Use of cheap, N-rich, and environmentally benign legume green manures to correct N deficiency in infertile soils is a very attractive option in the humid tropics. Understanding the influence of management and climate on their effectiveness, and quantifying their contribution to crop productivity, is therefore crucial for technology adoption and adaptation. Mineral N buildup and the contribution to N uptake in maize were studied in an Ultisol amended with fresh Gliricidia leaves. Net mineral N accumulation was compared in mulched and incorporated treatments in a field incubation study. The 15 N isotope dilution technique was used to quantify N supplied to maize by Gliricidia leaves in an alley cropping. Mineral N accumulation was slow, but was much greater after incorporation than after mulching. Also, N buildup was always higher in the topsoil (0 to 10 cm) than in the subsoil (10 to 20 cm). More NO3-N was leached than NH4-N, and the effect was greater in the incorporated treatment. Surface-applied Gliricidia leaves significantly increased N uptake by maize, and supplied >30% of the total N in the stover and >20% of that in the corn grain, even in the presence of hedgerows. Thus Gliricidia leaf mulch has immense potential to improve productivity in tropical soils.
A two-stage anoxic transformation process, involving growth of biomass utilizing two types of different electron acceptors, namely nitrate and nitrite, has been observed. The present water quality modules established for sewer processes cannot account for the two-stage process. This paper outlines the development of a model concept that enables the two-stage anoxic transformation process to be simulated. The proposed model is formulated in a matrix form that is similar to the Activated Sludge Models and Sewer Process Model matrices. The model was successfully applied to simulate changes in nitrate and nitrite concentrations during anoxic transformations in the bulkwater phase of municipal wastewater.
Nitrates in different water and wastewater streams raised concerns due to severe impacts on human and animal health. Diverse methods are reported to remove nitrate from water streams which almost fail to entirely treat nitrate, except biological denitrification which is capable of reducing inorganic nitrate compounds to harmless nitrogen gas. Review of numerous studies in biological denitrification of nitrate containing water resources, aquaculture wastewaters and industrial wastewater confirmed the potential of this method and its flexibility towards the remediation of different concentrations of nitrate. The denitrifiers could be fed with organic and inorganic substrates which have different performances and subsequent advantages or disadvantages. Review of heterotrophic and autotrophic denitrifications with different food and energy sources concluded that autotrophic denitrifiers are more effective in denitrification. Autotrophs utilize carbon dioxide and hydrogen as the source of carbon substrate and electron donors, respectively. The application of this method in bio-electro reactors (BERs) has many advantages and is promising. However, this method is not so well established and documented. BERs provide proper environment for simultaneous hydrogen production on cathodes and appropriate consumption by immobilized autotrophs on these cathodes. This survey covers various designs and aspects of BERs and their performances.
The degradation of (RS)-MCPP was investigated in an anaerobic membrane bioreactor (AnMBR) using nitrate as an available electron acceptor under different COD/NO(3)(-)-N ratios. Results showed high soluble COD removal efficiency (80-93%) when the reactor was operated at high COD/NO(3)(-)-N ratios. However, the COD removal started to decline (average 15%) at high nitrate concentrations coinciding with a drop in nitrate removal efficiency to 37%, suggesting that the denitrification activity dropped and affected the AnMBR performance when nitrate was the predominant electron acceptor. Additionally, the removal efficiency of (RS)-MCPP increased from 2% to 47% with reducing COD/NO(3)(-)-N ratios, whilst the (RS)-MCPP specific utilisation rate (SUR) was inversely proportional to the COD/NO(3)(-)-N ratio, suggesting that a lower COD/NO(3)(-)-N ratios had a positive influence on the (RS)-MCPP SUR. Although nitrate had a major impact on methane production rates, the methane composition was stable (approximately 80%) for COD/NO(3)(-)-N ratios of 23 or more.
