Document Type : علمی - پژوهشی


1 MSC Student of Dep. of Geographic Information System, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology

2 Prof., Dep. of Geographic Information System, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology


The models of the association between land use and air pollution have wide applications in urban studies, but the land-use role and its different parameters effective on the variability of air pollution concentration in various hours can be used for more accurate Spatio-temporal prediction of pollution. In this study, to make Spatio-temporal prediction of CO pollutants using hourly land-use regression (LUR), the effective parameters on Spatio-temporal variation of this pollutant are investigated during the day and night. The hourly data are collected from 21 air pollution monitoring stations for the summer  in Tehran and the predictive parameters including density and distance from different variables such as road network, vegetation, elevation, and different land-use are generated in the geographic information system (GIS). A general model and 8 hourly models are created at 3 am, 6 am, 9 am, 12 noon, 3 pm, 6 pm and 12 midnight. The coefficient of determination (R2) of the created model is equal to 0.7898, and it shows that the model has an  outstanding performance. By analyzing the generated hourly models, because of the differences in the parameters used in these models, it is denoted that both temporal variability and spatial variability play effective roles in forming the models during the day and night. The coefficient of determination (R2) of the hourly models ranges from 0.51 to 0.92 in which the lowest one and the highest one are related to the noon hours’ models and the nocturnal hours’ models, respectively. The parameters including local access roads and official/commercial areas have the most effect on increasing CO pollutants during the day and night, and the parameters including green space, sports, and medical centers lead to the locations with lower CO pollutants concentration.


