فراکاوی و مرور سیستماتیک ادغام داده‌های عمق نوری آئروسل برگرفته از ماهواره با یادگیری ماشین در برآورد غلظت ذرات معلق ریز (PM2.5)

جغرافیا و مخاطرات محیطی

مقاله 8، دوره 14، شماره 1 - شماره پیاپی 53، فروردین 1404، صفحه 151-186   

نوع مقاله: مقاله مروری

شناسه دیجیتال (DOI): 10.22067/geoeh.2024.88696.1497

نویسندگان

رسول طاهریان اصفهانی؛ مهدی قنبرزاده لک^*

گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه ارومیه، ارومیه، ایران

چکیده

قرار گرفتن در معرض ذرات ریز (PM_2.5) آلاینده هوا، به ویژه در مناطقی که میانگین سالانه آن از دستورالعمل‌های سازمان بهداشت جهانی فراتر است، به طور قابل توجهی بر سلامت عمومی تأثیر می‌گذارد. در ایران، افزایش سطح مواجهه با PM_2.5، به میزان قابل توجهی بر مرگ و میر در‌میان بزرگسالان نقش داشته است. محدودیت‌های پوشش مکانی و شکاف مقطعی داده‌های ایستگاه‌های پایش زمینی، چالش‌هایی را در مدیریت مؤثر کیفیت هوا ایجاد نموده است. محصولات فناوری‌های سنجش از دور، مانند عمق‌نوری‌آئروسل (AOD) برگرفته از حسگرهای MODIS، جایگزین امیدوارکننده‌ای در برآورد PM_2.5 به شمار می‌روند. در مقاله حاضر، تحقیقات پیشین در زمینه استفاده از الگوریتم‌های یادگیری ماشین در برآورد غلظت PM_2.5 بر اساس داده‌های AOD، مرور شده‌است. تجزیه و تحلیل ساختاری 127 مطالعه انتخاب شده، همبستگی‌های متفاوتی را بین AOD و PM_2.5 نشان داد (R² در محدوده 48 تا 99%)، که می‌تواند با بهره‌گیری از متغیرهای کمکی نظیر شرایط هواشناسی و عوامل محیطی، بهبود یابد. اگرچه ادغام این متغیرها، دقت برآورد را افزایش می‌دهد، ولی در عین حال، پیچیدگی و خطاهای بالقوه را در مدل‌های یادگیری‌ماشین افزایش خواهد داد. مدل‌های یادگیری ماشین ترکیبی عملکرد بهتری را در مقایسه با الگوریتم‌های فردی نشان می‌دهند، چراکه قابلیت انطباق، پردازش موازی و مدیریت داده‌های از دست رفته را دارند. علی‌رغم پیشرفت‌های اخیر، هنوز چالش‌هایی به دلیل عدم قطعیت داده‌ها و دینامیک بودن پدیده‌های هواشناسی، باقی مانده‌اند. الگوریتم‌های یادگیری ماشین، اگرچه ابزاری قوی در برآورد PM_2.5 بر اساس شاخص AOD ارائه می‌دهد، لیکن تحقیقات آتی در راستای رفع محدودیت‌ها و بهینه‌سازی عملکرد مدل متناسب با تغییرات محیطی، ضروری است.

کلیدواژه‌ها

ذرات PM2.5؛ عمق نوری آئروسل (AOD)؛ یادگیری ماشین؛ سنجنده MODIS؛ یادگیری گروهی؛ مدل‌های ترکیبی؛ مرور سیستماتیک؛ تأثیر بر سلامت عمومی

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