Nitrate Concentration Data Research Articles

Nitrate supply and uptake in the Atlantic Arctic sea ice zone: seasonal cycle, mechanisms and drivers.

Nutrient supply to the surface ocean is a key factor regulating primary production in the Arctic Ocean under current conditions and with ongoing warming and sea ice losses. Here we present seasonal nitrate concentration and hydrographic data from two oceanographic moorings on the northern Barents shelf between autumn 2017 and summer 2018. The eastern mooring was sea ice-covered to varying degrees during autumn, winter and spring, and was characterized by more Arctic-like oceanographic conditions, while the western mooring was ice-free year-round and showed a greater influence of Atlantic water masses. The seasonal cycle in nitrate dynamics was similar under ice-influenced and ice-free conditions, with biological nitrate uptake beginning near-synchronously in early May, but important differences between the moorings were observed. Nitrate supply to the surface ocean preceding and during the period of rapid drawdown was greater at the ice-free more Atlantic-like western mooring, and nitrate drawdown occurred more slowly over a longer period of time. This suggests that with ongoing sea ice losses and Atlantification, the expected shift from more Arctic-like ice-influenced conditions to more Atlantic-like ice-free conditions is likely to increase nutrient availability and the duration of seasonal drawdown in this Arctic shelf region. The extent to which this increased nutrient availability and longer drawdown periods will lead to increases in total nitrate uptake, and support the projected increases in primary production, will depend on changes in upper ocean stratification and their effect on light availability to phytoplankton as changes in climate and the physical environment proceed.This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.

Is the River a Chemostat?: Scale Versus Land Use Controls on Nitrate Concentration‐Discharge Dynamics in the Upper Mississippi River Basin

AbstractThe Upper Mississippi River Basin is the largest source of reactive nitrogen (N) to the Gulf of Mexico. Concentration‐discharge (C‐Q) relationships offer a means to understand both the terrestrial sources that generate this reactive N and the in‐stream processes that transform it. Progress has been made on identifying land use controls on C‐Q dynamics. However, the impact of basin size and river network structure on C‐Q relationships is not well characterized. Here, we show, using high‐resolution nitrate concentration data, that tile drainage is a dominant control on C‐Q dynamics, with increasing drainage density contributing to more chemostatic C‐Q behavior. We further find that concentration variability increases, relative to discharge variability, with increasing basin size across six orders of magnitude, and this pattern is attributed to different spatial correlation structures for C and Q. Our results show how land use and river network structure jointly control riverine N export.

How Important is Denitrification in Riparian Zones? Combining End‐Member Mixing and Isotope Modeling to Quantify Nitrate Removal from Riparian Groundwater

AbstractRiparian zones are important buffer zones for streams as they are hotspots of nitrate transformation and removal in agricultural catchments. However, mixing of water from different sources and various transformation processes can complicate the quantification of nitrate turnover in riparian zones. In this study, we analyzed nitrate concentration and isotope data in riparian groundwater along a 2‐km stream section in central Germany. We developed a mathematical model combining end‐member mixing and isotope modeling to account for mixing of river water and groundwater and quantify nitrate transformation in riparian groundwater. This enabled us to explicitly determine the extent of denitrification (as process leading to permanent nitrate removal from riparian groundwater) and transient nitrate removal by additional processes associated with negligible isotope fractionation (e.g., plant uptake and microbial assimilation) and to perform an extensive uncertainty analysis. Based on the nitrogen isotope data of nitrate, the simulations suggest a mean removal of up to 28% by additional processes and only about 9% by denitrification. Nitrate removal from riparian groundwater by additional processes exceeded denitrification particularly in winter and at larger distance from the river, underlining the role of the river as organic carbon source. This highlights that nitrate consumption by additional processes predominates at the field site, implying that a substantial fraction of agricultural nitrogen input is not permanently removed but rather retained in the riparian zone. Overall, our model represents a useful tool to better compare nitrogen retention to permanent nitrogen removal in riparian zones at various temporal and spatial scales.

Modelling Nitrate Pollution Vulnerability in the Brussel’s Capital Region (Belgium) Using Data-Driven Modelling Approaches

Groundwater vulnerability for nitrate pollution of groundwater in the Brussel’s Capital Region was modelled using data-driven modelling approaches. The land use in the study area is heterogeneous. The South South-Eastern part of the region is forested, while the remaining part is urbanised. Groundwater nitrate concentration data were determined at 48 measurement stations distributed over the study area. In addition, oxygen and nitrogen isotope concentration of the nitrates were determined. The data show that the groundwater body is degraded, particularly in the urbanised part of the study area. The contamination with nitrates at degraded stations is slightly decreasing, while the opposite is true for the nitrate contamination at the less degraded stations. We modelled the contamination and trends of nitrate contamination using linear and non-linear statistical modelling techniques. In total, we defined 23 spatially distributed proxy variables that could explain nitrate contamination of the groundwater body. These proxy variables were defined at the grid size of 10 m, and averaged over the influence zone of each measurement station. The influence zones were identified using a simplified particle tracking algorithm from the groundwater piezometric map. The calculated influence zones were consistent with results obtained from a detailed numerical groundwater flow and transport model. Stepwise regression allowed explaining 56% of the observed variability of nitrate contaminations, while non-linear artificial neural network modelling allows explaining nearly 60% of the variability. The dominant explaining variables are the percentage of impermeable surface, the percentage of the sewage system that is in a degradation state, the number of urban infrastructure construction permits with a high pollution risk, the size of the influence zone, and the depth of the groundwater sampling. These results illustrate the important role of urban infrastructure on groundwater degradation and are consistent with the isotopic signature of nitrates determined on the sampling stations. The overlay of the nitrate contamination data with the DRASTIC vulnerability model shows that this latter conceptual model captures partially the spatial signature of the observed contamination.

Evaluation of Meymeh Aquifer vulnerability to nitrate pollution by GIS and statistical methods

Increasing the concentration of nitrate ions in the soil solution and then leaching it to underground aquifers increases the concentration of nitrate in the water, and can cause many health and ecological problems. This study was conducted to evaluate the vulnerability of Meymeh aquifer to nitrate pollution. In this research, sampling of 10 wells was performed according to standard sampling principles and analyzed in the laboratory by spectrophotometric method, then; the nitrate concentration zonation map was drawn by using intermediate models. In the drastic model, the effective parameters for assessing the vulnerability of groundwater aquifers, including the depth of ground water, pure feeding, aquifer environment, soil type, topography slope, non-saturated area and hydraulic conductivity. Which were prepared in the form of seven layers in the ARC GIS software, and by weighting and ranking and integrating these seven layers, the final map of groundwater vulnerability to contamination was prepared. Drastic index estimated for the region between 75-128. For verification of the model, nitrate concentration data in groundwater of the region were used, which showed a relative correlation between the concentration of nitrate and the prepared version of the model. A combination of two vulnerability map and nitrate concentration zonation was provided a qualitative aquifer classification map. According to this map, most of the study areas are within safe and low risk, and only a small portion of the Meymeh Aquifer, which has a nitrate concentration of more than 50 mg / L in groundwater, is classified in a hazardous area.

Non-point source evaluation of groundwater nitrate contamination from agriculture under geologic uncertainty

The long-term effect of non-point source pollution on groundwater from agricultural practices is a major concern globally. Non-point source pollutants such as nitrate that occurs through fertilizers and animal waste eventually make their way into the aquifer by infiltrating soil. The goal of this study is to develop an approach for characterization of nitrate concentrations at potential source locations under conditions of geologic uncertainty. A Bayesian framework using the Markov Chain Monte Carlo (MCMC) approach is developed to estimate posterior probability distributions of non-point sources by incorporating nitrate concentration data as well as geologic uncertainties. The proposed approach is tested using hypothetical contamination scenarios and then validated using an application case study in North Carolina. Uncertainty existing in geologic formation (i.e., heterogeneous hydraulic conductivity field) is treated as prior and used in evaluating the likelihood function that measures the match between observed and simulated concentrations. The likelihood function computation involves a numerical model that simulates nitrate transport in groundwater from non-point agricultural sources and predicts nitrate concentrations at observation wells. Effectiveness of the MCMC approach is evaluated through a convergence analysis. Comparison among different sampling algorithms is carried out with respect to MCMC convergence diagnostics and making inference. The Bayesian inference analysis methodology developed in this research will help decision makers and water managers to identify potential areas for source containment and decide if further sampling is required.

