Understanding vegetation structures in green spaces to regulate atmospheric particulate matter and negative air ions

被引:24
作者
Niu, Xiang [1 ,3 ,4 ]
Li, Yu [1 ]
Li, Muni [1 ]
Zhang, Tong [1 ,2 ]
Meng, Huan [1 ,2 ]
Zhang, Zhi [1 ,2 ]
Wang, Bing [1 ,3 ,4 ]
Zhang, Weikang [1 ,2 ]
机构
[1] Shenyang Agr Univ, Dept Landscape Architecture, Landscape Planning Lab, Shenyang 110866, Liaoning, Peoples R China
[2] Key Lab Forest Tree Genet Breeding & Cultivat Liao, Beijing, Peoples R China
[3] Chinese Acad Forestry, Ecol & Nat Conservat Inst, Beijing 100091, Peoples R China
[4] State Forestry & Grassland Adm, Key Lab Forest Ecol & Environm, Beijing, Peoples R China
基金
美国国家科学基金会;
关键词
Vegetation types; Particulate matter; Negative ion; Canopy density; Green space; POLLUTION; DEPOSITION; QUALITY;
D O I
10.1016/j.apr.2022.101534
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Green spaces can effectively reduce atmospheric particulate matter (PM) pollution and regulate negative air ions (NAI), this ecological function of green spaces is directly determined by their types and structures. However, the mechanism of the interactions among the types and structures of green spaces, PM, and NAI are still unclear. In the present study, we selected Dongling Park in Shenyang, China as an example of a typical green space. We ran field experiments to analyze the influences of various green space types and structures on PM10, PM2.5, and NAI. The results showed that green spaces had significantly lower PM10 and PM2.5 and higher NAI concentrations than non-green spaces. Among the four vegetation types (tree-shrub-herb, tree-herb, shrub-herb, and tree-shrub), the tree-shrub-herb type most effectively decreased atmospheric PM10 and PM2.5 concentrations (32.54 +/- 8.14% and 21.26 +/- 4.07%, respectively) and increased NAI concentrations (115.43 +/- 10.00%) to a significantly greater extent than the other vegetation types (p < 0.05). Canopy density was strongly and significantly positively correlated with PM2.5 and PM10 concentrations (r = 0.84* and r = 0.86**, respectively). By contrast, shrub layer richness was significantly negatively correlated with PM2.5 and PM10 concentrations (r =-0.68* and r =-0.74*, respectively). Furthermore, significant positive correlations with NAI concentrations were detected in the canopy density and proportion of evergreen tree species significantly (r = 0.40* and r = 0.46*, respectively). The results illustrate that the green space vegetation types and structures, especially the density of tree canopy, the richness of shrub layer, and the proportion of evergreen tree species, can influence the airborne PM10, PM2.5 and NAI concentrations. Therefore, vegetation types and structures should be considered in the process of planning urban green spaces that perform the ecological function of improving air quality.
引用
收藏
页数:10
相关论文
共 48 条
[1]   Field investigations for evaluating green infrastructure effects on air quality in open-road conditions [J].
Abhijith, K., V ;
Kumar, Prashant .
ATMOSPHERIC ENVIRONMENT, 2019, 201 :132-147
[2]   Residential green space, air pollution, socioeconomic deprivation and cardiovascular medication sales in Belgium: A nationwide ecological study [J].
Aerts, Raf ;
Nemery, Benoit ;
Bauwelinck, Mariska ;
Trabelsi, Sonia ;
Deboosere, Patrick ;
Van Nieuwenhuyse, An ;
Nawrot, Tim S. ;
Casas, Lidia .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 712
[3]   Air ions and respiratory function outcomes: a comprehensive review [J].
Alexander, Dominik ;
Bailey, William ;
Perez, Vanessa ;
Mitchell, Meghan ;
Su, Steave .
JOURNAL OF NEGATIVE RESULTS IN BIOMEDICINE, 2013, 12
[4]   Numerical evaluation of urban green space scenarios effects on gaseous air pollutants in Tehran Metropolis based on WRF-Chem model [J].
Arghavani, Somayeh ;
Malakooti, Hossein ;
Bidokhti, Abbasali Aliakbari .
ATMOSPHERIC ENVIRONMENT, 2019, 214
[5]   Impact of fine particulate fluctuation and other variables on Beijing's air quality index [J].
Chen, Bo ;
Lu, Shaowei ;
Li, Shaoning ;
Wang, Bing .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2015, 22 (07) :5139-5151
[6]   The effects of negative air ions on cognitive function: an event-related potential (ERP) study [J].
Chu, Chien-Heng ;
Chen, Su-Ru ;
Wu, Chih-Han ;
Cheng, Yung-Chao ;
Cho, Yu-Min ;
Chang, Yu-Kai .
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2019, 63 (10) :1309-1317
[7]   How can vegetation protect us from air pollution? A critical review on green spaces' mitigation abilities for air-borne particles from a public health perspective-with implications for urban planning [J].
Diener, Arnt ;
Mudu, Pierpaolo .
SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 796
[8]   Research on public health and well-being associated to the vegetation configuration of urban green space, a case study of Shanghai, China [J].
Du, Hongyu ;
Zhou, Fengqi ;
Cai, Yongli ;
Li, Chunlan ;
Xu, Yanqing .
URBAN FORESTRY & URBAN GREENING, 2021, 59
[9]   Joint pollution and source apportionment of PM2.5 among three different urban environments in Sichuan Basin, China [J].
Fan, Jin ;
Shang, Yanan ;
Zhang, Xiaojiao ;
Wu, Xinni ;
Zhang, Meng ;
Cao, Jiayang ;
Luo, Bin ;
Zhang, Xiaoling ;
Wang, Shigong ;
Li, Shuangzhi ;
Liu, Hangqi ;
Wu, Pingli .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 714
[10]   Reduction of Atmospheric Suspended Particulate Matter Concentration and Influencing Factors of Green Space in Urban Forest Park [J].
Gao, Tian ;
Liu, Fang ;
Wang, Yang ;
Mu, Sen ;
Qiu, Ling .
FORESTS, 2020, 11 (09)