Abstract
Traffic-related urban air pollution is a pressing concern in Tehran, Iran, with severe health implications. This study aimed to create a dynamic spatiotemporal model to predict changes in urban traffic-related air pollution in Tehran using a land use regression (LUR) model. Two datasets were employed to model the spatiotemporal distribution of gaseous traffic-related pollutants—sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO). The first dataset incorporated remote sensing data, including land surface temperature (LST), the normalized difference vegetation index (NDVI), apparent thermal inertia (ATI), population density, altitude, land use, road density, road length, and distance to highways. The second dataset excluded remote sensing data, relying solely on population density, altitude, land use, road density, road length, and distance to highways. The LUR model was constructed using both datasets at three different buffer distances: 250, 500, and 1000 m. Evaluation based on the R2 index revealed that the 1000-m buffer distance achieved the highest accuracy. Without remote sensing data, R2 values for CO, NO2, and SO2 pollutants were respectively spring (0.77, 0.79, 0.51), summer (0.59, 0.71, 0.59), and winter (0.41, 0.52, 0.59). With remote sensing data, R2 values were respectively spring (0.82, 0.84, 0.74), summer (0.72, 0.87, 0.62), and winter (0.53, 0.59, 0.72). Incorporating remote sensing data notably improved the accuracy of modeling CO, NO2, and SO2 during all three seasons. The central, southern, and southeastern regions of Tehran consistently exhibited the highest pollutant concentrations throughout the year, while the northern areas maintained comparatively better air quality.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Agarwal R, Aggarwal SG (2023) A year-round study of ambient gaseous pollutants, their atmospheric chemistry and role in secondary particle formation at an urban site in Delhi. Atmos Environ 295:119557. https://doi.org/10.1016/j.atmosenv.2022.119557
Alvarez-Mendoza CI, Teodoro AC, Torres N, Vivanco V (2019) Assessment of remote sensing data to model PM10 estimation in cities with a low number of air quality stations: a case of study in Quito. Ecuador Environ 6(7):85. https://doi.org/10.3390/environments6070085
Amini H, Taghavi-Shahri SM, Henderson SB et al (2014) Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran Iran. Sci Total Environ 488:343–353. https://doi.org/10.1016/j.scitotenv.2014.04.106
Amini H, Nhung NTT, Schindler C et al (2019) Short-term associations between daily mortality and ambient particulate matter, nitrogen dioxide, and the air quality index in a Middle Eastern megacity. Environ Pollut 254:113121. https://doi.org/10.1016/j.envpol.2019.113121
Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area Iran. Remote Sens Environ 113(12):2606–2617. https://doi.org/10.1016/j.rse.2009.07.021
Atash F (2007) The deterioration of urban environments in developing countries: Mitigating the air pollution crisis in Tehran Iran. Cities 24(6):399–409. https://doi.org/10.1016/j.cities.2007.04.001
Beelen R, Hoek G, Vienneau D et al (2013) Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe-The ESCAPE project. Atmos Environ 72:10–23. https://doi.org/10.1016/j.atmosenv.2013.02.037
Bertazzon S, Johnson M, Eccles K, Kaplan GG (2015) Accounting for spatial effects in land use regression for urban air pollution modeling. Spat Spatiotemporal Epidemiol 14:9–21. https://doi.org/10.1016/j.sste.2015.06.002
Briggs DJ, Collins S, Elliott P et al (1997) Mapping urban air pollution using GIS: a regression-based approach. Int J Geogr Inf Sci 11(7):699–718. https://doi.org/10.1080/136588197242158
