Challenges with secondary use of multi-source water-quality data in the United States

Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales

Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months' nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.

Characterizing surface water concentrations of hundreds of organic chemicals in United States for environmental risk prioritization

BACKGROUND

Thousands of chemicals are observed in freshwater, typically at trace levels. Measurements are collected for different purposes, so sample characteristics vary. Due to inconsistent data availability for exposure and hazard, it is complex to prioritize which chemicals may pose risks.

OBJECTIVE

We evaluated the influence of data curation and statistical practices aggregating surface water measurements of organic chemicals into exposure distributions intended for prioritizing based on nation-scale potential risk.

METHODS

The Water Quality Portal includes millions of observations describing over 1700 chemicals in 93% of hydrologic subbasins across the United States. After filtering to maintain quality and applicability while including all possible samples, we compared concentrations across sample types. We evaluated statistical methods to estimate per-chemical distributions for chosen samples. Overlaps between resulting exposure ranges and distributions representing no-effect concentrations for multiple freshwater species were used to rank estimated chemical risks for further assessment.

RESULTS

When we apply explicit data quality and statistical assumptions, we find that there are 186 organic chemicals for which we can make screening-level estimates of surface water chemical concentration. Of the original 1700 observed chemicals, this number decreased primarily due to a predominance of censored values (that is, observations indicating concentrations too low to be measured). We further identify 423 chemicals where all measurements were censored but, through consideration of detection limits, risk might still be prioritized based on the detection limits themselves. In the final set of 1.5 million samples, the median environmental concentration of one chemical (acetic acid) exceeded the 5th percentile of no-effect concentrations for the most delicate freshwater species (the highest priority risk condition identified here), and a further 29 chemicals were identified for possible further evaluation based on a small margin between occurrence and toxicity values.

SIGNIFICANCE

This method shows the broad range of chemical concentrations seen for organic chemicals across the country and identifies methods of determining their central tendency, allowing for researchers to characterize higher-than-normal or lower-than-normal surface water conditions as well as providing an overall indication of the presence of organic chemicals in the United States. The highest chemical concentrations did not always indicate the highest-risk conditions. Even when accounting for the high level of uncertainty in these data due to differences in data collection and reporting across the set, some chemicals may still be categorized as higher environmental risk than others using this method, providing value to chemical safety decision makers and researchers by suggesting avenues for more focused investigation.

A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5-60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE > 0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.

Valuing water quality in the United States using a national dataset on property values

High-quality water resources provide a wide range of benefits, but the value of water quality is often not fully represented in environmental policy decisions, due in large part to an absence of water quality valuation estimates at large, policy relevant scales. Using data on property values with nationwide coverage across the contiguous United States, we estimate the benefits of lake water quality as measured through capitalization in housing markets. We find compelling evidence that homeowners place a premium on improved water quality. This premium is largest for lakefront property and decays with distance from the waterbody. In aggregate, we estimate that 10% improvement of water quality for the contiguous United States has a value of $6 to 9 billion to property owners. This study provides credible evidence for policymakers to incorporate lake water quality value estimates in environmental decision-making.

Estimation of dissolved organic carbon from inland waters at a large scale using satellite data and machine learning methods

Dissolved Organic Carbon (DOC) in inland waters plays an essential role in the global carbon cycle and has significant public health effects. Machine learning (ML) together with remote sensing has emerged as a powerful and promising combination to quantify water quality parameters from space. However, inland water sample data for DOC is limited. Hence, little is known about the potential to quantify DOC content in inland waters, especially over large-scale areas. This study presents the first attempt to estimate DOC in inland waters over a large-scale area using satellite data and ML methods with the newly published open-source dataset AquaSat. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were trained using more than 16 thousand samples across the continental United States matched with satellite data from Landsat 5, 7 and 8 missions. Satellite data from the Landsat missions were further extended with environmental data from the ERA5-Land product and used as input to train the ML algorithms. Our results show that including environmental data as inputs considerably improved the prediction of DOC for all ML algorithms, with GPR showing the most promising performance results with moderate estimation errors (RMSE: 4.08 mg/L). Permutation feature importance analysis showed that the wavelength range in the visible Green band (from Landsat) and the monthly average air temperature (from ERA5-Land) were the most important variables for the ML approaches. The results demonstrate the predictive strength of GPR and its useful feature to derive per pixel standard deviations for detailed analysis. Our results further highlight the important role of considering environmental processes to explain DOC variations over large scales. The application and performance of the GPR in mapping spatiotemporal variations of DOC in an entire water body were discussed by taking Lake Okeechobee (the 8^th largest freshwater lake in the U.S.) as an illustrative example. While performance evaluation showed that DOC concentrations can be retrieved with adequate accuracy, algorithm development was challenged by the heterogenous nature of large-scale open source in situ data, issues related to atmospheric correction, and the low spatial and temporal resolution of the environmental predictors. This research demonstrates how open source, large-scale datasets like AquaSat in combination with ML and satellite remote sensing can make research toward large-scale estimation of inland water DOC more realistic while highlighting its remaining limitations and challenges.

