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    Short-term associations between daily mortality and ambient particulate matter, nitrogen dioxide, and the air quality index in a Middle Eastern megacity

    , Article Environmental Pollution ; Volume 254 , 2019 ; 02697491 (ISSN) Amini, H ; Trang Nhung, N. T ; Schindler, C ; Yunesian, M ; Hosseini, V ; Shamsipour, M ; Hassanvand, M. S ; Mohammadi, Y ; Farzadfar, F ; Vicedo Cabrera, A. M ; Schwartz, J ; Henderson, S. B ; Künzli, N ; Sharif University of Technology
    Elsevier Ltd  2019
    Abstract
    There is limited evidence for short-term association between mortality and ambient air pollution in the Middle East and no study has evaluated exposure windows of about a month prior to death. We investigated all-cause non-accidental daily mortality and its association with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and the Air Quality Index (AQI) from March 2011 through March 2014 in the megacity of Tehran, Iran. Generalized additive quasi-Poisson models were used within a distributed lag linear modeling framework to estimate the cumulative effects of PM2.5, NO2, and the AQI up to a lag of 45 days. We further conducted multi-pollutant models and also stratified the analyses by... 

    Modelling and Prediction Air Polutants Level in Tehran Using Dynamic Neural Networks

    , M.Sc. Thesis Sharif University of Technology Khosravi, Neda (Author) ; Erhami, Mohammad (Supervisor)
    Abstract
    In parallel to the growing of population in Tehran metropolitan, air pollution in this city has become to a major problem. From which high concentration of pollutants have adverse effects on public health, accurate estimating and forecasting of concentrations for several days ahead, can provide the possibility to implement the management measures to reduce hazard and risks. Among the air pollution models, application of statistic models based on neural network in comparison to the traditional deterministic models are easier and less costly. In most studies, static models use a classical single MLP to predict one step ahead. For this purpose ANN models are required to estimate next value of... 

    Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

    , Article Environmental Science and Pollution Research ; Volume 20, Issue 7 , 2013 , Pages 4777-4789 ; 09441344 (ISSN) Arhami, M ; Kamali, N ; Rajabi, M. M ; Sharif University of Technology
    2013
    Abstract
    Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to... 

    Spatiotemporal description of BTEX volatile organic compounds in a middle eastern megacity: tehran study of exposure prediction for environmental health research (Tehran SEPEHR)

    , Article Environmental Pollution ; Volume 226 , 2017 , Pages 219-229 ; 02697491 (ISSN) Amini, H ; Hosseini, V ; Schindler, C ; Hassankhany, H ; Yunesian, M ; Henderson, S. B ; Künzli, N ; Sharif University of Technology
    Abstract
    The spatiotemporal variability of ambient volatile organic compounds (VOCs) in Tehran, Iran, is not well understood. Here we present the design, methods, and results of the Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR) on ambient concentrations of benzene, toluene, ethylbenzene, p-xylene, m-xylene, o-xylene (BTEX), and total BTEX. To date, this is the largest study of its kind in a low- and middle-income country and one of the largest globally. We measured BTEX concentrations at five reference sites and 174 distributed sites identified by a cluster analytic method. Samples were taken over 25 consecutive 2-weeks at five reference sites (to be used for... 

    Generic extraction medium: From highly polar to non-polar simultaneous determination

    , Article Analytica Chimica Acta ; Volume 1066 , 2019 , Pages 1-12 ; 00032670 (ISSN) Zeinali, S ; Khalilzadeh, M ; Bagheri, H ; Sharif University of Technology
    Elsevier B.V  2019
    Abstract
    Sample preparation for non-target analysis is challenging due to the difficulty in the extraction of polar and non-polar analytes simultaneously. Most commercial solid sorbents lack the proper comprehensiveness for extraction of analytes with different physiochemical properties. A possible key is the combination of hydrophobic polymer and hydrophilic surface functional groups in solid based extraction methods in order to generate the susceptibility for retaining both polar and non-polar analytes. To pursue this goal, in this study, four polar groups including [sbnd]NH 2 , [sbnd]NO 2 , [sbnd]COOH, and [sbnd]COCH 3 were chemically bound to Amberlite XAD-4 substrate in order to prepare a...