sulfur dioxide aqi modeling by artificial neural network in tehran between 2007 and 2013
نویسندگان
چکیده
background: air pollution and concerns about health impacts have been raised in metropolitan cities like tehran. trend and prediction of air pollutants can show the effectiveness of strategies for the management and control of air pollution. artificial neural network (ann) technique is widely used as a reliable method for modeling of air pollutants in urban areas. therefore, the aim of current study was to evaluate the trend of sulfur dioxide (so2) air quality index (aqi) in tehran using ann. methods: the dataset of so2 concentration and aqi in tehran between 2007 and 2013 for 2550 days were obtained from air quality monitoring fix stations belonging to the department of environment (doe). these data were used as input for the ann and nonlinear autoregressive (nar) model using matlab (r2014a) software. results: daily and annual mean concentration of so2 except 2008 (0.037 ppm) was less than the epa standard (0.14 and 0.03 ppm, respectively). trend of so2 aqi showed the variation of so2 during different days, but the study declined overtime and the predicted trend is higher than the actual trend. conclusion: the trend of so2 aqi in this study, despite daily fluctuations in ambient air of tehran over the period of the study have decreased and the difference between the predicted and actual trends can be related to various factors, such as change in management and control of so2 emissions strategy and lack of effective parameters in so2 emissions in predicting model.
منابع مشابه
Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013
Background: Air pollution and concerns about health impacts have been raised in metropolitan cities like Tehran. Trend and prediction of air pollutants can show the effectiveness of strategies for the management and control of air pollution. Artificial neural network (ANN) technique is widely used as a reliable method for modeling of air pollutants in urban areas. Therefore, the aim of current ...
متن کاملscour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network
today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...
Modeling of sulfur dioxide emissions in Ahvaz City, southwest of Iran during 2013
Sulfur dioxide has two important sources in the atmosphere and this is why most of scientists believe in a geographic split in the globe. Power plants, major emitter of SO2, are located in north hemisphere such as in Russia, China, Canada and the USA. In south hemisphere, phytoplankton produces a massive amount of dimethyl sulfide (DMS) and dimethyl disulfide (DMDS). Then these types of reduced...
متن کاملModeling and Simulation of Water Softening by Nanofiltration Using Artificial Neural Network
An artificial neural network has been used to determine the volume flux and rejections of Ca2+ , Na+ and Cl¯, as a function of transmembrane pressure and concentrations of Ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. The feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidde...
متن کاملDetecting Depression in Elderly People by Using Artificial Neural Network
Introduction: The possibility of depression is common in the elderly. Novel technologies allow us to monitor people related to depression. Hence, a model was provided to detect depression in elderly based on artificial neural network (ANN). Methods: The present study is an applied descriptive-survey research. Forty elderly people were randomly selected from the Elderly Care Center in Gonbad Ka...
متن کاملSolubility Prediction of Drugs in Supercritical Carbon Dioxide Using Artificial Neural Network
The descriptors computed by HyperChem® software were employed to represent the solubility of 40 drug molecules in supercritical carbon dioxide using an artificial neural network with the architecture of 15-4-1. The accuracy of the proposed method was evaluated by computing average of absolute error (AE) of calculated and experimental logarithm of solubilities. The AE (±SD) of data sets was 0.4 ...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
environmental health engineering and managementجلد ۲، شماره ۴، صفحات ۱۷۳-۱۷۸
کلمات کلیدی
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023