Long-term variability of particulate matter (PM2.5) in Tabriz using remote sensing data

Document Type : Full length article


1 1. Ph.D Student of Urban Climatology, Shahid Beheshti University, Tehran, Iran

2 Associate Professor of Climatology in Shahid Beheshti University, Tehran, Iran


Particulate matter (PM) or aerosols is a generic term used for a mixture of solid particles and liquid droplets in the atmosphere. Monitoring of natural (dust and volcanic ash) and anthropogenic particles (soot from biomass burning and industrial pollution) has attracted much attention in recent years. These particles can affect cloud properties, Earth's radiation budget, overall atmospheric circulation patterns, surface temperature, and precipitation. The emission of particulate matter and its associated stimuli comes from sources such as energy consumption and biomass burning in urban environments, and these two factors are commonly known as major contributors to PM2.5 concentrations in the atmosphere. However, surface PM2.5 concentrations are related to many factors such as meteorological conditions (eg temperature, wind speed, and relative humidity), land use type, population, and road networks, and so on. In recent years, many studies have been conducted using Aerosol Optical Depth (AOD) satellite measurements, AOD is a very important parameter for predicting Particulate matter (PM) at the Earth's surface in unmeasured locations or periods. AOD determines the amount of light absorbed or scattered by particulate matter. It is, therefore, an important parameter for predicting changes in PM although it may have deficiencies in this regard;
The purpose of this study was to estimate the particulate matter in the atmosphere of Tabriz city using a high spatial resolution weighted regression model (0.1-degree arc, 10 km apart). For estimating PM2.5 in Tabriz during the period 1998 to 2016 will be used combined data from SeaWiFS, MISR, and MODIS data.
Research Methodology
As mentioned earlier, meteorological conditions can substantially affect the relation of AOD-PM2.5. Aerosol concentration variability can change particle extinction properties and thus affect visibility. Visibility is an indicator of urban air quality, and particular matter is adversely associated with visibility impairment.
Geographically weighted regression (GWR) is a technique mainly intended to indicate where nonstationary is taking place on the map and that is where locally weighted regression coefficients move away from their global values. GWR is also a local form of linear regression used to model spatially varying relationships. Hourly data of particulate matter less than 2.5 µm (PM2.5) in air pollutants were obtained from Tabriz air quality control stations for 2016-2013. Aerosol Optical Depth (AOD) Data from Three Moderate Resolution Imaging Spectroradiometer (MODIS) of Terra and Aqua Satellites with Two Dark Target (DT) and Deep Blue (DB) Algorithms, Multi-angle Imaging SpectroRadiometer (MISR) and the GeoEye's OrbView-2 (AKA SeaStar) SeaWifs satellite sensor were used. The data were downloaded from the Ladsweb database of The National Aeronautics and Space Administration. Finally, non-parametric Mann-Kendall and Sens' slope estimator methods were used to investigate the trend and trend slope of the PM2.5 in Tabriz.
Results and discussion
Statistical indices of R2 and RMSE for PM2.5 showed that satellite data have high accuracy in estimating PM2.5. The R2 of Long-term time series data was 0.878 and RMSE was 1.330. The annual mean distribution of PM2.5 in Tabriz showed that PM2.5 was higher in western parts of the city than in other areas. Therefore, this area was identified as a polluted area in Tabriz. The PM2.5 in the city of Tabriz from 11.29 to 16.86 µ/m3. The southern and northern regions of the city showed the smallest PM2.5. The western and northwestern regions of the city, especially Tabriz's 4th, 6th and 7th districts, are the main areas of heavy industry, high density of roads and accelerated urban sprawl. This geographic environment has a significant impact on the emissions of primary greenhouse gases and secondary mineral pollutants. Unfavorable weather conditions on the planetary boundary layer (PBL), continuous inversion, and poor wind speed in winter can cause more pollutants to accumulate in a shallow layer. The annual mean long-term PM2.5 of Tabriz city was calculated to be 14.04 µ/m3. During the period of the first study period (1998-2002), PM2.5 was lower than the long average but from the second period onwards the particles with a steep slope in Tabriz have increased as in the third period (2012- 2008) and the fourth period (2016-2013) of particulate matter exceeded the long-term average value.
