Wastewater treatment is an important source of nitrous oxide (N₂O), which is a strong greenhouse gas and dominate ozone-depleting substance. The purpose of this study was to evaluate the effect of carbon source on N₂O emission from anoxic/oxic biological nitrogen removal process. The mechanisms of N₂O emission were also studied. Long-term experiments were operated to evaluate the effect of three different carbon sources (i.e., glucose, sodium acetate, and soluble starch) on N₂O emission characteristics. And batch experiments, in the presence or absence of specific inhibitors, were carried out to identify the sources of N₂O emission. The ammonia-oxidizing bacteria (AOB) and denitrifiers community compositions under different circumstances were also analyzed based on which the underlying mechanisms of N₂O emission were elucidated. The conversion ratios of N₂O in reactors with glucose, sodium acetate, and soluble starch were 5.3 %, 8.8 %, and 2.8 %, respectively. The primary process responsible for N₂O emission was nitrifier denitrification by Nitrosomonas-like AOB, while denitrification by heterotrophic denitrifiers acted as the sink. Reactor with sodium acetate showed the highest N₂O emission, together with the highest nitrogen and phosphate removal ratios. Carbon source has a significant impact on N₂O emission quantity and relatively minor effect on its production mechanism.

In this study, rice husk was modified with NaOH and used as adsorbent for dynamic adsorption of methylene blue (MB) from aqueous solutions. Continuous removal of MB from aqueous solutions was studied in a laboratory scale fixed-bed column packed with NaOH-modified rice husk (NMRH). Effect of different flow rates and bed heights on the column breakthrough performance was investigated. In order to determine the most suitable model for describing the adsorption kinetics of MB in the fixed-bed column system, the bed depth service time (BDST) model as well as the Thomas model was fitted to the experimental data. An artificial neural network (ANN)-based model was also developed for describing the dynamic dye adsorption process. An extensive error analysis was carried out between experimental data and data predicted by the models by using the following error functions: correlation coefficient (R ²), average relative error, sum of the absolute error and Chi-square statistic test (χ ²). Results show that with increasing bed height and decreasing flow rate, the breakthrough time was delayed. All the error functions yielded minimum values for the ANN model than the traditional models (BDST and Thomas), suggesting that the ANN model is the most suitable model to describe the fixed-bed adsorption of MB by NMRH. It is also more rational and reliable to interpret dynamic dye adsorption data through a process of ANN architecture.

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R (2) of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

Utilization of agrowaste materials for the production of activated carbon, as an excellent adsorbent with large surface area, is well established industrially, for dephenolation of wastewater. In the present work, dried pods of Prosopis cineraria—a novel and low-cost agrowaste material—were used to prepare activated carbons by zinc chloride activation. Batch adsorption experiments were carried out to study the effects of various physicochemical parameters such as initial phenol concentration, adsorbent dose, initial solution pH, and temperature. Pseudo-first-order second-order and diffusion kinetic models were used to identify the possible mechanisms of such adsorption process. The Langmuir and Freundlich equations were used to analyze the adsorption equilibrium. Maximum removal efficiency of 86 % was obtained with 25 mg L⁻¹ of initial phenol concentration. The favorable pH for maximum phenol adsorption was 4.0. Freundlich equation represented the adsorption equilibrium data more ideally than the Langmuir. The maximum adsorption capacity obtained was 78.32 mg g⁻¹ at a temperature of 30 °C and 25 mg L⁻¹ initial phenol concentration. The adsorption was spontaneous and endothermic. The pseudo-second-order model, an indication of chemisorption mechanism, fitted the experimental data better than the pseudo-first-order Lagergren model. Regeneration of spent activated carbon was carried out using Pseudomonas putida MTCC 2252 as the phenol-degrading microorganism. Maximum regeneration up to 57.5 % was recorded, when loaded phenol concentration was 25 mg L⁻¹. The data obtained in this study would be useful in designing and fabricating an efficient treatment plant for phenol-rich effluents.

Polycyclic aromatic hydrocarbons (PAHs) are widespread fused-ring contaminants formed during incomplete combustion of almost all kind of organic materials from both natural and anthropogenic sources. Some PAHs have been shown to be carcinogenic to humans, and a wide range of PAHs are found in wildlife all around the globe including avian species. The purpose of this project was to assess the effects of a standard mixture of 16 PAHs (United States Environmental Protection Agency) on the hepatic fatty acid β-oxidation in chicken embryos (Gallus gallus domesticus) exposed in ovo. The hepatic β-oxidation was measured using a tritium release assay with [9,10-³H]-palmitic acid (16:0) as substrate. Treated groups were divided into groups of 0.05, 0.1, 0.3, 0.5, and 0.8 mg PAHs/kg egg weight. The hepatic β-oxidation was reduced after exposure in ovo to the 16 PAHs mixture compared to control. The mechanisms causing reduced fatty acid oxidation in the present study are unclear, however may be due to deficient membrane structure, the functionality of enzymes controlling the rate of fatty acid entering into the mitochondria, or complex pathways connected to endocrine disruption. To the best of our knowledge, this is the first time a PAH-caused reduction of hepatic β-oxidation of fatty acids in avian embryos has been observed. The implication of this finding on risk assessment of PAH exposure in avian wildlife remains to be determined.

