The photo-degradation of nutrients in stormwater in photocatalytic reactor wet detention pond using nano titanium dioxide (TiO2) in concrete was investigated in a scale model as a new stormwater treatment method. Degradation of phosphate and nitrate in the presence of nano-TiO2 under natural ultra violet (UV) from tropical sunlight was monitored for 3 weeks compared with normal ponds. Two types of cement, including ordinary Portland and white cement mixed with TiO2 nano powder, were used as a thin cover to surround the body of the pond. Experiments with and without the catalyst were carried out for comparison and control. Average Anatase diameter of 25 nm and Rutile 100 nm nano particles were applied at three different mixtures of 3, 10 and 30% weight. The amounts of algae available orthophosphate and nitrate, which cause eutrophication in the ponds, were measured during the tests. Results revealed that the utilization of 3% up to 30% weight nano-TiO2 can improve stormwater outflow quality by up to 25% after 48 h and 57% after 3 weeks compared with the control sample in normal conditions with average nutrient (phosphate and nitrate) removal of 4% after 48 h and 10% after 3 weeks.
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.
Pulau Kapas is tropical island which dominantly depends on land-supplied and groundwater for freshwater sources. The groundwater quality was monitored monthly, to identify the possible factors effecting the groundwater quality throughout May to October 2022. Physico-chemical parameters were in-situ measured and groundwater were collected for nutrients analysis in the laboratory. The concentration of ammonium, phosphate, nitrite, and nitrate were in the range of 0.07-1.08 mg/L, 0.00-0.06 mg/L, BDL-18 × 10-4 mg/L and 0.01-0.19 mg/L, respectively. The cluster and principal component analysis unveiled the seawater intrusion for freshwater needs was the dominant factor affecting the groundwater. Followed by the dissolution of soil particles surrounds the groundwater table, and the surface run-off by rainfall. In conclusion, the groundwater was affected by geogenic factors as it was not extensively extracted due to movement control order of Covid-19 event. This has provided significant insight for a better management plan in sustaining the groundwater of Pulau Kapas.
Excessive nitrate intake via ingestion pathway and dermal absorption exposures has adverse health impacts on human health. This study evaluated groundwater (GW) nitrate concentrations and health risks which focused on ingestion and dermal exposures to residents in Bachok District, Kelantan, Malaysia. Three hundred (300) samples of private wells were collected and it is found that the nitrate concentrations ranging between 0.11 and 64.01 mg/L NO3-N with a mean value of 10.45 ± 12.67 mg/L NO3-N. The possible health hazards of nitrate by ingestion and dermal contact were assessed using USEPA human health risk assessment model for adult males and females. It is observed that the mean Hazard Quotient (HQ) values of adult males and females were 0.305 ± 0.364 and 0.261 ± 0.330, respectively. About 7.3% (n = 10) and 4.9% (n = 8) of adult males and females had HQ values more than 1, respectively. It was also observed that the mean of HQderm was lesser than HQoral for males and females. The spatial distribution of HQ by interpolation method showed high nitrate concentrations (> 10 mg/L NO3-N) were distributed from the centre to the southern part of the study location, which identified as an agricultural area, indicating the used of nitrogenous fertilizers as the main source of GW nitrate contamination in this area. The findings of this study are valuable for establishing private well water protection measures to stop further deterioration of GW quality caused by nitrate.
Acacia invasion has shown a negative impact on the water resources of forest streams. Therefore, studies need to be conducted demonstrating the importance of managing invasive species to preserve stream and forest ecosystems. This study investigates the effects of Acacia invasion on the water quality of Kerangas forest streams in Brunei. Water samples were collected from an Acacia-invaded (IN) stream and a non-invaded (NIN) stream during the dry season at three locations along each stream. Water properties, including pH, conductivity, salinity, total dissolved solids (TDS), phosphate, nitrate, ammonia, and nitrite, were analyzed using in situ and laboratory methods. The results showed that Acacia invasion significantly increased pH (from 4.01 to 5.68), nitrate (by 256%), and phosphate (by 250%) levels, while reducing conductivity (by 208%) and salinity (by 20%) compared to non-invaded streams. These findings suggest that Acacia invasion alters water chemistry, potentially posing risks to aquatic ecosystems. Effective management strategies, such as controlling Acacia spread and restoring native vegetation, are essential to mitigate these impacts and preserve forest water resources.
