Mitigation of atmospheric pollution has been a topic of concern over the past decades. In this study, tree rings have been used to reconstruct past climates as well as to assess the effects of recent climatic and environmental changes on tree growth. Vehicular emission is one of the major sources of pollutants in the atmosphere and this study focused on the Haatso-Atomic road which over the years has been a spot for heavy vehicular traffic. Swietenia mahagoni (Mahogany) tree was logged and the rings counted and age determined to be 61 years spanning from 1957 to 2018. X-ray fluorescence (XRF) was used to investigate the presence of the following heavy metals. Heavy metals (Cu, Mn, Zn, Pb, Cd and Ni) which ranged from (3.15—9.84mg/kg), (2.58 – 5.49 mg/kg), (8.18 – 15.78mg/kg), (0.12—0.60 mg/kg), (0.01—0.09 mg/kg) and (0.10 – 0.99 mg/kg) respectively, from vehicular emissions were determined for annual rings spanning from 1957 to 2018 and surprisingly an increasing trend was observed with some the heavy metals exceeding WHO guidelines. Tree growth rates were calculated through ring width measurements and related to annual precipitation data spanning over the sampling period. It was observed that wet seasons correlate with high growth rates of trees while low precipitations seasons related to low or no growth rate of trees.

The changes in diatom assemblages along an urban-to-rural gradient were characterized to assess the ecological status of the Sai Gon River, Vietnam. Diatoms and physico-chemical variables were measured at 10 stations during dry and rainy season. One-way ANOVA showed that diatom metrics and physicochemical variables were significantly different (p < 0.05) between the upper course sites and both the middle- and the lower sites. However, no significant differences were observed between the middle course sites and the lower course sites. Achnanthidium minutissimum and A. exigua were potential indicators of low nutrient in the upper course sites; Melosira granulata and Navicula viridula were preferred moderately eutrophic water in the middle course sites; while Navicula cryptocephala and Nitzschia palea were tolerant to very heavy pollution and dominant in the lower course sites. Canonical correlation analysis (CCA) results showed that concentration of TSS, TN, TP, BOD5 and COD were the most important factors in structuring benthic diatom communities in the Sai Gon River. The results of this study indicated that diatom community was sensitive to changes in urban condition and could be used as an indicator of urbanization.

Due to an increase in demand of petroleum products which are transported by vessels or exported by pipelines, oil spill management becomes a controversial issue in coastal environment safety as well as making serious financial problems. After spilling oil in the water body, oil spreads as a thin layer on the water surface. Currents, waves and wind are the main causes of oil slick transport. These phenomena depend on the overall interaction among gravity, viscosity, surface tension and interfacial tension of oil in water bodies. In the current study, Artificial Neural Network (ANN) models have been designed and trained for the prediction of oil spreading and advection under different hydrodynamic conditions. In this regard, results obtained from a multiphase Lagrangian numerical model are deployed to train ANN model. The mentioned numerical model which is based on the moving particle semi-implicit (MPS) method is developed in the earlier stage of the study. In this research study, the MPS numerical model is first validated and verified against the analytical formulas which are based on experimental data cited in the literature. Then, various hydrodynamic conditions and oil spill scenarios were chosen to obtain different numerical model results. Finally, numerical model results are then deployed for training ANN model to provide a useful tool for urgent prediction of oil slick trajectory in order to manage the oil slick transport in the coastal environments.

Heavy metal contaminated soil raises major global environmental and agricultural concern. Recently soil pollution through lead (Pb) and chromium (Cr) becoming serious problem and remediation or utilization of those contaminated soil with potential crops is of the outmost importance. The objectives of present study were to examine the effects of Pb and Cr on three different kenaf and mesta varieties for seed germination, seedling establishment and amount of Pb and Cr uptake by tested varieties in laboratory condition. Three varieties were used for the study namely, HC-95 (kenaf), CPL-72126 (mesta) and Samu-93 (mesta) and the treatments were combination of Pb and Cr chemical at (0,0), (60,60), (80,80), (100,100) and (120,120) mg/L. Increased level of lead and chromium gradually reduced the germination percentage and primary growth parameters compared to control. The shoot and root lengths were affected only little, whereas, the biomass showed a considerable reduction with the increase of Pb and Cr toxicity. Stress tolerance indices showed a gradual and negative response by the plant with the increase of metal concentrations. However, in all the levels of Pb and Cr treatment, the seedlings were capable to tolerate the toxicity and seedlings were established. Bioaccumulation of Cr was higher than that of Pb in all varieties and in all treatments. The interaction of Pb and Cr reduced the toxic effect of both metals to the plants. The findings are helpful for selecting fiber crop varieties for cultivation in contaminated soils or phytoremediation of Pb and Cr from the contaminated soils.

In this work, the backward Bayesian multiple linear regression (BBMLR) as a new approach is presented to predict the adsorption efficiency (AE) of Cd(II) ions by ostrich bone char (OBC) in the fixed bed adsorption with four operational variables consisting of pH (2-9), inlet Cd(II) concentration (Co= 25-100 mg/L), bed depth (h= 3-9 cm) and feed flow rate (Q= 0.5-30 mL/min). The performance of the BBMLR was evaluated using the coefficient of determination (R2), normalized root means square error (NRMSE) and mean residual error (MRE). The AE of Cd(II) ions by OBC increased from 42.3% to 99.9% when pH was increased from 2 to 9 with h of 6 cm and Q of 1.5 mL min-1. It was found that the AE dramatically increased from 70.5% to 99.9% with decreasing Q from 30 to 0.5 mL min-1 at pH of 7 and h of 6 cm. At pH= 7 and Q= 1.5 mL min-1, the AE increased from 71.9% to 100% when h increased from 3 to 9 cm. The BBMLR model presented excellent performance (NRMSE=6.69%) for predicting Cd(II) removal in a continuous adsorption system, although it gave a slight underestimation (approximately 3.52%). The BBMLR is more sensitive to the pH, followed by h and Q, while the Co has no significant effect on it. This research displays that OBC has great potential as an eco-friendly low-cost adsorbent in removing Cd(II) ions from the contaminated waters.