Uncertainty analysis of the relationship between discharge and nitrate concentration in the Lower Illinois River using a copula model

The complex relationship between water quality and river discharge has long been an active area of research. This research applies bivariate statistics to river nitrate concentration and discharge, and develops a procedure for selecting the best bivariate distribution for the chosen models. Nitrate concentration (NO3) data from five stations on the Lower Illinois River, USA, collected from 1972 to 2012 were used for demonstration of this procedure. To explore the statistical relationship between discharge and nitrate concentration, three copula statistical models were applied to determine optimal bivariate distributions. The generalized Pareto (GPA), generalized extreme value (GEV), and lognormal two- (LN2) and three-parameter (LN3) distributions were used as marginal distributions, and were combined with the Clayton, Frank, and Gumbel copula models. The probability plot correlation coefficient (PPCC) test and the Sn statistic were used to select the most suitable copula models. As a result, the GPA distributions for streamflow and nitrate concentration in the Frank copula model were more accurate for representing the data from the two mainstem stations, Havana (D-31) and Valley City (D-32), and those from the Oakford station along the Sangamon River (E-25). However, the Clayton copula model using NL3 for representing streamflow and GPA for representing nitrate concentration was more accurate for stations along the Spoon (DJ-08) and La Moine Rivers (DG-01). This study enabled the development of a procedure for determining an optimal copula for application in the environmental sciences and also provided a case study for applying copula models using discharge and nutrient concentration as hydrologic and water quality variables, respectively.

Environmental justice and drinking water quality: are there socioeconomic disparities in nitrate levels in U.S. drinking water?

BackgroundLow-income and minority communities often face disproportionately high pollutant exposures. The lead crisis in Flint, Michigan, has sparked concern about broader socioeconomic disparities in exposures to drinking water contaminants. Nitrate is commonly found in drinking water, especially in agricultural regions, and epidemiological evidence suggests elevated risk of cancer and birth defects at levels below U.S. EPA’s drinking water standard (10 mg/L NO3-N). However, there have been no nationwide assessments of socioeconomic disparities in exposures to nitrate or other contaminants in U.S. drinking water. The goals of this study are to identify determinants of nitrate concentrations in U.S. community water systems (CWSs) and to evaluate disparities related to wealth or race/ethnicity.MethodsWe compiled nitrate data from 39,466 U.S. CWSs for 2010–2014. We used EPA’s Safe Drinking Water Information System (SDWIS) to compile CWS characteristics and linked this information with both city- and county-level demographic data gathered from the U.S. Census Bureau. After applying multiple imputation methods to address censored nitrate concentration data, we conducted mixed-effects multivariable regression analyses at national and regional scales.Results5.6 million Americans are served by a CWS that had an average nitrate concentration ≥ 5 mg/L NO3-N between 2010 and 2014. Extent of agricultural land use and reliance on groundwater sources were significantly associated with nitrate. The percent of Hispanic residents served by each system was significantly associated with nitrate even after accounting for county-level cropland and livestock production, and CWSs in the top quartile of percent Hispanic residents exceeded 5 mg/L nearly three times as often as CWSs serving the lowest quartile. By contrast, the percent of residents living in poverty and percent African American residents were both inversely associated with nitrate.ConclusionsEpidemiological evidence for health effects associated with drinking water above 5 mg/L NO3-N raises concerns about increased risk for the 5.6 million Americans served by public water supplies with average nitrate concentrations above this level. The associations we observed between nitrate concentrations and proportions of Hispanic residents support the need for improved efforts to assist vulnerable communities in addressing contamination and protecting source waters. Future studies can extend our methods to evaluate disparities in exposures to other contaminants and links to health effects.

