Extinction Effect of Foliar Dust Retention on Urban Vegetation as Estimated by Atmospheric PM10 Concentration in Shenzhen, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Ground Datasets
2.3. Gaofen-4 Dataset
2.4. Data Availability
2.5. Optimal Spatial Scale between Remote-Sensing Data and Ground-Based Data
2.6. Theoretical Basis for Correlation between PM10 Concentration and δNDVI within a Station
3. Results
3.1. Space-Dependent δNDVI-PM10 Relationship
3.2. Time-Dependent δNDVI-PM10 Relationship
4. Discussion
4.1. NDVI-Dependent Performance in Estimating δNDVI by PM10
4.2. Three Scenarios of Spectral Characteristics of Foliar Dust Retention
4.3. Potential for EEFDR Modeling
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Chavez-Garcia, E.; Gonzalez-Mendez, B. Particulate matter and foliar retention: Current knowledge and implications for urban greening. Air Qual. Atmos. Health 2021, 14, 1433–1454. [Google Scholar] [CrossRef]
- Jia, M.Y.; Zhou, D.Q.; Lu, S.P.; Yu, J.P. Assessment of foliar dust particle retention and toxic metal accumulation ability of fifteen roadside tree species: Relationship and mechanism. Atmos. Pollut. Res. 2021, 12, 36–45. [Google Scholar] [CrossRef]
- Liu, L.; Guan, D.S.; Peart, M.R. The morphological structure of leaves and the dust-retaining capability of afforested plants in urban Guangzhou, South China. Environ. Sci. Pollut. Res. 2012, 19, 3440–3449. [Google Scholar] [CrossRef]
- Simon, E.; Baranyai, E.; Braun, M.; Cserhati, C.; Fabian, I.; Tothmeresz, B. Elemental concentrations in deposited dust on leaves along an urbanization gradient. Sci. Total Environ. 2014, 490, 514–520. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gajbhiye, T.; Pandey, S.K.; Kim, K.H.; Szulejko, J.E.; Prasad, S. Airborne foliar transfer of PM bound heavy metals in Cassia siamea: A less common route of heavy metal accumulation. Sci. Total Environ. 2016, 573, 123–130. [Google Scholar] [CrossRef]
- Kwak, M.J.; Lee, J.K.; Park, S.; Kim, H.; Lim, Y.J.; Lee, K.-A.; Son, J.; Oh, C.-Y.; Kim, I.; Woo, S.Y. Surface-based analysis of leaf microstructures for adsorbing and retaining capability of airborne particulate matter in ten woody species. Forests 2020, 11, 946. [Google Scholar] [CrossRef]
- Beckett, K.P.; Freer-Smith, P.H.; Taylor, G. Particulate pollution capture by urban trees: Effect of species and windspeed. Glob. Chang. Biol. 2000, 6, 995–1003. [Google Scholar] [CrossRef]
- Yang, J.; McBride, J.; Zhou, J.; Sun, Z. The urban forest in Beijing and its role in air pollution reduction. Urban For. Urban Green. 2005, 3, 65–78. [Google Scholar] [CrossRef]
- Tallis, M.; Taylor, G.; Sinnett, D.; Freer-Smith, P. Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London, under current and future environments. Landsc. Urban Plan. 2011, 103, 129–138. [Google Scholar] [CrossRef]
- Zhao, Y.B.; Lei, S.G.; Yang, X.C.; Gong, C.G.; Wang, C.J.; Cheng, W.; Li, H.; She, C.C. Study on spectral response and estimation of grassland plants dust retention based on hyperspectral data. Remote Sens. 2020, 12, 2019. [Google Scholar] [CrossRef]
- Zhu, J.Y.; Yu, Q.; Liu, X.X.; Yu, Y.; Yao, J.M.; Su, K.; Niu, T.; Zhu, H.; Zhu, Q.Y. Effect of leaf dust retention on spectral characteristics of Euonymus japonicas and its dust retention prediction. Spectrosc. Spectr. Anal. 2020, 40, 517–522. [Google Scholar] [CrossRef]