Chen J, Wang B, Huang S, Song M (2020) The influence of increased population density in China on air pollution. Sci Total Environ 735:139456. https://doi.org/10.1016/j.scitotenv.2020.139456
Dirgawati M, Barnes R, Wheeler AJ et al (2015) Development of land use regression models for predicting exposure to NO2 and NOx in metropolitan Perth, Western Australia. Environ Model Softw 74:258–267. https://doi.org/10.1016/j.envsoft.2015.07.008
Dong J, Ma R, Cai P et al (2021) Effect of sample number and location on accuracy of land use regression model in NO2 prediction. Atmos Environ 246:118057. https://doi.org/10.1016/j.atmosenv.2020.118057
Dons E, Van Poppel M, Panis LI et al (2014) Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution. Sci Total Environ 476:378–386. https://doi.org/10.1016/j.scitotenv.2014.01.025
El Kenawy AM, Lopez-Moreno JI, McCabe MF, Domínguez-Castro F, Peña-Angulo D, Gaber IM., …, Vicente-Serrano SM (2021) The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East. Environ Pollut 288:117802. https://doi.org/10.1016/j.envpol.2021.117802
Feizizadeh B, Blaschke T (2013) Examining urban heat island relations to land use and air pollution: multiple endmember spectral mixture analysis for thermal remote sensing. IEEE J Sel Top Appl Earth Obs Remote Sens 6(3):1749–1756. https://doi.org/10.1109/JSTARS.2013.2263425
Fritsch M, Behm S (2021) Agglomeration and infrastructure effects in land use regression models for air pollution–specification, estimation, and interpretations. Atmos Environ 253:118337. https://doi.org/10.1016/j.atmosenv.2021.118337
Fuladlu K, Altan H (2021) Examining land surface temperature and relations with the major air pollutants: a remote sensing research in case of Tehran. Urban Clim 39:100958. https://doi.org/10.1016/j.uclim.2021.100958
Gonzales M, Myers O, Smith L et al (2012) Evaluation of land use regression models for NO2 in El Paso, Texas, USA. Sci Total Environ 432:135–142. https://doi.org/10.1016/j.scitotenv.2012.05.062
Han X, Naeher LP (2006) A review of traffic-related air pollution exposure assessment studies in the developing world. Environ Int 32(1):106–120. https://doi.org/10.1016/j.envint.2005.05.020
Hassanpour Matikolaei SAH, Jamshidi H, Samimi A (2019) Characterizing the effect of traffic density on ambient CO, NO2, and PM2.5 in Tehran, Iran: an hourly land-use regression model. Transp Lett 11(8):436–446. https://doi.org/10.1080/19427867.2017.1385201
Henderson SB, Beckerman B, Jerrett M, Brauer M (2007) Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol 41(7):2422–2428. https://doi.org/10.1021/es0606780
Hoek G, Beelen R, De Hoogh K et al (2008) A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42(33):7561–7578. https://doi.org/10.1016/j.atmosenv.2008.05.057
Huang D, He B, Wei L et al (2021) Impact of land cover on air pollution at different spatial scales in the vicinity of metropolitan areas. Ecol Indic 132:108313. https://doi.org/10.1016/j.ecolind.2021.108313
Jerrett M, Arain A, Kanaroglou P (2005) A review and evaluation of intraurban air pollution exposure models. J Expo Sci Environ Epidemiol 15(2):185–204. https://doi.org/10.1038/sj.jea.7500388
Jiménez-Muñoz JC, Sobrino JA (2008) Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors. IEEE Geosci Remote Sens Lett 5(4):806–809. https://doi.org/10.1109/LGRS.2008.2001636
Jiménez-Muñoz JC, Sobrino JA, Skoković D (2014) Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett 11(10):1840–1843. https://doi.org/10.1109/LGRS.2014.2312032
Karimi B, Shokrinezhad B (2021) Spatial variation of ambient PM2.5 and PM10 in the industrial city of Arak, Iran: a land-use regression. Atmos Pollut Res 12(12):101235. https://doi.org/10.1016/j.apr.2021.101235
