[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 14, No. 4, pp.367-377
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Dec 2020
Received 26 May 2020 Revised 17 Aug 2020 Accepted 19 Aug 2020

# Dry Deposition of PM2.5 Nitrate in a Forest according to Vertical Profile Measurements

Mao Xu ; Kazuhide Matsuda*
United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo 183-8509, Japan

Correspondence to: * Tel: +81-42-367-5818 E-mail: kmatsuda@cc.tuat.ac.jp

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Abstract

The atmospheric nitrogen compounds can serve as a nutrient; however, its excess deposition has harmful effects on terrestrial ecosystems due to acidification and eutrophication. There are still large uncertainties concerning the dry deposition process of PM2.5 nitrate in forests, even though this process affects the accuracy of chemical transport model simulations. To better understand this process, we conducted vertical profile measurements of inorganic ions in PM2.5 and SO2 above and within a forest canopy in the Field Museum Tamakyuryo site in suburban Tokyo with a particular focus on the processes observed under both daytime and nighttime and both leafy and leafless conditions. We performed two observations during leafy periods (July 21-August 1, 2015, and September 27-October 11, 2016) and one observation during a leafless period (February 23-29, 2016). To obtain daytime and nighttime vertical profiles, we set filter holders at 4 or 5 heights on an observation tower in the forest and changed the filters for each daytime and nighttime. For the PM2.5, the vertical gradients of NO3- concentration were larger than those of SO42- during both the daytime and nighttime for all observational periods, particularly during the leafy periods. In addition, the decreasing rate of NO3- in the PM2.5 within the canopy was larger than that of SO2 for some observational periods. In the daytime, the air temperature was higher near the canopy surface during the leafy period and near the ground surface during the leafless period. As also suggested by past studies, the large gradients of NO3- in the PM2.5 during the leafy period were likely caused by the volatilization of NH4NO3 near the deposition surfaces due to the higher temperature in the daytime and the lower concentration of HNO3 caused by its fast removal during both the daytime and nighttime.

## Keywords:

Air-surface exchange, Gradient, Semi-volatile aerosols, Ammonium nitrate, Leaf area,

## 1. INTRODUCTION

A drastic increase in the emission of nitrogen compounds on a global scale has occurred due to human activities over the last century (Galloway et al., 2008). In particular, a drastic increase in the emission of nitrogen oxides (NOx) associated with energy consumption has been observed over East Asia in recent decades (Kurokawa et al., 2013; Ohara et al., 2007). NOx acts as a precursor of nitrate atmospheric particulate matter with diameters of less than 2.5 μm (PM2.5); such particulate matter is known to have adverse effects on human health. Moreover, these nitrogen compounds have harmful effects on terrestrial ecosystems due to acidification and eutrophication via the deposition of excess nitrogen.

PM2.5 simulations have been conducted using chemical transport models over Japan (Shimadera et al., 2018; Morino et al., 2015; Shimadera et al., 2014); however, such models have clearly overestimated the concentration of NO3- in PM2.5. Shimadera et al. (2014) suggested that the simulated concentration was highly dependent on the uncertainty in the dry deposition process of nitrogen compounds. Moreover, for NO3- in PM2.5, large uncertainties exist in the theoretical models used to estimate the dry deposition rates, particularly on forest surfaces (Flechard et al., 2011). However, the dry deposition process of PM2.5 has rarely been studied in East Asia, where anthropogenic emissions of NOx are higher than those found in Europe and North America. Therefore, a better understanding of the dry deposition process of NO3- in PM2.5 in this region will contribute to improving the model accuracy of estimated PM2.5 concentrations and nitrogen deposition rates.

