Ahvaz can be regarded as one of the most polluted cities in the world in terms of air pollution. Successive years of drought and weather conditions in recent years have resulted in particulate matter (PM10) concentration in Ahvaz. In this study, using probability distribution techniques, an appropriate threshold to identify the PM10 maximum extreme concentrations (MEC) has been detected. Based on log-logistics probability distribution, which has the best fit to the data of PM10 concentration in Ahvaz, the 0.99 percentile threshold which is specified by 1516 μg/m³ is known as the primary PM10 concentrations in Ahvaz air. Based on the mentioned threshold, 24 days in which the PM10 concentration was equal to or more than the threshold were selected for synoptic analysis. Analysis of the circulation of weather types showed that two weather types circulations at 500 hPa level provide the climatic conditions for the occurrence of (MEC) caused by PM10 concentration ≥1516 μg/m³ in Ahvaz in the first type (which is for hot days). Under such condition, the closed high pattern of 500 hPa level is accompanied by the ground low pressures. In the second type (which is for cold and transitional days) the closed high pattern of 500 hPa level is accompanied by the ground high pressures. In addition, this study showed that the (MEC) of PM10 in both models fed with several different sources at different levels and due to being multi-source, storms can create MEC.

Natural coagulants have received much attention for turbidity removal, thanks to their environmental friendliness. The present study investigates potential application of rice starch for removal of turbidity from aqueous solutions. It considers the effects of four main factors, namely settling time (40-140 min), pH (2-8), slow stirring speed (20-60 rpm), and rice starch dosage (0-200 mg/L), each at five levels, by means of central composite design. Results show that a quadratic model can adequately describe turbidity removal in case of non-autoclaved rice starch with statistics of R2= 0.95, R2adj.= 0.91, R2pred.= 0.77, AP = 23.75, and CV = 4.77. It has also been found that the performance of non-autoclaved rice starch is superior to the autoclaved variety, in terms of removal efficiency and floc size. In the optimal point, predicted by the model, a removal efficiency equal to 98.4% can be attained, using non-autoclaved rice starch, which is higher than that of the autoclaved rice starch (71.29%). The significant effective parameters have proven to be settling time along with pH. Overall, rice starch can be considered a promising high potential coagulant for removal of turbidity from water or wastewater.

The experiment was performed to evaluate effect of heavy metals on total phytoplankton load (TPL) using water of Turag River adjacent to Ashulia locating on the north-eastern side of Dhaka city, Bangladesh. Total phytoplankton load comprises of Euglena sp., Borodinella sp., Pediastrum biradiatum, Pinnularia sp., Fragillaria sp., Fragillaria crotonensis, Gloeocapsa sp., Navicula sp., Cynedra sp., Crucigenia sp., Chlorella sp., Spirogyra sp., Phacus acuminatus, Phacus circulatus., Nitzschia sp. and Nitzschia clausii. Phytoplankton load showed the abundances Bascillariophyceae (43.75%) > Chlorophyceae (37.50%) > Euglenophyceae (18.75%). The average maximum growth rate (log transformed) of TPL in control culture was -0.25μg/l and treated cultures using 1ppm, 3ppm, 5ppm, 7ppm concentration of heavy metals (Zn and Cu) were 0.03 μg/l, 0.03 μg/l, -0.11 μg/l and -0.26 μg/l, respectively. In treated culture using 1ppm concentration of heavy metals (Zn and Cu) the growth rate of phytoplankton load increased significantly whereas the growth rate decreased at higher concentrations (3ppm, 5ppm and 7ppm) of heavy metals. The implication of this finding can be used to monitor health of riverine ecosystems and management of river pollution.

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique (ARIMA-SVR). The main concept of generating a hybrid model is to combine different forecasting techniques so as to reduce the time-series forecasting errors. The data used in this study are annual CO2, CO, NOx, SO2, SO3, and SPM concentrations in Iran. According to the results, the ARIMA-TSVR Model is preferable over the other models, having the lowest error value among them which account for 0.0000076, 0.0000065, and 0.0001 for CO2; 0.0000043, 0.0000012, and 0.000022 for NOx; 0.00032, 0.00028., and 0.0012 for SO2; 0.000021, 0.000014, and 0.00038 for CO; 0.0000088, 0.0000005, and 0.00019 for SPM; and 0.000021, 0.000019, and 0.0044 for SO3. Furthermore, the accuracy of all models are checked in case of all pollutants, through RMSE, MAE, and MAPE value, with the results showing that the hybrid ARIMA-TSVR model has also been the best. Generally, results confirm that ARIMA-TSVR can be used satisfactorily to forecast air pollution concentration. Hence, the ARIMA-TSVR model could be employed as a new reliable and accurate data intelligent approach for the next 35 years’ forecasting.

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.