Modeling the Effects of Onsite Wastewater Treatment Systems on Nitrate Loads Using SWAT in an Urban Watershed of Metropolitan Atlanta.

Onsite wastewater treatment systems (OWTSs) can be a source of nitrogen (N) pollution in both surface and ground waters. In metropolitan Atlanta, GA, >26% of homes are on OWTSs. In a previous article, we used the Soil Water Assessment Tool to model the effect of OWTSs on stream flow in the Big Haynes Creek Watershed in metropolitan Atlanta. The objective of this study was to estimate the effect of OWTSs, including failing systems, on nitrate as N (NO-N) load in the same watershed. Big Haynes Creek has a drainage area of 44 km with mainly urban land use (67%), and most of the homes use OWTSs. A USGS gauge station where stream flow was measured daily and NO-N concentrations were measured monthly was used as the outlet. The model was simulated for 12 yr. Overall, the model showed satisfactory daily stream flow and NO-N loads with Nash-Sutcliffe coefficients of 0.62 and 0.58 for the calibration period and 0.67 and 0.33 for the validation period at the outlet of the Big Haynes Watershed. Onsite wastewater treatment systems caused an average increase in NO-N load of 23% at the watershed scale and 29% at the outlet of a subbasin with the highest density of OWTSs. Failing OWTSs were estimated to be 1% of the total systems and did not have a large impact on stream flow or NO-N load. The NO-N load was 74% of the total N load in the watershed, indicating the important effect of OWTSs on stream loads in this urban watershed.

Evaluation of the TBET Model for Potential Improvement of Southern P Indices.

Due to a shortage of available phosphorus (P)-loss datasets, simulated data from an accurate quantitative P transport model could be used to evaluate a P Index. The objective of this study was to compare predictions from the Texas Best Management Practice Evaluation Tool (TBET) against measured P-loss data to determine whether the model could be used to improve P Indices in the southern region. Measured P-loss data from field-scale study sites in Arkansas, Georgia, and North Carolina were used to assess the accuracy of TBET for predicting field-scale loss of P. We found that event-based predictions using an uncalibrated model were generally poor. Calibration improved runoff predictions and produced scatterplot regression lines that had slopes near one and intercepts near zero. However, TBET predictions of runoff met the performance criteria (Nash-Sutcliffe efficiency ≥ 0.3, percent bias ≤ 35%, and mean absolute error ≤ 10 mm) in only one out of six comparisons: North Carolina during calibration. Sediment predictions were imprecise, and dissolved P predictions underestimated measured losses. In North Carolina, total P-loss predictions were reasonably accurate because TBET did a slightly better job of predicting sediment losses from cultivated land. In Arkansas and Georgia, where the experimental sites were in forage production, the underprediction of dissolved P led directly to the underpredictions of total P. We conclude that TBET cannot be used to improve southern P Indices, but a curve number approach could be incorporated into P Indices to improve runoff predictions.

Comparing an Annual and a Daily Time-Step Model for Predicting Field-Scale Phosphorus Loss.

A wide range of mathematical models are available for predicting phosphorus (P) losses from agricultural fields, ranging from simple, empirically based annual time-step models to more complex, process-based daily time-step models. In this study, we compare field-scale P-loss predictions between the Annual P Loss Estimator (APLE), an empirically based annual time-step model, and the Texas Best Management Practice Evaluation Tool (TBET), a process-based daily time-step model based on the Soil and Water Assessment Tool. We first compared predictions of field-scale P loss from both models using field and land management data collected from 11 research sites throughout the southern United States. We then compared predictions of P loss from both models with measured P-loss data from these sites. We observed a strong and statistically significant ( < 0.001) correlation in both dissolved (ρ = 0.92) and particulate (ρ = 0.87) P loss between the two models; however, APLE predicted, on average, 44% greater dissolved P loss, whereas TBET predicted, on average, 105% greater particulate P loss for the conditions simulated in our study. When we compared model predictions with measured P-loss data, neither model consistently outperformed the other, indicating that more complex models do not necessarily produce better predictions of field-scale P loss. Our results also highlight limitations with both models and the need for continued efforts to improve their accuracy.

Evaluation of the APEX Model to Simulate Runoff Quality from Agricultural Fields in the Southern Region of the United States.

