Assessing the potential capacity of forests to remove airborne fine particulate matter: a case study in Beijing

Abstract

Forests play a vital role in removing airborne fine particulate matter (PM_2.5), which contributes to improvements in air quality and human health. However, comprehensively quantifying forest PM_2.5 removal (PMR) remains challenging, as the effects of forests on wet deposition and dust emission processes have rarely been incorporated into existing characterizations. Here, we develop an improved forest PMR quantification method that entails the integration of multiple processes, including dry deposition, resuspension, wash-off, and the effects of forests on both wet deposition and dust emissions, using a mass balance equation. By applying this method to a forest in Beijing, we demonstrate that this forest exhibits a significantly greater capacity to remove airborne PM_2.5 in May and November than the values obtained in existing quantifications. On the basis of measured data, the reduction in dust emissions induced by the forest dominates its PMR capacity, while the increase in wet deposition induced by the forest is negligible. In addition, the seasonal variations in forest PMR and potential PMR values are closely related to the wind speed, while species differences are related mainly to variations in the leaf area index. These findings highlight the underappreciated PMR capacity of forests and provide valuable implications for future afforestation initiatives.

Keywords

Forest / Particulate matter removal / Potential capacity / Dust emission

References

[1] Albarakat R, Le MH, Lakshmi V. Assessment of drought conditions over Iraqi transboundary rivers using FLDAS and satellite datasets. J Hydrol Reg Stud, 2022, 41. 101075

[2] Bell ML, Morgenstern RD, Harrington W. Quantifying the human health benefits of air pollution policies: review of recent studies and new directions in accountability research. Environ Sci Policy, 2011, 144357368.

[3] Boers N, Marwan N, Barbosa HMJ, Kurths J. A deforestation-induced tipping point for the South American monsoon system. Sci Rep, 2017, 7. 41489

[4] Cheng I, Al Mamun A, Zhang LM. A synthesis review on atmospheric wet deposition of particulate elements: scavenging ratios, solubility, and flux measurements. Environ Rev, 2021, 293340353.

[5] Chowdhury AI, Uddin MJ, Baul TK, Akhter J, Nandi R, Karmakar S, Nath TK. Quantifying the potential contribution of urban trees to particulate matters removal: a study in Chattogram city. Bangladesh J Clean Prod, 2022, 380: 135015.

[6] Ding JQ, Lun XX, Ma WF, Zhao L, Cao YY, Sun FB, Li RN. Deposition of water-soluble inorganic ions in PM_2.5 in a typical forestry system in Beijing, China. For Ecosyst, 2018, 5. 36

[7] Duce RA, Liss PS, Merrill JT, Atlas EL, Buat-Menard P, Hicks BB, Miller JM, Prospero JM, Arimoto R, Church TM, Ellis W, Galloway JN, Hansen L, Jickells TD, Knap AH, Reinhardt KH, Schneider B, Soudine A, Tokos JJ, Tsunogai S, Wollast R, Zhou M. The atmospheric input of trace species to the world ocean. Glob Biogeochem Cycles, 1991, 53193259.

[8] Gaglio M, Pace R, Muresan AN, Grote R, Castaldelli G, Calfapietra C, Fano EA. Species-specific efficiency in PM_2.5 removal by urban trees: from leaf measurements to improved modeling estimates. Sci Total Environ, 2022, 844. 157131

[9] Guan QY, Sun XZ, Yang J, Pan BT, Zhao SL, Wang L. Dust storms in northern China: long-term spatiotemporal characteristics and climate controls. J Climate, 2017, 301766836700.

[10] Guenther A, Hewitt CN, Erickson D, Fall R, Geron C, Graedel T, Harley P, Klinger L, Lerdau M, McKay WA, Pierce T, Scholes B, Steinbrecher R, Tallamraju R, Taylor J, Zimmerman P. A global model of natural volatile organic compound emissions. J Geophys Res, 1995, 100D588738892.

[11] Guo LC, Bao LJ, She JW, Zeng EY. Significance of wet deposition to removal of atmospheric particulate matter and polycyclic aromatic hydrocarbons: a case study in Guangzhou, China. Atmos Environ, 2014, 83: 136-144.