The highest Z-score of the Man-Kendall test was 2.69 in the western and northern parts of the city. Zones 1 and 2 also showed the lowest Z score of 1.75. The trend slope, which shows an increase in PM2.5 per head per year in Tabriz. According to the results, PM2.5 in Tabriz Variability between 0.250 to 0.25 µ/m3 (year-1). According to the results of Mann-Kendall test, Sense test also showed maximum gradient with 0.225 µ/m3 in western parts of Tabriz (4, 6 and 7 urban areas). The Z-score of the Man-Kendall test was 2.69 in the western and northern parts of the city. Zones 1 and 2 also showed the lowest Z score of 1.75. The trend slope, which shows an increase in PM2.5 per head per year in Tabriz.
In this study, Seawifs, MISR and MODIS satellite data were used to estimate PM2.5 using Geographic Weighted Regression (GWR). The results showed that the western and northwestern regions, along with significant parts of central Tabriz, have high PM2.5 values. In Tabriz, the lack of proper ventilation of wind speeds caused by urban buildings, along with the meteorological conditions of the local area, can be attributed to the high accumulation of particles. The least polluted areas were identified in the south and southeast of Tabriz. These areas are highly favorable for atmospheric dispersal due to low greenhouse gas emissions and meteorological conditions. The northern areas of Tabriz have a high PM2.5 due to the possible existence of farmland. However, economic and social factors such as industry, traffic, construction and burning of fossil fuels are direct sources of air pollution in Tabriz. But what is known is that socio-economic factors are less effective than natural factors in the city. Climatic conditions usually have a direct impact on PM2.5 in various aspects of wind-induced diffusion, precipitation of particulate matter, accumulation of particles in the air, and formation of secondary particles.

Keywords: PM2.5, Geographic Weighted Regression (GWR), Air quality control, Tabriz.


احمدی، م.؛ پریسا، چ. و داداشی رودباری، ع. (1397). مدل‏سازی روند بارش در منطقة غرب آسیا تحت واداشت دگرگونی‏های آب‏وهوایی، پژوهش‏های دانش زمین، 9(35): ۶۸-80.
احمدی، م. و داداشی رودباری، ع. (1398). توزیع زمانی- مکانی ذرات معلق (PM2.5) با رویکرد محیط زیست در غرب و جنوب ایران بر مبنای سنجنده‏های SeaWifs، MISR، و MODIS، محیط‏شناسی، 45(3): 379-394.
باباییان، ا.؛ رضایی‏پور، آ. و آهنگرزاده، ز. (1393). شبیه‏سازی نمایة آسایش اقلیمی در استان خراسان رضوی تحت سناریوهای تغییر اقلیم، مطالعات جغرافیایی مناطق خشک، 5(18): 95-112.
پهلوان، ا.؛ پهلوان، ر. و اسماعیلی، ع. (1393). برآورد غلظت آلاینده‏های PM10 و PM2.5 در کلان‏شهر تهران با استفاده از داده‏های سنجندة مودیس ماهواره‏های آکوا و ترا، نیوار، 38: 57-68.
حجازی، ع.؛ مباشری، م. و احمدیان ‏مرج، ا. (1391). تهیة نقشة توزیع مکانی ذرات معلق با قطر کمتر از دو نیم میکرومتر در هوای شهر تهران با استفاده از داده‏های سنجندة مودیس، نشریة تحقیقات کاربردی علوم جغرافیایی، 26: 161-178.
