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Deposition velocity of PM2.5 sulfate in the summer above a deciduous forest in central Japan
Atmospheric Environment 44 (2010) 4582e4587
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Deposition velocity of PM2.5 sulfate in the summer above a deciduous forest in central Japan Kazuhide Matsuda a, *, Yoshifumi Fujimura a, Kentaro Hayashi b, Akira Takahashi c, Ko Nakaya c a
School of Sciences and Engineering, Meisei University, 2-1-1 Hodokubo, Hino, Tokyo 191-8506, Japan National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan c Central Research Institute of Electric Power Industry, 1646 Abiko, Chiba 270-1194, Japan b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 April 2010 Received in revised form 5 August 2010 Accepted 5 August 2010
In order to increase knowledge of aerosol dry deposition for the regional assessment of acid deposition and transboundary air pollution in East Asia, an experimental study on PM2.5 sulfate deposition was implemented in the early summer of 2009. The experimental field was located in a deciduous forest at the foot of Mt. Asama, central Japan. Aerosol fluxes were obtained using the aerodynamic gradient method. Three aerosol samplers were placed on an experimental tower at 21, 24 and 27 m above the ground surface, and collected PM2.5 on filters for chemical analysis. Vertical concentration differences between 21 m and 27 m of PM2.5 sulfate were detected significantly when the concentration exceeded 1 mg m3. Mean deposition velocity was estimated to be 0.9 1.0 cm s1 in the daytime and 0.3 0.3 cm s1 in the nighttime. In the case that a height-dependent correction in the roughness sub-layer was taken into account, the deposition velocities increased more, especially in daytime. Higher deposition velocities in the daytime were associated with larger friction velocities and unstable conditions. The deposition velocities observed in this study were in agreement with other experimental results found in the literature. On the other hand, they were higher than those calculated by theoretical models. Two empirical parameterizations (Wesely, M.L., Cook, D.R., Hart, R.L., 1985. Measurement and parameterization of particulate sulfur dry deposition over grass. Journal of Geophysical Research 90, 2131e2143; Ruijgrok, W., Tieben, H., Eisinga, P., 1997. The dry deposition of particles to a forest canopy: a comparison of model and experimental results. Atmospheric Environment 31, 399e415) were validated by the observations. The general trend of higher daytime and lower nighttime deposition velocities was similar among the observation and the two parameterizations. The large variability found in the measurement was not reproduced by the parameterizations, because it is attributable to random error from the differences between the samplers. The observations were in accordance with the parameterization of Ruijgrok et al. (1997) for a forest, although much larger than that of Wesely et al. (1985) for grasslands. This indicates the large difference in aerosol deposition velocities between forests and grasslands. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Dry deposition Aerosols Flux measurement Parameterization East Asia
1. Introduction Sulfur oxides are extremely important species in the atmospheric environment in Asia. Transboundary air pollution of sulfur and the impact of sulfur deposition on the ecosystem are of concern. Total emission of sulfur dioxide from Asian countries has been increasing rapidly since 2000 (Ohara et al., 2007). The Acid Deposition Monitoring Network in East Asia (EANET), which is working on the acid deposition problem in East Asia, published the first periodic report on the state of acid deposition in East Asia (EANET, 2006). The chapter in the report, “Dry deposition”, highlights the * Corresponding author. Tel.: þ81 42 591 6216; fax: þ81 42 591 6196. E-mail address: [email protected] (K. Matsuda). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.08.015
significance of the long-range transport of sulfate aerosols over the region. On the other hand, the report did not evaluate dry deposition rates due to the lack of knowledge on deposition velocity in this region. According to a throughfall and stemflow measurement (Takahashi et al., 2002), dry deposition contributed approximately 27% of total sulfur deposition in a forest of central Japan. Much higher ratios of sulfate aerosols to SO2 at remote site in East Asia (EANET, 2006) indicates the importance of sulfate dry deposition in this region. Several field studies on dry deposition flux have been implemented in some countries in East Asia in recent years (Matsuda et al., 2007), for example in Japan (Takahashi et al., 2002; Matsuda et al., 2002, Hayashi et al., 2009), China (Sorimachi et al., 2003, 2004), Taiwan (Tsai et al., 2010) and Thailand (Matsuda et al., 2005, 2006).
K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
These studies were, however, mainly aimed at gases such as O3 and SO2. Relatively few field studies have been conducted on aerosol deposition (Takahashi and Wakamatsu, 2004). Petroff et al. (2008) reviewed the current knowledge of aerosol dry deposition on vegetative canopies. Almost all of the papers reviewed, which numbered more than fifty, were based on field studies in Europe and North America, and there were no papers derived from Asia. The Impacts of Aerosols in East Asia on Plants and Human Health Project (ASEPH) was implemented in 2008. As a part of this project, several field studies on aerosol deposition flux above different vegetation types have been carried out in this region. The purpose of this study, as an ASEPH activity, is to understand the deposition velocity of PM2.5 sulfate above a temperate forest in Japan using flux observations, as first step to accumulating knowledge on deposition velocities in East Asia.