Several field experiments to determine the deposition velocity (Vd) of PM2.5 nitrate in forests via gradient or relaxed eddy accumulation methods have been performed in Japan (Sakamoto et al., 2018; Honjo et al., 2016; Takahashi and Wakamatsu, 2004). These experiments suggest that the equilibrium shift of NH4NO3 into the gas phase likely enhances the dry deposition of nitrogen compounds, as indicated by previous studies in other regions (Nemitz et al., 2004; Wyers and Duyzer, 1997; Sievering et al., 1994; Huebert et al., 1988). Measurements of the vertical profiles of relevant matter are useful to understand dry deposition mechanisms in forests. Yamazaki et al. (2015) found different vertical profiles for NO3- and SO42- in PM2.5 in a forest in Tokyo over the course of a year, likely due to the abovementioned volatilization process. However, these studies conducted in Japan primarily focused on leafy forests and did not examine the diurnal deposition process. To improve the understanding of dry deposition processes for PM2.5 in East Asia further, we conducted intensive field observations in a forest in suburban Tokyo. We obtained the vertical profiles of the PM2.5 components together with those of SO2, a stereotypical gas, the deposition processes of which have been generalized (Nemitz, 2015), to compare the differences in the deposition processes of particle and gaseous matter. We particularly focused on the daytime and nighttime deposition processes of PM2.5 in a forest during leafy and leafless periods.

## 2. OBSERVATIONS AND METHODOLOGY

We conducted the measurements using an observation tower (Fig. 1) in a forest at the Field Museum Tamakyuryo (FM Tama) site of the Tokyo University of Agriculture and Technology, which is located in a western suburb of Tokyo, Japan (35°38′N, 139°23′E). Deciduous trees (Quercus) were the dominant tree species around the tower in addition to some Japanese cedar (Crytomeria). The canopy height around the tower was approximately 20 m. The deciduous trees were leafy from April and leafless from December. Other details concerning the measurement site have been described in Matsuda et al. (2015).

Schematic diagram of the observation tower at the Field Museum Tamakyuryo site.

We performed two observations during leafy periods and one during a leafless period from July 2015 to October 2016. We sampled PM2.5 and SO2 simultaneously using a filter pack (Tokyo Dylec Corporation, NILU filter folder NL-O) with an impactor and a pump unit (Tokyo Dylec Corporation, MCI sampler). The flow rate was set to 20 L min-1 in accordance with the PM2.5 cut off the impactor. PM2.5 was collected on glass fiber filters coated with Teflon. SO2 was collected on a cellulose filter impregnated with potassium carbonate following the PM2.5 filter. To obtain vertical concentration profiles during the daytime and nighttime, we set the filter holders at four or five heights, as indicated in Table 1, on the tower and changed the filters twice a day. Outlines of the samplings during each observational period are given in Table 1. To sufficiently detect the vertical gradients, we set the sampling time to more than 9 h after considering previous measurements at the same site (Yamazaki et al., 2015). Sampling was conducted continuously, except when it was raining. We obtained 36 valid samples in total at each height level. After the samples were collected, the inorganic ions in each filter were extracted into deionized water via ultrasonic extraction and then analyzed using ion chromatography (Thermo Scientific, Dionex ICS-1100).

Experimental periods and sampling strategies.

Meteorological conditions including the wind speed (WS), temperature (Temp), and relative humidity (RH) were observed at heights of 30 m, 25 m, 20 m, 10 m, 6 m, and 1 m along the tower using meteorological sensors (YOUNG 81000 and PREDE PHMP45A at 30 m; VAISALA WXT520 at 25 m, 20 m, 10 m, 6 m, and 1 m). The 10-min averages were used to obtain the vertical profiles of each parameter. The leaf area index (LAI) was measured using a plant canopy analyzer (LI-COR LAI-2200).

## 3. RESULTS AND DISCUSSION

### 3. 1. Overview of the Three Observational Periods

Table 2 shows the atmospheric conditions at the observation site for the three periods considered here. The estimated LAI shows a leafy canopy in 15-summer and 16-autumn and a leafless canopy in 16-winter, where 15-summer, 16-autumn, and 16-winter indicate the three observational periods during the summer of 2015, the autumn of 2016, and the winter of 2016, respectively. The WS values were highest during the daytime in 16-winter; the Temp values were highest during the daytime in 15-summer; and the RH values were highest during the nighttime in 15-summer. The concentrations of SO42- in the PM2.5 were highest in 15-summer, while the concentrations of NO3- in the PM2.5 were highest in 16-winter. In the three experiments, 78-83% of the inorganic ions in the PM2.5 consisted of NH4+, SO42-, and NO3-. The results were broadly similar at all heights. According to the relationship between the equivalent concentrations of NH4+ and SO42-+NO3- in each PM2.5 profile, the inorganic components primarily existed as (NH4)2SO4 or NH4NO3.

