Response of microbial communities to exogenous nitrate nitrogen input in black and odorous sediment.

Since nitrate nitrogen (NO3--N) input has proved an effective approach for the treatment of black and odorous river waterbody, it was controversial whether the total nitrogen concentration standard should be raised when the effluent from the sewage treatment plant is discharged into the polluted river. To reveal the effect of exogenous nitrate (NO3--N) on black odorous waterbody, sediments with different features from contaminated rivers were collected, and the changes of physical and chemical characteristics and microbial community structure in sediments before and after the addition of exogenous NO3--N were investigated. The results showed that after the input of NO3--N, reducing substances such as acid volatile sulfide (AVS) in the sediment decreased by 80 % on average, ferrous (Fe2+) decreased by 50 %, yet the changing trend of ammonia nitrogen (NH4+-N) in some sediment samples increased while others decreased. High-throughput sequencing results showed that the abundance of Thiobacillus at most sites increased significantly, becoming the dominant genus in the sediment, and the abundance of functional genes in the metabolome increased, such as soxA, soxX, soxY, soxZ. Network analysis showed that sediment microorganisms evolved from a single sulfur oxidation ecological function to diverse ecological functions, such as nitrogen cycle nirB, nirD, nirK, nosZ, and aerobic decomposition. In summary, inputting an appropriate amount of exogenous NO3--N is beneficial for restoring and maintaining the oxidation states of river sediment ecosystems.

Selective and Accurate Detection of Nitrate in Aquaculture Water with Surface-Enhanced Raman Scattering (SERS) Using Gold Nanoparticles Decorated with ß-Cyclodextrins.

A surface-enhanced Raman scattering (SERS) method for measuring nitrate nitrogen in aquaculture water was developed using a substrate of ß-cyclodextrin-modified gold nanoparticles (SH-ß-CD@AuNPs). Addressing the issues of low sensitivity, narrow linear range, and relatively poor selectivity of single metal nanoparticles in the SERS detection of nitrate nitrogen, we combined metal nanoparticles with cyclodextrin supramolecular compounds to prepare a AuNPs substrate enveloped by cyclodextrin, which exhibits ultra-high selectivity and Raman activity. Subsequently, vanadium(III) chloride was used to convert nitrate ions into nitrite ions. The adsorption mechanism between the reaction product benzotriazole (BTAH) of o-phenylenediamine (OPD) and nitrite ions on the SH-ß-CD@AuNPs substrate was studied through SERS, achieving the simultaneous detection of nitrate nitrogen and nitrite nitrogen. The experimental results show that BTAH exhibits distinct SERS characteristic peaks at 1168, 1240, 1375, and 1600 cm-1, with the lowest detection limits of 3.33 × 10-2, 5.84 × 10-2, 2.40 × 10-2, and 1.05 × 10-2 µmol/L, respectively, and a linear range of 0.1-30.0 µmol/L. The proposed method provides an effective tool for the selective and accurate online detection of nitrite and nitrate nitrogen in aquaculture water.

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement.

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein-nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

Effects of CO2 concentration and time on algal biomass film, NO3-N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II.

The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.

Effects of the nitrogen form ratios on photosynthetic productivity of poplar under condition of phenolic acids.

Phenolic acids can reduce nitrogen utilization rate of poplar, which seriously restrict the productivity of poplar plantation. In this study, three phenolic acid concentrations (T0, T1, and T2) and three ratios of nitrogen forms (NH4+-N to NO3--were 1:3, 1:7, and 1:14) were chosen for orthogonal experiment on poplar (Populus × euramericana "Neva") seedlings to study the effects of the nitrogen form ratios on photosynthetic productivity of poplar under environment of phenolic acids. Results showed that photosynthetic physiology parameters were influenced by both phenolic acid concentration and nitrogen form ratio. The order of net photosynthetic rate (PN) values obtained from 9 treatments were T1-1:3, T0-1:3, T2-1:3, T0-1:7, T1-1:7, T0-1:14, T2-1:7, T1-1:14, and T2-1:14 (from high to low). Under environment of phenolic acids, when poplar were treated with NH4+-N to NO3--N ratio of 1:14, the major limitation factor of photosynthesis was non stomatal factor. When poplar were treated with NH4+-N to NO3-N ratio of 1:3, the major limitation factor of photosynthesis changed to stomatal factor. The leaf nitrogen content and total biomass were obviously positively related with PN (p < 0.05). Phenolic acid inhibited photosynthetic productivity of poplar in a major way and this effect decreased with increase of the content of NH4+-N.

