Contribution of nitrate-nitrogen concentration in groundwater to stream water in an agricultural head watershed

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

A supervised machine learning approach to discriminate the effect of carcass leachate on shallow groundwater quality around on-farm livestock mortality burial sites

The evaluation of leachate leakage at livestock mortality burial sites is challenging, particularly when groundwater is previously contaminated by agro-livestock farming. Supervised machine learning was applied to discriminate the impacts of carcass leachate from pervasive groundwater contamination in the following order: data labeling, feature selection, synthetic data generation, and classification. Physicochemical data of 359 water samples were collected from burial pits (LC), monitoring wells near pits (MW), pre-existing shallow household wells (HW), and background wells with pervasive contamination (BG). A linear classification model was built using two representative groups (LC and BG) affected by different pollution sources as labeled data. A classifier was then applied to assess the impact of leachate leakage in MW and HW. As a result, leachate impacts were observed in 40% of MW samples, which indicates improper construction and management of some burial pits. Leachate impacts were also detected in six HW samples, up to 120 m downgradient, within one year. The quantitative decision-making tool to diagnose groundwater contamination with leachate leakage can contribute to ensuring timely responses to leakage. The proposed machine learning approach can also be used to improve the environmental impact assessment of water pollution by improper disposal of organic waste.

Effects of sunlight on the fate of graphene oxide and reduced graphene oxide nanomaterials in the natural surface water

Graphene nanomaterials have been commercialized for use in the electronic and biomedical industries, increasing their dissemination into surface waters and subsequent transformation in natural aquatic environment. While the photodegradation of graphene oxide nanomaterials has been investigated in the past, previous research did not consider actual natural aquatic environment and also focused on primarily graphene oxide nanomaterials. In this study, photodegradation of graphene nanomaterials with varying oxidation levels, including graphene oxide (GO) and partially reduced graphene oxide (rGO-2 h) are evaluated in Columbia River Water and compared with each other. Our results indicate that both direct and indirect photolysis of graphene-based nanomaterials will occur simultaneously in natural surface water. However, environmentally relevant concentrations of photosensitizers in surface water are not capable of producing sufficient ^·OH to initiate degradation of GO via indirect photolysis. For all conditions tested, GO showed more rapid photodegradation compared to rGO. Overall, direct and indirect photodegradation of graphene oxide nanomaterials in natural surface water is minimal and slow indicating that phototransformation of graphene-based nanomaterials will be insignificant in natural surface water.

Tracking anthropogenic nitrogen-compound sources of surface and groundwater in southwestern Nile Delta: hydrochemical, environmental isotopes, and modeling approach

This research aims to assign the specific and potential sources that control migration and transformation mechanisms of ammonium/nitrate contaminants of surface and groundwater systems in the southwestern Nile Delta, Egypt. To achieve that, an integration of hydrogeochemistry, multiple environmental stable isotopes (δ²H-H₂O, δ¹⁸O-H₂O, δ¹⁵N-NH₄, and δ¹⁵N-NO₃) coupled with three-dimensional nitrogen transport numerical model (MODFLOW-MT3D) was done. A set of representative water samples (20 canals and drainage water) and 14 groundwater samples were collected and analyzed for physical, chemical, and stable isotope analysis. NH₄⁺ and NO₃^- concentrations in surface water samples varied from 0.29 to 124 mg/l and 0.52 to 39.67 mg/l, respectively. For groundwater samples, NH₄⁺ and NO₃^- concentrations varied from 0.21 to 1.75 mg/l and 0.33 to 32.8 mg/l, respectively. Total risk quotient (THQ) level of nitrate (oral and dermal effects) from drinking water exceeds unity for all water samples indicating a potential noncancer risk for the southwestern Nile Delta residents. The potential sources of nitrogen compound pollution are water from sewage treatment plants used for irrigation, sludge and animal manure, septic tanks, soil nitrogen, and artificial fertilizers according to results of δ¹⁵N values. Results of ammonium/nitrate modeling in shallow groundwater aquifers are compared with observed concentrations and are found to be in good agreement. Some recommendations are given to decrease nitrogen loads in the study area through suggested a need for adoption of N-fertilizer management practices and treatment of sewage water before to application in agricultural activities.

Nitrate in Groundwater Resources of Hormozgan Province, Southern Iran: Concentration Estimation, Distribution and Probabilistic Health Risk Assessment Using Monte Carlo Simulation

High nitrate concentration in drinking water has the potential to cause a series of harmful effects on human health. This study aims to evaluate the health risk of nitrate in groundwater resources of Hormozgan province in four age groups, including infants, children, teenagers, and adults, based on the US EPA methodology and Monte Carlo technique to assess uncertainty and sensitivity analysis. A Geographic Information System (GIS) was used to investigate the spatial distribution of nitrate levels in the study area. The nitrate concentration ranged from 0.3 to 30 mg/L, with an average of 7.37 ± 5.61 mg/L. There was no significant difference between the average concentration of nitrate in all study areas (p > 0.05). The hazard quotient (HQ) was less than 1 for all age groups and counties, indicating a low-risk level. The HQ95 for infants and children in the Monte Carlo simulation was 1.34 and 1.22, respectively. The sensitivity analysis findings showed that the parameter with the most significant influence on the risk of toxicity in all age groups was the nitrate content. Therefore, implementing a water resources management program in the study area can reduce nitrate concentration and enhance water quality.

