Abstract
Urban atmospheric environmental problems have raised increasing attention in recent years. To confirm the impact of plant dust deposition capacity on urban atmosphere and spectral characteristics, this study carried out experiments in Xuhui District and Minhang District of Shanghai, and 4 common greening species were selected as research objects. In order to explore the changes in vegetation spectral characteristics, ASD FieldSpec 3 Spectrometer and 1/10000 electronic balance were used to measure the spectral data and dust data of samples. The results show as follows: (1) 380–680 nm and 750–1350 nm are the best spectral wavelengths to analyze the influence of dust deposition on spectrum. (2) The canopy reflectance spectra of tree species decrease with the increase of dust deposition, especially in the wavelength range of 750–1350 nm. (3) The first derivative and the second derivative are beneficial to observe the spectral changes and judge the position of the red edge. The red edge position of some tree species is easy to move under the interference of dust deposition. (4) Among the four tree species, the spectrum of Osmanthus fragrans is relatively undisturbed by dust deposition, and Osmanthus fragrans is a great tree species for urban greening. The research made a foundation for the future use of spectral information to estimate vegetation dust.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
References
Amato-Lourenco LF et al (2016) The influence of atmospheric particles on the elemental content of vegetables in urban gardens of Sao Paulo. Brazil Environ Pollut 216:125–134. https://doi.org/10.1016/j.envpol.2016.05.036
Bao L et al (2019) Foliar particulate matter distribution in urban road system of Beijing, China. Chin Geogr Sci 29(4):591–600. https://doi.org/10.1007/s11769-019-1057-8
Cai MF, Xin ZB, Yu XX (2017) Spatio-temporal variations in PM leaf deposition: a meta-analysis. Environ Pollut 231:207–218. https://doi.org/10.1016/j.envpol.2017.07.105
Cao ZX, Wang Q, Zheng CL (2015) Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments. Ecol Indic 54:96–107. https://doi.org/10.1016/j.ecolind.2015.02.027
Chai Y, Zhu N, Han H (2002) Dust removal effect of urban tree species in Harbin. J Appl Ecol 13(9):1121–1126. https://doi.org/10.13287/j.1001-9332.2002.0260
Chen LX et al (2016) Experimental examination of vegetation as bio-filter of particulate matters in the urban environment. Environ Pollut 208:198–208. https://doi.org/10.1016/j.envpol.2015.09.006
Chen LX et al (2017) Variation in tree species ability to capture and retain airborne fine particulate matter (PM2.5). Sci Rep 7(1):3206. https://doi.org/10.1038/s41598-017-03360-1
Gangale G, Prata AJ, Clarisse L (2010) The infrared spectral signature of volcanic ash determined from high-spectral resolution satellite measurements. Remote Sens Environ 114(2):414–425. https://doi.org/10.1016/j.rse.2009.09.007
Goetz AFH et al (1985) Imaging spectrometry for earth remote sensing. Science 228(4704):1147–1153. https://doi.org/10.1126/science.228.4704.1147
Haboudane D (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90(3):337–352. https://doi.org/10.1016/j.rse.2003.12.013
Han D et al (2019) A review on particulate matter removal capacity by urban forests at different scales. Urban For Urban Green 126565. https://doi.org/10.1016/j.ufug.2019.126565
Hansen PM, Schjoerring JK (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ 86(4):542–553. https://doi.org/10.1016/s0034-4257(03)00131-7
He C et al (2019) Particulate matter capturing capacity of roadside evergreen vegetation during the winter season. Urban For Urban Green 126510. https://doi.org/10.1016/j.ufug.2019.126510
Hong B, Lin BR, Qin HQ (2017) Numerical investigation on the effect of avenue trees on PM2.5 dispersion in urban street canyons. Atmosphere 8(12):129. https://doi.org/10.3390/atmos8070129
