Vol 5 No 4 (2020): Autumn 2020

Original Research

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    Introduction: Air pollution is a major problem in Isfahan, one of the major cities of Iran. A large number of jewelry making workshops are located in Isfahan, yet there is insufficient information about their pollutants emission rates. The aim of this study is to determine the emission factors of nitrogen oxides and volatile organic compounds (VOCs) in Isfahan’s jewelry making workshops.

    Methods: In the first step of this study, some jewelry making workshops were visited to find nitrogen oxides and VOCs emission sources. It was revealed that the only possible source of nitrogen oxides and VOCs in these workshops was use of the oxy fuel welding system used to melt gold. In the second step, a set of experiments was conducted to determine the emission factors of nitrogen oxides and VOCs while working with the oxy fuel welding system.

    Results: The results of this study showed that the emission factor of nitrogen oxides in the oxy fuel welding system was 0.64 kg/kg consumed natural gas. It was also found that no VOCs were emitted while working with the oxy fuel welding system, since sufficient pure oxygen was produced in this system. Interview with managers of some jewelry making workshops showed that the average natural gas consumption in each workshop was 22 kg. Therefore, each jewelry making workshop in Isfahan emitted nearly 14.08 kg of nitrogen oxide per month.

    Conclusion: It is revealed that in 2018, 81100.8 kg nitrogen oxides were emitted from jewelry making workshops into Isfahan’s atmosphere.


  • XML | PDF | downloads: 82 | views: 91 | pages: 209-222

    Introduction: Bioaerosols consist of aerosols which are biologically originated and can be present ubiquitously in different environments, including the indoor air of hospitals. The objective of this study was to survey the bioaerosol type and density in various environments of four governmental educational hospitals in Urmia, Iran, namely the intensive care unit (ICU), operating room, the internal medicine room, the infectious diseases room, the infectious diseases corridor, and ambient air.

    Materials and methods: Sampling was performed during summer and winter of 2019 at four different day-times using passive (sedimentation plate) and active methods (an Andersen one-stage viable impactor and Quick Take-30 sampling instrument) and by counting plates containing a bacterial and fungus-selective medium.

    Results: The results revealed that the highest microbial bioaerosol load was related to the infectious diseases corridor (100 and 150 CFU/m3 for total bacterial and fungal load, respectively). The highest bacterial and fungal density was observed in the afternoon at 17-18; and the concentration of bioaerosols was higher in summer than winter. A comparison of indoor and outdoor bacterial loads showed that the indoor bacterial concentration mean (49.1±23.8 CFU/m3) was higher than the outdoor value (47.1±21.5 CFU/m3), and the indoor levels of fungal contamination (83.3±31.9 CFU/m3) were significantly lower than outdoor values (182.5±48.0 CFU/m3). The predominantly isolated bacteria were Staphylococcus (95%) spp, and the main isolated fungi belong to the genera Aspergillus (50%) and Penicillium (32%).

    Conclusion: The results of this study can be useful in developing indoor air microbial quality guidelines in hospitals, which has not been done so far.

  • XML | PDF | downloads: 50 | views: 76 | pages: 223-232

    Introduction: Radiation gives tremendous benefit to mankind but unnecessary radiation may pose harm to worker and public. The purpose of the study
    is to continuous indoor radiation monitoring of Atomic Energy Centre Dhaka (AECD) campus to minimize the radiological risk on worker and public
    health in and around the campus.

    Materials and methods: Continuous indoor radiation monitoring was conducted in the AECD campus from November 2018-April 2019 using the
    Thermoluminescent dosimeters. The excess life-time cancer risk on worker
    and public health were estimated based on the continuous indoor radiation
    monitoring data.

    Results: The annual effective doses to the worker and public from indoor radiation were ranged from 0.28±0.11 mSv to 0.67±0.25 mSv and the mean was
    found to be 0.43±0.10 mSv. The excess life-time cancer risk (ELCR) on the
    radiation worker & public health were estimated based on the annual effective dose and ranged from 1.13 Χ 10-3 to 2.65 Χ 10-3 with an average of 1.72
    Χ 10-3.The average annual effective dose and ELCR on worker and public
    health were lower than those of the worldwide average values.

    Conclusion: The radiological hazard on worker and public health in and
    around the AECD campus is not significant because those values are lower
    than the recommended values of the international commission on radiological
    protection. Monitoring of these indoor places would help in keeping a record
    of safe working practices during the handling of the radioactive substances
    and radiation generating equipments in a radiological facility.

  • XML | PDF | downloads: 42 | views: 52 | pages: 233-242

    Introduction: Photocatalytic oxidation of gaseous pollutants in differential
    reactors is simulated using computational fluid dynamics.

    Materials and methods: The momentum equation and pollutant transport
    are solved by using ANSYS Fluent. The SIMPLE algorithm is used to treat
    the pressure-velocity coupling. The laminar flow and low Reynolds k−ε models are used to describe turbulence.

    Results: Velocity field distribution and degradation efficiency of different
    models at various flow rates were obtained and compared with the experimental data. The simulation results of degradation efficiency under different
    models are basically consistent.

    Conclusion: Although low Reynolds k-ε models have better simulation results for high inlet flow rates, in terms of computation complexity, laminar
    flow is recommended for simulation.

Review Article(s)

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    The ambient air pollutants that have a major role in causing respiratory diseases are particulate matter, sulfur dioxide, nitrogen dioxide, ozone, carbon
    monoxide, and heavy metals. In addition, respiratory infections, divided
    into upper respiratory tract and lower respiratory tract infection, are most
    commonly caused by viral agents. Thus, in light of the current COVID-19
    pandemic, this review has focused on the association between exposure to
    general air pollution including each of the mentioned air pollutants and viral
    respiratory infections. The gathered evidence from the reviewed studies in
    this article showed that most of these air pollutants have a positive correlation
    with mortality, severity, transmission, inflammation, and incidence of different viral respiratory infections. Whereas, some studies found contradictory
    results such as non-significant and negative connections between exposure
    to air pollutants and viral respiratory infections, which are further discussed
    in this text. Therefore, following the SARS-CoV-2 outbreak, these contradictions in the reported correlation between air pollution and different aspects
    of viral respiratory infections must be thoroughly investigated and cleared.

  • XML | PDF | downloads: 47 | views: 98 | pages: 259-272

    The issue of pollution in urban cities is a major problem these days especiallyin cities like the New Delhi is detected with more number of toxic gases in air, whic h has deduced the air quality of New Delhi. Thus, predictive analytics play a significant role in predicting the future instances of air quality based on the historical data. Forecasting the air quality of these cities is mandatory to overcome its consequences. Several machines learning algorithm is widely used these days to predict the future instances. Such as random forest, support vector machine, regression, classification, and so on. Main pollutants which present in the air are PM2.5, PM10, CO, NO2, SO2and O3 . In this paper we have focused mainly on data set of New Delhi for predicting ambient air pollution and quality using several machines learning algorithm.