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.

The presence of volatile organic compounds in the indoor environment and their unwanted effects on human health are inevitable. That's why different methods have been proposed to remove them from air. The present study examines using photocatalytic reaction system along with TiO2 particles coated on stainless steel webnet to study direct conversion of toluene using a new design. The study was carried out using UV radiation in a dynamic concentrator system. SEM and XRD analyses were performed to characterize prepared catalysts. Here, the aim was to employ photocatalytic oxidation (PCO) to optimize removal efficiency and elimination capacity using response surface methodology (RSM). To this end, initial concentration and flow rate were selected as independent variables. High removal efficiency and elimination capacity were realized using optimal settings. The findings indicated that PCO process with a new design other than RSM was an option to treat air pollution containing volatile organic compounds.

The pollution generated by metallic trace elements discharged by mines into the environment can become a very worrying source of contamination for soil, water and plants. The characterization of the chemical properties of metals in mine tailings and soils is of crucial importance to assess the risk of their potential mobility and therefore their bioavailability. In this paper, the bioavailability of metallic trace elements  in agricultural soils in the vicinity of the Draa Lasfar mine in the northwest of Marrakech city (Morocco) was studied by determining the contents of Cd, Cu, Pb and Zn in soils and in two plants: wheat (main food for the human population) and couch grass (main food for livestock). The results showed that these metals move from agricultural land to plants. They also showed that couch grass seems to strongly absorb and accumulate metallic trace elements present in the soil; it removes considerable amounts of metallic trace elements from the soil with its deeply penetrating root system.

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.

Gas storage is an important branch of technology that has many economic and environmental aspects. This technique could save gas to the need time and contribute to solving the CO2 and global warming problems. In this work, the structure and physicochemical properties of the prepared palladium complex were characterized in the solid and solution states using spectroscopic techniques. These examination methods include ultraviolet-visible (UV-Vis), Fourier transform infrared (FTIR), metal and elemental analyses, and measurements of magnetic susceptibility and conductivity at room temperature. Also, findings on the surface morphology and surface area were provided via Field emission scanning electron microscopy (FESEM) and Brunauer–Emmett–Teller (BET) techniques, respectively. High-pressure adsorption measurements were investigated by storing carbon dioxide, and the results proved that such materials own remarkable gas adsorption properties that make them a good option for gas separation and storage. Gases uptake at 323 K for the complexes leads to the highest CO2 uptake. The prepared material could pave the road for further exploitation of similar materials.

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.

BTEX is one of the common compounds in the breathing air of gas station workers, which can cause high carcinogenic and health risks. The present study was conducted to assess the carcinogenic and health risks of occupational exposure to BTEX compounds in gasoline fuel distribution stations in Karaj. This descriptive and cross-sectional study was conducted to assess the carcinogenic and health risks caused by exposure to BTEX compounds in 2021 during the summer and winter in six fuel distribution stations in Karaj. Occupational exposure to BTEX was measured according to the NIOSH 1501 method. Cancer and non-cancer risk assessment were performed according to the United States Environmental Protection Agency (USEPA) method. Data were analyzed in SPSS software version 26. The average occupational exposure to benzene, toluene, ethyl benzene, and xylene during a work shift among all participants in summer and winter were 83.33 - 89.33, 202 - 210.66, 126.55 - 136.83, and 168.81 - 174.83 µg.m-3, respectively. The highest concentration of BTEX compounds was observed in Gas station in the center of the city. The mean carcinogenic risk value of benzene and ethylbenzene were 139×10-2 and 27×10-2, respectively. The highest carcinogenic risk value due to exposure to benzene and ethyl benzene was observed in Gas station in the center of the city. The mean non-carcinogenic and health risks of occupational exposure to benzene, toluene, ethyl benzene, and xylene were 173.79, 14.19, 3.61, and 12.87, respectively.  The findings demonstrated the values of carcinogenic and non-carcinogenic risk in the majority of participants were within the definitive and unacceptable risk levels. Therefore, corrective measures are necessary to protect the employees from the non-cancer and cancer risks.