Katia P et al (2017) Quantification of fine dust deposition on different plant species in a vertical greening system. Ecol Eng 100. https://doi.org/10.1016/j.ecoleng.2016.12.032
Kayet N et al (2019) Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas. J Clean Prod 218:993–1006. https://doi.org/10.1016/j.jclepro.2019.01.305
Kim S et al (2017) Exploring sustainable street tree planting patterns to be resistant against fine particles (PM2.5). Sustainability 9(10):1709. https://doi.org/10.3390/su9101709
Klüser L et al (2015) Information content of space-borne hyperspectral infrared observations with respect to mineral dust properties. Remote Sens Environ 156:294–309. https://doi.org/10.1016/j.rse.2014.09.036
Liang J et al (2017) Heavy metal in leaves of twelve plant species from seven different areas in Shanghai, China. Urban For Urban Green 27:390–398. https://doi.org/10.1016/j.ufug.2017.03.006
Lin WP et al (2010) Analysis on urban vegetations reflectance characteristics in Shanghai. Spectrosc Spectr Anal 30(11):3111–3114. https://doi.org/10.3964/j.issn.1000-0593(2010)11-3111-04 (in Chinese)
Lin WP et al (2019) Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant. Ecol Indic 104:41–47. https://doi.org/10.1016/j.ecolind.2019.04.074
Lu S et al (2018) Effects of plant leaf surface and different pollution levels on PM2.5 adsorption capacity. Urban For Urban Green 34:64–70. https://doi.org/10.1016/j.ufug.2018.05.006
Lu T et al (2019) Atmospheric particle retention capacity and photosynthetic responses of three common greening plant species under different pollution levels in Hangzhou. Global Ecol Conserv 20. https://doi.org/10.1016/j.gecco.2019.e00783
Luo J et al (2019) Dynamic analysis of retention PM2.5 by plant leaves in rainfall weather conditions of six tree species. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1-12. https://doi.org/10.1080/15567036.2019.1602212
Markowicz KM et al (2012) Remote sensing measurements of the volcanic ash plume over Poland in April 2010. Atmos Environ 48:66–75. https://doi.org/10.1016/j.atmosenv.2011.07.015
Melanka B et al (2017) Hyperspectral leaf reflectance of Carpinus betulus L. saplings for urban air quality estimation. Environ Pollut 220:159–167. https://doi.org/10.1016/j.envpol.2016.09.035
Mori J et al (2015) Deposition of traffic-related air pollutants on leaves of six evergreen shrub species during a Mediterranean summer season. Urban For Urban Green 14(2):264–273. https://doi.org/10.1016/j.ufug.2015.02.008
Rai PK, Panda LLS (2013) Dust capturing potential and air pollution tolerance index (APTI) of some road side tree vegetation in Aizawl, Mizoram, India: an Indo-Burma hot spot region. Air Qual Atmos Health 7(1):3–101. https://doi.org/10.1007/s11869-013-0217-843
Rai PK, Panda LLS (2014) Leaf dust deposition and its impact on biochemical aspect of some roadside plants in Aizawl, Mizoram, North-east India. Int Res J Environ Sci 3(11):14–19
Ram SS et al (2014) Physico-chemical characterization of street dust and re-suspended dust on plant canopies: an approach for finger printing the urban environment. Ecol Indic 36:334–338. https://doi.org/10.1016/j.ecolind.2013.08.010
Shafri HZM et al (2012) Hyperspectral remote sensing of urban areas: an overview of techniques and applications. Res J Appl Ences Eng Technol 4(11):1557–1565
Shanghai Bureau of Statistics (2017) Shanghai Statistical Yearbook. Shanghai Dictionary Publishing House, Shanghai. http://www.stats-sh.gov.cn/html/sjfb/201701/1000200.html
Shao F et al (2018) Study on different particulate matter retention capacities of the leaf surfaces of eight common garden plants in Hangzhou, China. Sci Total Environ 652:939–951. https://doi.org/10.1016/j.scitotenv.2018.10.182