F ¼ ku*Dc=½lnððz2 dÞ=ðz1 dÞÞ jh ððz2 dÞ=LÞ þ jh ððz1 dÞ=LÞ;
(3)
Firstly, u* and L were averaged every 30 min, and then D, which is the transfer velocity (m s1) between 21 m and 27 m (a part of Eq. (3)), was computed using the following equation:
þ jh ððz1 dÞ=LÞ;
2.1. Experimental description The experimental field was located in a deciduous forest at the eastern foot of Mt. Asama (36 240 N, 138 350 E, 1380 m asl), Nagano Prefecture, central Japan. The ground is flat with a slight slope of approximately 3 over a distance of over 600 m in the main upwind direction. The experiment was carried out in early summer, from 2 to 8 July in 2009. The forest is classified as alpine temperate forest, and flourishes in this season. An experimental station with twin 28m-high walk-up towers was established in the forest with a canopy height of about 20 m. The dominant canopy tree species are birch (Betula ermanii) and other species, such as alder (Alnus hirsute). The sub-canopy layer under 10 m was covered with deciduous shrub. Displacement height and roughness length ranges from 8 m to 16 m and form 0.5 m to 1.5 m during a year, respectively. Aerosol fluxes were obtained by coupling aerosol samplers (MCI sampler, Tokyo Dylec Corporation) with an ultrasonic anemometer (METEK, USA-1) using the aerodynamic gradient method. PM2.5 was collected on a glass fiber filter coated with Teflon, using a filter holder with an impactor (flow rate 20 L min1). Three aerosol samplers were placed on a tower at heights of 21, 24 and 27 m above the ground surface, and sample filters were exchanged at 6:00, 10:00, 14:00, 18:00 (i.e. three times during the day and once at night). The ultrasonic anemometer and other meteorological instruments were placed on the other tower at a height of 28 m above the ground surface. After every sample was taken, each sample filter was sealed as soon as possible in a clean polypropylene test tube with a cap to avoid contamination. In the laboratory, inorganic ions were extracted from the samples into deionized water by ultrasonic extraction, and then analyzed by the ion chromatography. Since blank values of SO2 4 extracted from 5 blank filters showed less than 0.7% (average 0.2%) of the sample values, the blank values were ignored for the calculations of SO2 4 concentrations in the atmosphere. 2.2. Computation of sulfate fluxes In this study, the gradient method was used to estimate sulfate fluxes (Erisman and Draaijers, 1995). The fluxes, F, were computed using the following equation:
c* ¼ kDc=½lnððz2 dÞ=ðz1 dÞÞ jh ððz2 dÞ=LÞ (2)
(4)
Secondly, D was averaged every 4 h, and then F was computed from the product of D and Dc. From a previous study at the site, the leaf area index (LAI) was observed to range between 5e7, one sided, in July (Nakaya, 2008). Taking into account the relationship between d/h and LAI reported by Lovett and Reiners (1986) and Meyers et al. (1998), a d/h value of 0.8 (d ¼ 16 m) was adopted, where h is canopy height (20 m). The deposition velocity, Vd, is determined by the following equation adapted from Wesely and Hicks (1977):
Vd ¼ F=C;
(4)
where C is the PM2.5 sulfate concentration at height z2 (reference height zref ¼ z2 d ¼ 11 m). The fluxes of six periods, 6:00e10:00, 10:00e14:00, 14:00e18:00, 18:00e22:00, 22:00e2:00 and 2:00e6:00, were calculated during a day under the assumption that the concentration was constant during the nighttime sampling period (18:00e6:00). The influence of this assumption on nighttime Vd variation is discussed in section 3.4. Although the gradient method based on the MonineObukhov theory is valid in the inertial sub-layer (z >> z0) (Businger, 1986), this measurement were performed closer to the canopy top, in the roughness sub-layer. Possible effects on estimated Vd were discussed in section 3.3. 3. Results and discussion 3.1. Atmospheric conditions Table 1 shows the atmospheric conditions at the study site during the experimental period and Fig. 1 shows variations of wind speed, temperature, relative humidity and precipitation during this time. Precipitation events over 10 mm occurred twice in the nighttime of the 3rd and 4th July. Since relative humidity was also high in the nighttime (Table 1), the surface conditions of the forest canopy tended to be wet during this period. Mean wind speed in Table 1 Atmospheric conditions at the study site during the experimental period in July, 2009. All values indicate “mean standard deviation”, except for precipitation which is the total value.
(1)
where u* is the friction velocity and c* is the eddy concentration. c* is expressed by the following equation:
þ jh ððz1 dÞ=LÞ;
where Dc represents the differences in the concentrations between heights of z1 and z2, k is the Von Karman constant, L is the MonineObukhov length, Jh is the integrated stability correction function for heat defined by Erisman and Draaijers (1995), and d is the zero-plane displacement height. In this study, z1 and z2 were assigned to the lower sampling height of 21 m and the upper sampling height of 27 m, respectively. From the Eqs. (1) and (2), F could be expressed as follows:
D ¼ ku*=½lnððz2 dÞ=ðz1 dÞÞ jh ððz2 dÞ=LÞ
2. Experimental description and methodology
F ¼ u*c*;
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3 SO2 4 in PM2.5 (mg m ) Wind speed (m s1) Solar radiation (W m2) Temperature ( C) Relative humidity (%) Precipitation (mm)
Daytime
Nighttime
3.9 2.7 1.2 0.8 322 220 17.3 2.1 86 12 9
3.1 2.7 1.6 1.0 5 15 15.4 1.6 91 9 45
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K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
8
daytime
25
nighttime
27
20 15
4 10 2
5
26 Height (m)
6
Tem peratu re (ºC)
Win d speed (m s-1)
28
25 24 23 22 21 20 1
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
0
0
Fig. 2. Mean concentrations of SO2 4 in PM2.5 at heights of 21, 24 and 27 m in the daytime and nighttime during the experimental period in July 2009. Error bars indicate standard deviations in each height.
20
80
15
60 10 40 5
20 0
Precipitation (m m )
100
Relative h u m i dity (%)
1.5
7/ 18:00
7/ 6:00
6/ 18:00
6/ 6:00
5/ 18:00
5/ 6:00
4/ 18:00
4/ 6:00
3/ 18:00
3/ 6:00
2/ 18:00
0
Date Fig. 1. Variations of wind speed, temperature, relative humidity and precipitation at the study site during the experimental period in July, 2009.
the daytime was slightly lower than in the nighttime. Sulfate (SO2 4 in PM2.5) concentration in the daytime was higher than in the nighttime. 3.2. Sulfate gradients In highly rough canopy such as forests, it is difficult to detect the concentration gradient of fine particles because the gradient is very small (Businger, 1986). In particular, the gradient tends to decrease with concentration (Feliciano et al., 2001). Therefore, data sets with sulfate concentrations less than 1 mg m3 measured at a height of 27 m were omitted from the Vd analyses described below. The absolute values of the concentration difference between 21 m and 27 m, when the concentrations were more than 1 mg m3, was 0.15 0.12 mg m3 (average standard deviation), which was sufficiently higher than the detection limit of sulfate concentration of approximately 102 mg m3. Furthermore, a paired t-test between concentrations at 21 m and 27 m showed significantly higher concentrations at 27 m (p < 0.01). These results indicated that differences in the concentration were detected when the concentration exceeded 1 mg m3. On the other hand, random errors between samplers also influenced the concentration gradient. According to duplicate measurements made 7 times using the same specifications at the same height, a significant difference was not found between the samplers used at 21 m and 27 m (linier regression line: y ¼ 1.021x 0.031, r ¼ 0.999); average of the differences amounted to 0.09 0.14 (average standard deviation) mg m3, which probably contribute to an error in Vd of 60% (0.09/0.15) on average. Fig. 2 shows the average concentration gradients in the daytime and nighttime, respectively. The concentrations decreased with the
measurement height in both the daytime and nighttime. In addition, the gradients indicated the occurrence of downward fluxes (depositions), indicating that the forest could be regarded as a sulfate sink during the study period. The paired t-test between concentrations at 24 m and 27 m showed not significant differences. The test between 21 m and 24 m shows significant differences, however the gradients were closer to the random differences determined by the duplicate measurements than those between 21 m and 27 m, and then made large errors. Finally we concluded that Vd calculation was valid only between concentrations at 21 m and 27 m in this measurement. 3.3. Deposition velocities Table 2 shows mean deposition velocities in the daytime and nighttime together with micrometeorological parameters. The deposition velocities increased in the daytime and decreased in the nighttime, which corroborates the experimental results found in the literature (Petroff et al., 2008; Pryor et al., 2008). On the other hand, the deposition velocities observed in this study were higher than those calculated by theoretical models, approximately 0.1 cm s1 for submicron aerosols (e.g., Slinn, 1982). Regarding the deposition velocity of aerosols above forests, theoretical values tend to underestimate observed values (e.g., Erisman et al., 1997; Garland, 2001). In particular, Horváth (2003) suggested the necessity for the revision of the theoretical models of dry deposition velocity of PM2.5 aerosols according to long-term S- and N-balance estimations in a Norway spruce forest. The deposition velocities of aerosols are strongly influenced by aerodynamic conditions. Fig. 3 shows the relationship between mean deposition velocities and friction velocities classified into three categories. In the figure, the deposition velocities increased with friction velocities, and the slope of the relationship was in accordance with that found in experiments in a coniferous forest in The Netherlands (Wyers and Duyzer, 1997). The deposition velocities are influenced by atmospheric stability as well as friction velocity, and Wesely and Hicks (2000) report that they tend to
Table 2 Mean deposition velocities and micrometeorological parameters in the daytime and nighttime during the experimental period in July 2009. All values indicate “mean standard deviation”.
