Academic Journal
The data on the dispersion modeling of traffic-related PM10 and CO emissions using CALINE3; A case study in Tehran, Iran
| Title: | The data on the dispersion modeling of traffic-related PM10 and CO emissions using CALINE3; A case study in Tehran, Iran |
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| Authors: | Mohammad Hadi Dehghani, Sima Jarahzadeh, Mostafa Hadei, Nabiollah Mansouri, Yousef Rashidi, Mahmood Yousefi |
| Source: | Data in Brief, Vol 19, Iss , Pp 2284-2290 (2018) |
| Publisher Information: | Elsevier, 2018. |
| Publication Year: | 2018 |
| Collection: | LCC:Computer applications to medicine. Medical informatics LCC:Science (General) |
| Subject Terms: | Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390 |
| More Details: | CALINE3 model predicts the dispersion of pollutants released from roadways in the receptor places at a certain radius from the source. This model was used to evaluate the dispersion of particulate matter < 2.5 µm (PM10) and carbon monoxide (CO) emitted from Yadegar-e-Emam Expressway (YEE) as one of the most congested highways in Tehran. The hourly concentrations of PM10 and CO, and the count and speed of vehicles were obtained from Tehran׳s Air Quality Control Company (TAQCC). Wind speed and direction, the height of mixing zone, air temperature, relative humidity, and stability class were acquired from IRAN Meteorological Organization (IRIMO). The emission factors (EF) of vehicles were acquired from those proposed for UK. The dispersion of PM10 and CO was predicted over the nearby area, and the modeled concentrations were estimated for a specific point, where an air quality monitoring station was working. The major portion of PM10 and CO released by vehicles in YEE was dispersed to the east. The comparison between the modeled and measured concentrations revealed that CALINE3 underestimates the concentrations of PM10 and CO by about 50%. Keywords: Air pollution, CALINE3 model, Particulate matter, Carbon monoxide, Yadegar-e Emam Expressway |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2352-3409 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2352340918307923; https://doaj.org/toc/2352-3409 |
| DOI: | 10.1016/j.dib.2018.07.019 |
| Access URL: | https://doaj.org/article/ff4fb0a76b744d308e47119ef13f60c5 |
| Accession Number: | edsdoj.ff4fb0a76b744d308e47119ef13f60c5 |
| Database: | Directory of Open Access Journals |
| ISSN: | 23523409 |
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| DOI: | 10.1016/j.dib.2018.07.019 |
| Published in: | Data in Brief |
| Language: | English |