Data clustering: application and trends

Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

Harmonized nitrogen and phosphorus concentrations in the Mississippi/Atchafalaya River Basin from 1980 to 2018

Water quality monitoring can inform policies that address pollution; however, inconsistent measurement and reporting practices render many observations incomparable across bodies of water, thereby impeding efforts to characterize spatial patterns and long-term trends in pollution. Here, we harmonized 9.2 million publicly available monitor readings from 226 distinct water monitoring authorities spanning the entirety of the Mississippi/Atchafalaya River Basin (MARB) in the United States. We created the Standardized Nitrogen and Phosphorus Dataset (SNAPD), a novel dataset of 4.8 million standardized observations for nitrogen- and phosphorus-containing compounds from 107 thousand sites during 1980–2018. To the best of our knowledge, this dataset represents the largest record of these pollutants in a single river network where measurements can be compared across time and space. We addressed numerous well-documented issues associated with the reporting and interpretation of these water quality data, heretofore unaddressed at this scale, and our approach to water quality data processing can be applied to other nutrient compounds and regions.

Measurement(s)	Nitrogen Compound • phosphorus compound
Technology Type(s)	water monitors
Sample Characteristic - Environment	water body • watershed • amount of nitrogen atom in water • water pollution • pollution monitoring • amount of phosphorus in water
Sample Characteristic - Location	river, contiguous United States of America • Mississippi/Atchafalaya River Basin

Managing groundwater resources using a national reference database: the French ADES concept

Abstract

Groundwater is an integral part of the water cycle and an essential human resource. Humans must protect this ever-changing heritage and preserve it in a sustainable way by understanding the physical and chemical properties of aquifers and monitoring their quantity and quality. Numerous studies have collected immense volumes of data that are difficult to access and not always comparable or of adequate quality. A pioneering national-scale database, ADES, was created in 1999 to store and make available quality data on French groundwater. This tool is freely accessible for/to water managers, scientists and the public. The data management system used in the database satisfies two important objectives: it is interoperable and based on a recognised groundwater reference system and provides high quality data to a large public. Data from different producers require normalisation and standardisation of system requirements to allow data integration and exchange. The database designers set up shared data models, and based the system on communal repositories of water points and hydrogeological entities. Nearly 102 million groundwater quality records and over 17 million water-level records are currently available, describing almost 61,800 stations. ADES makes it possible to visualise in “real-time" water level data for approximately 1500 stations equipped with GPRS (General Packet Radio Service) technology. ADES also provides, on a public website and via web services, public quantitative and qualitative data. ADES is an essential tool for developing groundwater services based on the FAIR guiding principles: Findable, Accessible, Interoperable and Reusable data (Wilkinson et al. in SD 3:160018, 2016)

