Nitrate contamination in water resources,human health risks and its remediation through adsorption: a focused review

The level of nitrate in water has been increasing considerably all around the world due to vast application of inorganic nitrogen fertiliser and animal manure. Because of nitrate’s high solubility in water, human beings are getting exposed to it mainly through various routes including water, food etc. Various regulations have been set for nitrate (45–50 mgNO₃^?/L) in drinking water to protect health of the infants from the methemoglobinemia, birth defects, thyroid disease, risk of specific cancers, i.e. colorectal, breast and bladder cancer caused due to nitrate poisoning. Different methods like ion exchange, adsorption, biological denitrification etc. have the ability to eliminate the nitrate from the aqueous medium. However, adsorption process got preference over the other approaches because of its simple design and satisfactory results especially with surface modified adsorbents or with mineral-based adsorbents. Different types of adsorbents have been used for this purpose; however, adsorbents derived from the biomass wastes have great adsorption capacities for nitrate such as tea waste-based adsorbents (136.43 mg/g), carbon nanotube (142.86 mg/g), chitosan beads (104 mg/g) and cetyltrimethylammonium bromide modified rice husk (278 mg/g). Therefore, a thorough literature survey has been carried out to formulate this review paper to understand various sources of nitrate pollution, route of exposure to the human beings, ill effects along with discussing the key developments as well as the new advancements reported in procuring low-cost efficient adsorbents for water purification.

     

Optimization of co-digestion of various industrial sludges for biogas production and sludge treatment: Methane production potential experiments and modeling

Optimal biogas production and sludge treatment were studied by co-digestion experiments and modeling using five different wastewater sludges generated from paper, chemical, petrochemical, automobile, and food processing industries situated in Ulsan Industrial Complex, Ulsan, South Korea. The biomethane production potential test was conducted in simplex-centroid mixture design, fitted to regression equation, and some optimal co-digestion scenarios were given by combined desirability function based multi-objective optimization technique for both methane yield and the quantity of sludge digested. The co-digestion model incorporating main and interaction effects among sludges were utilized to predict the maximum possible methane yield. The optimization routine for methane production with different industrial sludges in batches were repeated with the left-over sludge of earlier cycle, till all sludges have been completely treated. Among the possible scenarios, a maximum methane yield of 1161.53 m³ is anticipated in three batches followed by 1130.33 m³ and 1045.65 m³ in five and two batches, respectively. This study shows a scientific approach to find a practical solution to utilize diverse industrial sludges in both treatment and biogas production perspectives.     

Kinetics of phenol biodegradation at high concentration by a metabolically versatile isolated yeast Candida tropicalis PHB5

A highly tolerant phenol-degrading yeast strain PHB5 was isolated from wastewater effluent of a coke oven plant and identified as Candida tropicalis based on phylogenetic analysis. Biodegradation experiments with C. tropicalis PHB5 showed that the strain was able to utilize 99.4 % of 2,400 mg l^?1 phenol as sole source of carbon and energy within 48 h. Strain PHB5 was also observed to grow on 18 various aromatic hydrocarbons. Haldane model was used to fit the exponential growth data and the following kinetic parameters were obtained: μ _max?=?0.3407 h^?1, K _S?=?15.81 mg l^?1, K _i?=?169.0 mg l^?1 (R ²?=?0.9886). The true specific growth rate, calculated from μ _max, was 0.2113. A volumetric phenol degradation rate (V _max) was calculated by fitting the phenol consumption data with Gompertz model and specific degradation rate (q) was calculated from V _max. The q values were fitted with Haldane model, yielding following parameters: q _max?=?0.2766 g g^?1 h^?1, K _S ′?=?2.819 mg l^?1, K _i ′?=?2,093 (R ²?=?0.8176). The yield factor (Y _X/S ) varied between 0.185 to 0.96 g g^?1 for different initial phenol concentrations. Phenol degradation by the strain proceeded through a pathway involving production of intermediates such as catechol and cis,cis-muconic acid which were identified by enzymatic assays and HPLC analysis.     

