Natural quartz mineral was examined as a new sorbent for Hg(II) removal from synthetic wastewater systems. Batch adsorption experiments of Hg(II)  onto quartz mineral were conducted under various conditions such as solution pH, sorbent dosage, contact time, initial Hg(II)  concentration. Adsorption experiments results of Hg(II) by quartz mineral showed good achievement after 180 min with 1.0 g/L sorbent mass at pH of 2.0, agitation speed of 200 rpm and a temperature of 25°C. Moreover, the Hg(II) concentration was directly related to increases the adsorption capacity, the maximum Hg(II) uptake by quartz  sample was 16.52 mg/g for 80 mg/L (C0 (Hg(II)].  Langmuir isotherm and pseudo-second-order kinetics (R2 > 0.99) were found to be the most appropriate models to describe the adsorption of Hg(II) by quartz mineral. The intra-particle diffusion model and the calculated Dubinin–Radushkevich adsorption energy (Eads = 0.78 kJmol-1), confirms a physisorption adsorption reaction occurring in three stages.

Boreal freshwater ecosystems are highly sensitive to pollution, but too little information is available on the use of both biotic and chemical indicators for estimation of the effect of wastewater on boreal rivers and streams. The purpose of this study was to assess the wastewater impact on the boreal river (Perm Krai, Russia).  Physicochemical parameters of major ions and trace elements were detected with a field portable unit, capillary electrophoresis, and ICP-MS. Fish data was collected by gillnets. To evaluate the level of pollution from the Tolych River upstream to downstream, we calculated heavy metal evaluation index (HEI), ecological risk index (ERI), and index of biotic integrity (IBI). The anthropogenic impact from upstream to downstream showed the range from a very high to medium level of pollution by ERI and from a high to medium level by HEI values, where most of the studied major ions and trace elements often exceeded aquatic life limits. We found significant thermal pollution of the observed river with the decreasing temperature gradient from pollution source down to the river mouth due to hydromorphological factors. Observed thermal pollution leads to the absence of thermally sensitive cold-water fish species and the abundance of ecologically flexible fish species. The water quality assessed by biotic IBI index showed low and very low quality of lower reach of the studied river, which contradicts the results of assessment by HEI and ERI indices.  The results show the importance of using aquatic organisms as bioindicators for assessing ecological water quality.

Laboratory wastewater is categorized as hazardous waste that should not be released into the environment since it poses a serious threat to environmental safety. In the present study, the use of Sulphate-Reducing Bacteria (SRB) colonies in an anaerobic reactor to treat heavy metals-containing laboratory wastewater was examined. SRB was initially cultivated with the treatment of fermented compost and Postgate's medium before being attached to the laboratory-size anaerobic reactor to treat laboratory waste containing heavy metal. Within the 15 days of initial incubation under the room temperature of 28 °C, we discovered that SRB optimally grew on the medium with the composition of 5% Postgate B solution, 30% fermented compost liquid, and 5% active suspension liquid, with a total population of cell colonies was 1.2 x 105 CFU/ml. After SRB colonies from the most optimum medium were affixed to the reactor, the reactor attained 89% of lead (Pb) removal, 69.78% of iron (Fe) removal, and 48.93% of copper (Cu) removal for 15 days treatment periods. On the 21st days of treatment time, the removal efficiency increased significantly to 91.57%, 78.09%, and 83.56% of Pb, Fe, and Cu removed, respectively.