Su K et al (2019) Inversion and effect research on dust distribution of urban forests in Beijing. Forests 10(5):418. https://doi.org/10.3390/f10050418
Sun TT et al (2017) Effect of different dust weight levels on unban canopy reflectance spectroscopy. Spectrosc Spectr Anal 37(08):2539–2545. https://doi.org/10.3964/j.issn.1000-0593(2017)08-2539-07 (in Chinese)
Venera AJ et al (2002) Differences in accumulation of PAHs and metals on the leaves of Tilia×euchlora and Pyrus calleryana. Environ Pollut 120(2): 0-338. https://doi.org/10.1016/s0269-7491(02)00121-5
Wang C et al (2018) Estimation model for dust-retention content of main green plants in South China based on the red edge of reflectance. IGARSS 2018-2018 IEEE Int Geosci Remote Sens Symposium 3355-3357. https://doi.org/10.1109/igarss.2018.8519063
Wu CY, Wang XF (2016) Research of foliar dust content estimation by reflectance spectroscopy of Euonymus japonicus Thunb. Environ Nanotechnol Monitor Manag 5:54–61. https://doi.org/10.1016/j.enmm.2015.09.001
Xu JH, Yu JT (2013) Air dustfall impact on spectrum of Ficus Microcarpa’s leaf. Adv Mater Res 655-657:813–815. https://doi.org/10.4028/www.scientific.net/amr.655-657.813
Xu X et al (2019) Size distribution of particulate matter in runoff from different leaf surfaces during controlled rainfall processes. Environ Pollut 113234. https://doi.org/10.1016/j.envpol.2019.113234
Yan X et al (2014) Estimation of atmospheric dust deposition on plant leaves based on spectral features. Spectrosc Lett 47(7):536–542. https://doi.org/10.1080/00387010.2013.820761
Yan X et al (2015) Mapping dustfall distribution in urban areas using remote sensing and ground spectral data. Sci Total Environ 506-507:604–612. https://doi.org/10.1016/j.scitotenv.2014.11.036
Zhang F, Zhou G (2019) Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol 19(1). https://doi.org/10.1186/s12898-019-0233-0
Zhu J et al (2019) Response of dust particle pollution and construction of a leaf dust deposition prediction model based on leaf reflection spectrum characteristics. Environ Sci Pollut Res 26(36):36764–36775. https://doi.org/10.1007/s11356-019-06635-4
Zhu J et al (2020) Response of plant reflectance spectrum to simulated dust deposition and its estimation model. Sci Rep 10:15803. https://doi.org/10.1038/s41598-020-73006-2
Žibret G, Kopačková V (2018) Comparison of two methods for indirect measurement of atmospheric dust deposition: street-dust composition and vegetation-health status derived from hyperspectral image data. Ambio 48(4):423–435. https://doi.org/10.1007/s13280-018-1093-0
Zulfa AW et al (2020) Discriminating trees species from the relationship between spectral reflectance and chlorophyll contents of mangrove forest in Malaysia. Ecol Indic 111:106024. https://doi.org/10.1016/j.ecolind.2019.106024
Funding
This research was funded by the National Natural Science Foundation of China, grant number 41571047.
Author information
Authors and Affiliations
Contributions
Conceptualization, Wenpeng Lin and Dan Wang; formal analysis, Xumiao Yu; funding acquisition, Wenpeng Lin; investigation, Xumiao Yu; methodology, Xumiao Yu and Ying Li; software, Ying Li; validation, Xumiao Yu, Ying Li, and Yue Sun; visualization, Yue Sun; writing—original draft, Wenpeng Lin and Ying Li; writing—review and editing, Xumiao Yu and Dan Wang. All authors have read and agreed to the published version of the manuscript.
Corresponding authors
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yu, X., Lin, W., Wang, D. et al. Identification and characteristic analysis of urban vegetation spectra under different dust deposition. Environ Sci Pollut Res 30, 21299–21312 (2023). https://doi.org/10.1007/s11356-022-23704-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-022-23704-3