The present study aims to identify the impact of polluted aquatic body i.e. River Hindon on two selected riparian flora i.e. Azadirachta indica and Acacia nilotica. During the course of study the average concentration of different metals in river water was found as Iron (Fe) 11.27ppm±3.50, Manganese (Mn) 4.00ppm±1.26, Cadmium (Cd) 0.08ppm±0.07, Nickel (Ni) 0.63ppm±0.17 and Zinc (Zn) 1.46ppm±0.38 respectively. The average concentration of heavy metals in A. indica of sampling site was found as Iron (Fe) 24.76ppm±6.25, Manganese (Mn) 5.04ppm±1.38, Cadmium (Cd) 0.05ppm±0.05, Nickel (Ni) 0.34ppm±0.20 and Zinc (Zn) 53.92ppm±19.29 respectively while in control site plant average concentration was found as Iron (Fe) 17.18ppm±3.96, Manganese (Mn) 3.63ppm±1.63, Cadmium (Cd) 0.02ppm±0.03, Nickel (Ni) 0.16ppm±0.06 and Zinc (Zn) 31.26ppm±12.11 respectively and average concentration in A. nilotica of sampling sites was found as Iron (Fe) 45.78ppm±10.67, Manganese (Mn) 42.08ppm±11.98, Cadmium (Cd) 0.59ppm±0.51, Nickel (Ni) 40.83ppm±12.16 and Zinc (Zn) 144.10ppm±49.94 respectively while average concentration in control site plant was found as Iron (Fe) 27.76ppm±9.49, Manganese (Mn) 22.75ppm±7.09, Cadmium (Cd) 0.42ppm±0.27, Nickel (Ni) 23.53ppm±8.02 and Zinc (Zn) 96.61ppm±24.78 respectively. One way ANOVA shows statistically significant difference between sampling site plant and control site plant for all the studied metals except Cr in A. nilotica F (3, 42) = 0.589, P= 0.626. A big difference was found in the concentration of metals between sampling site plants and control site plant. In case of metal uptake A. nilotica was found more efficient as comparison to A. indica.

The ever increasing pile-up of electronic waste in dumping sites, especially in developing countries such as China, Pakistan, India and several African countries, might have caused a significant alteration in the microbial community of the contaminated sites. This change in the microbial population may have significant impact to the soil ecology function. The major pollutants of electronic waste are heavy metals like cadmium, lead, nickel, mercury, hexavalent chromium, arsenic and persistent organic pollutants like polychlorinated biphenyls and polybrominated diphenyl ethers. In general, the toxic pollutants reduce the normal soil microbial biota but give rise to increase in the heavy metal resistant and organic pollutants remediating microbes. With the development of culture- independent approach as a tool for studying microbial diversity, the microbial community structures in toxic waste contaminated sites have been revealed gradually. Studies on the microbial community structure of electronic waste contaminated sites show that there are significant differences between the contaminated and the non-contaminated sites. Soil pH in the e-waste contaminated sites of various regions has been reported in a wide range varying from pH 4 to pH 12. However, the predominant phyla so far identified in the electronic waste contaminated sites, based on studies through culture independent approach, are Firmicutes, Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, Crenarchaeota and Bacteroidetes accounting for more than 80% of the total sequence reads on an average. The genera like Pseudomonas, Bacillus, Clostridium, Rhodococcus, Achromobacter and many unclassified bacteria are the common types in the contaminated sites.

Present study aims to unravel the hydro geochemical interaction of sediment and water of Saheb bandh lake, West Bengal, India with an emphasis on heavy metal assessment. Lake water belongs to Ca2+–HCO3− type hydro geochemical faces and water-rock interaction primarily controls the lake water chemistry. Based on different Hydro chemical characteristics it is suggested that silicate weathering is the major hydro geochemical process operating in Saheb bandh lake water. Regarding point source contribution of pollutants the average value of NO3-N, TP and Hg are much higher in inlet water (7.5 mg/L, 1.29 mg/L and 8.5 μg/L) than the lake water (1.5 mg/L, 0.05 mg/L and 0.42 μg/L). Risk assessment indices suggest advanced decline of the sediment quality. Water-sediment interaction of heavy metals reveals that Cd, As, Pb and Hg metals enter into lake water as a result of not only natural processes but also of direct and indirect activities of humans. This study recommends that continuous monitoring of these metals in water and sediment and other aquatic biota of Saheb bandh should be directed to assess the risk of these vital heavy metals in order to maintain the safe ecology in the vicinity of this lake.

