Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model

As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some cor...

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Autores principales:	Lirong Yin, Lei Wang, Weizheng Huang, Shan Liu, Bo Yang, Wenfeng Zheng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolution neural network Moran’s <i>I</i> LISA aggregation graph haze spatial autocorrelation Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/60b180760bb84d958137e447f8db4169
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id	oai:doaj.org-article:60b180760bb84d958137e447f8db4169
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spelling	oai:doaj.org-article:60b180760bb84d958137e447f8db41692021-11-25T16:44:14ZSpatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model10.3390/atmos121114082073-4433https://doaj.org/article/60b180760bb84d958137e447f8db41692021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1408https://doaj.org/toc/2073-4433As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s <i>I</i> to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s <i>I</i>, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.Lirong YinLei WangWeizheng HuangShan LiuBo YangWenfeng ZhengMDPI AGarticleconvolution neural networkMoran’s <i>I</i>LISA aggregation graphhazespatial autocorrelationMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1408, p 1408 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolution neural network Moran’s <i>I</i> LISA aggregation graph haze spatial autocorrelation Meteorology. Climatology QC851-999
spellingShingle	convolution neural network Moran’s <i>I</i> LISA aggregation graph haze spatial autocorrelation Meteorology. Climatology QC851-999 Lirong Yin Lei Wang Weizheng Huang Shan Liu Bo Yang Wenfeng Zheng Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model
description	As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s <i>I</i> to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s <i>I</i>, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.
format	article
author	Lirong Yin Lei Wang Weizheng Huang Shan Liu Bo Yang Wenfeng Zheng
author_facet	Lirong Yin Lei Wang Weizheng Huang Shan Liu Bo Yang Wenfeng Zheng
author_sort	Lirong Yin
title	Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model
title_short	Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model
title_full	Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model
title_fullStr	Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model
title_full_unstemmed	Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model
title_sort	spatiotemporal analysis of haze in beijing based on the multi-convolution model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/60b180760bb84d958137e447f8db4169
work_keys_str_mv	AT lirongyin spatiotemporalanalysisofhazeinbeijingbasedonthemulticonvolutionmodel AT leiwang spatiotemporalanalysisofhazeinbeijingbasedonthemulticonvolutionmodel AT weizhenghuang spatiotemporalanalysisofhazeinbeijingbasedonthemulticonvolutionmodel AT shanliu spatiotemporalanalysisofhazeinbeijingbasedonthemulticonvolutionmodel AT boyang spatiotemporalanalysisofhazeinbeijingbasedonthemulticonvolutionmodel AT wenfengzheng spatiotemporalanalysisofhazeinbeijingbasedonthemulticonvolutionmodel
_version_	1718413020185493504

Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model

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