Nitrate Concentrations In Surface Waters Research Articles

Surface-Water Nitrate Exposure to World Populations Has Expanded and Intensified during 1970-2010.

Excessive nitrate in surface waters deteriorates the water quality and threatens human health. Human activities have caused increased nitrate concentrations in global surface waters over the past 50 years. An assessment of the long-term trajectory of surface-water nitrate exposure to world populations and the associated potential health risks is imperative but lacking. Here, we used global spatially explicit data on surface-water nitrate concentrations and population density, in combination with thresholds for health risks from epidemiological studies, to quantify the long-term changes in surface-water nitrate exposure to world populations at multiple spatial scales. During 1970-2010, global populations potentially affected by acute health risks associated with surface-water nitrate exposure increased from 6 to 60 million persons per year, while populations at potential chronic health risks increased from 169 to 1361 million persons per year. Potential acute risks have increasingly affected Asian countries. Populations potentially affected by chronic risks shifted from dominance by high-income countries (in Europe and North America) to middle-income countries (in Asia and Africa). To mitigate adverse health effects associated with surface-water nitrate exposure, anthropogenic nitrogen inputs to natural environments should be drastically reduced. International and national standards of maximum nitrate contamination may need to be lowered.

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed

Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater. Previous studies have applied regression and classification ML algorithms to predict nutrient concentrations in surface water and/or groundwater, or to categorize an output variable using a limited number of input variables. However, there have been no studies that examined the application of different ML classification algorithms in agricultural settings to classify various output variables using a wide range of input variables. In this study, twenty-four ML classification algorithms were implemented on a dataset from three locations within the Upper Parkhill watershed, an agricultural watershed in southern Ontario, Canada. Nutrient concentrations in surface water were classified using geochemical and physical water parameters of surface water and groundwater (e.g., pH), climate and field conditions as the input variables. The performance of these algorithms was evaluated using four evaluation metrics (e.g., classification accuracy) to identify the optimal algorithm for classifying the output variables. Ensemble bagged trees was found to be the optimal ML algorithm for classifying nitrate concentration in surface water (accuracy of 90.9%), while the weighted KNN was the most appropriate algorithm for categorizing the total phosphorus concentration (accuracy of 87%). The ensemble subspace discriminant algorithm gave the highest overall classification accuracy for the concentration of soluble reactive phosphorus and total dissolved phosphorus in surface water with an accuracy of 79.2% and 77.9%, respectively. This study exemplifies that ML algorithms can be used to signify exceedance of recommended concentrations of nutrients in surface waters in agricultural watersheds. Results are useful for decision makers to develop nutrient management strategies.

Douglas-fir plantations impact stream and groundwater chemistry in western Europe: Insights from three case studies in France and Luxembourg

In rural areas, nitrate concentrations in surface waters most often originate from the leaching of excess N fertilizer in agricultural lands, whereas forested catchments often have good water quality. However, Douglas-fir plantations may induce nitrogen cycle unbalances which may lead to an excess of nitrate production in the soil. We hypothesize that the excess of production of nitrate in the soil and nitrate leaching to streamwater is greater in catchments planted with Douglas fir. We used paired catchments in both France and Luxembourg with different land covers (Douglas-fir, Spruce, Deciduous, Grassland and clearcut) which were monitored over a 3–5 year period in order to assess the effect of Douglas-fir plantations on the chemical composition of surface water. Nitrate concentration in the soil and groundwater were also monitored. The results show that nitrate concentrations in streams draining Douglas-fir catchments were two to ten times higher than in streams draining other land covers, but were similar to the clearcut catchment. Nitrate concentrations under Douglas-fir in groundwater (up to 50 mg L−1) and in the soil were also higher than under all other land covers. Soil nitrate concentration was related to stream nitrate concentration. This suggests that soil processes, through excessive nitrate production under Douglas-fir, are driving the nitrate concentration in the stream water and our hypothesis of a transfer of a fairly large proportion of this excessive production from the soil to the stream is supported. This study also shows that nitrate concentrations in surface and ground waters in rural areas could also originate from Douglas fir forested catchments. The impact of Douglas-fir is nevertheless reduced downstream through a dilution effect: mixing tree species at the catchment scale could thus be a solution to mitigate the effect of Douglas-fir on nitrate concentration in surface waters.

