مقاله انگلیسی رایگان در مورد رمزگشایی از ترافیک شهری موثر بر کیفیت هوا – الزویر ۲۰۲۳

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله رمزگشایی از ترافیک شهری تاثیرگذار بر کیفیت هوا با استفاده از یادگیری عمیق و فهرست آلاینده ها
عنوان انگلیسی مقاله Deciphering urban traffic impacts on air quality by deep learning and emission inventory
انتشار مقاله سال ۲۰۲۳
تعداد صفحات مقاله انگلیسی ۱۳ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) JCR – Master Journal List – Scopus – Medline
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۵٫۸۲۶ در سال ۲۰۲۰
شاخص H_index ۹۹ در سال ۲۰۲۱
شاخص SJR ۱٫۳۱۶ در سال ۲۰۲۰
شناسه ISSN ۱۰۰۱-۰۷۴۲
شاخص Quartile (چارک) Q1 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط محیط زیست – مهندسی کامپیوتر
گرایش های مرتبط آلودگی هوا – هوش مصنوعی
نوع ارائه مقاله
ژورنال
مجله  Journal of Environmental Sciences – مجله علوم زیست محیطی
دانشگاه University of Science and Technology of China, Hefei, China
کلمات کلیدی پیش بینی غلظت PM25، آلاینده های ترافیک، یادگیری عمیق، مکانیسم توجه، ماشین های جدید انرژی
کلمات کلیدی انگلیسی PM2.5 concentration forecast, Traffic emissions, Deep learning, Attention mechanism, New energy vehicles
شناسه دیجیتال – doi
https://doi.org/10.1016/j.jes.2021.12.035
کد محصول E16197
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:

Abstract

Introduction

Material and methods

Results and discussion

Conclusions

Acknowledgments

Supplementary materials

Reference

بخشی از متن مقاله:

Abstract

     Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 μg/m3 to 12.283 μg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.

Introduction

     Air pollution is a large obstacle to the world’s future sustainable developments, and millions of people die from air pollution-related diseases every year around the world (Zheng et al., 2017). This is seriously severe in some developing countries like China (Lelieveld et al., 2015), which has the highest country-level values globally for the population-weighted annual average concentration of PM2.5 (Tichenor and Sridhar, 2019; Zhang et al., 2012) and has been a major public health concern in recent years (Li et al., 2019a) . Shanghai, one of the most developed and populous cities in China, has suffered severe increasing haze episodes mostly attributed to the severe particle pollution especially fine particles (particles ≤۲٫۵μm in aerodynamic diameter; PM2.5)(Han et al., 2020) since 1990s with the rapid urbanization and industrialization (Wang et al., 2015) . Under these circumstances, air pollution-related diseases have emerged gradually, such as respiratory diseases in the elderly and preterm birth and low birth weight for birth when maternal exposure to PM2.5 in Shanghai(Li et al., 2019a; Liu et al., 2017) .

Conclusions

     In this article, we propose a novel method to quantify the influence of anthropogenic emissions on PM2.5 concentration with a novel and traceable deep learning model. Our experiment results indicate that the proposed model could achieve better fitting and prediction performances than the LSTM, GBRT, Seq2Seq, DA-RNN model and other deep learning models. Furthermore, we output the contributions of input parameter to the prediction in LRP method and visualize and analyze the spatial correlation between traffic flow and PM2.5.

     In addition, we discover that transportation emissions will play the most dominate role in future urban air pollution, and how to reduce traffic emissions are an unavoidable issue on achieving sustainable developments in modern cities. Meanwhile, to some extent, new energy vehicles can be considered as an effective way to reduce traffic emissions. However, the current promotion policies and efforts are far from enough. To further improve urban air quality, we need some more effective and powerful measurements in response to the rapid growth of urban traffic emissions.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد.