Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul

We analyzed the correlation between air pollution indicators representing the earth changes in urban areas and socio-economic indicators representing human influences in urban areas spatially. This study is meaningful as it spatially represents the correlation between human influences and Earth syst...

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Autores principales:	Gyu-eun Lee, Ji-Hyun Lee
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Anthropocene Great acceleration Earth system change Socio-economic indicators Spatial model Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/2adefd1ed03d4020b680bc523b180b1b
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spelling	oai:doaj.org-article:2adefd1ed03d4020b680bc523b180b1b2021-12-01T04:47:54ZSpatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul1470-160X10.1016/j.ecolind.2021.107535https://doaj.org/article/2adefd1ed03d4020b680bc523b180b1b2021-06-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21002004https://doaj.org/toc/1470-160XWe analyzed the correlation between air pollution indicators representing the earth changes in urban areas and socio-economic indicators representing human influences in urban areas spatially. This study is meaningful as it spatially represents the correlation between human influences and Earth system changes. Seoul and its surrounding metropolitan area were selected as the case study area, as these areas have experienced urbanization at the same rates depicted in the Great Acceleration graphs of the Anthropocene. Data corresponding to the socio-economic and Earth system change indicators used in the Great Acceleration graphs were collected to create a spatial model. Four cases with different spatial ranges and variables were composed and compared. Of the four cases, the Seoul range model using ground-level ozone rate was observed to have the highest explanatory power. Ground-level ozone rate was highly correlated with air pollutant emissions, number of parking lots, number of residential facilities, and reconstruction projects in urban areas. The model that had used fine particulate matter rate as the dependent variable did not significantly correlate with the explanatory variables. The results from the spatial correlation analysis were meaningful as they may be used to inform policy decisions relating to mitigating changes in the district system in local urban areas.Gyu-eun LeeJi-Hyun LeeElsevierarticleAnthropoceneGreat accelerationEarth system changeSocio-economic indicatorsSpatial modelEcologyQH540-549.5ENEcological Indicators, Vol 125, Iss , Pp 107535- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anthropocene Great acceleration Earth system change Socio-economic indicators Spatial model Ecology QH540-549.5
spellingShingle	Anthropocene Great acceleration Earth system change Socio-economic indicators Spatial model Ecology QH540-549.5 Gyu-eun Lee Ji-Hyun Lee Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul
description	We analyzed the correlation between air pollution indicators representing the earth changes in urban areas and socio-economic indicators representing human influences in urban areas spatially. This study is meaningful as it spatially represents the correlation between human influences and Earth system changes. Seoul and its surrounding metropolitan area were selected as the case study area, as these areas have experienced urbanization at the same rates depicted in the Great Acceleration graphs of the Anthropocene. Data corresponding to the socio-economic and Earth system change indicators used in the Great Acceleration graphs were collected to create a spatial model. Four cases with different spatial ranges and variables were composed and compared. Of the four cases, the Seoul range model using ground-level ozone rate was observed to have the highest explanatory power. Ground-level ozone rate was highly correlated with air pollutant emissions, number of parking lots, number of residential facilities, and reconstruction projects in urban areas. The model that had used fine particulate matter rate as the dependent variable did not significantly correlate with the explanatory variables. The results from the spatial correlation analysis were meaningful as they may be used to inform policy decisions relating to mitigating changes in the district system in local urban areas.
format	article
author	Gyu-eun Lee Ji-Hyun Lee
author_facet	Gyu-eun Lee Ji-Hyun Lee
author_sort	Gyu-eun Lee
title	Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul
title_short	Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul
title_full	Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul
title_fullStr	Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul
title_full_unstemmed	Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul
title_sort	spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: a case study of seoul
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2adefd1ed03d4020b680bc523b180b1b
work_keys_str_mv	AT gyueunlee spatialcorrelationanalysisusingtheindicatorsoftheanthropocenefocusingonatmosphericpollutionacasestudyofseoul AT jihyunlee spatialcorrelationanalysisusingtheindicatorsoftheanthropocenefocusingonatmosphericpollutionacasestudyofseoul
_version_	1718405715925663744

Spatial correlation analysis using the indicators of the anthropocene focusing on atmospheric pollution: A case study of Seoul

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