Temporal and Spatial Distribution Characteristics and Source Analysis of Nitrate in Surface Water of Wuding River Basin

The Wuding River Basin is a first-class tributary of the Yellow River, and the quality of its water ecological environment has a profound impact on the ecological protection and high-quality development of the Yellow River Basin. In order to identify the source of nitrate pollution in the Wuding River Basin, surface water samples of the Wuding River were collected from 2019 to 2021, and the temporal and spatial distribution characteristics and influencing factors of nitrate concentration in surface water in the basin were explored. Nitrogen and oxygen isotope tracer technology and the MixSIAR model were used to qualitatively and quantitatively determine the sources of surface water nitrate and their contribution rates. The results showed that there were significant spatial and temporal differences in nitrate concentrations in the Wuding River Basin. In terms of time, the mean concentration of NO-3-N in surface water in the wet season was higher than that in the flat-water period; spatially, the mean concentration of NO-3-N in the downstream surface water was higher than that in the upstream. The spatial and temporal differences in surface water nitrate concentrations were mainly affected by rainfall runoff, soil types, and land use types. The main sources of nitrates in the surface water of the Wuding River Basin during the wet season were domestic sewage, manure, chemical fertilizers, and soil organic nitrogen, whose contribution rates were 43.3%, 27.6%, and 22.1%, respectively, and the contribution rate of precipitation was only 7.0%. There were differences in the contribution rate of nitrate pollution sources in surface water of different river sections. The contribution rate of soil nitrogen in the upstream was significantly higher than that in the downstream, which was 26.5%. The contribution rate of domestic sewage and manure in the downstream was significantly higher than that in the upstream, which was 48.9%. To provide a basis for the analysis of nitrate sources and pollution control in Wuding River and even rivers in arid and semi-arid regions.

Measuring nitrate concentration in surface waters with a microfluidic device facilitated by a miniaturized capacitive deionization cell

Abstract Excessive nitrate in surface waters poses a great threat to the health of human beings. Traditional measuring tools require either hazardous chemicals or organic matter compensation. In this work, we proposed an online microfluidic device incorporated with a miniaturized capacitive deionization cell that separates organic matter and nitrate ions before the measurement and afterwards determines the nitrate concentration with a 235-nm LED. The optimal operational parameter setting, which is a combination of 600-s charging duration and 0.5-V cell potential, was also obtained in order to achieve the maximum fractionation of nitrate ions. Promising results were obtained by our new approach, revealing that this device could serve as a functional and effective tool for the determination of nitrate concentration in surface water.

Machine learning models to predict nitrate concentration in a river basin

Aquifer-stream interactions affect the water quality in Mediterranean areas; therefore, the coupling of surface water and groundwater models is generally used to solve water-planning and pollution problems in river basins. However, their use is limited because model inputs and outputs are not spatially and temporally linked, and the data update and fitting are laborious tasks. Machine learning models have shown great potential in water quality simulation, as they can identify the statistical relationship between input and output data without the explicit requirement of knowing the physical processes. This allows the ecological, hydrological, and environmental variables that influence water quality to be analysed with a holistic approach. In this research, feature selection (FS) methods and algorithms of artificial intelligence—random forest (RF) and eXtreme Gradient Boosting (XGBoost) trees—are used to simulate nitrate concentration and determine the main drivers related to nitrate pollution in Mediterranean streams. The developed models included 19 inputs and sampling of nitrate concentration in 159 surface water quality-gauging stations as explanatory variables. The models were trained on 70 percent data, with 30 percent used to validate the predictions. Results showed that the combination of FS method with local knowledge about the dataset is the best option to improve the model’s performance, while RF and XGBoost simulate the nitrate concentration with high performance (r = 0.93 and r = 0.92, respectively). The final ranking, based on the relative importance of the variables in the RF and XGBoost models, showed that, regarding nitrogen and phosphorus concentration, the location explained 87 percent of the nitrate variability. RF and XGBoost predicted nitrate concentration in surface water with high accuracy without using conditions or parameters of entry and enabled the observation of different relationships between drivers. Thus, it is possible to identify and delimit zones with a spatial risk of pollution and approaches to implementing solutions.