The Agricultural Policy Environmental eXtender (APEX) model has been widely applied to assess phosphorus (P) loss in runoff water and has been proposed as a model to support practical decisions regarding agricultural P management, as well as a model to evaluate tools such as the P Index. The aim of this study is to evaluate the performance of APEX to simulate P losses from agricultural systems to determine its potential use for refinement or replacement of the P Index in the southern region of the United States. Uncalibrated and calibrated APEX model predictions were compared against measured water quality data from row crop fields in North Carolina and Mississippi and pasture fields in Arkansas and Georgia. Calibrated models satisfactorily predicted event-based surface runoff volumes at all sites (Nash-Sutcliffe efficiency [NSE] > 0.47, |percent bias [PBIAS]| < 34) except Arkansas (NSE < 0.11, |PBIAS| < 50) but did not satisfactory simulate sediment, dissolved P, or total P losses in runoff water. The APEX model tended to underestimate dissolved and total P losses from fields where manure was surface applied. The model also overestimated sediments and total P loads during irrigation events. We conclude that the capability of APEX to predict sediment and P losses is limited, and consequently so is the potential for using APEX to make P management recommendations to improve P Indices in the southern United States.

Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison.

Phosphorus (P) Indices in the southern United States frequently produce different recommendations for similar conditions. We compared risk ratings from 12 southern states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Texas) using data collected from benchmark sites in the South (Arkansas, Georgia, Mississippi, North Carolina, Oklahoma, and Texas). Phosphorus Index ratings were developed using both measured erosion losses from each benchmark site and Revised Universal Soil Loss Equation 2 predictions; mostly, there was no difference in P Index outcome. The derived loss ratings were then compared with measured P loads at the benchmark sites by using equivalent USDA-NRCS P Index ratings and three water quality models (Annual P Loss Estimator [APLE], Agricultural Policy Environmental eXtender [APEX], and Texas Best Management Practice Evaluation Tool [TBET]). Phosphorus indices were finally compared against each other using USDA-NRCS loss ratings model estimate correspondence with USDA-NRCS loss ratings. Correspondence was 61% for APEX, 48% for APLE, and 52% for TBET, with overall P index correspondence at 55%. Additive P Indices (Alabama and Texas) had the lowest USDA-NRCS loss rating correspondence (31%), while the multiplicative (Arkansas, Florida, Louisiana, Mississippi, South Carolina, and Tennessee) and component (Georgia, Kentucky, and North Carolina) indices had similar USDA-NRCS loss rating correspondence-60 and 64%, respectively. Analysis using Kendall's modified Tau suggested that correlations between measured and calculated P-loss ratings were similar or better for most P Indices than the models.

Isolating the impact of septic systems on fecal pollution in streams of suburban watersheds in Georgia, United States.

The presence of multiple sources of fecal pollution at the watershed level presents challenges to efforts aimed at identifying the influence of septic systems. In this study multiple approaches including targeted sampling and monitoring of host-specific Bacteroidales markers were used to identify the impact of septic systems on microbial water quality. Twenty four watersheds with septic density ranging from 8 to 373 septic units/km2 were monitored for water quality under baseflow conditions over a 3-year period. The levels of the human-associated HF183 marker, as well as total and ruminant Bacteroidales, were quantified using quantitative polymerase chain reaction. Human-associated Bacteroidales yield was significantly higher in high density watersheds compared to low density areas and was negatively correlated (r = -0.64) with the average distance of septic systems to streams in the spring season. The human marker was also positively correlated with the total Bacteroidales marker, suggesting that the human source input was a significant contributor to total fecal pollution in the study area. Multivariable regression analysis indicates that septic systems, along with forest cover, impervious area and specific conductance could explain up to 74% of the variation in human fecal pollution in the spring season. The results suggest septic system impact through contributions to groundwater recharge during baseflow or failing septic system input, especially in areas with >87 septic units/km2. This study supports the use of microbial source tracking approaches along with traditional fecal indicator bacteria monitoring and land use characterization in a tiered approach to isolate the influence of septic systems on water quality in mixed-use watersheds.

Confirmation of the Impact of Onsite Wastewater Treatment Systems on Stream Base-Flow Nitrogen Concentrations in Urban Watersheds of Metropolitan Atlanta, GA.

