Abstract
Major items concerning air and weather researches include the amount of aerosols and particulate matter (PM) present in the air. Satellite-retrieved, aerosol optical depth (AOD) is a widely used method for the mapping of particulate matter (PM10) concentrations. Precise estimate and mapping of PM10 depend on the resolution of AOD data and the mathematical model, which considers the spatially non-stationary relationship between PM10 and AOD. Khuzestan province of Iran is deficient in a powerful and validated resolved model of PM10 with high spatial temporal resolution. Therefore, the purpose of this study is to investigate and monitor the concentration of PM10 in 26 air quality monitoring stations along with meteorological data obtained from 21 synoptic stations, aerosol optical depth (AOD) extracted MODIS Terra/Aqua, and their relationship which were analyzed using GIS services, statistical models, and remote sensing throughout Khuzestan province of Iran from January 2008 to December 2018. This research verified the MCD19A2 AOD product and then proved that MCD19A2 could accurately indicate the aerosol distribution in the Khuzestan province of Iran. Analysis of average annual MCD19A2 AOD data revealed 2008 and 2009 as the most polluted years in Khuzestan province during 11 years (2008–2018) of study. The study of dust trends showed a significant increase in spring and summer in the study area. The results of this study indicated that PM10 is influenced by AOD and meteorological parameters. Meteorological data together with simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution were applied as the predictors for the linear regression (LR), multiple linear regression (MLR), the ordinary least squares (OLS), and geographically weighted regression (GWR) models to predict the spatial distribution of PM10 concentrations. Among all the statistical models, the GWR performed better and had higher accuracy. Also, the investigation of indicators such as root mean squared errors (RMSE), mean absolute error (MAE), Akaike’s information criterion (AICc), adjusted coefficient of determination (R2), normal (Z) scores, and Moran’s I of the model residuals, to evaluate the accuracy of the studied models, showed high accuracy and excellent performance of the GWR model in predicting the amount of particulate matter in the study area. These results suggested that the GWR model could provide a reliable way to predict the spatial distribution of PM10 concentrations over Khuzestan province. Assessment of short- and long-term human exposures and then investigation of the effects of particulate matter will be possible through our model.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abdollahi S, Madadi M, Ostad-Ali-Askari K (2021) Monitoring and investigating dust phenomenon on using remote sensing science, geographical information system and statistical methods. Appl Water Sci 11:111. https://doi.org/10.1007/s13201-02101419-z
Amani M, Ghorbanian A, Ahmadi SA, Kakooei M, Moghimi A, Mirmazloumi SM, Alizadeh Moghaddam SH, Mahdavi S, Ghahremanloo M, Parsian S, Wu Q, Brisco B (2020) Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE J Sel Top Appl Earth Obs Remote Sens 13:5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052
Ali MA, Assiri M, Dambul R (2017) Seasonal aerosol optical depth (AOD) variability using satellite data and its comparison over Saudi Arabia for the period 2002–2013. Aerosol Air Qual Res 17(5):1267–1280. https://doi.org/10.4209/aaqr.2016.11.0492
