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Authors: Jingtian Xu 1 ; Simengyu Li 1 ; Xuesong Zhang 1 and Ruifang Wang 1 ; 2

Affiliations: 1 College of Agriculture and Forestry, Puer University, Puer, Yunnan Province, 665000, China ; 2 Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, China

Keyword(s): Antipollution, Alee-Tree, Pu ’er Avenue.

Abstract: Garden or urban street construction planting plants with high pollution resistance can not only play a remedial role in the surrounding environment, such as reducing dust and noise, absorbing toxic gases and substances, but also beautify the environment and play a role in shade and soil preservation, climate regulation, etc. Three street trees, Cassia nodosa, Cinnamomum camphora and Spathodea campanulata were selected as the objects, conductivity, chlorophyll content and plant leaf dust retention were used to reflect the pollution resistance characteristics of these three plants. The results showed that C. Camphor had the strongest anti-pollution characteristics, followed by Spathodea Campanulata, and Cassia Nodosa was the weakest. This conclusion provides theoretical guidance for the selection and utilization of street trees.

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Paper citation in several formats:
Xu, J.; Li, S.; Zhang, X. and Wang, R. (2022). Pollution Resistance Characteristics of Street Trees. In Proceedings of the 2nd Conference on Artificial Intelligence and Healthcare - CAIH; ISBN 978-989-758-594-4, SciTePress, pages 343-347. DOI: 10.5220/0011369700003444

@conference{caih22,
author={Jingtian Xu. and Simengyu Li. and Xuesong Zhang. and Ruifang Wang.},
title={Pollution Resistance Characteristics of Street Trees},
booktitle={Proceedings of the 2nd Conference on Artificial Intelligence and Healthcare - CAIH},
year={2022},
pages={343-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011369700003444},
isbn={978-989-758-594-4},
}

TY - CONF

JO - Proceedings of the 2nd Conference on Artificial Intelligence and Healthcare - CAIH
TI - Pollution Resistance Characteristics of Street Trees
SN - 978-989-758-594-4
AU - Xu, J.
AU - Li, S.
AU - Zhang, X.
AU - Wang, R.
PY - 2022
SP - 343
EP - 347
DO - 10.5220/0011369700003444
PB - SciTePress