Klompmaker JO, Janssen N, Andersen ZJ et al (2021) Comparison of associations between mortality and air pollution exposure estimated with a hybrid, a land-use regression and a dispersion model. Environ Int 146:106306. https://doi.org/10.1016/j.envint.2020.106306
Knibbs LD, Coorey CP, Bechle MJ et al (2018) Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia. Environ Res 163:16–25. https://doi.org/10.1016/j.envres.2018.01.046
Kramer M (2013) Our built and natural environments : a technical review of the interactions between land use, transportation, and environmental quality. United States Environmental Protection Agency. Washington, D.C. https://rosap.ntl.bts.gov/view/dot/16166
Lee C, Martin RV, Van Donkelaar A et al (2011) SO2 emissions and lifetimes: estimates from inverse modeling using in situ and global, space‐based (SCIAMACHY and OMI) observations. J Geophys Res Atmos 116(D6). https://doi.org/10.1029/2010JD014758
Lenney MP, Woodcock CE, Collins JB, Hamdi H (1996) The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sens Environ 56(1):8–20. https://doi.org/10.1016/0034-4257(95)00152-2
Li H, Zhang J, Wen B et al (2022) Spatial-temporal distribution and variation of NO2 and its sources and chemical sinks in Shanxi Province China. Atmosphere 13(7):1096. https://doi.org/10.3390/atmos13071096
Liang S (2005) Quantitative remote sensing of land surfaces (Vol. 30). John Wiley & Sons.
Lin C, Labzovskii LD, Mak HWL, Fung JC, Lau AK, Kenea ST (2020) Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring. Atmos Environ 227:117410. https://doi.org/10.1016/j.atmosenv.2020.117410
Luminati O, de Campos BLDA, Flückiger B, Brentani A, Röösli M, Fink G, de Hoogh K (2021) Land use regression modelling of NO2 in Sao Paulo. Brazil Environ Pollut 289:117832. https://doi.org/10.1016/j.envpol.2021.117832
Luo D, Kuang T, Chen YX et al (2021) Air pollution and pregnancy outcomes based on exposure evaluation using a land use regression model: a systematic review. Taiwan J Obstet Gynecol 60(2):193–215. https://doi.org/10.1016/j.tjog.2021.01.004
Malik MN, Khan HH, Chofreh AG et al (2019) Investigating students’ sustainability awareness and the curriculum of technology education in Pakistan. Sustainability 11(9):2651. https://doi.org/10.3390/su11092651
Matthaios VN, Kramer LJ, Sommariva R et al (2019) Investigation of vehicle cold start primary NO2 emissions inferred from ambient monitoring data in the UK and their implications for urban air quality. Atmos Environ 199:402–414. https://doi.org/10.1016/j.atmosenv.2018.11.031
Meng X, Chen L, Cai J et al (2015) A land use regression model for estimating the NO2 concentration in Shanghai, China. Environ Res 137:308–315. https://doi.org/10.1016/j.envres.2015.01.003
Miri M, Derakhshan Z, Allahabadi A, Ahmadi E, Conti GO, Ferrante M, Aval HE (2016) Mortality and morbidity due to exposure to outdoor air pollution in Mashhad metropolis Iran the AirQ Model Approach. Environ Res 151:451–457. https://doi.org/10.1016/j.envres.2016.07.039
Miri M, Ghassou Y, Dovlatabadi A et al (2019) Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model. Ecotoxicol Environ Saf 174:137–145. https://doi.org/10.1016/j.ecoenv.2019.02.070
Morley DW, Gulliver J (2018) A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment. Environ Model Softw 105:17–23. https://doi.org/10.1016/j.envsoft.2018.03.030
Motlagh SHB, Pons O, Hosseini SA (2021) Sustainability model to assess the suitability of green roof alternatives for urban air pollution reduction applied in Tehran. Build Environ 194:107683. https://doi.org/10.1016/j.buildenv.2021.107683