Using ethanol in gasoline is considered one of the most significant goals in the 2030 agenda, which has been set a 15-year plan in order to achieve it since 2015. Appropriately, this project was planned for predicting the value of the most important engine parameters such as the equivalence air-fuel ratio (φ), fuel consumption (ṁf), and brake thermal efficiency nb. th, and brake-specific fuel consumption (BSFC) by regression models. According to the protocol of this project, first, the determined percentages of ethanol were added (0, 20, 40, 60, and 80%) to gasoline at different engine speeds (850, 1000, 2000, 3000, and 4000 rpm and the New European Driving Cycle test). After testing, calculating, mathematical programming, and fitting the regression models for the two SI-engine (TU5 and EF7) with different properties of engine design,12 regression equations have been determined for each of the ‘ (positive linear model), (ṁf) (negative linear model), nb.th (negative second-order polynomial model), and BSFC (positive second-order polynomial model), respectively. Clearly, these 48 regression equations with different line slopes will be able to predict the exact value of the ‘, (ṁf), nb.th, and BSFC for each concentration of ethanol at different engine speeds in order to help automotive industries for trend predicting them in other similar engines.

Persistent pollution of surface waters by hydrocarbon compounds is one of the foremost threats to limited global freshwater resources. This study analyzed the abundance, diversity and degradative capacities of hydrocarbon-utilizing bacteria in chronically polluted Kono River in the Nigerian Niger Delta in order to establish the bacterial drivers of ecological regeneration of the river after an oil spill. The study further aimed to develop a specialized bacterial consortium for application in bioremediation interventions. Bacillus, Pseudomonas and Enterobacter spp. were predominant out of the 82 isolates obtained. Klebsiella pneumoniae and two species of Enterobacter cloacae were identified as the most efficient hydrocarbon utilizers. The isolates were also confirmed as biosurfactant producers and possessed the alkB1 and nahAc genes for degradation of aliphatics and aromatics. E. cloacae-K11, K. pneumoniae-K05, E. cloacae-K12 and their consortium were able to degrade the total petroleum hydrocarbons and polycyclic aromatic hydrocarbons in batch systems by 59.37% – 96.06% and 68.40% – 92.46% respectively. K. pneumoniae-K05 showed the greatest petroleum degradation capacity of the three isolates but hydrocarbon degradation was most efficient with the bacterial consortium. The results obtained showed no significant differences at p≤0.05 between the degradation capacities of K. pneumoniae-K05 and the consortium for PAHs but a significant difference (p≤0.05) was seen with TPH degradation. A viable hydrocarbon degrading bacterial consortium was developed at the end of the study and it was concluded that the polluted river water displayed inherent potential for effective natural attenuation.

Researchers are interested in developing techniques to monitor, manage and predict the risks of gases and particles emitted from cement factories, which have a direct and negative impact on human health. Deep learning (DL) is a critical component of data mining, which further involves statistics and prediction. In this study, we developed a deep learning prediction model called the Deep Pollutant Prediction Model (DPPM). The data used for DPPM are separated into two types: observed data from a pollution monitoring station of the Institute of Mental Health in Ahmedabad City, India coded as (GJ001), to validate the model and simulated data generated using the Gaussian Plume Model for the hypothetical receptor (Laylan District, Kirkuk, Iraq) to predict the pollution that emitted from Kirkuk Cement Plant 5 km apart from the study area. The findings indicated that the DPPM has high efficiency in both Allahabad and Laylan stations, with more closed results for the data in the Laylan station, which is based on the Gaussian equation simulated data. Since the highest loss function value in the Laylan is 0.0221 of the CaO parameter, while it is 4.466 of the AQI parameter for the Allahabad Station, and the smallest loss function value in the Laylan is equal to 0.0041of both Fe2O3 and MgO parameters, it corresponds to 0.038 of Xylene for the Allahabad station. The results of the study proved that data continuity and non-volatility produce excellent outcomes for DPPM.