پیش بینی غلظت آلاینده های هوای تهران بر اساس متغیرهای هواشناسی با استفاده از شبکه عصبی مصنوعی و رگرسیون خطی در فصول گرم و سرد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشدعلوم و مهندسی محیط زیست، دانشکده علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران

2 نانوتکنولوژی

3 استادیار، دانشکده محیط زیست، دانشگاه تهران

4 استادیار، دانشکده ریاضیات و علوم کامپیوتر، دانشگاه حکیم سبزواری

چکیده

تهران آلوده‌ترین شهر کشور محسوب می‌شود که این آلودگی می‌تواند آثار دراز مدت و کوتاه مدتی بر سلامت انسان داشته باشد. از این‌رو پیش‌بینی غلظت آلاینده‌ها می‌تواند در برنامه‌ریزی‌های پیشگیری و کنترل مفید واقع شود. روشهای متفاوتی برای پیش‌بینی وجود دارد و دراین میان سالها، روش‌های شبکه‌ی عصبی پیشرفت قابل توجهی در پیش‌بینی آلودگی هوا داشته است. در این مطالعه، از شبکه‌ی عصبی مصنوعی پرسپترون سه‌لایه به‌منظور پیش‌بینی غلظت آلاینده‌های PM10، CO و شاخص کیفیت هوا (AQI) در هوای شهر تهران استفاده شد. داده‌های غلظت آلاینده‌ها از اداره‌ی کنترل کیفیت هوای تهران جمع‌آوری شد و داده‌های هواشناسی از اداره‌‌ی کل سازمان هواشناسی کشور طی سال‌های 1392 و 1393 جمع‌آوری شد. بیشترین ضریب همبستگی (R2) برای آلاینده PM10 با مقدار 0.83 در فصول گرم بود و بیشترین ضریب همبستگی آلاینده CO مربوط به فصول سرد بود (R2=0.74). در نهایت بیشترین ضریب همبستگی AQI در فصل سرد (R2=0.57) بود. در مدل رگرسیون خطی بیشترین ضریب همبستگی با مقدار 0.58 برای آلاینده PM10 در فصول گرم بود. بیشترین ضریب همبستگی در این مدل برای آلاینده CO با مقدار 0.33 در فصل سرد بود. درنهایت بیشترین ضریب همبستگی AOI (R2=0.31) در فصل گرم بود. این به این معنی است که با تغییرات متغیرهای هواشناسی، غلظت CO و ذرات معلق و مقادیر شاخص AQI تغییر می‌کند به گونه‌ای که افزایش باد باعث پراکنش آلاینده و کاهش غلظت آن می‌شود و افزایش درجه حرارت باعث افزایش غلظت آلاینده می‌شود. بنابراین بین آنها ارتباط وجود دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of Air Pollutants Concentration Based on Meteorological Factors in Warm and Cold Season by Artificial Neural Network and Linear Regression, Case Study: Tehran

نویسندگان [English]

  • Razieh Farhadi 1
  • mojtaba hadavifar 2
  • Mazaher Moeinaddini 3
  • Mahmood Amintoosi 4

1 Department of environmental Science of Hakim Sabzevari University, Sabzevar, Iran

3 Assistant professor, environmental Science Department, University of Tehran, Tehran, Iran

4 Assistant professor, Faculty of Mathematics and Computer Science, Hakim Sabzevari University, Sabzevar, Iran

چکیده [English]

Tehran is the most polluted city in the country, which can have long-term and short-term effects on human health. Therefore, predicting the concentration of pollutants can be useful in prevention and control planning. There are different methods for predicting, and over the years, neural network methods have made significant progress in predicting pollution. In this study, an artificial neural network of three-layer perceptron was used to predict the concentrations of PM10, CO and air quality index (AQI) in air in Tehran. The concentrations of pollutants were collected from the Tehran Air Quality Control Department and the weather data collected from the Office of the Iranian Meteorological Organization during 2013 and 2015. The highest correlation coefficient (R2) for PM10 pollutant was 0.83 in warm seasons and the highest CO emission factor correlated with cold seasons (R2 = 0.74). Finally, the highest correlation coefficient of AQI was in cold season (R2 = 0.57). In linear regression model, the highest correlation coefficient with 0.58 for PM10 pollutant was in hot seasons. The highest correlation coefficient in this model was for the CO pollutant (0.33) in the cold season. Finally, the highest correlation coefficient was AOI (R2 = 0.31) in the warm season.

کلیدواژه‌ها [English]

  • Air pollution forecast
  • Carbon monoxid
  • Particulate matter
  • Air quality index
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