Wastewater and lawn fertilizer potentially contribute to degraded water quality in urban watersheds. Previously we described a study from 2011 to 2012 in which we examined the effect of the density of onsite wastewater treatment systems (OWTS) on nitrogen concentrations in 24 small streams in metropolitan Atlanta. Our objective in this study was to confirm that the impact on water quality that we observed was due to OWTS and not lawn fertilizer. We sampled the same 24 streams again in 2013 and 2014, representing watersheds ranging in area from 0.18 to 8.8 km. We conducted regression analysis of the effect of OWTS and season, used dual-isotope analysis (nitrogen and oxygen in nitrate) to identify sources and determine the effect of denitrification and mixing, and conducted stream walks to identify areas where animals had access to the streams. Twelve streams were characterized as high-density (HD, more than 75 systems km) OWTS and 12 as low-density (LD, less than 75 systems km) OWTS. Water samples were collected three times a year under base-flow conditions, from November 2011 to July 2014, and analyzed for nitrate (NO-N), ammonium (NH-N), and total Kjeldahl nitrogen. Total nitrogen and NO-N concentrations increased linearly with increasing OWTS density above a threshold of about 75 OWTS km. Dual-isotope analysis of NO showed that stream NO originated predominantly from OWTS in HD watersheds and from a combination of animal waste and perhaps organic N in LD watersheds. Stream walks showed that livestock had access to some of the LD streams with high N concentrations. Our results confirm that HD OWTS can significantly degrade water quality at the watershed scale.

Applicability of models to predict phosphorus losses in drained fields: a review.

Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P losses can occur in drainage water from artificially drained fields. In this review, we assess the applicability of nine models to predict this type of P loss. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P into runoff, macropore flow, and matrix flow. Within the soil profile, sorption and desorption of dissolved P and filtering of particulate P will be important. Eight models are reviewed (ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAMDB, PLEASE, and SWAT) along with P Indexes. Few of the models are designed to address P loss in drainage waters. Although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. ADAPT, HSPF, and most P Indexes do not simulate flow to tiles or drains. DRAINMOD simulates drains but does not simulate P. The ICECREAMDB model from Sweden is an exception in that it is designed specifically for P losses in drainage water. This model seems to be a promising, parsimonious approach in simulating critical processes, but it needs to be tested. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.

Laccase mediated transformation of 17ß-estradiol in soil.

It is known that 17ß-estradiol (E2) can be transformed by reactions mediated by some oxidoreductases such as laccase in water. Whether or how such reactions can happen in soil is however unknown although they may significantly impact the environmental fate of E2 that is introduced to soil by land application of animal wastes. We herein studied the reaction of E2 in a model soil mediated by laccase, and found that the reaction behaviors differ significantly from those in water partly because of the dramatic difference in laccase stability. We also examined E2 transformation in soil using (14)C-labeling in combination with soil organic matter extraction and size exclusion chromatography, which indicated that applied (14)C radioactivity was preferably bound to humic acids. The study provides useful information for understanding the environmental fate of E2 and for developing a novel soil remediation strategy via enzyme-enhanced humification reactions.

Relationship between soil test phosphorus and phosphorus in runoff: effects of soil series variability.

Phosphorus loss in runoff from agricultural fields has been identified as an important contributor to eutrophication. The objective of this research was to determine the relationship between phosphorus (P) in runoff from a benchmark soil (Cecil sandy loam; fine, kaolinitic, thermic Typic Kanhapludult) and Mehlich III-, deionized water-, and Fe(2)O(3)-extractable soil P, and degree of phosphorus saturation (DPS). Additionally, the value of including other soil properties in P loss prediction equations was evaluated. Simulated rainfall was applied (75 mm h(-1)) to 54 1-m(2) plots installed on six fields with different soil test phosphorus (STP) levels. Runoff was collected in its entirety for 30 min and analyzed for total P and dissolved reactive phosphorus (DRP). Soil samples were collected from 0- to 2-, 0- to 5-, and 0- to 10-cm depths. The strongest correlation for total P and DRP occurred with DPS (r(2) = 0.72). Normalizing DRP by runoff depth resulted in improved correlation with deionized water-extractable P for the 0- to 10-cm sampling depth (r(2) = 0.81). The STP levels were not different among sampling depths and analysis of the regression equations revealed that soil sampling depth had no effect on the relationship between STP and P in runoff. For all forms of P in runoff and STP measures, the relationship between STP and runoff P was much stronger when the data were split into groups based on the ratio of oxalate-extractable Fe to Al. For all forms of P in runoff and all STP methods, R(2) increased with the inclusion of oxalate-extractable Al and Fe in the regression equation. The results of this study indicate that inclusion of site-specific information about soil Al and Fe content can improve the relationship between STP and runoff P.