Assessing critical loads and exceedances for acidification and eutrophication in the forests of East and Southeast Asia: A comparison with EANET monitoring data

Spatial variations in sulfur (S) and nitrogen (N) deposition have changed in East and Southeast Asia in recent decades. Nevertheless, in this region, including the tropics, regional-scale assessments of the long-term risk of acidification and eutrophication (N saturation) for terrestrial ecosystems using a critical load approach have not been updated since 2001. To evaluate future risks, maps of critical loads and exceedances were updated using recently acquired spatial datasets of soil properties, soil minerals, climate, tree plantations, and the annual S and N depositions estimated using the Community Multiscale Air Quality (CMAQ) model. The resulting maps were verified using data on long-term trends in soil pH and nitrate concentration in surface water acquired by the Acid Deposition Monitoring Network in East Asia (EANET). It was found that N deposition exceeded the critical load for eutrophication not only in East Asia but also in some parts of the tropical monsoon and humid regions in Southeast Asia, whereas S deposition partly exceeded the critical load for soil acidification in China and small parts of the tropical monsoon region. The high-risk areas for eutrophication coincided well with the EANET sites, where the increase in nitrate concentration in the surface water was significant over the last 20 years. Hence, the estimated map of the critical load exceedance for eutrophication is more plausible for assessing the risk in East and Southeast Asia than that for acidification, although the critical load exceedance for acidification would be sufficiently significant as an updated risk map based on the latest input values. This update also suggests that increased N deposition around megacities, water discharge, and tree plantations may play an important role in the spatial variability of eutrophication risks in the tropics of Southeast Asia.

Ultraviolet‐visual spectroscopy estimation of nitrate concentrations in surface waters via machine learning

AbstractHigh‐frequency acquisition of nutrient concentrations in rivers is needed to generate nutrient loading estimates commensurate with flow and discharge data. Although the combination of field sampling and laboratory analysis is the standard approach to riverine water quality analysis, this strategy is expensive and can miss important storm‐related events. Ultraviolet‐visual (UV–Vis) spectroscopy is widely used in drinking water and wastewater systems for high‐frequency concentration estimates. However, surface waters present a unique challenge as co‐occurring constituents in environmental samples cause spectral interference at the wavelengths used to measure concentrations of dissolved nutrients. Partial least squares regression (PLSR), Lasso regression (Lasso), and stepwise multivariate linear regression (Stepwise) models can be effective predictors of nitrate concentrations using UV–Vis absorbance and are used in many available in‐situ nitrate sensors; however, the proliferation of user‐friendly open‐source machine learning (ML) algorithms offers an opportunity to use sophisticated big‐data techniques to predict nutrient concentrations in surface waters. We collected samples from four rivers across southern Ontario with a variety of nitrate concentrations, flow regimes, and interfering co‐contaminants. We demonstrated that ML applications of random forest and gradient boosting models significantly outperformed PLSR, Lasso, and Stepwise methodologies to estimate nitrate concentrations in complex environmental samples via UV–Vis absorbance. Importantly, ML applications outcompete current models at low concentrations. This new predictive methodology provides regulators and stakeholders an opportunity to establish low cost, continuous monitoring environmental programs using UV–Vis approaches.

Riparian Forest Cover Modulates Phosphorus Storage and Nitrogen Cycling in Agricultural Stream Sediments.

Watershed land cover affects in-stream water quality and sediment nutrient dynamics. The presence of natural land cover in the riparian zone can reduce the negative effects of agricultural land use on water quality; however, literature evaluating the effects of natural riparian land cover on stream sediment nutrient dynamics is scarce. The objective of this study was to assess if stream sediment phosphorus retention and nitrogen removal varies with riparian forest cover in agricultural watersheds. Stream sediment nutrient dynamics from 28 sites with mixed land cover were sampled three times during the growing season. Phosphorus dynamics and nitrification rates did not change considerably throughout the study period. Sediment total phosphorus concentrations and nitrification rates decreased as riparian forest cover increased likely due to a decline in fine, organic material. Denitrification rates were strongly correlated to surface water nitrate concentrations. Denitrification rate and denitrification enzyme activity decreased with an increase in forest cover during the first sampling period only. The first sampling period coincided with the greatest connectivity between the watershed and in-stream processing, indicating that riparian forest cover indirectly decreased denitrification rates by reducing the concentrations of dissolved nutrients entering the stream. This reduction in load may allow the sediment to maintain greater nitrogen removal efficiency, because bacteria are not saturated with nitrogen. Riparian forest cover also appeared to lessen the effect of agriculture in the watershed by decreasing the amount of fine material in the stream, resulting in reduced phosphorus storage in the stream sediment.

