Monitoring the Areas of Dust Production and Assessing the Damages Caused by This Phenomenon to the Agriculture Sector (Case study: Alborz Province, Iran)

Document Type : Research Paper

Authors

1 Environment and Natural Resources Faculty, Islamic Azad University, Tehran, Iran.

2 Research Group of Environmental Assessment and Risks, Research Center of Environment and Sustainable Development (RCESD), Department of Environment, Tehran, Iran.

10.22059/jdesert.2023.93543

Abstract

In recent years, the dust phenomenon has become one of the most important environmental challenges throughout the world, and one of its negative effects is on the agriculture sector. The aim of this research is first to determine dust emission sources in Alborz Province and then to estimate the willingness to pay (WTP) for reducing the effects of dust on the agriculture of dust emission sources and their surrounding areas. The Index of Land Susceptibility to Wind Erosion (ILSWE) was used to determine dust emission sources. ILSWE consisted of the combination of 5 effective factors in wind erosion, namely climate erosivity, soil erodibility, soil crust, vegetation cover, and surface roughness. In the next step, according to the produced map of dust emission sources, the affected rural districts were identified, and then using the contingent valuation method (CVM), individuals’ WTP for preventing and reducing the negative impacts of the dust phenomenon on the agriculture was calculated by 400 questionnaires. According to the results, the classification map of ILSWE indicated that while classifying the areas in terms of their sensitivity to wind erosion 7.8% of the study area was placed in the very high sensitivity class. This class was considered the center of dust production, which was located chiefly in the southern parts of Alborz Province. Using the CVM method, the expected value rate and the WTP were calculated as 1654231 Rials (approximately $ 5.5). According to the population of the affected area, the total value of protecting the agricultural products against dust phenomenon is 27433766904 Rials ($91445.89) annually. 

