Skip to main content

Advertisement

SpringerLink
Log in

Response of dust particle pollution and construction of a leaf dust deposition prediction model based on leaf reflection spectrum characteristics

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Urban plants can improve several environmental pollution problems in cities, especially dust prevention, noise reduction, purification of the atmosphere, etc. To explore the influence of dust deposition on the spectral characteristics of the leaf, a foliar dust deposition prediction model based on high-spectrum data was established. Taking Euonymus japonicus L., the common greening tree species in Beijing, as the research object, high (T1), medium (T2), and low (T3) dust pollution gradients were set and hyperspectral data were collected. Results showed that: (1) in the dust-contaminated environment with different concentrations, the trend of the reflectance curve of the leaves of Euonymus japonicus L. was generally consistent. The spectral reflectance of the leaf surface was positively correlated with the amount of leaf dust. (2) There were five obvious reflection peaks and five main absorption valleys with the same positions and ranges in the 350–2500 nm range. (3) The spectral reflectance of leaf flour dust particles of Euonymus japonicus L. was significantly different before and after dusting, and its size was generally clean leaves > dust-depositing leaves. The sensitive range of its spectral response was 695–1400 nm. (4) The overall trend of the first derivative spectrum was basically the same. The red edge slope and the blue edge slope appeared as T3 > T2 > T1, the red edge position and the blue edge position appeared as T1 < T2 < T3. The red edge position of the leaf surface after dust deposition had an obvious "blueshift", and the moving distance increases with the increase of dust retention on leaf surface. (5) The leaf water index (y = − 1.18x2 + 0.5424x + 0.9917, R2 = 0.8030, RMSE = 0.187) had the highest accuracy in the regression model of leaf surface dust deposition using spectral parameters. The test showed that the R2 reached 0.9019, which indicated that the model has a good fitting effect. This prediction model can effectively estimate the dust deposition of the leaf surface of Euonymus japonicus L.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Aaron J, Baosen Z, Mari O, Kirschen DS (2018) Real-time prediction of the duration of distribution system outages. IEEE Trans Power Syst:1–1

  • Al-Chalabi AS, Hawker D (1996) Deposition and exchange behaviour of vehicular lead in street dusts from major roads. Sci Total Environ 187(2):105–119

    CAS  Google Scholar 

  • Ansquer P, Duru M, Theau JP, Cruz P (2009) Functional traits as indicators of fodder provision over a short time scale in species-rich grasslands. Ann Bot 103(1):117–126

    Google Scholar 

  • Beckett KP, Freer-Smith PH, Taylor G (1998) Urban woodlands: their role in reducing the effects of particulate pollution. Environ Pollut 99(3):347–360

    CAS  Google Scholar 

  • Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76(2):156–172

    Google Scholar 

  • Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88(4):677–684

    CAS  Google Scholar 

  • Chaoyang WU, Zheng N, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148(8-9):1230–1241

    Google Scholar 

  • Cho MA, Skidmore AK (2006) A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sens Environ 101(2):181–193

    Google Scholar 

  • Chudnovsky A, Ben-Dor E (2008) Application of visible, near-infrared, and short-wave infrared (400–2500 nm) reflectance spectroscopy in quantitatively assessing settled dust in the indoor environment. Case study in dwellings and office environments. Sci Total Environ 393(2-3):198–213

    CAS  Google Scholar 

  • Clark ML, Roberts DA, Clark DB (2005) Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens Environ 96(3-4):375–398

    Google Scholar 

  • Craine JM, Tilman D, Wedin D, Reich P, Tjoelker M, Knops J (2010) Functional traits, productivity and effects on nitrogen cycling of 33 grassland species. Funct Ecol 16(5):563–574

    Google Scholar 

  • De Deyn GB, Cornelissen JH, Bardgett RD (2010) Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol Lett 11(5):516–531

    Google Scholar 

  • Díaz S, Cabido M, Zak M, Carretero EM, Araníbar J (2010) Plant functional traits, ecosystem structure and land-use history along a climatic gradient in central-western Argentina. J Veg Sci 10(5):651–660

    Google Scholar 

  • Erel Y, Dayan U, Rabi R, Rudich Y, Stein M (2006) Trans boundary transport of pollutants by atmospheric mineral dust. Environ Sci Technol 40(9):2996–3005

    CAS  Google Scholar 

  • Farfel MR, Orlova AO, Lees PSJ, Rohde C, Ashley PJ, Chisolm JJ (2003) A study of urban housing demolitions as sources of lead in ambient dust: demolition practices and exterior dust fall. Environ Health Perspect 111(9):1228–1234

    CAS  Google Scholar 

  • Fernández Espinosa AJ, Rossini Oliva S (2005) The composition and relationships between trace element levels in inhalable atmospheric particles (PM10) and in leaves of Nerium oleander L. and Lantana camara L. Chemosphere 62(10):1665–1672

    Google Scholar 

  • Gray AL, Houk RS, Williams JG (1987) Langmuir probe potential measurements in the plasma and their correlation with mass spectral characteristics in inductively coupled plasma mass spectrometry. J Anal At Spectrom 2(1):13–20

    CAS  Google Scholar 

  • Greenberg JA, Dobrowski SZ, Ustin SL (2005) Shadow allometry: estimating tree structural parameters using hyperspatial image analysis. Remote Sens Environ 97(1):15–25

    Google Scholar 

  • Grote R, Samson R, Alonso R, Amorim JH, Cariñanos P, Churkina G, Fares S, Thiec DT, Niinemets Ü, Mikkelsen TN, Paoletti E, Tiwary A, Calfapietra C (2017) Functional traits of urban trees: air pollution mitigation potential. Front Ecol Environ 14(10):543–550

    Google Scholar 

  • Hde R, Ajm U, Meinen E, Prins A (1990) Influence of surfactants and plant species of leaf deposition of spray solutions. Weed Sci 38(6):567–572

    Google Scholar 

  • Heijden MGAVD, Scheublin TR (2010) Functional traits in mycorrhizal ecology: their use for predicting the impact of arbuscular mycorrhizal fungal communities on plant growth and ecosystem functioning. New Phytol 174(2):244–250

    Google Scholar 

  • Kahmen S, Poschlod P (2004) Plant functional trait responses to grassland succession over 25 years. J Veg Sci 15(1):21–32

    Google Scholar 

  • Khavanin Zadeh AR, Veroustraete F, Buytaert JA, Dirckx J, Samson R (2013) Assessing urban habitat quality using spectral characteristics of tilia leaves. Environ Pollut 178:7–14

    CAS  Google Scholar 

  • Knauer U, Matros A, Petrovic T, Zanker T, Scott ES, Seiffert U (2017) Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. Plant Methods 13:47

    Google Scholar 

  • Kou BF, Liu QZ (2015) Wetting behavior of hydrophobic dust and dust-fall theory of fine droplets. Braz J Phys 45(6):708–712

    Google Scholar 

  • Kueger S, Steinhauser D, Willmitzer L, Giavalisco P (2012) High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions. Plant J 70(1):39–50

    CAS  Google Scholar 

  • Kushida K, Yoshino K (2010) Estimation of lai and fapar by constraining the leaf and soil spectral characteristics in a radiative transfer model. Int J Remote Sens 31(9):2351–2375

    Google Scholar 

  • Landhäusser SM, Lieffers VJ (2010) Leaf area renewal, root deposition and carbohydrate reserves in a clonal tree species following above-ground disturbance. J Ecol 90(4):658–665

    Google Scholar 

  • Langner M, Kull M, Endlicher WR (2011) Determination of PM10 deposition based on antimony flux to selected urban surfaces. Environ Pollut 159(8-9):2028–2034

    CAS  Google Scholar 

  • Li Z, Liang Y, Zhou J, Sun X (2014) Impacts of de-icing salt pollution on urban road greenspace: a case study of Beijing. Front Env Sci Eng 8(5):747–756

    CAS  Google Scholar 

  • Liu Y, Wu J, Yu D, Ma Q (2018) The relationship between urban form and air pollution depends on seasonality and city size. Environ Sci Pollut Res 25(16):15554–15567

    CAS  Google Scholar 

  • Lu Y, Wang Y, Zuo J, Jiang H, Huang D, Rameezdeen R (2018) Characteristics of public concern on haze in china and its relationship with air quality in urban areas. Sci Total Environ 637–638:1597–1606

    Google Scholar 

  • Maňkovská B, Steinnes E (1995) Effects of pollutants from an aluminium reduction plant on forest ecosystems. Sci Total Environ 163(1-3):11–23

    Google Scholar 

  • Manzo C, Salvini R, Guastaldi E, Nicolardi V, Protano G (2013) Reflectance spectral analyses for the assessment of environmental pollution in the geothermal site of Mt. Amiata (Italy). Atmos Environ 79:650–665

    CAS  Google Scholar 

  • Matthew W (2018) United Kingdom unveils ambitious air pollution plan. Science 360(6392):953–953

    Google Scholar 

  • Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans.geosci. &. Remote Sens 42(8):1778–1790

    Google Scholar 

  • Mukherjee A, Agrawal M (2018) The influence of urban stress factors on responses of ground cover vegetation. Environ Sci Pollut Res 25