- Chaston, K.; Doley, D. Mineral Particulates and Vegetation: Effects of Coal Dust, Overburden and Flyash on Light Interception and Leaf Temperature. Clean Air Environ. Qual. 2006, 40, 40–44. [Google Scholar]
- Yan, X.; Shi, W.Z.; Zhao, W.J.; Luo, N.N. Mapping dustfall distribution in urban areas using remote sensing and ground spectral data. Sci. Total Environ. 2015, 506, 604–612. [Google Scholar] [CrossRef] [PubMed]
- Lin, W.; Yu, X.; Xu, D.; Sun, T.; Sun, Y. Effect of dust deposition on chlorophyll concentration estimation in urban plants from reflectance and vegetation indexes. Remote Sens. 2021, 13, 3570. [Google Scholar] [CrossRef]
- Xu, J.H.; Yu, J.T. Air dustfall impact on spectrum of Ficus microcarpa’s leaf. In Proceedings of the 3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012), Beihai, China, 22–23 December 2012; pp. 813–815. [Google Scholar]
- Peng, J.; Wang, J.Q.; Xiang, H.Y.; Niu, J.L.; Chi, C.M.; Liu, W.Y. Effect of Foliar Dustfall Content (FDC) on High Spectral Characteristics of Pear Leaves and Remote Sensing Quantitative Inversion of FDC. Spectrosc. Spectr. Anal. 2015, 35, 1365–1369. [Google Scholar] [CrossRef]
- Zhang, X.Y.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84, 471–475. [Google Scholar] [CrossRef]
- Yu, Y.; Song, Z.; Fan, W.; Yang, X. Scale conversion from canopy spectra to leaf spectra. Geomat. Inf. Sci. Wuhan Univ. 2018, 43, 1560–1565. [Google Scholar] [CrossRef]
- Hilker, T.; Wulder, M.A.; Coops, N.C.; Linke, J.; McDermid, G.; Masek, J.G.; Gao, F.; White, J.C. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens. Environ. 2009, 113, 1613–1627. [Google Scholar] [CrossRef]
- Ollinger, S.V. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 2011, 189, 375–394. [Google Scholar] [CrossRef] [PubMed]
- Leonard, R.J.; McArthur, C.; Hochuli, D.F. Particulate matter deposition on roadside plants and the importance of leaf trait combinations. Urban For. Urban Green. 2016, 20, 249–253. [Google Scholar] [CrossRef]
- Litschke, T.; Kuttler, W. On the reduction of urban particle concentration by vegetation—A review. Meteorol. Z. 2008, 17, 229–240. [Google Scholar] [CrossRef]
- Deng, Y.; Wang, J.; Xu, J.; Du, Y.; Chen, J.; Chen, D. Spatiotemporal variation of NDVI and its response to climatic factors in Guangdong province. Ecol. Environ. Sci. 2021, 30, 37–43. [Google Scholar] [CrossRef]
- Zhang, T.; Hong, X.; Sun, L.; Liu, Y. Particle-retaining characteristics of six tree species and their relations with micro-configurations of leaf epidermis. J. Beijing For. Univ. 2017, 39, 70–77. [Google Scholar] [CrossRef]
- Qiu, L.; Liu, F.; Zhang, X.; Gao, T. Difference of airborne particulate matter concentration in urban space with different green coverage rates in Baoji, China. Int. J. Environ. Res. Public Health 2019, 16, 1465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Meteorological Bureau of Shenzhen Municipality. Available online: http://weather.sz.gov.cn (accessed on 16 June 2021).
- Sun, Y.; Qin, Q.; Ren, H.; Zhang, T. Consistency analysis of surface reflectance and NDVI between GF-4/PMS and GF-1/WFV. Trans. Chin. Soc. Agric. Eng. 2017, 33, 167–173. [Google Scholar] [CrossRef]
- China Centre for Resources Satellite Data Application. Available online: http://www.cresda.com (accessed on 16 June 2021).
