The composite media, ZeliacTM was developed with the initial aim to provide low cost adsorbent with promising adsorption capacity. This study was conducted to investigate the removal of UV absorbance at 254 nm (UV254) in Kerian river water using ZeliacTM as the media. Batch experiments study was carried out to determine the optimum removal of UV254 by ZeliacTM. The experimental data were fitted to Langmuir and Freundlich isotherms to investigate the adsorption mechanism. The results from batch study exhibit that ZeliacTM is capable to remove 74.4% UV254 at the dosage of 7g/100 ml. Linear isotherm analysis suggests that the best fitting linear line is Freundlich isotherm with R2 values of 0.9294 indicating multilayer adsorption. Similarly, non-linear regression analysis reveals that the adsorption of UV254 by ZeliacTM is attributed by physisorption. The non-linear Freundlich isotherm gives a better fit to the adsorption of UV254 than Langmuir isotherm with R2 values of 0.9488. The results are supported with low values of X2, ARE, HYBRID and MPSED from the error function analysis. Additionally, it is noted that the linear analysis overestimates the constant parameters’ values for Freundlich isotherm, which cause larger errors as estimated by the error function analysis. Hence, non-linear analysis is more appropriate in explaining the batch experiment data.
Water resources are subjected to different pollution sources. Point source water pollution of surface water is an important issue when considering the limited surface water availability as well as the potential knock-on effects of this pollution on human health as well as habitat and land degradation. One of the main point sources of water pollution is Olive Mill Waste Water (OMWW). OMWW is the liquid by-product generated during olive oil production. However, there is no standardized method to assess the risk of water pollution by OMWW for any given river basin. This research addressed the above issue by designing a detailed quantitative risk assessment methodology, which utilizes Geographic Information System (GIS) modeling to classify within a watershed individual sub-catchment risk of water pollution occurring from olive mill waste discharges. The research presents the proposed criteria and calculations required to estimate sub-catchment risk significance and comments on the potential of the method for wider application. This research combines elements from risk assessment frameworks, Multi-Criteria Analysis (MCA), and GIS. MCA helped in aggregating different aspects and elements associated with this environmental problem, while GIS modeling tools helped in obtaining many criterion values and providing insight into how different objects interact in nature and how these interactions influence risk at the watershed level. The proposed method was tested in the Keritis watershed in Crete, Greece, where OMWW is one of the main stressors influencing water quality, and the results indicated that this method has the potential to be a useful guide to prioritize risk management actions and mitigation measures to be incorporated in River Basin Management Plans.
Laboratories produce a large volume of wastewaters containing different chemical indicators, organic species for which there is no complete knowledge about their effects in the aquatic environment.
The aim of this work was to evaluate the ecotoxicity of four chemical indicator substances commonly used in titrations (sodium diphenylamine-4-sulfonate, phenolphthalein, methyl orange, and eriochrome black T) by applying two distinct bioassays that evaluated the growth inhibition of the microalga Chlorella vulgaris and the acute immobilization of the microcrustacean Daphnia magna.
All the indicators showed growth inhibition rates in the chronic test performed with the alga C. vulgaris. Only phenolphthalein and eriochrome black-T showed high immobilization rates on the acute test for D. magna. C. vulgaris showed higher sensitivity to the chemical indicators tested than D. magna. Eriochrome black T was the most toxic for both test organisms and, according to the effective concentration that causes inhibition on 50% of C. vulgaris population, it can be considered as “highly toxic to aquatic organisms”. Phenolphthalein and methyl orange may be classified as “toxic to aquatic organisms” and sodium diphenylamine-4-sulfonate is the least toxic, only being considered as “harmful”.
This work increases the awareness of the hazardous effects of these chemical indicators and reinforces the need of improved solutions to manage and treat laboratory effluents.
