The influence of different nitrate concentrations on aerobic sludge granulation and the role of extracellular polymeric substances

This study investigated the influence of nitrate on aerobic granular sludge (AGS) granulation. The introduction of nitrate at 5, 15 and 20 mg L-1 promoted AGS granulation, and the promoting effect was positively correlated with nitrate concentrations. Meanwhile, exogenous nitrate significantly increased denitrification rate in the AGS system. However, granular disintegration appeared at a long-term addition of nitrate. An in-deep analysis showed that nitrate stimulated the secretion of extracellular polymeric substances (EPS), especially the content of proteins, which might be the main reason for the AGS granulation. However, the rapid and excessive increase in EPS might cause granular disintegration, as excessive EPS blocked the transmission of substrates, leading to the increase of dead cells in the granules. Besides, nitrate also altered the hydrophobicity of EPS and the content of α-helix, 3-turned helix and polymeric chain that favored aggregation, which also affected AGS granulation. From the microbial community level, nitrate induced the enrichment of denitrifying bacteria, including those that also functioned as EPS producers, such as Micropruina and Flavobacterium, resulting in the rapid increase of functional enzymes associated with amino acid synthesis, thereby promoting the secretion of proteins in EPS. Conversely, disintegration caused by mass transfer blockage might lead to the loss of EPS producing bacteria and subsequent decrease in EPS content, further accelerating granular disintegration.

Accurate assessment of the effect of Rhodamine 6G solution as a spectral converter on biomass production of microalgae Chlorella sp., nitrate uptake, and energy consumption by the light source

Spectral conversion of light introduced as a new technique to manage incoming light photons into the culture medium of photosynthetic microorganisms which can increase biomass production. In this work, a much more accurate value of the effect of spectral conversion on the growth of Chlorella sp. was obtained. For this purpose, the light source and Rhodamine 6G solution, as a spectral converter, was placed in the center of the culture flask, which was covered with aluminum foil. The loss of photons was largely avoided due to the additional homogeneous distribution of light. Modification of white light using the solution has increased biomass productivity up to 300%. Also, for biomass productivity of 1 mg L^-1 day^-1, the energy consumption of the white light source was reduced up to 75%. Nitrate uptake has also increased up to 28%, and intracellular lipid content has decreased, as well.

Accuracy assessment of inverse distance weighting interpolation of groundwater nitrate concentrations in Bavaria (Germany)

For the designation of nitrate vulnerable zones under the EU Nitrate Directive, some German federal states use inverse distance weighting (IDW) as interpolation method. Our study quantifies the accuracy of IDW with respect to the designation of areas with a groundwater nitrate concentration above the threshold of 50 mg NO₃/l using a dataset of 5790 groundwater monitoring sites in Bavaria. The results show that the absolute differences of nitrate concentrations between the monitoring sites are only weakly correlated within a range of no more than 0.4 km. The IDW cross-validated nitrate concentration of measurement sites shows a mean absolute error of 7.0 mg NO₃/l and the number of measurement sites above 50 mg NO₃/l is 44% too low by interpolation for all sites as a whole. The corresponding values for interpolation separately for the 18 hydrogeological regions in Bavaria are 7.1 mg NO₃/l and 38%. The sensitivity and the accuracy of nitrate concentration maps due to the variation of IDW parameters and the position of sampling points are analysed by Monte Carlo IDW interpolations using a Random Forest modelled map as reference spatial distribution. Compared to this reference map, the area with a concentration above 50 mg NO₃/l in groundwater is estimated by IDW to be 46% too low for the best IDW parametrization. Overall, IDW interpolation systematically underrates the occurrence of higher range nitrate concentrations. In view of these underestimations, IDW does not appear to be a suitable regionalization method for the designation of nitrate vulnerable zones, neither when applied for a federal state as a whole nor when interpolated separately for hydrogeological regions.

A comparative analysis on groundwater vulnerability models-fuzzy DRASTIC and fuzzy DRASTIC-L

Groundwater vulnerability assessment using the fuzzy logic technique is attempted in this study. A hierarchical fuzzy inference system is created to serve the selected objective. The parameters considered in this study are similar to the seven parameters used in conventional DRASTIC methods; however, the effect of land use and land cover is studied by including it as an additional parameter in a model. A hierarchy is created by comparing two input parameters, say (D and R), and the output of the same is paired as an input with the third parameter (A) and so on using the fuzzy toolbox in MATLAB. Thus, the final output of fuzzy inference systems six and seven (FI6 and FI7) is defuzzified and mapped using ArcGIS to obtain the groundwater vulnerability zones by fuzzy DRASTIC and fuzzy DRASTIC-L. Each map is grouped into five vulnerability classes: very high, high, moderate, low, and very low. Further, the results were validated using the observed nitrate concentration from 51 groundwater sampling points. The receiver operating curve (ROC) technique is adopted to determine the best suitable model for the selected study. From this, area under the curve is estimated and found to be 0.83 for fuzzy DRASTIC and 0.90 for fuzzy DRASTIC-L; the study concludes that fuzzy DRASTIC-L has a better value of AUC suits best for assessing the groundwater vulnerability in Thoothukudi District.