    CAS  Google Scholar 

  • Nowak DJ, Crane DE, Stevens JC (2006) Air pollution removal by urban trees and shrubs in the United States. Urban For Urban Green 4(3-4):115–123

    Google Scholar 

  • Okin GS, Roberts DA, Murray B, Okin WJ (2001) Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens Environ 77(2):212–225

    Google Scholar 

  • Prusty BAK, Mishra PC, Azeez PA (2005) Dust accumulation and leaf pigment content in vegetation near the national highway at Sambalpur, Orissa, India. Ecotoxicol Environ Saf 60(2):228–235

    CAS  Google Scholar 

  • Schleicher NJ, Norra S, Chai FH, Chen YZ, Wang SL, Cen K, Yu Y, Stüben D (2011) Temporal variability of trace metal mobility of urban particulate matter from Beijing—a contribution to health impact assessments of aerosols. Atmos Environ 45(39):7248–7265

    CAS  Google Scholar 

  • Selmi W, Weber C, Rivière E, Blond N, Mehdi L, Nowak D (2016) Air pollution removal by trees in public green spaces in Strasbourg City, France. Urban For Urban Green 17:192–201

    Google Scholar 

  • Simkhovich BZ, Kleinman MT, Kloner RA (2008) Air pollution and cardiovascular injury-epidemiology, toxicology, and mechanisms. J Am Coll Cardiol 52(9):719–726

    CAS  Google Scholar 

  • Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81(2-3):337–354

    Google Scholar 

  • Stober1 F, Lichtenthaler HK (2015) Studies on the localization and spectral characteristics of the fluorescence emission of differently pigmented wheat leaves. Plant Biol 106(5):365–370

    Google Scholar 

  • Strothmann W, Ruckelshausen A, Hertzberg J, Scholz C, Langsenkamp F (2017) Plant classification with in-field-labeling for crop/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system. Comput Electron Agric 134:79–93

    Google Scholar 

  • Tarabalka Y, Benediktsson JA, Chanussot J (2009) Spectral–spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE T Geosci Remote 47(8):2973–2987

    Google Scholar 

  • Thenkabail PS, Enclona EA, Ashton MS, Meer BVD (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91(3-4):354–376

    Google Scholar 

  • Townsend PA, Walsh SJ (2001) Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in Southeastern USA. Plant Ecol 157(2):129–149

    Google Scholar 

  • Underwood E, Ustin S, Dipietro D (2003) Mapping nonnative plants using hyperspectral imagery. Remote Sens Environ 86(2):150–161

    Google Scholar 

  • Wagner PR, Fürstner Barthlott W, Neinhuis C (2003) Quantitative assessment to the structural basis of water repellency in natural and technical surfaces. J Exp Bot 54(385):1295–1303

    CAS  Google Scholar 

  • Zhao X, Cheng H, He S, Cui X, Pu X, Lu L (2018) Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area. Environ Res 164:173–183

    CAS  Google Scholar 

Download references

Acknowledgments

The English language in this document has been checked by at least two professional editors; both were native speakers of English.

Funding

This research is supported by the “Special fund project for basic scientific research business fees of the Central University of Beijing Forestry University: Study on Ecological Network Structure and Its Crash Threshold in the Northeastern Edge of Wulanbu Desert (BLX201806)” and the “China Postdoctoral Science Foundation Grant: Study on Structural Characteristics and Crash Threshold of Complex Ecological Network in Desert Oasis Ecotone (2018 M641218)”.

Author information

Authors and Affiliations

  1. Beijing Forestry University, Beijing, 100083, China

    Jiyou Zhu, Qiang Yu, Chengyang Xu, Juyang Liao & Kai Su

  2. Guangxi Medical College, Nanning, 530012, Guangxi, China

    Hua Zhu & QiuYu Zhu

  3. Forestry College, Guangxi University, Nanning, 530005, Guangxi, China

    Weijun He

  4. Hunan Forest Botanical Garden, Changsha, 410116, China

    Juyang Liao

Authors
  1. Jiyou Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weijun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengyang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Juyang Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. QiuYu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kai Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.Z. and Q.Y. conceived and designed the study. J.Z., H.Z., Q.Y., J.Y., C.X., and K.S. contributed materials and tools. J.Z., Q.Y., W.H., and G.Q. performed the experiments. J.Z. contributed to data analysis and paper preparation.

Corresponding author

Correspondence to Qiang Yu.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, J., Yu, Q., Zhu, H. et al. 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, 36764–36775 (2019). https://doi.org/10.1007/s11356-019-06635-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-019-06635-4

Keywords

Search

Navigation