Groundwater inflow in rivers as a controlling factor to surface water nitrate concentrations and impact of groundwater age distribution on response times for remediation strategies

Diffuse groundwater contamination by intense fertilizer use is a widespread problem in most of the agricultural regions in West-Europe and in many other countries worldwide. As a result elevated nitrate concentrations in groundwater are found, and as groundwater is the source of baseflow into rivers and streams, also surface waters show increased nitrate levels. Flanders in the north of Belgium is no exception and despite restrictions on the fertilizer use and remediation practices initiated in the mid-1990s, and still ongoing, in many stream monitoring points, peak concentrations are measured above the 50 mg/l criterium. In many monitoring stations there is apparently a long delay between the start of the restrictions and effect on water quality. Often this delay is related to high concentrations in the groundwater inflow and long residence and travel times of groundwater feeding the streams. To better understand this behavior and estimate response times a tool was developed that incorporates the main water flows in a typical catchment and considers the effects of varying groundwater ages. This tool is conceived as a parsimonious lumped parameter model, simple and easy enough to be used by many people but still capturing the main mechanisms end processes. In this paper the tool is explained and it is applied on two testcases in Flanders to show its performance.

Baseflow nitrate dynamics within nested watersheds of an agricultural stream in Nebraska, USA

There is a need to evaluate high surface nitrate concentrations across agricultural watersheds, both spatially and temporally, to increase understanding of source and timing of nitrogen loads in streams and rivers. Bazile Creek is a high-nitrate stream originating in the agriculturally intensive Bazile Groundwater Management Area of Eastern Nebraska, USA. It is a gaining stream that receives groundwater with high nitrate concentrations originating from nonpoint sources. The objective of this study was to determine spatial and temporal variability of baseflow nitrate concentrations in Bazile Creek and its tributaries and to relate this variability to watershed characteristics. Surface-water nitrate samples were collected monthly from July 2018 through September 2019 from nine sites in the watershed and were analyzed for nitrate concentration. Average surface water nitrate-N concentrations within the watershed ranged from 2.7 to 15 mg L−1 and were significantly different between the sites (p < 0.05). Surface water nitrate-N concentrations varied seasonally in the main channel, recording the highest concentrations in winter (December-February, average = 14.4 mg L−1) when the discharge was minimum. High nitrate-N concentrations were observed in two of the five sampled tributaries, suggesting steady inputs of high-nitrate groundwater. The results of this study reveal substantial spatial variation in surface-water nitrate concentrations in the headwaters despite the close proximity of sampling sites. This study demonstrates that sampling tributaries along with the main channel of a stream is beneficial in determining nitrate inputs, variability and overall contaminant loading to a watershed.

Modeling field scale nitrogen non-point source pollution (NPS) fate and transport: Influences from land management practices and climate

The use of nitrogen (N) fertilizer marked the start of modern agriculture that boosted food production to help alleviate food shortages across the globe but at the cost of severe environmental issues and critical stress to the agroecosystem. This paper was aimed at determining the fate and transport of nitrite and ammonia under future climate projections by adapting the recommended land management practices that are supposed to reduce nitrate N in surface water to state government target. To accomplish these objectives, a fully-distributed physical-based hydrologic model, MIKE SHE, and a hydrodynamic river model, MIKE 11, were coupled with MIKE ECO-Lab to simulate the fate and transport of different forms of N in the agro-ecosystem in the Upper Sangamon River Basin (USRB). Twelve (12) combinations of land management and climate projections were simulated to evaluate the N fate and transport in the USRB from 2020 to 2050. Under the current land management, the nitrate concentration in surface water was expected to exceed the EPA limit of 10 ppm up to 2.5% of the days in the simulation period. Regulating the fertilizer application rates to approximately 50% of the current rate will ensure this limit will not be exceeded in the future. Implementing cover cropping alone can potentially decrease nitrate N concentrations by 33% in surface water under dry climate and in the saturated zone under future projections. By combining the cover cropping and regulated application rate management, the nitrate N concentration in the saturated zone was expected to decrease by 67% compared with historic baseline. The modeling framework developed and used in this study can help evaluate the effectiveness of different management schemes aimed at reducing future nutrient load in our surface water and groundwater.