Article highlights

Climate extremes as drivers of surface-water-quality trends in the United States

Surface-water quality can change in response to climate perturbations, such as changes in the frequency of heavy precipitation or droughts, through direct effects, such as dilution and concentration, and through physical processes, such as bank scour. Water quality might also change through indirect mechanisms, such as changing water demand or changes in runoff interaction with organic matter on the landscape. Many studies predict future changes in water-quality related to climate changes; however, fewer studies specifically document changes in water quality related to changes in climate, and they are usually limited in geographic scope. Recently, the U.S. Geological Survey's National Water-Quality Program reported nearly 12,000 trends in concentration and load for numerous water-quality constituents, including nutrients, sediment, major ions, and carbon. The results provide an unprecedented opportunity to examine sites across the conterminous United States for changes in water quality related to climate changes. We used published water-quality trends, modeled using the method of Weighted Regressions on Time, Season and Discharge, and calculated trends in climate extremes indices, using a modified Mann-Kendall trend method. The water-quality and the climate extremes trends were combined to identify areas in the conterminous United States where changes in climate extremes may have changed water quality. We investigated the water-quality trends in these areas to determine whether the trends related to changes in climate. We found that it was important to go beyond spatial correlation and examine trends on a watershed scale to investigate key drivers of trends. We found successful management practices in Iowa to reduce chloride concentrations, despite increases in icing days. For sediment, it appeared that management practices were having a larger effect than climate changes. For nutrients, complex forces affecting water quality make it difficult to unequivocally attribute water-quality change to climate change.

Development of a mixed-source, single pesticide database for use in ecological risk assessment: quality control and data standardization practices

Inclusion of pesticide monitoring data in pesticide risk assessment is important yet challenging for several reasons, including infrequent or irregular data collection, disparate sources procedures and associated monitoring periods, and interpretation of the data itself in a policy context. These challenges alone, left unaddressed, will likely introduce unintentional and unforeseen risk assessment conclusions. While individual water quality monitoring programs report standard operating procedures and quality control practices for their own data, cross-checking data for duplicated data from one database to another does not routinely occur. Consequently, we developed a novel quality control and assurance methodology to identify errors and duplicated records toward creating an aggregated, single pesticide database toward use in ecological risk assessment. This methodology includes (1) standardization and reformatting practices, (2) data error and duplicate record identification protocols, (3) missing or inconsistent limit of detection and quantification reporting, and (4) site metadata scoring and ranking procedures to flag likely duplicate records. We applied this methodology to develop an aggregated (multiple-source), national-scale database for atrazine from a diverse set of surface water monitoring programs. The resultant database resolved and/or removed approximately 31% of the total ~ 385,000 records that were due to duplicated records. Identification of sample replicates was also developed. While the quality control and assurances methodologies developed in this work were applied to atrazine, they generally demonstrate how a properly constructed and aggregated single pesticide database would benefit from the methods described herein before use in subsequent statistical and data analysis or risk assessment.

Management pathways for the successful reduction of nonpoint source nutrients in coastal ecosystems

Eutrophication remains a threat to coastal habitats and water quality worldwide. The U.S. Clean Water Act resulted in reductions of nutrient loading from point sources but management of nonpoint sources (NPS) of nutrients remains challenging despite efforts over at least three decades. The hydrological factors, best management practices (BMPs) and regulatory mechanisms that target nutrient NPS and improve coastal ecosystem function are poorly understood. We identified three case study sites in the U.S. with sufficient NPS management and monitoring history to quantify changes in estuarine habitat and water quality following BMP implementation and regulation targeting nutrient NPS. Utilizing publicly available data, we compared sites that are geographically distant and hydrologically distinct. We found that BMPs targeting NPS loads from surface waters into Roberts Bay (Florida) and Newport Bay (California) significantly reduced nutrient concentrations and harmful algal blooms within ~20 years. Improvements occurred despite concurrent human population growth within both watersheds. Conversely, we found that the majority of BMPs implemented within the Peconic Estuary (New York) watershed targeted surface waters despite a dominance of nitrogen inputs (97%) from groundwater and atmospheric sources. Declines in habitat and water quality in Peconic Estuary may be due to a failure to control the dominant nutrient sources and the long residence time of nitrogen in groundwater. Compared to surface water, reducing groundwater and atmospheric nutrients face greater technical and financial challenges. Improvements to Peconic Estuary may occur with further reductions in surface water inputs and as nutrients leach out of the groundwater. Although the effectiveness of specific NPS BMPs has been examined at small spatial scales, our study is the first to quantify improvements at a watershed scale. We showed that successful NPS management pathways are those which targeted the dominant sources of nutrients to coastal ecosystems and applied multiple BMPs within watersheds.

An approach for decomposing river water-quality trends into different flow classes

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN_2Q, which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS ("Weighted Regressions on Time, Discharge, and Season") method. The FN_2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985-2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN_2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN_2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.

