نشریه محیط زیست طبیعی

پیش‌بینی و تحلیل مکانی-زمانی ذرات ریز جوی و اثرپذیری آن از دما و پوشش گیاهی در سطح ایران با استفاده از رویکرد هموارسازی نمایی در پایتون

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران-محیط زیست، دانشکده محیط زیست، دانشگاه تهران، ایران.

2 دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران.

10.22059/jne.2023.354696.2521

چکیده

پیش‌بینی نسبتاً دقیق غلظت آلاینده ­ها و متغیرهای محیط زیستی کوتاه‌مدت و بلندمدت، گام مهمی در کاهش آسیب‌های ناشی از کیفیت پایین هوا می­ باشد. در این مطالعه، ابتدا به تحلیل مکانی-زمانی و سپس با استفاده از تکنیک‌های پیش‌بینی، به پیش ­بینی غلظت ذرات ریز جوی (PM2.5)، دما و شاخص پوشش گیاهی (NDVI) بر روندPM2.5  در دورة زمانی 5 ساله (1400-1396) در سطح کشور ایران پرداخته شده است. داده­ های غلظت ذرات ریز جوی PM2.5، دما و شاخص پوشش گیاهی بر مبنای مدل­های ماهواره ­ای MERRA-2، FLDAS و MODIS استخراج شده است. در دورة پنج سالة مطالعاتی، یک روند تا حدودی نزولی برای غلظت هوای PM2.5 مشاهده گردید. نتایج کمترین میانگین سالانة غلظت ذرات ریز جوی را در طی سال­ های 1398 و 1399 نشان داد. همچنین یک همبستگی قوی بین غلظت PM2.5 و دما به‌دست آمد. بیشترین میانگین غلظت PM2.5 در شمال غربی، غرب و جنوب غرب ایران رخ داده است. در مرحلة بعد، برای پیش‌بینی وضعیت غلظت آتی ذرات ریز جویPM2.5  هوا، دما و شاخص پوشش گیاهی از رویکرد هموارسازی نمایی (Exponential Smoothing) در کتابخانة آماری پایتون (Statsmodels) برای مدل‌سازی سری‌های زمانی ماهانه استفاده شد. ارزیابی مدل­ها با دو معیار خطای جذر میانگین مربعات (RMSE) و ضریب تعیین (R2) به‌منظور حداقل نمودن خطای برآورد و یافتن مناسب ­ترین مدل از میان یازده مدل پیش ­بینی شده انجام شد. نتایج حاصل بیانگر آن است که مدل­ های هموارسازی نمایی دوگانه برای پیش­بینی غلظتPM2.5  و مدل های هموارسازی نمایی سه ­گانه با روند Holt-Winter برای پیش ­بینی داده ­های دما و NDVI مناسب­ تر است.  این مطالعه می ­تواند به درک بهتر اثرات اقتصادی، بهداشتی و محیط زیستی متأثر از آلودگی هوا با پیش ­بینی دوره­ای که سطوح آلودگی هوا ممکن است به‌ویژه بالا باشد، برای برنامه ­ریزی بهتر به مؤسسات دولتی و خصوصی کمک نماید.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction and spatiotemporal analysis of atmospheric Fine Particles and their effect on temperature and vegetation cover in Iran using Exponential Smoothing approach in Python

نویسندگان [English]

  • Faezeh Borhani 1
  • Amir Houshang Ehsani 1
  • Helia Sadat Hosseini Shekarabi 2

1 Department of Civil-Environmental Engineering, School of Environment, University of Tehran, Tehran, Iran.

2 Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran. Head of the General Department of Environment and Sustainable Development, Deputy of Municipal Service

چکیده [English]

A relatively accurate prediction of the concentration of pollutants and environmental variables, short-term and long-term, is an important step in reducing the damage caused by poor air quality. In this study, first by spatiotemporal analysis and then, using prediction techniques, to predict the concentration of fine atmospheric particles (PM2.5), temperature and vegetation cover index (NDVI) on the trend of PM2.5 in a period of 5 years (2017-2022) was discussed at the level of Iran. The data of PM2.5 concentration, temperature and vegetation index were extracted based on MERRA-2, FLDAS and MODIS satellite models. In the five-year study period, a somewhat downward trend was observed for the air concentration of PM2.5. The results showed the lowest annual average concentration of fine atmospheric particles in 2019 and 2020. Also, a strong correlation between PM2.5 concentration and temperature was obtained. The highest average concentration of PM2.5 occurred in the northwest, west, and southwest of Iran. In the next step, in order to predict the future concentration of PM2.5 air particles, temperature and vegetation index, the Exponential Smoothing approach was used in the Python statistical library (Statsmodels) to model monthly time series. Evaluation of the models with two criteria of root mean square error (RMSE) and coefficient of determination (R2) was done to minimize the estimation error and find the most suitable model among the eleven predicted models. The results show that double exponential smoothing models are more suitable for predicting PM2.5 concentration and triple exponential smoothing models with Holt-Winter trend are more suitable for predicting temperature and NDVI data. This study can help public and private institutions to better understand economic, health and environmental condition affected by air pollution effects by predicting the period when air pollution levels may be particularly high.

کلیدواژه‌ها [English]

  • Temperature
  • Atmospheric fine particles (PM2.5)
  • Normalized difference vegetation index (NDVI)
  • Exponential Smoothing
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نشریه محیط زیست طبیعی
دوره 76، شماره 2 - شماره پیاپی 2
تیر 1402
صفحه 325-344