Atmospheric conditions at a height of 30 m at the tower and the leaf area index. WS, Temp, RH, and LAI indicate the wind speed, temperature, relative humidity, and leaf area index, respectively. All values except LAI are given as the average±standard deviation from the 10-min means of the data for the meteorological parameters and the half-day data for the concentrations.

The temporal variations in the mass concentrations of SO42- and NO3- in the PM2.5 at heights of 30 m, 23 m, 8 m, and 1 m during the observational periods are shown in Fig. 2. During the three observational periods, there were no significant differences in the mass concentrations of SO42- between these heights except between 8 m and 1 m (p<0.01), but there were significant differences in the mass concentrations of NO3- (p<0.01). The NO3- concentration clearly decreased from the top of the canopy to the forest floor during the observations compared to the SO42- concentration.

Temporal variations in the mass concentrations of SO42− and NO3− in the PM2.5 at 30 m, 23 m, 8 m, and 1 m at the tower during the three observational periods. D and N indicate daytime and nighttime, respectively.

### 3. 2 Vertical Profiles and Decreasing Rates

The vertical concentration profiles of SO42- and NO3- in the PM2.5 and those of SO2 during the daytime and nighttime for the observational periods are shown in Fig. 3. The daytime and nighttime averages at each height were used to obtain the vertical profiles. The concentrations at all heights were normalized by those at 30 m. In general, the dry deposition mechanisms of aerosols are thought to depend on the physical processes at each particle size. In that case, the vertical profiles of SO42- and NO3- in the PM2.5 should show similar tendencies. However, the decreasing trend of NO3- below the canopy was clearly larger than that of SO42- during both the daytime and nighttime for all observational periods, especially during the leafy seasons (15-summer and 16-autumn). In the leafless season (16-winter), the differences in the SO42- and NO3- vertical profiles decreased due to the smaller decrease in NO3-.

Normalized vertical profiles of the mass concentrations of SO42− in the PM2.5 (open circles), NO3− in the PM2.5 (closed circles), and SO2 (open squares) during the daytime and nighttime for the three observational periods. The relative concentration is the concentration ratio with respect to the concentration at 30 m. The gray layers indicate the leafy canopies.

The decreasing rate (hereafter referred to as DR) is a proper index to understand the tendency of whether the target component is removed or not between each of the heights. The DR is defined as the following:

 $DR=\left({C}_{Z1}-{C}_{Z0}\right)/{C}_{Z1}$ (1)

where CZ1 and CZ0 is the concentration of the target component at Z1 [m] and Z0 [m] (Z1>Z0), respectively. Therefore, differences in DR indicate the differences in the removal efficiency between components. Because SO2 is a gas that is easy to deposit on forest surfaces due to its reactive and water-soluble properties, its value of DR is assumed larger than that of fine particulate matter (e.g., Erisman and Draaijers, 1995). This assumption holds between SO2 and SO42- for all observations in the canopy. However, the DR value of NO3- below the canopy was larger than that of SO2 during some observational periods (daytime in 15-summer and 16-autumn) (Fig. 3). Therefore, there are likely other factors that enhance the deposition of NO3- in PM2.5 in addition to physical processes.

The DR30-1 values (the DR values between 30 m and 1 m) of SO42- and NO3- for the observational periods are shown in Fig. 4. Each circle shows a daytime or nighttime daily value. The DR30-1 values of NO3- were clearly larger than those of SO42-, particularly in 15-summer and 16-autumn. The DR30-1 values of SO42- varied independently of LAI for all observations. Conversely, the DR30-1 values of NO3- were clearly larger during the leafy periods and smaller during the leafless period. These results indicate that the variation in the NO3- decrease may be closely related to the leaf condition. In effect, the removal efficiency of NO3- by the leaf canopy was larger than that of SO42- regardless of the time of day.

Distributions of the decreasing rates of SO42− and NO3− in the PM2.5 from 30 m to 1 m for the three observational periods. Each circle shows a daytime or nighttime daily value. The bars indicate the mean values.

### 3. 3 Effect of the Equilibrium Shift of NH4NO3 on the Deposition Process

(NH4)2SO4 particles have a very low vapor pressure and exist as an aerosol under atmospheric conditions. Conversely, NH4NO3 particles are semi-volatile and have an equilibrium relationship with NH3 and HNO3 in the atmosphere. The dry deposition of NH4NO3 is affected by its volatilization process and depends on Temp, RH, and the concentrations of either HNO3 or NH3. Therefore, differences in the DR30-1 values between SO42- and NO3- were likely caused by differences in their chemical properties.