Time series analysis of water use and indirect reuse within a HUC-4 basin (Wabash) over a nine year period

Anthropogenic water use and reuse represent major components of the water cycle. In the context of climate change, water reuse and recycling are considered necessary components for an integrated water management approach. Unplanned, or de facto, indirect water reuse occurs in most of the U.S. river systems, however, there is little real-time documentation of it. Despite the fact that there are national and state agencies that systematically collect data on water withdrawals and wastewater discharges, their databases are organized and managed in a way that makes it challenging to use them for water resource management analysis. The ability to combine reported water data to perform large scale analysis about water use and reuse is severely limited. In this paper, we apply a simple but effective methodology to complete a time series watershed-scale analysis of water use and unplanned indirect reuse for the Wabash River Watershed. Results document the occurrence of indirect water reuse, ranging from 3% to 134%, in a water-rich area of the U.S. The time series analysis shows that reported data effectively describe the water use trends through nine years, from 2009 to 2017, clearly reflecting both anthropogenic and natural events in the watershed, such as the retirement of thermoelectric power plants, and the occurrence of an extreme drought in 2012. We demonstrate the feasibility and significance of using available water datasets to perform large scale water use analysis, describe limitations encountered in the process, and highlight areas for improvement in water data management.

Lakes at Risk of Chloride Contamination

Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but there is little knowledge of the prevalence and spatial distribution of freshwater salinization. Here, we use a quantile regression forest (QRF) to leverage information from 2773 lakes to predict the chloride concentration of all 49 432 lakes greater than 4 ha in a 17-state area. The QRF incorporated 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest road and interstate. Model predictions had an r² of 0.94 for all chloride observations, and an r² of 0.86 for predictions of the median chloride concentration observed at each lake. The four predictors with the largest influence on lake chloride concentrations were low and medium intensity development in the watershed, crop density in the watershed, and distance to the nearest interstate. Almost 2000 lakes are predicted to have chloride concentrations above 50 mg L^-1 and should be monitored. We encourage management and governing agencies to use lake-specific model predictions to assess salt contamination risk as well as to augment their monitoring strategies to more comprehensively protect freshwater ecosystems from salinization.

Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework

Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (∼1 km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994-2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average.

Landscape Drivers of Dynamic Change in Water Quality of U.S. Rivers

Water security is a top concern for social well-being, and dramatic changes in the availability of freshwater have occurred as a result of human uses and landscape management. Elevated nutrient loading and perturbations to major ion composition have resulted from human activities and have degraded freshwater resources. This study addresses the emerging nature of streamwater quality in the 21st century through analysis of concentrations and trends in a wide variety of constituents in streams and rivers of the U.S. Concentrations of 15 water quality constituents including nutrients, major ions, sediment, and specific conductance were analyzed over the period 1982-2012 and a targeted trend analysis was performed from 1992 to 2012. Although environmental policy is geared toward addressing the long-standing problem of nutrient overenrichment, these efforts have had uneven success, with decreasing nutrient concentrations at urbanized sites and little to no change at agricultural sites. Additionally, freshwaters are being salinized rapidly in all human-dominated land use types. While efforts to control nutrients are ongoing, rapid salinity increases are ushering in a new set of poorly defined issues. Increasing salinity negatively affects biodiversity, mobilizes sediment-bound contaminants, and increases lead contamination of drinking water, but its effects are not well integrated into current paradigms of water management.

Variable impacts of contemporary versus legacy agricultural phosphorus on US river water quality

Management of agricultural phosphorus (P) has been in effect for decades with limited improvements in downstream water quality. Accumulated legacy (historical) P sources can mobilize, serve as a continual nutrient source, and mask the effects of conservation efforts to improve water quality through reductions in contemporary agricultural inputs to surface waters. We used a proxy estimate of legacy sources and assessed if P lingering in soils long after application was a major contributor to river export. For most watersheds, contributions of legacy P to river export were small in comparison to contributions from contemporary surpluses (fertilizer + manure > crop uptake). Estimating the magnitude of contemporary versus legacy P sources provides critical information to support effective implementation of management plans.

