A total of 58 Cadmium tolerant bacterial isolates were isolated from 26 samples collected from 20 villages/city of different contaminated water samples from industrial and mining affected areas of Chhattisgarh (India). Out of 58 bacterial isolates, 15 bacterial isolates were able to grow in presence of 40 mM cadmium chloride. These fifteen were further screened by biochemical characterization, antibiotic susceptibility and presence of czcA gene. However, finally five selected isolates (BSWC3, RgCWC2, RgUWC1, RpSWC3, KDWC1) were identified by 16S rRNA gene sequencing belonged to the genus Serratia liquefaciens, Klebsiella quasipneumoniae subsp. similipneumoniae, Klebsiella pneumoniae, Pantoea dispersa and Enterobacter tabaci, respectively. Among these two best culture Serratia liquefaciens BSWC3 and Klebsiella pneumoniae RpSWC3 were testes for their bioremediation efficiency individually as well as in mixed culture. Atomic Absorption spectrophotometer analysis of samples revealed that cadmium (Cd) tolerant bacterial isolates BSWC3, RpSWC3 and Combination of BSWC3 and RpSWC3 were significantly reduce of cadmium concentration i.e. 44.46%, 40% and 50.92%, respectively as compared to control. Therefore, the finding of the present study revealed the use of mixed culture or consortium of indigenous isolates is the better option for bioremediation of heavy metals.

Hydrocarbon Processing Industries (HPIs) emit large amounts of highly reactive hydrocarbons and Nitrogen Oxides to the atmosphere. Such simultaneous emissions of ozone precursors result in rapid and high yields ozone (O3) formation downwind. The climate of the Middle East has been shown to be favorable for O3 formation in summer. There are also vast activities in processing oil and gas in this region. This study aimed to investigate the influence of HPIs located in the Middle East on ozone formation. We chose the South Pars Zone (SPZ) located in the coastal area of the Persian Gulf with concentrated HPIs as a case study. To do this, after developing an emission inventory for O3 precursors, we used OZIPR, a Lagrangian photochemical model, coupled with SAPRC-07 chemical mechanism to describe the effects of HIPs on ozone formation in the SPZ and downwind area from June to August of (2017). Results indicate that the SPZ has a far-reaching and wide-ranging impact on O3 formation in downwind areas and an area at a distance of 300 km can be affected profoundly (Average 0.06 ppm and maximum increase 0.24 ppm). Given the large numbers of HPIs located in the Middle East, we predict that the transport of O3 and its precursors from this region play an important role in the ozone air pollution in a much wider area and the role of these industries should be taken into account for regional and interregional ozone concentration modeling.

The present research studies the performance of Converter Sludge (CL)as a nano-activator of persulfate (PS) in Permeable Reactive Barrier (PRB) as an in-situ technology for leachate treatment.In batch experiments, the acidic conditions (pH = 3) have been the most suitable for removal operations, where COD and NH3 removal efficiencies are 69.15% and 60.96%, respectively. The Box–Behnken design (BBD) has been employed to optimize three parameters, namely PS/ COD ratio, CS dose, and pore volume (PV), using COD and NH3 of leachate landfill as the target pollutant. The BBD is considered a satisfactory model to optimize the process. Under optimal conditions (PS/COD ratio: 3.47, CS dose: 3.09 g L-1,and PV: 4.27), the measured values of the COD and NH3 removal efficiencies have been 74.2 and 66.8, respectively, all within the 95%-prediction intervals, which indicate the model’s success in predicting removal values. The biodegradability (BOD5/COD) of the real leachate has been enhanced from 0.25 to 0.77, with the toxicity of real leachate getting decreased by more than 90%.

Several studies have been conducted to develop more accurate and precise indices for hydrocarbons source apportionment. The present study, however, develops a new multi-metric index for hydrocarbons source apportionment. It measures Poly Aromatic Hydrocarbons (PAHs) concentration at six stations with well known petrogenic origin, calculating Phe/An, Flu/Py, Chr/BaA, BaA/Chr, An/(An+Ph), Flu/(Flu+Pyr), and IP/(IP+Bghi) indices. All the indices could correctly determine the source of hydrocarbons, except for IP/(IP+Bghi). Subsequently, it uses principle component analysis method to create a combined multi-metric index, based on PAHs, the concentration of which also contributes to the evaluation of new index performance in stations with known origins. Results show that the new multi-metric index can determine the source of hydrocarbons with greater certainty. Then, using this index, the potential source of contamination in the area has been divided into six sections, namely HPY, MPY, LPY, MPE, HPE, and LPE, which indicate origin of high, moderate, and low risk of petrogenic contamination, as well as source of pyrolytic contamination with high, moderate, and low probabilities.

Due to its wide range of hazardous hydrocarbons and even heavy metal ions, oily sludge has become a great environmental challenge which must be dealt with quite quickly. As a result, ther have been numerous efforts during recent years to develop an efficient method for sludge recovery. The current research studies the effectiveness of solvent extraction with toluene and Fe2O3 nanoparticles for recovery and upgrading of oily sludge. Having employed Design of Experiment (DOE), it has found optimum conditions for sludge recovery with solvent extraction, namely a temperature of 55°C and mixing time of 17 minutes with solvent to sludge ratio of 6.4/4.2. Under these conditions, the sludge recovery has been 37%, which is the maximum available with toluene. Furthermore, it has studied the effectiveness of Fe2O3 nanoparticles for improvement of sludge pyrolysis efficiency in order to upgrade the oily sludge, wherein it has been observed that nanoparticles can significantly decrease the temperature and time of reaching maximum conversion during sludge pyrolysis process. The temperature and time of reaching to the maximum conversion, by means of gamma Fe2O3 nanoparticles, is about 200°C and 1200 s, respectively, which is lower than the condition in which pure sludge is being pyrolyzed.

The term bioremediation describes biological machinery of recycling wastes to make them harmless and useful to some extent. Bioremediation is the most proficient tool to manage the polluted environment and recover contaminated river water.  Bioremediation is very much involved in the degradation, eradication, restriction, or reclamation varied chemical and physical hazardous substances from the nearby with the action of all-inclusive microorganisms. The fundamental principle of bioremediation is disintegrating and transmuting pollutants such as hydrocarbons, oil, heavy metal, pesticides and so on. Different microbes like aerobic, anaerobic, fungi and algae are incorporated in bioremediation process. At present, several methods and approaches like bio stimulation, bio augmentation, and monitoring natural recovery are common and functional in different sites around the world for treating contaminated river water. However, all bioremediation procedures it has its own pros and cons due to its own unambiguous application. Above all, utilization of bioremediation paving a minimal inconsiderably contaminated, healthy as well as safe and sound future.

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.