خوش‏سیما، م.؛ ثابت‏قدم، س. و علی‏اکبری بیدختی، ع. (1394). تخمین تمرکز ذرات معلق (PM10) در جو با استفاده از داده‏های سنجش‏ از دور ماهواره‏ای و زمین پایه و پراسنج‏های هواشناختی: کاربست شبکۀ عصبی مصنوعی، فیزیک زمین و فضا، 41: 499-510.
صفوی، ن.؛ موسوی، م.؛ دهقان‏زاده ریحانی، ر. و شاکری، م. (1395). پهنه‏بندی فصلی و مکانی شاخص کیفیت هوا و آلاینده‏های هوای محیطی شهر تبریز به کمک نرم‏افزار GIS و بررسی مشکلات اجرایی موجود، سلامت و بهداشت، ۷ (۲): ۱۵۸-۱۷۷.
Babaeian, A.; Rezaeipour, A. and Ahangarzadeh, Z. (2015). Simulation of Bio-Climatic Comfort Index over Khorasan Razavi under Climate Scenarios. Arid Regions Geographic Studies, 5(18): 95-112 (In Persian).
Chen, Z. H.; Cheng, S. Y.; Li, J. B.; Guo, X. R.; Wang, W. H. and Chen, D. S. (2008). Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China. Atmospheric Environment, 42(24): 6078-6087.
De Hoogh, K.; Héritier, H.; Stafoggia, M.; Künzli, N. and Kloog, I. (2018). Modelling daily PM2. 5 concentrations at high spatio-temporal resolution across Switzerland. Environmental Pollution, 233: 1147-1154.
Dominici, F.; Peng, R. D.; Bell, M. L.; Pham, L.; McDermott, A.; Zeger, S. L. and Samet, J. M. (2006). Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. Jama, 295(10): 1127-1134.
Friedl, M. A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A. and Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote sensing of Environment, 114(1): 168-182.
Gauderman, W. J.; Avol, E.; Gilliland, F.; Vora, H.; Thomas, D.; Berhane, K. ... and Margolis, H. (2004). The effect of air pollution on lung development from 10 to 18 years of age. New England Journal of Medicine, 351(11): 1057-1067.
GBD 2016 Risk Factors Collaborators (2017). Global, regional, and national comparative risk assessment of 84 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet, 390(10100): 1345-1422.
Gu, B.; Sutton, M. A.; Chang, S. X.; Ge, Y. and Chang, J. (2014). Agricultural ammonia emissions contribute to China's urban air pollution. Frontiers in Ecology and the Environment, 12(5): 265-266.
Guan, Q.; Cai, A.; Wang, F.; Yang, L.; Xu, C. and Liu, Z. (2017). Spatio-temporal variability of particulate matter in the key part of Gansu Province, Western China. Environmental pollution, 230: 189-198.
Gupta, P. and Christopher, S. A. (2009). Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. Journal of Geophysical Research: Atmospheres, 114(D20).
Hejazi, A.; Mobasheri, M. and Ahmadian, A. (2012). Preparation of Spatial Distribution Map of Particles matter 2.5 Micrometer in Tehran Air Using Modis Sensor Data. Journal of Geographical Sciences Applied Research, 26: 161-178 (In Persian).
Hu, X.; Waller, L. A.; Al-Hamdan, M. Z.; Crosson, W. L.; Estes Jr, M. G.; Estes, S. M. ... and Liu, Y. (2013). Estimating ground-level PM2. 5 concentrations in the southeastern US using geographically weighted regression. EnvironmentalResearch, 121: 1-10.
Jimenez, J. L.; Canagaratna, M. R.; Donahue, N. M.; Prevot, A. S. H.; Zhang, Q.; Kroll, J. H. ...  and Aiken, A. C. (2009). Evolution of organic aerosols in the atmosphere. Science, 326(5959): 1525-1529.
Kendall, M. G. (1955). Rank correlation methods.
