Ali Jalalian Moghadam; Mohammad Nourmohammadi; Ramezan Mirzaei; Jamshid Jamali
Abstract
Background and purpose: Volatile organic compounds (VOCs), such as toluene, are among the major air pollutants due to their persistence, hydrophobicity, and adverse effects on human health and the environment. This study aimed to evaluate the feasibility of using cutting oil as an organic phase in two-phase ...
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Background and purpose: Volatile organic compounds (VOCs), such as toluene, are among the major air pollutants due to their persistence, hydrophobicity, and adverse effects on human health and the environment. This study aimed to evaluate the feasibility of using cutting oil as an organic phase in two-phase bioscrubbers to enhance the biological removal of toluene.Materials and Methods: First, the biocompatibility of cutting oil at concentrations of 10%, 20%, and 30% was assessed using activated sludge cultures in media containing the oil. Subsequently, the toluene absorption performance in the presence of the organic phase was evaluated under various flow rates by measuring the mass transfer to the liquid phase. Bioaerosol concentrations at the scrubber outlet were also determined using NIOSH method 0800.Results: Results indicated that at a 10% concentration, cutting oil had no adverse effects on microbial growth and led to a 5.7-fold increase in toluene absorption compared to pure water. At higher concentrations, microbial growth declined significantly, and the overall mass transfer coefficient (KLa) decreased. The lowest bioaerosol emission was also observed at the 10% oil concentration.Conclusion: In conclusion, adding cutting oil as an organic phase in bioscrubbers, particularly at lower concentrations, can enhance the absorption capacity of toluene, extend its residence time in the liquid phase, and provide more favorable conditions for biodegradation. Given its low cost, availability, and favorable physical properties, cutting oil appears to be a promising and efficient alternative for application in two-phase bioscrubber systems for VOC removal. Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Amir Zarei; Sirvan Zarei; Seyyed Erfan Momenpour; Mohamad Reza Golanbaryan; Abass Solimanpor
Abstract
Background and purpose: Solar radiation is one of the important and influential parameters in agricultural hydrology and meteorology studies. According to the purpose of the research, which is to predict the amount of solar radiation, the type of research can be considered user . Materials and Methods: ...
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Background and purpose: Solar radiation is one of the important and influential parameters in agricultural hydrology and meteorology studies. According to the purpose of the research, which is to predict the amount of solar radiation, the type of research can be considered user . Materials and Methods: The statistical year in question is for one year (from the first of Farudin 1402 to the end of Farudin 1403) and the data used are the daily data of Tehran synoptic station. It includes the maximum and minimum temperature, maximum and minimum relative humidity and maximum wind speed (5 cases) and PM10 data (PM2.5 from Tehran city pollution measurement station). Correlation relationship between solar radiation as a dependent variable and other parameters (independent components) was done through Pearson's correlation coefficient in SPSS.26 software.Results: and the results showed that solar radiation with minimum and maximum temperature, maximum wind speed, and PM10 have a direct correlation with maximum and minimum relative humidity, wind direction and PM2.5 have an inverse correlation. The estimation of solar radiation has been done using multiple linear regression and artificial neural network (MLP) methods, with a coefficient of determination (R2) of nearly 87% that performs better than the other two methods. (Enter and Stepwise)Conclusion: Also, the comparison of the training and test period of the model shows that the ANN method with the mean square error (MSE) of megajoules per square meter per day, the absolute value of the error is 82.02 and the explanation coefficient is 88.0 in the test phase and 1.87 megajoules respectively. 35.86 and 81.0 Joules per square centimeter per day in the training phase of the models have performed better than other models in estimating solar radiation. Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Reza Peykanpour fard; Mahsa Tamjidi; Manije Mahdi abadi; Ferial Farasat
Abstract
Background and Objective: Today, many people are exposed to air pollution, a phenomenon that threatens human health in various ways. Air pollution is defined as the presence of a pollutant at concentrations exceeding permissible limits in the natural environment. The primary objective of this study is ...
