You are hereHome | Assessment and prediction of benzene concentrations in a street canyon using a variety of models, in the means of environmental management purposes
Assessment and prediction of benzene concentrations in a street canyon using a variety of models, in the means of environmental management purposesView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000689</doi_batch_id><timestamp>1713511296</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 3</title></titles><publication_date><month>11</month><year>2024</year></publication_date><journal_volume><volume>8</volume></journal_volume><issue>3</issue></journal_issue><journal_article><titles><title>Assessment and prediction of benzene concentrations in a street canyon using a variety of models, in the means of environmental management purposes</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>Urban air quality nowadays is one of the major environmental issues, due to its impact to various health problems, caused by the daily exposure of the population in dangerous air pollutants. The highest levels of air pollutants are usually observed in street canyons, emitted by urban traffic. To understand the way that pollutants dispersed in a street canyon environment, various modeling techniques are used. Both the accuracy of the predictions and the quickness of the calculations, are significant factors to adopt a modeling technique. In the present study, we used benzene as an indicator of traffic pollution. Two different modeling techniques, an artificial neural network (ANN) and a semi empirical deterministic model (DET) are used, to predict benzene concentrations in a street canyon environment. The ANN was based on a training procedure using measurements collected in a specific street canyon (benzene concentrations, traffic density, vehicle’s type distribution), while the DET model was based on road traffic emission rate, wind speed and direction, and the geometrical characteristics of the road. After the validation of models, their response to preselected “what if” scenarios was attempted. Although both models produced very good results, given the limited amount of data available, the ANN succeeded slightly better than DET in predicting benzene concentrations. On the contrary, ANN presents lower response in predicting the effect of significant changes in traffic flow patterns on benzene concentrations. The results from the simulations indicate that the ANN is a promising technique for benzene modeling in an urban environment, since DET seems to be ideal for environmental management purposes.</p></abstract><publication_date><month>04</month><day>29</day><year>2013</year></publication_date><pages><first_page>265</first_page><last_page>271</last_page></pages><publisher_item><identifier id_type="pii">374</identifier></publisher_item><doi_data><doi>10.30955/gnj.000374</doi><timestamp>1713511296</timestamp><resource>https://journal.gnest.org/publication/374</resource></doi_data></journal_article></journal></body></doi_batch> </div>
