Chinese surface waters are severely polluted by nutrients. This study addresses three challenges in nutrientmodeling for rivers in China: (1) difficulties in transferring modeling results across biophysical and administrative scales, (2) poor representation of the locations of point sources, and (3) limited incorporation of the direct discharge of manure to rivers. The objective of this study is, therefore, to quantify inputs of nitrogen (N) and phosphorus (P) to Chinese rivers from different sources at multiple scales. We developed a novel multi-scale modeling approach including a detailed, state-of-the-art representation of point sources of nutrients in rivers. The model results show that the river pollution and source attributions differ among spatial scales. Point sources accounted for 75% of the total dissolved phosphorus (TDP) inputs to rivers in China in 2012, and diffuse sources accounted for 72% of the total dissolved nitrogen (TDN) inputs. One-third of the sub-basins accounted for more than half of the pollution. Downscaling to the smallest scale (polygons) reveals that 14% and 9% of the area contribute to more than half of the calculated TDN and TDP pollution, respectively. Sources of pollution vary considerably among and within counties. Clearly, multi-scale modeling may help to develop effective policies for water pollution.

Leverage existing nutrient input/output data from the Chesapeake Bay Program's CAST database to develop nitrogen and phosphorus inventories. These inventories will highlight spatiotemporal variability in point and non-point source pollution throughout the watershed, and can be used to monitor progress in decreasing pollution sources as well as retrospective water quality analyses looking to explain past water quality trends. This dataset is associated with the following publication: Sabo, R., B. Sullivan, C. Wu, E. Trentacoste, Q. Zhang, G. Shenk, G. Bhatt, and L. Linker. Major point and nonpoint sources of nutrient pollution to surface water have declined throughout the Chesapeake Bay watershed. Environmental Research Communications. IOP Publishing, PHILADELPHIA, PA, USA, 4: 1-19, (2022).

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.

Extensive time and financial resources have been dedicated to address nonpoint sources of nitrogen and phosphorus in watersheds. Despite these efforts, many watersheds have not seen substantial improvement in water quality. The objective of this study is to review the literature and investigate key factors affecting the lack of improvement in nutrient levels in waterways in urban and agricultural regions. From 94 studies identified in the academic literature, we found that, although 60% of studies found improvements in water quality after implementation of Best Management Practices (BMPs) within the watershed, these studies were mostly modeling studies rather than field monitoring studies. For studies that were unable to find improvements in water quality after the implementation of BMPs, the lack of improvement was attributed to lack of knowledge about BMP functioning, lag times, nonoptimal placement and distribution of BMPs in the watershed, postimplementation BMP failure, and socio-political and economic challenges. We refer to these limiting factors as known unknowns. We also acknowledge the existence of unknown unknowns that hinder further improvement in BMP effectiveness and suggest that machine learning, approaches from the field of business and operations management, and long-term convergent studies could be used to resolve these unknown unknowns.

Nutrient pollution is a widespread problem in rivers in China. Managing nutrient pollution requires better knowledge of in-stream processes governing the surface water quality. As current nutrient models for China mainly focus on river export of nutrients to seas, in-stream surface water quality and their contributing sources and processes are, therefore not well understood. This requires accounting for combined effects of nutrient inputs to rivers from produced waste, biochemistry of different forms of nutrients and their transport by river network. Moreover, improvements can be made in evaluating the model performance of large-scale nutrient models based on water quality measurements in China (using the surface water quality classes from 1 to 6). The objective of this study is to quantify the spatial variation in in-stream water quality for nutrients, and associated sources, for water quality classes in China. Our new Model to Assess River Inputs of Nutrients to seAs (MARINA 3.0) for instream water quality distinguishes different nutrient forms including dissolved inorganic (DIN, DIP) and organic (DON, DOP) nitrogen and phosphorus and was applied for the year 2012. Our model simulations compare reasonably well with measurements across 155 river sections. Results show that between 12% and 66% of the streams are highly polluted (exceeding water quality class 3) and depending on nutrient form. Diffuse sources dominate in 76% of the streams for DIN. Point sources such as direct discharges of animal manure dominate in 46%–59% of the streams for DON, DIP and DOP. The dominant sources vary considerably between rivers and nutrient forms. This indicates the need account for nutrient forms in models and national monitoring programs. Our model results could support effective management to reduce nutrient pollution in China.

