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Effects of Urban Landscapes on Pollutant Concentrations in Chengdu Plain Urban Agglomeration

Frequent air pollution due to urbanization poses a severe threat to urban environments. In this study, the effects of urban landscapes on pollutant concentrations at different spatial and temporal scales are examined using “3S” technology, based on land-use (LU) classification maps for two time phas...

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Published in:Atmosphere 2022-09, Vol.13 (9), p.1492
Main Authors: Hu, Hua, Zeng, Shenglan, Han, Xiao
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description Frequent air pollution due to urbanization poses a severe threat to urban environments. In this study, the effects of urban landscapes on pollutant concentrations at different spatial and temporal scales are examined using “3S” technology, based on land-use (LU) classification maps for two time phases (2015 and 2018) in conjunction with monitoring data for air pollutants of the same periods. The results showed that: (1) A very high overall LU ratio was found for Chengdu Plain urban agglomeration (CPUA), with only 0.3% of the land being unused. Farmlands (43%), forests (36.45%), and grasslands (14.17%) were identified as the main landscape types. A decrease in the proportions of the total area occupied by farmlands and grasslands was 0.44% and 0.72%, respectively, and 0.10%, 0.04%, and 0.97% increased in the proportions of the total area occupied by forests, water bodies, and developed lands, respectively, were found from 2015 to 2018. (2) NO2, PM2.5, and PM10 were mainly distributed in the central and eastern parts of the study area, while SO2 was mainly distributed in the southwest. In 2018, compared with 2015, the maximum concentration of NO2 decreased from 60.36 μg/m3 to 49.75 μg/m3, and the distribution range of high concentration NO2 was reduced and concentrated in Chengdu; the concentrations of PM10 and PM2.5 decreased significantly, and the maximum concentration decreased by 20.53% and 23.93%, respectively, but the concentration of pollution in the northeast increased significantly. The scope of SO2 pollution had shifted from the south to the southwest, and the pollution level had decreased from south to north. (3) The effects of various landscape types on pollutant concentrations were complex. At a patch-type level, increasing the area proportions of “pollution-reducing” landscape types could reduce pollutant concentrations. Specifically, increasing the area, largest patch index, and patch cohesion of forests and grasslands, as well as reducing the area, largest patch index, and patch cohesion of farmlands and developed lands, could effectively lower pollutant concentrations. From a landscape pattern perspective, high shape regularity and low diversity of landscape patches resulted in high concentrations of NO2, PM10, and PM2.5. In contrast, high levels of dominance and aggregation of landscapes lead to low concentrations of SO2.
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In this study, the effects of urban landscapes on pollutant concentrations at different spatial and temporal scales are examined using “3S” technology, based on land-use (LU) classification maps for two time phases (2015 and 2018) in conjunction with monitoring data for air pollutants of the same periods. The results showed that: (1) A very high overall LU ratio was found for Chengdu Plain urban agglomeration (CPUA), with only 0.3% of the land being unused. Farmlands (43%), forests (36.45%), and grasslands (14.17%) were identified as the main landscape types. A decrease in the proportions of the total area occupied by farmlands and grasslands was 0.44% and 0.72%, respectively, and 0.10%, 0.04%, and 0.97% increased in the proportions of the total area occupied by forests, water bodies, and developed lands, respectively, were found from 2015 to 2018. (2) NO2, PM2.5, and PM10 were mainly distributed in the central and eastern parts of the study area, while SO2 was mainly distributed in the southwest. In 2018, compared with 2015, the maximum concentration of NO2 decreased from 60.36 μg/m3 to 49.75 μg/m3, and the distribution range of high concentration NO2 was reduced and concentrated in Chengdu; the concentrations of PM10 and PM2.5 decreased significantly, and the maximum concentration decreased by 20.53% and 23.93%, respectively, but the concentration of pollution in the northeast increased significantly. The scope of SO2 pollution had shifted from the south to the southwest, and the pollution level had decreased from south to north. (3) The effects of various landscape types on pollutant concentrations were complex. At a patch-type level, increasing the area proportions of “pollution-reducing” landscape types could reduce pollutant concentrations. Specifically, increasing the area, largest patch index, and patch cohesion of forests and grasslands, as well as reducing the area, largest patch index, and patch cohesion of farmlands and developed lands, could effectively lower pollutant concentrations. From a landscape pattern perspective, high shape regularity and low diversity of landscape patches resulted in high concentrations of NO2, PM10, and PM2.5. 