Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China

This case study was conducted to quantify the effects of urban greenspace patterns on particle matter (PM) concentration in Zhengzhou, China by using redundancy and variation partitioning analysis. Nine air-quality monitoring stations (AQMS) were selected as the central points. Six distances of 1 km...

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Main Authors: Yakai Lei, Yanbo Duan, Dan He, Xiwen Zhang, Lanqi Chen, Yonghua Li, Yu Gary Gao, Guohang Tian, Jingbiao Zheng
Format: Article
Language:English
Published: MDPI AG 2018-05-01
Series:Atmosphere
Subjects:
Online Access:http://www.mdpi.com/2073-4433/9/5/199
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spelling doaj-873b5a6e9fc044cc8db09673f8516a702020-11-24T22:08:57ZengMDPI AGAtmosphere2073-44332018-05-019519910.3390/atmos9050199atmos9050199Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, ChinaYakai Lei0Yanbo Duan1Dan He2Xiwen Zhang3Lanqi Chen4Yonghua Li5Yu Gary Gao6Guohang Tian7Jingbiao Zheng8Forestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaForestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaForestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaForestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaForestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaForestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaOSU South Centers, The Ohio State University, 1864 Shyville Road, Piketon, OH 45661, USAForestry College, Henan Agricultural University, 95 Wenhua Road, Zhengzhou 450002, ChinaInstitute of Geographic, Space Information Engineer University, Zhengzhou 450001, ChinaThis case study was conducted to quantify the effects of urban greenspace patterns on particle matter (PM) concentration in Zhengzhou, China by using redundancy and variation partitioning analysis. Nine air-quality monitoring stations (AQMS) were selected as the central points. Six distances of 1 km, 2 km, 3 km, 4 km, 5 km, and 6 km were selected as the side lengths of the squares with each AQMS serving as the central point, respectively. We found: (1) the fine size of PM (PM2.5) and coarse size of PM (PM10) among four seasons showed significant differences; during winter, the concentration of PM2.5 and PM10 were both highest, and PM2.5 and PM10 concentration in summer were lowest. (2) To effectively reduce the PM2.5 pollution, the percentage of greenspace, the differences in areas among greenspace patches, and the edge complexity of greenspace patches should be increased at distances of 2 km and 3 km. To effectively reduce PM10, the percentage of greenspace at a distance of 4 km, the edge density at distances of 2 km and 4 km, and the average area of greenspace patches at a distance of 1 km should be increased. (3) Greenspace pattern significantly affected PM2.5 at a distance of 3 km, and PM10 at a distance of 4 km. From shorter distance to longer distance, the proportion of variance explained by greenspace showed a decline–increase–decline–increase trend for PM2.5, and a decline–increase–decline trend for PM10. At shorter distances, the composition of greenspace was more effective in reducing the PM pollution, and the configuration of greenspace played a more important role at longer distances. The results should lead to specific guidelines for more cost-effective and environmentally sound greenspace planning.http://www.mdpi.com/2073-4433/9/5/199particulate matterspatial patterngreenspaceredundancy analysis
collection DOAJ
language English
format Article
sources DOAJ
author Yakai Lei
Yanbo Duan
Dan He
Xiwen Zhang
Lanqi Chen
Yonghua Li
Yu Gary Gao
Guohang Tian
Jingbiao Zheng
spellingShingle Yakai Lei
Yanbo Duan
Dan He
Xiwen Zhang
Lanqi Chen
Yonghua Li
Yu Gary Gao
Guohang Tian
Jingbiao Zheng
Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China
Atmosphere
particulate matter
spatial pattern
greenspace
redundancy analysis
author_facet Yakai Lei
Yanbo Duan
Dan He
Xiwen Zhang
Lanqi Chen
Yonghua Li
Yu Gary Gao
Guohang Tian
Jingbiao Zheng
author_sort Yakai Lei
title Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China
title_short Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China
title_full Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China
title_fullStr Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China
title_full_unstemmed Effects of Urban Greenspace Patterns on Particulate Matter Pollution in Metropolitan Zhengzhou in Henan, China
title_sort effects of urban greenspace patterns on particulate matter pollution in metropolitan zhengzhou in henan, china
publisher MDPI AG
series Atmosphere
issn 2073-4433
publishDate 2018-05-01
description This case study was conducted to quantify the effects of urban greenspace patterns on particle matter (PM) concentration in Zhengzhou, China by using redundancy and variation partitioning analysis. Nine air-quality monitoring stations (AQMS) were selected as the central points. Six distances of 1 km, 2 km, 3 km, 4 km, 5 km, and 6 km were selected as the side lengths of the squares with each AQMS serving as the central point, respectively. We found: (1) the fine size of PM (PM2.5) and coarse size of PM (PM10) among four seasons showed significant differences; during winter, the concentration of PM2.5 and PM10 were both highest, and PM2.5 and PM10 concentration in summer were lowest. (2) To effectively reduce the PM2.5 pollution, the percentage of greenspace, the differences in areas among greenspace patches, and the edge complexity of greenspace patches should be increased at distances of 2 km and 3 km. To effectively reduce PM10, the percentage of greenspace at a distance of 4 km, the edge density at distances of 2 km and 4 km, and the average area of greenspace patches at a distance of 1 km should be increased. (3) Greenspace pattern significantly affected PM2.5 at a distance of 3 km, and PM10 at a distance of 4 km. From shorter distance to longer distance, the proportion of variance explained by greenspace showed a decline–increase–decline–increase trend for PM2.5, and a decline–increase–decline trend for PM10. At shorter distances, the composition of greenspace was more effective in reducing the PM pollution, and the configuration of greenspace played a more important role at longer distances. The results should lead to specific guidelines for more cost-effective and environmentally sound greenspace planning.
topic particulate matter
spatial pattern
greenspace
redundancy analysis
url http://www.mdpi.com/2073-4433/9/5/199
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