The data set contains data required to run the logistics network, nutrient fate and transport, and algae growth models: geographical nodes, nutrient and product demand data, nutrient limit in agricultural land, nutrient emission factor of waste, technology design and capacity data, nutrient source inventory location and capacity, algae cell data, waterbody reservoir temperature profile, weather data, and other parameters described in the manuscript's Figure 5 (data flow of the modeling framework). This dataset is associated with the following publication: Hu, Y., A.M. Sampat, G.J. Ruiz-Mercado, and V.M. Zavala. Logistics Network Management of Livestock Waste for Spatiotemporal Control of Nutrient Pollution in Water Bodies. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, USA, 7(22): 18359-18374, (2019).

Students use data on nitrogen and phosphorus levels in streams and macrobenthic insect biodiversity to consider issues of nutrient pollution and stream health while learning to filter, summarize, and plot data.

The MANAGE (Measured Annual Nutrient loads from AGricultural Environments) database was developed to be a readily-accessible, easily-queried database of site characteristic and field-scale nutrient export data (Harmel et al., 2006). Initial funding for MANAGE was provided by USDA-ARS to support the USDA Conservation Effects Assessment Project (CEAP) and the Texas State Soil and Water Conservation Board as part of their mission to understand and mitigate agricultural impacts on water quality. The original version of MANAGE, which drew heavily from an early 1980’s compilation of nutrient export data (Reckhow et al., 1980; Beaulac, 1980; Beaulac and Reckhow, 1982), created an electronic database with nutrient load data and corresponding site characteristics from 40 studies on agricultural (cultivated and pasture/range) land uses. The first revision in 2008 added N and P load data from 15 additional studies along with N and P runoff concentration data for all 55 studies (Harmel et al., 2008). The second revision in 2016 added 30 runoff studies from forested land uses, 91 drainage water quality studies from drained land, and 12 additional runoff studies from cultivated and pasture/range (Christianson and Harmel, 2015; Harmel et al., 2016). In this expansion, fertilizer application timing, crop yield, and N and P uptake data were added to facilitate analysis of 4R Nutrient Stewardship. The latest revision (Harmel et al., 2022) added 27 studies and Level II ecoregion delineations for each of the 94 studies such that data are now available from 11 of the 50 North American Level II ecoregions, representing the major U.S. agricultural regions. With these updates, MANAGE contains data from a vast majority of published peer-reviewed N and P export studies on homogeneous cultivated, pasture/range, and forested land uses in the US under natural rainfall-runoff conditions, as well as artificially drained agricultural land. Thus MANAGE facilitates expanded spatial analyses and improved understanding of regional differences, management practice effectiveness, and impacts of land use conversions and management techniques, and it provides valuable data for modeling and decision-making related to agricultural runoff. The Manage Database v5 04-04-2018 zip file resource superseded the previously available v4 and was added to this record on May 30, 2018. Resource MANAGE Database v6 added Nov 17, 2022. Resources in this dataset:Resource Title: Manage Database v5 04-04-2018. File Name: MANAGEv5 4-4-18.zipResource Description: Contains the MANAGE v5 Microsoft Access DatabaseResource Title: MANAGE Database v6. File Name: MANAGEv6 11-17-21.accdbResource Description: Contains the MANAGE v6 Microsoft Access database. Zip file containing Access database: managev4ag+forest+yield+drain4-1-16.accdb

https://github.com/zavalab/JuliaBox/tree/master/HiddenImpacts This folder provides supporting codes for the paper "Valuing Economic Impact Reductions of Nutrient Pollution from Livestock Waste". * The folder "sensitivity_analysis" contains the code and data files for different values of the economic impact/value of service (vos). * The folder "GIS_data" contains the code and data files used to generate the maps of the Upper Yahara watershed region presented in the paper. * In each case, we have three Julia scripts: "market_model.jl", "market_Run.jl", and "market_print.jl". One should run "market_Run.jl" first, this script will automatically read the "market_model.jl" script, establish the model, and solve the model. Then, the "market_print.jl" should be run in order to print out all the result files. * If a sensitivity analysis on the VOS needs to be conducted (similar to the paper), one can change the lambda value in line 26 in "market_model.jl". * We recommend use Julia 0.6.4 and Gurobi 8.1 to run all code files for sensitivity analysis. This dataset is associated with the following publication: Sampat, A.M., A. Hicks, G. Ruiz-Mercado, and V.M. Zavala. Valuing economic impact reductions of nutrient pollution from livestock waste. Resources, Conservation and Recycling. Elsevier Science BV, Amsterdam, NETHERLANDS, 164: 105199, (2021).