[12] He LC, Zhang JF. Particulate matter (PM) oxidative potential: measurement methods and links to PM physicochemical characteristics and health effects. Crit Rev Environ Sci Technol, 2023, 532177197.

[13] Hicks BB, Matt DR, McMillen RT. A micrometeorological investigation of surface exchange of O₃, SO₂ and NO₂: a case study. Boundary layer studies and applications, 1989Springer Netherlands321336.

[14] Hirabayashi S, Kroll CN, Nowak DJ. Development of a distributed air pollutant dry deposition modeling framework. Environ Pollut, 2012, 171: 9-17.

[15] HJ 618-2011 (Replace GB 6921-86) (2011) Determination of atmospheric articles PM₁₀ and PM_2.5 in ambient air by gravimetric method (China)

[16] Huang YC, Cai Y, Jiao JJ, Pan CY, Wang GY, Li C, Jia ZC, Chen ZH, Zhou YF, Zhou GM. The impact of meteorological factors and canopy structure on PM_2.5 dynamics under different urban functional zones in a subtropical city. Forests, 2025, 163479

[17] Katul GG, Grönholm T, Launiainen S, Vesala T. The effects of the canopy medium on dry deposition velocities of aerosol particles in the canopy sub-layer above forested ecosystems. Atmos Environ, 2011, 45512031212.

[18] Kwon HS, Ryu MH, Carlsten C. Ultrafine particles: unique physicochemical properties relevant to health and disease. Exp Mol Med, 2020, 523318328.

[19] Lamchin M, Lee WK, Jeon SW, Wang SW, Lim CH, Song C, Sung M. Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data. Sci Total Environ, 2018, 618: 1089-1095.

[20] Landis JD. Quantifying new versus old aerosol deposition in forest canopies: throughfall mass balance with fallout radionuclide chronometry. Biogeosciences, 2025, 222056075624.

[21] Li GX, Xue M, Zeng Q, Cai Y, Pan XC, Meng Q. Association between fine ambient particulate matter and daily total mortality: an analysis from 160 communities of China. Sci Total Environ, 2017, 599–600: 108-113.

[22] Li TK, Bi XH, Dai QL, Liu BS, Han Y, You HY, Wang L, Zhang JY, Cheng Y, Zhang YF, Wu JH, Tian YZ, Feng YC. Improving spatial resolution of soil fugitive dust emission inventory using RS-GIS technology: an application case in Tianjin, China. Atmos Environ, 2018, 191: 46-54.

[23] Li TK, Bi XH, Dai QL, Wu JH, Zhang YF, Feng YC. Optimized approach for developing soil fugitive dust emission inventory in “2+26” Chinese cities. Environ Pollut, 2021, 285. 117521

[24] Li WW, Jiang YX, Duan YH, Bai JH, Zhou DM, Ke YH. Where and how to restore wetland by utilizing storm water at the regional scale: a case study of Fangshan, China. Ecol Indic, 2021, 122. 107246

[25] Li SW, Li Y, Meng H, Cao M, Wang XY, Zhang T, Zhang Z, Zhang WK. Effect of multiscale plant traits on particulate matter resuspension. Environ Pollut, 2025, 387. 127330

[26] Liang H, Chen XS, Yin JG, Da LJ. The spatial-temporal pattern and influencing factors of negative air ions in urban forests, Shanghai, China. J for Res, 2014, 254847856.

[27] Liu XH, Yu XX, Zhang ZM. PM_2.5 concentration differences between various forest types and its correlation with forest structure. Atmosphere (Basel), 2015, 61118011815.

[28] Liu SJ, Jiang XT, Tsona NT, Lv C, Du L. Effects of NO_x, SO₂ and RH on the SOA formation from cyclohexene photooxidation. Chemosphere, 2019, 216: 794-804.

[29] Liu YJ, Yang Z, Liu QY, Qi XK, Qu JM, Zhang SM, Wang XX, Jia K, Zhu MH. Study on chemical components and sources of PM_2.5 during heavy air pollution periods at a suburban site in Beijing of China. Atmos Pollut Res, 2021, 124188199.