Mechanistic Insights Into Molecular Proxies Through Comparison of Subannually Resolved Sedimentary Records With Instrumental Water Column Data in the Santa Barbara Basin, Southern California

AbstractLipid biomarkers archived in marine sediments include widely applied proxies to reconstruct sea surface temperature (SST). Two prominent groups of SST sensitive biomarkers are long‐chain alkenones from haptophyte algae and glycerol dibiphytanyl glycerol tetraethers (GDGTs) from planktonic Thaumarchaeota. The corresponding proxies, and TEX86, respectively, are strongly correlated with mean annual SST. However, culture experiments suggest that other factors such as nutrients could also influence the proxy signals. We created monthly resolved records of molecular SST proxies via mass spectrometry imaging at 200‐μm resolution in varved sediments from Santa Barbara Basin deposited between 1984 and 2009. Direct comparison to coeval water column data determined at seasonal resolution allows new mechanistic insights into environmental parameters influencing proxy signal formation. The responds sensitively to SST variations, while also being influenced by seasonal variations in nutrient concentrations. Surface water nitrate concentrations above or below ~2 μmol/L are associated with values that overestimate or underestimate actual SST, respectively. Distributions of the two major GDGTs, expressed as the Crenarchaeol‐Caldarchaeol‐Tetraether index (CCaT), are highly similar to those of the minor cycloalkylated compounds GDGT‐1 to GDGT‐3, presented as the corresponding ring index. The CCaT is correlated with subsurface chemical properties, which are controlled by interannual variations in upwelling intensity linked to dynamics of the California Current System. This relationship is attributed to the impact of nutrients on thaumarchaeal growth rates and GDGT cyclization. The coupling of CCaT to SST was weak but enhanced for temperatures averaged across the upper 100 m of the water column.

Land Use Effects on Sediment Nutrient Processes in a Heavily Modified Watershed Using Structural Equation Models

AbstractContemporary land use can affect sediment nutrient processes in rivers draining heavily modified watersheds; however, studies linking land use to sediment nutrient processes in large river networks are limited. In this study, we developed and evaluated structural equation models for denitrification and phosphorus retention capacity to determine direct and indirect linkages between current land use and sediment nutrient processes during base flow in the Fox River watershed, WI, USA. A large spatial‐scale dataset used for this study included sediment nitrogen and phosphorus retention measurements and land use information for 106 sites. The structural equation models for the Fox River watershed identified direct links between current land use and in‐stream sediment nutrient processes. Subwatersheds with agricultural land consisting of more natural land cover had lower surface water nitrate concentrations and higher denitrification enzyme activity than subwatersheds with less alternative cover. This indicates that best management practices implemented in the Fox River watershed that restore natural land cover can improve water quality through nitrogen removal on the agricultural landscape and in the river network. Best management practices are not having the same measurable effects on phosphorus in the river network, most likely due to legacy phosphorus stored in the sediment.

Distribution and Source Analysis of Nitrate in Surface Waters of China

The nitrate pollution of surface water has attracted worldwide attention, and it is not optimistic in China. To identify the distribution, sources, and transformation mechanisms of nitrate in China's surface waters, the nitrate data of 71 major rivers from 7 regions were systematically collected. The spatial distribution of nitrate concentrations in surface water was analyzed, and the main nitrate pollution sources were revealed based on nitrogen and oxygen isotopes of nitrate. The results show that approximately 7.83% of rivers in China exceeded the Chinese drinking water standard for nitrate (45 mg·L-1). The concentrations of nitrate in Mudanjiang, Haihe River, and the estuary of Yangtze River was even more than 90 mg·L-1, which indicates a serious pollution phenomenon. The isotopic compositions of surface water in China ranged from -23.5‰ to 26.99‰ for δ15N-NO3 and from -12.7‰ to 83.5‰ for δ18O-NO3. The main sources of nitrate are domestic sewage in Northeast, Central, and East China, while those are inorganic fertilizers and domestic sewage in Southwest and South China. The nitrate sources of surface water in Northwest and North China are complex, mainly from domestic sewage, inorganic fertilizer, and nitration of soil organic matter. Through correlation analysis, it is found that the nitrate concentrations of surface water have a positive relationship with population, wastewater discharge, agricultural nitrogen fertilizer application, and GDP per capita in China. It is urgent to solve the problem of pollution and prevent the further pollution of China's surface waters. The new "10-Point Water Plan" issued by the Chinese government solved the previous problems, but it will take decades to control and repair the polluted surface waters. In Northwest, North China, Southwest, and South China, not only the scale of sewage treatment plants in cities and counties should be increased but also the use of agricultural fertilizers should be controlled and managed by the government. Northeast, Central, and East China need to further control point source pollution and reduce the discharge of industrial wastewater and domestic sewage into rivers.

