The performance of moving bed sequencing batch reactors (MBSBRs) added with 8 % (v/v) of polyurethane (PU) foam cubes as carrier media in nitrogen removal was investigated in treating low COD/N wastewater. The results indicate that MBSBR with 8-mL cubes achieved the highest total nitrogen (TN) removal efficiency of 37% during the aeration period, followed by 31%, 24% and 19 % for MBSBRs with 27-, 64- and 125-mL cubes, respectively. The increased TN removal in MBSBRs was mainly due to simultaneous nitrification and denitrification (SND) process which was verified by batch studies. The relatively lower TN removal in MBSBR with larger PU foam cubes was attributed to the observation that larger PU foam cubes were not fully attached by biomass. Higher concentrations of 8-mL PU foam cubes in batch reactors yielded higher TN removal.
This study was undertaken to compare the chemical properties and yields of pineapple leaf residue (PLR) char produced by field burning (CF) with that produced by a partial combustion of air-dried PLR at 340 °C for 3 h in a furnace (CL). Higher total C, lignin content, and yield from CL as well as the presence of aromatic compounds in the Fourier Transform Infrared spectra of the char produced from CL suggest that the CL process was better in sequestering C than was the CF process. Although the C/N ratio of char produced from CL was low indicating a high N content of the char, the C in the char produced from CL was dominated by lignin suggesting that the decomposition of char produced from CL would be slow. To sequester C by char application, the PLR should be combusted in a controlled process rather than by burning in the field.
The potential application of Chlorella vulgaris UMACC 001 for bioremediation of textile wastewater (TW) was investigated using four batches of cultures in high rate algae ponds (HRAP) containing textile dye (Supranol Red 3BW) or TW. The biomass attained ranged from 0.17 to 2.26 mg chlorophyll a/L while colour removal ranged from 41.8% to 50.0%. There was also reduction of NH(4)-N (44.4-45.1%), PO(4)-P (33.1-33.3%) and COD (38.3-62.3%) in the TW. Supplementation of the TW with nutrients of Bold's Basal Medium (BBM) increased biomass production but did not improve colour removal or reduction of pollutants. The mechanism of colour removal by C. vulgaris is biosorption, in accordance with both the Langmuir and Freundlich models. The HRAP using C. vulgaris offers a good system for the polishing of TW before final discharge.
In the present study, Electrochemical Oxidation was used to remove COD and color from semi-aerobic landfill leachate collected from Pulau Burung Landfill Site (PBLS), Penang, Malaysia. Experiments were conducted in a batch laboratory-scale system in the presence of NaCl as electrolyte and aluminum electrodes. Central composite design (CCD) under Response surface methodology (RSM) was applied to optimize the electrochemical oxidation process conditions using chemical oxygen demand (COD) and color removals as responses, and the electrolyte concentrations, current density and reaction time as control factors. Analysis of variance (ANOVA) showed good coefficient of determination (R(2)) values of >0.98, thus ensuring satisfactory fitting of the second-order regression model with the experimental data. In un-optimized condition, maximum removals for COD (48.77%) and color (58.21%) were achieved at current density 80 mA/cm(2), electrolyte concentration 3,000 mg/L and reaction time 240 min. While after optimization at current density 75 mA/cm(2), electrolyte concentration 2,000 mg/L and reaction time 218 min a maximum of 49.33 and 59.24% removals were observed for COD and color respectively.
