شناسایی کانون‌های دارای پتانسیل تولید گرد و غبار با منطق فازی در محدوده استان همدان

آلودگی هوا یکی از بحران‌های محیط زیستی است که آثار منفی آن بر ابعاد مختلف زندگی انسان و سایر موجودات نمود پیدا کرده است. گرد و غبار یکی از انواع آلاینده‌های هوا است که در سال‌های اخیر به ویژه در مناطق خشک و نیمه خشک مشکلات فراوانی را ایجاد کرده است. مهمترین راهکار مدیریت پدیده گرد و غبار، شناسایی کانون تولید به منظور کنترل آن از منشا است. این مطالعه با هدف شناسایی مناطق دارای پتانسیل تولید گرد و غبار در محدوده استان همدان صورت گرفته است. در این راستا ضمن در نظر گرفتن حریم 15 کیلومتری در اطراف مرز استان همدان، مهمترین شاخص‌های موثر در ایجاد طوفان‌های گرد و غبار بررسی، شناسایی و نقشه‌سازی شدند. سپس نقشه‌های کاربری اراضی، پوشش گیاهی، رطوبت خاک و زبری سطح زمین بر اساس تصاویر ماهواره‌ای برای سال‌های 2001، 2009 و 2018 تهیه و با استفاده از توابع فازی طبقه‌بندی شدند. در نقشه کاربری اراضی، آن دسته از فعالیت‌هایی که می‌توانند منشا تولید گرد و غبار باشند شناسایی و اولویت‌بندی شدند. در شاخص‌های دیگر نیز کاهش پوشش گیاهی، کاهش رطوبت خاک و کاهش زبری زمین به عنوان شاخص‌های موثر بر افزایش پتانسیل گردو غبار در نظر گرفته شدند. در نهایت ضمن وزن‌دهی شاخص‌های مختلف از تلفیق شاخص‌ها، مناطق دارای بیشترین پتانسیل ایجاد گرد و غبار شناسایی و اولویت بندی شدند. حساس‌ترین مناطق در شمال غرب استان و همچنین بخش‌هایی از شرق و مرکز واقع شده اند. از سوی دیگر، گسترش بسیار شدید مناطق با پتانسیل بالای تولید گرد و غبار در سطح استان، طی دوره مورد مطالعه، مشاهده می شود. این مساله، لزوم توجه جدی و انجام اقدامات مدیریتی را نمایان می‌سازد.

Identifying Dust generation potential sources using fuzzy logic in Hamadan province

Extended abstract1 IntroductionDust is a term used in meteorology to refer to very small, solid, light particles of silt, clay, or sand created by wind erosion and desertification. It is transported over long distances. Dust is more common in arid and semiarid lands, which is related to the climatic characteristics of these areas and has become one of the main problems in these arid areas. Studying the effects and consequences of this phenomenon, as well as proper management to reduce the effects of dust and focusing on areas which are the origin of the storm can be important in identifying areas as the source of dust. In other words, proper identification of dust sources is of great importance due to its impact on the environment. Soil erosion is the most important cause of dust, and the most important parameters for soil erosion include vegetation, soil moisture, surface roughness, and land use. 2 MethodologyTo identify the potential of dust generation in areas of Hamadan province and the effected ranges, a multicriteria evaluation method was applied. Land use information, vegetation, roughness, and soil moisture were extracted from the Landsat 8 (OLI) and Landsat 5 (TM) images for three periods of 2001, 2009 and 2018. Images were corrected radiometrically. ENVI 5.3 software was used to correct the radiometric and atmospheric images. All of the layers were standardized to a range of 0255 as a fuzzy method. to standardize the land use layer, the userdefined function was used and fallow and uncultivated lands were given a value of 255 while, 150 to lowdensity pastures, 95 to fallow grounds, 75 to dryland farming, 25 to builtup areas, 6 to Mountain and rocky outcrop, 3 to gardens, 2 to irrigated agriculture and zero value to snow and water cover. The NDVI index was used to extract the vegetation map and standardized using the Linear Symmetric function. the soil moisture index was obtained by the NDMI index, and Linear Monotonically decreasing function was applied to standardization this layer. The surface roughness coefficient was extracted from ASTER imagery and a Linear Monotonically decreasing was employed to standardize this layer. The WLC approach was applied to integrate the above criteria. According to expert choice, the humidity score is 5 compared to vegetation cover, humidity to roughness is 7, moisture to land use is 9, vegetation to roughness is 3, vegetation to land use is 5 and roughness to land use is 3. Then the fuzzy maps of the area were merged so that higher values ​​and closer to 255 meant more desirability as dust sources and lower values ​​meant less desirability. Finally, to select the potential hotspots, using the dust output desirability image, the threshold levels were adjusted based on the threshold. 3 Results The results showed that the incompatibility rate from the comparisons made between the factors was estimated to be 0.66, which is acceptable. The final map of possible dust sources was prepared based on utility levels using threshold values ​​for 2001, 2009 and 2018. By contractually dividing the suitability map into 5 classes based on the threshold, areas with the highest potential (255225), high potential (225200), medium potential (150200), low potential (150100) and lowest potential (1000) were identified and the 5th category (255225) was selected as the most likely sources. The soil erosion map was then overlaid with the dust hotspot map and the result showed that of the total 227,483 hectares of land identified as dust hotspot, 121023 hectares are located in lands with catastrophic erosion, 56956 hectares in lands with extraordinary erosion, 17718 hectares in lands with Extreme erosion and 24272 hectares in lands with high erosion, respectively. In other words, more than 96 percent of the identified hotspots are situated in areas with moderate erosion and higher. 4 Discussion ConclusionsThe results of this study showed that fourdefined factors including soil moisture, vegetation, land surface roughness, and land use type can have a high ability to identify potential dust sources. Overlaying identified areas with erosion maps, which show an overlap of about 97 percent between these sources and areas with aboveaverage erosion, confirms this fact that the main internal sources of dust are due to soil degradation on lands. Accordingly, the lack of proper management can change these potentially identified areas to actual dust production centers. Therefore, it is necessary to plan protection programs to prevent dust storms such as planting vegetation around the borders of the province or county and to prevent pasture overgrazing.