Biofilter aquaponic system for nutrients removal from fresh market wastewater

Aquaponics is a significant wastewater treatment system which refers to the combination of conventional aquaculture (raising aquatic organism) with hydroponics (cultivating plants in water) in a symbiotic environment. This system has a high ability in removing nutrients compared to conventional methods because it is a natural and environmentally friendly system (aquaponics). The current chapter aimed to review the possible application of aquaponics system to treat fresh market wastewater with the intention to highlight the mechanism of phytoremediation occurs in aquaponic system. The literature revealed that aquaponic system was able to remove nutrients in terms of nitrogen and phosphorus

Accumulation of heavy metals in the leaves of different tree species and its association with the levels of atmospheric PM(2.5)-bond heavy metals in Isfahan

This study aimed to biomonitor air pollution by measuring heavy metals (HMs) accumulation levels in the leaves of common urban trees, Cupressus arizonica Greene, Melia azedarach L., Morus alba L. and Buxus colchica in different regions of Isfahan. Their association with the levels of PM(2.5) and PM(2.5)-bond HMs was also investigated. PM(2.5) were collected on a glass-fiber filter and measured by gravimetric method. The HM contents of the PM(2.5) and tree leaves were extracted and analyzed by ICP-OES. The average PM(2.5) concentrations in ambient air of all areas varied from 52.34 to 103.96 mug/m(3). The mean HMs levels in the leaves were in the following orderZn(31.2) > Cu(11.04) > Pb(4.38) > Ni(4.01) > Cr(3.03) > Co(0.61) > Cd(0.04) (mug/g). The highest level of HMs was detected in the leaves of Morus alba L., followed by Buxus colchica, Melia azedarach L. and Cupressus arizonica Greene. There was a significant correlation between the amounts of Pb and Cu in tree leaves and those in ambient PM(2.5) (p value </= 0.05). In conclusion, tree leaves can be used as a suitable bioindicator in the evaluation of air pollution. Morus alba L. compared to the other species can be confidently used for green space development. Isfahan is one of the most populated, industrialized cities in Iran experiencing serious air pollution. The currently operated air pollution monitoring stations in the city do not measure atmospheric heavy metals, a hazardous component of PM(2.5). To assess air pollution and level of exposure, biomonitor species such as tree leaves are the best tool due to their availability and low cost. Tree leaves can absorb and retain air pollutants. In this study, we found a good correlation between the concentration of heavy metals especially Pb and Cu in the leaves of commonly used tree species and their concentration in the airborne PM(2.5). Also, the results revealed that among the tree species, Morus alba L. retains more heavy metals followed by Buxus colchica, Melia azedarach L., and Cupressus arizonica Greene, which can be used for green space development

Investigating the Knowledge, Attitude, and Performance of the Health Workers of Khorramabad City, Iran, Regarding Indoor Air Pollution and Their Correlation with Demographic Factors

Background: Indoor air quality is a critical factor influencing the health and well-being of individuals in society, as polluted indoor air can lead to various diseases. Consequently, this study examined the awareness, attitudes, and performance of health workers in Khorramabad City, Iran, concerning indoor air pollution, as well as the correlation of these factors with demographic variables. Methods: This descriptive-analytical cross-sectional study was conducted in 2021 using a researcher-developed questionnaire to assess the knowledge, attitudes, and performance of health workers in the health centers of Khorramabad City. A total of 149 individuals from the Khorramabad health center participated in the study, and after receiving education, 140 of them agreed to complete the questionnaire. The collected data were analyzed using independent t-tests, analysis of variance (ANOVA), and Pearson's correlation coefficient via SPSS software. Findings: Health workers' performance regarding indoor air quality had the highest score of 60.50, while the scores for attitude and knowledge were 51.85 and 17.82, respectively. There was no statistically significant difference in the scores of knowledge, attitude, and performance between men and women (P > 0.05). The level of education had a significant relationship with the average scores of knowledge and performance. Consequently, the highest scores in knowledge, attitude, and performance were observed among individuals with a bachelor's degree or higher. In contrast, no significant relationship was found between the scores of knowledge, attitude, and performance with age or work experience. Conclusion: Given the limited awareness among health workers regarding indoor air pollution, coupled with their significant role in disseminating health information to the community, it is essential to conduct training courses on this topic to enhance the knowledge of health workers. © 2025, Isfahan University of Medical Sciences(IUMS). All rights reserved

Spatio-temporal variations of asbestos fibres levels in ambient air of a densely populated and industrialized city of Iran