Non-Gaussian Covariate-Dependent Spatial Measurement Error Model for Analyzing Big Spatial Data

Spatial models based on the Gaussian distribution have been widely used in environmental sciences. However, real data could be highly non-Gaussian and may show heavy tails features. Moreover, as in any type of statistical models, in spatial statistical models, it is commonly assumed that the covariates are observed without errors. Nonetheless, for various reasons such as measurement techniques or instruments used, measurement error (ME) can be present in the covariates of interest. This article concentrates on modeling heavy-tailed geostatistical data using a more flexible class of ME models. One novelty of this article is to allow the spatial covariance structure to depend on ME. For this purpose, we adopt a Bayesian modeling approach and utilize Markov chain Monte Carlo techniques and data augmentations to carry out the inference. However, when the number of observations is large, statistical inference is computationally burdensome, since the covariance matrix needs to be inverted at each iteration. As another novelty, we use a prediction-oriented Bayesian site selection scheme to tackle this difficulty. The proposed approach is illustrated with a simulation study and an application to nitrate concentration data. Supplementary materials accompanying this paper appear online.

Stream Runoff and Nitrate Recovery Times After Forest Disturbance in the USA and Japan

AbstractTo understand mechanisms of long‐term hydrological and biogeochemical recovery after forest disturbance, it is important to evaluate recovery times (i.e., time scales associated with the return to baseline or predisturbance conditions) of stream runoff and nitrate concentration. Previous studies have focused on either the response of runoff or nitrate concentration, and some have specifically addressed recovery times following disturbance. However, controlling factors have not yet been elucidated. Knowing these relationships will advance our understanding of each recovery process. The objectives of this study were to explore the relationship between runoff and nitrate recovery times and identify potential factors controlling each. We acquired long‐term runoff and stream water nitrate concentration data from 20 sites in the USA and Japan. We then examined the relationship between runoff and nitrate recovery times at these multiple sites and use these relationships to discuss the ecosystem dynamics following forest disturbance. Nitrate response was detected at all study sites, while runoff responses were detected at all sites with disturbance intensities greater than 75% of the catchment area. The runoff recovery time was significantly correlated with the nitrate recovery time for catchments that had a runoff response. For these catchments, hydrological recovery times were slower than nitrate recovery times. The relationship between these two recovery times suggests that forest regeneration was a common control on both recovery times. However, the faster recovery time for nitrate suggests that nitrogen was less available or less mobile in these catchments than water.

Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements.

Microcystin (MCY)-producing harmful cyanobacterial blooms (cHABs) are an annual occurrence in Lake Erie, and buoys equipped with water quality sondes have been deployed to help researchers and resource managers track cHABs. The objective of this study was to determine how well water quality sondes attached to buoys measure total algae and cyanobacterial biomass and water turbidity. Water samples were collected next to two data buoys in western Lake Erie (near Gibraltar Island and in the Sandusky subbasin) throughout summers 2015, 2016, and 2017 to determine correlations between buoy sonde data and water sample data. MCY and nutrient concentrations were also measured. Significant (P < 0.001) linear relationships (R2 > 0.75) occurred between cyanobacteria buoy and water sample data at the Gibraltar buoy, but not at the Sandusky buoy; however, the coefficients at the Gibraltar buoy differed significantly across years. There was a significant correlation between buoy and water sample total chlorophyll data at both buoys, but the coefficient varied considerably between buoys and among years. Total MCY concentrations at the Gibraltar buoy followed similar temporal patterns as buoy and water sample cyanobacterial biomass data, and the ratio of MCY to cyanobacteria-chlorophyll decreased with decreased ambient nitrate concentrations. These results suggest that buoy data are difficult to compare across time and space. Additionally, the inclusion of nitrate concentration data can lead to more robust predictions on the relative toxicity of blooms. Overall, deployed buoys with sondes that are routinely cleaned and calibrated can track relative cyanobacteria abundance and be used as an early warning system for potentially toxic blooms.

A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.

