Journal
SUSTAINABILITY
Volume 13, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/su13010206
Keywords
air pollution control; historical evaluation; deep learning; forecast; Chengdu-Chongqing region of China
Funding
- National and Regional Research Projects of Ministry of Education of the People's Republic of China in 2020 [2020-N53]
- Decision-making Consultation Research Key Project of Shanghai Municipal Government [2020-A-003-B]
- Decision Consulting Cultivation Project of University of Shanghai for Science and Technology [2020-JCPY01]
- Connotation Construction Project for University Think Tank of Shanghai Municipal Education Commission [2020 Xuanchuan 6-109]
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This study focuses on the Chengdu-Chongqing region in China, using historical data and deep learning methods to assess and predict trends in air quality. It found that various cities in the region showed visible improvements in air quality, providing relevant recommendations for future air pollution control policy formulation.
Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya'an's lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong's lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.
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