[30] McNally A, Arsenault K, Kumar S, Shukla S, Peterson P, Wang SG, Funk C, Peters-Lidard CD, Verdin JP. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data, 2017, 4. 170012

[31] MDHS-14/3 (2000) Methods for the determination of hazardous substances health and safety laboratory—general methods for sampling and gravimetric analysis of respirable and inhalable dust. Health and Safety Executive (February).

[32] Meier R, Schwaab J, Seneviratne SI, Sprenger M, Lewis E, Davin EL. Empirical estimate of forestation-induced precipitation changes in Europe. Nat Geosci, 2021, 147473478.

[33] Mishra AK, Sinha B, Kumar R, Barth M, Hakkim H, Kumar V, Kumar A, Datta S, Guenther A, Sinha V. Cropland trees need to be included for accurate model simulations of land-atmosphere heat fluxes, temperature, boundary layer height, and ozone. Sci Total Environ, 2021, 751. 141728

[34] Nguyen T, Yu XX, Zhang ZM, Liu MM, Liu XH. Relationship between types of urban forest and PM_2.5 capture at three growth stages of leaves. J Environ Sci, 2015, 27: 33-41.

[35] Nowak DJ, Greenfield EJ. Tree and impervious cover change in U.S. cities. Urban for Urban Green, 2012, 1112130.

[36] Nowak DJ, Hirabayashi S, Bodine A, Hoehn R. Modeled PM_2.5 removal by trees in ten U.S. cities and associated health effects. Environ Pollut, 2013, 178: 395-402.

[37] Pace R, Grote R. Deposition and resuspension mechanisms into and from tree canopies: a study modeling particle removal of conifers and broadleaves in different cities. Front for Glob Change, 2020, 3: 26.

[38] Pace R, Guidolotti G, Baldacchini C, Pallozzi E, Grote R, Nowak DJ, Calfapietra C. Comparing i-tree eco estimates of particulate matter deposition with leaf and canopy measurements in an urban Mediterranean holm oak forest. Environ Sci Technol, 2021, 551066136622.

[39] Ryu J, Kim JJ, Byeon H, Go T, Lee SJ. Removal of fine particulate matter (PM_2.5) via atmospheric humidity caused by evapotranspiration. Environ Pollut, 2019, 245: 253-259.

[40] Schaubroeck T, Deckmyn G, Neirynck J, Staelens J, Adriaenssens S, Dewulf J, Muys B, Verheyen K. Multilayered modeling of particulate matter removal by a growing forest over time, from plant surface deposition to washoff via rainfall. Environ Sci Technol, 2014, 48181078510794.

[41] Sinharay R, Gong JC, Barratt B, Ohman-Strickland P, Ernst S, Kelly FJ, Zhang JJ, Collins P, Cullinan P, Chung KF. Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study. Lancet, 2018, 39110118339349.

[42] Spracklen DV, Baker JCA, Garcia-Carreras L, Marsham JH. The effects of tropical vegetation on rainfall. Annu Rev Environ Resour, 2018, 43: 193-218.

[43] Srivastava D, Xu JS, Vu TV, Liu D, Li LJ, Fu PQ, Hou SQ, Moreno Palmerola N, Shi ZB, Harrison RM. Insight into PM_2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing. Atmos Chem Phys, 2021, 21191470314724.

[44] Szopa S, Naik V, Adhikary B, Artaxo P, Berntsen T, Collins W, et al. (2021) Short-lived climate forcers climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change In: V Masson-Delmotte et al. (eds) Cambridge, United Kingdom and New York, USA, Cambridge University Press, pp 817−922

[45] 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, 1032129138.

[46] TCVN 5067-1995 (1995) Air quality - Weighing method for determination of dust content (Vietnam).

