پژوهش های محیط زیست

پژوهش های محیط زیست

ارزیابی تاثیر عمق اٌپتیکی آئروسل (AOD) بر سلامت پوشش گیاهی (مطالعه موردی: حوضه آبریز دریاچه مهارلو)

نوع مقاله : مقاله پژوهشی

نویسنده
استادیار، گروه آموزش جغرافیا، دانشگاه فرهنگیان، تهران، ایران
10.22034/eiap.2025.217510
چکیده
دریاچه مهارلو از سه بخش دریاچه فعلی، پوشش نمکی و پوشش گلی تشکیل شده که خشک شدن آن می‌تواند تاثیر مستقیمی بر میزان گردوغبار و پوشش گیاهی بگذارد. لذا در این پژوهش به بررسی ارتباط عمق نوری هواویزها (AOD) و پوشش گیاهی در حوضه آبریز دریاچه مهارلو پرداخته شد. برای این منظور داده‌های (AOD) در بازه آماری 2010 تا 2023 از پایگاه داده MERRA-2 و تصاویر 5 مسیر و گذر متفاوت لندست 8 برای ارزیابی شاخص‌های پوشش گیاهی (NDVI، ARVI، CRI2) اخذ گردید. در ادامه با استفاده از رگرسیون وزن‌دار جغرافیایی (GWR) و ضریب همبستگی پیرسون، برای چهار فصل زمستان، بهار، تابستان و پاییز مقادیر ، ، و AICc بین شاخص‌های پوشش گیاهی و AOD محاسبه گردید. نتایج بیانگر همبستگی منفی بین AOD و شاخص‌های پوشش گیاهی مذکور در تمام فصول مورد بررسی بود. بیشترین همبستگی منفی در بین تمام پوشش‌های گیاهی مورد بررسی در فصل تابستان (430/0-) و کمترین میزان همبستگی (134/0-) مربوط به فصل زمستان است. همچنین نتایج به‌دست‌آمده از GWR بیانگر بیشترین میزان  و  به ترتیب مربوط به شاخص NDVI با مقدار عددی 819/0 و 627/0و کمترین میزان آن نیز به ترتیب مربوط به شاخص CRI2 با مقدار عددی 454/0 و 195/0 می‌باشد. کمترین میزان AICc با مقدار عددی 25/272 مربوط به شاخص NDVI و بیشترین میزان با مقدار عددی 75/377 مربوط به شاخص CRI2 بود که به ترتیب نشان‌دهنده مناسب و نامناسب‌ترین برازش بین آئروسل‌ها با شاخص‌های پوشش گیاهی موردبررسی می‌باشد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Evaluating the Effect of Aerosol Optical Depth (AOD) on the Health of Vegetation (Case Study: Maharlo Lake Catchment)

نویسنده English

Mehdi Asadi
Assistant Professor, Department of Geography Education, Farhangian University, Tehran, Iran
چکیده English

Maharlo Lake consists of the current lake, salt cover, and mud cover. The drying of these parts can directly affect the amount of dust and vegetation. Therefore, this research investigates the relationship between Aerosol Optical Depth (AOD) and vegetation in the catchment area of Maharlo Lake. To achieve this, AOD data from the MERRA-2 database for 2010 to 2023 were obtained, along with images from 5 different Rows and Paths of Landsat 8 to evaluate vegetation indicators (NDVI, ARVI, CRI2). Geographically weighted regression (GWR) and Pearson's correlation coefficient ( , , and AICc values) were calculated to assess the correlation between vegetation indices and AOD for the four seasons: winter, spring, summer, and fall. The results demonstrate a negative correlation between AOD and vegetation indices mentioned in all seasons. The strongest negative correlation (-0.430) was observed in the summer, while the weakest correlation (-0.134) was found in the winter. GWR results indicate that the NDVI index has the highest  and  values (0.819 and 0.627, respectively), while the CRI2 index has the lowest values (0.454 and 0.195, respectively). The lowest AICc value (272.25) was associated with the NDVI index, indicating a better fit between aerosols and the investigated vegetation indices. Conversely, the CRI2 index had the highest AICc value (377.75), suggesting a less appropriate fit.