A ten year record of nitrate retention and solute trends in a Wisconsin sand plains stream: temporal variation at multiple scales

Nitrate retention is an important ecosystem service provided by streams which reduces nitrogen loads to downstream ecosystems. However, most inferences about nitrate retention and uptake in streams are based on measurements at sub-annual scales. Our objective was to evaluate variation in nitration retention and solute concentrations at weekly to decadal time scales. We measured nitrate and chloride concentration, nitrate flux and nitrate retention in Emmons Creek, a groundwater-dominated, nitrate-rich stream in central Wisconsin during a 10-year period. We used a two-station mass balance approach to measure nitrate retention while accounting for groundwater inputs. Surface water nitrate concentration, nitrate yield and nitrate retention exhibited strong seasonal variation. Seasonal Kendall tests revealed positive trends for surface water discharge, nitrate and chloride concentrations and groundwater chloride concentration, but not nitrate retention. Generalized additive mixed models (GAMM), accounting for serial autocorrelations, also indicated positive trends in surface water nitrate and chloride concentrations but no trend for nitrate retention. Nitrate retention averaged 370 mg NO3-N m−2 d−1 (± 316 SD) and was highest during the spring (512 mg NO3-N m−2 d−1 ± 319), followed by summer (460 ± 322), fall (340 ± 281) and winter (165 ± 212). Nitrate retention rates in Emmons Creek were relatively high compared to most published estimates. However, our results suggest that Emmons Cr. may have saturated its capacity to retain nitrate during a period when nitrate yield from its watershed increased, which is an important finding given the increases in N availability in many regions of earth.

Consequences of Groundwater-Model Vertical Discretization in Risk-Based Decision-Making.

One of the first and most important decisions facing practitioners when constructing a numerical groundwater model is vertical discretization. Several factors will influence this decision, such as the conceptual model of the system and hydrostratigraphy, data availability, resulting computational burden, and the purpose of the modeling analysis. Using a coarse vertical discretization is an attractive option for practitioners because it reduces data requirements and model construction efforts, can increase model stability, and can reduce computational demand. However, using a coarse vertical discretization as a form of model simplification is not without consequence; this may give rise to unwanted side-effects such as biases in decision-relevant simulated outputs. Given its foundational role in the modeled representation of the aquifer system, herein we investigate how vertical discretization may affect decision-relevant simulated outputs using a paired complex-simple model analysis. A Bayesian framework and decision analysis approach are adopted. Two case studies are considered, one of a synthetic, linked unsaturated-zone/surface-water/groundwater hydrologic model and one of a real-world linked surface-water/groundwater hydrologic-nitrate transport model. With these models, we analyze decisions related to abstraction-induced changes in ecologically important streamflow characteristics and differences in groundwater and surface-water nitrate concentrations and mass loads following potential land-use change. We show that for some decision-relevant simulated outputs, coarse vertical discretization induces bias in important simulated outputs, and can lead to incorrect resource management action. For others, a coarse vertical discretization has little or no consequence for resource management decision-making.

Insights Into the Biogeochemical Cycling of Iron, Nitrate, and Phosphate Across a 5,300 km South Pacific Zonal Section (153°E–150°W)

