Unmanned aerial systems for modelling air pollution removal by urban greenery |
| |
| Institution: | 1. Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha, Suchdol 165 00, Czech Republic;2. Silesian University in Opava, Institute of Physics in Opava, Bezručovo náměstí 1150/13, CZ-746 01 Opava, Czech Republic;3. Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, CZ-603 00 Brno, Czech Republic;4. Department of Geology and Soil Science, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 3, CZ-613 00 Brno, Czech Republic;5. Institute of Environmental Technology, Energy and Environmental Technology Centre, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic;1. Department for International Scientific Cooperation in Southeast Europe – EFISEE, Croatian Forest Research Institute, Cvjetno naselje 41, 10450 Jastrebarsko, Croatia;2. Humboldt Universität zu Berlin, Rudower Chaussee 16, 12489 Berlin, Germany;3. Helmholtz Centre for Environmental Research – UFZ, Department of Computational Landscape Ecology, Permoserstraße 15, 04318 Leipzig, Germany;1. Key Laboratory of Wetland Ecology and Management, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. College of Natural Resources, University of Wisconsin-Stevens Point, 800 Reserve Street, Stevens Point, WI 54481, USA;4. Department of Human Resources Management, School of Business and Management, Jilin University, Changchun 130021, China;5. Environment and Resources College, Dalian Minzu University, Dalian 116600, China;1. Research Institute of Forestry, Chinese Academy of Forestry / Urban Forest Research Center, National Forestry and Grassland Administration, Beijing 100091, China;2. Beijing Turenscape Company Limited, Beijing 100080, China;1. Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya, Yokohama, Kanagawa 240-8501, Japan;2. Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.;3. Department of Earth System Science, Faculty of Science, Fukuoka University, 8-19-1, Nanakuma, Jonan-ku, Fukuoka 814-0180, Japan;1. Department of Horticulture, National Chung Hsing University, Taichung City, Taiwan;2. Innovation and Development Center of Sustainable Agriculture from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan, Taiwan;3. Department of Physical Therapy and Graduate Institute of Rehabilitation Science, China Medical University, 406040 Taichung, Taiwan;4. Department of Physical Medicine and Rehabilitation, Asia University Hospital, Asia University, 413505 Taichung, Taiwan;5. Department of Physical Medicine and Rehabilitation, China Medical University Hospital, 404332 Taichung, Taiwan;1. Faculty of Design and Environment, Technological and Higher Education Institute of Hong Kong, 133 Shing Tai Road, Chai Wan, Hong Kong, China;2. Landscape Division, Highways Department, Spectrum Tower, 53 Hung To Road, Kwun Tong, Kowloon, Hong Kong, China;3. Greening, Landscape and Tree Management Section, Development Bureau, 2 Tim Mei Avenue, Tamar, Hong Kong, China |
| |
| Abstract: | Urban greenery plays an important role in reducing air pollution, being one of the often-used, nature-based measures in sustainable and climate-resilient urban development. However, when modelling its effect on air pollution removal by dry deposition, coarse and time-limited data on vegetation properties are often included, disregarding the high spatial and temporal heterogeneity in urban forest canopies. Here, we present a detailed, physics-based approach for modelling particulate matter (PM10) and tropospheric ozone (O3) removal by urban greenery on a small scale that eliminates these constraints. Our procedure combines a dense network of low-cost optical and electrochemical air pollution sensors, and a remote sensing method for greenery structure monitoring derived from Unmanned aerial systems (UAS) imagery processed by the Structure from Motion (SfM) algorithm. This approach enabled the quantification of species- and individual-specific air pollution removal rates by woody plants throughout the growing season, exploring the high spatial and temporal variability of modelled removal rates within an urban forest. The total PM10 and O3 removal rates ranged from 7.6 g m-2 (PM10) and 12.6 g m-2 (O3) for mature trees of Acer pseudoplatanus to 0.1 g m-2 and 0.1 g m-2 for newly planted tree saplings of Salix daphnoides. The present study demonstrates that UAS-SfM can detect differences in structures among and within canopies and by involving these characteristics, they can shift the modelling of air pollution removal towards a level of individual woody plants and beyond, enabling more realistic and accurate quantification of air pollution removal. Moreover, this approach can be similarly applied when modelling other ecosystem services provided by urban greenery. |
| |
| Keywords: | Dry deposition Ground-level ozone Leaf area index Particulate matter Structure from motion Unmanned aerial systems |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