TEMPORAL DYNAMICS OF EUTROPHICATION LEVEL AND SEDIMENTATION RATE IN CORAL REEF AREA OF SPERMONDE AND SEMBILAN ISLANDS, SOUTH SULAWESI

Spermonde and Sembilan Islands (Bone Bay) are the central distribution of coral reefs in South Sulawesi. These archipelagos are likely to be at risk from eutrophication and sedimentation from intensive agriculture and aquaculture activities, in particular through transport of nutrients and materials discharged to the river systems on the Sulawesi mainland. The aim of this study was to analyse the temporal dynamics of eutrophication levels and sedimentation rates on coral reefs area of Spermonde and Sembilan Islands. Nutrient concentration and sedimentation rate were collected at monthly intervals over 4 months, at six stations: three in the Spermonde Islands (Laiya, Kodingareng, and Samalona Islands), and three in the Sembilan Islands, Sinjai District (Batanglampe, Kambuno, and Burungloe Islands), with two data collection points/replicates at each station/island. The results showed that phosphate concentration and sedimentation rates were higher in the Sembilan Islands, whereas nitrate concentrations were similar in both island groups. Nitrate concentration data indicated that eutrophication levels was varied, ranging from oligotrophic to eutrophic conditions, depends on months. In July and September, eutrophication was observed at all stations in both locations. Sedimentation rates were higher in the Sembilan Islands, but declined gradually until the end of the study time. Conversely, at stations in the Spermonde Islands, especially Samalona and Kodingarengkeke Islands, sedimentation level was increased significantly until the end of the study.

Calibration of groundwater vulnerability mapping using the generalized reduced gradient method

Groundwater vulnerability assessment studies are essential in water resources management. Overlay-and-index methods such as DRASTIC are widely used for mapping of groundwater vulnerability, however, these methods mainly suffer from a subjective selection of model parameters. The objective of this study is to introduce a calibration procedure that results in a more accurate assessment of groundwater vulnerability. The improvement of the assessment is formulated as a parameter optimization problem using an objective function that is based on the correlation between actual groundwater contamination and vulnerability index values. The non-linear optimization problem is solved with the generalized-reduced-gradient (GRG) method, which is numerical algorithm based optimization method. To demonstrate the applicability of the procedure, a vulnerability map for the Tahtali stream basin is calibrated using nitrate concentration data. The calibration procedure is easy to implement and aims the maximization of correlation between observed pollutant concentrations and groundwater vulnerability index values. The influence of each vulnerability parameter in the calculation of the vulnerability index is assessed by performing a single-parameter sensitivity analysis. Results of the sensitivity analysis show that all factors are effective on the final vulnerability index. Calibration of the vulnerability map improves the correlation between index values and measured nitrate concentrations by 19%. The regression coefficient increases from 0.280 to 0.485. It is evident that the spatial distribution and the proportions of vulnerability class areas are significantly altered with the calibration process. Although the applicability of the calibration method is demonstrated on the DRASTIC model, the applicability of the approach is not specific to a certain model and can also be easily applied to other overlay-and-index methods.

Time lags in watershed-scale nutrient transport: an exploration of dominant controls

Unprecedented decreases in atmospheric nitrogen (N) deposition together with increases in agricultural N-use efficiency have led to decreases in net anthropogenic N inputs in many eastern US and Canadian watersheds as well as in Europe. Despite such decreases, N concentrations in streams and rivers continue to increase, and problems of coastal eutrophication remain acute. Such a mismatch between N inputs and outputs can arise due to legacy N accumulation and subsequent lag times between implementation of conservation measures and improvements in water quality. In the present study, we quantified such lag times by pairing long-term N input trajectories with stream nitrate concentration data for 16 nested subwatersheds in a 6800 km2, Southern Ontario watershed. Our results show significant nonlinearity between N inputs and outputs, with a strong hysteresis effect indicative of decadal-scale lag times. The mean annual lag time was found to be 24.5 years, with lags varying seasonally, likely due to differences in N-delivery pathways. Lag times were found to be negatively correlated with both tile drainage and watershed slope, with tile drainage being a dominant control in fall and watershed slope being significant during the spring snowmelt period. Quantification of such lags will be crucial to policy-makers as they struggle to set appropriate goals for water quality improvement in human-impacted watersheds.