Ali G, Abbas S, Mueen Qamer F, Sing Wong M, Rasul G, Irteza SM, Shahzad N (2021) Environmental impacts of shifts in energy, emissions, and urban heat island during the COVID-19 lockdown across Pakistan. J Clean Prod 291(125806):1–12. https://doi.org/10.1016/j.jclepro.2021.125806
Al-Saadi J, Szykman J, Pierce RB, Kittaka C, Neil D, Chu DA, Remer L, Gumley L, Prins E, Weinstock L, MacDonald C, Wayland R, Dimmick F, Fishman J (2005) Improving national air quality forecasts with satellite aerosol observations. Bull Am Meteorol Soc 86:1249–1261. https://doi.org/10.1175/BAMS-86-9-1249
Azizi G, Miri M, Nabavi O (2012) Detection of Dust Phenomenon in Western Part of Iran. Iran J Arid Reg Geogr Stud 7(2):63–81
Balakrishnan K, Sankar S, Parikh J, Padmavathi R, Srividya K, Venugopal V, Prasad S, Pandey VL (2002) Daily average exposures to respirable particulate matter from combustion of biomass fuels in rural households of Southern India. Environ Health Perspect 110(11):1069–1075. https://doi.org/10.1289/ehp.021101069
Ballester F, Estarlich M, Iniguez C, Llop S, Ramon R, Esplugues A, Lacasana M, Rebagliato M (2010) Air pollution exposure during pregnancy and reduced birth size: a prospective birth cohort study in Valencia. Spain Environ Health 9(6):1–11. https://doi.org/10.1186/1476-069X-9-6
Bhaskar BV, Jeba Rajasekhar RV, Muthusubramanian P, Kesarkar AP (2008) Measurement and modelling of respirable particulate (PM10) and lead pollution over Madurai, India. Air Qual Atmos Health 1:45–55. https://doi.org/10.1007/s11869-008-0004-0
Broomandi P, Dabir B, Bonakdarpour B, Rashidi Y (2018) Identification of the sources of dust storms in the city of Ahvaz by HYSPLIT. Pollution 3(2):341–348. https://doi.org/10.7508/PJ.2017.02.015
Chu DA, Kaufman YJ, Zibordi G, Chern JD, Mao J, Li C, Holben BN (2003) Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectrora-diometer (MODIS). J Geophys Res 108(D21):4661. https://doi.org/10.1029/2002JD003179
Chu Y, Liu Y, Li X, Liu Z, Lu H, Lu Y, Mao Z, Chen X, Li N, Ren M, Liu F, Tian L, Zhu Z, Xiang H (2017) A review on predicting ground PM2.5 concentration using satellite aerosol optical depth. Atmos 7(129):1–25. https://doi.org/10.3390/atmos7100129
de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025
Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J (2016) Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environ Sci Technol 50:4712–4721. https://doi.org/10.1021/acs.est.5b06121
Dinoi A, Perrone MR, Burlizzi P (2010) Application of MODIS products for air quality studies over Southeastern Italy. Remote Sens 2(7):1767–1796. https://doi.org/10.3390/rs2071767
Dominici F, Peng RD, Bell ML, Pham L, McDermontt A, Zeger SL, Samet JM (2006) Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295(10):1127–1134. https://doi.org/10.1001/jama.295.10.1127
Engel-Cox JA, Holloman CH, Coutant BW, Hoff RM (2004) Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmos Environ 38(16):2495–2509. https://doi.org/10.1016/j.atmosenv.2004.01.039
Environment in Iraq: UNEP Progress Report 2003 Retrieved from Switzerland
Faridi S, Shamsipour M, Krzyzanowski M, Künzli N, Amini H, Azimi F, Malkawi M, Momeniha F, Gholampour A, Hassanvand MS, Naddafi K (2018) Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 2006–2015. Environ Int 114:37–49. https://doi.org/10.1016/j.envint.2018.02.026
Farsani MH, Shirmardi M, Alavi N, Maleki H, Sorooshian A, Babaei A, Asgharnia H, Bagherian Marzouni M, Goudarzi G (2018) Evaluation of the relationship between PM10 concentrations and heavy metals during normal and dusty days in Ahvaz. Iran Aeolian Res 13:12–22. https://doi.org/10.1016/j.aeolia.2018.04.001