There are some previous studies mentioned below that indicate a higher Vd value for NO3- particles compared to SO42- particles. For a crested wheatgrass field in the Boulder Atmospheric Observatory, a research facility in Colorado in the United States, Huebert et al. (1988) found that the vertical gradients of the NO3- concentrations in particles were larger than those of SO42- and sometimes exceeded those of HNO3, which has a very high Vd value. These results are consistent with the predictions of a model coupling the volatilization of NH4NO3 to the rapid dry deposition of HNO3 presented in Brost et al. (1988). Wyers and Duyzer (1993) determined the Vd values of SO42- and NO3- above a coniferous forest in Speulderbos in the Netherlands using the gradient method. They found that the Vd value of NO3- was larger than that of the maximum theoretically possible value when the temperature was high (above 20°C) and much larger than that of SO42-, possibly due to the equilibrium shift from NH4NO3 to NH3 and HNO3. Van Oss et al. (1998) used these results in their simulations, which considered the influence of the gas-to-particle conversion on surface exchange processes above a forest. Their simulation results suggest that the volatilization of particulate NH4NO3 during the daytime can lead to the emission of HNO3 and NH3 above the forest, along with the observed anomalously large Vd value for NO3- compared to the theoretical value. Based on the observations in Nemiz et al. (2004) of a dry inland heath dominated by Calluna vulgaris in Elspeetsche Veld in the Netherlands, the surface concentration products of HNO3 and NH3 were well below the thermodynamic equilibrium value and the Damkohler numbers indicated that the chemical conversion was sufficiently fast to modify the exchange fluxes. Considering these studies, there are two possible sources of the differences in the dry deposition mechanisms between (NH4)2SO4 and NH4NO3:

(1) An equilibrium shift of NH4NO3 due to the higher temperature near the deposition surfaces and/or

(2) An equilibrium shift of NH4NO3 due to the low concentrations of HNO3 caused by fast removal near the deposition surfaces.

The variations in the ensemble mean air temperature over the observational periods are shown in Fig. 5. In the daytime, Temp at 30 m was lower than Temp at 20 m, which was close to the canopy surface during 15-summer and 16-autumn, and was lower than Temp at 1 m, which was close to the ground surface during 16-winter. This occurred because direct sunlight struck the canopy surface during the daytime in the leafy periods (15-summer and 16-autumn) while it struck the ground surface in the leafless period (16-winter). In addition, Temp values of forest surfaces exposed to sunlight tend to be higher than the Temp values near the surfaces (Nakahara et al., 2019). Therefore, the volatilization of NH4NO3 was likely enhanced close to the canopy surface during the leafy periods and close to the ground surface during the leafless period. This is in agreement with the higher daytime DR30-1 values of NO3- than SO42- during the three periods (Fig. 4). Conversely, the air temperature gradients mentioned above were not clearly seen in the nighttime data during the three periods, even though the DR30-1 values of NO3- were also higher than SO42- in the nighttime. The NH4NO3 aerosols have an equilibrium relationship with the concentrations of the HNO3 and NH3 gases in the atmosphere. The Vd value of HNO3 is known to be greatly higher than those of NH3 and NH4NO3. The Vd values calculated by the resistance models are 3-10 times higher than those of other gaseous and particulate matter (Ban et al., 2016). During the leafy periods, HNO3 was quickly removed by the canopy surface due to the high Vd values and the large leaf area. When HNO3 is quickly removed by deposition surfaces, its concentration near these surfaces drastically decreases. Then, the gas-particle equilibrium is shifted to the gas phase. After that, NH4NO3 near the surfaces volatilizes to resupply the decreased level of HNO3 and quickly deposits to the surfaces as gaseous matter. This can cause a high DR30-1 value for NO3- not only in the daytime but also in the nighttime. This is likely because NH4NO3 can be removed from the atmosphere just as quickly as SO2 (Fig. 3) and therefore the DR30-1 value of NH4NO3 can become significantly larger than that of (NH4)2SO4 (p<0.01) (Fig. 4).