Effect of transpiration on Pb uptake by lettuce and on water soluble low molecular weight organic acids in rhizosphere

The effect of transpiration (high and low) on Pb uptake by leaf lettuce and on water soluble low molecular weight organic acids (LMWOAs) in rhizosphere has been studied. After two weeks of growth the plants were cultured in greenhouse for more four weeks and two days. Pb(NO(3))(2) solutions of different concentrations (100, 200, and 300 mg l(-1) of Pb) were then added to the quartz sand pots of different plants and studies were initiated. Blank experiments (without treating the quartz sand pots with Pb(NO(3))(2) solutions) were also run in parallel. No significant differences in the growth of the plants with the concentrations of added Pb(NO(3))(2) solutions were observed by both low and high transpirations at the end of the 0, 3rd, and 10th days of studies. The total evaporation of the volatiles during 10 days did not depend on the concentration of Pb(2+) but with high transpiration the rate of evaporation was significantly higher than with low transpiration. Uptake of Pb by shoots and roots of the plants was found to be proportional to the concentration of various Pb(NO(3))(2) solutions added and more accumulation was observed in roots than in shoots at the end of 3rd and 10th days. High transpiration created more Pb uptake than low transpiration did. One volatile acid, propionic acid and nine non-volatile acids, lactic, glycolic, oxalic, succinic, fumaric, oxalacetic, D-tartaric, trans-aconitic, and citric acids in rhizosphere quartz sands were identified and quantified by gas chromatography (GC) analysis. D-Tartaric and citric acids were major among the non-volatile acids. The amount of LMWOAs in rhizosphere quartz sands increased with the higher amount of Pb uptake and also with the duration of studies. The total quantities of the LMWOAs in the rhizosphere quartz sands were significantly higher under high transpiration with 300 mg l(-1) Pb solution addition at the end of 10th day. The present study shows prominent correlation between transpiration and uptake of heavy metal and interesting correlation between Pb contaminated level and quantity of water soluble LMWOAs in rhizosphere quartz sands. The latter thus deserves of further studies.     

Minimum turbulence assumptions and u* and L estimation for dispersion models during low-wind stable conditions

The U.S. Environmental Protection Agency (EPA) short-distance dispersion model, AERMOD, has been shown to overpredict by a factor of as much as 10 when compared with observed concentrations from continuous releases at the Oak Ridge, TN (OR), and Idaho Falls, ID (IF), field experiments during stable periods when wind speeds often dropped below 1 m/sec. Some of this overprediction tendency can be reduced by revising AERMOD's meteorological preprocessor's parameterizations of the friction velocity, u _* , during low-wind stable conditions, thus increasing the calculated σ _v and σ _w and hence the lateral and vertical dispersion rates. Observations show that as the mean wind speed approaches zero at night, there is always significant σ _v and σ _w over time periods of 15 to 60 min, while standard Monin–Obukhov Similarity Theory (MOST) predicts that σ _v and σ _w will approach zero. This paper focuses on the u _* estimation methods and the minimum turbulence (σ _v and σ _w ) assumptions in AERMOD (beta option 4) and two widely used U.S. operational dispersion models, AERMOD (v12345) and SCICHEM. The U.S. EPA has provided results of its tests with the OR and IF data, with its base AERMOD version and its December 2012 modified versions, which assume adjustments to the low-wind u _* and increases in the minimum σ _v parameterization. SCICHEM has relatively small mean bias for both data sets. The revised AERMOD shows much less mean bias, agreeing more with SCICHEM.

Implications:

Suggestions are made for improvements to dispersion models such as AERMOD to correct overpredictions during light-wind stable conditions. Methods for estimating u_*, L, and the minimum turbulence parameters (σ_v and σ_w) are reviewed and compared. SCICHEM and the current operational version and an optional beta version (December 2012) of AERMOD are evaluated with tracer data from low-wind stable field experiments in Idaho Falls and Oak Ridge. It is seen that the operational version of AERMOD overpredicts by a factor of 2 to 10, while the optional beta version of AERMOD and SCICHEM have much less bias.