Fotheringham AS, Charlton ME, Brunsdon C (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ Plan A 30(11):1905–1927
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New York
Ghotbi S, Sotoudeheian S, Arhami M (2016) Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model. Atmos Environ 141:333–346. https://doi.org/10.1016/j.atmosenv.2016.06.057
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Grgurić S, Križan J, Gašparac G, Antonić O, Špirić Z, Mamouri RE, Christodoulou A, Nisantzi A, Agapiou A, Themistocleous K, Fedra K, Panayiotou C, Hadjimitsis D (2014) Relationship between MODIS based aerosol optical depth and PM10 over Croatia. Cent Eur J Geosci 6(1):2–16. https://doi.org/10.2478/s13533-012-0135-6
Han Y, Wu Y, Wang T, Zhuang B, Li S, Zhao K (2015) Impacts of elevated-aerosol layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China. Sci Total Environ 532:195–207. https://doi.org/10.1016/j.scitotenv.2015.05.136
Ha Trang N, Tripathi NK (2014) Spatial correlation analysis between particulate matter 10 (PM10) hazard and respiratory diseases in Chiang Mai Province, Thailand. Int. Arch. Photogramm. Remote Sens. Spatial Inf Sci XL-8:185-191. https://doi.org/10.5194/isprsarchives-XL-8-185-2014
Hatzianastassiou N, Matsoukas C, Drakakis E, Stackhouse JPW, Koepke P, Fotiadi A, Pavlakis KG, Vardavas I (2007) The direct effect of aerosols on solar radiation based on satellite observations, reanalysis datasets, and spectral aerosol optical properties from Global Aerosol Data Set (GADS). Atmos Chem Phys 7:2585–2599
Haywood JM, Ramaswamy V (1998) Global sensitivity studies of the direct radiative forcing due to anthropogenic sulfate and black carbon aerosols. J Geophys Res 103:6043–6058
Hird JN, DeLancey ER, McDermid GJ, Kariyeva J (2017) Google Earth Engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sens 9(12):1315. https://doi.org/10.3390/rs9121315
Hu X, Waller LA, Al-Hamdan MZ, Crosson WL, Estes MG Jr, Estes SM, Quattrochi DA, Sarnat JA, Liu Y (2013) Estimating ground-level PM2.5 concentrations in the Southeastern U.S. using geographically weighted regression. Environ Res 121:1–10. https://doi.org/10.1016/j.envres.2012.11.003
Hu X, Waller LA, Lyapustin A, Wang Y, Al-Hamdan MZ, Crosson WL, Estes MG Jr, Estes SM, Quattrochi DA, Puttaswamyet SJ, Liu Y (2014) Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Rem Sens Environ 140:220–232. https://doi.org/10.1016/j.rse.2013.08.032
Hurvich CM, Simonoff JS, Tsai CL (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc Series B Stat Methodol 60(2):271–293. https://doi.org/10.1111/1467-9868.00125
Javadian M, Behrangi A, Sorooshian A (2019) Impact of drought on dust storms: case study over Southwest Iran. Environ Res Lett 14(12):1–9. https://doi.org/10.1088/1748-9326/ab574e
Kamarul Zaman NAF, Kanniah KD, Kaskaoutis DG (2017) Estimating particulate matter using satellite based aerosol optical depth and meteorological variables in Malaysia. Atmos Res 193:142–162. https://doi.org/10.1016/j.atmosres.2017.04.019
Karagulian F, Temimi M, Ghebreyesus D, Weston M, Kumar Kondapalli N, Krishnan Valappil V, Aldababesh A, Lyapustin A, Chaouch N, Al Hammadi F, Al Abdooli A (2019) Analysis of a severe dust storm and its impact on air quality conditions using WRF-Chem modeling, satellite imagery, and ground observations. Air Qual Atmos Health 12:453–470. https://doi.org/10.1007/s11869-019-00674-z