Keywords

References

References
Albugami, S., S. Palmer, J. Cinnamon, J. Meersmans, 2019. Spatial and temporal variations in the incidence of dust storms in Saudi Arabia revealed from in situ observations. Geosciences, 9(4), pp. 1-20.
Al-Hemoud, A., A. Al-Dousari, R. Misak, M. Al-Sudairawi, A. Naseeb, H. Al-Dashti, N. Al-Dousari, 2019. Economic impact and risk assessment of sand and dust storms (SDS) on the oil and gas industry in Kuwait. Sustainability, 11(1), pp. 1-19.
Alizadeh‐Choobari, O., P. Ghafarian, E. Owlad, 2016. Temporal variations in the frequency and concentration of dust events over Iran based on surface observations. International Journal of Climatology, 36(4), 2050-2062.
Ardakani, A. F., 2016. Estimating willingness to pay in order to prevent external intangible effects of dust in Yazd-Ardakan plain. International journal of environmental science and technology, 13(6), 1489-1496.
Ashrafi, K., M. S. Motlagh, S. E. Neyestani, 2017. Dust storms modeling and their impacts on air quality and radiation budget over Iran using WRF-Chem. Air Quality, Atmosphere & Health, 10(9), 1059-1076.
Bishop, R. C., T. A. Heberlein, 1979. Measuring values of extramarket goods: Are indirect measures biased?. American journal of agricultural economics, 61(5), 926-930.
Borrelli, P., C. Ballabio, P. Panagos, L. Montanarella, 2014. Wind erosion susceptibility of European soils. Geoderma, 232, 471-478.
Borrelli, P., P. Panagos, L. Montanarella, 2015. New insights into the geography and modelling of wind erosion in the european agricultural land. Application of a spatially explicit indicator of land susceptibility to wind erosion. Sustainability, 7(7), 8823-8836.
Borrelli, P., P. Panagos, C. Ballabio, E. Lugato, M. Weynants, L. Montanarella, 2016. Towards a pan‐European assessment of land susceptibility to wind erosion. Land Degradation & Development, 27(4), 1093-1105.
Cao, H., F. Amiraslani, J. Liu, N. Zhou, 2015. Identification of dust storm source areas in West Asia using multiple environmental datasets. Science of the Total Environment, 502, 224-235.
Darand, M., M. M. Sohrabi, 2018. Identifying drought-and flood-prone areas based on significant changes in daily precipitation over Iran. Natural Hazards, 90(3), 1427-1446.
Deiravi Pour, M., H. Mohammadasgari, S. Farhadi, I. Najafi, 2019. Dust storm detection in the Southwest of Iran using NDDI and BTD indexes and neural network. Scientific-Research Quarterly of Geographical Data (SEPEHR), 28(111), 217-234.
Deiravipour, M., H. Mohammad Asgari, S. Farhadi, 2022. Detection of dust storms overnight in the South West of Iran using satellite images. Desert, 27(1), 35-53.
Department of Environment, 2014. The environment of Alborz Province, threats, opportunities and proposed solutions. Department of Environment Karaj, Iran.
Duffield, J. W., D. A. Patterson, 1991. Inference and optimal design for a welfare measure in dichotomous choice contingent valuation. Land Economics, 67(2), 225-239.
Ebrahimi Khusfi, Z., F. Roustaei, M. Ebrahimi Khusfi, S. Naghavi, 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.
Ebrahimpour, M., J. Rahimi, A. Nikkhah, J. Bazrafshan, 2015. Monitoring agricultural drought using the standardized effective precipitation index. Journal of Irrigation and Drainage Engineering, 141(1), 04014044.
Effati, M., H. A. Bahrami, M. Gohardoust, E. Babaeian, M. Tuller, 2019. Application of satellite remote sensing for estimation of dust emission probability in the Urmia Lake Basin in Iran. Soil Science Society of America Journal, 83(4), 993-1002
FAO., (1979). A provisional methodology for soil degradation assessment. Food and Agriculture Organization, Rome, Italy.
FAO/IIASA/ISRIC/ISSCAS/JRC., 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria. (accessed 17 May 2019). http://www.fao.org/3/aq361e/aq361e.pdf.
Fenta, A. A., A. Tsunekawa, N. Haregeweyn, J. Poesen, M. Tsubo, P. Borrelli,... Y. Kurosaki, 2020. Land susceptibility to water and wind erosion risks in the East Africa region. Science of the Total Environment, 703, 135016.
Feuerstein, S., K.  Schepanski, 2019. Identification of dust sources in a Saharan dust hot-spot and their implementation in a dust-emission model. Remote Sensing, 11(1), 4.
Fryrear, D. W., J. D. Bilbro, A. Saleh, H. Schomberg, J. E. Stout, T. M. Zobeck, 2000. RWEQ: Improved wind erosion technology. Journal of Soil and Water Conservation, 55(2), 183-189.
Fryrear, D. W., C. A. Krammes, D. L. Williamson, T. M. Zobeck, 1994. Computing the wind erodible fraction of soils. Journal of Soil and Water Conservation, 49(2), 183-188.
