Mazdak Arabi

Correlation Between Upstream Human Activities and Riverine Antibiotic Resistance Genes

Antimicrobial resistance remains a serious and growing human health challenge. The water environment may represent a key dissemination pathway of resistance elements to and from humans. However, quantitative relationships between landscape features and antibiotic resistance genes (ARGs) have not previously been identified. The objective of this study was to examine correlations between ARGs and putative upstream anthropogenic sources in the watershed. sul1 (sulfonamide) and tet(W) (tetracycline) were measured using quantitative polymerase chain reaction in bed and suspended sediment within the South Platte River Basin, which originates from a pristine region in the Rocky Mountains and runs through a gradient of human activities. A geospatial database was constructed to delineate surface water pathways from animal feeding operations, wastewater treatment plants, and fish hatchery and rearing units to river monitoring points. General linear regression models were compared. Riverine sul1 correlated with upstream capacities of animal feeding operations (R(2) = 0.35, p < 0.001) and wastewater treatment plants (R(2) = 0.34, p < 0.001). Weighting for the inverse distances from animal feeding operations along transport pathways strengthened the observed correlations (R(2) = 0.60-0.64, p < 0.001), suggesting the importance of these pathways in ARG dissemination. Correlations were upheld across the four sampling events during the year, and averaging sul1 measurements in bed and suspended sediments over all events yielded the strongest correlation (R(2) = 0.92, p < 0.001). Conversely, a significant relationship with landscape features was not evident for tet(W), which, in contrast to sul1, is broadly distributed in the pristine region and also relatively more prevalent in animal feeding operation lagoons. The findings highlight the need to focus attention on quantifying the contribution of water pathways to the antibiotic resistance disease burden in humans and offer insight into potential strategies to control the spread of ARGs.

MODELING LONG-TERM WATER QUALITY IMPACT OF STRUCTURAL BMPS

Structural best management practices (BMPs) that reduce soil erosion and nutrient losses have been recommended and installed on agricultural land for years. A structural BMP is expected to be fully functional only for a limited period after installation, after which degradation of the BMP is likely to lead to a reduction in the water quality improvement provided by the BMP. Assessing the impact of BMPs on water quality is of widespread interest, but no standard methods exist to determine the water quality impact of structural BMPs, particularly as the impact changes through time. The objective of this study was to determine the long-term (~20 year) impact of structural BMPs in two subwatersheds of Black Creek on sediment and phosphorus loads using the Soil and Water Assessment Tool (SWAT) model. The BMPs were represented by modifying SWAT parameters to reflect the impact the practice has on the processes simulated within SWAT, both when practices are fully functional and as their condition deteriorates. The current condition of the BMPs was determined using field evaluation results from a previously developed BMP condition evaluation tool. Based on simulations in the two subwatersheds, BMPs in good condition reduced the average annual sediment yield by 16% to 32% and the average annual phosphorus yield by 10% to 24%. BMPs in their current condition reduced sediment yield by only 7% to 10% and phosphorus yield by 7% to 17%.

Tracking Antibiotic Resistance Genes in the South Platte River Basin Using Molecular Signatures of Urban, Agricultural, And Pristine Sources

A novel approach utilizing antibiotic-resistance-gene (ARG) molecular signatures was applied to track the sources of ARGs at sites along the Cache la Poudre (Poudre) and South Platte Rivers in Colorado. Two lines of evidence were employed: (1) detection frequencies of 2 sulfonamide and 11 tetracycline ARGs and (2) tet(W) phylotype and phylogenetic analysis. A GIS database indicating the locations of wastewater treatment plants (WWTPs) and animal feeding operations (AFOs) in the watershed was also constructed to assess congruence of the surrounding landscape with the putative sources identified by ARG molecular signatures. Discriminant analysis was performed on detection frequencies of tetARG groups that were previously identified to be associated with either WWTPs or AFOs. All but one (South Platte River-3, just downstream from the confluence with the Poudre River) of the eight sites were classified as primarily WWTP-influenced based on discriminant analysis of ARG detection frequencies. tet(W) phylotype analysis also aligned South Platte River-3 with putative AFO sources, while phylogenetic analysis indicated that it was not significantly different from the AFOs or WWTPs investigated. South Platte River-3 is situated in an intense agricultural area, but the upstream portion of the South Platte River receives substantial loading from metropolitan Denver. By contrast, tet(W) phylotype and phylogenetics of site Poudre River-4, located 4 km downstream of a WWTP, was also characterized and found to be significantly different from the AFO lagoons (p < 0.05), as expected. In general, a good correspondence was found between classification of the impacted river sites and the surrounding landscape. While the overall approach could be extended to other watersheds, the general findings indicate that transport of ARGs from specific sources is likely the dominant mechanism for ARG proliferation in this riverine environment relative to selection of ARGs among native bacteria by antibiotics and other pollutants.

UNCERTAINTY IN TMDL MODELS

Although the U.S. Congress established the Total Maximum Daily Load (TMDL) program in the original Clean Water Act of 1972, Section 303(d), it did not receive attention until the 1990s. Currently, two methods are available for tracking pollution in the environment and assessing the effectiveness of the TMDL process on improving the quality of impaired water bodies: field monitoring and mathematical/computer modeling. Field monitoring may be the most appropriate method, but its use is limited due to high costs and extreme spatial and temporal ecosystem variability. Mathematical models provide an alternative to field monitoring that can potentially save time, reduce cost, and minimize the need for testing management alternatives. However, the uncertainty of the model results is a major concern. Uncertainty is defined as the estimated amount by which an observed or calculated value may depart from the true value, and it has important policy, regulatory, and management implications. The source and magnitude of uncertainty and its impact on TMDL assessment has not been studied in depth. This article describes the collective experience of scientists and engineers in the assessment of uncertainty associated with TMDL models. It reviews sources of uncertainty (e.g., input variability, model algorithms, model calibration data, and scale), methods of uncertainty evaluation (e.g., first-order approximation, mean value first-order reliability method, Monte Carlo, Latin hypercube sampling with constrained Monte Carlo, and generalized likelihood uncertainty estimation), and strategies for communicating uncertainty in TMDL models to users. Four case studies are presented to highlight uncertainty quantification in TMDL models. Results indicate that uncertainty in TMDL models is a real issue and should be taken into consideration not only during the TMDL assessment phase, but also in the design of BMPs during the TMDL implementation phase. First-order error (FOE) analysis and Monte Carlo simulation (MCS) or any modified versions of these two basic methods may be used to assess uncertainty. This collective study concludes that a more scientific method to account for uncertainty would be to develop uncertainty probability distribution functions and transfer such uncertainties to TMDL load allocation through the margin of safety component, which is selected arbitrarily at the present time. It is proposed that explicit quantification of uncertainty be made an integral part of the TMDL process. This will benefit private industry, the scientific community, regulatory agencies, and action agencies involved with TMDL development and implementation.

Identification of antibiotic-resistance-gene molecular signatures suitable as tracers of pristine river, urban, and agricultural sources.

Animal feeding operations (AFOs) and wastewater treatment plants (WWTPs) are potential sources of antibiotic resistance genes (ARGs) in rivers and/or antibiotics that may select for ARGs in native river bacteria. This study aimed to identify ARG distribution patterns that unambiguously distinguish putative sources of ARG from a native river environment. Such molecular signatures may then be used as tracers of specific anthropogenic sources. Three WWTPs, six AFO lagoons, and three sites along a pristine region of the Cache la Poudre (Poudre) River were compared with respect to the frequency of detection (FOD) of 11 sulfonamide and tetracycline ARGs. Principle-component and correspondence analyses aided in identifying the association of tet(H), tet(Q), tet(S), and tet(T) (tet group HQST) with AFO environments and tet(C), tet(E), and tet(O) (tet group CEO) with WWTPs. Discriminant analysis indicated that both tet group HQST and tet group CEO correctly classified the environments, but only the tet group HQST provided a significant difference in FOD among the environments (p < 0.05). Sul(I) was detected in 100% of the source environments but just once in the pristine Poudre River, which was dominated by tet(M) and tet(W). Tet(W) libraries generated from the pristine Poudre River, WWTPs, and AFO lagoons were also discernible based on restriction fragment length polymorphism and phylogenetic analysis. Thus, a novel approach was developed and demonstrated to be effective for the model river system, taking an important step in advancing the fundamental understanding of ARG transport in the environment.

Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.

A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling

Failure to consider major sources of uncertainty may bias model predictions in simulating watershed behavior. A framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), was developed utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Applications of the framework at the Eagle Creek Watershed in Indiana shows that watershed behavior was more realistically represented when the four uncertainty sources were considered jointly without having to embed watershed behavior constraints in auto-calibration. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty.

Improving conservation practices programming to protect water quality in agricultural watersheds: Lessons learned from the National Institute of Food and Agriculture-Conservation Effects Assessment Project

Nutrient enrichment and sedimentation of water resources is a significant problem in the United States and globally (Carpenter et al. 2011; Dubrovsky et al. 2010; Hilton et al. 2006). Specifically, in the United States, over 6,908 water bodies are listed as being nutrient impaired and 6,165 are sediment impaired (USEPA 2012). Agricultural nonpoint source pollution contributes, in part, to impaired water resources in many of these watersheds (NRC 2008; USEPA 2010). Conservation practices, including conservation tillage, nutrient management, and riparian buffers, are routinely used to reduce off-site losses of sediment, nutrients, pesticides, and bacteria from agricultural operations. Many research studies, generally conducted at the plot- or field-scale, report ranges in effectiveness of such conservation practices, from being negative to 100% effective (Gagnon et al. 2004; Gagnon et al. 2008; Jokela et al. 2004; Line et al. 2001; Richards and Baker 2002; Schnepf and Cox 2006; Sharpley et al. 2006; Shepard 2005; Smith et al. 2006). Documentation of combined practice impacts on water quality at the watershed scale has been more difficult than in plot or field-scale studies. The Black Creek Project in northeastern Indiana and the Model Implementation Program (MIP) promoted by the USDA and US Environmental Protection Agency (USEPA)…

Performance implications of multi-tier application deployments on Infrastructure-as-a-Service clouds: Towards performance modeling

Hosting a multi-tier application using an Infrastructure-as-a-Service (IaaS) cloud requires deploying components of the application stack across virtual machines (VMs) to provide the applications infrastructure while considering factors such as scalability, fault tolerance, performance and deployment costs (# of VMs). This paper presents results from an empirical study which investigates implications for application performance and resource requirements (CPU, disk and network) resulting from how multi-tier applications are deployed to IaaS clouds. We investigate the implications of: (1) component placement across VMs, (2) VM memory size, (3) VM hypervisor type (KVM vs. Xen), and (4) VM placement across physical hosts (provisioning variation). All possible deployment configurations for two multi-tier application variants are tested. One application variant was computationally bound by the application middleware, the other bound by geospatial queries. The best performing deployments required as few as 2 VMs, half the number required for VM-level service isolation, demonstrating potential cost savings when components can be consolidated. Resource utilization (CPU time, disk I/O, and network I/O) varied with component deployment location, VM memory allocation, and the hypervisor used (Xen or KVM) demonstrating how application deployment decisions impact required resources. Isolating application components using separate VMs produced performance overhead of ~1%-2%. Provisioning variation of VMs across physical hosts produced overhead up to 3%. Relationships between resource utilization and performance were assessed using multiple linear regression to develop a model to predict application deployment performance. Our model explained over 84% of the variance and predicted application performance with mean absolute error of only ~0.3 s with CPU time, disk sector reads, and disk sector writes serving as the most powerful predictors of application performance.

Practical Use of Computationally Frugal Model Analysis Methods

Three challenges compromise the utility of mathematical models of groundwater and other environmental systems: (1) a dizzying array of model analysis methods and metrics make it difficult to compare evaluations of model adequacy, sensitivity, and uncertainty; (2) the high computational demands of many popular model analysis methods (requiring 1000s, 10,000 s, or more model runs) make them difficult to apply to complex models; and (3) many models are plagued by unrealistic nonlinearities arising from the numerical model formulation and implementation. This study proposes a strategy to address these challenges through a careful combination of model analysis and implementation methods. In this strategy, computationally frugal model analysis methods (often requiring a few dozen parallelizable model runs) play a major role, and computationally demanding methods are used for problems where (relatively) inexpensive diagnostics suggest the frugal methods are unreliable. We also argue in favor of detecting and, where possible, eliminating unrealistic model nonlinearities-this increases the realism of the model itself and facilitates the application of frugal methods. Literature examples are used to demonstrate the use of frugal methods and associated diagnostics. We suggest that the strategy proposed in this paper would allow the environmental sciences community to achieve greater transparency and falsifiability of environmental models, and obtain greater scientific insight from ongoing and future modeling efforts.