Repository of Research and Investigative Information

Repository of Research and Investigative Information

Kurdistan University of Medical Sciences

Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran

(2014) Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran. Science of the Total Environment.

Full text not available from this repository.

Official URL:


The Middle Eastern city of Tehran, Iran has poor air quality compared with cities of similar size in Europe and North America. Spatial annual and seasonal patterns of SO2 and PM10 concentrations were estimated using land use regression (LUR) methods applied to data from 21 air quality monitoring stations. A systematic algorithm for LUR model building was developed to select variables based on (1) consistency with a priori assumptions about the assumed directions of the effects, (2) a p-value of <0.1 for each predictor, (3) improvements to the leave-one-out cross-validation (LOOCV) R2, (4) a multicollinearity index called the variance inflation factor, and (5) a grouped (leave-25-out) cross-validation (GCV) for final model. In addition, several new predictive variables and variable types were explored. The annual mean concentrations of SO2 and PM10 across the stations were 38ppb and 100.8μg/m3, respectively. The R2 values ranged from 0.69 to 0.84 for SO2 models and from 0.62 to 0.67 for PM10 models. The LOOCV and GCV R2 values ranged, respectively, from 0.40 to 0.56 and 0.40 to 0.50 for the SO2 models; they were 0.48 to 0.57 and 0.50 to 0.55, respectively, for the PM10 models. There were clear differences between the SO2 and PM10 models, but the warmer and cooler season models were consistent with the annual models for both pollutants. Although there was limited similarity between the SO2 and PM10 predictive variables, measures of street density and proximity to airport or air cargo facilities were consistent across both pollutants. In 2010, the entire population of Tehran lived in areas where the World Health Organization guidelines for 24-hour mean SO2 (7ppb) and annual average PM10 (20μg/m3) were exceeded. © 2014 Elsevier B.V.

Item Type: Article
Keywords: Air quality; Geographic information systems; Land use; Pollution; Regression analysis; Sulfur, Air pollution exposures; Air quality monitoring stations; Land use regression; Land-use regression models; Leave-one-out cross-validation (LOOCV); Particulate Matter; Tehran; World Health Organization, Sulfur dioxide, sulfur dioxide, air quality; algorithm; annual variation; atmospheric pollution; concentration (composition); GIS; land use; particulate matter; pollution exposure; seasonal variation; spatial variation; sulfur dioxide, air monitoring; air pollutant; air quality; algorithm; article; concentration (parameters); Iran; land use; linear system; particulate matter; prediction; priority journal; regression analysis; seasonal variation; statistical model; validation study, Iran; Tehran Iran, Air pollution exposure modeling; Geographic Information Systems (GIS); Land use regression (LUR); Particulate matter; Sulfur dioxide; Tehran, Air Pollutants; Air Pollution; Environmental Monitoring; Iran; Models, Statistical; Particulate Matter; Seasons; Sulfur Dioxide
Page Range: pp. 343-353
Journal or Publication Title: Science of the Total Environment
Volume: 488-48
Number: 1
Publisher: Elsevier
Identification Number: 10.1016/j.scitotenv.2014.04.106
ISSN: 00489697
Depositing User: مهندس جمال محمودپور

Actions (login required)

View Item View Item