The source and quantity of nutrients available to plants can affect the quality of leafy herbs. A study was conducted to compare quality of Cosmos caudatus in response to rates of organic and mineral-based fertilizers. Organic based fertilizer GOBI (8% N:8% P₂O₅:8% K₂O) and inorganic fertilizer (15% N, 15% P₂O₅, 15% K₂O) were evaluated based on N element rates at 0, 30, 60, 90, 120 kg h⁻¹. Application of organic based fertilizer reduced nitrate, improved vitamin C, antioxidant activity as well as nitrogen and calcium nutrients content. Antioxidant activity and chlorophyll content were significantly higher with increased fertilizer application. Fertilization appeared to enhance vitamin C content, however for the maximum ascorbic acid content, regardless of fertilizer sources, plants did not require high amounts of fertilizer.
Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
Edible bird nests (EBNs) are important ethnomedicinal commodity in the Chinese community. Recently, But and others showed that the white EBNs could turn red by vapors from sodium nitrite (NaNO2) in acidic condition or from bird soil, but this color-changing agent remained elusive. The aim of this study was to determine the prevalence of nitrite and nitrate contents and its affects on EBN's color. EBNs were collected from swiftlet houses or caves in Southeast Asia. White EBNs were exposed to vapor from NaNO2 in 2% HCl, or bird soil. The levels of nitrite (NO2-) and nitrate (NO3-) in EBNs were determined through ion chromatography analysis. Vapors from NaNO2 in 2% HCl or bird soil stained white bird nests to brown/red colors, which correlated with increase nitrite and nitrate levels. Moreover, naturally formed cave-EBNs (darker in color) also contained higher nitrite and nitrate levels compared to white house-EBNs, suggesting a relationship between nitrite and nitrate with EBN's color. Of note, we detected no presence of hemoglobin in red "blood" nest. Using infrared spectra analysis, we demonstrated that red/brown cave-EBNs contained higher intensities of C-N and N-O bonds compared to white house-EBNs. Together, our study suggested that the color of EBNs was associated with the prevalence of the nitrite and nitrate contents.
Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.
In this work, the DRASTIC and GALDIT models were employed to determine the groundwater vulnerability to contamination from anthropogenic activities and seawater intrusion in Kapas Island. In addition, the work also utilized sensitivity analysis to evaluate the influence of each individual parameter used in developing the final models. Based on these effects and variation indices of the said parameters, new effective weights were determined and were used to create modified DRASTIC and GALDIT models. The final DRASTIC model classified the island into five vulnerability classes: no risk (110-140), low (140-160), moderate (160-180), high (180-200), and very high (>200), covering 4, 26, 59, 4, and 7 % of the island, respectively. Likewise, for seawater intrusion, the modified GALDIT model delineates the island into four vulnerability classes: very low (<90), low (90-110), moderate (110-130), and high (>130) covering 39, 33, 18, and 9 % of the island, respectively. Both models show that the areas that are likely to be affected by anthropogenic pollution and seawater intrusion are within the alluvial deposit at the western part of the island. Pearson correlation was used to verify the reliability of the two models in predicting their respective contaminants. The correlation matrix showed a good relationship between DRASTIC model and nitrate (r = 0.58). In a similar development, the correlation also reveals a very strong negative relationship between GALDIT model and seawater contaminant indicator (resistivity Ωm) values (r = -0.86) suggesting that the model predicts more than 86 % of seawater intrusion. In order to facilitate management strategy, suitable areas for artificial recharge were identified through modeling. The result suggested some areas within the alluvial deposit at the western part of the island as suitable for artificial recharge. This work can serve as a guide for a full vulnerability assessment to anthropogenic pollution and seawater intrusion in small islands and will help policy maker and manager with understanding needed to ensure sustainability of the island's aquifer.
A detailed study on the solution chemistry of red soil in South China is presented. Data are collected from two simulated column-leaching experiments with an improved setup to evaluate the effects of atmospheric N deposition (ADN) composition and ADN flux on agricultural soil acidification using a (15)N tracer technique and an in situ soil solution sampler. The results show that solution pH values decline regardless of the increase of the NH4(+)/NO3(-) ratio in the ADN composition or ADN flux, while exchangeable Al(3+), Ca(2+), Mg(2+), and K(+) concentrations increase at different soil depths (20, 40, and 60 cm). Compared with the control, ADN (60 kg per ha per year N, NH4(+)/NO3(-) ratio of 2 : 1) decreases solution pH values, increases solution concentrations of NO3(-)-N, Al(3+), Ca(2+) and Mg(2+) at the middle and lower soil depths, and promotes their removal. NH4(+)-N was not detected in red soil solutions of all the three soil layers, which might be attributed to effects of nitrification, absorption and fixation in farmland red soil. Some of the NO3(-)-N concentrations at 40-60 cm soil depth exceed the safe drinking level of 10 mg L(-1), especially when the ADN flux is beyond 60 kg ha(-1) N. These features are critical for understanding the ADN agro-ecological effects, and for future assessment of ecological critical loads of ADN in red soil farmlands.