Increasing the ratio of ammonia nitrogen fertilizer in soil can effectively reduce the toxic effect of phenolic acids on poplar and improve the photosynthetic productivity of poplar.

The Beneficial Fungus Mortierella hyalina Modulates Amino Acid Homeostasis in Arabidopsis under Nitrogen Starvation.

Non-mycorrhizal but beneficial fungi often mitigate (a)biotic stress-related traits in host plants. The underlying molecular mechanisms are mostly still unknown, as in the interaction between the endophytic growth-promoting soil fungus Mortierella hyalina and Arabidopsis thaliana. Here, abiotic stress in the form of nitrogen (N) deficiency was used to investigate the effects of the fungus on colonized plants. In particular, the hypothesis was investigated that fungal infection could influence N deficiency via an interaction with the high-affinity nitrate transporter NRT2.4, which is induced by N deficiency. For this purpose, Arabidopsis wild-type nrt2.4 knock-out and NRT2.4 reporter lines were grown on media with different nitrate concentrations with or without M. hyalina colonization. We used chemical analysis methods to determine the amino acids and phytohormones. Experimental evidence suggests that the fungus does not modulate NRT2.4 expression under N starvation. Instead, M. hyalina alleviates N starvation in other ways: The fungus supplies nitrogen (15N) to the N-starved plant. The presence of the fungus restores the plants' amino acid homeostasis, which was out of balance due to N deficiency, and causes a strong accumulation of branched-chain amino acids. We conclude that the plant does not need to invest in defense and resources for growth are maintained, which in turn benefits the fungus, suggesting that this interaction should be considered a mutualistic symbiosis.

Sustainability of cover cropping practice with changing climate in Illinois.

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3-N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021-2040) and the far-term future (2041-2060) were compared with the baseline scenario (2001-2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (-6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy.

Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.

Advance in the sulfur-based electron donor autotrophic denitrification for nitrate nitrogen removal from wastewater.

In the field of wastewater treatment, nitrate nitrogen (NO3--N) is one of the significant contaminants of concern. Sulfur autotrophic denitrification technology, which uses a variety of sulfur-based electron donors to reduce NO3--N to nitrogen (N2) through sulfur autotrophic denitrification bacteria, has emerged as a novel nitrogen removal technology to replace heterotrophic denitrification in the field of wastewater treatment due to its low cost, environmental friendliness, and high nitrogen removal efficiency. This paper reviews the advance of reduced sulfur compounds (such as elemental sulfur, sulfide, and thiosulfate) and iron sulfides (such as ferrous sulfide, pyrrhotite, and pyrite) electron donors for treating NO3--N in wastewater by sulfur autotrophic denitrification technology, including the dominant bacteria types and the sulfur autotrophic denitrification process based on various electron donors are introduced in detail, and their operating costs, nitrogen removal performance and impacts on the ecological environment are analyzed and compared. Moreover, the engineering applications of sulfur-based electron donor autotrophic denitrification technology were comprehensively summarized. According to the literature review, the focus of future industry research were discussed from several aspects as well, which would provide ideas for the application and optimization of the sulfur autotrophic denitrification process for deep and efficient removal of NO3--N in wastewater.

Relationship between the vertical distribution of fine roots and residual soil nitrogen along a gradient of hardwood mixture in a conifer plantation.

In forest ecosystems, understanding the relationship between the vertical distribution of fine roots and residual soil nitrogen is essential for clarifying the diversity-productivity-water purification relationship. Vertical distributions of fine-root biomass (FRB) and concentrations of nitrate-nitrogen (NO3 -N) in soil water were investigated in a conifer plantation with three thinning intensities (Control, Weak and Intensive), in which hardwood abundance and diversity were low, moderate and high, respectively. Intensive thinning led to the lowest NO3 -N concentration in soil water at all depths (0-100 cm) and highest FRB at shallow depths (0-50 cm). The NO3 -N concentration at a given depth was negatively correlated with total FRB from the surface to the depth at which NO3 -N concentration was measured, especially at shallow depths, indicating that more abundant fine roots led to lower levels of downward NO3 -N leaching. FRB contributed positively to nitrogen content of hardwood leaves. These findings demonstrate that a hardwood mixture in conifer plantations resulted in sufficient uptake of NO3 -N from soil by well developed fine-root systems, and translocation to canopy foliage. This study suggests that productivity and water purification can be achieved through a hardwood mixture in conifer plantations.

