Correlation of air pollutants with land use and traffic measures in Tehran, Iran: A preliminary statistical analysis for land use regression modeling

• Main Authors: Hassan Amini, Seyed-Mahmood Taghavi-Shahri, Kazem Naddafi, Ramin Nabizadeh, Masud Yunesian
• Format: Article
• Language: English
• Published: Kurdistan University of Medical Sciences 2013-01-01
• Series: Journal of Advances in Environmental Health Research
• Subjects: land use regression; land use types; traffic measures; particulate matter; sulfur dioxide; nitrogen dioxide
• Online Access: http://jaehr.muk.ac.ir/article_40118_49020c3b4e386a1b1d66037e30fefe00.pdf

Land use regression (LUR) models have been globally used to estimate long-term air pollution exposures. The present study aimed to analyze the association of different land use types and traffic measures with air pollutants in Tehran, Iran, as part of the future development of LUR models. Data of the particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) were extracted from 23 Tehran’s air quality monitors for 2010. The data of different land use types and traffic measures within the circular buffer radii 100 to 1000 meters and distances to them were calculated using Geographic Information System (GIS). Thereafter, the association of the mentioned air pollutants was evaluated with land use types and traffic measures. The annual average concentrations of PM10, SO2 and NO2 were 100.8 µg/m3, 38 parts per billion (ppb), and 53.2 ppb, respectively. The PM10 was associated with transportation area, other areas, and with distance to the other nearest land use (P < 0.05). The SO2 concentration was associated with official or commercial land use, and with other area land use (P < 0.05). Noteworthy, the NO2 concentration was associated with official or commercial land use, and with other areas (P < 0.05). The air pollutant concentrations was analyzed with different land use types and traffic measures as a preliminary work for development of LUR models in Tehran. It is hoped these analyses lead to successful development of LUR models in the near future.