Fryrear, D.W., A. Saleh, J. D. Bilbro, H. M. Schomberg, J. E. Stout, T.M. Zobeck, 1998. Revised Wind Erosion Equation (RWEQ). Wind Erosion and Water Conservation Research Unit, USDA-ARS, Southern Plains Area Cropping Systems Research Laboratory. Technical Bulletin No. 1. pp. 185. (accessed 2019). https://www.csrl.ars.usda.gov/wewc/rweq/rweq.pdf.
Funk, R., H. I. Reuter, 2006. Wind erosion. In Soil erosion in Europe, Boardman J, Poesen J. (eds). Wiley: Chichester; 563–582.
Hansen, F. V. 1993.  Surface roughness lengths. ARL Technical Report U. S. Army, White Sands Missile Range, NM 88002-5501, pp. 51. (accessed 4 December 2018).
Hengl, T.,  J. Mendes de Jesus, G. B. Heuvelink, M. Ruiperez Gonzalez, M. Kilibarda, A. Blagotić, ... B. Kempen,  2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748
Heydari Alamdarloo, E., H. Khosravi, P. Dehghan Rahimabadi, M. Ghodsi, 2021. The Effect of Climate Fluctuations on Vegetation Dynamics in West and Northwest of Iran. Desert Ecosystem Engineering Journal, 3(2), 19-28.
Heydari Alamdarloo, E., H. Khosravi, S. Nasabpour, A. Gholami, 2020. Assessment of drought hazard, vulnerability and risk in Iran using GIS techniques. Journal of Arid Land, 12(6), 984-1000. ‏
Heydari Alamdarloo, E., E. Moradi, M. Abdolshahnejad, Y. Fatahi, H. Khosravi, A. M. Da Silva, 2021. Analyzing WSTP trend: a new method for global warming assessment. Environmental monitoring and assessment, 193(12), 1-15.
Hojan, M., M. Rurek, M. Więcław, A. Krupa, 2019. Effects of extreme dust storm in agricultural areas (Poland, the Greater Lowland). Geosciences, 9(3), 106.
Huang, M., G. Peng, J. Zhang, S. Zhang, 2006. Application of artificial neural networks to the prediction of dust storms in Northwest China. Global and Planetary change, 52(1-4), 216-224
Iran Meteorological Organization, 2014. Review of climatic characteristics of Alborz Province. Meteorological Organization of Alborz Province, Karaj, Iran.
Jebali, A., M. Zare, M. R. Ekhtesasi, R.Jafari, 2021. Detection of areas prone to wind erosion and air pollution using DSI and PDSI indices. Natural Hazards, 1-15.
Jenks, G. F., 1977. Optimal data classification for choropleth maps. Department of Geographiy, University of Kansas Occasional Paper.
Jeong, D. Y., 2008. Socio-economic costs from yellow dust damages in South Korea. Korean Soc. Sci. J, 35, 1-29
Ji, S., Y. Choi, C. K. Lee, J. W. Mjelde, 2018. Comparing willingness-to-pay between residents and non-residents using a contingent valuation method: case of the Grand Canal in China. Asia Pacific Journal of Tourism Research, 23(1), 79-91.
Jin, M., Y. Juan, Y. Choi, C. K. Lee, 2019. Estimating the preservation value of world heritage site using contingent valuation method: The case of the Li River, China. Sustainability, 11(4), 1100
Khosravi, H., E. Haydari, G. Zehtabian, J. Bazrafshan, 2016. Analysis of spatial and temporal trends of groundwater index (GRI)(case study: Yazd-Ardakan plain). Iranian Journal of Range and Desert Research, 22(4).
Kim, H. M., I. G. Kim, B. Lim, S. H. Yoo, 2021. Estimating the Economic Value of Improving the Asian Dust Aerosol Model in the Korean Household Sector: A Choice Experiment. Sustainability, 13(21), 12054.
Komleh, S. P.,  A. Keyhani, S. H. Rafiee, P. Sefeedpary, 2011. Energy use and economic analysis of corn silage production under three cultivated area levels in Tehran province of Iran. Energy, 36(5), 3335-3341.
Lee, C. K., S. Y. Han, 2002.  Estimating the use and preservation values of national parks’ tourism resources using a contingent valuation method. Tourism management, 23(5), 531-540.
Maghsood, F. F., H. Moradi, R. Berndtsson, M. Panahi, A. Daneshi, H. Hashemi, A. R. M. Bavani, 2019. Social acceptability of flood management strategies under climate change using contingent valuation method (CVM). Sustainability, 11(18), 5053.
Manesh, M. B., H. Khosravi, E. H. Alamdarloo, M. S. Alekasir, A. Gholami, V. P. Singh, 2019. Linkage of agricultural drought with meteorological drought in different climates of Iran. Theoretical and Applied Climatology, 138(1), 1025-1033.
Mansouri, Z., H. M. Asgari, 2021. Dust Distribution and Emission Modeling (Study Area: Khuzestan province). Desert (2008-0875), 26(1).
Mehrabi, S., S. Soltani, R. Jafari, 2015. Analyzing the Relationship Between Dust Storm Occurrence and Climatic Parameters. Journal of Water and Soil Science. 19 (71) :69-81
Mei, D., L. Xiushan, S. Lin, W. A. N. G. Ping, 2008. A dust-storm process dynamic monitoring with multi-temporal MODIS data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 965-970.‏
Middleton, N., U. Kang, 2017. Sand and dust storms: impact mitigation. Sustainability 9, 1053.