Genome Identification and Expression Profiles in Response to Nitrogen Treatment Analysis of the Class I CCoAOMT Gene Family in Populus.

Lignin is essential for the characteristics and quality of timber. Nitrogen has significant effects on lignin contents in plants. Nitrogen has been found to affect wood quality in plantations and lignin content in plants. Caffeoyl-CoA 3-O-methyltransferase (CCoAOMT) is an important methyltransferase in lignin biosynthesis. However, the classification of woody plant CCoAOMT gene family members and the regulation mechanism of nitrogen are not clear. Bioinformatics methods were used to predict the members, classification, and transcriptional distribution of the CCoAOMT gene family in Populus trichocarpa. The results showed that there were five PtCCoAOMTs identified, and they could be divided into three sub-groups according to their structural and phylogenetic features. The results of tissue expression specificity analysis showed that: PtCCoAOMT1 was highly expressed in roots and internodes; PtCCoAOMT2 was highly expressed in roots, nodes, and internodes, PtCCoAOMT3 was highly expressed in stems; PtCCoAOMT4 was highly expressed in young leaves, and, PtCCoAOMT5 was highly expressed in roots. Different forms and concentrations of nitrogen had varying effects on the expression patterns of genes in different plant tissue types. The results of real-time PCR showed that the expression levels of PtCCoAOMT1 and PtCCoAOMT2 in stems increased significantly under different forms of nitrogen. PtCCoAOMT3 and PtCCoAOMT4 were induced by nitrate nitrogen in upper stems and lower leaves, respectively. PtCCoAOMT4 and PtCCoAOMT5 were induced by different concentrations of nitrate nitrogen in lower stems and roots, respectively. These results could provide valuable information for revealing the differences between functions and expression patterns of the various CCoAOMT gene family members under different forms and concentrations of exogenous nitrogen in poplar.

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy.

Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH4-N and NO3-N in different soils were found at 978 cm-1 and 1044 cm-1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation (Rp2) of multiple linear regression prediction models for NH4-N and NO3-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400-2000 cm-1 were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the Rp2 was above 0.99, the root mean square errors of prediction (RMSEP) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging.

Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH4-N), sodium nitrate (nitrate nitrogen, NO3-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH4-N, R2p = 0.92, RMSEP = 0.77% and RPD = 3.63; for NO3-N, R2p = 0.92, RMSEP = 0.74% and RPD = 4.17; for urea-N, R2p = 0.96, RMSEP = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.

Influence of the catchment area use on the water quality in the Utrata River.

The present paper discusses the impact of land use and seasons on the concentration of nutrients in the waters of the Utrata River (Pruszków Poviat, Mazowieckie Voivodeship) from April 2018 to March 2019. The pollution of rivers by nutrients is a major problem for society. Surface water is a source of drinking water, water used for industrial and agricultural purposes. With the increasing pollution of rivers, the purification process for these purposes becomes more expensive and more challenging. To assist in carrying out activities aimed at reducing the inflow of biogenic substances into large river systems and then down to the Baltic Sea, we analyzed the spatial and temporal dynamics of loads from the entire Utrata River catchment area. We divided the entire catchment area into three impact zones: grasslands and wastelands, urbanized areas, and agricultural land and examined changes in nutrient concentrations (total phosphorus, nitrate nitrogen, ammonium nitrogen) in each of the zones. The results were statistically processed using the 1-factor ANOVA method with the p-value of significance below 0.05. Research indicates an increase in the concentration of total phosphorus and nitrogen forms down the course of the river in urban and agricultural areas with persistently low concentrations of these biogenic substances in grasslands.

A support vector regression model to predict nitrate-nitrogen isotopic composition using hydro-chemical variables.