سجادیان، ن.، 1394، پیش‌بینی آلودگی هوای ناشی از حمل‌ونقل شهری کلان‌شهر تهران با بهره‌گیری از تلفیق GIS با مدل LUR و شبکة عصبی مصنوعی، سپهر، دورة 24، شمارة 95.
متکان، ع.ا.، شکیبا، ع.، پورعلی، س.ح.، بهارلو، ا.، 1388، تعیین تغییرات مکانی و زمانی آلودگی‌های منواکسید کربن و ذرات معلق با استفاده از تکنیک‌های GIS در شهر تهران، سنجش از دور و GIS ایران، سال اول، شمارة 1.‎
محمدی، ا.، قرخلو، م.، زیاری، ک.، پوراحمد، ا.، 1397، استفاده از مدل رگرسیون کاربری اراضی (LUR) برای پیش‌بینی آلاینده‌های NO2، CO و PM10 (مطالعة موردی: شهر تهران)، پژوهش‌های جغرافیای انسانی، دورة 50، شمارة 1.
Adam-Poupart, A., Brand, A., Fournier, M., Jerrett, M. & Smargiassi, A., 2014, Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches,Environmental health perspectives, 122(9), P. 970.
Aguilera, I., Sunyer, J., Fernández-Patier, R., Esteban, R.G., Bomboi, T. & Alvarez-Pedrerol, M., 2007, Using Land-use Regression Modeling to Estimate Exposure to VOCs in a Cohort of Pregnant Women, Epidemiology, 18(5), PP. S42-S43.
Amini, H., Schindler, C., Hosseini, V., Yunesian, M. & Künzli, N., 2017, Land Use Regression Models for Alkyl-benzenes in a Middle Eastern Megacity: Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR),Environmental science & technology, 51(15), PP. 8481-8490.
Amini, H., Taghavi-Shahri, S.M., Henderson, S.B., Naddafi, K., Nabizadeh, R. & Yunesian, M., 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, 488, PP. 343-353.
Beelen, R., Hoek, G., Vienneau, D., Eeftens, M., Dimakopoulou, K., Pedeli, X., Tsai, M.-Y., Künzli, N., Schikowski, T. & Marcon, A., 2013, Development of NO2 and NOx land Use Regression Models for Estimating Air Pollution Exposure in 36 Study Areas in Europe–the ESCAPE Project,Atmospheric environment, 72, PP. 10-23.
Briggs, D., 2005, The Role of GIS: Coping with Space (and Time) in Air Pollution Exposure Assessment,Journal of Toxicology and Environmental Health, Part A, 68(13-14), PP. 1243-1261.
Briggs, D.J., Collins, S., Elliott, P., Fischer, P., Kingham, S., Lebret, E., Pryl, K., Van Reeuwijk, H., Smallbone, K. & Van Der Veen, A., 1997, Mapping Urban Air Pollution Using GIS: A Regression-Based Approach,International Journal of Geographical Information Science, 11(7), PP. 699-718.
Chen, S., Yang, J., Qin, P. & Xu, J., 2016, Truth Behind Chinese Superstition: Non-linear Effects of Vehicle Traffic on Urban Air Quality in Beijing (No. EfD DP 16-16).
Eeftens, M., Beelen, R., de Hoogh, K., Bellander, T., Cesaroni, G., Cirach, M. & Dimakopoulou, K., 2012 Development of Land Use Regression Models for PM2. 5, PM2. 5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project,Environmental Science & Technology, 46(20), PP. 11195-11205.
Gilbert, N.L., Goldberg, M.S., Beckerman, B., Brook, J.R. & Jerrett, M., 2005, Assessing Spatial Variability of Ambient Nitrogen Dioxide in Montreal, Canada, with a Land-use Regression Model,Journal of the Air & Waste Management Association, 55(8), PP. 1059-1063.
Gulliver, J., de Hoogh, K., Hansell, A. & Vienneau, D., 2013, Development and Back-extrapolation of NO2 Land Use Regression Models for Historic Exposure Assessment in Great Britain,Environmental Science & Technology, 47(14), PP. 7804-7811.
Hassanpour Matikolaei, S.A.H., Jamshidi, H. & Samimi, A., 2017, Characterizing the Effect of Traffic Density on Ambient CO, NO2, and PM2. 5 in Tehran, Iran: An Hourly Land-use Regression Model,Transportation Letters, PP. 1-11.
Hoek, G., Beelen, R., De Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P. & Briggs, D., 2008, A Review of Land-use Regression Models to Assess Spatial Variation of Outdoor Air Pollution,Atmospheric Environment, 42(33), PP. 7561-7578.
Holmes, N.S. & Morawska, L., 2006, A Review of Dispersion Modelling and its Application to the Dispersion of Particles: An Overview of Different Dispersion Models Available,Atmos-pheric Environment, 40(30), PP. 5902-5928.
Hystad, P., Setton, E., Cervantes, A., Poplawski, K., Deschenes, S., Brauer, M., van Donkelaar, A., Lamsal, L., Martin, R. & Jerrett, M., 2011, Creating National Air Pollution Models for Population Exposure Assessment in Canada,Environmental health perspectives, 119(8), PP. 1123.
Jerrett, M., Arain, A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., Morrison, J. & Giovis, C., 2005, A Review and Evaluation of Intraurban Air Pollution Exposure Models,Journal of Exposure Science and Environmental Epidemiology, 15(2), PP. 185.
Johnson, M., MacNeill, M., Grgicak-Mannion, A., Nethery, E., Xu, X., Dales, R., Rasmussen, P. & Wheeler, A., 2013, Development of Temporally Refined Land-use Regression Models Predicting Daily Household-level Air Pollution in a Panel Study of Lung Functionamong Asthmatic Children,Journal of Exposure Science and Environmental Epidemiology, 23(3), PP. 259.
Knowlton, K., Rosenthal, J.E., Hogrefe, C., Lynn, B., Gaffin, S., Goldberg, R. & Kinney, P.L., 2004, Assessing Ozone-related Health Impacts under a Changing Climate,Environmental Health Perspectives, 112(15), PP. 1557.
Liao, D., Peuquet, D.J., Duan, Y., Whitsel, E.A., Dou, J., Smith, R.L. ... & Heiss, G., 2006, GIS Approaches for the Estimation of Residential-level Ambient PM Concentrations, Environ-mental Health Perspectives, 114(9), PP. 1374.
Liu, H.-L. & Shen, Y.-S., 2014, The Impact Of Green Space Changes on Air Pollution and Microclimates: A Case Study of the Taipei Metropolitan Area,Sustainability, 6(12), PP. 8827-8855. Marshall, J.D., Nethery, E. & Brauer, M., 2008, Within-urban Variability in Ambient Air Pollution: Comparison of Estimation Methods,Atmospheric Environment, 42(6), PP. 1359-1369.
Ryan, P.H. & LeMasters, G.K., 2007, A Review of Land-use Regression Models for Characterizing Intraurban Air Pollution Exposure,Inhalation Toxicology, 19(sup1), PP. 127-133.
Saraswat, A., Apte, J.S., Kandlikar, M., Brauer, M., Henderson, S.B. & Marshall, J.D., 2013, Spatiotemporal Land Use Regression Models of Fine, Ultrafine, and Black Carbon Particulate Matter in New Delhi, India,Environmental Science & Technology, 47(22), PP. 12903-12911.
Wu, J., Li, J., Peng, J., Li, W., Xu, G. & Dong, C., 2015, Applying Land Use Regression Model to Estimate Spatial Variation of PM2. 5 in Beijing, China,Environmental Science and Pollution Research, 22(9), PP. 7045-7061.
Wu, J., Wilhelm, M., Chung, J. & Ritz, B., 2011, Comparing Exposure Assessment Methods for Traffic-Related Air Pollution in an Adverse Pregnancy Outcome Study,Environmental Research, 111(5), PP. 685-692.