The present study aims at evaluating the risk of Mutagen X (MX) (3-chloro-4-(dichloromethyl)-5-hydroxy-2 (5H)-furanone) and adverse health effects, associated with direct ingestion of chlorinated drinking water in west of Tehran, supplied by chlorinated drinking water from surface and underground water sources. For one year, MX concentrations in tap water samples has been measured for consumers in four different zones in western Tehran. It has been found that average MX concentration in the whole study area is 24.16 ng/L, with the highest concentration being in Zone 1 with a value of 38 ng/L. Also, the role of water sources, seasonal changes, and effective factors such as Total Organic Carbon (TOC) have been evaluated on MX formation. The highest of excess lifetime cancer risk (ELCR), estimated as 0.0037E-05, belongs to Zone 1, which uses surface water to supply drinking water, while the lowest can be seen in Zone 4, being 0.0021E-05. This latter zone utilizes underground water as the water source. In all zones, the highest risk of excessive cancer is related to winter, ranging from 0.0045E-5 in Zone 1 to 0.0023E-5 in Zone 4. The estimated number of cancer cases for Zones 1 to 4 have been 0.012, 0.016, 0.016, and 0.004, respectively, based on their population. The estimated average risk and the number of ELCR, caused by exposure to MX, through direct ingestion of drinking water have been 0.0030E-5 and 0.047, respectively, in the entire studied area for the duration of one year.

The main purpose of this research is to determine the generation quantity of each generator in a power system. In this way, not only will the electricity demanded by the system be supplied, but the cost of fuel along with the level of pollution can be minimized. Obviously, calculation of the optimal layout of power plants with the aim of minimizing fuel costs and pollutants level contributes to sustainable socio-economic development. For this purpose, modeling a multi-objective decision making framework by means of the weighting method makes it possible to attain the mentioned goals. After modeling the goals and constraints of the power system, the problem associated with economic-environmental load dispatch with the Institute of Electrical and Electronics Engineers 30-Bus data is optimized by means of the Lagrange approach. Moreover, the sensitivity analysis in connection with the weight of short-term costs is conducted to determine the final point of the system usage. Results show that if the importance coefficient of the fuel cost reduction is 1 (W=1), the economic and environmental load dispatch will pose some problems for the economic load dispatch. In contrast, if the importance coefficient of the reducing fuel cost is zero (W=0), the economic and environmental load dispatch will become problematic for environmental load dispatch. Incidentally, the trade off curve of the fuel cost and the pollutant amount involves the functional information for the system operator. The current research is mainly innovative in its use of a method to reduce fuel consumption and environmental impacts on emission at optimization process. This can, in turn, lead to generation of sustainable energy.

The presence of synthetic dyes in textile wastewater is a problematic issue for environmentalist. Nowadays, dye removal is practiced via different methods. Among all these methods, biodecolorization is an ideal technique. The present research apples vermicompost microflora to remove reactive black- C, pH = 7, and under anaerobic condition. At 36h, removal efficiencies of 94.79%, 94.06%, and 93.6% are obtained for concentrations of 800, 850, and 950 mg/ L, respectively. It has also been observed that when the initial concentration rises to 1400 mg/ L, the efficiency drops to 51.57% at 36h. Also, methyl red, methyl orange, eriochrome black-t, and acid blue-113 could be decolorized by the isolated bacterial strain with an efficiency of 94.29%, 92.10%, 90.83%, and 88.95%, respectively. Phytotoxicity Test shows that the parent form of reactive black-5 has not been toxic for the seeds (100% germination for Triticum aestivum and 90% for Maize). When reactive black-5 is treated with isolated bacterial strain under anaerobic condition, none of the seeds remain germinated which might be due to the possible formation of toxic aromatic amines intermediates. Therefore, ultraviolet C + 100 mM H2O2 has been used as the post-treatment process for detoxifying of by-products. After the integrated treatment of synthetic wastewater, containing RB-5, complete germination (100%) of Triticum aestivum and Maize is observed. In the post-treatment process, due to the generation and activation of hydroxyl radicals, the toxic aromatic amines compounds convert to the less toxic compounds.

Arsenic (As) contamination in the groundwater of Bangladesh is one of the major public health concerns. It has become a challenge to remove As from groundwater and a great deal of efforts employed in this regards with limited success. Cerium oxide is one of the important medias of arsenic removal techniques. Nine units of cerium-based arsenic technology were tested with seven different well waters in five hydro-geological areas in Bangladesh. Interestingly, the same technology showed variable results in terms of As removal performance from well water. Therefore, this study aimed to investigate the reasons behind the variant performance of the As removal technology. The studied wells were contaminated with a range of 283 to 873 μg/L of arsenic, 0.35 to 10.4 mg/L of iron, 0.29 to 6.83 mg/L of phosphate, 32.5 to 49.5 mg/L of silicate, 0.08 to 0.25 mg/L of sulfate and pH range was 7.11 to 7.65. The cerium-based As removal technology consistently produced As safe water from three wells containing more than 80% As (III) of total arsenic (As) and >3 mg/L of iron and reduced As concentration to below 50 μg/L consistently but failed at other four wells containing less than 75% As (III) of tAs and

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.