Vd (cm s1) u* (m s1) 1/L (m1) Vd/u* (e)
Daytime
Nighttime
0.9 1.0 0.3 0.2 0.07 0.25 0.03 0.03
0.3 0.3 0.2 0.2 0.06 0.2 0.01 0.01
K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
Estimated Vd would be affected by the increase of the eddy diffusivity in the roughness sub-layer mentioned above. Wyers and Duyzer (1997) applied modified flux-profile relations to the flux calculation for gradient measurements that were partly performed within the roughness sub-layer. The modification concerned a height-dependent correction factor, which ranged from 0.73 for z ¼ 22 m to 0.9 for z ¼ 34 m for a canopy height of about 20 m. To evaluate the effects on this measurement, a correction factor of 0.75 was tentatively applied to the flux calculations, taking this range into account. As a result, mean deposition velocity increased in 33% in the daytime and 28% in the nighttime. The increases of Vd, especially in daytime, are consistent with the characteristics of Vd discussed in this section.
2.5 2
Vd (cm s-1)
1.5 1 0.5 0
3.4. Comparison between measured and parameterized Vd
-0.5 <0.15
0.15~0.25 u* (m
0.25<
s-1)
Fig. 3. Relationship between mean deposition velocity and friction velocity classified into three categories. Error bars indicate standard deviations in each category.
increase markedly during the daytime when there are dynamically unstable conditions near the ground surface. The deposition velocity normalized by the friction velocity (Vd/u*) also increased in the daytime (Table 2). According to previous studies (Wesely et al., 1985; Petroff et al., 2008; Pryor et al., 2008), Vd/u* is constant under stable conditions, whereas it increases with a decrease in 1/L under unstable conditions. Fig. 4 shows the relationship between mean Vd/u* and stability conditions. The results, shown in Fig. 4, also show the increase of mean Vd/u* under unstable conditions. Therefore, the high deposition velocity in the daytime observed in this study was associated with larger friction velocity and unstable conditions in the day. A large error is evident in the friction velocity class over 0.25 m s1, which was caused by a negative value of deposition velocity to be discussed in section 3.4.
0.09
0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 -0.01 -0.02
To evaluate regional atmospheric deposition in East Asia, the applicability of parameterizations developed in other regions has to be considered. Two empirical parameterizations (Wesely et al., 1985; Ruijgrok et al., 1997) were compared with our observations. The framework of aerosol deposition velocity estimation is generally parameterized by an aerodynamic term and a surface term as follows (Slinn, 1982; Wesely et al., 1985):
Vd ¼
unstable
near neutral
stable
Fig. 4. Relationship between mean Vd/u* and stability conditions into three categories (“unstable”; L < 100, “near neutral”; 100 < L < 100, “stable”; L > 100). Error bars indicate standard deviations in each category.
1 Ra þ Vds
1
(5)
where Ra is the aerodynamic resistance and Vds is the surface deposition velocity. Ra was computed based on Erisman and Draaijers (1995). The roughness length, z0, for the Ra computation was set at z0/h ¼ 0.1, taking into account the relationship between z0/h and LAI (Meyers et al., 1998). The Vds parameterization of Wesely et al. (1985) is an empirical fit to the field experiment data above grassland in North America. This is parameterized as:
Vds ¼ u*=500; neutral or stable conditions; i h Vds ¼ ðu*=500Þ 1 þ ð300=ð LÞÞ2=3 ; unstable conditions:
(6)
The Ruijgrok et al. (1997) parameterization is an empirical fit to field experiment data above a forest in Europe. This can be is parameterized as:
Vds ¼
0.08
Vd/u*
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u*2 =uh E
(7)
where uh is the wind speed at the top of the canopy and E is the total collection efficiency with which the canopy captures particles defined by Ruijgrok et al. (1997). The E depends on u*, relative humidity and surface conditions. Fig. 5 shows the variations between the deposition velocities observed in this study and those calculated by the two parameterizations. Error bars indicate measurement errors from the differences between the samplers and estimation errors from the assumption of the nighttime concentrations. The former was determined from the maximum difference (mean þ standard deviation) between the samplers divided by the mean concentration difference between 21 m and 27 m, and the latter was determined from the mean coefficient of variation of Dc/C in daytime that was assumed to be same in nighttime. The general trend of high values in the daytime and low values in the nighttime was similar among the observed and the two parameterized deposition velocities. The Ruijgrok et al. (1997) parameterization reproduced the observation relatively well, while the Wesely et al. (1985) parameterization underestimated the observations (Fig. 5). The large variability observed between the measurements was not reproduced by either parameterization. Hole et al. (2008) obtained similar results from gradient measurements at a semi-alpine site in
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K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
minimum values during each time period. The use of median deposition velocities is considered to reduce the influences of the random error and extreme values. The observations were in accordance with the Ruijgrok et al. (1997) parameterization and markedly larger than the Wesely et al. (1985) parameterization, indicating the existence of large differences between aerosol deposition above forests and grasslands. From the results, the Ruijgrok et al. (1997) parameterization is applicable to the forest in the present study, and possibly broadly to deciduous forests in Japan.
9.0 Wesely85
8.0
Ruijgrok97 7.0
Vd obs
6.0 5.0 4.0 3.0 2.0 1.0
4. Conclusion
0.0 -1.0
07/ 18:00
07/ 6:00
06/ 18:00
06/ 6:00
05/ 18:00
05/ 6:00
04/ 18:00
04/ 6:00
03/ 18:00
03/ 6:00
02/ 18:00
-2.0
Date
Fig. 5. Variations of deposition velocities observed in this study (Vd obs) and calculated by two parameterizations (Ruijgrok 97: Ruijgrok et al., 1997; Wesely85: Wesely et al., 1985) during the experimental period in July 2009. Error bars indicate measurement errors from the differences between the samplers and estimation errors from the assumption of the nighttime concentrations.
southern Norway, and explained that the difference was caused by the parameterizations not considering some processes and/or some extreme values from measurement uncertainties. It is possible that the random error arising from the differences between the samplers mentioned above contributed to the marked variability observed in the measurements in this study. A negative value appeared in the 14:00e18:00 sample collected on 7 July (Fig. 5), which showed a large discrepancy with the parameterizations in particular. During this period, 6 mm of rain was observed. The discrepancy is possibly associated with the parameterizations not consider processes of upward flux, rain, and/or the measurement uncertainties. The large error bar shown in the friction velocity class over 0.25 m s1 in Fig. 3 was caused by this negative value. Fig. 6 shows the diurnal variation in median deposition velocities. The bars of the observation data indicate maximum and
3.5 Vd obs Ruijgrok97
3
Wesely85
2.5
Vd (cm s-1)
2 1.5 1
The experimental study of PM2.5 sulfate deposition was implemented in the early summer of 2009 in a deciduous forest at the foot of Mt. Asama in central Japan. Aerosol fluxes were obtained using the aerodynamic gradient method. The absolute values of the sulfate concentration differences between the heights of 21 m and 27 m, when the concentrations were more than 1 mg m3, were 0.15 0.12 mg m3, which was considerably higher than the detection limit. Furthermore, a paired t-test between the two heights showed significant difference with higher concentrations at 27 m (p < 0.01). The deposition velocities increased during the daytime (0.9 1.0 cm s1) and decreased during the nighttime (0.3 0.3 cm s1). In the case that a heightdependent correction in the roughness sub-layer was taken into account, the deposition velocities increased more, especially in daytime. The high deposition velocities observed in the daytime during this study were associated with larger friction velocities and unstable conditions in the day. In addition, the deposition velocities observed in this study were in accordance with experimental results reported in recent reviews (Petroff et al., 2008; Pryor et al., 2008). On the other hand, the deposition velocities were higher than those calculated by theoretical models and indicate the necessity for the revision of the theoretical models of deposition velocity of PM2.5 aerosols, as suggested by Horváth (2003). Two empirical parameterizations (Wesely et al., 1985; Ruijgrok et al., 1997) were validated by the observations. The general trend of high values in the daytime and low values in the nighttime was similar between the observations and the two parameterizations. The large variability observed in the measurements was not reproduced by the two parameterizations, and was considered to be due to random error arising from differences between the samplers. The observations were in accordance with the Ruijgrok et al. (1997) parameterization, and were considerably higher than the Wesely et al. (1985) parameterization. Taken together, these findings indicate the marked differences in aerosol deposition above forest compared to grasslands. From the results, the Ruijgrok et al. (1997) parameterization is considered to be most applicable to this forest, and possibly broadly to deciduous forests in Japan. Acknowledgements
0.5 0 -0.5 2~6
6~10
10~14
14~18
18~22
22~2
The authors extend their gratitude to Prof. S. Hatakeyama and Prof. T. Izuta, Tokyo University of Agriculture and Technology, Japan, for their support of this study. The authors also thank Mr. T. Miyake and students of Matsuda Laboratory, Meisei University, for their assistance with measurements. This work was supported by Grant-in-Aid for Scientific Research on Innovative Areas (No. 20120012) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
Time interval References Fig. 6. Diurnal variations of median deposition velocities observed in this study (Vd obs), and calculated by two parameterizations (Ruijgrok 97: Ruijgrok et al., 1997; Wesely85: Wesely et al., 1985) during the experimental period in July, 2009. Error bars indicate maximum and minimum values during each time interval.
Businger, J.A., 1986. Evaluation of the accuracy with which dry deposition can be measured with current micrometeorological techniques. Journal of Climate and Applied Meteorology 25, 1100e1124.
K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587 EANET (Acid Deposition Monitoring Network in East Asia), 2006. Periodic report on the state of acid deposition in East Asia e executive summary. http://www. eanet.cc/product/index.html. Erisman, J.W., Draaijers, G.P.J., 1995. Atmospheric Deposition in Relation to Acidification and Eutrophication, Studies in Environmental Science, vol. 63. ELSEVIER, pp. 55e75. Erisman, J.W., Draaijers, G., Duyzer, J., Hofshreuder, P., van Leeuwen, N.F.M., Römer, F., Ruijgrok, W., Wyers, P., Gallagher, M., 1997. Particle deposition to forestssummary of results and application. Atmospheric Environment 31, 321e332. Feliciano, M.S., Pio, C.A., Vermeulen, A.T., 2001. Evaluation of SO2 dry deposition over short vegetation in Portugal. Atmospheric Environment 35, 3633e3643. Garland, J.A., 2001. On the size dependence of particulate deposition. Water, Air, and Soil Pollution: Focus 1, 323e332. Hayashi, K., Takagi, K., Noguchi, I., Fukuzawa, K., Takahashi, H., Fukazawa, T., Shibata, H., Fujinuma, Y., 2009. Ammoniacal nitrogen emission from a young larch ecosystem afforested after clear-cutting of a pristine forest in northernmost Japan. Water, Air, and Soil Pollution 200, 33e46. Hole, L.R., Brunner, S.H., Hanssen, J.E., Zhang, L., 2008. Low cost measurements of nitrogen and sulphur dry deposition velocities at a semi-alpine site: gradient measurements and a comparison with deposition model estimates. Environmental Pollution 154, 473e481. Horváth, L., 2003. Dry deposition velocity of PM2.5 ammonium sulfate particles to a Norway spruce forest on the basis of S- and N-balance estimations. Atmospheric Environment 37, 4419e4424. Lovett, G.M., Reiners, W.A., 1986. Canopy structure and cloud water deposition in subalpine coniferous forest. Tellus 38B, 319e327. Matsuda, K., Aoki, M., Zhang, S., Kominami, T., Fukuyama, T., Fukuzaki, N., Totsuka, T., 2002. Dry deposition velocity of sulfur dioxides on a red pine forest in Nagano, Japan. Journal of Japan Society for Atmospheric Environment 37, 387e392. Matsuda, K., Watanabe, I., Wingpud, V., Theramongkol, P., Khummongkol, P., Wangwongwatana, S., Totsuka, T., 2005. Ozone dry deposition above a tropical forest in the dry season in northern Thailand. Atmospheric Environment 39, 2571e2577. Matsuda, K., Watanabe, I., Wingpud, V., Theramongkol, P., Ohizumi, T., 2006. Deposition velocity of O3 and SO2 in the dry and wet season above a tropical forest in northern Thailand. Atmospheric Environment 40, 7557e7564. Matsuda, K., Takahashi, A., Hayashi, K., Sorimachi, A., 2007. A review of field studies on dry deposition in East Asia. Journal of Japan Society for Atmospheric Environment 42, 261e270 (in Japanese). Meyers, T.P., Finkelstein, P., Clarke, J., Ellestad, T.G., Sims, P.F., 1998. A multilayer model for inferring dry deposition using standard meteorological measurements. Journal of Geophysical Research 103, 22645e22661. Nakaya, K., 2008. Study of improvement on elvaluation method of energy and mass exchange between atmosphere and forest ecosystem. Memoirs of the Research Faculty of Agriculture Hokkaido University 29 (2), 149e213 (in Japanese).
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Ohara, T., Akimoto, H., Kurokawa, J., Horii, N., Yamaji, K., Yan, X., Hayasaka, T., 2007. An Asian emission inventory of anthropogenic emission sources for the period 1980e2020. Atmospheric Chemistry and Physics 7, 4419e4444. Petroff, A., Mailliat, A., Amielh, M., Anselmet, F., 2008. Aerosol dry deposition on vegetative canopies. Part I: Review of present knowledge. Atmospheric Environment 42, 3625e3653. Pryor, S.C., Gallagher, M., Sievering, H., Larsen, S.E., Barthelmie, R.J., Birsan, F., Nemitz, E., Rinne, J., Kulmala, M., Grönholm, T., Taipale, R., Vesala, T., 2008. A review of measurement and modelling results of particle atmosphereesurface exchange. Tellus 60B, 42e75. Ruijgrok, W., Tieben, H., Eisinga, P., 1997. The dry deposition of particles to a forest canopy: a comparison of model and experimental results. Atmospheric Environment 31, 399e415. Slinn, W.G.N., 1982. Predictions for particle deposition to vegetative canopies. Atmospheric Environment 16, 1785e1794. Sorimachi, A., Sakamoto, K., Ishihara, H., Fukuyama, T., Utiyama, M., Liu, H., Wang, W., Tang, D., Dong, X., Quan, H., 2003. Measurements of sulfur dioxide and ozone dry deposition over short vegetation in northern China-a preliminary study. Atmospheric Environment 37, 3157e3166. Sorimachi, A., Sakamoto, K., Sakai, M., Ishihara, H., Fukuyama, T., Utiyama, M., Liu, H., Wang, W., Tang, D., Dong, X., Quan, H., 2004. Laboratory and field measurements of dry deposition of sulfur dioxide onto Chinese loess surfaces. Environmental Science and Technology 38, 3396e3404. Takahashi, A., Sato, K., Wakamatsu, T., Fujita, S., Yoshikawa, K., 2002. Estimation of dry deposition of sulfur to a forest using an inferential method eInfluence of canopy wetness on SO2 dry deposition. Journal of Japan Society for Atmospheric Environment 37, 192e205 (in Japanese). Takahashi, A., Wakamatsu, T., 2004. Estimation of deposition velocity of particles to a forest using the concentration gradient method. Journal of Japan Society for Atmospheric Environment 39, 53e61 (in Japanese). Tsai, J., Chen, C., Tsuang, B., Kuo, P., Tseng, K., Hsu, Ti., Sheu, B., Liu, C., Hsueh, M., 2010. Observation of SO2 dry deposition velocity at a high elevation flux tower over an evergreen broadleaf forest in Central Taiwan. Atmospheric Environment 44, 1011e1019. Wesely, M.L., Hicks, B.B., 1977. Some factors that affect the deposition rates of sulfur dioxide and similar gases on vegetation. Journal of the Air Pollution Control Association 27, 1110e1116. Wesely, M.L., Cook, D.R., Hart, R.L., 1985. Measurement and parameterization of particulate sulfur dry deposition over grass. Journal of Geophysical Research 90, 2131e2143. Wesely, M.L., Hicks, B.B., 2000. A review of the current status of knowledge on dry deposition. Atmospheric Environment 34, 2261e2282. Wyers, G.P., Duyzer, J.H., 1997. Micrometeorological measurement of the dry deposition flux of sulphate and nitrate aerosols to coniferous forest. Atmospheric Environment 31, 333e343.
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Deposition velocity of PM2.5 sulfate in the summer above a deciduous forest in central Japan Kazuhide Matsuda a, *, Yoshifumi Fujimura a, Kentaro Hayashi b, Akira Takahashi c, Ko Nakaya c a
School of Sciences and Engineering, Meisei University, 2-1-1 Hodokubo, Hino, Tokyo 191-8506, Japan National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan c Central Research Institute of Electric Power Industry, 1646 Abiko, Chiba 270-1194, Japan b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 April 2010 Received in revised form 5 August 2010 Accepted 5 August 2010
In order to increase knowledge of aerosol dry deposition for the regional assessment of acid deposition and transboundary air pollution in East Asia, an experimental study on PM2.5 sulfate deposition was implemented in the early summer of 2009. The experimental field was located in a deciduous forest at the foot of Mt. Asama, central Japan. Aerosol fluxes were obtained using the aerodynamic gradient method. Three aerosol samplers were placed on an experimental tower at 21, 24 and 27 m above the ground surface, and collected PM2.5 on filters for chemical analysis. Vertical concentration differences between 21 m and 27 m of PM2.5 sulfate were detected significantly when the concentration exceeded 1 mg m3. Mean deposition velocity was estimated to be 0.9 1.0 cm s1 in the daytime and 0.3 0.3 cm s1 in the nighttime. In the case that a height-dependent correction in the roughness sub-layer was taken into account, the deposition velocities increased more, especially in daytime. Higher deposition velocities in the daytime were associated with larger friction velocities and unstable conditions. The deposition velocities observed in this study were in agreement with other experimental results found in the literature. On the other hand, they were higher than those calculated by theoretical models. Two empirical parameterizations (Wesely, M.L., Cook, D.R., Hart, R.L., 1985. Measurement and parameterization of particulate sulfur dry deposition over grass. Journal of Geophysical Research 90, 2131e2143; Ruijgrok, W., Tieben, H., Eisinga, P., 1997. The dry deposition of particles to a forest canopy: a comparison of model and experimental results. Atmospheric Environment 31, 399e415) were validated by the observations. The general trend of higher daytime and lower nighttime deposition velocities was similar among the observation and the two parameterizations. The large variability found in the measurement was not reproduced by the parameterizations, because it is attributable to random error from the differences between the samplers. The observations were in accordance with the parameterization of Ruijgrok et al. (1997) for a forest, although much larger than that of Wesely et al. (1985) for grasslands. This indicates the large difference in aerosol deposition velocities between forests and grasslands. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Dry deposition Aerosols Flux measurement Parameterization East Asia
1. Introduction Sulfur oxides are extremely important species in the atmospheric environment in Asia. Transboundary air pollution of sulfur and the impact of sulfur deposition on the ecosystem are of concern. Total emission of sulfur dioxide from Asian countries has been increasing rapidly since 2000 (Ohara et al., 2007). The Acid Deposition Monitoring Network in East Asia (EANET), which is working on the acid deposition problem in East Asia, published the first periodic report on the state of acid deposition in East Asia (EANET, 2006). The chapter in the report, “Dry deposition”, highlights the * Corresponding author. Tel.: þ81 42 591 6216; fax: þ81 42 591 6196. E-mail address: [email protected] (K. Matsuda). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.08.015
significance of the long-range transport of sulfate aerosols over the region. On the other hand, the report did not evaluate dry deposition rates due to the lack of knowledge on deposition velocity in this region. According to a throughfall and stemflow measurement (Takahashi et al., 2002), dry deposition contributed approximately 27% of total sulfur deposition in a forest of central Japan. Much higher ratios of sulfate aerosols to SO2 at remote site in East Asia (EANET, 2006) indicates the importance of sulfate dry deposition in this region. Several field studies on dry deposition flux have been implemented in some countries in East Asia in recent years (Matsuda et al., 2007), for example in Japan (Takahashi et al., 2002; Matsuda et al., 2002, Hayashi et al., 2009), China (Sorimachi et al., 2003, 2004), Taiwan (Tsai et al., 2010) and Thailand (Matsuda et al., 2005, 2006).
K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
These studies were, however, mainly aimed at gases such as O3 and SO2. Relatively few field studies have been conducted on aerosol deposition (Takahashi and Wakamatsu, 2004). Petroff et al. (2008) reviewed the current knowledge of aerosol dry deposition on vegetative canopies. Almost all of the papers reviewed, which numbered more than fifty, were based on field studies in Europe and North America, and there were no papers derived from Asia. The Impacts of Aerosols in East Asia on Plants and Human Health Project (ASEPH) was implemented in 2008. As a part of this project, several field studies on aerosol deposition flux above different vegetation types have been carried out in this region. The purpose of this study, as an ASEPH activity, is to understand the deposition velocity of PM2.5 sulfate above a temperate forest in Japan using flux observations, as first step to accumulating knowledge on deposition velocities in East Asia.
F ¼ ku*Dc=½lnððz2 dÞ=ðz1 dÞÞ jh ððz2 dÞ=LÞ þ jh ððz1 dÞ=LÞ;
(3)
Firstly, u* and L were averaged every 30 min, and then D, which is the transfer velocity (m s1) between 21 m and 27 m (a part of Eq. (3)), was computed using the following equation:
þ jh ððz1 dÞ=LÞ;
2.1. Experimental description The experimental field was located in a deciduous forest at the eastern foot of Mt. Asama (36 240 N, 138 350 E, 1380 m asl), Nagano Prefecture, central Japan. The ground is flat with a slight slope of approximately 3 over a distance of over 600 m in the main upwind direction. The experiment was carried out in early summer, from 2 to 8 July in 2009. The forest is classified as alpine temperate forest, and flourishes in this season. An experimental station with twin 28m-high walk-up towers was established in the forest with a canopy height of about 20 m. The dominant canopy tree species are birch (Betula ermanii) and other species, such as alder (Alnus hirsute). The sub-canopy layer under 10 m was covered with deciduous shrub. Displacement height and roughness length ranges from 8 m to 16 m and form 0.5 m to 1.5 m during a year, respectively. Aerosol fluxes were obtained by coupling aerosol samplers (MCI sampler, Tokyo Dylec Corporation) with an ultrasonic anemometer (METEK, USA-1) using the aerodynamic gradient method. PM2.5 was collected on a glass fiber filter coated with Teflon, using a filter holder with an impactor (flow rate 20 L min1). Three aerosol samplers were placed on a tower at heights of 21, 24 and 27 m above the ground surface, and sample filters were exchanged at 6:00, 10:00, 14:00, 18:00 (i.e. three times during the day and once at night). The ultrasonic anemometer and other meteorological instruments were placed on the other tower at a height of 28 m above the ground surface. After every sample was taken, each sample filter was sealed as soon as possible in a clean polypropylene test tube with a cap to avoid contamination. In the laboratory, inorganic ions were extracted from the samples into deionized water by ultrasonic extraction, and then analyzed by the ion chromatography. Since blank values of SO2 4 extracted from 5 blank filters showed less than 0.7% (average 0.2%) of the sample values, the blank values were ignored for the calculations of SO2 4 concentrations in the atmosphere. 2.2. Computation of sulfate fluxes In this study, the gradient method was used to estimate sulfate fluxes (Erisman and Draaijers, 1995). The fluxes, F, were computed using the following equation:
c* ¼ kDc=½lnððz2 dÞ=ðz1 dÞÞ jh ððz2 dÞ=LÞ (2)
(4)
Secondly, D was averaged every 4 h, and then F was computed from the product of D and Dc. From a previous study at the site, the leaf area index (LAI) was observed to range between 5e7, one sided, in July (Nakaya, 2008). Taking into account the relationship between d/h and LAI reported by Lovett and Reiners (1986) and Meyers et al. (1998), a d/h value of 0.8 (d ¼ 16 m) was adopted, where h is canopy height (20 m). The deposition velocity, Vd, is determined by the following equation adapted from Wesely and Hicks (1977):
Vd ¼ F=C;
(4)
where C is the PM2.5 sulfate concentration at height z2 (reference height zref ¼ z2 d ¼ 11 m). The fluxes of six periods, 6:00e10:00, 10:00e14:00, 14:00e18:00, 18:00e22:00, 22:00e2:00 and 2:00e6:00, were calculated during a day under the assumption that the concentration was constant during the nighttime sampling period (18:00e6:00). The influence of this assumption on nighttime Vd variation is discussed in section 3.4. Although the gradient method based on the MonineObukhov theory is valid in the inertial sub-layer (z >> z0) (Businger, 1986), this measurement were performed closer to the canopy top, in the roughness sub-layer. Possible effects on estimated Vd were discussed in section 3.3. 3. Results and discussion 3.1. Atmospheric conditions Table 1 shows the atmospheric conditions at the study site during the experimental period and Fig. 1 shows variations of wind speed, temperature, relative humidity and precipitation during this time. Precipitation events over 10 mm occurred twice in the nighttime of the 3rd and 4th July. Since relative humidity was also high in the nighttime (Table 1), the surface conditions of the forest canopy tended to be wet during this period. Mean wind speed in Table 1 Atmospheric conditions at the study site during the experimental period in July, 2009. All values indicate “mean standard deviation”, except for precipitation which is the total value.
(1)
where u* is the friction velocity and c* is the eddy concentration. c* is expressed by the following equation:
þ jh ððz1 dÞ=LÞ;
where Dc represents the differences in the concentrations between heights of z1 and z2, k is the Von Karman constant, L is the MonineObukhov length, Jh is the integrated stability correction function for heat defined by Erisman and Draaijers (1995), and d is the zero-plane displacement height. In this study, z1 and z2 were assigned to the lower sampling height of 21 m and the upper sampling height of 27 m, respectively. From the Eqs. (1) and (2), F could be expressed as follows:
D ¼ ku*=½lnððz2 dÞ=ðz1 dÞÞ jh ððz2 dÞ=LÞ
2. Experimental description and methodology
F ¼ u*c*;
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3 SO2 4 in PM2.5 (mg m ) Wind speed (m s1) Solar radiation (W m2) Temperature ( C) Relative humidity (%) Precipitation (mm)
Daytime
Nighttime
3.9 2.7 1.2 0.8 322 220 17.3 2.1 86 12 9
3.1 2.7 1.6 1.0 5 15 15.4 1.6 91 9 45
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8
daytime
25
nighttime
27
20 15
4 10 2
5
26 Height (m)
6
Tem peratu re (ºC)
Win d speed (m s-1)
28
25 24 23 22 21 20 1
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
0
0
Fig. 2. Mean concentrations of SO2 4 in PM2.5 at heights of 21, 24 and 27 m in the daytime and nighttime during the experimental period in July 2009. Error bars indicate standard deviations in each height.