Phosphorus (P) fertilizer has contributed to the eutrophication of freshwater ecosystems. Watershed-based conservation programs aiming to reduce external P loading to surface waters have not resulted in significant water-quality improvements. One factor that can help explain the lack of water-quality response is remobilization of accumulated legacy (historical) P within the terrestrial-aquatic continuum, which can obscure the beneficial impacts of current conservation efforts. We examined how contemporary river P trends (between 1992 and 2012) responded to estimated changes in contemporary agricultural P balances [(fertilizer + manure inputs)—crop uptake and harvest removal] for 143 watersheds in the conterminous United States, while also developing a proxy estimate of legacy P contribution, which refers to anthropogenic P inputs before 1992. We concluded that legacy sources contributed to river export in 49 watersheds because mean contemporary river P export exceeded mean contemporary agricultural P balances. For the other 94 watersheds, agricultural P balances exceeded river P export, and our proxy estimate of legacy P was inconclusive. If legacy contributions occurred in these locations, they were likely small and dwarfed by contemporary P sources. Our continental-scale P mass balance results indicated that improved incentives and strategies are needed to promote the adoption of nutrient-conserving practices and reduce widespread contemporary P surpluses. However, a P surplus reduction is only 1 component of an effective nutrient plan as we found agricultural balances decreased in 91 watersheds with no consistent water-quality improvements, and balances increased in 52 watersheds with no consistent water-quality degradation.

Scientific integrity issues in Environmental Toxicology and Chemistry: Improving research reproducibility, credibility, and transparency

High-profile reports of detrimental scientific practices leading to retractions in the scientific literature contribute to lack of trust in scientific experts. Although the bulk of these have been in the literature of other disciplines, environmental toxicology and chemistry are not free from problems. While we believe that egregious misconduct such as fraud, fabrication of data, or plagiarism is rare, scientific integrity is much broader than the absence of misconduct. We are more concerned with more commonly encountered and nuanced issues such as poor reliability and bias. We review a range of topics including conflicts of interests, competing interests, some particularly challenging situations, reproducibility, bias, and other attributes of ecotoxicological studies that enhance or detract from scientific credibility. Our vision of scientific integrity encourages a self-correcting culture that promotes scientific rigor, relevant reproducible research, transparency in competing interests, methods and results, and education. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

Combining national and state data improves predictions of microcystin concentration

Data collected from lakes at national (regional) scales and state (local) scales can provide different insights regarding relationships between environmental factors and biological responses, and combining these two types of data can potentially yield more precise and accurate understanding of ecological phenomena. National data can include many measures, cover large spatial areas, and span broad environmental gradients. Because of these characteristics, analyses of these data can yield accurate estimates of relationships among different lake characteristics. However, the number of samples in a national data set that is available for estimating a relationship specific to waterbodies within a smaller region, like a single state, is limited. Conversely, state monitoring data provide intensive sampling of lakes within a smaller area, but these data span a narrower range of conditions and may only include a subset of relevant measurements. Here, a Bayesian network model is described that represents the causal linkages between observations of chlorophyll a concentration, cyanobacterial biovolume, and microcystin concentration. This network model was fit to national data and provided a context for modeling observations of chlorophyll a and microcystin collected from lakes in Iowa. Using the knowledge inherent in the national network model improved the accuracy of predictions of microcystin concentrations in Iowa compared to a model based only on Iowa data.

What Goes Up Must Come Down: Integrating Air and Water Quality Monitoring for Nutrients

Excess nitrogen and phosphorus ("nutrients") loadings continue to affect ecosystem function and human health across the U.S. Our ability to connect atmospheric inputs of nutrients to aquatic end points remains limited due to uncoupled air and water quality monitoring. Where connections exist, the information provides insights about source apportionment, trends, risk to sensitive ecosystems, and efficacy of pollution reduction efforts. We examine several issues driving the need for better integrated monitoring, including: coastal eutrophication, urban hotspots of deposition, a shift from oxidized to reduced nitrogen deposition, and the disappearance of pristine lakes. Successful coordination requires consistent data reporting; collocating deposition and water quality monitoring; improving phosphorus deposition measurements; and filling coverage gaps in urban corridors, agricultural areas, undeveloped watersheds, and coastal zones.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.

LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600–12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990

The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate-all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast U.S. (LAGOS-NE), a collection of preexisting lake data mostly from state agencies. LAGOS-NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake-specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year^-1 , while TP, TN:TP, and Chl did not change. A minority (7%-16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN:TP trends were related to region-level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region-level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved.