Deposition velocity of PM2.5 sulfate in the summer above a deciduous forest in central Japan

Matsuda, Kazuhide; Fujimura, Yoshifumi; Hayashi, Kentaro; Takahashi, Akira; Nakaya, Ko

doi:10.1016/j.atmosenv.2010.08.015

Cited by 72 publications

(59 citation statements)

References 23 publications

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“…Matsuda et al [22] analyzed sulfate flux of PM 2.5 in deciduous forests of varied heights during the central Japanese summer, finding that PM 2.5 concentration at 27 m is significantly higher than at 21 m, which indicates retention and adsorption of pollutants descending the foliage column. As air filters through foliated strata, PM 2.5 concentration significantly decreases, as well as sulfur concentration and the settlement rate of PM 2.5.…”

Section: Introductionmentioning

confidence: 99%

“…In Germany, mixed forests were estimated to reduce atmospheric PM 2.5 by about 12% [20][21]. For instance, it has been found that coniferous forests and Norway spruce forests have notably changed the sulfur concentration and PM 2.5 deposition rates in central Japan [22][23]. Hwang et al [4] chose five tree species to perform a chamber experiment whose results show that the intercept potential of conifers is higher than that of broadleaf trees.…”

Section: Introductionmentioning

confidence: 99%

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Pollution Remediation by Urban Forests: PM2.5 Reduction in Beijing, China

Chen¹,

Lu²,

Zhao³

et al. 2016

Pol. J. Environ. Stud.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pollution Remediation by Urban Forests: PM2.5 Reduction in Beijing, China

Chen¹,

Lu²,

Zhao³

et al. 2016

Pol. J. Environ. Stud.

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“…Urban forests play an important role in reducing atmospheric pollution and improving the quality of the urban environment. Forest vegetation can capture aerosols, particulates, and other pollutants more efficiently and are characterized by higher dry deposition than other land surfaces [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing

Chen

Sun

et al. 2015

Atmosphere

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Particulate matter is a serious source of air pollution in urban areas, where it exerts adverse effects on human health. This article focuses on the study of subduction of shelterbelts for atmospheric particulates. The results suggest that (1) the PM mass concentration is higher in the morning or both morning and noon inside the shelterbelts and lower mass concentrations at other times; (2) the particle mass concentration inside shelterbelt is higher than outside; (3) the particle interception efficiency of the two forest belts over the three months in descending order was PM10 > PM1 > PM2.5; and (4) the two shelterbelts captured air pollutants at rates of 1496.285 and 909.075 kg/month and the major atmospheric pollutant in Beijing city is PM10. Future research directions are to study PM mass concentration variation of shelterbelt with different tree species and different configuration.

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“…A similar comparison between measured and modeled V d was performed using three studies (Wesely et al, 1983;Pryor, 2006;and Matsuda et al, 2010) for deciduous forest. In these studies, the largest variations (ranges are given in the parentheses) were associated with u * (0.12-1.13 m s −1 ), Evidently, none of the parameterizations performed consistently better for all the three studies.…”

Section: Evaluation Of Dry Deposition To Deciduous Forestmentioning

confidence: 99%

“…For coniferous forest, modeled deposition velocities were compared with measurements from Lamaud et al (1994), Wyzers and Duyzer (1996), Gallagher et al (1997), Ruijgrok et al (1997), Buzorius et al (2000), Rannik et al (2000), Gaman et al (2004), Pryor et al (2007), and Grönholm et al (2009). Experiments conducted over deciduous forest are limited, and only three studies (Wesely et al, 1983;Pryor, 2006;Matsuda et al, 2010) were used in the present paper.…”

Section: An Evaluation Of the Dry Deposition Parameterizationsmentioning

confidence: 99%

Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models

Khan

Perlinger

2017

Geosci. Model Dev.

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Abstract. Despite considerable effort to develop mechanistic dry particle deposition parameterizations for atmospheric transport models, current knowledge has been inadequate to propose quantitative measures of the relative performance of available parameterizations. In this study, we evaluated the performance of five dry particle deposition parameterizations developed by Zhang et al. (2001) ("Z01"), Petroff and Zhang (2010) ("PZ10"), Kouznetsov and Sofiev (2012) ("KS12"), Zhang and He (2014) ("ZH14"), and Zhang and Shao (2014) ("ZS14"), respectively. The evaluation was performed in three dimensions: model ability to reproduce observed deposition velocities, V d (accuracy); the influence of imprecision in input parameter values on the modeled V d (uncertainty); and identification of the most influential parameter(s) (sensitivity). The accuracy of the modeled V d was evaluated using observations obtained from five land use categories (LUCs): grass, coniferous and deciduous forests, natural water, and ice/snow. To ascertain the uncertainty in modeled V d , and quantify the influence of imprecision in key model input parameters, a Monte Carlo uncertainty analysis was performed. The Sobol' sensitivity analysis was conducted with the objective to determine the parameter ranking from the most to the least influential. Comparing the normalized mean bias factors (indicators of accuracy), we find that the ZH14 parameterization is the most accurate for all LUCs except for coniferous forest, for which it is second most accurate. From Monte Carlo simulations, the estimated mean normalized uncertainties in the modeled V d obtained for seven particle sizes (ranging from 0.005 to 2.5 µm) for the five LUCs are 17, 12, 13, 16, and 27 % for the Z01, PZ10, KS12, ZH14, and ZS14 parameterizations, respectively. From the Sobol' sensitivity results, we suggest that the parameter rankings vary by particle size and LUC for a given parameterization. Overall, for d p = 0.001 to 1.0 µm, friction velocity was one of the three most influential parameters in all parameterizations. For giant particles (d p = 10 µm), relative humidity was the most influential parameter. Because it is the least complex of the five parameterizations, and it has the greatest accuracy and least uncertainty, we propose that the ZH14 parameterization is currently superior for incorporation into atmospheric transport models.

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Deposition velocity of PM2.5 sulfate in the summer above a deciduous forest in central Japan

Cited by 72 publications

References 23 publications

Pollution Remediation by Urban Forests: PM2.5 Reduction in Beijing, China

Pollution Remediation by Urban Forests: PM2.5 Reduction in Beijing, China

The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing

Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models

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