Evaluation of nitrate levels in groundwater under agricultural fields in two pilot areas in central Chile: A hydrogeological and geochemical approach

The aim of this study is to evaluate the impact of the application of industrial fertilizers and liquid swine manure in groundwater in two pilot agricultural areas, San Pedro and Pichidegua, which have been under long term historic use of fertilizers. A comprehensive hydrogeological investigation was carried out to define the geology and the groundwater flow system. Chemical and isotopic tools were used to evaluate the distribution and behavior of the nitrate in the groundwater. The isotopic tools included δ18O, δ2H and 3H, which provide information about the origin and residence time of the groundwater; δ15N-NO3- and δ18O-NO3-, which provide information about nitrate sources and processes that can affect nitrate along the groundwater flow system. The application rate of liquid manure and other fertilizers all together with land uses were also evaluated. The hydrogeological investigation identified the presence of a confined aquifer underneath a thick low permeability aquitard, whose extension covers most of the two study areas. The nitrate concentration data, excepting a few points in zones located near recharge areas in the upper part of the basins and lower areas at the valley outlets (San Pedro), showed nitrate concentration below 10 mgN/L at the regional scale. The isotope data for nitrate showed no influence of the liquid swine manure in the groundwater at the regional scale, except for the high part of the basins and the outlet of the San Pedro valley, which are areas fertilized by manure. This data showed that the regional aquifer on both pilot study areas is protected by the thick low permeability aquitard, which is playing an important role on nitrate attenuation. Evidence of denitrification were also found on both shallow and deep groundwater in the Pichidegua site. This study showed that a comprehensive hydrogeological characterization complemented by chemical and isotope data is key for understanding nitrate distribution and concentration in aquifers from areas with intensive agriculture activities.

Produced water disposal injection in the southern San Joaquin Valley: No evidence of groundwater quality effects due to upward leakage

Upward migration of brine because of pressurization resulting from injection is a risk of disposal of water produced with oil and geologic carbon storage. Analysis of the net production in each zone associated with oil production activities in the southern San Joaquin Valley, California, determined that net injection caused by disposal of water produced with oil occurred in zones above the shallowest zone with net production in several oil fields. The zones with net injection are also variously at depths just greater than the shallowest depths for geologic carbon storage or at depths intermediate between more typical geologic carbon storage depths and overlying groundwater with a total dissolved solids concentration appropriate for domestic use. As such, these net injections provide analogs for brine pressurization caused by geologic carbon storage, either in the injection zone around the CO2 plume or in overlying zones caused by vertical leakage of brine or CO2. Hundreds of newspaper articles regarding groundwater contamination in the main newspaper in the southern San Joaquin area collectively reported on effects on groundwater from tens of sources at tens of locations. These effects resulted in the closure of about 100 water supply wells. However, no effects caused by upward migration of brine were reported. Of the shallowest zones with oil production–related activity in each field, the Fruitvale field, Main area, Etchegoin pool had the largest cumulative net injection volume. This pool is also intersected by numerous faults and approximately 900 wells related to oil production, each providing a potential pathway for upward fluid migration. Total dissolved solids and nitrate concentration data are available from greater than 100 water supply wells overlying this pool. Analysis of these data determined there was no significant groundwater quality change likely attributable to upward migration of brine (p < 0.05). It is not known if this is because the application of current underground injection control regulations is effective or because upward migration of brine, which is a dense phase, to groundwater is unlikely. The different engineering and economic implications of these two hypotheses suggest the need for future work to ascertain which is correct under different conditions.

Denitrification and dilution along fracture flowpaths influence the recovery of a bedrock aquifer from nitrate contamination