The dynamics of physicochemical properties within rivers are essential for understanding the health and functioning of aquatic ecosystems. This study investigated the spatial and seasonal variability of water quality in both water and sediment phases across rivers with different pollution sources in the Jinjing Basin: Tuojia River (TR), Tuojia River substream (TRS) (farmland), Guojia River (GR), Guojia River substream (GRS) (woodlands) and Jinjing River (JR) (residential). Samples were collected during wet and dry seasons and analyzed using multivariate statistical approaches. Farmland-dominated rivers (TR and TRS) exhibited the highest nutrient concentrations in both water and sediment phases, with elevated nutrients, soil organic matter (SOM), and dissolved organic carbon (DOC), driven by fertilizer runoff and organic inputs. In contrast, woodland rivers (GR and GRS) displayed the lowest nutrient levels, benefiting from dense vegetation and natural nutrient retention processes. Seasonal variability revealed higher nutrient concentrations in the water phase and increased levels of ammonium nitrogen (NH4+-N) and SOM in the sediment phase during the wet season. In the dry season, reduced flow enhanced photosynthesis, resulting in higher pH and dissolved oxygen levels in the water phase and elevated pH and DOC in sediment. Principal component analysis further confirmed that nutrient pollution is predominantly influenced by agricultural runoff during the wet season, while reduced runoff in the dry season allowed natural processes to dominate. The findings underscore the importance of managing nutrient loads in both water and sediment, especially in farmland areas to ensure the sustainability of water resource management in the Jinjing Basin.

Coastal eutrophication is a prevalent threat to the healthy functioning of ecosystems globally. While degraded water quality can be detected by monitoring oxygen, nutrient concentrations, and algal abundance, establishing regulatory guidelines is complicated by a lack of baseline data (e.g., pre-Anthropocene). We use historical carbon and nitrogen isoscapes from sediment cores to reconstruct spatial and temporal changes in nutrient dynamics for a central California estuary, where development and agriculture dramatically enhanced nutrient inputs over the past century. We found strong contrasts between current sediment stable isotopes and those from the recent past, demonstrating shifts exceeding those in previously studied eutrophic estuaries and substantial increases in nutrient inputs. Comparisons of contemporary with historical isoscapes also revealed that nitrogen sources shifted from a marine-terrestrial gradient to amplified denitrification at the head and mouth of the estuary. Geo..., To examine interrelationships between nitrogen pollution and anthropogenic sources over the past century, we parametrized a model of nitrogen inputs to the watershed. Our model was based on the Nitrogen Loading Model (NLM). We applied the NLM model to calculate watershed sources of nitrogen over time in decadal increments from 1930–2010. We compiled historical data on changes in human population from census data, atmospheric deposition, homes with wastewater treatment, the areal extent of cultivated and natural lands and impervious surface cover, and estimated changes in fertilizer application rates in the Elkhorn watershed (based on annual "Commercial Fertilizers" and "Fertilizing Materials" reports published by the California Department of Agriculture 1925–2012). Eighty-five ~ three-meter-deep sediment cores were collected during 2010 from the vertices of a 200 m x 200 m grid superimposed over the tidal and never-diked portions of the estuary. Most of the sediment cores were collected..., Excel, R, and a GIS software such as ArcGIS or QGIS.

This dataset contains measurements of fecal indicator bacteria (Escherichia coli and enterococci), microbial source tracking markers (HF183 and GFD), nutrients, and environmental parameters from two freshwater Florida streams. Water and sediment was collected over a 26-month period. Fecal indicator bacteria were cultured from water and sediment, microbial source tracking markers were assessed in water by qPCR, and nutrients were measured in water and sediment.

It should be noted that this data is now somwhat dated!

Nutrients can enter streams from point, diffuse from various extensive land- uses or natural sources. In this indicator only industrial point sources (mines, quarries and chemical plants) and intensive agricultural production and urban point sources (abattoirs, dairy, livestock production, sewerage) are considered. The Wild Rivers data set (Environment Australia) includes national data on point sources of pollution (1:250K).

This data set can be separated into industrial (mines, quarries, chemical) point sources and nutrient point sources (abattoirs, dairy, livestock production, sewage). These data were obtained from state and federal sources and much of the contaminated sites, chemical pollution and nutrient pollution data are far from comprehensive (i.e. available for NSW, Victoria and SA only).

Reliability of the available data is good, but the data set is incomplete. Only nutrient point source data is used in this indicator. The density of nutrient point sources is not an unequivocal indicator of excessive nutrient levels in nearby waterways, since diffuse agricultural sources and natural sources are not included. As for the industrial point sources, there is no distinction made between types of nutrients exported, discharge magnitude and frequency, existing environmental safe guards and proximity to stream network.