Variations in the ensemble mean values of the air temperature at 30 m, 25 m, 20 m, 10 m, 6 m, and 1 m for the three observational periods. The figures ((b), (d), (f)) are an expansion of the period from 10:00 to 14:00.

Katata et al. (2020) applied a new multi-layer land surface model, which was coupled with the NH4NO3 gas-particle conversion processes, to our results of the 16-autumn observation. The model reproduced the differences in the vertical profiles between NO3- and SO42- in the PM2.5 well, particularly in the daytime. This revealed that the volatilization of NH4NO3 below the canopy under dry conditions enhanced the deposition flux of HNO3 converted from NH4NO3. The DR30-1 value of NO3- calculated by the model was similar to that observed in the daytime but was smaller than that observed in the nighttime. These results are likely due to the uncertainty in the process of the equilibrium shift of NH4NO3 due to the low concentrations of HNO3 and other nighttime processes.

## 4. CONCLUSIONS

To better understand the dry deposition process of NO3- in PM2.5 in a forest, we conducted vertical profile measurements of SO42- and NO3- in PM2.5, as well as SO2, in a forest in suburban Tokyo, Japan, focusing in particular on the daytime/nighttime and leafy/leafless conditions. The observations were performed during the daytime and nighttime during two leafy periods and one leafless period. The vertical gradients of NO3- were clearly larger than those of SO42- in the PM2.5 during both the daytime and nighttime, especially for the leafy periods. Moreover, the daytime decreasing rate of NO3- in the PM2.5 below the canopy was larger than that of SO2 during the leafy periods.

The large NO3- gradients in the PM2.5 were caused by the equilibrium shift from NH4NO3 to NH3 and HNO3 near the deposition surfaces. In the daytime, the air temperature was higher near the canopy surface during the leafy periods and near the ground surface during the leafless period. These conditions enhanced the volatilization of NH4NO3 near the deposition surfaces in the daytime. Moreover, the lower concentration of HNO3 near the surfaces caused by its fast removal enhanced the volatilization during both the daytime and nighttime. Therefore, NO3- in the PM2.5 was quickly removed by the forest and its vertical gradients were larger than those of SO42- in the PM2.5, even to the point of being equal to those of SO2.

Because the abovementioned chemical processes during dry deposition are not treated in current chemical transport models, future studies focused on the quantification of these processes are required to improve the model accuracy to estimate PM2.5 and nitrogen deposition.

## Acknowledgments

We gratefully acknowledge the support of Mr. Takaaki Honjo and Mr. Taiichi Sakamoto (Tokyo University of Agriculture and Technology) in the observations of this study. We would like to thank Dr. Genki Katata (Ibaraki University), Dr. Atsuyuki Sorimachi (Fukushima Medical University) and Dr. Akira Takahashi (Central Research Institute of Electric Power Industry) for useful discussions. We would also like to thank Enago (www.enago.jp) for the English language review. This work was supported by JSPS KAKENHI Grant Number 16H029 33 and the Steel Foundation for Environmental Protection Technology (36th and 37th grants).

## References

• Ban, S., Matsuda, K., Sato, K., Ohizumi, T. (2016) Long-term assessment of nitrogen deposition at remote EANET sites in Japan. Atmospheric Environment, 146, 70-78. [https://doi.org/10.1016/j.atmosenv.2016.04.015]
• Brost, R.A., Delany, A.C., Huebert, B.J. (1988) Numerical modeling of concentrations and fluxes of HNO3, NH3, and NH4NO3 near the surface. Journal of Geophysical Research, 93, 7137-7152.
• Erisman, J.W., Draaijers, G.P.J. (1995) Atmospheric deposition in relation to acidification and eutrophication. Studies in Environmental Research, vol. 63. Elsevier, The Netherlands, pp. 85-97.
• Flechard, C.R., Nemitz, E. Smith, R.I., Fowler, D., Vermeulen, A.T., Bleeker, A., Erisman, J.W., Simpson, D., Zhang, L., Tang, Y.S., Sutton, M.A. (2011) Dry deposition of reactive nitrogen to European ecosystems: a comparison of inferential models across the NitroEurope network. Atmospheric Chemistry and Physics, 11, 2703-2728. [https://doi.org/10.5194/acp-11-2703-2011]
• Galloway, J.N., Townsend, A.R., Erisman, J.W., Bekunda, M., Cai, Z., Freney, J.R., Martinelli, L.A., Seitzinger, S.P., Sutton, M.A. (2008) Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions. Science, 320, 889-892. [https://doi.org/10.1126/science.1136674]
• Honjo, T., Takahashi, A., Matsuda, K. (2016) Deposition velocity of sulfate and nitrate in PM2.5 above a forest in suburban Tokyo using relaxed eddy accumulation. Journal of Japan Society for Atmospheric Environment, 51, 257-265 (in Japanese).
• Huebert, B.J., Luke, W.T., Delany, A.C., Brost, R.A. (1988) Measurements of concentrations and dry surface fluxes of atmospheric nitrates in the presence of ammonia. Journal of Geophysical Research, 93, 7127-7136.
• Katata, G., Matsuda, K., Sorimachi, A., Kajino, M., Takagi, K. (2020) Effects of aerosol dynamics and gas-particle conversion on dry deposition of inorganic reactive nitrogen in a temperate forest. Atmospheric Chemistry and Physics, 20, 4933-4949. [https://doi.org/10.5194/acp-20-4933-2020]
• Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., Akimoto, H. (2013) Emissions of air pollutants and greenhouse gases over Asian regions during 2000-2008: Regional Emission inventory in ASia (REAS) version 2. Atmospheric Chemistry and Physics, 13, 11019-11058. [https://doi.org/10.5194/acp-13-11019-2013]
• Matsuda, K., Watanabe, I., Mizukami, K., Ban, S., Takahashi, A. (2015) Dry deposition of PM2.5 sulfate above a hilly forest using relaxed eddy accumulation. Atmospheric Environment, 107, 255-261. [https://doi.org/10.1016/j.atmosenv.2015.02.050]
• Morino, Y., Nagashima, T., Sugata, S., Sato, K., Tanabe, K., Noguchi, T., Takami, A., Tanimoto, H., Ohara, T. (2015) Verification of chemical transport models for PM2.5 chemical composition using simultaneous measurement data over Japan. Aerosol and Air Quality Research, 15, 2009-2023. [https://doi.org/10.4209/aaqr.2015.02.0120]
• Nakahara, A., Takagi, K., Sorimachi, A., Katata, G., Matsuda, K. (2019) Enhancement of dry deposition of PM2.5 nitrate in a cool-temperate forest. Atmospheric Environment, 212, 136-141. [https://doi.org/10.1016/j.atmosenv.2019.05.053]
• Nemitz, E., Sutton, M.A., Wyers, G.P., Otjes, R.P., Mennen, M.G., van Putten, E.M., Gallagher, M.W. (2004) Gas-particle interactions above a Dutch heathland: II. Concentrations and surface exchange fluxes of atmospheric particles. Atmospheric Chemistry and Physics, 4, 1007-1024. [https://doi.org/10.5194/acp-4-1007-2004]
• Nemitz, E. (2015) Surface/atmosphere Exchange of Atmospheric Acids and Aerosols, Including the Effect and Model Treatment of Chemical Interactions. Review and Integration of Biosphere-Atmosphere Modelling of Reactive Trace Gases and Volatile Aerosols. Springer, pp. 115-149. [https://doi.org/10.1007/978-94-017-7285-3_5]
• 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 1980-2020. Atmospheric Chemistry and Physics, 7, 4419-4444. [https://doi.org/10.5194/acp-7-4419-2007]
• Sakamoto, T., Nakahara, A., Takahashi, A., Sorimachi, A., Katata, G., Matsuda, K. (2018) Deposition velocity of PM2.5 nitrate and gaseous nitric acid above a forest in suburban Tokyo using relaxed eddy accumulation with denuder sampling technique. Journal of Japan Society for Atmospheric Environment, 53, 136-143 (in Japanese).
• Shimadera, H., Hayami, H., Chatani, S., Morino, Y., Mori, Y., Morikawa, T., Yamaji, K., Ohara, T. (2014) Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate. Journal of the Air & Waste Management Association, 64, 374-387. [https://doi.org/10.1080/10962247.2013.778919]
• Shimadera, H., Hayami, H., Chatani, S., Morikawa, T., Morino, Y., Mori, Y., Yamaji, K., Nakatsuka, S., Ohara, T. (2018) Urban Air Quality Model Inter-Comparison Study (UMICS) for improvement of PM2.5 simulation in greater Tokyo area of Japan. Asian Journal of Atmospheric Environment, 12, 139-152. [https://doi.org/10.5572/ajae.2018.12.2.139]
• Sievering, H., Enders, G., Kins, L., Kramm, G., Ruoss, K., Roider, G., Zelger, M., Anderson, l., Dlugi, R. (1994) Nitric acid, particulate nitrate and ammonium profiles at the Bayerisher Wald: evident for large deposition rates of total nitrate. Atmospheric Environment, 28, 311-315. [https://doi.org/10.1016/1352-2310(94)90106-6]
• 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, 53-61 (in Japanese).
• Van Oss, R., Duyzer, J., Wyers, P. (1998) The influence of gas-to-particle conversion on measurements of ammonia exchange over forest. Atmospheric Environment, 32, 465-471. [https://doi.org/10.1016/S1352-2310(97)00280-X]
• Wyers, G.P., Duyzer, J.H. (1997) Micrometeorological measurement of the dry deposition flux of sulphate and nitrate aerosols to conifer forest. Atmospheric Environment, 31, 333-343. [https://doi.org/10.1016/S1352-2310(96)00188-4]
• Yamazaki, T., Takahashi, A., Matsuda, K. (2015) Differences of dry deposition between sulfate and nitrate in PM2.5 to a forest in suburban Tokyo by vertical profile observations. Journal of Japan Society for Atmospheric Environment, 50, 167-175 (in Japanese).

