Analyzing the Relationships Between Aerosol Optical Depth and Environmental Variables Using Geographically and Temporally Weighted Regression Model

Document Type : Original Research Paper

Authors

Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, P.O. Box 416 Sanandaj, Iran

10.22059/poll.2026.411243.3295

Abstract

This study aimed to investigate the effect of environmental variables on the distribution of Aerosol Optical Depth (AOD) in Sanandaj County over a six-year period from 2019 to 2024 using a Geographically and Temporally Weighted Regression (GTWR) Model. In order to analyze the relationships between AOD and the factors affecting it, five environmental variables including soil moisture, wind speed, NDVI, LST and rainfall were selected. The GTWR model was implemented using GTWR-Addins, a software package in ArcGIS software. To improve its performance, GTWR was compared with OLS, GWR and TWR in terms of goodness of fit and other statistical measures. The GTWR model was able to identify spatial and temporal heterogeneities simultaneously and had higher explanatory power (R²=0.80) than other models. The spatial and temporal coefficients obtained from this model showed that wind speed and LST have a positive and stable effect on AOD and are considered the most important increasing factors. Soil moisture and NDVI have variable spatio-temporal behavior and in most cases have a reducing effect on AOD. Rainfall has a small-scale and nonlinear effect that varies depending on the spatial pattern and intensity of precipitation. These findings demonstrate the high importance of environmental variables in controlling AOD dynamics and the necessity of simultaneously considering spatial and temporal dimensions in environmental modeling. For future studies, the GTWR model can be used to analyze the AOD impact coefficients at multiple spatial scales and add significant factors such as population, Gross Domestic Product (GDP) and road density.

