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Article: Do green spaces affect the spatiotemporal changes of PM2.5in Nanjing?

TitleDo green spaces affect the spatiotemporal changes of PM<inf>2.5</inf>in Nanjing?
Authors
KeywordsEdge density
Green space
Nanjing
Nature-based solution (NBS)
PM 2.5
Pollution control
Seasonal variation
Issue Date2016
Citation
Ecological Processes, 2016, v. 5, n. 7 How to Cite?
Abstract© 2016 Chen et al. Introduction: Among the most dangerous pollutants is PM 2.5 , which can directly pass through human lungs and move into the blood system. The use of nature-based solutions, such as increased vegetation cover in an urban landscape, is one of the possible solutions for reducing PM 2.5 concentration. Our study objective was to understand the importance of green spaces in pollution reduction. Methods: Daily PM 2.5 concentrations were manually collected at nine monitoring stations in Nanjing over a 534-day period from the air quality report of the China National Environmental Monitoring Center (CNEMC) to quantify the spatiotemporal change of PM 2.5 concentration and its empirical relationship with vegetation and landscape structure in Nanjing. Results: The daily average, minimum, and maximum PM2.5 concentrations from the nine stations were 74.0, 14.2, and 332.0 μg m -3 , respectively. Out of the 534 days, the days recorded as â excellentâ and â goodâ conditions were found mostly in the spring (30.7%), autumn (25.6%), and summer (24.5%), with only 19.2% of the days in the winter. High PM 2.5 concentrations exceeding the safe standards of the CNEMC were recorded predominately during the winter (39.3-100.0%). Our hypothesis that green vegetation had the potential to reduce PM 2.5 concentration was accepted at specific seasons and scales. The PM 2.5 concentration appeared very highly correlated (R 2 > 0.85) with green cover in spring at 1-2 km scales, highly correlated (R 2 > 0.6) in autumn and winter at 4 km scale, and moderately correlated in summer (R 2 > 0.4) at 2-, 5-, and 6-km scales. However, a non-significant correlation between green cover and PM 2.5 concentration was found when its level was > 75 μg m -3 . Across the Nanjing urban landscape, the east and southwest parts had high pollution levels. Conclusions: Although the empirical models seemed significant for spring only, one should not devalue the importance of green vegetation in other seasons because the regulations are often complicated by vegetation, meteorological conditions, and human activities.
Persistent Identifierhttp://hdl.handle.net/10722/251226

 

DC FieldValueLanguage
dc.contributor.authorChen, Jiquan-
dc.contributor.authorZhu, Liuyan-
dc.contributor.authorFan, Peilei-
dc.contributor.authorTian, Li-
dc.contributor.authorLafortezza, Raffaele-
dc.date.accessioned2018-02-01T01:54:57Z-
dc.date.available2018-02-01T01:54:57Z-
dc.date.issued2016-
dc.identifier.citationEcological Processes, 2016, v. 5, n. 7-
dc.identifier.urihttp://hdl.handle.net/10722/251226-
dc.description.abstract© 2016 Chen et al. Introduction: Among the most dangerous pollutants is PM 2.5 , which can directly pass through human lungs and move into the blood system. The use of nature-based solutions, such as increased vegetation cover in an urban landscape, is one of the possible solutions for reducing PM 2.5 concentration. Our study objective was to understand the importance of green spaces in pollution reduction. Methods: Daily PM 2.5 concentrations were manually collected at nine monitoring stations in Nanjing over a 534-day period from the air quality report of the China National Environmental Monitoring Center (CNEMC) to quantify the spatiotemporal change of PM 2.5 concentration and its empirical relationship with vegetation and landscape structure in Nanjing. Results: The daily average, minimum, and maximum PM2.5 concentrations from the nine stations were 74.0, 14.2, and 332.0 μg m -3 , respectively. Out of the 534 days, the days recorded as â excellentâ and â goodâ conditions were found mostly in the spring (30.7%), autumn (25.6%), and summer (24.5%), with only 19.2% of the days in the winter. High PM 2.5 concentrations exceeding the safe standards of the CNEMC were recorded predominately during the winter (39.3-100.0%). Our hypothesis that green vegetation had the potential to reduce PM 2.5 concentration was accepted at specific seasons and scales. The PM 2.5 concentration appeared very highly correlated (R 2 > 0.85) with green cover in spring at 1-2 km scales, highly correlated (R 2 > 0.6) in autumn and winter at 4 km scale, and moderately correlated in summer (R 2 > 0.4) at 2-, 5-, and 6-km scales. However, a non-significant correlation between green cover and PM 2.5 concentration was found when its level was > 75 μg m -3 . Across the Nanjing urban landscape, the east and southwest parts had high pollution levels. Conclusions: Although the empirical models seemed significant for spring only, one should not devalue the importance of green vegetation in other seasons because the regulations are often complicated by vegetation, meteorological conditions, and human activities.-
dc.languageeng-
dc.relation.ispartofEcological Processes-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectEdge density-
dc.subjectGreen space-
dc.subjectNanjing-
dc.subjectNature-based solution (NBS)-
dc.subjectPM 2.5-
dc.subjectPollution control-
dc.subjectSeasonal variation-
dc.titleDo green spaces affect the spatiotemporal changes of PM<inf>2.5</inf>in Nanjing?-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s13717-016-0052-6-
dc.identifier.scopuseid_2-s2.0-85024875874-
dc.identifier.volume5-
dc.identifier.issue7-
dc.identifier.spagenull-
dc.identifier.epagenull-
dc.identifier.eissn2192-1709-
dc.identifier.issnl2192-1709-

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