Nitrate is a prominent pollutant in surface and groundwater bodies worldwide. Isotopes in nitrate provide a powerful approach for tracing nitrate sources and transformations in waters. Given that analytical techniques for determining isotopic compositions are generally time-consuming, laborious and expensive, alternative methods are warranted to supplement and enhance existing approaches. Hence, we developed a support vector regression (SVR) model and explored its feasibility to predict nitrogen isotopic composition of nitrate (δ15N-NO3-) in a rural-urban river system in Southeastern China. A total of 16 easily obtained hydro-chemical variables were measured in the wet season (September 2019) and dry season (January 2020) and used to develop the SVR prediction model. The grading method utilized ~75% (35) of the samples for model building while the remaining 11 samples assessed model performance. Principal component analysis (PCA) extracted 7 principal components for SVR model inputs as PCA reduces superfluous variables. We optimized tuning parameters in the SVR model using a grid search technique coupled with V-fold cross-validation. The optimized SVR model provided accurate δ15N-NO3- predictions with a determination coefficient (R2) of 0.88, Nash-Sutcliffe (NS) of 0.87, and mean square error (MSE) of 0.53‰ in the testing step, and performed much better than the corresponding multivariate linear regression model (R2 = 0.60, NS = 0.58 and MSE = 1.76‰) and general regression neural network model (R2 = 0.66, NS = 0.65 and MSE = 1.45‰). Overall, the SVR model provides a potential indirect method to predict environmental isotope values for water quality management that will complement and enhance the interpretation of direct measurements of δ15N-NO3-.

An All-Solid-State Nitrate Ion-Selective Electrode with Nanohybrids Composite Films for In-Situ Soil Nutrient Monitoring.

In this paper, an all-solid-state nitrate doped polypyrrole (PPy(NO3-) ion-selective electrode (ISE) was prepared with a nanohybrid composite film of gold nanoparticles (AuNPs) and electrochemically reduced graphene oxide (ERGO). Preliminary tests on the ISE based in-situ soil nitrate-nitrogen (NO3--N) monitoring was conducted in a laboratory 3-stage column. Comparisons were made between the NO3--N content of in-situ soil percolate solution and laboratory-prepared extract solution. Possible influential factors of sample depth, NO3--N content, soil texture, and moisture were varied. Field-emission scanning electron microscopy (FESEM) and X-ray powder diffraction (XRD) characterized morphology and content information of the composite film of ERGO/AuNPs. Due to the performance excellence for conductivity, stability, and hydrophobicity, the ISE with ERGO/AuNPs illustrates an acceptable detection range from 10-1 to 10-5 M. The response time was determined to be about 10 s. The lifetime was 65 days, which revealed great potential for the implementation of the ERGO/AuNPs mediated ISE for in-situ NO3--N monitoring. In-situ NO3--N testing results conducted by the all-solid-state ISE followed a similar trend with the standard UV-VIS method.

An ISE-based On-Site Soil Nitrate Nitrogen Detection System.

Soil nitrate-nitrogen (NO3--N) is one of the primary factors used to control nitrogen topdressing application during the crop growth period. The ion-selective electrode (ISE) is a promising method for rapid lower-cost in-field detection. Due to the simplification of sample preparation, the accuracy and stability of ISE-based in-field detection is doubted. In this paper, a self-designed prototype system for on-site soil NO3--N detection was developed. The procedure of spinning centrifugation was used to avoid interference from soil slurry suspension. A modified Nernstian prediction model was quantitatively characterized with outputs from both the ISE and the soil moisture sensor. The measurement accuracy of the sensor fusion model was comparable with the laboratory ISE detections with standard sample pretreatment. Compared with the standard spectrometric method, the average absolute error (AE) and root-mean-square error (RMSE) were found to be less than 4.7 and 6.1 mg/L, respectively. The on-site soil testing efficiency was 4-5 min/sample, which reduced the operation time by 60% compared with manual sample preparation. The on-site soil NO3--N status was dynamically monitored for 42 consecutive days. The declining peak of NO3--N was observed. In all, the designed ISE-based detection system demonstrated a promising capability for the dynamic on-site monitoring of soil macronutrients.

A High-Performance Optoelectronic Sensor Device for Nitrate Nitrogen in Recirculating Aquaculture Systems.