کلیدواژه‌ها English

AOD
Vegetation
Landsat
MODIS
MERRA-2
Maharlo
Allen, B. 2015. Atmospheric aerosols: What are they, and why are they so important? National Aeronautics and Space Administration, 6.
Asadi, M., & Kamran, K. V. 2022. Comparison of SEBAL, METRIC, and ALARM algorithms for estimating actual evapotranspiration of wheat crop. Theoretical and Applied Climatology, 149(1): 327-337.
Asadi, M., & Kamran, K. V. 2023. Estimating selected cultivated crop water requirement-based surface energy balance algorithm. Arabian Journal of Geosciences, 16(5): 298.
Asadi, M., & Karami, M. 2019. Spatial and Temporal Distribution of Dust in Iran. Environmental Researches, 10(19), 293-300. (In Persian).
Asadi, M., & Karami, M. 2022. Modeling of relative humidity trends in Iran. Model. Earth Syst. nviron. 8, 1035–1045.
Bahrami, H.A., Jalali, M., Darvishi Balorani, A., & Azizi, R. 2012. Spatial-temporal modeling of dust storms in Khuzestan province, Iranian Journal of Remote Sensing & GIS, 5(2), 95-114. (In Persian).
Behrang Manesh M, Khosravi H, Azarnivand H, & Senatore A. 2020. Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province), Journal of Plant Ecosystem Conservation, 7 (15): 295-318. (In Persian).
Behrooz, R. D., Esmaili-Sari, A., Bahramifar, N., Kaskaoutis, D. G., Saeb, K., & Rajaei, F. 2017. Trace-element concentrations and water-soluble ions in size-segregated dust-borne and soil samples in Sistan, southeast Iran. Aeolian Research, 25, 87-105.
Butt, E. W., Turnock, S. T., Rigby, R., Reddington, C. L., Yoshioka, M., & Johnson, J. S. 2017. Global and regional trends in particulate air pollution and attributable health burden over the past 50 years. Environmental Research Letters, 12.
Buya, S., Gokon, H., Dam, H. C., Usanavasin, S., & Karnjana, J. 2024. Estimating Ground-level Hourly PM 2.5 Concentrations in Thailand using Satellite Data: A Log-linear Model with Sum Contrast Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Charlton, M., Fotheringham, S., & Brunsdon, C. 2009. Geographically weighted regression. White paper. National Centre for Geocomputation. National University of Ireland Maynooth, 2.
Del Aguila, A., Sorribas, M., Lyamani, H., Titos, G., Olmo, F. J., & Arruda‐Moreira, G. 2018. Sources and physicochemical characteristics of submicron aerosols during three intensive campaigns in Granada (Spain). Atmospheric Research, 213, 398–410.
Ebrahimi Khusfi, Z., Roustaei, F., Ebrahimi Khusfi, M., & Naghavi, S. 2020. Investigation of the relationship between dust storm index, climatic parameters, and normalized difference vegetation index using the ridge regression method in arid regions of Central Iran. Arid land research and management, 34(3): 239-263.
Eshghizadeh, M., & Esmaeilian, Y. 2020. Evaluation possibility of rangelands biomass estimation using Landsat 8 satellite data. Iranian Journal of Range and Desert Research, 27(1): 159-176.
Fotheringham, A. S., Crespo, R., & Yao, J. 2015. Geographical and temporal weighted regression (GTWR). Geographical Analysis, 47(4): 431-452.
Froehlich‐Nowoisky, J., Kampf, C. J., Weber, B., Huffman, J. A., Poehlker, C., & Andreae, M. O. 2016. Bioaerosols in the earth system: Climate, health, and ecosystem interactions. Atmospheric Research, 182, 346–376.
Gitelson, A.A., Viña, A., Ciganda, V., Rundquist, D. & Arkebauer, T.J. 2005. Remote Estimation of Canopy Chlorophyll Content in Crops, Geophysical Research Letter, 32, P. L08403.
Golreyhan, F., Valizadeh Kamran, K., mokhtari, D., & rasouli, A. A. 2024. The Effect of Salt Dust Storms on the Health of Plants in the Eastern Basin of Urmia Lake. Iranian Journal of Remote Sensing & GIS, 15(4): 101-118.
Guo, Y., Hong, S., Feng, N., Zhuang, Y., & Zhang, L. 2012. Spatial distributions and temporal variations of atmospheric aerosols and the affecting factors: a case study for a region in central China, International journal of remote sensing, 33(12): 3672-3692.
Gutierrez‐Avila, I., Rojas‐Bracho, L., Riojas‐Rodriguez, H., Kloog, I., Just, A. C., & Rothenberg, S. J. 2018. Cardiovascular and cerebrovascular mortality associated with acute exposure to PM2.5 in Mexico City. Stroke, 49(7): 1734–1736.
Han, X., Zhang, M., Zhu, L., & Xu, L. 2013. Model analysis of influences of aerosol mixing state upon its optical properties in East Asia. Advances in Atmospheric Sciences, 30, 1201-1212.
IPCC (The Intergovernmental Panel on Climate Change)-Fifth Assessment Report–Climate Change. 2013. Www.ipcc.ch.
Kaufman, Y. J., & Tanre, D. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2): 261-270.