Interpretation is also confounded by the incompletedness of the data set. At best, this is an indicator of nutrient hazard. It has not been validated against the assessment question. The 500 and AWRC maps give a similar picture, with the major point sources located in the Murray-Darling Basin and central Victoria. The Bunyip, Moorabool, Kiewa and Ovens Rivers in Victoria, the Namoi River in NSW and the Torrens River in South Australia have a notably high nutrient point source hazard.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

This dataset reflects the spatial distribution of area specific inputs to the sea of total nitrogen and total phosphorus from different land-based sources. Area specific inputs to the sea are calculated for each sub-catchment designated for the HELCOM PLC assessment. The sub-catchments are the smallest areas for which loads of nutrients and hazardous substances are assessed. Sub-catchments are always located within borders of a single country and drain into one Baltic Sea sub-basin. Sub-catchments may correspond to either monitored or unmonitored areas. Unmonitored sub-catchments may represent either unmonitored parts of river catchments or aggregated unmonitored areas (depending on the reporting format of each Contracting Party). Area specific inputs to the sea are calculated applying the load-oriented approach (see PLC-Water Guideline, http://www.helcom.fi/Lists/Publications/PLC-Water%20Guidelines.pdf). It means that computed values reflect nutrient inputs from identified sources into respective Baltic Sea sub-basins, considering also retention in downstream sub-catchments. The data on sources of nutrients is obtained through national monitoring programmes and periodically reported by Contracting Parties to the HELCOM Pollution Loads Compilation (PLC-water) database as an integral part of HELCOM Pollution Loades Compilation process. The data is produced, compiled and quality assured in accordance with the HELCOM Guidelines for the annual and periodical compilation and reporting of waterborne pollution inputs to the Baltic Sea (PLC-Water Guideline, http://www.helcom.fi/Lists/Publications/PLC-Water%20Guidelines.pdf). Dataset content: Polygonal feature class contains the following attributes: 1. Source_Code: Unique code for sub-catchment 2. Name: Name of river catchment or unmonitored area 3. SubBasin: Sub-basin of the Baltic Sea the sub-catchment drains to. 4. Country: Country on which territory the sub-catchment is located. 5. Polygon area in sq.km. 6. Retention of nitrogen (%) 7. Retention of phosphorus (%) 8. Area specific inputs of Nitrogen to the sea (kg/km2) 9. Area specific inputs of Phosphorus to the sea (kg/km2) 10. NBL_TN: Area specific background inputs of Nitrogen to the sea (kg/km2) 11. NBL_TP: Area specific background inputs of Phosphorus to the sea (kg/km2) 12. Diffuse_TN: Area specific total diffuse inputs of Nitrogen to the sea (kg/km2) 13. Diffuse_TP: Area specific total diffuse inputs of Phosphorus to the sea (kg/km2) 14. AGL_TN: Area specific agricultural inputs of Nitrogen to the sea (kg/km2) 15. AGL_TP: Area specific agricultural inputs of Phosphorus to the sea (kg/km2) 16. Dif_other_TN: Area specific diffuse nitrogen inputs to the sea from sources other than agriculture (kg/km2) 17. Dif_other_TP: Area specific diffuse phosphorus inputs to the sea from sources other than agriculture (kg/km2) 18. Trans_TN: Area specific transboundary nitrogen inputs to the sea (kg/km2). 19. Trans_TP: Area specific transboundary phosphorus inputs to the sea (kg/km2).

Visualization: The following maps visualize the data on area specific loads of nutrients from various sources: • Retention of Nitrogen (%) • Retention of Phosphorus (%) • Area specific total inputs of Nitrogen to the sea (kg/km2) • Area specific total inputs to the sea of Phosphorus to the sea (kg/km2) • Area specific background inputs of Nitrogen to the sea (kg/km2) • Area specific background inputs of Phosphorus to the sea (kg/km2) • Area specific total diffuse inputs of Nitrogen to the sea (kg/km2) • Area specific total diffuse inputs of Phosphorus to the sea (kg/km2) • Area specific agricultural inputs of Nitrogen to the sea (kg/km2) • Area specific agricultural inputs of Phosphorus to the sea (kg/km2) • Area specific diffuse Nitrogen inputs to the sea from sources other than agriculture (kg/km2) • Area specific diffuse Phosphorus inputs to the sea from sources other than agriculture (kg/km2) • Area specific transboundary inputs of Nitrogen load to the sea (kg/km2). • Area specific transboundary input of Phosphorus load to the sea (kg/km2).

