Quantifying Variability in Four US Streams Using a Long-Term Data Set: Patterns in Water Quality Endpoints

Hysteresis analysis of nitrate dynamics in the Neuse River, NC

Anthropogenic activities have caused N saturation in many terrestrial ecosystems. The transfer of nutrients and sediments to freshwater environments has resulted in water quality impairments including eutrophication, increased turbidity, ecosystem acidification, and loss of biodiversity. Storm events account for the transport of a large proportion of nutrients and sediments found in watersheds on an annual basis. To implement effective water-quality management strategies, the importance of surface and subsurface flow paths during storm events and low flow conditions need to be quantified. The increased availability of optical in-situ sensors makes high-frequency monitoring of catchment fluxes practical. In this study, we present a high-resolution nitrate monitoring record over a 10-year period in the Neuse River Basin near Clayton, North Carolina. The relationship between discharge and nitrate concentration for 365 storm events are categorized into hysteresis classes that indicate different transport mechanisms into the river. Storm events over the entire period of this study are divided between clockwise, counter-clockwise, and complex hysteresis patterns, indicating multiple nitrate flow paths during different seasons and years. Logistic regression of a suite of environmental variables demonstrates that antecedent soil moisture is a significant factor in determining the storm hysteresis class, with the odds of counter-clockwise hysteresis increasing by 10.3% for every 1 percentage point increase in the soil moisture. There is also an overlying seasonal effect, which indicates that dry soil conditions and frequent small storms during summer leads to greater nitrate transport on the rising limb, in contrast to slower, groundwater-driven inputs during the rest of the year.

Developing and applying control charts to detect changes in water chemistry parameters measured in the Athabasca River near the oil sands: A tool for surveillance monitoring

Control charting is a simple technique to identify change and is well suited for use in water quality programs. Control charts accounting for covariation associated with discharge and in some cases time were used to explore example and representative variables routinely measured in the Athabasca River near the oil sands area for indications of change. The explored variables include 5 major ions (chloride, sodium, sulfate, calcium, magnesium), 5 total metals (aluminum, iron, thallium, molybdenum, vanadium), and total suspended solids at two sites straddling the developments north of Fort McMurray. Regression equations developed from reference data (1988-2009) were used to predict observations and calculate residuals from later test data (2010-2016). Evidence of change was sought in the deviation of residual errors from the test period compared with the patterns expected and defined from probability distributions of the reference residuals using the odds ratio. In most cases, the patterns in test residuals were not statistically different from those expected from the reference period at either site, especially when data were examined annually. However, differences were found at both locations, more were found at the downstream site, and more differences emerged as data accumulated and were analyzed over time. In sum, the analyses at the downstream site suggest higher concentrations than predicted in most major ions, but the source of the changes is uncertain. In contrast, the concentrations of most metals at during the test period were lower than expected, which may be related to deposition patterns of materials or weathering of minerals during construction activities of the 2000s which influence the reference data used. The analyses also suggest alternative approaches may be necessary to understand change in some variables. Despite this, the results support the use of control charts to detect changes in water chemistry parameters and the value of the tool in surveillance phases of long-term and adaptive monitoring programs. Environ Toxicol Chem 2018;37:2296-2311. © 2018 SETAC.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R²=0.82), but it was not robust in extrapolation (R²=0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.

Using adaptive processes and adverse outcome pathways to develop meaningful, robust, and actionable environmental monitoring programs

The primary goals of environmental monitoring are to indicate whether unexpected changes related to development are occurring in the physical, chemical, and biological attributes of ecosystems and to inform meaningful management intervention. Although achieving these objectives is conceptually simple, varying scientific and social challenges often result in their breakdown. Conceptualizing, designing, and operating programs that better delineate monitoring, management, and risk assessment processes supported by hypothesis-driven approaches, strong inference, and adverse outcome pathways can overcome many of the challenges. Generally, a robust monitoring program is characterized by hypothesis-driven questions associated with potential adverse outcomes and feedback loops informed by data. Specifically, key and basic features are predictions of future observations (triggers) and mechanisms to respond to success or failure of those predictions (tiers). The adaptive processes accelerate or decelerate the effort to highlight and overcome ignorance while preventing the potentially unnecessary escalation of unguided monitoring and management. The deployment of the mutually reinforcing components can allow for more meaningful and actionable monitoring programs that better associate activities with consequences. Integr Environ Assess Manag 2017;13:877-891. © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).