Mozumder C, Reddy KV, Pratap D (2013) Air pollution modeling from remotely sensed data using regression techniques. J Indian Soc Remote Sens 41(2):269–277. https://doi.org/10.1007/s12524-012-0235-2
Price JC (1985) On the analysis of thermal infrared imagery: the limited utility of apparent thermal inertia. Remote Sens Environ 18(1):59–73. https://doi.org/10.1016/0034-4257(85)90038-0
Rajeshwari A, Mani ND (2014) Estimation of land surface temperature of Dindigul district using Landsat 8 data. Int J Res Eng Technol 3(5):122–126. https://doi.org/10.15623/ijret.2014.0305025
Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM (2021) Effects of air pollution in spatio-temporal modeling of asthma-prone areas using a machine learning model. Environ Res 200:111344. https://doi.org/10.1016/j.envres.2021.111344
Ryan PH, LeMasters GK (2007) A review of land-use regression models for characterizing intraurban air pollution exposure. Inhal Toxicol 19(sup1):127–133. https://doi.org/10.1080/08958370701495998
Rybarczyk Y, Zalakeviciute R (2018) Machine learning approaches for outdoor air quality modelling: a systematic review. Appl Sci 8(12):2570. https://doi.org/10.3390/app8122570
Ryu J, Park C, Jeon SW (2019) Mapping and statistical analysis of NO2 concentration for local government air quality regulation. Sustainability 11(14):3809. https://doi.org/10.3390/su11143809
Sahsuvaroglu T, Arain A, Kanaroglou P (2006) A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario Canada. J Air Waste Manag Assoc 56(8):1059–1069. https://doi.org/10.1080/10473289.2006.10464542
Sanchez M, Ambros A, Milà C et al (2018) Development of land-use regression models for fine particles and black carbon in peri-urban South India. Sci Total Environ 634:77–86. https://doi.org/10.1016/j.scitotenv.2018.03.308
Saucy A, Röösli M, Künzli N et al (2018) Land use regression modelling of outdoor NO2 and PM25 concentrations in three low income areas in the western cape province, South Africa. Int J Environ Res Public Health 15(7):1452. https://doi.org/10.3390/ijerph15071452
Saunders LJ, Russell RA, Crabb DP (2012) The coefficient of determination: what determines a useful R2 statistic? Investig Ophthalmol Vis Sci 53(11):6830–6832. https://doi.org/10.1167/iovs.12-11354
Shi Y, Lau AKH, Ng E et al (2021) A multiscale land use regression approach for estimating intraurban spatial variability of PM2.5 concentration by integrating multisource datasets. Int J Environ Res Public Health 19(1):321. https://doi.org/10.3390/ijerph19010321
Shogrkhodaei SZ, Razavi-Termeh SV, Fathnia A (2021) Spatio-temporal modeling of pm2.5 risk mapping using three machine learning algorithms. Environ Pollut 289:117859. https://doi.org/10.1016/j.envpol.2021.117859
Sobrino JA, Li ZL, Stoll MP, Becker F (1997) Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. Oceanogr Lit Rev 2(44):162–163. https://doi.org/10.1080/01431169608948760
Son Y, Osornio-Vargas ÁR, O’Neill MS et al (2018) Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters. Sci Total Environ 639:40–48. https://doi.org/10.1016/j.scitotenv.2018.05.144
Taghavi-Shahri SM, Fassò A, Mahaki B, Amini H (2020) Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization. Atmos Environ 224:117202. https://doi.org/10.1016/j.atmosenv.2019.117202
Tian Y, Yao X, Chen L (2019) Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics. Comput Environ Urban Syst 75:35–48. https://doi.org/10.1016/j.compenvurbsys.2019.01.003
Tularam H, Ramsay LF, Muttoo S (2021) A hybrid air pollution/land use regression model for predicting air pollution concentrations in Durban South Africa. Environ Pollut 274:116513. https://doi.org/10.1016/j.envpol.2021.116513