Mirmousavi, S. H. (2016). Regional modeling of wind erosion in the North West and South West of Iran. Eurasian Soil Science, 49(8), 942-953
Moghaddam, M. H. R., A. Sedighi, S. Fasihi, M. K. Firozjaei, 2018. Effect of environmental policies in combating aeolian desertification over Sejzy Plain of Iran. Aeolian research, 35, 19-28.‏
Mohammad Asgari, H., H. Mojiri-Forushani, M. Mahboubi, 2023. Temporal and spatial pattern of dust storms, their polycyclic aromatic hydrocarbons, and human health risk assessment in the dustiest region of the world. Environmental Monitoring and Assessment, 195(1), 76.
Nasabpour, S., H. Khosravi, E. Heydari Alamdarloo, 2017. National assessment of climate resources for tourism seasonality in Iran using the tourism climate index. Desert, 22(2), 175-186.
Papi, R., S. Attarchi, A. Darvishi Boloorani, N. Neysani Samany, 2022. Characterization of Hydrologic Sand and Dust Storm Sources in the Middle East. Sustainability, 14(22), 15352.
Qu, J. J., X. Hao, M. Kafatos, L. Wang, 2006. Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements. IEEE Geoscience and remote sensing letters, 3(4), 484-486.
Rayegani, B. 2019. Identification of potential dust sources using remote sensing data (Case Study: Alborz Province). Journal of Natural Environmental Hazards, 8, 1-20.
Rayegani, B., S. Barati, H. Goshtasb, S. Gachpaz, J. Ramezani, H. Sarkheil, 2020. Sand and dust storm sources identification: A remote sensing approach. Ecological Indicators, 112, 106099.
Rayegani, B., S. Barati, T.A. Sohrabi, B. Sonboli, 2016. Remotely sensed data capacities to assess soil degradation. The Egyptian Journal of Remote Sensing and Space Science, 19, 207-222.
Rayegani, B., G. Zehtabian, H. Azarnivand, S.K. Alavipanah, S.J. Khajeddin, 2015. LADA method Performance evaluation on soil degradation assessment in the East of Esfahan. Journal of Range and Watershed Managment, 68, 109-129.
Raygani, B., Z. Kheirandish, F. Kermani, M. Mohammdi Miyab, A. Torabinia, 2017. Identification of active dust sources using remote sensing data and air flow simulation (Case study: Alborz province). Desert Management, 4, 15-26
Sarmadian, F., R. Taghi Zadeh Mehrjerdi, 2010. A comparison of interpolation methods for preparing soil quality maps: case study:(Agricultural faculty experimental field). Iranian journal of soil and water research, 40(2).
Shao, Y., 2008. Physics and modelling of wind erosion (Vol. 37). Springer Science & Business Media.
Shao, Y., M. Klose, K. H. Wyrwoll, 2013. Recent global dust trend and connections to climate forcing. Journal of Geophysical Research: Atmospheres, 118(19), 11-107.‏
Sorkheh, M., H. M. Asgari, I. Zamani, F. Ghanbari, 2022. The relationship between dust sources and airborne bacteria in the southwest of Iran. Environmental Science and Pollution Research, 29(54), 82045-82063.
Stefanski, R., M. V. K. Sivakumar, 2009. Impacts of sand and dust storms on agriculture and potential agricultural applications of a SDSWS. In IOP Conference Series: Earth and Environmental Science (Vol. 7, No. 1, p. 012016). IOP Publishing.
Tajiki, F., H. M. Asgari, I. Zamani, F. Ghanbari, 2021. Assessing the relationship between airborne fungi and potential dust sources using a combined approach. Environmental Science and Pollution Research, 1-12.
TA-Luft., 2001. Erste Allgemeine Verwaltungsvorschrift zum Bundes-Immissionsschutzgesetz. Technische Anleitung zur Reinhaltung der Luft – TA-Luft Stand 12.06.2001.
Tussupova, K., Berndtsson, R., Bramryd, T., & Beisenova, R. (2015). Investigating willingness to pay to improve water supply services: application of contingent valuation method. Water, 7(6), 3024-3039.
Wever, N., 2012. Quantifying trends in surface roughness and the effect on surface wind speed observations. Journal of Geophysical Research: Atmospheres, 117(D11).
Xuan, J., I. N. Sokolik, J. Hao, F. Guo, H. Mao, G. Yang, 2004. Identification and characterization of sources of atmospheric mineral dust in East Asia. Atmospheric Environment, 38(36), 6239-6252.
Zhang, K., S. Li, W. Peng, B. Yu, 2004. Erodibility of agricultural soils on the Loess Plateau of China. Soil and Tillage Research, 76(2), 157-165.
Zhang, L., H. Fukuda, Z. Liu, 2019. Public willingness to pay for sand and dust weather mitigation: A case study in Beijing, China. Journal of cleaner production, 217, 639-645
Zobeck, T. M., 1991. Soil properties affecting wind erosion. Journal of Soil and Water Conservation, 46(2), 112-118.
Desert
Volume 28, Issue 1
June 2023
Pages 65-83

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