Biomedical subjects

Craig A Stow

Publications and source records attributed to Craig A Stow.

6 recordsLinked to original sources

PubMed2005

N2O emissions from streams in the Neuse river watershed, North Carolina.

We present N2O emission data from 11 sites in the Neuse River watershed. Emissions were measured using a static surface enclosure technique deployed on eight sites on the main river channel and three tributary sites. Ancillary data collected included dissolved oxygen, nitrate, total nitrogen, ammonium, dissolved organic carbon, total phosphorus, and temperature. Analysis using standard linear models, and classification and regression trees (CART), indicated nitrate to be the primary driving variable associated with N2O emission, although dissolved organic carbon concentration and water temperature were positively related with N2O emission as well. Relationships between nitrate concentration and N2O emission were consistent with those found in previous studies, although the data presented here represent the lower end of the range for both variables among published studies. Using our measured N2O emission rates along with literature values for the ratio of nitrogen gas to N2O produced during denitrification, we estimate N loss via denitrification in the Neuse River is approximately 17% of the annual N load delivered to the estuary.

Air Pollutants↗

PubMed2005

Declining threshold for hypoxia in the Gulf of Mexico.

The northwestern Gulf of Mexico shelf has been nicknamed "The Dead Zone" due to annual summertime (May-September) bottom-water hypoxia (dissolved oxygen < or =2 mg L(-1)) that can be extensive (>20 000 km2) and last for several months. Hypoxia has been attributed to eutrophication caused by increasing nitrogen loads, although directly linking hypoxia to nitrogen is difficult. While the areal extent of hypoxia has been shown to increase with Mississippi River flow, it is unclear whether this increase results from enhanced vertical water-column stratification or from eutrophication caused by river-borne nutrients. Disentangling the relative contributions of eutrophication versus stratification has important management consequences. Our analysis indicates that the top:bottom salinity difference is an important predictor of hypoxia, exhibiting a threshold, where the probability of hypoxia increases rapidly, at approximately 4.1 ppt. Using a Bayesian change-point model, we show that this stratification threshold decreased from 1982 to 2002, indicating the degree of stratification needed to induce hypoxia has gone down. Although this declining threshold does not link hypoxia and nitrogen, it does implicate a long-term factor transcending yearly flow-induced stratification differences. Concurrently, we show that surface temperature increased, while surface dissolved oxygen decreased, suggesting that factors in addition to nitrogen may be influencing the incidence of hypoxia in the bottom water.

Atlantic Ocean↗

PubMed2004

Will Lake Michigan lake trout meet the Great Lakes Strategy 2002 PCB reduction goal?

The Great Lakes Strategy 2002 establishes the long-term goal that all Great Lakes fish should be safe to eat without restriction. As an indicator of progress toward that goal, the Strategy specifies that lake trout PCBs will decline 25% from 2000 to 2007. We estimated the plausibility of achieving this near-term goal by examining a time-series of Lake Michigan lake trout PCB concentrations from 1972 to 2000. We used two different Bayesian approaches, Bayesian model averaging (BMA) and dynamic linear models (DLM), to model the trajectory of these historical data and forecast concentrations through 2007. Both approaches indicate that the probability of a 25% reduction from 2000 to 2007 is negligible. The most likely lake trout PCB declines predicted by the BMA and DLM over this time period are 6.8% and 8.9%, respectively. Our results suggest that declines in lake trout PCBs will be in the range of 5-10% assuming conditions similar to recent years. This rate of decline will be difficult to discern without adequate data collection. If sufficient data are not gathered to document further declines, then the relaxation of lake trout consumption advisories is indeed a long-term prospect.

Animals↗

PubMed2004

Bayesian methods for regional-scale eutrophication models.

We demonstrate a Bayesian classification and regression tree (CART) approach to link multiple environmental stressors to biological responses and quantify uncertainty in model predictions. Such an approach can: (1) report prediction uncertainty, (2) be consistent with the amount of data available and (3) be flexible enough to permit updates and improvements. Tree-based methods are a flexible approach useful for variable subset selection and when the analyst suspects global nonlinearity and cannot (or does not want to) specify the functional form of possible interactions a priori. We use the US EPA National Eutrophication Survey data to fit three models demonstrating the methods and to highlight important differences arising from slightly different model specifications. The Bayesian approach offers many advantages, including the estimation of the value of new information and proper probability distributions on the variable of interest as an output, which can be directly used in risk assessment or decision-making.

Bayes Theorem↗

PubMed2003

Assessing TMDL effectiveness using flow-adjusted concentrations: a case study of the Neuse River, North Carolina.

Integrated control of both point and nonpoint source water pollution using Total Maximum Daily Load (TMDL) assignments will be a major regulatory focus over the next decade. We propose the use of "flow-adjusted" pollutant concentrations to evaluate the effectiveness of management actions taken to meet approved TMDLs. Pollutant concentrations are usually highly correlated with streamflow, and flow is strongly weather-dependent. Thus, pollutant loads, which are calculated as pollutant concentration multiplied by streamflow, have a large weather-dependent variance component. This natural variation can be removed by calculating flow-adjusted concentrations. While such values are not a direct measure of pollutant load, they make it easier to discern changes in streamwater quality. Additionally, they are likely to be a better predictor of pollutant concentrations in the receiving waterbody. We demonstrate the use of this technique using long-term nutrient data from the Neuse River in North Carolina. The Neuse River Estuary has suffered many eutrophication symptoms, and a program to reduce nutrient loading has been in place for several years. We show that, in addition to revealing recent reductions in nutrient inputs, annual flow-adjusted riverine nutrient concentrations show a more pronounced relationship with estuarine nutrient concentrations than do annual nutrient loads. Thus, we suggest that the calculation of flow-adjusted concentrations is a useful technique to aid in assessment of TMDL implementation.

Environmental Monitoring↗

PubMed2002

Predicting the frequency of water quality standard violations: a probabilistic approach for TMDL development.

To address the impaired condition of the water bodies listed under Section 303(d) of the Clean Water Act, over 40 000 total maximum daily loads (TMDLs) for pollutants must be developed during the next 10-15 years. Most of these will be based on the results of water quality simulation models. However, the failure of most models to incorporate residual variability and parameter uncertainty in their predictions makes them unsuitable for TMDL development. The percentile-based standards increasingly used by the EPA and the requirement for a margin of safety in TMDLs necessitate that model predictions include quantitative information on uncertainty. We describe a probabilistic approach to model-based TMDL assessment that addresses this issue and is suitable for use with any type of mathematical model. To demonstrate our approach, we employ a eutrophication model for the Neuse River Estuary, North Carolina, and evaluate compliance with the state chlorophyll a standard. Any observed variability in chlorophyll athatis notexplained bythe model is explicitly incorporated via a residual error term. This probabilistic term captures the effects of any processes that are not considered in the model and allows for direct assessment of the frequency of standard violations. Additionally, by estimating and propagating the effects of parameter uncertainty on model predictions, we are able to provide an explicit basis for choosing a TMDL that includes a margin of safety. We conclude by discussing the potential for models currently supported by the EPA to be adapted to provide the type of probabilistic information that is necessary to support TMDL decisions.

Decision Making↗