The determination of nitrate nitrogen (NO3-N) in recirculating aquaculture systems is of great significance for the health assessment of the living environment of aquatic animals. Unfortunately, the commonly used spectrophotometric methods often yield unstable results, especially when the ambient temperature varies greatly in the field measurement. Here, we have developed a novel handheld absorbance measurement sensor based on the thymol-NO3-N chromogenic rearrangement reaction. In terms of hardware, the sensor adopts a dual channel/dual wavelength colorimeter structure that features a modulated light source transmitter and a synchronous detector receiver. The circuit measures the ratio of light absorbed by the sample and reference containers at two LEDs with peak wavelengths at 420 nm and 450 nm. Using the modulated source and synchronous detector rather than a constant (DC) source eliminates measurement errors due to ambient light and low frequency noise and provides higher accuracy. In terms of software, we design a new quantitative analysis algorithm for absorbance by studying colloid absorbing behavior. The application of a buffer operator embedded in the algorithm makes the sensor get the environmental correction function. The results have shown that the sensitivity, repeatability, precision and environmental stability are higher than that by ordinary spectrophotometry. Lastly, we have a brief overview of future work.

Effectiveness of a newly reconstructed floodplain oxbow to reduce NO3-N loads from a spring flood.

Enhancing NO3-N processing in floodplains offers opportunities to achieve water quality improvements in agricultural watersheds but few studies have quantified the effectiveness of newly reconstructed oxbows to reduce loads delivered from floods. In this study, we evaluated NO3-N retention during a spring storm water runoff event in a newly reconstructed oxbow (<1 year old) located along Morgan Creek in eastern Iowa. A 30-h flood connected the oxbow to the creek for approximately nine hours and delivered 14.7 kg of NO3-N into the oxbow. Using a NO3-N sensor, oxbow NO3-N concentrations were observed to increase from 0.7 to 5.3 mg/l after the flood event, but decreased to background conditions over the next 21 days. We estimated NO3-N retention to be 0.30 g N m-2 d-1 and the NO3-N retention efficiency to be 74.2% for the single flood event. The NO3-N mass reduction in the oxbow intersected with predicted mass reduction from a first-order denitrification decay model after 21 days which suggests that denitrification was largely responsible for the observed NO3-N decrease. However, the effectiveness of the oxbow for reducing watershed-scale N loads appears to be limited, since the oxbow is located in a low-nutrient floodplain and would only retain NO3-N loads when delivered to the oxbow via flooding. Study results suggest that oxbows provides valuable ecosystem services during non-flooding periods and are activated for NO3-N load reduction during floods.

Enhanced nitrate-nitrogen removal by modified attapulgite-supported nanoscale zero-valent iron treating simulated groundwater.

Attapulgite (or palygorskite) is a magnesium aluminium phyllosilicate. Modified attapulgite-supported nanoscale zero-valent iron (NZVI) was created by a liquid-phase reduction method and then applied for nitrate-nitrogen (NO3-N) removal (transformation) in simulated groundwater. Nanoscale zero-valent iron was sufficiently dispersed on the surface of thermally modified attapulgite. The NO3-N removal efficiency reached up to approximately 83.8% with an initial pH values of 7.0. The corresponding thermally modified attapulgite-supported nanoscale zero-valent iron (TATP-NZVI) and NO3-N concentrations were 2.0 g/L and 20 mg/L respectively. Moreover, 72.1% of the water column NO3-N was converted to ammonium-nitrogen (NH4-N) within 6 h. The influence of environmental boundary conditions including dissolved oxygen (DO) concentration, light illumination and water temperature on NO3-N removal was also investigated with batch experiments. The results indicated that the DO concentration greatly impacted on NO3-N removal in the TATP-NZVI-contained solution, and the NO3-N removal efficiencies were 58.5% and 83.3% with the corresponding DO concentrations of 9.0 and 0.3 mg/L after 6 h of treatment, respectively. Compared to DO concentrations, no significant (p > 0.05) effect of light illumination on NO3-N removal and NH4-N generation was detected. The water temperature also has great importance concerning NO3-N reduction, and the removal efficiency of NO3-N at 25 °C was 1.25 times than that at 15 °C. For groundwater, therefore, environmental factors such as water temperature, anaerobic conditions and darkness could influence the NO3-N removal efficiency when TATP-NZVI is present. This study also demonstrated that TATP-NZVI has the potential to be developed as a suitable material for direct remediation of NO3-N-contaminated groundwater.