Quantifying the Potential Contribution of Urban Forest to PM2.5 Removal in the City of Shanghai, China

被引:7
作者
Zhang, Biao [1 ,2 ]
Xie, Zixia [3 ]
She, Xinlu [4 ]
Gao, Jixi [5 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
[3] Guangdong Prov Acad Environm Sci, Guangzhou 510045, Peoples R China
[4] Minist Nat Resources Peoples Republ China, Beijing 100812, Peoples R China
[5] Minist Ecol & Environm Ctr Satellite Applicat Eco, Beijing 100094, Peoples R China
关键词
urban forest; particulate matter; PM; (2 5) removal; potential contribution; DRY DEPOSITION VELOCITY; AIR-POLLUTION; PARTICULATE MATTER; PARTICLES; VEGETATION; ASSOCIATIONS; IMPROVEMENT; POLLUTANTS; MORTALITY; NETWORK;
D O I
10.3390/atmos12091171
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Climate change and air pollution pose multiple health threats to humans through complex and interacting pathways, whereas urban vegetation can improve air quality by influencing pollutant deposition and dispersion. This study estimated the amount of PM2.5 removal by the urban forest in the city of Shanghai by using remote sensing data of vegetation and a model approach. We also identified its potential contribution of urban forest presence in relation to human population and particulate matter concentration. Results show that the urban forest in Shanghai reached 46,161 ha in 2017, and could capture 874 t of PM2.5 with an average of 18.94 kg/ha. There are significant spatial heterogeneities in the role of different forest communities and administrative districts in removing PM2.5. Although PM2.5 removal was relatively harmonized with the human population distribution in terms of space, approximately 57.41% of the urban forest presented low coupling between removal capacity and PM2.5 concentration. Therefore, we propose to plant more trees with high removal capacity of PM2.5 in the western areas of Shanghai, and increase vertical planting in bridge pillars and building walls to compensate the insufficient amount of urban forest in the center area.
引用
收藏
页数:16
相关论文
共 66 条
[1]   Spatial estimation of urban air pollution with the use of artificial neural network models [J].
Alimissis, A. ;
Philippopoulos, K. ;
Tzanis, C. G. ;
Deligiorgi, D. .
ATMOSPHERIC ENVIRONMENT, 2018, 191 :205-213
[2]  
[Anonymous], 2019, 2018 REV WORLD URB P
[3]  
Cao H., 2016, J. Shanghai Jiaotong Univ. (Agric. Sci.), V34, P76, DOI DOI 10.3969/J.ISSN.1671-9964.2016.05.011
[4]   Characterization and Cytotoxicity of PM<0.2, PM0.2-2.5 and PM2.5-10 around MSWI in Shanghai, China [J].
Cao, Lingling ;
Zeng, Jianrong ;
Liu, Ke ;
Bao, Liangman ;
Li, Yan .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2015, 12 (05) :5076-5089
[5]   Dry deposition velocity of total suspended particles and meteorological influence in four locations in Guangzhou, China [J].
Chen, Leifu ;
Peng, Shaolin ;
Liu, Jingang ;
Hou, Qianqian .
JOURNAL OF ENVIRONMENTAL SCIENCES, 2012, 24 (04) :632-639
[6]   Increased incidence of allergic rhinitis, bronchitis and asthma, in children living near a petrochemical complex with SO2 pollution [J].
Chiang, Tzu-Ying ;
Yuan, Tzu-Hsuen ;
Shie, Ruei-Hao ;
Chen, Chen-Fang ;
Chan, Chang-Chuan .
ENVIRONMENT INTERNATIONAL, 2016, 96 :1-7
[7]  
China Meteorological Information Center, 2005, Chinese building thermal environment analysis of specialized meteorological data collection
[8]  
Fang Yao-yao, 2015, Shengtaixue Zazhi, V34, P1516
[9]   Airborne foliar transfer of PM bound heavy metals in Cassia siamea: A less common route of heavy metal accumulation [J].
Gajbhiye, Triratnesh ;
Pandey, Sudhir Kumar ;
Kim, Ki-Hyun ;
Szulejko, Jan E. ;
Prasad, Satgur .
SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 573 :123-130
[10]  
Gao X., 2016, STUDY MONITORING DIS