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Background and Objective: Today, many people are exposed to air pollution, a phenomenon that threatens human health in various ways. Air pollution is defined as the presence of a pollutant at concentrations exceeding permissible limits in the natural environment. The primary objective of this study is to examine the effect of stack height on the concentration of pollutants emitted from an industrial stack.Materials and Methods: In this descriptive-analytical study, the AERMOD software was employed to model the dispersion of SO₂ emissions at four different stack heights (63, 97, 103, and 110 meters). The input data included: technical specifications of the stack (flow rate, temperature, and exit gas velocity), hourly meteorological data from the Shiraz synoptic station (2021-2022), and a 90-meter resolution digital elevation model of the study area. The modeling domain was divided into a 300×300 meter grid within a 10 km radius of the stack.Results: The findings demonstrated that increasing the stack height from 63 to 110 meters (a %42 increase) resulted in a %32 reduction in SO₂ concentrations. At the 63-meter height, the maximum 1-hour SO₂ concentration reached 263 μg/m³ (exceeding the 196 μg/m³ standard limit), while at 110 meters, this value decreased to 178 μg/m³. The minimum effective height for compliance with air quality standards was determined to be 103 meters. Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/Conclusion: This research demonstrates that modifying stack height significantly affects both the concentration and spatial distribution of pollutants. Future studies should investigate the combined effects of other physical stack parameters to develop more comprehensive emission control strategies.
Narges Atarodi; Mitra Mohammadi; Zahra Hajali oghli
Abstract
Background and Purpose: This study investigates and predicts the concentration of PM10 pollutant in Mashhad using simple statistical techniques and also the LSTM model with a focus on traffic restrictions before and during the COVID-19 pandemic.Materials and Methods: First, data related to the concentration ...
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Background and Purpose: This study investigates and predicts the concentration of PM10 pollutant in Mashhad using simple statistical techniques and also the LSTM model with a focus on traffic restrictions before and during the COVID-19 pandemic.Materials and Methods: First, data related to the concentration of PM10 pollutant were collected from air pollution monitoring stations in Mashhad. Then, using a paired t-test, the statistical changes in PM10 concentration before and during the quarantine period were investigated. Also, the LSTM machine learning model was used to predict the effect of quarantine on PM10 levels during this period, which included data processing, model training, and evaluation of prediction accuracy using various criteria. Results: The results of the paired t-test showed a 16% decrease in the average concentration of PM10 during the quarantine period, which is specified by a mean difference of 4.397 μg/m3. Although this decrease was not statistically significant, the relative improvement in air quality during this time period is remarkable. Also, in the study of the 210-day period before and after COVID-19, the results showed the significant impact of quarantine measures on air quality, and these changes did not occur randomly. In the next step, the LSTM machine learning model was used to predict the effect of quarantine on PM10 levels. The value of the coefficient of determination (0.8) indicates a strong correlation between the predictions and the actual concentration of PM10. Conclusion: The values of mean square error (3.01) and mean absolute error (2.56) also indicate the high accuracy of the LSTM model predictions and their proximity to the actual values. These results demonstrate that the LSTM model has been able to predict the concentration of PM10 pollutant with high accuracy and confirms its high efficiency in analyzing time series data. Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Soheyl Eskandari; Paria Miri; Mahsa Karimi Srazameleh; Saeed Aghebat-Bekheir; Bahare Mohamadi
Abstract
Background and Objective: It is important to investigate unnecessary heavy metals such as lead and cadmium, which can be harmful to humans even at low concentrations. The toxicity of these metals is influenced by factors such as concentration, exposure, and individual characteristics, and may lead to ...
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Background and Objective: It is important to investigate unnecessary heavy metals such as lead and cadmium, which can be harmful to humans even at low concentrations. The toxicity of these metals is influenced by factors such as concentration, exposure, and individual characteristics, and may lead to neurological, renal, hormonal, and cancer damage. Given the environmental and health concerns associated with heavy metals in polymer containers used in food packaging, it is crucial to investigate and determine the concentrations of these metals. This study aimed to measure the concentrations of lead and cadmium in 35 polymer container samples from 9 different brands.Materials & Methods: Polymer containers were digested using food simulant solvents under controlled conditions, and heavy metals were extracted. The concentration of total heavy metals (lead and cadmium) after chemical digestion was measured with an atomic absorption spectrometer (AAS), and the data were analyzed with SPSS version 26.Results: Statistical analysis of data obtained from the concentration of total lead and cadmium in samples of different polymer containers, where the concentration of total cadmium and lead metals in the samples varied between 0.013 and 0.9305 ppm. The lowest value was observed in two-layer polystyrene containers, and the highest value in single-layer polyethylene containers. The analysis method was validated with international guidelines, and the LOD and LOQ values were in the ranges of 0.0009–0.0176 and 0.0018–0.0582 μg/L, respectively.Conclusion: The total concentration of cadmium and lead in all the samples was within the range of international standards. However, continuous monitoring and production by national standards are essential to reduce the risk of food contamination and ensure public health safety. Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
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