- Liu, X.Y.; Sun, L.; Yang, Y.K.; Zhou, X.Y.; Wang, Q.; Chen, T.T. Cloud and cloud shadow detection algorithm for Gaofen-4 satellite data. Acta Opt. Sin. 2019, 39, 12. [Google Scholar] [CrossRef]
- Rouse, J.W., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 1, 309–317. [Google Scholar]
- Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Jordan, C.F. Derivation of leaf-area index from quality of light on the forest floor. Ecology 1969, 50, 663–666. [Google Scholar] [CrossRef]
- Jiang, Z.Y.; Huete, A.R.; Didan, K.; Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 2008, 112, 3833–3845. [Google Scholar] [CrossRef]
- Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 1994, 48, 119–126. [Google Scholar] [CrossRef]
- Ma, B.D.; Pu, R.L.; Wu, L.X.; Zhang, S. Vegetation index differencing for estimating foliar dust in an ultra-low-grade magnetite mining area using Landsat imagery. IEEE Access 2017, 5, 8825–8834. [Google Scholar] [CrossRef]
- Zhang, P.; Zhu, M.; Liu, Y.; Yang, Z. Leaf surface micro-morphological features and its retention ability of particulate matters for 9 plant species at the roadside of Beijing. Ecol. Environ. Sci. 2017, 26, 2126–2133. [Google Scholar] [CrossRef]
- Xu, X.; Xia, J.; Gao, Y.; Zheng, W. Additional focus on particulate matter wash-off events from leaves is required: A review of studies of urban plants used to reduce airborne particulate matter pollution. Urban For. Urban Green. 2020, 48, 126559. [Google Scholar] [CrossRef]
- Przybysz, A.; Saebo, A.; Hanslin, H.M.; Gawronski, S.W. Accumulation of particulate matter and trace elements on vegetation as affected by pollution level, rainfall and the passage of time. Sci. Total Environ. 2014, 481, 360–369. [Google Scholar] [CrossRef]
- Yin, S.; Shen, Z.M.; Zhou, P.S.; Zou, X.D.; Che, S.Q.; Wang, W.H. Quantifying air pollution attenuation within urban parks: An experimental approach in Shanghai, China. Environ. Pollut. 2011, 159, 2155–2163. [Google Scholar] [CrossRef]
- Jing, W.L.; Zhou, X.; Zhang, C.; Wang, C.Y.; Jiang, H. Machine learning for estimating leaf dust retention based on hyperspectral measurements. J. Sens. 2018, 2018, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Raynor, G.S.; Hayes, J.V.; Ogden, E.C. Particulate dispersion into and within a forest. Bound. Layer Meteorol. 1974, 7, 429–456. [Google Scholar] [CrossRef]
- Hanel, G. Influence of relative-humidity on aerosol deposition by sedimentation. Atmos. Environ. 1982, 16, 2703–2706. [Google Scholar] [CrossRef]
- Chen, Y.; Zheng, R.; Chen, J.; Xu, L.; Hong, Y. The phenomenon and formation causes of mass concentration reversal of PM10 and PM2. 5 during automatic monitoring in a coastal city. Environ. Monit. China 2021, 37, 54–64. [Google Scholar] [CrossRef]
- Gao, J.H.; Wang, D.M.; Zhao, L.; Wang, D.D. Airborne dust detainment by different plant leaves: Taking Beijing as an example. J. Beijing For. Univ. 2007, 29, 94–99. [Google Scholar] [CrossRef]
- Ould-Dada, Z.; Baghini, N.M. Resuspension of small particles from tree surfaces. Atmos. Environ. 2001, 35, 3799–3809. [Google Scholar] [CrossRef]
- Freer-Smith, P.H.; El-Khatib, A.A.; Taylor, G. Capture of particulate pollution by trees: A comparison of species typical of semi-arid areas (Ficus nitida and Eucalyptus globulus) with European and North American species. Water Air Soil Pollut. 2004, 155, 173–187. [Google Scholar] [CrossRef]
- Wang, Z.; Li, J. Capacity of dust uptake by leaf surface of Euonymus Japonicus Thunb. and the morphology of captured particle in air polluted city. Ecol. Environ. Sci. 2006, 15, 327–330. [Google Scholar] [CrossRef]
- Zhang, L.; Brook, J.R.; Vet, R. A revised parameterization for gaseous dry deposition in air-quality models. Atmos. Chem. Phys. 2003, 3, 2067–2082. [Google Scholar] [CrossRef] [Green Version]
- Zadeh, A.R.K.; Veroustraete, F.; Wuyts, K.; Kardel, F.; Samson, R. Dorsi-ventral leaf reflectance properties of Carpinus betulus L.: An indicator of urban habitat quality. Environ. Pollut. 2012, 162, 332–337. [Google Scholar] [CrossRef]
