Selective Sulfur Dioxide Absorption from Simulated Flue Gas Using Various Aqueous Alkali Solutions in a Polypropylene Hollow Fiber Membrane Contactor: Removal Efficiency and Use of Sulfur Dioxide

Biochemical engineering for elemental sulfur from flue gases through multi-enzymatic based approaches - A review

Flue gases are the gases which are produced from industries related to chemical manufacturing, petrol refineries, power plants and ore processing plants. Along with other pollutants, sulfur present in the flue gas is detrimental to the environment. Therefore, environmentalists are concerned about its removal and recovery of resources from flue gases due to its activation ability in the atmosphere to transform into toxic substances. This review is aimed at a critical assessment of the techniques developed for resource recovery from flue gases. The manuscript discusses various bioreactors used in resource recovery such as hollow fibre membrane reactor, rotating biological contractor, sequential batch reactor, fluidized bed reactor, entrapped cell bioreactor and hybrid reactors. In conclusion, this manuscript provides a comprehensive analysis of the potential of thermotolerant and thermophilic microbes in sulfur removal. Additionally, it evaluates the efficacy of a multi-enzyme engineered bioreactor in this process. Furthermore, the study introduces a groundbreaking sustainable model for elemental sulfur recovery, offering promising prospects for environmentally-friendly and economically viable sulfur removal techniques in various industrial applications.

Preparation of an innovative series of respiratory nano-filters using polystyrene fibrous films containing KCC-1 dendrimer and ZnO nanostructures for environmental assessment of SO2, NO2 and CO2

Air pollution has become a major challenge that threatens human health. The use of respiratory filters is one of the proposed solutions. In this study, using polystyrene (PS) fibers and various nanomaterials, improved respiratory filters were fabricated to remove air pollutants. In this context, ZnO nanoparticles (ZnO NPs) integrated into dendritic structures of KCC-1 silica were used to improve the filters' ability to absorb pollutants. For the first time, the removal of gasses by modified filters with a novel polymeric nanocomposite (PS/ZnO-KCC-1) stabilized on the surface of respiratory filters was investigated. Moreover, two different methods including stabilized- and solution-based techniques were used to prepare the filters with different amounts of ZnO NPs and their efficiency was evaluated. All synthesized nanocomposites and developed filters were characterized by FT-IR, FESEM, TGA and XRD methods. The successful stabilization of nanostructures on the fibers was proved and the performance of the fibers was investigated with some tests, such as pressure drop and removal of suspended particles and CO2 (89%), NO2 (86%), and SO2 (83%) gases. PS/KCC-1-ZnO (5%) has better performance than other prepared fibers. The results showed that the removal of suspended particles in the filter containing ZnO and KCC-1 (M5) nanostructures was improved by 18% compared to the filter consisting of polystyrene fibers. The pressure drop increased with the addition of nanostructures and reached 180 Pa in the M5 filter. The filter containing ZnO NPs showed antibacterial activity against Staphylococcus (S.) aureus and Escherichia (E.) coli as Gram-positive and Gram-negative model bacteria using the Agar disk-diffusion method. Based on the results, the use of improved respiratory filters is recommended as an effective solution for combating air pollution and protecting human health.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.