Investigation of the impact of urban vegetation on air pollutants based on remotely sensed measurements: a case study in Shenzhen, China

Weimin Wang; Lijun Yang; Liming Wang; Qinghuai Zeng; Lihuan He

doi:10.1117/12.2323686

18 September 2018 Investigation of the impact of urban vegetation on air pollutants based on remotely sensed measurements: a case study in Shenzhen, China

Weimin Wang, Lijun Yang, Liming Wang, Qinghuai Zeng, Lihuan He

Author Affiliations +

Proceedings Volume 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV; 107670G (2018) https://doi.org/10.1117/12.2323686
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

The spatial and temporal distribution of atmospheric pollutants (especially PM_2.5), which may cause adverse effects on human health and the environment, are affected by urban vegetation through deposition and dispersion processes. Although lots of studies have been conducted to investigate this effect, we still lack the knowledge of how urban vegetation reduces PM_2.5 and other air pollutions quantitatively. In this study, Landsat 8 data are used to retrieve the urban surface parameters (including Leaf Area Index and Normalized Difference Impervious Surface Index). Meanwhile, hourly PM_2.5 and other air pollutions concentrations over a period of one year (from Jan. 1, 2016 to Dec. 31, 2016) were obtained at a real-time air quality monitoring system in Shenzhen, P.R. China. It is found that, urban vegetation has little effect on air pollutants. Meanwhile, the results show that, a strong relationship between impervious surface and air pollutants are found.

Conference Presentation

Citation Download Citation

Weimin Wang, Lijun Yang, Liming Wang, Qinghuai Zeng, and Lihuan He "Investigation of the impact of urban vegetation on air pollutants based on remotely sensed measurements: a case study in Shenzhen, China", Proc. SPIE 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV, 107670G (18 September 2018); https://doi.org/10.1117/12.2323686

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