Kliengchuay W, Cooper Meeyai A, Worakhunpiset S, Tantrakarnapa K (2018) Relationships between meteorological parameters and particulate matter in Mae Hong Son Province, Thailand. Int J Environ Res Public Health 15(12):2801. https://doi.org/10.3390/ijerph15122801
Kloog I, Nordio F, Coull BA, Schwartz J (2012) Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. Environ Sci Technol 46(21):11913–11921. https://doi.org/10.1021/es302673e
Koelemeijer RBA, Homan CD, Matthijsen J (2006) Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmos Environ 40(27):5304–5315. https://doi.org/10.1016/j.atmosenv.2006.04.044
Kolhe AR, Pawar GV, Varpe SR, Kumar PP, Devara PCS, Aher GR (2016) Multi-year analysis of aerosol properties retrieved from the Ångström parameters for different spectral ranges over Pune. Aerosol Air Qual Res 16(12):3266–3280. https://doi.org/10.4209/aaqr.2016.06.0268
Lee M, Kloog I, Chudnovsky A, Lyapustin A, Wang Y, Melly S, Coull B, Koutrakis P, Schwartz J (2015) Spatiotemporal prediction of fine particulate matter using high resolution satellite images in the Southeastern US 2003–2011. J Expo Sci Environ Epidemiol 26(4):377–384. https://doi.org/10.1038/jes.2015.41
Liu Z, Liu Q, Lin HC, Schwartz CS, Lee YH, Wang T (2011) Three-dimensional variational assimilation of MODIS aerosol optical depth: implementation and application to a dust storm over East Asia. J Geophys Res Atmos 116(D23206):1–19. https://doi.org/10.1029/2011JD016159
Liou K-N (2002) An introduction to atmospheric radiation. Academic press
Lyapustin A, Wang Y, Korkin S, Huang D (2018) MODIS collection 6 MAIAC algorithm. Atmos Meas Tech 11:5741–5765. https://doi.org/10.5194/amt-11-5741-2018
Ma J, Zhang W, Luo L, Xu Y (2013) China Statistical Yearbook- 2013
Mirzaei M, Amanollahi J, Tzanis CG (2019) Evaluation of linear, nonlinear, and hybrid models for predicting PM2.5 based on a GTWR model and MODIS AOD data. Air Qual Atmos Health 12:1215–1224. https://doi.org/10.1007/s11869-019-00739-z
Misra A, Jayaraman A, Ganguly D (2015) Validation of version 5.1 MODIS aerosol optical depth (deep blue algorithm and dark target approach) over a semi-arid location in Western India. Aerosol Air Qual Res 15:252–262. https://doi.org/10.4209/aaqr.2014.01.0004
Mokhtari A, Tashayo B, Deilami K (2021) Implications of nonstationary effect on geographically weighted total least squares regression for PM2.5 estimation. Int J Environ Res Public Health 18(13):7115. https://doi.org/10.3390/ijerph18137115
Noori R, Hoshyaripour G, Ashrafi K, NadjarArrabi B (2010) Uncertainty analysis of developed ANN and ANFIS model in prediction of carbon monoxide daily concentration. Atmos Environ 44:476–482. https://doi.org/10.1016/j.atmosenv.2009.11.005
Noroozi A, Shoaei Z (2018) Identifying areas with dust generation potential in the South West of Iran, case study: Khuzestan province. Watershed Eng Manage 10(3):398–409
Papadimas CD, Hatzianastassiou N, Mihaloppoulos N, Kanakidou M, Katsoulis BD, Vardavas I (2008) Assessment of the MODIS collections C005 and C004 aerosol optical depth products over the Mediterranean basin. Atmos Chem Phys 9:2987–2999. https://doi.org/10.5194/acp-9-2987-2009
Pawar GV, Devara PCS, Aher GR (2015) Identification of aerosol types over an urban site based on air-mass trajectory classification. Atmos Res 164:142–155. https://doi.org/10.1016/j.atmosres.2015.04.022
Pope CA, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J Am Med Assoc 287(9):1132–1141. https://doi.org/10.1001/jama.287.9.1132
Pope CA, Brook RD, Burnett RT, Dockery DW (2011a) How is cardiovascular disease mortality risk affected by duration and intensity of fine particulate matter exposure? An integration of the epidemiologic evidence. Air Qual Atmos Health 4:5–14. https://doi.org/10.1007/s11869-010-0082-7