Kaufman, Y. J., & Tanre, D. 1996. Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote sensing of Environment, 55(1): 65-79.
Molod, A., Takacs, L., Suarez, M., & Bacmeister, J. 2015. Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2. Geoscientific Model Development, 8(5): 1339-1356.
Nasr Azadani, A. 2014. Investigation of changes in soil moisture and rainfall and their effect on dust production in the Inter-Nahrain region using MODIS system and TRMM satellite data from 2001 to 2014, thesis Senior Science, Faculty of Basic Sciences , Zanjan University of Basic Sciences. (In Persian).
Obregón, M. D. L. A., Costa, M. J., Silva, A. M., & Serrano, A. 2018. Impact of aerosol and water vapour on SW radiation at the surface: Sensitivity study and applications. Atmospheric research, 213, 252-263.
Pacitto, A., Stabile, L., Viana, M., Scungio, M., Reche, C., & Querol, X. 2018. Particle‐related exposure, dose and lung cancer risk of primary school children in two European countries. Science of the Total Environment, 616, 720–729.
Pippal, P. S., Kumar, R., Kumar, R., & Singh, A. 2024. Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India. Science of the Total Environment, 922, 170835.
Ranjan, A. K., Patra, A. K., & Gorai, A. K. 2021. A review on estimation of particulate matter from satellite-based aerosol optical depth: Data, methods, and challenges. Asia-Pacific Journal of Atmospheric Sciences, 57, 679-699.
Rashki, A., Kaskaoutis, D. G., Rautenbach, C. D., Eriksson, P. G., Qiang, M., & Gupta, P. 2012. Dust storms and their horizontal dust loading in the Sistan region, Iran. Aeolian Research, 5, 51-62.
Real-Rangel, R., Pedrozo-Acuña, A., Breña-Naranjo, J. A., & Alcocer-Yamanaka, V. 2017. Evaluation of the hydroclimatological variables derived from GLDAS-1, GLDAS-2 and MERRA-2 in Mexico, E-proceedings of the 37th IAHR World Congress August 13–18, Kuala Lumpur, Malaysia.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., & Bloom, S. 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of climate, 24(14): 3624-3648.
Sahak, A. S., & Karsli, F. 2024. A new approach for the assessment of urban eco-environmental quality based on remote sensing: a case study of Herat City, Afghanistan. Journal of Spatial Science, 1-26.
Sofue, Y., Hoshino, B., Demura, Y., Kai, K., Baba, K., Nduati, E., Kondoh, A. and Sternberg, T. 2018. Satellite monitoring of vegetation response to precipitation and dust storm outbreaks in Gobi Desert Regions. Land, 7(1): 19.
Streets, D. G., Yan, F., Chin, M., Diehl, T., Mahowald, N., Schultz, M., & Yu, C. 2009. Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006. Journal of Geophysical Research: Atmospheres, 114(D10).
Tariq, S., Nawaz, H., Ul-Haq, Z., & Mehmood, U. 2021. Investigating the relationship of aerosols with enhanced vegetation index and meteorological parameters over Pakistan. Atmospheric Pollution Research, 12(6): 101080.
Tariq, S., Ul-Haq, Z., Mahmood, K., & Rana, A. D. 2018. Spatio-temporal distributions and trends of aerosol parameters over Pakistan using remote sensing. Applied Ecology & Environmental Research, 16(3).
Teillet, P. M., Staenz, K., & William, D. J. 1997. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 61(1): 139-149.
Thenkabail, P. S., Smith, R. B., & De Pauw, E. 2002. Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric engineering and remote sensing, 68(6): 607-622.
Tucker, C.J. 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8, 127-150.
Valsaraj, K. T., Kommalapati, R. R., & Kommalapati, R.R. 2009. Atmospheric aerosols: Characterization, chemistry, modeling, and climate, (Vol. 1005). USA: Oxford University Press.
Wang, H., Yang, L., Zhao, M., Du, W., Liu, P., & Sun, X. 2019. The normalized difference vegetation index and angular variation of surface spectral polarized reflectance relationships: Improvements on aerosol remote sensing over land. Earth and Space Science, 6(6): 982-989.
Yang, Y., Zhao, C., Dong, X., Fan, G., Zhou, Y., & Wang, Y. 2019. Toward understanding the process‐level impacts of aerosols on microphysical properties of shallow cumulus cloud using aircraft observations. Atmospheric Research, 221, 27–33.
Yoo, D. 2019. Geographically Weighted Regression: A Method for Spatial Analysis in Socio-Historical Research. Arch Iran Med, 22(3): 155-160.
Zhang, W., He, Q., Wang, H., Cao, K., & He, S. 2018. Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change. Ecological indicators, 93, 458-469.
Zhang, Z., Xiong, J., Fan, M., Tao, M., Wang, Q., & Bai, Y. 2023. Satellite-observed vegetation responses to aerosols variability. Agricultural and Forest Meteorology, 329, 109278.