Electrical conductivity and dissolved oxygen as predictors of nitrate concentrations in shallow groundwater in Erhai Lake region

Elevated nitrogen (N) concentration in shallow groundwater is becoming increasingly problematic, putting water resources under pressure. For more effective management of such a resource, more precise predictors of N level in groundwater using smart monitoring networks are needed. However, external factors such as land use type, rainfall, and N loads from multiple sources (residential and agricultural) make it difficult to accurately predict the spatial and temporal variations of N concentration. In order to identify the key factors affecting spatial and temporal N concentration in shallow groundwater and develop a predictive model, 635 groundwater samples from drinking wells in residential areas and agricultural wells in croplands of a typical agricultural watershed in the Erhai Lake Basin, southwest China, in the period from 2018 to 2020, were collected and analyzed. The results showed that the type of land use and seasonal variations significantly affected the N forms and their concentrations in the shallow groundwater, as the ratios of ON and NO₃^--N to TN were 30%-39% and 52%-59% for the two land uses and 25%-44% and 46%-66% for seasonal changes. Their variations were reflected by electrical conductivity (EC) and redox environment. EC and dissolved oxygen (DO) had a positive non-linear relationship with the concentrations of total nitrogen (TN) and nitrate (NO₃^--N). The fitted non-linear quantitative models were established separately to predict TN and NO₃^--N concentrations in groundwater using easily available indictors (EC and DO). The high accuracy and performance of the models were investigated and approved by rRMSE, MAE, and 1:1 line. These findings can provide technical support for the rapid prediction and evaluation of N pollution in shallow groundwater through easily available indicators.

Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone

Nitrate-nitrogen (NO₃-N) contamination in groundwater is a major problem of drinking and domestic waters in rural areas. This study revealed the influence of land use type on shallow alluvial groundwaters in a typical rural area in South Korea by applying a self-organizing map (SOM), principal component analysis (PCA), and hierarchical cluster analysis (HCA). The uncertainty of spatial information on land use was improved by using a buffer zone of the average influence radius of 32.65 m surrounding wells. Two major land-use types, forests (44.9%) and rice fields (28.8%), occupied a total of 73.7% of the rural area. The higher concentrations of NO₃-N in public facilities and livestock areas were demonstrated to directly recharge groundwater pollutants. NO₃-N contamination in rice paddies, which also contained chlorine (Cl) and sulfate (SO₄), was assessed according to the nutrients and residual salt in the soil. In addition, different NO₃-N concentrations for the same land use indicate various biochemical reactions and NO₃-N recharge types into the groundwater system. The shallow groundwaters in the study area were classified into three clusters according to their chemical constituents and land-use properties, especially NO₃-N concentration, including pH, Cl, and SO₄, using a SOM, PCA, and HCA. Unlike existing studies, we applied a buffer zone based on the Cooper-Jacob equation to obtain an improved SOM model prediction accuracy approximately 10% greater than that using the original dataset.

Important factors when simulating the water and nitrogen balance in a tile-drained agricultural field under long-term monitoring

Despite the effectiveness of tile drain systems as a water management practice in naturally poorly drained soils, they facilitate the transport of NO₃^--N to surface water bodies. In order to improve the risk assessment of this significant transport under increased applications of N fertilisers in agriculture, it is imperative to delineate the controlling factors and processes. The aim of this study was to acquire such knowledge using the 1D Daisy model to simulate water and N balance based on comprehensive data from a ten-year monitoring study of a tile-drained loamy field in Denmark under the actual crop rotation of winter wheat, sugar beet, spring barley, winter rape and maize. The model simulated the cumulative drainage and NO₃^--N leaching over the ten-year period satisfactorily with NSE of 1.00 and 0.87 respectively. While the annual N input to the model was 181 kg N ha^-1, an average of 139 kg N ha^-1 was harvested in the crop, 22 kg N ha^-1 was leached through deep percolation, 17 kg N ha^-1 was leached to the tile drains, and 14 kg N ha^-1 was lost due to denitrification. Although the model satisfactorily captured the monitored data, the results of this study highlight: (i) the requirement for improved parameterisation of winter crops, (ii) the need to give further consideration in the model to soil surface and macropore processes that govern water infiltration and (iii) that measured and simulated NO₃^--N concentrations in the drainage exceeded the limit defined by the European Drinking Water Directive and Nitrates Directive for drinking water and hence improved N management strategies are essential for tile-drained agricultural fields in temperate regions under conventional crop rotations.

Peptide-directed Pd-decorated Au and PdAu nanocatalysts for degradation of nitrite in water

In this work, a palladium binding peptide, Pd4, has been used for the synthesis of catalytically active palladium-decorated gold (Pd-on-Au) nanoparticles (NPs) and palladium–gold (Pd_xAu_100−x) alloy NPs exhibiting high nitrite degradation efficiency. Pd-on-Au NPs with 20% to 300% surface coverage (sc%) of Au showed catalytic activity commensurate with sc%. Additionally, the catalytic activity of Pd_xAu_100−x alloy NPs varied based on palladium composition (x = 6–59). The maximum nitrite removal efficiency of Pd-on-Au and Pd_xAu_100−x alloy NPs was obtained at sc 100% and x = 59, respectively. The synthesized peptide-directed Pd-on-Au catalysts showed an increase in nitrite reduction three and a half times better than monometallic Pd and two and a half times better than Pd_xAu_100−x NPs under comparable conditions. Furthermore, peptide-directed NPs showed high activity after five reuse cycles. Pd-on-Au NPs with more available activated palladium atoms showed high selectivity (98%) toward nitrogen gas production over ammonia.

In this work, a palladium binding peptide, Pd4, has been used for the synthesis of catalytically active palladium-decorated gold (Pd-on-Au) nanoparticles (NPs) and palladium–gold (Pd_xAu_100−x) alloy NPs exhibiting high nitrite degradation efficiency.