Any proper operation could be translated as a constrained optimization problem inside a WWTP, whose nonlinear behavior renders its control problems quite attractive for performance of multivariable optimization–based control technique algorithms, such as NMPC. The main advantage of this control technique lies in its ability to handle model nonlinearity as well as various types of constraints on the actuators and state variables. The current study presents the process of BSM1 building, step by step, proposing appropriate numerical methods are creating the simulation model in MATLAB environment. It also makes a detailed comparison of the proposed NMPC with five recent predictive control schemes, namely LMPC, hierarchical MPC+ff, EMPC, and MPC+fuzzy, along with the default PI. The performance of predictive control schemes is much better than the default PI; however, something of highest importance is the ability to use the proposed control scheme in real systems, for a real application faces several limitations, especially in terms of the equipment. Finally, in order to compare predictive controllers, it is necessary to determine the same conditions so that results from more days can be used, and, if needed, more than 28 days have to be simulated. MOI index can help determine which of the proposed control scheme is really applicable.

The present study synthesizes a novel adsorbent by coating Fe3O4 magnetic nanoparticles with amino functionalized mesoporous silica. The FTIR spectrums indicate that silica has been successfully coated on the surface of Fe3O4 and 3-aminopropyl tri methoxysilane compound have been grafted to the surface of silica-coated Fe3O4. The XRD analysis shows the presence of magnetite phase with cubic spinel as a highly crystalline structure, before and after silica coating. The study also investigates the potentials of amino functionalized silica-coated Fe3O4 magnetic nanoparticles for extraction of Pb2+ and Cd2+ cations from aqueous solutions, where it has used flame atomic absorption spectrometry to determine ion concentration in both recovery and sample solutions. The optimum conditions of removal of Pb2+ and Cd2+ ions turn out to be pH= 4-8 with a stirring time of 20 minutes. The minimum amount of 3M nitric acid to strip ions from functionalized magnetic nanoparticles is 10 mL. The experimental data show the adsorption isotherms have been well described by Langmuir isotherm model, with the maximum capacity of the adsorbent being 1000.0 (± 1.4) μg, 454.5 (± 1.6) μg of Pb2+, and Cd2+ per each mg of functionalized magnetic nanoparticles, respectively. Finally, the proposed adsorbent is successfully applied to remove Pb2+ and Cd2+ ions in wastewater samples.

Pyrolysis is an applicable method that has been widely used to recover hydrocarbons from Used Lubricating Oil (ULO). However, large-scale application of this approach has been limited by its noticeably energy and time consuming nature. In the present research, it has been attempted to modify the energy and time requirements of ULO pyrolysis using the catalytic effects of metal oxide nanoparticles (NPs). The impacts of γ-Al2O3, γ-Fe2O3 and ZnO NPs on the kinetic features of ULO pyrolysis were studied using thermogravimetric analysis (TGA). The kinetic parameters of the pyrolysis process were calculated based on Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozava (FWO) models. The activation energy of virgin ULO pyrolysis had been calculated to be 161.505 and 162.087 kJ/mol using KAS and FWO models, respectively. However, in the present work, utilization of γ-Fe2O3 NPs significantly reduced the activation energy of ULO pyrolysis to 133.511 and 138.289 kJ/mol through KAS and FWO models, respectively. The catalytic effect of ZnO NPs was not as noticeable as that of γ-Fe2O3 NPs, resulting in activation energies of 155.568 and 158.501 kJ/mol using KAS and FWO models, respectively. Moreover, based on the results of this study, γ-Al2O3 NPs had no significant impact on the kinetics of ULO pyrolysis.

The present study monitors BTEX concentration in outdoor and indoor air of eight different microenvironments during summer 2017 and winter 2018 at Asaloyeh city, Iran's energy capital. It samples BTEX compounds by charcoal tubes, analyzing the samples by means of a gas chromatograph with a flame ionization detector. According to the obtained results, outdoor concentrations of BTEX have been higher than the indoor ones, for both seasons, with the highest outdoor and indoor BTEX being 21.70 and 18.59 μg/m3, respectively. Toluene has been the most abundant substance, among the investigated BTEX in all sampling points. Based on the MIR scale, m, p-xylene is the most dominant contributor to ozone formation potential among BTEX species. Indoor to outdoor (I/O) ratios of BTEX compounds range from 0.53 to 0.88 and 0.41 to 0.77 in winter and summer, respectively. The cumulative hazard index (HI) is within an acceptable range. The LTCR value of benzene concentration, obtained, exceeds the value of 1.0E-06, recommended by USEPA. Sensitivity analysis shows that benzene concentration, exposure duration, and inhalation rate have a greater impact on health risk assessment.