Eutrophication is a process driven by enrichment of water by nutrients, especially compounds of nitrogen and/or phosphorus, leading to increased biomass of algae, changes in the balance of organisms, and water quality degradation. In coastal waters, excessive nutrient inputs primarily come from human sources including agricultural fertilizers, livestock waste and outlets from wastewater treatment plants. Eutrophication can lead to harmful algal blooms, hypoxia, fish kills, seagrass die off, loss of coral reef and nearshore hard bottom habitats, and health hazards to swimmers and fishers.The United Nations' Sustainable Development Goals (SDGs) provide a framework for the conservation and sustainable use of oceans, seas, and marine resources. In support of this framework, the rates of nutrient pollution are collected and analyzed, over time, within the Exclusive Economic Zone of each country. Learn more about these goals and methodologies here.The Coastal Eutrophication application is a work of Esri’s Living Atlas of the World. To access this data directly, visit this ArcGIS Online resource. Please direct questions or comments to Keith VanGraafeiland. The code for the application can be found on Esri Codehub.

In the future, rivers may export more pollutants to coastal waters, driven by socio-economic development, increased material consumption, and climate change. However, existing scenarios often ignore multi-pollutant problems. Here, we aim to explore future trends in river exports of nutrients (N and P), plastics (macro and micro), and emerging contaminants (triclosan and diclofenac) at the sub-basin scale in the world by developing and applying the process-based MARINA-Multi model for diverging scenarios. In our MARINA-Multi (Model to Assess River Inputs of pollutaNts to the seAs) model, we implemented two new multi-pollutant scenarios: “Sustainability-driven Future” (SD) and “Economy-driven Future” (ED). In ED, river exports of nutrients and microplastics will double by 2100 globally. For SD, a decrease of up to 83% is projected for all pollutants by 2100. Diffuse sources such as fertilizers are largely responsible for increasing nutrient pollution in the two scenarios. Point sources namely sewage systems are largely responsible for increasing microplastic pollution in the ED scenario. In both scenarios, the Indian Ocean will receive up to 400% more pollutants from rivers by 2100 because of growing population, urbanization, and poor waste management in the African and Asian basins. The situation is different for the Mediterranean Sea and the Pacific Ocean (mainly less future pollution) and the Atlantic Ocean and Arctic Ocean (more or less future pollution depending on sub-basin and scenario). Globally, 56-78% of people are expected to live in more polluted river basins in the future, challenging sustainable development goals for clean waters.

Degradation of water quality from nutrient pollution continues to be a challenge for water resource managers. The development of effective management strategies begins with tools that facilitate an understanding of nutrient sources and transport. SPARROW (SPAtially Referenced Regression On Watershed attributes) is a spatially explicit model platform that correlates water-quality observations with sources and transport-related properties of the watershed to predict constituent loads for streams and catchments. Several large-scale regional SPARROW models have been previously developed by the USGS that predict nutrient loads for large portions of the U.S. (see https://www.usgs.gov/mission-areas/water-resources/science/sparrow-mappers). However, relatively smaller-scaled and more focused, state-based SPARROW models may also be of particular benefit to state and local resource managers related to assessment of total maximum daily loads, nutrient criteria, and prioritizing nutrient reduction strategies. For this study, the USGS, in cooperation with Mississippi Department of Environmental Quality (MDEQ), developed state-based SPARROW models for Mississippi using RSPARROW software (Alexander and Gorman Sanisaca, 2019). Mississippi SPARROW models were developed by utilizing published input datasets of nutrients sources and delivery variables compiled from the Midwest (Saad and Robertson, 2020) and Southeast regional SPARROW models (Roland and Hoos, 2020). Source and delivery variables included land characteristics (urban coverage), instream and reservoir attenuation, and various sources of nitrogen and phosphorus (atmospheric deposition, point-sources, fertilizer/manure application, geologic material, etc.). Updated load estimates were calculated using streamflow and nutrient data for the period of 2005 through 2020 from USGS streamgages throughout Mississippi and portions of Alabama, Georgia, Tennessee, North Carolina, and Virginia. Load estimates were used to calibrate total nitrogen and total phosphorus SPARROW models for the base year of 2018. This data release includes the raw input and output files used in the development of Mississippi SPARROW models. Please note: the spatial footprint of these datasets includes calibration sites, stream reaches, and catchments that extend outside of the state of Mississippi; however, the associated report by Roland and Gain (2025) and web-based mapper (https://sparrow.wim.usgs.gov/sparrow-mississippi/) only include model results for stream reaches and catchments that are within or drain into the state of Mississippi. Included datasets: ms_sparrow_data1.csv (model input data file "data1") ms_sparrow_catchments.zip (catchment shapefile) ms_sparrow_reaches.zip (stream reach shapefile) ms_sparrow_output_TN.csv (total nitrogen model results - predicted loads and yields) ms_sparrow_output_TP.csv (total phosphorus model results - predicted loads and yields) Cited works: Alexander, Richard B., and Gorman Sanisaca, Lillian. (2019). RSPARROW: An R system for SPARROW modeling. U.S. Geological Survey Software release. DOI: https://doi.org/10.5066/P9UAZ6FO. Roland, V.L., II, and Hoos, A.B., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 Base Year: U.S. Geological Survey data release, https://doi.org/10.5066/P9A682GW. Saad, D.A., and Robertson, D.M., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 Base Year: U.S. Geological Survey data release, https://doi.org/10.5066/P93QMXC9.