- Kayet, N.; Pathak, K.; Chakrabarty, A.; Kumar, S.; Chowdary, V.M.; Singh, C.P.; Sahoo, S.; Basumatary, S. 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. 2019, 218, 993–1006. [Google Scholar] [CrossRef]
- Su, K.; Yu, Q.; Hu, Y.H.; Liu, Z.L.; Wang, P.C.; Zhang, Q.B.; Zhu, J.Y.; Niu, T.; Yue, D.P. Inversion and effect research on dust distribution of urban forests in Beijing. Forests 2019, 10, 418. [Google Scholar] [CrossRef] [Green Version]
- Wang, H.F.; Fang, N.; Yan, X.; Chen, F.; Xiong, Q.; Zhao, W. Retrieving dustfall distribution in Beijing City based on ground spectral data and remote sensing. Spectrosc. Spectr. Anal. 2016, 36, 2911–2918. [Google Scholar] [CrossRef]
- Tian, F.; Wang, Y.J.; Fensholt, R.; Wang, K.; Zhang, L.; Huang, Y. Mapping and evaluation of NDVI trends from synthetic time series obtained by blending Landsat and MODIS data around a coalfield on the Loess Plateau. Remote Sens. 2013, 5, 4255–4279. [Google Scholar] [CrossRef] [Green Version]
- Stellmes, M.; Udelhoven, T.; Roder, A.; Sonnenschein, R.; Hill, J. Dryland observation at local and regional scale—Comparison of Landsat TM/ETM plus and NOAA AVHRR time series. Remote Sens. Environ. 2010, 114, 2111–2125. [Google Scholar] [CrossRef]
- Weerakkody, U.; Dover, J.W.; Mitchell, P.; Reiling, K. Topographical structures in planting design of living walls affect their ability to immobilise traffic-based particulate matter. Sci. Total Environ. 2019, 660, 644–649. [Google Scholar] [CrossRef]
Name | Abbreviation | Longitude | Latitude | Dominant Vegetation Type | Precipitation (mm) | ||
---|---|---|---|---|---|---|---|
10 February | 26 February | 6 March | |||||
Nanhai | NH01 | 113.922 | 22.511 | Garden vegetation | 38.5 | 9.4 | 11.8 |
Huaqiaocheng | HQ01 | 113.982 | 22.54 | Garden vegetation | 43.5 | 16.8 | 6.4 |
Henggang | HG01 | 114.176 | 22.643 | Garden vegetation | 44.1 | 31.4 | 13.9 |
Longgang | LG01 | 114.217 | 22.722 | Artificial evergreen broad-leaved forest | 38.4 | 9.2 | 11.5 |
Meisha | MS01 | 114.296 | 22.597 | South subtropical evergreen broad-leaved forest | 38.2 | 4.4 | 22.2 |
Yantian | YT01 | 114.236 | 22.566 | Artificial evergreen broad-leaved forest | 39.2 | 9.3 | 12.5 |
Kuiyong | KY01 | 114.41 | 22.634 | Artificial evergreen broad-leaved forest | 33.2 | 6.8 | 22.9 |
Nan’ao | NA01 | 114.491 | 22.538 | Artificial evergreen broad-leaved forest | 38.5 | 9.4 | 11.8 |
Xixiang | XX01 | 113.891 | 22.58 | Garden vegetation | 35.5 | 13.5 | 11.4 |
Lianhua | LH01 | 114.053 | 22.557 | Artificial evergreen broad-leaved forest | 44.3 | 29.1 | 7.4 |
Tongxinling | TX01 | 114.096 | 22.545 | Garden vegetation | 26.2 | 4.1 | 5.2 |
Honghu * | HH01 | 114.115 | 22.568 | Garden vegetation | 36.4 | 1.3 | 12.2 |
Minzhi | MZ01 | 114.017 | 22.615 | Orchard | 38 | 12.9 | 10.2 |
Guanlan | GL01 | 114.056 | 22.735 | Garden vegetation | 38.1 | 8.3 | 10.8 |
Pingshan | PS01 | 114.343 | 22.711 | Garden vegetation | 43.4 | 3.1 | 18.6 |
Index | Acronym | Formula * | Reference |
---|---|---|---|
normalized difference vegetation index | NDVI | [30] | |
soil-adjusted vegetation index | SAVI | [31] | |
ratio vegetation index | RVI | [32] | |
two-band enhanced vegetation index | EVI2 | [33] | |
modified soil-adjusted vegetation index | MSAVI | [34] |
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Yu, T.; Wang, J.; Chao, Y.; Zeng, H. Extinction Effect of Foliar Dust Retention on Urban Vegetation as Estimated by Atmospheric PM10 Concentration in Shenzhen, China. Remote Sens. 2022, 14, 5103. https://doi.org/10.3390/rs14205103
Yu T, Wang J, Chao Y, Zeng H. Extinction Effect of Foliar Dust Retention on Urban Vegetation as Estimated by Atmospheric PM10 Concentration in Shenzhen, China. Remote Sensing. 2022; 14(20):5103. https://doi.org/10.3390/rs14205103
Chicago/Turabian StyleYu, Tianfang, Junjian Wang, Yiwen Chao, and Hui Zeng. 2022. "Extinction Effect of Foliar Dust Retention on Urban Vegetation as Estimated by Atmospheric PM10 Concentration in Shenzhen, China" Remote Sensing 14, no. 20: 5103. https://doi.org/10.3390/rs14205103