Khoshsima, M.; Sabet Ghadam, S. and Aliakbari Bidokhti, A. (2015). Estimation of atmospheric particulate matter (PM10) concentration based on remote sensing measurements and meteorological parameters: application of artificial neural network. Journal of the Earth and Space Physics, 41(3): 499-510. doi: 10.22059/jesphys.2015.54528 (In Persian).
Li, T.; Shen, H.; Zeng, C.; Yuan, Q. and Zhang, L. (2017). Point-surface fusion of station measurements and satellite observations for mapping PM2. 5 distribution in China: Methods and assessment. Atmosphericenvironment, 152: 477-489.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245-259.
Nowak, D. J.; Hirabayashi, S.; Bodine, A. and Greenfield, E. (2014). Tree and forest effects on air quality and human health in the United States. Environmentalpollution, 193: 119-129.
Pahlavan, A.; Pahlavan, R. and Esmaeili, A. (2014). Estimating PM10 and PM2.5 in Tehran mega city using MODIS data of Terra and Aqua satellites. Nivar, 38(85-84): 57-68 (In Persian).
Philip, S.; Martin, R. V.; van Donkelaar, A.; Lo, J. W. H.; Wang, Y.; Chen, D. ... and Lu, Z. (2014). Global chemical composition of ambient fine particulate matter for exposure assessment. Environmental science & technology, 48(22): 13060-13068.
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379-1389.
Stafoggia, M.; Bellander, T.; Bucci, S.; Davoli, M.; de Hoogh, K.; De'Donato, F. ... and Scortichini, M. (2019). Estimation of daily PM10 and PM2. 5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environment international, 124: 170-179.
Theil, H. (1992). A rank-invariant method of linear and polynomial regression analysis. In Henri Theil’s Contributions to Economics and Econometrics (pp. 345-381). Springer Netherlands.
Van Donkelaar, A.; Martin, R. V.; Brauer, M.; Hsu, N. C.; Kahn, R. A.; Levy, R. C. ... and Winker, D. M. (2016). Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental science & technology, 50(7): 3762-3772.
Wang, J. and Christopher, S. A. (2003). Intercomparison between satellite‐derived aerosol optical thickness and PM2. 5 mass: Implications for air quality studies. Geophysical research letters, 30(21).
Wang, J.; Hu, Z.; Chen, Y.; Chen, Z. and Xu, S. (2013). Contamination characteristics and possible sources of PM10 and PM2. 5 in different functional areas of Shanghai, China. Atmospheric Environment, 68: 221-229.
Wang, S. H.; Lin, N. H.; Chou, M. D.; Tsay, S. C.; Welton, E. J.; Hsu, N. C. ... and Holben, B. N. (2010). Profiling transboundary aerosols over Taiwan and assessing their radiative effects. Journal of Geophysical Research: Atmospheres, 115(D7).
West, J. J.; Smith, S. J.; Silva, R. A.; Naik, V.; Zhang, Y.; Adelman, Z. ... and Lamarque, J. F. (2013). Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health. Nature climate change, 3(10): 885.
WHO (World Health Organization) (2018). Available at. https://www.who.int/en/newsroom/ fact-sheets/detail/ambient-(outdoor)-air-quality-and-health, Accessed date: 22 August 2019.
Wu, Y.; Guo, J.; Zhang, X.; Tian, X.; Zhang, J.; Wang, Y. ... and Li, X. (2012). Synergy of satellite and ground based observations in estimation of particulate matter in eastern China. Science of the Total Environment, 433: 20-30.
Zhu, J.; Xia, X.; Wang, J.; Che, H.; Chen, H.; Zhang, J. ... and Ayoub, M. (2017). Evaluation of aerosol optical depth and aerosol models from VIIRS retrieval algorithms over North China Plain. Remotesensing, 9(5): 432.
Volume 52, Issue 3
October 2020
Pages 467-480
  • Receive Date: 05 January 2020
  • Revise Date: 24 June 2020
  • Accept Date: 24 June 2020
  • First Publish Date: 22 September 2020