In this study, the treatment of septage (originating from septic tanks) was carried out in a pilot-scale, two-staged, vertical-flow engineered wetland (VFEW). Palm kernel shells (PKS) were incorporated as part of the VFEW's substrate (B-PKS), to compare its organic matter (OM) and nitrogen (N) removal efficiency against wetlands with only sand substrates (B-SD). The results revealed satisfactory OM removal with >90% reduction efficiencies at both wetlands B-PKS and B-SD. No increment of chemical oxygen demand (COD) concentration was observed in the effluent of B-PKS. Ammonia load removal efficiencies were comparable (>91% and 95% in wetland B-PKS and B-SD, respectively). However, nitrate accumulation was observed in the effluent of B-SD where PKS was absent. This was due to the limited denitrification in B-SD, as sand is free of carbon. A lower nitrate concentration was associated with higher COD concentration in the effluent at B-PKS. This study has shown that the use of PKS was effective in improving the N removal efficiency in engineered wetlands.
Improper use of urea may cause environmental pollution through NH3 volatilization and NO3 (-) leaching from urea. Clinoptilolite zeolite and compost could be used to control N loss from urea by controlling NH4 (+) and NO3 (-) release from urea. Soil incubation and leaching experiments were conducted to determine the effects of clinoptilolite zeolite and compost on controlling NH4 (+) and NO3 (-) losses from urea. Bekenu Series soil (Typic Paleudults) was incubated for 30, 60, and 90 days. A soil leaching experiment was conducted for 30 days. Urea amended with clinoptilolite zeolite and compost significantly reduced NH4 (+) and NO3 (-) release from urea (soil incubation study) compared with urea alone, thus reducing leaching of these ions. Ammonium and NO3 (-) leaching losses during the 30 days of the leaching experiment were highest in urea alone compared with urea with clinoptilolite zeolite and compost treatments. At 30 days of the leaching experiment, NH4 (+) retention in soil with urea amended with clinoptilolite zeolite and compost was better than that with urea alone. These observations were because of the high pH, CEC, and other chemical properties of clinoptilolite zeolite and compost. Urea can be amended with clinoptilolite zeolite and compost to improve NH4 (+) and NO3 (-) release from urea.
Nitrogen (N) transport from land to water is a dominant contributor of N in estuarine waters leading to eutrophication, harmful algal blooms, and hypoxia. Our objectives were to (1) investigate the composition of inorganic and organic N forms, (2) distinguish the sources and biogeochemical mechanisms of nitrate-N (NO3-N) transport using stable isotopes of NO3- and Bayesian mixing model, and (3) determine the dissolved organic N (DON) bioavailability using bioassays in a longitudinal gradient from freshwater to estuarine ecosystem located in the Tampa Bay, Florida, United States. We found that DON was the most dominant N form (mean: 64%, range: 46-83%) followed by particulate organic N (PON, mean: 22%, range: 14-37%), whereas inorganic N forms (NOx-N: 7%, NH4-N: 7%) were 14% of total N in freshwater and estuarine waters. Stable isotope data of NO3- revealed that nitrification was the main contributor (36.4%), followed by soil and organic N sources (25.5%), NO3- fertilizers (22.4%), and NH4+ fertilizers (15.7%). Bioassays showed that 14 to 65% of DON concentrations decreased after 5-days of incubation indicating utilization of DON by microbes in freshwater and estuarine waters. These results suggest that despite low proportion of inorganic N forms, the higher concentrations and bioavailability of DON can be a potential source of N for algae and bacteria leading to water quality degradation in the estuarine waters.
Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors.
