow, and an appropriately estimated removal timescale. Our model shows that net fractional export of TN loading ranges from 0.3 to 0.5 over the 28-yr period.The models can be employed for other substances and water bodies if the underlying assumptions are applicable

A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored

Mapping for the EQ-5D-5L for Use in Cost-Utility Analysis

Cost-utility analysis (CUA) assesses the cost-effectiveness of health technologies by comparing their costs and health outcomes. The utility is incorporated in the health outcome measures of CUA, and the EQ-5D-5L is one of the most common instruments to estimate utility values. When utility values are not available, mapping from non-preference-based instruments to a preference-based instrument is a popular technique. When CUAs use different preference-based measures, mapping between these measures can transfer the utility values and allow for better comparison across CUAs. However, there are many concerns regarding studies reporting mapped utility values, such as extrapolation issues and the uncertainty of this methodology. The quality of mapping studies has become an important criterion when using them in economic evaluations. The first study of my thesis assessed the reporting quality of mapping studies onto the EQ-5D-5L, especially their completeness of information for CUA applications. The second study developed a novel mapping algorithm from the Edmonton Symptom Assessment System Revised: Renal (ESAS-r: Renal) to the EQ-5D-5L among patients with end-stage renal disease (ESRD). The first objective of my systematic review was to identify new mapping studies onto the EQ-5D-5L by updating a previous systematic search made by the Health Economics Research Centre (HERC). The second objective was to assess all the EQ-5D-5L mapping studies on their reporting quality, especially the completeness of information for CUA, with the use of two reporting quality checklists. The third objective was to explore whether using reporting quality checklists was associated with improved reporting quality. The review identified 14 new studies since 2018 which were not included in the HERC database. In the assessment of all 39 published studies (including 25 from the HERC database), the overall reporting quality was mostly good. In several areas I identified problems that would require improvements including 1) estimation of predicted utilities, 2) reporting variances, covariances, and error terms, 3) final model calculation examples, 4) parameter uncertainty, and 5) individual uncertainty. A preliminary comparison showed that the checklists could help to improve the reporting quality of the studies. The second study of this thesis mapped the ESAS-r: Renal to the EQ-5D-5L in patients with ESRD using data from the Evaluation of Routinely Measured Patient-reported Outcomes in Hemodialysis Care (EMPATHY) trial, a multi-centre clustered randomized-controlled trial of routine measurement of patient-reported outcomes in hemodialysis units in Northern Alberta. Several models were explored in the mapping analysis using data from one study arm, including linear models, censored dependent variable models, mixture models and response mapping. Internal validation was conducted to evaluate the predictive power of the models, and the validation sample was from another arm of the EMPATHY trial. Statistical fit and predictive power were measured by mean absolute error (MAE) and mean squared error (MSE), which, along with theoretical backgrounds, were the selection criteria for the best model. The final sample size for model estimation was 506, after excluding missing records (missing rate: 57.6%). All models produced relatively similar statistical fit and predictive power (Estimation: MAE: 0.056 - 0.120, MSE: 0.007 - 0.028; Validation: MAE: 0.136 - 0.155, MSE: 0.032 - 0.046). All models showed great prediction properties for relatively healthy health states, but poor prediction properties for worse health states. With the consideration of all selection criteria, the generalized estimating equations (Estimation: MAE: 0.120, MSE: 0.027; Validation: MAE:0.140, MSE:0.034 ) and generalized linear models (Estimation: MAE: 0.116, MSE: 0.028; Validation: MAE: 0.136, MSE: 0.034) on selected ESAS-r: Renal items were considered the best models. Since the models have not been externally validated, they should be applied in populations with similar patient characteristics as our study sample. Overall, mapping is a feasible and useful technique to estimate the utility values for conducting CUA. The issues identified in current mapping studies could inform further mapping studies on how to improve reporting quality, especially ensuring the completeness of information for employing mapping algorithms in CUA. The empirical mapping study on ESAS-r: Renal provided mapping algorithms which could be used to predict utility values for patients with ESRD when only ESAS-r: Renal is available

Role of Baroclinic Processes on Flushing Characteristics in a Highly Stratified Estuarine System, Mobile Bay, Alabama

Flushing of an estuary quantifies the overall water exchange between the estuary and coastal ocean and is crucially important for water quality as well as biological and geochemical processes within the system. Flushing times and freshwater age in Mobile Bay were numerically calculated under realistic and various controlled forcing conditions. Their responses to external forcing were explained by the three‐dimensional characteristics of general circulation in the system. The flushing time ranges from 10 to 33 days under the 25th–75th percentile river discharges, nearly half of the previous estimates based on barotropic processes only, suggesting the important contribution of baroclinic processes. Their influence, quantified as the “new ocean influx,” is on the same order of the river discharge under low to moderate river discharge conditions. The baroclinic influence increases and then decreases with increasing river discharge, aligning with the response of horizontal density gradient. By enhancing the net influx from the ocean mainly through density‐driven circulation, baroclinic processes contribute to reduce flushing times. The three‐dimensional circulation, which differs greatly between the wet and dry seasons, explains the temporal and spatial variations of the flushing characteristics. Wind forcing influences the three‐dimensional circulation in the system with easterly and northerly winds tending to reduce the flushing time, while southerly and westerly winds the opposite

Use of settlement patterns and geochemical tagging to test population connectivity of eastern oysters Crassostrea virginica

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gancel, H. N., Carmichael, R. H., Du, J., & Park, K. Use of settlement patterns and geochemical tagging to test population connectivity of eastern oysters Crassostrea virginica. Marine Ecology Progress Series, 673, (2021): 85–105, https://doi.org/10.3354/meps13796.Freshwater-dominated estuaries experience large fluctuations in their physical and chemical environments which may influence larval dispersal, settlement, and connectivity of populations with pelagic larval stages. We used settlement patterns and natural tagging along with numerical hydrodynamic model results to assess settlement and connectivity among oysters across the freshwater-dominated Mobile Bay-eastern Mississippi Sound (MB-EMS) system. Specifically, we (1) tested how freshwater inputs and associated environmental attributes influenced settlement patterns during high and low discharge conditions in 2014 and 2016, respectively, and (2) analyzed trace element (TE) ratios incorporated into multiple shell types (larval and settled shell of spat and adult shells) to determine if shells collected in situ incorporate temporally stable site-specific signatures. We also assessed if TE ratios compared between adult (TE natal signature proxy) and larval shells could infer connectivity. Larval settlement was 4× higher during low discharge than during high discharge when oyster larvae only settled in higher salinity regions (EMS). Spat and adult shells incorporated site-specific TE ratios that varied from weeks to months. Connectivity results (May-June 2016 only) suggest that EMS is an important larval source to EMS and lower MB. While we were able to infer probable connectivity patterns using adult and larval shells, more study is needed to assess the utility of adult shells as proxies for natal-location TE signatures. Results provide a baseline for measuring future larval connectivity and adult distribution changes in the MB-EMS system. Biological and geochemical data demonstrate the potential to identify environmental attributes that spatiotemporally mediate settlement and connectivity in dynamic systems.This work was funded by the Mississippi−Alabama Sea Grant Consortium (project number #R/ SFA-03) and the Food and Drug Administration and MESC/ Dauphin Island Sea Lab Collaboration (award numbers: 5U19FD005923-04 and 5U19FD004277-04)