Finding an environment-friendly and affordable method to remove contaminated soils from Polycyclic Aromatic Hydrocarbons (PAHs) has now become an attractive field for researchers, with super-critical fluid extraction being an innovative process in the field of contaminated soil treatment. Extraction with super-critical fluid is a simple and rapid extraction process that uses super-critical fluids as solvents. The present study has investigated the extraction of contaminated soil with Polycyclic Aromatic Hydrocarbons (PAHs) by means of batch supercritical water reactor, employing variables like pressure (100–300 bar), temperature (60–140 ◦C), residence time (0.5–3 hours), and base, acidic, and neutral pH values. In order optimize the process parameters, Response Surface Methodology (RSM) has been used. Results show that removal efficiency of PAHs is between 82%-100%, where the highest PAHs removal efficiency (100%) has been observed in Test No. 22, with a pressure of 300 bars, temperature of 500°C, acidic pH equal to 5, and duration of 3 hours. In addition, the lowest removal efficiency of these compounds (82%) has been obtained in Test No. 26, with a pressure of 300 bars, temperature of 350°C, base pH of 9, and duration of half an hour. According to the results from this study, it has become clear that residence time is the most important and most effective parameter for removing PAHs from contaminated soil. Afterwards, temperature and pH are most influential with pressure showing the least effect. Using supercritical water method in appropriate conditions can eliminate more than 99% of aromatic contamination.

The present research investigates the ability of graphene oxide nanosheets for treatment of dairy wastewater, using In Situ Sludge Magnetic Impregnation” (ISSMI) to separate sludge after adsorption process. To increase the interaction between magnetic nanoparticles and graphene oxide, the former has been functionalized, using 3-Aminopropyl triethoxysilane, with the synthesized graphene oxide and magnetic nanoparticles being characterized by FT-IR, SEM, and NCHS analysis. The experiments have been conducted on the effluent of Pegah factory. The batch adsorption experiments have been carried out to investigate the effect of adsorbent dose, contact time, and pH on the removal of total nitrogen, total phosphorus, COD, and turbidity. At adsorbent dose of 320 mg L-1, the removal efficiencies of 90, 80, 84, and 94% have been observed for TN, TP, COD, and turbidity, respectively. The adsorbent data has been modeled by Langmuir and Freundlich isotherms, giving results that are compatible with Freundlich isotherm. TN, TP, and COD are mostly particulate materials in dairy wastewaters; therefore, when nanosheets aggregate, particulate materials are trapped between GO nanosheets; as a result, pollutants are distributed heterogeneously on the adsorbent's surface. Consequently, adsorption does not occur as monolayer on the surface of GO; for this reason, adsorption follows Freundlich model. Maximum absorption capacity of the adsorbent turns out to be 730 mg g-1 for total nitrogen, 600 mg g-1 for total phosphorus, 26000 mg g-1 for COD, and 5500 mg g-1 for turbidity. Adsorption kinetic has been studied with the first and second order equation, giving results that are compatible with second order equation.

The present study is the first attempt to examine temporal and spatial characteristics of aerosol properties and classify their modes over Iran. The data used in this study include the records of Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) from MODerate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Index (AI) from the Ozone Monitoring Instrument (OMI), obtained from 2005 to 2015. The high concentration of AOD and AI values (representing high-high cluster) have been observed in the southwest and east regions, while their low concentrations (representing low-low cluster) have been found in the high mountainous areas. Based on AE values, Iran has been divided into three distinct regions, including fine, mixture, and coarse aerosol modes in each season. Results show that the maximum/minimum area under fine aerosols mode has occurred in the autumn, covering an area of 84.15% and in the spring, covering an area of 40.5%. In the case of coarse mode, the maximum/minimum area has been found in the spring, covered area=53.5% / in the Autumn covered area=12. 5%. The different aerosol modes regions strongly coincide with the topographical structure. To analyze the relation between aerosol properties and topography, Aerosol Properties Index (API) has been developed by combining OMI and MODIS datasets. API is a simple indicator, capable of showing the degree of aerosol coarseness in each pixel. There is a negative correlation between API and topography over the studied region, meaning that aerosol concentrations are high in the lowlands, but low in the highlands. However, this relation differs in various geographic regions, as Geographically Weighted Regression (GWR) model shows a higher determination coefficient in all seasons, in comparison to Ordinary Least Squares (OLS).