### Fig. 1.

Schematic diagram of the observation tower at the Field Museum Tamakyuryo site.

### Fig. 2.

Temporal variations in the mass concentrations of SO42− and NO3− in the PM2.5 at 30 m, 23 m, 8 m, and 1 m at the tower during the three observational periods. D and N indicate daytime and nighttime, respectively.

### Fig. 3.

Normalized vertical profiles of the mass concentrations of SO42− in the PM2.5 (open circles), NO3− in the PM2.5 (closed circles), and SO2 (open squares) during the daytime and nighttime for the three observational periods. The relative concentration is the concentration ratio with respect to the concentration at 30 m. The gray layers indicate the leafy canopies.

### Fig. 4.

Distributions of the decreasing rates of SO42− and NO3− in the PM2.5 from 30 m to 1 m for the three observational periods. Each circle shows a daytime or nighttime daily value. The bars indicate the mean values.

### Fig. 5.

Variations in the ensemble mean values of the air temperature at 30 m, 25 m, 20 m, 10 m, 6 m, and 1 m for the three observational periods. The figures ((b), (d), (f)) are an expansion of the period from 10:00 to 14:00.

### Table 1.

Experimental periods and sampling strategies.

Season Experimental period Daytime sampling Nighttime sampling Measurement heights
Summer (leafy) July 21-August 1, 2015 06:00-18:00 18:00-06:00 30 m, 23 m, 8 m, 1 m
Winter (leafless) February 23-29, 2016 08:00-17:00 17:00-08:00
Autumn (leafy) September 27-October 11, 2016 30 m, 23 m, 16 m, 8 m, 1 m

### Table 2.

Atmospheric conditions at a height of 30 m at the tower and the leaf area index. WS, Temp, RH, and LAI indicate the wind speed, temperature, relative humidity, and leaf area index, respectively. All values except LAI are given as the average±standard deviation from the 10-min means of the data for the meteorological parameters and the half-day data for the concentrations.

Experiment WS (m s−1) Temp (°C) RH (%) Concentration (μg m−3) LAI
SO42− NO3
Summer 2015 (15-summer) 4.4
Daytime 3.1±2.4 29.2±2.3 64±11 6.3±4.0 0.6±0.4
Nighttime 2.8±1.5 26.6±1.7 77±9 6.7±4.8 1.9±1.3
Winter 2016 (16-winter) 1.7
Daytime 3.4±2.1 6.5±3.4 45±18 2.6±1.5 2.9±1.3
Nighttime 2.8±1.4 4.0±2.9 65±15 3.0±2.1 3.7±1.2
Autumn 2016 (16-autumn) 4.3
Daytime 1.8±0.7 25.7±3.2 61±14 2.0±1.3 1.5±0.8
Nighttime 2.9±2.1 20.9±3.8 71±16 2.1±1.2 1.0±0.7