Air fresheners are the synthetic products, used to improve the quality of indoor air by removing unpleasant or disturbing odours, in addition they disinfect the air by removing allergens and in turn add pleasant odours. However, these fresheners since they contain varied chemicals, which on magnification in a closed environment may cause respiratory illness. Therefore, constant usage of these air fresheners would deteriorate the ambient quality of indoor air. Even air fresheners which claim to be “green”, since these lack regulatory norms, they too emit hazardous or chemically harmful compounds. Hence there is a dire need to use alternative products that substantiate the quality of indoor air.  The present study aimed at exploring the efficacy of medicinal plant extracts of Azadirachta indica, Menta piperita and Aloe barbadensis in controlling air borne fungi in indoor environments by creating a simulation of an indoor environment and checking the efficiency of these natural air fresheners. About 60-70% reduction in the vegetative structures (colony diameter) and 30% reduction in reproductive structures were observed after exposure for 11 days to environment containing Azadirachta indica and Menta piperita extracts. Thus this study has novelty in formulating herbal based air fresheners based on the proven antifungal activities of these medicinal plant extracts, thereby replacing the usage of commercial air fresheners in the near future in controlling indoor air borne fungi. Since these natural formulations undoubtedly disinfect the indoor air, has commercial prospects and are eco-friendly, cost-effective with no health implications.

In this study, the concentrations of heavy metals were studied using atomic absorption spectroscopy of samples from the sediments of the Tigris River within the boundaries of the city of Mosul, northern Iraq, and the environmental parameters of heavy metals were calculated. The results showed that the average concentrations of Co, Cu, Cd, Pb, Zn, and Ni in (ppm) were (8.78, 30.42, 0.179, 12.04, 75.53, and 144.75), respectively, where these results were higher than the international accepted average. It indicates that the main factor in the high concentrations of heavy metals in the environment of the Tigris River in the city of Mosul is the pollution caused by human activities. The results of the environmental treatments for the studied heavy metals showed that the values of the enrichment factor (EF) were moderately contaminated with Cu, Cd, Ni, and Zn and not contaminated with Co and Pb. The value of the contamination factor (CF) for the sediments of the Tigris River in the studied areas showed that the sediments of those areas are moderately polluted with Co, Ni, and Zn metals. The degree of contamination (Cdeg) for the sediments of the study area in general ranges from low - medium pollution, the pollution load index (PLI) average of (1.03) showed that the sediments of the study area were contaminated with heavy metals. Therefore, we conclude that the environment of the Tigris River is polluted with heavy metals, but it is not at the level that causes concern at present.

The high amount of Electrical Conductivity (EC) in the groundwater is one of the major negative Geo-environmental problems which has a considerable effect on the quality of drinking water. To address this challenging problem we proposed an intelligent Machine Learning (ML) based approach to predict EC in urban areas. We applied the deep learning technique as one of the most applicable ML techniques with high capabilities for intelligent predictions. Five different deep neural networks (Net 1 to Net 5) were developed in this study and their reliability to predict EC with an emphasis on different settings of inputs, features, functions, and the number of hidden layers was evaluated. The achieved results showed that deep neural networks can predict EC parameters using minimum and economic input parameters. Results showed parameters Cl and SO4 with a high range of correlation and pH with a low range of Pearson correlation properties are influential parameters to be used as the input of neural networks. Activation function Relu, optimization function Adam with a learning rate of 0.0005 and loss function Mean Squared Error with the minimum of two hidden dense layers from Keras laboratory of Tensor Flow developed an efficient and fast network to predict the EC parameter in urban areas. Maximum epochs for developed networks were defined up to 2000 iterations while epochs are reducible up to 200 to drive minimum loss function outcome. The maximum training and testing R2 for developed networks was 0.99 in both the training and testing parts.

Benzene is considered a toxic and hazardous pollutant in Tehran metropolis. The storage tanks of petroleum products and refueling in gas stations are among the main sources of benzene emissions. Using the software AERMOD and reviewing the benzene dispersion maps at different distances from 412 storage tanks at 148 gas stations, it was found the permissible distance of the emission source is dependent on various variables such as the number of loading times and the storage capacity. When, storage capacity in the range of 60,000 L to 96,000 L and the number of loading is in the range of 675 to 1328 times a year, the concentration of benzene at a distance of 30 m of the emission source reaches the annual standard of 5 μg/ m3. While, storage capacity in the range of 80,000 L to 128,000 L and the number of loading is in the range of 1329 to 1834 times a year, the concentration of benzene at a distance of 40 m of the emission source reaches the annual standard of 5 μg/ m3. Also, based on the analysis of data and the linear regression equation, the permissible distance of the emission source can be predicted.