Keywords

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AlNasser, F., Chehbouni, A., & Entekhabi, D. (2025). Influences of soil moisture and vegetation cover on dust emission using satellite observations. Aeolian Research, 72, 100961.  https://doi.org/10.1016/j.aeolia.2025.100961
Azami, M., Mirzaee, E., & Mohammadi, A. (2015). Recognition of urban unsustainability in Iran (case study: Sanandaj City). Cities, 49, 159-168. http://dx.doi.org/10.1016/j.cities.2015.08.005
Bai, Y., Wu, L., Qin, K., Zhang, Y., Shen, Y., & Zhou, Y. (2016). A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD. Remote Sensing, 8(262): 1-21. https://doi.org/10.3390/rs8030262
Bao, C., Yong, M., Bi, L., Gao, H., Li, J., & Bao, Y. (2021). Impacts of underlying surface on the dusty weather in central Inner Mongolian steppe, China. Earth and Space Science, 8, e2021EA001672.  https://doi.org/10.1029/2021EA001672
Barben, M., Wunderle, S., & Dupuis, S. (2024). A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective. Remote Sensing, 16(19): 3686. https://doi.org/10.3390/rs16193686
Bilal, M., Nazeer, M., Nichol, J., Qiu, Z., Wang, L., & Bleiweiss, M.P. (2019). Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014. Remote Sensing, 11(486): 1-16. https://doi.org/10.3390/rs11050486
Brunsdon, C., Fotheringham, A.S., & Charlton, M. (1996). Geographically weighted regression: a method for exploring spatial non-stationarity. Geographical Analysis, 28, 281-298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Chu, H.J., Huang, B., & Lin, C.Y. (2015). Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship. Atmospheric Environment, 102, 176e182. http://dx.doi.org/10.1016/j.atmosenv.2014.11.062
Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., & Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Quarterly Journal of the Royal Meteorological Society, 144, 1-21. https://doi.org/0.1002/qj.3244
Filonchyk, M., & Hurynovich, V. (2020). Validation of MODIS Aerosol Products with AERONET Measurements of Different Land Cover Types in Areas over Eastern Europe and China. Journal of Geovisualization and Spatial Analysis, 4(1): 1-10. https://doi.org/10.1007/s41651-020-00052-9
He, Q., & Huang, B. (2018). Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling. Remote Sensing of Environment, 206, 72-83. https://doi.org/10.1016/j.rse.2017.12.018
He, Q., Zhang, M., & Huang, B. (2016). Spatio-temporal variation and impact factors analysis of satellite-based aerosol optical depth over China from 2002 to 2015. Atmospheric Environment, 129, 79-90. https://doi.org/10.1016/j.atmosenv.2016.01.002
Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3): 383-401. https://doi.org/10.1080/13658810802672469
Jin, Q., & Wang, C. (2018). The greening of Northwest Indian subcontinent and reduction of dust abundance resulting from Indian summer monsoon revival. Scientific Reports, 8, 4573. https://doi.org/10.1038/s41598-018-23055-5.j.atmosenv.2004.12.029
Jolliffe, I.T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065): 20150202. https://doi.org/10.1098/rsta.2015.0202
Jourdier, B. (2020). Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROMEto simulate wind power production over France. Advances in Science and Research, 17, 63-77. http://dx.doi.org/10.5194/asr-17-63-2020.
Li, J., Garshick, E., Al-Hemoud, A., Huang, S., & Koutrakis, P. (2020). Impacts of meteorology and vegetation on surface dust concentrations in Middle Eastern countries. Science of The Total Environment, 712, 136597. https://doi.org/10.1016/j.scitotenv.2020.136597
Li, L., & Wang, Y. (2014). What drives the aerosol distribution in Guangdong-the most developed province in Southern China? Scientific Reports, 4, 5972. 
Liu, Y., Wang, G., Hu, Z., Shi, P., Lyu, Y., Zhang, G., Gu, Y., Liu, Y., & Liu, L. (2020). Dust storm susceptibility on different land surface types in arid and semiarid regions of northern China. Atmospheric Research, 243, 105031. https://doi.org/10.1016/j.atmosres.2020.105031
Ma, X., Zhang, J., Ding, C., & Wang, Y. (2018). A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 70, 113-124. https://doi.org/10.1016/j.compenvurbsys.2018.03.001
Oneill, P.E., Chan, S., Njoku, E., Jackson, T., Bindlish, R., Chaubell, M., & Colliander, A. (2023). SMAP enhanced L3 radiometer global and polar grid daily 9 km EASE-gridsoil moisture, version 6. http://dx.doi.org/10.5067/M20OXIZHY3RJ, URLhttps://nsidc.org/data/spl3smp_e/versions/6.
Pan, Q., Li, S., Li, J., Xu, M., & Yang, X. (2025). A Village-Scale Study Regarding Landscape Evolution and Ecological Effects in a Coastal Inner Harbor. Land, 14(319): 1-21. https://doi.org/10.3390/land14020319
Price, D.J., Kacarab, M., Cocker, D.R., Purvis-Roberts, K.L and Silva, P.J. (2016) Effects of temperature on the formation of secondary organic aerosol from amine precursors. Aerosol Science and Technology, 50, 1216-1226. https://doi.org/10.1080/ 02786 826. 2016. 12361 82
Quan, W., Xia, N., Guo, Y., Hai, W., Song, J., & Zhang, B. (2023). PM2.5 concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China. Plos One, 1-25. https://doi.org/10.1371/journal.pone.0285610
Rahman, M.M., Shults, R., Hasan, M.G., Arshad, A., Alsubhi, Y.H., & Alsubhi, A.S. (2024). Exploring the Trends of Aerosol Optical Depth and Its Relationship with Climate Variables over Saudi Arabia. Earth Systems and Environment, 1-20. https://doi.org/10.1007/s41748-024-00452-7
Sehler, R., Li, J., Reager, J.T., & Ye, H. (2019). Investigating Relationship Between Soil Moisture and Precipitation Globally Using Remote Sensing Observations. Journal of Contemporary Water Research and Eductaion, 168, 106-118.  https://doi.org/10.1111/j.1936-704X.2019.03324.x
Shi, H., He. Q., & Zhang, W. (2018). Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity. Atmosphere, 9(156): 1-14. https://doi.org/doi:10.3390/atmos9040156
Waring, R., Coops, N., Fan, W., & Nightingale, J. (2006). MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous U.S.A. Remote Sensing of Environment, 103(2): 218-226. http://dx.doi.org/10.1016/j.rse.2006.05.007
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. International Journal of Environmental Research and Public Health, 16, 5107. https://doi.org/10.3390/ijerph16245107
West, R. (2014). Soil moisture active and passive mission (SMAP) L1B S0, L1C S0algorithm theoretical basis document (ATBD). Tech. rep., NASA Jet Propulsion Laboratory, Version 1.
Wu, C., Ren, F., Hu, W., & Du, Q. (2019). Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices. International Journal of Geographical Information Science, 33(3): 489-511. https://doi.org/10.1080/13 658816.2018.1545158
Yao, W., Gui, K., Wang, Y., Che, H., & Zhang, X. (2021). Identifying the dominant local factors of 2000–2019 changes in dust loading over East Asia. Science of The Total Environment, 777, 146064. https://doi.org/10.1016/j.scitotenv.2021.146064
Yuan, J., Wang, X., Feng, Z., Zhang, Y., & Yu, M. (2023). Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations. Remote Sensing, 15(4613): 1-25. https://doi.org/10.3390/rs1518
Zhang, Z., Ding, J.I., Wang, J.I., Chen, X.Y., Liu, X.T., & Osman A. (2021). Temporal and Spatial Distribution Characteristics of Aerosol Optical Properties in Urban Agglomerations on the North Slope of the Tianshan Mountains. Environmental Science, 42(05): 2202-2212. https://doi.org/10.13227/j.hjkx.202009083 PMID: 33884789
Zhao, R., Zhan, L., Yao, M., & Yang, L. (2020). A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5. Sustainable Cities and Society, 56, 102106. https://doi.org/10.1016/j.scs.2020.102106
Zhou, W., Wang, H., & Ge, Q. (2024). Contributions of climatic factors and vegetation cover to the temporal shift in Asian dust events. climate and atmospheric science, 7(328): 1-10. https://doi.org/10.1038/s41612-024-00887-9
Zhu, Z., Zhang, Z., Liu, F., Chen, Z., Ren, Y., & Ren, Q. (2023). Study on Accuracy Evaluation of MCD19A2 and Spatiotemporal Distribution of AOD in Arid Zones of Central Asia. Sustainability, 15, 13959. https://doi.org/10.3390/su151813959
Zurqani, H.A. (2024). High-resolution forest canopy cover estimation in ecodiverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment. Remote Sensing Applications: Society and Environment, 33, 101095. https://doi.org/10.1016/j.rsase.2023.101095