The Interactive Map of Eutrophication & Hypoxia represents 762 coastal areas impacted by eutrophication and/or hypoxia. There are 479 sites identified as experiencing hypoxia, 55 sites that once experienced hypoxia but are now improving, and 228 sites that experience other symptoms of eutrophication, including algal blooms, species loss, and impacts to coral reef assemblages. These data were compiled using a literature search conducted by Dr. Robert Diaz of VIMS and WRI staff. Cautions Because this map depends on available data, geographic areas with more data availability (such as the United States) may show relatively more problem areas compared to areas with less data. We need your help to ensure that this dataset is accurate and up to date. Please let us know if we are missing a site, or if you believe a site on the map is misclassified. Please also note that this dataset was last updated in 2011; conditions have likely changed since then. Citation Diaz, R., M. Selman. and C. Chique. 2011. 'Global Eutrophic and Hypoxic Coastal Systems.' Washington, DC: World Resources Institute. Eutrophication and Hypoxia: Nutrient Pollution in Coastal Waters. Available online at:

IntroductionBest management practices (BMPs) are important tools for mitigating the impact of non-point source pollutants on water quality. Drivers of the high variance observed in BMP performance field tests are not well documented and present challenges for planning BMP construction and forecasting water quality improvements.MethodsWe conducted a systematic review of published nonpoint source water quality BMP studies conducted in the United States and used a meta-analysis approach to describe variance in pollutant removal performance. We used meta-regression to explore how much BMP pollutant removal process, influent pollutant concentration, and aridity effected BMP performance.ResultsDespite high variance, we found the BMPs on average were effective at reducing fecal indicator bacteria (FIB), total nitrogen (TN), total phosphorus (TP), and total suspended sediment (TSS) concentrations. We found that influent concentration and interaction effect between the BMP pollutant removal process and aridity explained a substantial amount of variance in BMP performance in FIB removal. Influent concentration explained a small amount of variability in BMP removal of TP and orthophosphate (PO4). We did not find evidence that any of our chosen variables moderated BMP performance in nitrogen or TSS removal. Through our systematic review, we found inadequate spatial representation of BMP studies to capture the underlying variability in climate, soil, and other conditions that could impact BMP performance.

ABSTRACT: The goal of this study was to quantify the water, soil, and soluble nutrient losses during high-intensity rainfall simulated in two soil preparation systems with four sources of fertilization. Forty-five days after the corn seeding, a 120 mm h-1 intensity rainfall was simulated during 90 min in field plots with conventional tillage (CT) or no-tillage (NT). Each system had four repetitions with the fertilizer treatments, including without fertilization, mineral, urban waste compost (UWC), and pig slurry. P, K, Ca, and K concentrations were measured in soluble form, in addition to electrical conductivity, pH, water, and soil losses. As expected, the greatest soil losses occurred with CT; however, the greatest water losses occurred with NT. Among the fertilizers, UWC was more efficient because it had the highest infiltration rates. The concentrations of P, K, Ca, and Mg did not exhibit any interaction between fertilization and soil tillage treatments. K was the nutrient that presented the greatest losses (kg ha-1) at the end of the simulated rainfall because of the highest concentrations (mg L-1) added to high runoff coefficients of 45% for CT and 77% for NT. Thus, the evaluated system with cover crops and minimum soil tillage was not sufficient to control nutrient transfer in the soluble form during intense rainfall events.