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بررسی آلودگی هوا در دوران کرونا و پیش از آن در کلان‌شهرهای تهران، اصفهان و قم

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار گروه سنجش از دور و GIS، دانشگاه تربیت مدرس

2 دانشیار دانشکده اقتصاد، مدیریت و علوم اجتماعی ، دانشگاه شیراز

چکیده

فعالیت‌های صنعتی و ترافیک شهری منجر به افزایش آلودگی هوا در کلان‌شهرها می‌شود و این آلودگی سبب افزایش بیماری‌های بسیاری در افراد شده است؛ بنابراین بررسی و مطالعة مناطق آلوده برای مدیریت شهرها مهم است. با توجه ‌به اهمیت موضوع، هدف از این مطالعه بررسی وضعیت آلودگی هوا در کلان‌شهرهای تهران، اصفهان و قم از نظر آلاینده‌های NO2، CO2، CO، و CH4، پیش از کرونا (2019-2018) و حین کرونا (2021-2020) طی چهار فصل متفاوت سال است. همچنین با استفاده از روش همبستگی پیرسون و شبکه‌های عصبی RBF (شبکة عصبی تابع شعاعی پایه)، ارتباط بین دما و آلاینده‌ها بررسی شد. نتایج این مطالعه نشان داد که در کلان‌شهرهای تهران و اصفهان، میزان آلودگی هوا بیشتر از سایر مناطق است؛ همچنین میزان آلودگی در دوران کرونا در قیاس با پیش از کرونا، کاهش چشمگیری داشته است. افزون‌براینها نتایج حاصل از روش رگرسیون بیان کرد که افزایش دما با میزان آلودگی ارتباط معنی‌داری دارد (R2=0.981)؛ به‌گونه‌ای‌که در مناطق دارای آلودگی، میزان دما هم بیشتر بوده است. نتایج استفاده از روش RBF نیز حاکی از دقت بالای مدل در پیش‌بینی میزان آلودگی هوا بوده است (R2 = 0.85 ، RMSE = 0.08)؛ در نتیجه، این تحقیق بر نیاز به اقدامات جامع به‌منظور کاهش آلودگی هوا، به‌ویژه در مناطق بسیار آلوده، تأکید می‌کند.
 

کلیدواژه‌ها

عنوان مقاله [English]

Investigating Air Pollution during the Corona Era and before that in the Metropolises of Tehran, Isfahan and Qom

نویسندگان [English]

  • M Shaygan 1
  • Marzieh Mokarram 2

1 Assistant Professor, Dept. of Remote Sensing & GIS, Tarbiat Modares University, Tehran, Iran

2 Associate Professor, Department of Geography, Faculty of Economics, Management and Social sciences, Shiraz University, Shiraz, Iran

چکیده [English]

Industrial activities and urban traffic contribute to increased air pollution in large cities, resulting in a rise in various diseases among the population. Consequently, studying and investigating polluted areas is crucial for effective city management. This study aims to examine the air pollution levels in Tehran, Isfahan, and Qom cities, focusing on NO2, CO2, CO, and CH4 pollutants, during two distinct periods: pre-COVID-19 (2018-2019) and during COVID-19 (2020-2021), across all four seasons. By employing the Pearson correlation method and RBF neural networks (radial basis function neural network), the relationship between temperature and pollutants was explored. The findings reveal higher levels of air pollution in Tehran and Isfahan compared to other regions. Moreover, the study demonstrates a significant reduction in pollution during the COVID-19 era compared to the pre-COVID-19 period. Additionally, the regression analysis highlights a strong correlation between temperature increase and pollution levels (R2=0.981). Furthermore, the RBF method exhibits high accuracy in predicting air pollution levels (R2 = 0.85, RMSE = 0.08). In conclusion, this research underscores the urgent need for comprehensive measures to mitigate air pollution, particularly in highly polluted areas, and emphasizes the role of temperature as a crucial factor affecting pollution levels.

کلیدواژه‌ها [English]

  • air pollution
  • remote sensing
  • neural networks
  • metropolis
  • earth surface temperature
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نشریه سنجش از دور و GIS ایران
دوره 15، شماره 2 - شماره پیاپی 58
مرداد 1402
صفحه 101-116
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