Controversy exists as to whether working or living in the vicinity of a petroleum refinery (RF) increases the risk of haematological cancer (HC). The European Pollutant Release and Transfer Register obliges petroleum refineries to notify their emissions of toxic substances which include carcinogenic substances. Our objective is to determine if living in the proximity of an RF is associated with a greater risk of mortality due to HC in the census tracts (CTs) of the Spanish cities of Bilbao, Cartagena, Castellón, La Coruña, Huelva, and Santa Cruz de Tenerife. This is an ecological study of mortality in the years 1996–2007 which includes 968 CTs with 1,263,371 inhabitants. Exposure has been measured as the distance from the centroid of each CT to the RF. The Besag–York–Mollié autoregressive spatial model has been fitted by R-INLA to estimate the relative risk (RR) and 95 % credible intervals (95 % CrI) for distance in quintiles. The most distant quintile has been taken as the reference. A total of 2,574 persons died of HC. The distances from the CTs to RFs ranged from 0.5 to 22.5 km (median = 7.6 km). All of the RRs for the quintiles of distances in Huelva were greater than 1. Statistically significant excess risk was shown in Cartagena in the nearest CT (1.8 to 6.8 km; RR = 1.43, 95 % CrI 1.02 to 2.02). Radial effects have not been detected between the CT of residence and the petroleum RF in mortality due to HC in any of the cities.

Health care waste includes all the waste generated by health care establishments, research facilities, and laboratories. This constitutes a variety of chemical substances, such as pharmaceuticals, radionuclides, solvents, and disinfectants. Recently, scientists and environmentalists have discovered that wastewater produced by hospitals possesses toxic properties due to various toxic chemicals and pharmaceuticals capable of causing environmental impacts and even lethal effects to organisms in aquatic ecosystems. Many of these compounds resist normal wastewater treatment and end up in surface waters. Besides aquatic organisms, humans can be exposed through drinking water produced from contaminated surface water. Indeed, some of the substances found in wastewaters are genotoxic and are suspected to be potential contributors to certain cancers. The aim of this study was to evaluate the genotoxic and cytotoxic potential of wastewaters from two hospitals and three clinical diagnostic centers located in Jaipur (Rajasthan State), India using the prokaryotic Salmonella mutagenicity assay (Ames assay) and the eukaryotic Saccharomyces cerevisiae respiration inhibition assay. In the Ames assay, untreated wastewaters from both of the health care sectors resulted in significantly increased numbers of revertant colonies up to 1,000-4,050 as measured by the Salmonella typhimurium TA98 and TA100 strains (with and without metabolic activation) after exposure to undiluted samples, which indicated the highly genotoxic nature of these wastewaters. Furthermore, both hospital and diagnostic samples were found to be highly cytotoxic. Effective concentrations at which 20 % (EC20) and 50 % (EC50) inhibition of the respiration rate of the cells occurred ranged between ∼0.00 and 0.52 % and between 0.005 and 41.30 % (calculated with the help of the MS excel software XLSTAT 2012.1.01; Addinsoft), respectively, as determined by the S. cerevisiae assay. The results indicated that hospital wastewaters contain genotoxic and cytotoxic components. In addition, diagnostic centers also represent small but significant sources of genotoxic and cytotoxic wastes.

Phytoremediation provides an ecofriendly alternative for the treatment of pollutants like textile dyes. The purpose of this study was to explore phytoremediation potential of Petunia grandiflora Juss. by using its wild as well as tissue-cultured plantlets to decolorize Brilliant Blue G (BBG) dye, a sample of dye mixture and a real textile effluent. In vitro cultures of P. grandiflora were obtained by seed culture method. The decolorization experiments were carried out using wild as well as tissue-cultured plants independently. The enzymatic analysis of the plant roots was performed before and after decolorization of BBG. Metabolites formed after dye degradation were analyzed using UV–vis spectroscopy, high-performance liquid chromatography, Fourier transform infrared spectroscopy, and gas chromatography–mass spectrometry. Phytotoxicity studies were performed. Characterization of dye mixture and textile effluent was also studied. The wild and tissue-cultured plants of P. grandiflora showed the decolorized BBG up to 86 %. Significant increase in the activities of lignin peroxidase, laccase, NADH-2,6-dichlorophenol-indophenol reductase, and tyrosinase was found in the roots of the plants. Three metabolites of BBG were identified as 3-{[ethyl(phenyl)amino]methyl}benzenesulfonic acid, 3-{[methyl (phenyl)amino]methyl}benzenesulfonic amino acid, and sodium-3-[(cyclohexa-2,5-dien-1-ylideneamino)methyl]benzenesulfonate. Textile effluent sample and a synthetic mixture of dyes were also decolorized by P. grandiflora. Phytotoxicity test revealed the nontoxic nature of metabolites. P. grandiflora showed the potential to decolorize and degrade BBG to nontoxic metabolites. The plant has efficiently treated a sample of dye mixture and textile effluent.

In this study, the effects of cadmium (Cd) stress on the activities of disaccharidases (sucrase, lactase, and maltase), amylase, trypsin, pepsase, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and malondialdehyde (MDA) content in the alimentary system of freshwater crabs Sinopotamon henanense were studied. Results showed that the enzyme activities in the stomach, intestine, and hepatopancreas changed with Cd concentration. In terms of digestive enzymes, Cd exposure had an inhibitory effect on the activities of the disaccharidases, amylase, and pepsase (only in the stomach). Significant induction of trypsin activity by Cd at a lower concentration was observed, but as Cd concentration increased, trypsin activity decreased. Maltase activity showed a slight recovery after inhibition by Cd. The activities of SOD and CAT increased initially and decreased subsequently. Cd significantly inhibited the activity of GPx. MDA content increased with increasing concentration of Cd. These results showed that acute Cd exposure led to harmful effects on the alimentary system of crabs, which are likely linked to Cd induced oxidative stress.