Impact of Urea and Ammoniacal Nitrogen Wastewaters on Soil: Field Study in a Fertilizer Industry (Bahía Blanca, Argentina)

Endophytes in Agriculture: Potential to Improve Yields and Tolerances of Agricultural Crops

Endophytic fungi and bacteria live asymptomatically within plant tissues. In recent decades, research on endophytes has revealed that their significant role in promoting plants as endophytes has been shown to enhance nutrient uptake, stress tolerance, and disease resistance in the host plants, resulting in improved crop yields. Evidence shows that endophytes can provide improved tolerances to salinity, moisture, and drought conditions, highlighting the capacity to farm them in marginal land with the use of endophyte-based strategies. Furthermore, endophytes offer a sustainable alternative to traditional agricultural practices, reducing the need for synthetic fertilizers and pesticides, and in turn reducing the risks associated with chemical treatments. In this review, we summarise the current knowledge on endophytes in agriculture, highlighting their potential as a sustainable solution for improving crop productivity and general plant health. This review outlines key nutrient, environmental, and biotic stressors, providing examples of endophytes mitigating the effects of stress. We also discuss the challenges associated with the use of endophytes in agriculture and the need for further research to fully realise their potential.

Self-supported spinel nanosphere as bifunctional electrocatalysts for energy-saving hydrogen production via urea-water electrolysis

Electrochemical oxidation of urea is of great importance in the removal and energy exchange and storage of urea from wastewater as well as of potential applications in potable dialysis of end-stage renal disease. However, the lack of economical electrocatalysts hinders its widespread application. In this study, we successfully fabricated ZnCo₂O₄ nanospheres with bifunctional catalysis on nickel foam (NF). The catalytic system has high catalytic activity and durability for urea overall electrolysis. The urea oxidation and hydrogen evolution reactions required only 1.32 V and -80.91 mV to obtain ± 10 mA cm^-2. Only 1.39 V was needed to obtain 10 mA cm^-2 for 40 h without noticeably declining activity. The excellent performance could be attributed to the fact that the material can provide multiple redox couplings and a three-dimensional porous structure to facilitate the release of gases from the surface.

The effect of tensile strain in Pd-Ni core-shell nanocubes with tuneable shell thickness on urea electrolysis selectivity

Expanding our understanding of the structure-performance relationship in nanoscale electrocatalysts for urea electrolysis is crucial for efficient urea waste treatment and concomitant cathodic hydrogen production or CO₂ reduction. Here, we elucidate the effect of the lattice strain in Pd-Ni core-shell nanocubes on the dominance of urea overoxidation pathway.

Health risk assessment of groundwater nitrogen pollution in Yinchuan plain

High nitrogen concentration of groundwater poses a threat to human health. This study evaluated the potential health risk of nitrogen pollution in Yinchuan plain by geostatistical analysis and triangular stochastic model considering different land use types, and identified the uncertainties of the parameters. 163 samples were collected from groundwater wells in different land use types. The results show that the concentration of NO₃^--N ranges from 0.059 to 450 mg/L, with an average of 22.439 mg/L. Approximately 32% of the samples exceed Grade III threshold (20 mg/L of N). The concentration of NH₄⁺-N ranges from 0.011 to 11 mg/L, with an average of 0.456 mg/L. The concentration of NO₂^--N ranges from 0.003 to 9.09 mg/L The NO₃^--N and NH₄⁺-N concentration in the groundwater of the unutilized land use is significantly lowest among all the land types. The concentration of nitrogen is highest in farmland use. The ranking of non-carcinogenic risk under different land types for infants, children, adult males and females is: farmland use > residential land use> unutilized land use. The non-carcinogenic risk value of farmland use is three times as much as that of the residential land use. Drinking groundwater can be potentially harmful to human health, and nitrogen pollutants pose an even greater threat to infant. At the same time, considering the impact of different land use types on groundwater would avoid overestimating or underestimating regional risk value. Triangular stochastic model is more sensitive to data changes and can reduce uncertainty. The contribution rate of nitrate concentration to risk is more than 83%, indicating that random sampling is needed to improve the reliability of evaluation results. The research results of this study will provide a new way to solve the uncertainty in groundwater security management.

Imputation of Ammonium Nitrogen Concentration in Groundwater Based on a Machine Learning Method

Ammonium is one of the main inorganic pollutants in groundwater, mainly due to agricultural, industrial and domestic pollution. Excessive ammonium can cause human health risks and environmental consequences. Its temporal and spatial distribution is affected by factors such as meteorology, hydrology, hydrogeology and land use type. Thus, a groundwater ammonium analysis based on limited sampling points produces large uncertainties. In this study, organic matter content, groundwater depth, clay thickness, total nitrogen content (TN), cation exchange capacity (CEC), pH and land-use type were selected as potential contributing factors to establish a machine learning model for fitting the ammonium concentration. The Shapley Additive exPlanations (SHAP) method, which explains the machine learning model, was applied to identify the more significant influencing factors. Finally, the machine learning model established according to the more significant influencing factors was used to impute point data in the study area. From the results, the soil organic matter feature was found to have a substantial impact on the concentration of ammonium in the model, followed by soil pH, clay thickness and groundwater depth. The ammonium concentration generally decreased from northwest to southeast. The highest values were concentrated in the northwest and northeast. The lowest values were concentrated in the southeast, southwest and parts of the east and north. The spatial interpolation based on the machine learning imputation model established according to the influencing factors provides a reliable groundwater quality assessment and was not limited by the number and the geographical location of samplings.