Asbestos fibre is one of the hazardous pollutants that may present in urban air. The aim of this study was to determine the concentration of asbestos fibres in the ambient air of urban areas of Isfahan, second industrialised and densely populated city of Iran, and to evaluate their spatial-temporal variation. Sampling was carried out from eleven points covering traffic areas of the Isfahan city during cold and warm seasons (winter and summer) in 2015. Scanning Electron Microscope (SEM) coupled to an energy dispersive x-ray (EDX) system was utilised to count and identify the asbestos fibres. Spatial mapping was carried out using the inverse distance weighting (IDW) method. Seasonal geometry meansof the concentration of airborne asbestos fibres in the studied regions were 14.48 ± 5 and 9.34 ± 4.90 SEM f/L in winter and summer, respectively, which was higher than the WHO guideline (2.2 SEM f/L‏).‏There was a significant correlation between the concentration of asbestos fibres and atmospheric temperature and humidity (p < 0.05). Also, the spatial distribution maps of asbestos fibres showed that the eastern area of Isfahan was more polluted than the western areas. Heavy traffic showed major contribution in the asbestos fibre emissions. Therefore, some effective course of actions such as traffic management, industrial replacement should be taken to regulate airborne asbestos fibres emission. © 2020 Informa UK Limited, trading as Taylor & Francis Group

Influence of meteorological parameters and PM2.5 on the level of culturable airborne bacteria and fungi in Abadan, Iran

In recent years, monitoring of airborne bacteria and fungi concentrations has obtained increasing universal attraction not only for influences on ecological balance but also for evaluating their public health consequences. In this study, we aimed to investigate culturable airborne bacteria and fungi levels in different sites of Abadan, and their association with meteorological parameters and PM2.5 levels. Abadan is one of the most industrialized cities in the southwest of Iran where over the current decade has experienced lots of dust storm episodes. In total, 400 air samples were collected in 6 months (autumn and winter) using a single-stage viable Andersen cascade impactor for sampling airborne bacteria and fungi and portable DustTrak Aerosol Monitor 8520 for measuring PM2.5 concentrations and meteorological parameters. Microbial concentrations showed a significant difference between various sites over the study period with averages of 569.57 +/- 312.64 and 482.73 +/- 242.86 CFU/M-3 for bacteria and fungi, respectively. The air temperature had a significant effect on the concentration of both airborne bacteria and fungi. A significant positive correlation between relative humidity and fungi but no correlation between relative humidity and bacteria concentrations were observed. The average airborne PM2.5 concentrations of all sites among the study period was 93.24 +/- 116.72 mu g/m(3). The atmospheric bacterial and fungal communities were strongly positively correlated with the ambient PM2.5 level. The levels of airborne bacteria and fungi along with PM2.5 in the air of the city were relatively higher than the recommended levels. Therefore, the best course of action is needed to control emission sources. Further studies are also needed to evaluate the clinical analysis of the health effects of exposure to these pollutants

Indoor radon measurement in buildings of a university campus in central Iran and estimation of its effective dose and health risk assessment

Indoor radon is a serious health concern and contributes about 10 of deaths from lung cancer in the USA and Europe. In this study, radon and thoron levels of 20 multi-floor buildings on the campus of Isfahan University of Medical Sciences were measured in cold and hot seasons of a year. SARAD- RTM1688 radon and thoron monitor was used for measurement. The annual effective dose of radon exposure was also estimated for residences on the campus. The results showed that radon concentration was below the WHO guideline (100 Bq m(- 3)) in most of the buildings. The ranges of radon were from 3 +/- 10 to 322 +/- 15 Bq m(- 3) in winter and from below the detectable level to 145 +/- 8 Bq m(- 3) in summer. Mostly, the radon concentration in the basement or ground floors was higher than upper floors, however, exceptions were observed in some locations. For thoron, no special trends were observed, and in the majority of buildings, its concentration was below the detectable level. However, in a few locations besides radon, thoron was also measured at a high level during both seasons. The average annual effective dose via radon exposure was estimated to be 0.261 +/- 0.339 mSv y(- 1). The mean excess lung cancer risk (ELCR) was estimated to be 0.10. It was concluded that indoor air ventilation, buildings' flooring and construction materials, along with the geological structure of the ground could be the factors influencing the radon concentration inside the buildings. Thus, some applicable radon prevention and mitigation techniques were suggested

Modelling the phytoremediation of formaldehyde from indoor air by Chamaedorea Elegans using artificial intelligence, genetic algorithm and response surface methodology

Recently, indoor air quality has become a great concern for human health. Volatile organic compounds (VOCs), are the most common pollutants in the indoor air. Some VOCs such as formaldehyde are toxic and carcinogenic. A lot of researches have proved that potted plants through phytoremediation can help to reduce these pollutants, but modelling on these systems is not developed adequately and without creating the appropriate model, there will be no ability to predict the performance of phytoremediation. In this study, Formaldehyde removal from indoor air by Chamaedorea Elegans was modelled through Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), and optimized by genetic algorithm (GA). Here, the formaldehyde concentration, leaf surface area, light intensity and relative humidity were considered as input variables. The experimental design was conducted using full factorial design method. The RSM results showed that the second-order nonlinear equation was in high accordance with the experiments. A Multilayer Perceptron ANN was developed and network performance was evaluated with several training algorithms, in which, the Levenberg Marquardt (LM) algorithm showed the best performance. Finally, the optimum value of the variables was determined by GA. The results indicated that the ANN model exhibits a higher degree of delicacy and accuracy compared to the RSM

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

BACKGROUND: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19. METHODS: A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19. RESULTS: In 750 confirmed patients, Common symptoms were: fever () >37.5 °C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model. CONCLUSION: The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection