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Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform

For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and...

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Autores principales: Su, Yana, Sha, Yongzhong, Zhai, Guangyu, Zong, Shengliang, Jia, Jiehua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546662/
https://www.ncbi.nlm.nih.gov/pubmed/28432621
http://dx.doi.org/10.1007/s11356-017-8959-8
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author Su, Yana
Sha, Yongzhong
Zhai, Guangyu
Zong, Shengliang
Jia, Jiehua
author_facet Su, Yana
Sha, Yongzhong
Zhai, Guangyu
Zong, Shengliang
Jia, Jiehua
author_sort Su, Yana
collection PubMed
description For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and impact factor of the air pollution time series. The analysis showed that (1) air pollution in the two cities was non-stationary and nonlinear, had multiple timescales, and exhibited the characteristics of high in winter and spring and low in summer and autumn. (2) The monthly variation in air pollution in Shanghai was not significant, whereas the seasonal variation of air pollution in Lanzhou was obvious. (3) Air pollution in Shanghai showed an ascending tendency, whereas that in Lanzhou presented a descending tendency. Overall, air pollution in Lanzhou was higher than that in Shanghai, but the situation has reversed since 2015. (4) The primary cycles of air pollution in these two cities were close, but the secondary cycles were significantly different. The aforementioned differences were mainly due to the impact of topographical and meteorological factors in Lanzhou, the weather process and the surrounding environment in Shanghai. These conclusions have reference significance for Shanghai and Lanzhou to control air pollution. The multi-timescale variation and local features of the wavelet analysis method used in this study can be applied to varied aspects of air pollution analysis. The identification of cycle characteristics and the monitoring, forecasting, and controlling of air pollution can yield valuable reference.
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spelling pubmed-65466622019-06-19 Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform Su, Yana Sha, Yongzhong Zhai, Guangyu Zong, Shengliang Jia, Jiehua Environ Sci Pollut Res Int Reducing air and soil pollution in China: issues in environmental technologies and management For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and impact factor of the air pollution time series. The analysis showed that (1) air pollution in the two cities was non-stationary and nonlinear, had multiple timescales, and exhibited the characteristics of high in winter and spring and low in summer and autumn. (2) The monthly variation in air pollution in Shanghai was not significant, whereas the seasonal variation of air pollution in Lanzhou was obvious. (3) Air pollution in Shanghai showed an ascending tendency, whereas that in Lanzhou presented a descending tendency. Overall, air pollution in Lanzhou was higher than that in Shanghai, but the situation has reversed since 2015. (4) The primary cycles of air pollution in these two cities were close, but the secondary cycles were significantly different. The aforementioned differences were mainly due to the impact of topographical and meteorological factors in Lanzhou, the weather process and the surrounding environment in Shanghai. These conclusions have reference significance for Shanghai and Lanzhou to control air pollution. The multi-timescale variation and local features of the wavelet analysis method used in this study can be applied to varied aspects of air pollution analysis. The identification of cycle characteristics and the monitoring, forecasting, and controlling of air pollution can yield valuable reference. Springer Berlin Heidelberg 2017-04-21 2019 /pmc/articles/PMC6546662/ /pubmed/28432621 http://dx.doi.org/10.1007/s11356-017-8959-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Reducing air and soil pollution in China: issues in environmental technologies and management
Su, Yana
Sha, Yongzhong
Zhai, Guangyu
Zong, Shengliang
Jia, Jiehua
Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
title Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
title_full Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
title_fullStr Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
title_full_unstemmed Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
title_short Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
title_sort comparison of air pollution in shanghai and lanzhou based on wavelet transform
topic Reducing air and soil pollution in China: issues in environmental technologies and management
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546662/
https://www.ncbi.nlm.nih.gov/pubmed/28432621
http://dx.doi.org/10.1007/s11356-017-8959-8
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