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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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In this study, the effects of urban landscapes on pollutant concentrations at different spatial and temporal scales are examined using “3S” technology, based on land-use (LU) classification maps for two time phases (2015 and 2018) in conjunction with monitoring data for air pollutants of the same periods. The results showed that: (1) A very high overall LU ratio was found for Chengdu Plain urban agglomeration (CPUA), with only 0.3% of the land being unused. Farmlands (43%), forests (36.45%), and grasslands (14.17%) were identified as the main landscape types. A decrease in the proportions of the total area occupied by farmlands and grasslands was 0.44% and 0.72%, respectively, and 0.10%, 0.04%, and 0.97% increased in the proportions of the total area occupied by forests, water bodies, and developed lands, respectively, were found from 2015 to 2018. (2) NO2, PM2.5, and PM10 were mainly distributed in the central and eastern parts of the study area, while SO2 was mainly distributed in the southwest. In 2018, compared with 2015, the maximum concentration of NO2 decreased from 60.36 μg/m3 to 49.75 μg/m3, and the distribution range of high concentration NO2 was reduced and concentrated in Chengdu; the concentrations of PM10 and PM2.5 decreased significantly, and the maximum concentration decreased by 20.53% and 23.93%, respectively, but the concentration of pollution in the northeast increased significantly. The scope of SO2 pollution had shifted from the south to the southwest, and the pollution level had decreased from south to north. (3) The effects of various landscape types on pollutant concentrations were complex. At a patch-type level, increasing the area proportions of “pollution-reducing” landscape types could reduce pollutant concentrations. 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In this study, the effects of urban landscapes on pollutant concentrations at different spatial and temporal scales are examined using “3S” technology, based on land-use (LU) classification maps for two time phases (2015 and 2018) in conjunction with monitoring data for air pollutants of the same periods. The results showed that: (1) A very high overall LU ratio was found for Chengdu Plain urban agglomeration (CPUA), with only 0.3% of the land being unused. Farmlands (43%), forests (36.45%), and grasslands (14.17%) were identified as the main landscape types. A decrease in the proportions of the total area occupied by farmlands and grasslands was 0.44% and 0.72%, respectively, and 0.10%, 0.04%, and 0.97% increased in the proportions of the total area occupied by forests, water bodies, and developed lands, respectively, were found from 2015 to 2018. (2) NO2, PM2.5, and PM10 were mainly distributed in the central and eastern parts of the study area, while SO2 was mainly distributed in the southwest. In 2018, compared with 2015, the maximum concentration of NO2 decreased from 60.36 μg/m3 to 49.75 μg/m3, and the distribution range of high concentration NO2 was reduced and concentrated in Chengdu; the concentrations of PM10 and PM2.5 decreased significantly, and the maximum concentration decreased by 20.53% and 23.93%, respectively, but the concentration of pollution in the northeast increased significantly. The scope of SO2 pollution had shifted from the south to the southwest, and the pollution level had decreased from south to north. (3) The effects of various landscape types on pollutant concentrations were complex. At a patch-type level, increasing the area proportions of “pollution-reducing” landscape types could reduce pollutant concentrations. Specifically, increasing the area, largest patch index, and patch cohesion of forests and grasslands, as well as reducing the area, largest patch index, and patch cohesion of farmlands and developed lands, could effectively lower pollutant concentrations. From a landscape pattern perspective, high shape regularity and low diversity of landscape patches resulted in high concentrations of NO2, PM10, and PM2.5. In contrast, high levels of dominance and aggregation of landscapes lead to low concentrations of SO2.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/atmos13091492</doi><orcidid>https://orcid.org/0000-0002-2406-3351</orcidid><oa>free_for_read</oa></addata></record>
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subjects Agglomeration
Aggregation
Agricultural land
Air monitoring
Air pollution
Air quality
Cities
Cohesion
Environmental aspects
Environmental monitoring
Forests
Grasslands
inverse distance weighted interpolation
Land use
Landscape
Low concentrations
Metropolitan areas
Nitrogen dioxide
Outdoor air quality
Particulate matter
pollutant concentration
Pollutants
Pollution control
Pollution levels
Pollution monitoring
pollution-reducing landscape
Sulfur dioxide
uniform grid sampling
Urban areas
Urban environments
Urbanization
title Effects of Urban Landscapes on Pollutant Concentrations in Chengdu Plain Urban Agglomeration
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