Pope CA, Burnett RT, Turner MC, Cohen A, Krewski D, Jerrett M, Gapstur SM, Thun MJ (2011b) Lung cancer and cardiovascular disease mortality associated with particulate matter exposure from ambient air pollution and cigarette smoke:shape of the exposure–response relationships. Environ Health Perspect 119:1616–1621. https://doi.org/10.1289/ehp.1103639
Ramanathan V, Carmichael G (2008) Global and regional climate changes due to black carbon. Nat Geosci 1:221–227. https://doi.org/10.1038/ngeo156
Seo S, Kim J, Lee H, Jeong U, Kim W, Holben BN, Kim S-W, Song CH, Lim JH (2015) Estimation of PM10 concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign. Atmos Chem Phys 15:319–334. https://doi.org/10.5194/acp-15-319-2015
Soleimany A, Grubliauskas R, Šerevičienė V (2021) Application of satellite data and GIS services for studying air pollutants in Lithuania (case study: Kaunas city). Air Qual Atmos Health 14:411–429. https://doi.org/10.1007/s11869-020-00946-z
Stafoggia M, Schwartz J, Badaloni C, Bellander T, Alessandrini E, Cattani G, de’Donato F, Gaeta A, Leone G, Lyapustin A, Sorek-Hamer M, de Hoogh K, Di Q, Forastiere F, Kloog I (2016) Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology. Environ Int 99:234–244. https://doi.org/10.1016/j.envint.2016.11.024
Stafoggia M, Bellander T, Bucci S, Davoli M, de’Hoogh K, de Donato F, Gariazzo C, Lyapustin A, Michelozzi P, Renzi M, Scortichini M, Shtein A, Viegi G, Kloog I, Schwartz J (2019) Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environ Int 124:170–179. https://doi.org/10.1016/j.envint.2019.01.016
Tian J, Chen D (2010) Spectral, spatial, and temporal sensitivity of correlating MODIS aerosol optical depth with ground-based fine particulate matter (PM2.5) across southern Ontario. Can J Remote Sens 36:119–128. https://doi.org/10.5589/m10-033
Wang Z, Zhou Y, Zhao R, Wang N, Biswas A, Shi Z (2021) High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model. J Clean Prod 297(126493):1–11. https://doi.org/10.1016/j.jclepro.2021.126493
Wei Q, Zhang L, Duan W, Zhen Z (2019) Global and geographically and temporally weighted regression models for modeling PM2.5 in Heilongjiang, China from 2015 to 2018. Int J Environ Res Public Health 16:5107. https://doi.org/10.3390/ijerph16245107
Wu L, Li N, Yang Y (2018) Prediction of air quality indicators for the Beijing-Tianjin-Hebei region. J Clean Prod 196:682–687. https://doi.org/10.1016/j.jclepro.2018.06.068
Xiao L, Lang Y, Christakos G (2018) High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique. Atmos Environ 173(295):305. https://doi.org/10.1016/j.atmosenv.2017.10.062
Zhang L, Gove JH, Heath LS (2005) Spatial residual analysis of six modeling techniques. Ecol Model 186:154–177
Zhang L, Ma Z, Guo L (2009) An evaluation of spatial autocorrelation and heterogeneity in the residuals of six regression models. Forest Sci 55(6):533–548
Zou B, Pu Q, Bilal M, Weng Q, Zhai L, Nichol JE (2016) High-resolution satellite mapping of fine particulates based on geographically weighted regression. IEEE Geosci Remote Sens Lett 13:495–499
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Soleimany, A., Solgi, E., Ashrafi, K. et al. Temporal and spatial distribution mapping of particulate matter in southwest of Iran using remote sensing, GIS, and statistical techniques. Air Qual Atmos Health 15, 1057–1078 (2022). https://doi.org/10.1007/s11869-022-01179-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11869-022-01179-y