Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China
Fine particulate matter (PM2.5) pollution has become one of the greatest urban issues in China. Studies have shown that PM2.5 pollution is strongly related to the land use pattern at the micro-scale and optimizing the land use pattern has been suggested as an approach to mitigate PM2.5 pollution. Ho...
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doaj-4da2287d7b444dbfb3a6e76b176f238c2020-11-24T22:26:23ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012017-04-0114546210.3390/ijerph14050462ijerph14050462Impact of Land Use on PM2.5 Pollution in a Representative City of Middle ChinaHaiou Yang0Wenbo Chen1Zhaofeng Liang2College of Forestry, Jiangxi Agricultural University, Nanchang 330045, ChinaKey Laboratory of Landscape and Environment, Jiangxi Agricultural University, Nanchang 330045, ChinaKey Laboratory of Landscape and Environment, Jiangxi Agricultural University, Nanchang 330045, ChinaFine particulate matter (PM2.5) pollution has become one of the greatest urban issues in China. Studies have shown that PM2.5 pollution is strongly related to the land use pattern at the micro-scale and optimizing the land use pattern has been suggested as an approach to mitigate PM2.5 pollution. However, there are only a few researches analyzing the effect of land use on PM2.5 pollution. This paper employed land use regression (LUR) models and statistical analysis to explore the effect of land use on PM2.5 pollution in urban areas. Nanchang city, China, was taken as the study area. The LUR models were used to simulate the spatial variations of PM2.5 concentrations. Analysis of variance and multiple comparisons were employed to study the PM2.5 concentration variances among five different types of urban functional zones. Multiple linear regression was applied to explore the PM2.5 concentration variances among the same type of urban functional zone. The results indicate that the dominant factor affecting PM2.5 pollution in the Nanchang urban area was the traffic conditions. Significant variances of PM2.5 concentrations among different urban functional zones throughout the year suggest that land use types generated a significant impact on PM2.5 concentrations and the impact did not change as the seasons changed. Land use intensity indexes including the building volume rate, building density, and green coverage rate presented an insignificant or counter-intuitive impact on PM2.5 concentrations when studied at the spatial scale of urban functional zones. Our study demonstrates that land use can greatly affect the PM2.5 levels. Additionally, the urban functional zone was an appropriate spatial scale to investigate the impact of land use type on PM2.5 pollution in urban areas.http://www.mdpi.com/1660-4601/14/5/462fine particulate matter (PM2.5)land useland use regression (LUR)statistical analysisurban functional zone |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Haiou Yang Wenbo Chen Zhaofeng Liang |
spellingShingle |
Haiou Yang Wenbo Chen Zhaofeng Liang Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China International Journal of Environmental Research and Public Health fine particulate matter (PM2.5) land use land use regression (LUR) statistical analysis urban functional zone |
author_facet |
Haiou Yang Wenbo Chen Zhaofeng Liang |
author_sort |
Haiou Yang |
title |
Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China |
title_short |
Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China |
title_full |
Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China |
title_fullStr |
Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China |
title_full_unstemmed |
Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China |
title_sort |
impact of land use on pm2.5 pollution in a representative city of middle china |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1660-4601 |
publishDate |
2017-04-01 |
description |
Fine particulate matter (PM2.5) pollution has become one of the greatest urban issues in China. Studies have shown that PM2.5 pollution is strongly related to the land use pattern at the micro-scale and optimizing the land use pattern has been suggested as an approach to mitigate PM2.5 pollution. However, there are only a few researches analyzing the effect of land use on PM2.5 pollution. This paper employed land use regression (LUR) models and statistical analysis to explore the effect of land use on PM2.5 pollution in urban areas. Nanchang city, China, was taken as the study area. The LUR models were used to simulate the spatial variations of PM2.5 concentrations. Analysis of variance and multiple comparisons were employed to study the PM2.5 concentration variances among five different types of urban functional zones. Multiple linear regression was applied to explore the PM2.5 concentration variances among the same type of urban functional zone. The results indicate that the dominant factor affecting PM2.5 pollution in the Nanchang urban area was the traffic conditions. Significant variances of PM2.5 concentrations among different urban functional zones throughout the year suggest that land use types generated a significant impact on PM2.5 concentrations and the impact did not change as the seasons changed. Land use intensity indexes including the building volume rate, building density, and green coverage rate presented an insignificant or counter-intuitive impact on PM2.5 concentrations when studied at the spatial scale of urban functional zones. Our study demonstrates that land use can greatly affect the PM2.5 levels. Additionally, the urban functional zone was an appropriate spatial scale to investigate the impact of land use type on PM2.5 pollution in urban areas. |
topic |
fine particulate matter (PM2.5) land use land use regression (LUR) statistical analysis urban functional zone |
url |
http://www.mdpi.com/1660-4601/14/5/462 |
work_keys_str_mv |
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