The fluctuation of domestic wastewater characteristic inhibits the current conventional microbial-based treatment. The bioremediation fungi has received attention and reported to be an effective alternative to treat industrial wastewater. Similar efficient performance is envisaged for domestic wastewater whereby assessed performance of fungi for varying carbon-to-nitrogen ratios in domestic wastewater is crucial. Thus, the performance of pre-grown wild-Serbian Ganoderma lucidum mycelial pellets (GLMPs) was evaluated on four different synthetic domestic wastewaters under different conditions of initial pH (pH 4, 5, and 7) and chemical oxygen demand (COD) to nitrogen (COD/N) ratio of 3.6:1, 7.1:1, 14.2:1, and 17.8:1 (C3.6N1, C7.1N1, C14.2N1, and C17.8N1). The COD/N ratios with a constant concentration of ammonia-nitrogen (NH3-N) were chosen on the basis of the urban domestic wastewater characteristics sampled at the inlet basin of a sewage treatment plant (STP). The parameters of pH, COD, and NH3-N were measured periodically during the experiment. The wild-Serbian GLMPs efficiently removed the pollutants from the synthetic sewage. The COD/N ratio of C17.8N1 wastewater had the best COD and NH3-N removal, as compared to the lower COD/N ratio, and the shortest treatment time was obtained in an acidic environment at pH 4. The highest percentage for COD and NH3-N removal achieved was 96.0% and 93.2%, respectively. The results proved that the mycelium of GLMP has high potential in treating domestic wastewater, particularly at high organic content as a naturally sustainable bioremediation system.
The analysis of total organic carbon (TOC) by the American Public Health Association (APHA) closed-tube reflux colorimetric method requires potassium dichromate (K2Cr2O7), silver sulfate (AgSO4), and mercury (HgSO4) sulfate in addition to large volumes of both reagents and samples. The method relies on the release of oxygen from dichromate on heating which is consumed by carbon associated with organic compounds. The method risks environmental pollution by discharging large amounts of chromium (VI) and silver and mercury sulfates. The present method used potassium monochromate (K2CrO4) to generate the K2Cr2O7 on demand in the first phase. In addition, miniaturizing the procedure to semi microanalysis decreased the consumption of reagents and samples. In the second phase, mercury sulfate was eliminated as part of the digestion mixture through the introduction of sodium bismuthate (NaBiO3) for the removal of chlorides from the sample. The modified method, the potassium monochromate closed-tube colorimetry with sodium bismuthate chloride removal (KMCC-Bi), generates the potassium dichromate on demand and eliminates mercury sulfate. The semi microanalysis procedure leads to a 60% reduction in sample volume and ≈ 33.33 and 60% reduction in monochromate and silver sulfate consumption respectively. The LOD and LOQ were 10.17 and 33.90 mg L-1 for APHA, and 4.95 and 16.95 mg L-1 for KMCC-Bi. Recovery was between 83 to 98% APHA and 92 to 104% KMCC-Bi, while the RSD (%) ranged between 0.8 to 5.0% APHA and 0.00 to 0.62% KMCC-Bi. The method was applied for the UV-Vis spectrometry determination of COD in water and wastewater. Statistics was done by MINITAB 17 or MS Excel 2016. ᅟ Graphical abstract.
Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.
Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
In this article, gelatin/copper activated faujasites (CAF) composite scaffolds were fabricated by lyophilisation technique for promoting partial thickness wound healing. The optimised scaffold with 0.5% (w/w) of CAF, G (0.5%), demonstrated pore size in the range of 10-350 μm. Agar disc diffusion tests verified the antibacterial role of G (0.5%) and further supported that bacterial lysis was due to copper released from the core of CAF embedded in the gelatin matrix. The change in morphology of bacteria as a function of CAF content in gelatin scaffold was studied using SEM analysis. The confocal images revealed the increase in mortality rate of bacteria with increase in concentration of incorporated CAF in gelatin matrix. Proficient oxygen supply to needy cells is a continuing hurdle faced by tissue engineering scaffolds. The dissolved oxygen measurements revealed that CAF embedded in the scaffold were capable of increasing oxygen supply and thereby promote cell proliferation. Also, G (0.5%) exhibited highest cell viability on NIH 3T3 fibroblast cells which was mainly attributed to the highly porous architecture and its ability to enhance oxygen supply to cells. In vivo studies conducted on Sprague Dawley rats revealed the ability of G (0.5%) to promote skin regeneration in 20 days. Thus, the obtained data suggest that G (0.5%) is an ideal candidate for wound healing applications.
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.
The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment.
We tested the hypothesis that NO contamination of hospital compressed air also improves PaO(2) in patients with acute lung injury (ALI) and following lung transplant (LTx).