Landfills are one of the main point sources of groundwater pollution. This research mainly aims to assess the risk of nitrate [Formula: see text] transport from the unlined landfill to subsurface layers and groundwater using experimental results and the SESOIL model. Samples from 12 groundwater wells downstream of the landfill were collected and analyzed in 2008, 21 years after the landfill construction. The average [Formula: see text] concentration in the wells was 54 mg/L, slightly higher than the World Health Organization ([Formula: see text] 50 mg/L) standards. SESOIL model was used to predict the [Formula: see text] concentration at the bottom of the unsaturated zone. Results indicated that the current mean [Formula: see text] concentration at the bottom of the unsaturated zone is 75 mg/L. the model predicted that the level of NO3 will increased up to 325 mg/L within 30 years. Accordingly, the [Formula: see text] concentration in groundwater wells near the landfill area is expected to gradually increase with time. Although the current risk associated with the [Formula: see text] level might not be harm to adults, however, it might pose severe risks to both adults and infants in the near future due to [Formula: see text] leaching. Urgent mitigation measures such as final cell cover (cap), lining system and vertical expansion should be considered at the landfill to protect the public health in the area.
Macrophytes have been used to mitigate eutrophication and upgrade effluent quality via their nutrient removal capability. However, the available data are influenced by factors such as microbial activities, weather, and wastewater quality, making comparison between nutrient removal performance of different macrophytes almost impossible. In this study, phytoremediation by Spirodela polyrhiza, Salvinia molesta and Lemna sp. were carried out axenically in synthetic wastewater under controlled condition to precisely evaluate nutrient removal efficiency of NO3--N, PO43-, NH3-N, COD and pH in the water sample. The results showed that ammonia removal was rapid, significant for S. polyrhiza and Lemna sp., with efficiency of 60% and 41% respectively within 2 days. S. polyrhiza was capable of reducing 30% of the nitrate. Lemna sp. achieved the highest phosphate reduction of 86% at day 12 to mere 1.07 mg/L PO43--P. Correlation was found between COD and TC, suggesting the release of organic substances by macrophytes into the medium. All the macrophytes showed biomass increment. S. polyrhiza outperformed other macrophytes in nutrient removal despite lower biomass production. The acquired nutrient removal profiles can serve as a guideline for the selection of suitable macrophytes in wastewater treatment and to evaluate microbial activity in non-aseptic phytoremediation system.
Nanocrystalline lead sulfide (PbS) thin films have been successfully grown on glass substrate using the chemical bath deposition technique. Microwave oven was used as a heating source to facilitate the growth process of the thin films. Aqueous solutions of lead nitrate Pb(NO3) and thiourea [SC(NH2)2] were used as lead and sulfur ion sources, respectively. Structural, morphological and optical analyses revealed good quality growth of nanocrystalline PbS thin films. This study introduced a facile and low cost method to prepare high quality nanocrystalline PbS thin films in a relatively short growth time for optoelectronic applications.
Current study report the growth of Zinc Oxide (ZnO) nanorods (NRs) by a facile and low temperature method on Zinc (Zn) foil in deionized (DI) water. These ZnO NRs have a typical length of 500-700 nm and average diameter of 50-70 nm. By using different volume of DI water, the morphology of ZnO nanostructures are tunable from rod-like to flower-like structures. Under the presence of Zn nitrate precursor, mixture of rod/wall-like structures are formed. Both of ZnO NRs and combined nanorods/nanowalls render higher diffraction for the (002) peak reveals, which implies preferred orientation growth along c-axis take place. However, photoluminescence (PL) study indicates that ZnO NRs have strong emission located at ~380 nm if compared to that of combined ZnO nanorods/nanowalls. This shows that ZnO NRs have higher-densities of defects.
Benthic dinoflagellates are known to occur in the water column. The reason they can be found in different parts of the ecosystem is not clear. This study aims to determine the species and the cell abundance of benthic dinoflagellates in the water column and macrophytes collected from two different locations i.e. semi-enclosed lagoon and open coastal waters. The physico-chemical parameters (temperature, salinity, pH, dissolved oxygen) and nutrients (nitrate and phosphate) were determined. Results showed that in the lagoon, the most abundant dinoflagellate species on the macrophytes was also the most abundant dinoflagellate species in the water column. The species that dominated the water column and marophytes in the lagoon was Bysmatrum caponii. In the coastal area the trend was not clear. Coolia dominated the macrophytes whereas Peridinium quinquecorne dominated the water column. The physico-chemical parameters determined were similar at both sites except for nutrients. Results show that type of substrates and different geomorphology effects benthic dinoflagellates cell abundance in the macrophytes and water column.
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.
In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.