Four regions of the Farahzad River Valley with different topography were selected to fully survey it and study the effects of morphology on local climate. then one of the hot days of the month of June 2021 (June 6th) was selected because the wind speeds increase in spring. According to the comparison of the simulation results with the existing site plans, the temperature in area 3 was the highest, 39.60 degrees, and the wind speed was 3.57 m/s. On the other hand, the study and analysis of the maps showed that the temperature of the roads in regions 3 and 4 were higher than the other two regions with a temperature range of 37.69-38.40, so the presence of impervious asphalt surfaces on the roads is very effective in increasing the air temperature in these areas.  Comparisons also showed that tall buildings and vegetation create shaded areas and increase wind speed. Based on this, two scenarios were designed. In the first scenario, doubling the height of buildings increased wind speed in Region 3 by 3.42 m/s and decreased temperatures by 1.59 degrees. In the second scenario, when tall trees were planted at certain distances around the streets, the temperature in Region 3 decreased by 1.68 degrees and the wind speed increased by 1.68 m/s. The results show that the differences in the topography of urban valleys cause ventilation of the environment and that the effect of this feature in other environments is more effective through planting than through buildings.

Human developmental activities always result to waste generation; that invariably pollute the environment, if not properly managed. The aim of this study is to determine soil quality around Odo Iya-Alaro at Ojota, Lagos. A total of 12 soil samples were collected from 0 -15 cm and 15- 30 cm at three different spots of 100 and 500 m (control) away from the bank of the river. Samples were analysed for pH, EC, NO3, TOC; Zn, Na, K, Ca, Mg, Cu, Fe, Cd, Cr, Ni, and Pb using standard analytical methods. The results were subjected to both differential and inferential statistics using statistical package (SPSS 22.0 version). Subsequently, the data were compared with Earth crust values. The soil pollution was evaluated using pollution, ecological risk, and geo-accumulation index. Cr (50.43), Ni (29.47), and Cu (104.10) mg/kg at 100 m were higher than their controls; (12.09), (8.14), and (86.06) mg/kg respectively, but lower than their respective Earth crusts; (100), (80) except (50) mg/kg. The soil was moderately polluted with pH (1.15), Na (3.00), K (2.11), Mg (1.87), Ca (1.26) and Cu (1.21); considerably polluted with EC (3.82), TOC (3.39), and Ni (3.62); and very highly polluted with Fe (8.26). Fe (711.73) had a very high ecological risk.  The Geo – accumulation index was moderately - strongly polluted with Zn (2.61), and very strongly polluted with pH (5.37), EC (14.90), NO3 (9.66), Na (15.41), K (11.31), Mg (9.51), Ca (17.08), Fe (15.32), Cu (12.54), Cr (8.67), and Ni (7.32). The soil was polluted. and urgently needs reclamation for Garden Park (relaxation).

Increasing the level of environmental knowledge of consumers and their level of concern towards the environment and green brands are among the important factors that have been considered in the discussion of consumer behavior in recent years. Marketing managers also try to attract customers' attention to their green products and brands in order to succeed in selling and marketing their products. Therefore, the present study was conducted with the aim of investigating the effect of green packaging on attracting and retaining environmentally friendly customers with the mediating role of green brand image. In terms of the purpose of the research, the type of applied research, the method of collecting descriptive data is of the correlation type. The statistical population of this research is the students of University of Mohaghegh Ardabili, 373 of them were selected as a sample by referring to Morgan's table and using the available sampling method. The required data were collected using a questionnaire and analyzed based on the structural equation modeling method and using SPSS and Smart PLS software. The results of the research showed that green packaging has a positive and significant effect on attracting and retaining environmentally friendly customers as well as the image of the green brand. On the other hand, the image of the green brand has a positive and significant effect on attracting and retaining environmentally friendly customers. Finally, green brand image acts as a mediating factor in the relationship between green packaging and attracting and retaining environmentally friendly customers.