20
80
15
60 10 40 5
20 0
Precipitation (m m )
100
Relative h u m i dity (%)
1.5
7/ 18:00
7/ 6:00
6/ 18:00
6/ 6:00
5/ 18:00
5/ 6:00
4/ 18:00
4/ 6:00
3/ 18:00
3/ 6:00
2/ 18:00
0
Date Fig. 1. Variations of wind speed, temperature, relative humidity and precipitation at the study site during the experimental period in July, 2009.
the daytime was slightly lower than in the nighttime. Sulfate (SO2 4 in PM2.5) concentration in the daytime was higher than in the nighttime. 3.2. Sulfate gradients In highly rough canopy such as forests, it is difficult to detect the concentration gradient of fine particles because the gradient is very small (Businger, 1986). In particular, the gradient tends to decrease with concentration (Feliciano et al., 2001). Therefore, data sets with sulfate concentrations less than 1 mg m3 measured at a height of 27 m were omitted from the Vd analyses described below. The absolute values of the concentration difference between 21 m and 27 m, when the concentrations were more than 1 mg m3, was 0.15 0.12 mg m3 (average standard deviation), which was sufficiently higher than the detection limit of sulfate concentration of approximately 102 mg m3. Furthermore, a paired t-test between concentrations at 21 m and 27 m showed significantly higher concentrations at 27 m (p < 0.01). These results indicated that differences in the concentration were detected when the concentration exceeded 1 mg m3. On the other hand, random errors between samplers also influenced the concentration gradient. According to duplicate measurements made 7 times using the same specifications at the same height, a significant difference was not found between the samplers used at 21 m and 27 m (linier regression line: y ¼ 1.021x 0.031, r ¼ 0.999); average of the differences amounted to 0.09 0.14 (average standard deviation) mg m3, which probably contribute to an error in Vd of 60% (0.09/0.15) on average. Fig. 2 shows the average concentration gradients in the daytime and nighttime, respectively. The concentrations decreased with the
measurement height in both the daytime and nighttime. In addition, the gradients indicated the occurrence of downward fluxes (depositions), indicating that the forest could be regarded as a sulfate sink during the study period. The paired t-test between concentrations at 24 m and 27 m showed not significant differences. The test between 21 m and 24 m shows significant differences, however the gradients were closer to the random differences determined by the duplicate measurements than those between 21 m and 27 m, and then made large errors. Finally we concluded that Vd calculation was valid only between concentrations at 21 m and 27 m in this measurement. 3.3. Deposition velocities Table 2 shows mean deposition velocities in the daytime and nighttime together with micrometeorological parameters. The deposition velocities increased in the daytime and decreased in the nighttime, which corroborates the experimental results found in the literature (Petroff et al., 2008; Pryor et al., 2008). On the other hand, the deposition velocities observed in this study were higher than those calculated by theoretical models, approximately 0.1 cm s1 for submicron aerosols (e.g., Slinn, 1982). Regarding the deposition velocity of aerosols above forests, theoretical values tend to underestimate observed values (e.g., Erisman et al., 1997; Garland, 2001). In particular, Horváth (2003) suggested the necessity for the revision of the theoretical models of dry deposition velocity of PM2.5 aerosols according to long-term S- and N-balance estimations in a Norway spruce forest. The deposition velocities of aerosols are strongly influenced by aerodynamic conditions. Fig. 3 shows the relationship between mean deposition velocities and friction velocities classified into three categories. In the figure, the deposition velocities increased with friction velocities, and the slope of the relationship was in accordance with that found in experiments in a coniferous forest in The Netherlands (Wyers and Duyzer, 1997). The deposition velocities are influenced by atmospheric stability as well as friction velocity, and Wesely and Hicks (2000) report that they tend to
Table 2 Mean deposition velocities and micrometeorological parameters in the daytime and nighttime during the experimental period in July 2009. All values indicate “mean standard deviation”.
Vd (cm s1) u* (m s1) 1/L (m1) Vd/u* (e)
Daytime
Nighttime
0.9 1.0 0.3 0.2 0.07 0.25 0.03 0.03
0.3 0.3 0.2 0.2 0.06 0.2 0.01 0.01
K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
Estimated Vd would be affected by the increase of the eddy diffusivity in the roughness sub-layer mentioned above. Wyers and Duyzer (1997) applied modified flux-profile relations to the flux calculation for gradient measurements that were partly performed within the roughness sub-layer. The modification concerned a height-dependent correction factor, which ranged from 0.73 for z ¼ 22 m to 0.9 for z ¼ 34 m for a canopy height of about 20 m. To evaluate the effects on this measurement, a correction factor of 0.75 was tentatively applied to the flux calculations, taking this range into account. As a result, mean deposition velocity increased in 33% in the daytime and 28% in the nighttime. The increases of Vd, especially in daytime, are consistent with the characteristics of Vd discussed in this section.
2.5 2
Vd (cm s-1)
1.5 1 0.5 0
3.4. Comparison between measured and parameterized Vd
-0.5 <0.15
0.15~0.25 u* (m
0.25<
s-1)
Fig. 3. Relationship between mean deposition velocity and friction velocity classified into three categories. Error bars indicate standard deviations in each category.
increase markedly during the daytime when there are dynamically unstable conditions near the ground surface. The deposition velocity normalized by the friction velocity (Vd/u*) also increased in the daytime (Table 2). According to previous studies (Wesely et al., 1985; Petroff et al., 2008; Pryor et al., 2008), Vd/u* is constant under stable conditions, whereas it increases with a decrease in 1/L under unstable conditions. Fig. 4 shows the relationship between mean Vd/u* and stability conditions. The results, shown in Fig. 4, also show the increase of mean Vd/u* under unstable conditions. Therefore, the high deposition velocity in the daytime observed in this study was associated with larger friction velocity and unstable conditions in the day. A large error is evident in the friction velocity class over 0.25 m s1, which was caused by a negative value of deposition velocity to be discussed in section 3.4.
0.09
0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 -0.01 -0.02
To evaluate regional atmospheric deposition in East Asia, the applicability of parameterizations developed in other regions has to be considered. Two empirical parameterizations (Wesely et al., 1985; Ruijgrok et al., 1997) were compared with our observations. The framework of aerosol deposition velocity estimation is generally parameterized by an aerodynamic term and a surface term as follows (Slinn, 1982; Wesely et al., 1985):
Vd ¼
unstable
near neutral
stable
Fig. 4. Relationship between mean Vd/u* and stability conditions into three categories (“unstable”; L < 100, “near neutral”; 100 < L < 100, “stable”; L > 100). Error bars indicate standard deviations in each category.