Ammonia emissions is an important issue during composting because it can cause secondary pollution and a significant of nitrogen loss. Based on research adding Bacillus stearothermophilus can reduce ammonia emissions during composting because it can use sugar in organic matter fermentation to produce organic acids over 50 °C. This study conducted the batch experiments by adding different concentrations of Bacillus stearothermophilus to reduce the ammonia emissions and find out its characteristic during layer manure composting by using an aerobic composting reactor with sawdust as a bulking agent. The results show that the application of Bacillus stearothermophilus can accelerate the rate of temperature and significantly decrease pH, the warming period was 2 days in the treatment with Bacillus stearothermophilus, while it was 4 days in the treatment without Bacillus stearothermophilus. Ammonia emissions were mainly occurred in warming and high temperature period during composting. The ammonia emissions in the treatment with 8.00 g/kg initial Bacillus stearothermophilus were significantly lower than the other lower Bacillus stearothermophilus treatment and control during composting (p 0.05). MiSeq System Sequencing results find that the addition of Bacillus stearothermophilus changed the bacterial community structure under warming and high-temperature periods during composting, increased the relative abundance of lactic acid bacillus and nitrification bacteria. Therefore, the reason for the low ammonia emission in 8.00 g/kg initial Bacillus stearothermophilus treatments might be not only due to the Bacillus stearothermophilus itself, but also Bacillus stearothermophilus can change the indigenous microorganism community, including increase the relative content of lactic acid Bacillus and nitrification bacteria, thus reducing the pH and promoting nitrification, and reducing ammonia emissions.
Monitoring the groundwater chemical composition and identifying the presence of pollutants is an integral part of any comprehensive groundwater management strategy. The present study was conducted in a part of West Tripura, northeast India, to investigate the presence and sources of trace metals in groundwater and the risk to human health due to direct ingestion of groundwater. Samples were collected from 68 locations twice a year from 2016 to 2018. Mixed Ca-Mg-HCO3, Ca-Cl and Ca-Mg-Cl were the main groundwater types. Hydrogeochemical methods showed groundwater mineralization due to (1) carbonate dissolution, (2) silicate weathering, (3) cation exchange processes and (4) anthropogenic sources. Occurrence of faecal coliforms increased in groundwater after monsoons. Nitrate and microbial contamination from wastewater infiltration were apparent. Iron, manganese, lead, cadmium and arsenic were above the drinking water limits prescribed by the Bureau of Indian Standards. Water quality index indicated 1.5% had poor, 8.7% had marginal, 16.2% had fair, 66.2% had good and 7.4% had excellent water quality. Correlation and principal component analysis reiterated the sources of major ions and trace metals identified from hydrogeochemical methods. Human exposure assessment suggests health risk due to high iron in groundwater. The presence of unsafe levels of trace metals in groundwater requires proper treatment measures before domestic use.
Stormwater runoff is recognized as a cause of water quality degradation because it may carry nitrogen (N) and other pollutants to aquatic ecosystems. Stormwater ponds are a stormwater control measure often used to manage stormwater runoff by holding a permanent pool of water, which reduces the peak flow, magnitude of runoff volume, and concentrations of nutrients and pollutants. We instrumented the outlet of a stormwater pond in an urban residential neighbourhood in Florida, United States to (1) investigate the concentration and composition of N forms during the summer rainy season (May to September 2016), and (2) determine the bioavailability of organic N in the stormwater pond with a bioassay experiment. A total of 144 outflow water samples over 13 storm events were collected at the outlet of the stormwater pond that collects runoff from the residential catchment. Samples were analysed for various inorganic N [ammonium (NH4-N), nitrate (NO3-N)], and organic N forms [dissolved organic nitrogen (DON), and particulate organic nitrogen (PON)]. Flow-weighted mean concentration of total N (TN) in pond outflow for all collected storm events was 1.3±1.42 mg L-1, with DON as the dominant form (78%), followed by PON and NO3-N (each at 8%), and NH4-N (6%). In the bioassay experiment, organic N (DON+PON) was significantly decreased by 25-28% after 5 days of incubation, suggesting that a portion of the DON carried from the pond outflow to receiving water bodies may be bioavailable. These results suggest that efforts to mitigate stormwater N outflows from urban ponds should incorporate both inorganic and organic N in management plans.