Groundwater vulnerability and contamination risk mapping of semi-arid Totko river basin, India using GIS-based DRASTIC model and AHP techniques

Totko river basin is a semi-arid watershed, which undergoes severe water crisis during the dry season. Presently, due to increase in population, demand of food has increased leading to a rise in growth of high yield crop variety and usage of chemical fertilizers and pesticides. So, surface water as well as groundwater is getting polluted. In this study assessment of groundwater vulnerability of Totko river basin has been done using DRASTIC and Analytic Hierarchy Process (AHP) models. For this, seven hydrogeological parameters have been considered which are; Depth to water level (D), Net Recharge (R), Aquifer media (A), Soil media (S), Topography (T), Impact of vadose zone (I) and Hydraulic Conductivity (C). Weight and rating analysis of the seven criteria and their sub-criteria have been done using generic DRASTIC algorithm and AHP comparison matrix. Ground Water Vulnerability Map (GWV) obtained from DRASTIC and AHP analysis has been divided into five vulnerable classes. Area of very high vulnerable zone is 6.53% more in AHP based vulnerability as compared to Generic DRASTIC. Similarly, these regions show a high nitrate concentration (30-50 ppm) in groundwater. GWV maps have been validated through nitrate concentration and the accuracy of the models have been assessed through Pearson's correlation coefficient and Kappa coefficient. To prevent groundwater contamination proper land use planning and watershed management are necessary, for which vulnerable zones need to be demarcated and DRASTIC is a useful model for vulnerability assessment.

In vitro-derived plants grown from low nitrate medium produced quality tubers in Decalepis hamiltonii Wight & Arn

In the present study, influence of nitrate concentration on in vitro rooting and its ex vitro hardened tubers were investigated in D. hamiltonii. In vitro multiple shoots established on the MS medium were used as explants for this study and concentration of indole-3-butyric acid (1.23 µM) required for root initiation was determined. The effect of different nitrate concentrations in MS medium for in vitro rooting was investigated and positive (¼ and ½ strength) influence was observed on 14 days inoculation. The in vitro rooted plants (IVP) showed 80% survival upon hardening and exhibited similar growth pattern to seedling plants. The 1-year grew ex vitro hardened plants were evaluated for their tuber quality with reference to their yield and flavor metabolite 2-hydroxy-4-methoxy benzaldehyde (2H4MB) content. The IVP grown from ¼ nitrate strength media produced 155 ± 4.85 g FW of tuber biomass which is observed to be higher than full nitrate strength IVP (112 ± 2.52 g) which are grown under similar conditions in the greenhouse. The flavor metabolite content, total phenolics, total flavonoids, and antioxidant potential of these IVP tubers were evaluated. Upregulation of flavor biosynthetic pathway genes DhPAL, DhC4H and DhCoMT were observed in tubers of potted plants that developed from low nitrate strength culture media. In this study, superiority of tissue cultured plants was evident, wherein in vitro plants developed in low nitrate strength medium and acclimatized could produce a higher yield in tuber biomass and maintain relative content of flavor compounds in this endangered plant.

Multi-objective optimal model for sustainable management of groundwater resources in an arid and semiarid area using a coupled optimization-simulation modeling

Excessive exploitation of groundwater resources can increase the concentration of pollutants in addition to the progressive drawdown of groundwater table. In this research, to achieve aquifer quantitative and qualitative (QQ) sustainable development, an optimal scenario for withdrawing from operation wells is proposed. At the first step, the aquifer QQ simulation was carried out with the GMS model. The developed code in MATLAB2018b in the second step provides the link between the simulation and the NSGA-II optimization tools. In the third step, a multi-objective coupled optimization-simulation model based on GMS and NSGA-II developed. Finally, optimal scenario was chosen based on applying the multiple criteria decision-making (MCDM) and Berda Aggregation Method (BAM). The results show that reducing the current withdrawal rate to 51.55% can establish the QQ stability of the aquifer. This decrease in groundwater abstraction has led to a 4.6 m increase in groundwater level (GWL) over 3 years (average 19 cm per month). The spatial and temporal distribution of nitrate concentration after applying the optimal discharge of wells shows the nitrate concentration in central and eastern parts of the aquifer has greatly reduced. Developed sustainable management model can be used to provide a real operation planning of wells to improvement of the QQ status of groundwater in each unconfined aquifer.

Experiment and multicomponent model based analysis on the effect of flow rate and nitrate concentration on denitrification in low-permeability media

To better understand the combined effects of flow rate and NO₃^- concentration on denitrification rate and NO₃^- removal efficiency in the low-permeability media, a set of column experiments with different flow rates and injected NO₃^- concentrations were conducted. Denitrification processes under these different conditions were simulated using the PHREEQC code that couples the biogeochemical reactions and hydrological transport processes. In these reactive transport models, Monod kinetics were applied to describe the denitrification process. It was found that, among the experiments conducted in this study, the low flow rate (0.023 m/d) resulted in the low denitrification rate but high NO₃^- removal efficiency. Meanwhile, NO₃^- removal efficiency was the highest (85%) under moderate NO₃^- concentration of 1.29 mmol/L, although denitrification rate increased in response to the increase of NO₃^- concentration. The model results also indicated that NO₃^- removal efficiency of 97% can be achieved with relatively low flow rate and high influent NO₃^- concentration. The results in this study provide insights into NO₃^- remediation, and the temporal and spatial flow rate, as well as NO₃^- concentration distribution, should be pre-evaluated for the effective removal strategies.