[47] Wu YN, Ma WM, Liu JK, Zhu LJ, Cong L, Zhai JX, Wang Y, Zhang ZM. Sabina chinensis and Liriodendron chinense improve air quality in Beijing, China. PLoS ONE, 2018, 131e0189640

[48] Wu CL, Lin ZH, Shao YP, Liu XH, Li Y. Drivers of recent decline in dust activity over East Asia. Nat Commun, 2022, 13. 7105

[49] Xie CK, Yan LB, Liang AZ, Che SQ. Understanding the washoff processes of PM_2.5 from leaf surfaces during rainfall events. Atmos Environ, 2019, 214. 116844

[50] Xu DZ, Lin YL. Impacts of irrigation and vegetation growth on summer rainfall in the Taklimakan Desert. Adv Atmos Sci, 2021, 381118631872.

[51] Xu P, Chen YF, Ye XJ. Haze, air pollution, and health in China. Lancet, 2013, 38299102067.

[52] Xu XW, Zhang ZM, Bao L, Mo L, Yu XX, Fan DX, Lun XX. Influence of rainfall duration and intensity on particulate matter removal from plant leaves. Sci Total Environ, 2017, 609: 11-16.

[53] Xu XW, Yu XX, Mo L, Xu YS, Bao L, Lun XX. Atmospheric particulate matter accumulation on trees: a comparison of boles, branches and leaves. J Clean Prod, 2019, 226: 349-356.

[54] Yang YH, Qin TL, Yan DH, Liu SS, Feng JM, Wang QL, Liu HX, Gao HY. Analysis of the evolution of ecosystem service value and its driving factors in the Yellow River Source Area, China. Ecol Indic, 2024, 158. 111344

[55] Yang W, Lin WP, Li Y, Shi YW, Xiong Y. Estimating the seasonal and spatial variation of urban vegetation’s PM_2.5 removal capacity. Environ Pollut, 2025, 369. 125800

[56] Yao JQ, Wu SQ, Cao YQ, Wei J, Tang XM, Hu LR, Wu JJ, Yang HC, Yang JH, Ji XH. Dry deposition effect of urban green spaces on ambient particulate matter pollution in China. Sci Total Environ, 2023, 900. 165830

[57] Yu Y, Notaro M, Wang FY, Mao JF, Shi XY, Wei YX. Observed positive vegetation-rainfall feedbacks in the Sahel dominated by a moisture recycling mechanism. Nat Commun, 2017, 8. 1873

[58] Zhang R, Ma KM. The impact of climate factors on airborne particulate matter removal by plants. J Clean Prod, 2021, 310. 127559

[59] Zhang BE, Jiao LM, Xu G, Zhao SL, Tang X, Zhou Y, Gong C. Influences of wind and precipitation on different-sized particulate matter concentrations (PM_2.5, PM₁₀, PM_2.5 –10). Meteorol Atmos Phys, 2018, 1303383392.

[60] Zhang L, Zhang ZQ, Chen LX, McNulty S. An investigation on the leaf accumulation-removal efficiency of atmospheric particulate matter for five urban plant species under different rainfall regimes. Atmos Environ, 2019, 208: 123-132.

[61] Zhao J, Li J, Liu QH, Xu BD, Yu WT, Lin SR, Hu Z. Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory. Int J Appl Earth Obs Geoinf, 2020, 90. 102112

[62] Zhao DC, Miuqian K, Dong WH, Lin YL, Jian BD, Li Y, Zhang BL (2026) Evaluation of clouds and precipitation in the WRF convection-permitting model over the Tibetan Plateau. J Geophys Res 131(15): e2025JD046099

[63] Zheng M, Salmon LG, Schauer JJ, Zeng LM, Kiang CS, Zhang YH, Cass GR. Seasonal trends in PM_2.5 source contributions in Beijing, China. Atmos Environ, 2005, 392239673976.

[64] Zhou CW, Fu BJ, Wang XF, Yin LC, Feng XM. The regional impact of ecological restoration in the arid steppe on dust reduction over the metropolitan area in northeastern China. Environ Sci Technol, 2020, 541377757786.

[65] Zhou SJ, Yan GX, Wu YN, Zhai JX, Cong L, Zhang ZM. The PM removal process of wetland plant leaves with different rainfall intensities and duration. J Environ Manage, 2020, 275. 111239

Funding

National Natural Science Foundation of China(Grants 42430601 and 42405159)

Rights & permissions

Northeast Forestry University