Wang R, Henderson SB, Sbihi H et al (2013) Temporal stability of land use regression models for traffic-related air pollution. Atmos Environ 64:312–319. https://doi.org/10.1016/j.atmosenv.2012.09.056
Weissert LF, Salmond JA, Miskell G et al (2018) Development of a microscale land use regression model for predicting NO2 concentrations at a heavy trafficked suburban area in Auckland, NZ. Sci Total Environ 619:112–119. https://doi.org/10.1016/j.scitotenv.2017.11.028
Weng Q, Yang S (2006) Urban air pollution patterns, land use, and thermal landscape: an examination of the linkage using GIS. Environ Monit Assess 117(1):463–489. https://doi.org/10.1007/s10661-006-0888-9
Wu CD, Chen YC, Pan WC et al (2017) Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability. Environ Pollut 224:148–157. https://doi.org/10.1016/j.envpol.2017.01.074
Xiang W, Yuan J, Wu Y, Luo H, Xiao C, Zhong N, … & He Y (2022) Working principle and application of photocatalytic optical fibers for the degradation and conversion of gaseous pollutants. Chin Chem Lett 33(8):3632–3640. https://doi.org/10.1016/j.cclet.2021.11.074
Xu H, Bechle MJ, Wang M et al (2019) National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci Total Environ 655:423–433. https://doi.org/10.1016/j.scitotenv.2018.11.125
Xu H, Wang X, Tian Y, Tian J, Zeng Y, Guo Y, … & Feng G (2022) Short-term exposure to gaseous air pollutants and daily hospitalizations for acute upper and lower respiratory infections among children from 25 cities in China. Environ Res 212:113493. https://doi.org/10.1016/j.envres.2022.113493
Zeng L, Hang J, Wang X, Shao M (2022) Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: a land use regression study in Shenzhen China. J Environ Sci 114:485–502. https://doi.org/10.1016/j.jes.2021.12.002
Zhang Z, Wang J, Hart JE et al (2018) National scale spatiotemporal land-use regression model for PM2.5, PM10 and NO2 concentration in China. Atmos Environ 192:48–54. https://doi.org/10.1016/j.atmosenv.2018.08.046
Zheng S, Zhou X, Singh RP, Wu Y, Ye Y, Wu C (2017) The spatiotemporal distribution of air pollutants and their relationship with land-use patterns in Hangzhou city China. Atmosphere 8(6):110. https://doi.org/10.3390/atmos8060110
Zheng C, Zhao C, Li Y et al (2018) Spatial and temporal distribution of NO2 and SO2 in Inner Mongolia urban agglomeration obtained from satellite remote sensing and ground observations. Atmos Environ 188:50–59. https://doi.org/10.1016/j.atmosenv.2018.06.029
Zheng S, Zhang C, Wu X (2022) Estimating PM2. 5 concentrations using an improved land use regression model in Zhejiang China. Atmosphere 13(8):1273. https://doi.org/10.3390/atmos13081273
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Seyedeh Zeinab Shogrkhodaei: data creation; S.Z Shogrkhodaei: formal analysis; Amanollah Fathnia: investigation; Seyed Vahid Razavi-Termeh: methodology; S.Z Shogrkhodaei: project administration; S.V.R Termeh and Amanollah Fathnia: resources; S.Z Shogrkhodaei, S.V.R Termeh, and Sirous Hashemi Dareh Badami: software; A. Fathnia: Supervision; A.F: validation; S.Z Shogrkhodaei, and Khalifa M. Al-Kindi: writing original draft; Seyedeh Zeinab Shogrkhodaei, A. Fathnia and S.V.R Termeh: writing—review and editing: Khalifa M. Al-Kindi, A. Fathnia and S.V.R Termeh, Sirous Hashemi Dareh Badami.
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Shogrkhodaei, S.Z., Fathnia, A., Razavi-Termeh, S.V. et al. Application of dynamic spatiotemporal modeling to predict urban traffic–related air pollution changes. Air Qual Atmos Health 17, 439–454 (2024). https://doi.org/10.1007/s11869-023-01456-4
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DOI: https://doi.org/10.1007/s11869-023-01456-4