AbstractIron, phosphate, and nitrate are essential nutrients for phytoplankton growth, and hence, their supply into the surface ocean controls oceanic primary production. Here we present a GEOTRACES zonal section (GP13; 30–33°S, 153°E–150°W) extending eastward from Australia to the oligotrophic South Pacific Ocean gyre outlining the concentrations of these key nutrients. Surface dissolved iron concentrations are elevated at &gt;0.4 nmol L−1 near continental Australia (west of 165°E) and decreased eastward to ≤0.2 nmol L−1 (170°W–150°W). The supply of dissolved iron into the upper ocean (&lt;100 m) from the atmosphere and vertical diffusivity averaged 11 ± 10 nmol m−2 d−1. In the remote South Pacific Ocean (170°W–150°W), atmospherically sourced iron is a significant contributor to the surface dissolved iron pool with average supply contribution of 23 ± 17% (range 3% to 55%). Surface water nitrate concentrations averaged 5 ± 4 nmol L−1 between 170°W and 150°W, while surface water phosphate concentrations averaged 58 ± 30 nmol L−1. The supply of nitrogen into the upper ocean is primarily from deeper waters (24–1647 μmol m−2 d−1) with atmospheric deposition and nitrogen fixation contributing &lt;1% to the overall flux along the eastern part of the transect. The deep water N:P ratio averaged 14.5 ± 0.5 but declined to &lt;1 above the deep chlorophyll maximum (DCM) indicating a high N:P assimilation ratio by phytoplankton leading to almost quantitative removal of nitrate. The supply stoichiometry for iron and nitrogen relative to phosphate at and above the DCM declines eastward leading to two biogeographical provinces: one with diazotroph production and the other without diazotroph production.

Nitrate pollution in the Warta River (Poland) between 1958 and 2016: trends and causes

The article presents analyses of long-term water quality data from the Warta River between 1958 and 2016. A clear increasing trend in nitrate concentrations was observed from 1958 to the early 1990s. This trend was mainly related to the increasing use of fertilizers in Poland in this period. Then, after the early 1990s, a slow decreasing trend related to improvements in water and sewage management and more rational fertilizer use was observed after political and economic changes in Poland. The influence of long-term hydrological droughts on nitrate concentrations was also investigated. Sharp increases in the nitrate concentration in surface water were related to the accumulation of contaminants in the soil and aeration zone during drought periods and the subsequent transport of these contaminants to groundwater and surface water via recharge infiltration after each drought period. The presented results highlight the importance of surface water–groundwater interactions and suggest that groundwater protection in an entire catchment area is essential for surface water quality protection.

Reconnecting crop and cattle farming to reduce nitrogen losses to river water of an intensive agricultural catchment (Seine basin, France): past, present and future

Nitrate and pesticide contamination of surface and groundwater has become a major problem in intensive farming regions in Europe, with nitrate concentrations reaching values above the standard defined in 2000 by the European Water Framework Directive. In the Seine basin, a major issue is the closure and abandonment of drinking-water wells, which force water managers and drinking-water producers to explore solutions for water resource protection. Organic farming has appeared as a credible alternative to conventional farming, and this study explores the potential of organic farming to reconcile agricultural production and water quality. On the basis of agricultural statistics, survey questionnaires and experimental data, the nitrogen soil surface balance (N-SSB) has been established at the scale of a small 104-km2 catchment (The Orgeval sub-basin), representative of the intensive cash crop farming in the Seine basin. The N-surplus for arable land in specialized organic cash crop systems has been found to be half that of current conventional systems (15kg Nha−1yr−1 versus 30kgNha−1yr−1, respectively). The N-yield in organic systems is 21% lower than in conventional systems, but total fertilization (mostly symbiotic N fixation) is also 26% lower. Whereas 2–3 years of forage legume (e.g., alfalfa) as a starter crop of the typical 7- to 10-year diversified rotation builds up N soil fertility and helps prevent weeds without pesticides, the existence of an outlet for this fodder production is a limiting factor for the economic sustainability and the environmental benefits of these farming systems. Therefore, we explored the possibility of a reconnection of livestock and crop farming systems in the Orgeval catchment, a traditional dairy farming and Brie cheese production region. We calculated the N-SSB for this type of a reconnected livestock and cropping system and found a value very close to the specialized organic cash crop system with full utilization of fodder production, leading to profitable animal production, essentially as milk in this farm design. This reconnected system is compared with the estimated situation in 1955 before separation of plant and livestock production. Furthermore, the N-SSB values were converted into infiltrating sub-root concentrations and used as a boundary condition to a biogeochemical model. Organic cropping and organic reconnected livestock cropping systems result in a 50% reduction of surface water nitrate concentrations, a surface water quality 20% better than that reconstructed for 1955, with an overall higher protein production.