Maxent Estimation of Aquatic Escherichia Coli Stream Impairment

Background.The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment

Maxent Estimation of Aquatic Escherichia Coli Stream Impairment

Background: The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment. Methods: We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results: Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion: Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic the plethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making. Methods We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic theplethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making

Comparison Study of Sediment Microbial Enzyme Activities to Biochemical Oxygen Demand, Nitrate Concentration, Phosphate Concentration in the Sediments of a Fecally-Contaminated Stream in Northeast Tennessee Relative to Season and Land Use

Microbial metabolism reacts quickly to environmental conditions. These reactions are dependent on the need for nutrients and respiration and can be measured using an assay of individual microbial enzyme activities (MEA’s). In this study, we measured MEA’s in the sediments of a stream in northeast Tennessee that had an approved fecal coliform Total Maximum Daily Load (TMDL). These values were compared to biochemical oxygen demand (BOD), phosphate concentration and nitrate concentration in the water column of this stream. Comparisons were grouped by season and land use. Stream sediments and water were collected monthly for one year and then quarterly for an additional two years at 14 sites located in agricultural, urban and forest regions. Dehydrogenase (DHA), a measure of microbial respiration, along with acid phosphatase (AcidPA), alkaline phosphatase (AlkPA), galactosidase (GalA) and glucosidase (GluA) activities were measured using colorimetric assays. BOD was determined using the standard 5-day BOD test (BOD5). Nitrate and phosphate concentrations were measured using colorimetric procedures. There were significant positive and negative correlations (p5, DHA vs. nitrate concentration, and DHA vs. phosphate concentration. Also in the fall months there were significant negative correlations between GalA and GluA vs. BOD5, and concentrations of nitrate and phosphate. There was also a negative correlation between AcidPA and BOD5. In the warmer months of spring and summer, there were positive correlations between AcidPA, AlkPA, GalA and GluA vs. the BOD5 ’s, and the concentrations of nitrate and phosphate. The only negative correlation in a warmer season was in the summer between AlkPA vs. BOD5 and phosphate concentration. No significant correlations were found by land use type. Results indicate that significant relationships may exist between MEA’s and other water quality measures (e.g. BOD5, nitrate concentration, and phosphate concentration) that could make it possible to use MEA’s as another tool for water quality assessment

Comparison Study of the Averaged Sediment Microbial Enzyme Activities in Four Fecally-Contaminated streams in the Same Watershed in Northeast Tennessee to Biochemical Oxygen Demand, Nitrate Concentration, and Phosphate Concentration

Microbial enzyme activities (MEA’s) are measurements of microbial metabolism. These activities are dependent on the need for nutrients and respiration. This extended study evaluated four streams in the same watershed that had an approved fecal coliform Total Maximum Daily Load. Sediment and water samples were collected monthly for the first year of each specific stream study, and then quarterly to the end of 2006. Dehydrogenase, a measure of microbial respiration, along with acid phosphatase, alkaline phosphatase, galactosidase and glucosidase activities were measured using colorimetric assays. Biochemical oxygen demand (BOD) was determined using the standard 5-day test (BOD5). Nitrate and phosphate concentrations were measured using colorimetric procedures. Sediment MEA values were compared to the BOD, nitrate concentration and phosphate concentration in the overlying water. Seasonal means of each parameter were not significantly different (p5, nitrate concentration, and phosphate concentration). This suggests to us that MEA’s may be an alternative tool for water quality assessments

Comparison Study of the Averaged Sediment Microbial Enzyme Activities in Four Fecally-Contaminated streams in the Same Watershed in Northeast Tennessee to Biochemical Oxygen Demand, Nitrate Concentration, and Phosphate Concentration

Microbial enzyme activities (MEA’s) are measurements of microbial metabolism. These activities are dependent on the need for nutrients and respiration. This extended study evaluated four streams in the same watershed that had an approved fecal coliform Total Maximum Daily Load. Sediment and water samples were collected monthly for the first year of each specific stream study, and then quarterly to the end of 2006. Dehydrogenase, a measure of microbial respiration, along with acid phosphatase, alkaline phosphatase, galactosidase and glucosidase activities were measured using colorimetric assays. Biochemical oxygen demand (BOD) was determined using the standard 5-day test (BOD5). Nitrate and phosphate concentrations were measured using colorimetric procedures. Sediment MEA values were compared to the BOD, nitrate concentration and phosphate concentration in the overlying water. Seasonal means of each parameter were not significantly different (p5, nitrate concentration, and phosphate concentration). This suggests to us that MEA’s may be an alternative tool for water quality assessments

Comparison of Microbial Water Quality Parameters of Four Geographically Similar Creeks in Northeast Tennessee

Four creeks within the Watauga River watershed in Northeast Tennessee are routinely monitored for water quality assessments. To identify sources and monitor remediation, Sinking Creek, Cash Hollow Creek, Buffalo Creek and Boones Creek are monitored for chemical and microbial parameters. These parameters include phosphates, nitrates, BOD and fecal coliforms. Sinking Creek is a tributary of the Watauga River with 10 miles of impaired water. Cash Hollow Creek enters the Watauga River at river mile 11.4 with 3.4 miles of impaired water. Boones Creek contains 18.6 impaired miles while the status of water quality in Buffalo Creek is not yet determined. Agricultural input is a major source of pollution for Sinking and Boones Creek. Cash Hollow Creek is impacted by a combination of sources of which urban runoff is the largest due to storm sewers and land development. Boones, Cash Hollow and Sinking Creeks are considered impaired and are on the state’s 303(d) list due to pathogen loading but only Sinking and Cash Hollow Creek have TMDLs. The seasonal and spatial patterns are more obvious for microbial than for chemical parameters. From 2002 - 2005, 14 stations on Sinking Creek were sampled quarterly. Fecal coliforms were elevated and always greater than 200 CFU/100ml for stations 1 – 5. Due to agricultural land use adjacent to stations 1 – 4, this would be expected. There was also a seasonal trend with higher concentrations found in the fall and spring. Cash Hollow Creek’s 9 stations were sampled monthly from 2002 - 2005. Although very high fecal coliforms concentrations were found, there were no obvious patterns. The 12 stations on Buffalo Creek were sampled quarterly from June 2004 to June 2005. Fecal coliform concentrations were high at station 8, which is adjacent to agricultural land. Boones Creek was sampled monthly from March 2005 to present and no obvious trends have been noted. The objective of this research is to compare patterns in these geographically similar creeks to identify any common patterns associated with various pollution sources. We will discuss the preliminary results and conclusions about the usefulness of these data to accomplish this objective