In 2000, elevated nitrate concentrations ranging from 12 to 34mg/L NO3N were discovered in groundwater from numerous domestic bedrock wells adjacent to a large dairy farm in central Vermont. Long-term plots and contours of nitrate vs. time for bedrock wells showed “little/no”, “moderate”, and “large” change patterns that were spatially separable. The metasedimentary bedrock aquifer is strongly anisotropic and groundwater flow is controlled by fractures, bedding/foliation, and basins and ridges in the bedrock surface. Integration of the nitrate concentration vs. time data and the physical and chemical aquifer characterization suggest two nitrate sources: a point source emanating from a waste ravine and a non-point source that encompasses the surrounding fields. Once removed, the point source of NO3 (manure deposited in a ravine) was exhausted and NO3 dropped from 34mg/L to <10mg/L after ~10years; however, persistence of NO3 in the 3 to 8mg/L range (background) reflects the long term flux of nitrates from nutrients applied to the farm fields surrounding the ravine over the years predating and including this study. Inferred groundwater flow rates from the waste ravine to either moderate change wells in basin 2 or to the shallow bedrock zone beneath the large change wells are 0.05m/day, well within published bedrock aquifer flow rates. Enrichment of 15N and 18O in nitrate is consistent with lithotrophic denitrification of NO3 in the presence of dissolved Mn and Fe. Once the ravine point-source was removed, denitrification and dilution collectively were responsible for the down-gradient decrease of nitrate in this bedrock aquifer. Denitrification was most influential when NO3N was >10mg/L. Our multidisciplinary methods of aquifer characterization are applicable to groundwater contamination in any complexly-deformed and metamorphosed bedrock aquifer.

Mapping the groundwater vulnerability for pollution at the pan African scale

We estimated vulnerability and pollution risk of groundwater at the pan-African scale. We therefore compiled the most recent continental scale information on soil, land use, geology, hydrogeology and climate in a Geographical Information System (GIS) at a resolution of 15km×15km and at the scale of 1:60,000,000. The groundwater vulnerability map was constructed by means of the DRASTIC method. The map reveals that groundwater is highly vulnerable in Central and West Africa, where the watertable is very low. In addition, very low vulnerability is found in the large sedimentary basins of the African deserts where groundwater is situated in very deep aquifers. The groundwater pollution risk map is obtained by overlaying the DRASTIC vulnerability map with land use. The northern, central and western part of the African continent is dominated by high pollution risk classes and this is very strongly related to shallow groundwater systems and the development of agricultural activities. Subsequently, we performed a sensitivity analysis to evaluate the relative importance of each parameter on groundwater vulnerability and pollution risk. The sensitivity analysis indicated that the removal of the impact of vadose zone, the depth of the groundwater, the hydraulic conductivity and the net recharge causes a large variation in the mapped vulnerability and pollution risk. The mapping model was validated using nitrate concentration data of groundwater as a proxy of pollution risk. Pan-African concentration data were inferred from a meta-analysis of literature data. Results shows a good match between nitrate concentration and the groundwater pollution risk classes. The pan African assessment of groundwater vulnerability and pollution risk is expected to be of particular value for water policy and for designing groundwater resources management programs. We expect, however, that this assessment can be strongly improved when better pan African monitoring data related to groundwater pollution will be integrated in the assessment methodology.

Biogeochemical variations at the Porcupine Abyssal Plain sustained Observatory in the northeast Atlantic Ocean, from weekly to inter-annual timescales

Abstract. We present high-resolution autonomous measurements of carbon dioxide partial pressure p(CO2) taken in situ at the Porcupine Abyssal Plain sustained Observatory (PAP-SO) in the northeast Atlantic (49° N, 16.5° W; water depth of 4850 m) for the period 2010–2012. Measurements of p(CO2) made at 30 m depth on a sensor frame are compared with other autonomous biogeochemical measurements at that depth (including chlorophyll a fluorescence and nitrate concentration data) to analyse weekly to seasonal controls on p(CO2) flux in the inter-gyre region of the North Atlantic. Comparisons are also made with in situ regional time series data from a ship of opportunity and mixed layer depth (MLD) measurements from profiling Argo floats. There is a persistent under-saturation of CO2 in surface waters throughout the year which gives rise to a perennial CO2 sink. Comparison with an earlier data set collected at the site (2003–2005) confirms seasonal and inter-annual changes in surface seawater chemistry. There is year-to-year variability in the timing of deep winter mixing and the intensity of the spring bloom. The 2010–2012 period shows an overall increase in p(CO2) values when compared to the 2003–2005 period as would be expected from increases due to anthropogenic CO2 emissions. The surface temperature, wind speed and MLD measurements are similar for both periods of time. Future work should incorporate daily CO2 flux measurements made using CO2 sensors at 1 m depth and the in situ wind speed data now available from the UK Met Office Buoy.