Unmanned aerial systems for modelling air pollution removal by urban greenery

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F22%3A00564478" target="_blank" >RIV/86652079:_____/22:00564478 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43410/22:43922144 RIV/60460709:41330/22:91590 RIV/47813059:19630/22:A0000221 RIV/61989100:27710/22:10251660

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1618866722003004?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1618866722003004?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ufug.2022.127757" target="_blank" >10.1016/j.ufug.2022.127757</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unmanned aerial systems for modelling air pollution removal by urban greenery

Popis výsledku v původním jazyce

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 (O-3) 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 O-3 removal rates ranged from 7.6 g m(-2) (PM10) and 12.6 g m(-2) (O-3) 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.

Název v anglickém jazyce

Unmanned aerial systems for modelling air pollution removal by urban greenery

Popis výsledku anglicky

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 (O-3) 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 O-3 removal rates ranged from 7.6 g m(-2) (PM10) and 12.6 g m(-2) (O-3) 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Urban Forestry & Urban Greening

ISSN

1618-8667

e-ISSN

1610-8167

Svazek periodika

78

Číslo periodika v rámci svazku

DEC

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

12

Strana od-do

127757

Kód UT WoS článku

000880161100006

EID výsledku v databázi Scopus

2-s2.0-85140805916