1 Ra þ Vds
1
(5)
where Ra is the aerodynamic resistance and Vds is the surface deposition velocity. Ra was computed based on Erisman and Draaijers (1995). The roughness length, z0, for the Ra computation was set at z0/h ¼ 0.1, taking into account the relationship between z0/h and LAI (Meyers et al., 1998). The Vds parameterization of Wesely et al. (1985) is an empirical fit to the field experiment data above grassland in North America. This is parameterized as:
Vds ¼ u*=500; neutral or stable conditions; i h Vds ¼ ðu*=500Þ 1 þ ð300=ð LÞÞ2=3 ; unstable conditions:
(6)
The Ruijgrok et al. (1997) parameterization is an empirical fit to field experiment data above a forest in Europe. This can be is parameterized as:
Vds ¼
0.08
Vd/u*
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u*2 =uh E
(7)
where uh is the wind speed at the top of the canopy and E is the total collection efficiency with which the canopy captures particles defined by Ruijgrok et al. (1997). The E depends on u*, relative humidity and surface conditions. Fig. 5 shows the variations between the deposition velocities observed in this study and those calculated by the two parameterizations. Error bars indicate measurement errors from the differences between the samplers and estimation errors from the assumption of the nighttime concentrations. The former was determined from the maximum difference (mean þ standard deviation) between the samplers divided by the mean concentration difference between 21 m and 27 m, and the latter was determined from the mean coefficient of variation of Dc/C in daytime that was assumed to be same in nighttime. The general trend of high values in the daytime and low values in the nighttime was similar among the observed and the two parameterized deposition velocities. The Ruijgrok et al. (1997) parameterization reproduced the observation relatively well, while the Wesely et al. (1985) parameterization underestimated the observations (Fig. 5). The large variability observed between the measurements was not reproduced by either parameterization. Hole et al. (2008) obtained similar results from gradient measurements at a semi-alpine site in
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K. Matsuda et al. / Atmospheric Environment 44 (2010) 4582e4587
minimum values during each time period. The use of median deposition velocities is considered to reduce the influences of the random error and extreme values. The observations were in accordance with the Ruijgrok et al. (1997) parameterization and markedly larger than the Wesely et al. (1985) parameterization, indicating the existence of large differences between aerosol deposition above forests and grasslands. From the results, the Ruijgrok et al. (1997) parameterization is applicable to the forest in the present study, and possibly broadly to deciduous forests in Japan.
9.0 Wesely85
8.0
Ruijgrok97 7.0
Vd obs
6.0 5.0 4.0 3.0 2.0 1.0
4. Conclusion
0.0 -1.0
07/ 18:00
07/ 6:00
06/ 18:00
06/ 6:00
05/ 18:00
05/ 6:00
04/ 18:00
04/ 6:00
03/ 18:00
03/ 6:00
02/ 18:00
-2.0
Date
Fig. 5. Variations of deposition velocities observed in this study (Vd obs) and calculated by two parameterizations (Ruijgrok 97: Ruijgrok et al., 1997; Wesely85: Wesely et al., 1985) during the experimental period in July 2009. Error bars indicate measurement errors from the differences between the samplers and estimation errors from the assumption of the nighttime concentrations.
southern Norway, and explained that the difference was caused by the parameterizations not considering some processes and/or some extreme values from measurement uncertainties. It is possible that the random error arising from the differences between the samplers mentioned above contributed to the marked variability observed in the measurements in this study. A negative value appeared in the 14:00e18:00 sample collected on 7 July (Fig. 5), which showed a large discrepancy with the parameterizations in particular. During this period, 6 mm of rain was observed. The discrepancy is possibly associated with the parameterizations not consider processes of upward flux, rain, and/or the measurement uncertainties. The large error bar shown in the friction velocity class over 0.25 m s1 in Fig. 3 was caused by this negative value. Fig. 6 shows the diurnal variation in median deposition velocities. The bars of the observation data indicate maximum and
3.5 Vd obs Ruijgrok97
3
Wesely85
2.5
Vd (cm s-1)
2 1.5 1
The experimental study of PM2.5 sulfate deposition was implemented in the early summer of 2009 in a deciduous forest at the foot of Mt. Asama in central Japan. Aerosol fluxes were obtained using the aerodynamic gradient method. The absolute values of the sulfate concentration differences between the heights of 21 m and 27 m, when the concentrations were more than 1 mg m3, were 0.15 0.12 mg m3, which was considerably higher than the detection limit. Furthermore, a paired t-test between the two heights showed significant difference with higher concentrations at 27 m (p < 0.01). The deposition velocities increased during the daytime (0.9 1.0 cm s1) and decreased during the nighttime (0.3 0.3 cm s1). In the case that a heightdependent correction in the roughness sub-layer was taken into account, the deposition velocities increased more, especially in daytime. The high deposition velocities observed in the daytime during this study were associated with larger friction velocities and unstable conditions in the day. In addition, the deposition velocities observed in this study were in accordance with experimental results reported in recent reviews (Petroff et al., 2008; Pryor et al., 2008). On the other hand, the deposition velocities were higher than those calculated by theoretical models and indicate the necessity for the revision of the theoretical models of deposition velocity of PM2.5 aerosols, as suggested by Horváth (2003). Two empirical parameterizations (Wesely et al., 1985; Ruijgrok et al., 1997) were validated by the observations. The general trend of high values in the daytime and low values in the nighttime was similar between the observations and the two parameterizations. The large variability observed in the measurements was not reproduced by the two parameterizations, and was considered to be due to random error arising from differences between the samplers. The observations were in accordance with the Ruijgrok et al. (1997) parameterization, and were considerably higher than the Wesely et al. (1985) parameterization. Taken together, these findings indicate the marked differences in aerosol deposition above forest compared to grasslands. From the results, the Ruijgrok et al. (1997) parameterization is considered to be most applicable to this forest, and possibly broadly to deciduous forests in Japan. Acknowledgements
0.5 0 -0.5 2~6
6~10
10~14
14~18
18~22
22~2
The authors extend their gratitude to Prof. S. Hatakeyama and Prof. T. Izuta, Tokyo University of Agriculture and Technology, Japan, for their support of this study. The authors also thank Mr. T. Miyake and students of Matsuda Laboratory, Meisei University, for their assistance with measurements. This work was supported by Grant-in-Aid for Scientific Research on Innovative Areas (No. 20120012) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
Time interval References Fig. 6. Diurnal variations of median deposition velocities observed in this study (Vd obs), and calculated by two parameterizations (Ruijgrok 97: Ruijgrok et al., 1997; Wesely85: Wesely et al., 1985) during the experimental period in July, 2009. Error bars indicate maximum and minimum values during each time interval.
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