In this study, an analytical methods for determination of antidepressants including (diazepam (DZP), lorazepam (LZP), carbamazepine (CBZ), fluoxetine (FLU)) was developed. The concentration of antidepressants was monitored in Konya Urban Wastewater Treatment Plant influent and effluent samples for one years. Environmental risk assessment was performed with detected concentration with treatment plant effluent wastewater by hazard quotient (HQ) methods. Oasis HLB cartridges with SPE system was used for extraction of wastewater samples. The effect of sample volume and pH, different analyte concentration, pretreatment and matrix on efficiency of cartridges were optimized. Quantitative analyses of the target compounds were performed by LC/MS-MS system. Recoveries of antidepressant compounds from fortified wastewater were over 87% for three different fortification levels. The limits of detection were determined between 0.006 and 0.118 ng l-1. CBZ, FLU and LZP concentration in influent varied from 6.3 to 135.6 ng l-1, <dl to 2.6 ng l-1, <dl to 4.8 ng l-1, respectively, while DZP was not detected. CBZ, DZP, FLU and LZP concentrations in the effluent was determined as <dl-245.1 ng l-1, <dl-0.21 ng l-1, <dl-2.7 ng l-1, <dl-2.2 ng l-1, respectively. Antidepressants were generally removed in January, February, and August. The high removal was determined for CBZ compounds as %100 at January. The low removal was determined for FLU compounds as %2.4 at January.
While the HQ values for DZP, LZP, CBZ were determined below 0.1 which means insignificant risk to aquatic organisms, HQ values for FLU determined above 0.1 which means low risk to aquatic organism
Low concentrations of contaminants in rainwater is the primary benefit of transforming it as an alternative source of drinking water. However, treatment is necessary as rainwater collects contaminants that are persistent in the environment such as lindane and E.coli. The combination of photocatalysis and adsorption processes was chosen as the treatment method in this study. From the results obtained, photocatalysis treatment process was able to degrade lindane in synthetic rainwater under different experimental conditions such as pH, titanium dioxide (TiO2) dosage, and initial concentration. The photodegradation process of lindane followed pseudo kinetic first order. In adsorption process, the adsorbents used in the process are limestone and laterite soil. The performance of these adsorbents are determined by carrying out an equilibrium batch study.The experimental works show that limestone and laterite soil able to remove E. coli at 99%. The optimum dosage of limestone and laterite soil to remove E. coli from synthetic rainwater is 6g and 2g, respectively. The results were then analysed by developing Langmuir and Freundlich isotherm model. Overall, the processes of photocatalysis and adsorption showed a good performance in removing lindane and E.coli from the synthetic rainwater respectively.
The decomposition of sodium dodecylbenzene sulfonate (SDBS), which is a dangerous and anionic surfactant, was investigated by the electro/Fe2+/persulfate process from aqueous solutions. The activation of persulfate anion and production of active radicals were performed by means of heat, UV and iron ions (released from iron electrodes). The finding illustrated that the pH value of the solution, initial concentration of persulfate anion, the amount of input voltage and iron dosage were entirely effective in SDBS removal. Nearly 100% of SDBS was removed under the optimum conditions: pH 3, voltage 10 V, anion persulfate concentration 25 mM/L, iron ion dosage 0.25 g/L and reaction time 25 min. Furthermore, the application of the persulfate anion process in concert with the electrochemical process, in order to generate electrical iron and persulfate activation, had a better performance compared to separate methods.
In last decades the search for new low cost sorbents that have heavy metal ions binding capabilities is a hot topic in the field of clean-up technologies. In this study, wastes of Romanian silver tree (Abies alba) bark were explored for first time as green and economical sorbent for the removal of Cd(II) ions from aqueous solutions. The effect of various experimental parameters such as initial solution pH, sorbent dose, initial Cd(II) concentration, temperature and contact time has been investigated under batch conditions.
The Langmuir and Freundlich models were used to describe the equilibrium isotherms and both models have been fitted very well. According to the evaluation using the Langmuir equation, the maximum sorption capacity of Cd (II) ions on Abies alba bark waste was found to be 11.98
mg g-1 at 293 K. The thermodynamic parameters showed that the process of Cd(II) sorption on silver fir tree bark was feasible, spontaneous and endothermic. Kinetic data were properly fitted with the pseudo–second order model. The obtained results strongly suggest that Romanian silver tree (Abies alba) bark is eligible as an efficient sorbent for the decontamination of toxic metals from wastewaters.