In an effort to determine the reason behind excellent nitrate remediation capacity at Kelantan region, a multivariate approach is employed to evaluate extent to which the influence of sea on soil geochemical composition affect variation pattern of groundwater quality. The results obtained from geochemical analysis of paleo-beach soil in coastal site at Bachok revealed multiple redox activity at different soil strata, involving both heterotrophic and autotrophic denitrification. In soil and water analysis, eight of the fourteen hydro-geochemical parameters (conductivity, temperature, soil texture, oxidation reduction potential, pH, total organic carbon, Fe, Cu, Mn, Cl-, SO42-, NO2-, NO3- and PO43-) measured using standard procedures were subjected to multivariate analysis. Evaluation of general variation pattern across the area reveals that the principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) are in consonance with one another on apportioning three parameters (SO42-, Cl- and conductivity) to the coastal sites and two parameters (Fe and NH4+ or NO3-) to inland sites. The step forward analysis of LDA reveals four parameters in order of decreasing significance as Cl-, Fe and SO42-, while the two-way HCA identifies three clusters on location basis, respectively. In addition to the significant data reduction obtained, the results indicate that proximity to sea and location/geological-based influence are more significant than temporal-based influence in denitrification. By extension, the research reveals that influence of labile portion of natural resources is explorable for broader application in other remediation strategies.
Changes in nitrate and nitrite contents (leaves and stem) of Amaranthus gangeticus (AG) and Amaranthus paniculatus (AP), resulting from blanching, storage time (0-4 days), storage temperature (0 and 4ºC), and reheating were analysed. Results showed that fresh AG (1859 ± 7.07 mg/kg) had higher nitrite content than AP (1262 ± 2.12 mg/kg). Nitrites content was 506 ± 2 and 825 ± 3.5 mg/kg for AG and AP, respectively. Reheating and storage times significantly increased the conversion of nitrate to nitrite in AG and AP. Storage at 0 and 4oC exhibited a significant change (P < 0.05) in nitrate and nitrite contents for both samples. Higher nitrite content was found in AP when stored at 4oC and 0oC. The present study indicated that storage time and temperature affected the nitrite contents in blanched AG and AP when stored in low temperatures. Apart from that reheating was also found to increase the formation of nitrite.
The flow of unprocessed sewage through municipal sewers is a great source of water contamination. This study aims to observe the pollutants removal efficiencies of walnut shells as an efficient low-cost adsorbent material compared to gravel materials as an anaerobic filter medium. Two models of the De-Centralized Wastewater Treatment System (DEWATS) were constructed. The wastewater flowing from toilets and handwashing places was connected to anaerobic filters filled with walnut shells and gravel. The efficiency of both filter media in the removal of biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total dissolved solids (TDS), nitrate (NO3), and phosphate (PO43), pH and temperature were observed at the influent of the settler tank and then at the effluent of the collection tank (CT). Temperature and pH were within the acceptable limit of wastewater discharge. The results also indicated that the walnut shells filter media was more efficient at removing organic pollutants (TSS 94%, BOD5 88%, COD 85%, Nitrate 57%, phosphate 46%, and TDS 29%) than the gravel (TSS 81%, BOD5 82%, COD 84%, Nitrate 35%, phosphate 38%, and TDS 26%) at the successive stages. The average removal efficiency of the walnut shell was 88% while in the gravel case, it was 83%. The removal efficiency of walnut shell filters was extensively better over the complete experiment compared to gravel filters for the removal of pollutants, representing the high sorption capability of the walnut shell material. The results of this study show that the walnut shells may be a very useful substitute for other conventional fillers for anaerobic treatment in the anaerobic filter of DEWATS.
A study was performed to determine the effect of Conway and f/2 media on the growth of microalgae genera. Genera of Chlorella sp., Dunaliella sp., Isochrysis sp., Chaetoceros sp., Pavlova sp. and Tetraselmis sp. were isolated from the South China Sea. During the cultivation period, the density of cells were determined using Syringe Liquid Sampler Particle Measuring System (SLS-PMS) that also generated the population distribution curve based on the size of the cells. The population of the microalgae genera is thought to consist of mother and daughter generations since these microalgae genera reproduce by releasing small non-motile reproductive cells (autospores). It was found that the reproduction of Tetraselmis sp., Dunaliella sp. and Pavlova sp. could be sustained longer in f/2 Medium. Higher cell density was achieved by genus Dunaliella, Chlorella and Isochrysis in Conway Medium. Different genera of microalgae had a preference for different types of cultivation media.
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.
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.
Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO3 (primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, X(max), and lipid yield, P(max)/X(max). RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Pr(lipid). A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO3 (4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of X(max) to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation.
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.