Relative performance of different data mining techniques for nitrate concentration and load estimation in different type of watersheds

The increasing availability of water quality datasets has led to a greater focus on hydrologic and water quality analysis, thus requiring more efficient and accurate modelling methods. Data mining techniques have been increasingly used for water quality analysis and prediction of the concentration and load of nitrogen pollutants instead of more traditional simulation methods. In this study, we tested the multilayer perceptron (MLP), k-nearest neighbor (k-NN), random forest, and reduced error pruning tree (REPTree) methods, along with the traditional linear regression, to predict nitrate levels based on long-term data from six watersheds with different land-use practices in the midwestern United States. Both the concentration and load results indicated that REPTree had the best performance, with an R² of 0.61-0.85 and a relative absolute error of <75.8%. The different watershed types, however, influenced the performance of the data mining methods, where all four methods showed a higher accuracy for urban dominant watershed and lower accuracy for agricultural and forest watersheds. Out of these four methods, classification tree methods (REPTree and RF) performed better than cluster methods (MLP and k-NN) for agricultural and forested watersheds. Our results indicated that both the data structure based on the dominant land use and type of algorithmic method should be carefully considered for selecting a data mining method to predict nitrate concentration and load for a watershed.

Enhancing production of lutein by a mixotrophic cultivation system using microalga Scenedesmus obliquus CWL-1

This work studies a series of strategies in the production of lutein by Scenedesmus obliquus CWL-1 under mixotrophic cultivation. Our experimental results revealed that the optimal conditions associated with light-related strategies were 12 h light period followed by a 12 h dark period and blue to red light under mixotrophic cultivation. Under such conditions, the biomass, lutein content and lutein productivity were maximized to 9.88 (g/L), 1.78 (mg/g) and 1.43 (mg/L/day), respectively. Moreover, the assimilation of 4.5 g/L of calcium nitrate into S. obliquus CWL-1 increased the maximal biomass (12.73 g/L) and the highest maximal lutein productivity (3.06 mg/L/day), while the assimilation of 1.5 g/L of calcium nitrate yielded the highest maximal lutein content of 2.45 mg/g. The highest maximal lutein productivity of 4.96 (mg/L/day) was obtained when fed-batch fermentation was conducted, and this value was approximately 11-folds that obtained using the batch system.

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85-0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R² = 0.71) that were relatively similar to RF (RMSE = 10.41, R² = 0.72) and higher than SVM (RMSE = 13.28, R² = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions.

Cloning of nitrite reductase gene from Haematococcus pluvialis and transcription and enzymatic activity analysis at different nitrate and phosphorus concentration

Haematococcus pluvialis is an economic microalga to produce astaxathin. To study the nitrogen metabolic process of H. pluvialis, the transcription level and enzyme content of nitrite reductase at different nitrate and phosphorus concentrations were studied. In this research, nitrite reductase gene (nir) was first cloned from H. pluvialis, which consists of 5592 nucleotides and includes 12 introns. The cDNA ORF is 1776 bp, encoding a 592 amino acid protein with two conserved domains. Phylogenetic analysis showed that the nir gene in H. pluvialis had the highest affinity with other freshwater green algae. Nitrogen and phosphorus play an important role in the growth of H. pluvialis. The single factor experiments of nitrogen on growth conditions showed that the group with 0.2 g/L NaNO₃ had a relative high biomass. The single factor experiments of phosphorus on growth conditions showed that the group with 0.06 g/L K₂HPO₄ had a relative high biomass. The transcription level and enzymatic activity of nitrite reductase were detected at different nitrate and phosphorus concentrations. In the absence of nitrogen and phosphorus in the medium, nitrite reductase activity is the highest. This research provides theoretical guidance for optimization of culture medium for H. pluvialis and also provides an experimental basis for understanding of nitrogen metabolism pathway in H. pluvialis.

Spatial Prediction of Nitrate Concentration Using GIS and ANFIS Modelling in Groundwater

Groundwater contamination by nitrate becomes a major problem in agricultural areas. Nitrate pollution in the groundwater of Coimbatore district of India has been investigated. Seventy-one observation wells within the study area were selected. The samples were collected in three stages with 6 months interval during the years 2011 and 2012. The input for creating an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model is the average of calcium, electrical conductivity, sodium, chlorides, hardness, potassium and the concentration of nitrate was the output from the model. Results of the best ANFIS model were assessed by deterministic, geostatistical and kernel smoothing methods using GIS. Out of the five models developed, ANFIS model V predicts nitrate concentration with 90% accuracy and less root mean square error (0.0934). The results from model V are given as input in GIS to predict spatial nitrate concentration in groundwater. Kriging method is more accurate in estimating nitrate concentration over the study area.