Application of nano particle in the treatment of municipal solid waste leachate is of recent interest. In this paper, the effectiveness of silica nano particles synthesized from blast furnace slag and iron nano particle synthesized from chemicals was studied for the removal of organic pollutants and color. The synthesized nano particles were characterized using SEM, TEM, EDX and FTIR analysis. Batch experiments were conducted to remove the BOD, COD and color from Aged landfill leachate (ALL) and leachate from the composting yard (CYL).Influencing parameters like pH, contact time, nano particle dosage and Hydrogen peroxide concentration were studied. The maximum removal was achieved at the pH of 6 for both the nano particle, contact time 90 minutes for silica nano particle and 120 minutes for iron nano particle, silica nano particle dose as 0.4g/50 ml, iron nano particle dose as 0.3g/50ml and hydrogen peroxide concentration was found to be 3M and 4M for silica and iron nano particles respectively. The removal efficiency in CYL and ALL using silica nano particle was obtained as 87.15%, 72.72%, 83.15% and 82.5%, 62.5%, 77.34% for color, BOD and COD respectively. Similarly for iron nano particle, the removal efficiency was found to be 60.3%, 65%, 67.43% and 57.06%, 57.27%, 67% for the removal of color, BOD and COD in CYL and ALL, respectively.
In the present study an attempt has been made to assess the seasonal ground water quality variations at Kancheepuram region, Tamil Nadu, India. In order to evaluate the seasonal ground water quality for drinking and irrigation purposes, 68 groundwater well data were collected and analyzed for various physico-chemical parameters such as electrical conductivity (EC), pH, total dissolved solids (TDS), total hardness (TH), Na+, Ca2+, Mg2+, Cl-, SO42-, HCO3- and F-. Nine parameters viz., pH, TDS, TH, Ca2+, Mg2+, Cl-, SO42- , HCO3- and F- were used to calculate the Water Quality Index (WQI) using weighted arithmetic index for drinking purpose. EC and Sodium Adsorption Ratio (SAR) were the two important indicators used to assess the irrigation water quality. Mapping was done to examine the spatial distribution of water quality parameters using Geographical Information System (GIS). From the analysis, more than half of the samples record good water quality whereas only 1% falls under unsuitable category for drinking purpose. Approximately 33% of samples falls under poor quality for drinking. In terms of irrigation, the salinity hazard occurs in 23 to 42% of sampling locations depicting the uncertainty towards agricultural production in the study region. From the study, it is observed that the water quality is highly degraded where Industrialization and urbanization took place when compared to areas where agriculture is being practiced. Hence, suitable measures to ameliorate the pollution effect of urbanization and industrializations are suggested.
Snow depletion curves (SDCs) are important in hydrological studies for predicting snowmelt generated runoff in high mountain catchments. The present study deals with the derivation of the average snow depletion pattern in the Mago basin of Arunachal Pradesh, which falls in the eastern Himalayan region and the generation of climate affected SDCs in future years (2020, 2030, 2040, and 2050) under different projected climatic scenarios. The MODIS daily snow cover product at 500m resolution from both the Aqua and Terra satellites was used to obtain daily snow cover maps. MOD10A1 and MYD10A1 images were compared to select cloud free or minimum cloud image to obtain the temporal distribution of snow cover area (SCA). Snow accumulation and depletion patterns were obtained by analysing SCA at different days. For most of the years, two peaks were observed in the SCA analysis. The conventional depletion curve (CDC) representing present climate was derived by determining and interpolating the SCA from cloud-free (cloud<5%) images for the selected hydrological year 2007. The investigation shows that the SCA was highest in February and lowest in May. Ten years meteorological data were used to normalize the temperature and precipitation data of the selected hydrological year (2007) to eliminate the impact of their yearly fluctuations on the snow cover depletion. The temperature and precipitation changes under four different projected climatic scenarios (A1B, A2, B1, and IPCC Commitment) were analysed for future years. Changes in the cumulative snowmelt depth with respect to the present climate for different future years were studied by a degree-day approach and were found to be highest under A1B, followed by A2, B1, and IPCC Commitment scenarios. It was observed that the A1B climatic scenario affected the depletion pattern most, making the depletion of snow to start and complete faster than under different scenarios. Advancing of depletion curve for different future years was found to be highest under A1B and lowest under IPCC Commitment scenarios with A2 and B1 in-between them.