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Asian Journal of Atmospheric Environment - Vol. 11 , No. 3

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 11, No. 3
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Sep 2017
Received 18 Apr 2017 Revised 26 May 2017 Accepted 02 Jun 2017

Characteristics of Atmosphere-rice Paddy Exchange of Gaseous and Particulate Reactive Nitrogen in Terms of Nitrogen Input to a Single-cropping Rice Paddy Area in Central Japan
Kentaro Hayashi* ; Keisuke Ono ; Kazuhide Matsuda1) ; Takeshi Tokida ; Toshihiro Hasegawa2)
Institute for Agro-Environmental Sciences, NARO, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan
1)Faculty of Agriculture Field Science Center, Tokyo University of Agriculture and Technology, 3-5-8 Saiwaicho, Fuchu, Tokyo 183-8509, Japan
2)Tohoku Agricultural Research Center, NARO, 4 Akahira, Shimokuriyagawa, Morioka, Iwate 020-0198, Japan

Correspondence to : * Tel: +81-29-838-8225, E-mail:

Funding Information ▼


Nitrogen (N) is an essential macronutrient. Thus, evaluating its flows and stocks in rice paddy ecosystems provides important insights into the sustainability and environmental loads of rice production. Among the N sources of paddy fields, atmospheric deposition and irrigation inputs remain poorly understood. In particular, insufficient information is available for atmosphere-rice paddy exchange of gaseous and particulate reactive N (Nr, all N species other than molecular N) which represents the net input or output through dry deposition and emission. In this study, we assessed the N inputs via atmospheric deposition and irrigation to a Japanese rice paddy area by weekly monitoring for 2 years with special emphasis on gas and particle exchange. The rice paddy during the cropping season acted as a net emitter of ammonia (NH3) to the atmosphere regardless of the N fertilizer applications, which reduced the effects of dry deposition to the N input. Dry N deposition was quantitatively similar to wet N deposition, when subtracting the rice paddy NH3 emissions from N exchange. The annual N inputs to the rice paddy were 3.2 to 3.6 kg N ha-1 yr-1 for exchange, 8.1 to 9.8 kg N ha-1 yr-1 for wet deposition, and 11.1 to 14.5 kg N ha-1 yr-1 for irrigation. The total N input, 22.8 to 27.5 kg N ha-1 yr-1, corresponded to 38% to 55% of the N fertilizer application rate and 53% to 67% of the brown rice N uptake. Monitoring of atmospheric deposition and irrigation as N sources for rice paddies will therefore be necessary for adequate N management.

Keywords: Dry deposition, Emission, Nitrogen cycling, Nitrogen deposition, Reactive nitrogen, Wet deposition


Nitrogen (N) is essential for life and is thus indispensable for crop production to achieve satisfactory yields. Plants and animals need forms of N that are chemically available, which are collectively described as “reactive N” (Nr), i.e., N compounds other than molecular N (N2). The two largest Nr sources in the biosphere are conversion from N2 to ammonia (NH3) by means of biological N fixation (BNF), which is a microbial process, and anthropogenic N fixation to produce mainly fertilizer using the Haber-Bosch process since the early 20th century. The annual world consumption of inorganic N fertilizers was 112.1 Tg N yr-1 in 2010/2011 (FAO, 2015).

Fertilizer N applied to croplands is not fully utilized by crops. The N-use efficiency (NUE) of the global cropping system has stayed at around 47% during the last 30 years (Lassaletta et al., 2014). NUE is defined as the ratio of the N in the harvested crops to the total N input; here, the total N input comprises applications of inorganic fertilizer and manure, BNF, and atmospheric N deposition. After excluding N stored in soils, the N that is not utilized by crops is lost to the atmosphere, aquifers, and water bodies through a variety of biogeochemical processes, creating an N load on the environment.

Reactive N lost to the surroundings can lead to serious environmental issues: air pollution, water pollution, climate change, stratospheric ozone depletion, acidification, eutrophication, and biodiversity loss (Sutton et al., 2011). A recent scrutiny of the planetary boundaries showed that the anthropogenic impact on global biochemical N flows creates an uncertain but high risk of destabilizing the current climate and eco-systems (Steffen et al., 2015).

Improving cropland NUE would be an effective way to address these environmental issues because it would greatly reduce N losses. To determine the magnitude of the potential improvement, we need accurate information on N inputs to croplands so we can assess the N budget and accurately evaluate NUE. In this paper, we will focus our attention on paddy rice (Oryza sativa L.). In addition to N inputs in applications of inorganic fertilizer, manure, and crop residues, rice paddies receive significant N inputs through BNF, N deposition, and irrigation; where, the two latter categories remain poorly understood.

For Japanese paddy fields, Koyama (1981) reported that the N input equaled the N output in harvested crops, with both at 170 kg N ha-1 in 1980. More recent estimates for single-cropping rice paddies based on a combination of measurements and a literature review revealed N inputs and outputs in harvested crops of 132.0 and 119.2 kg N ha-1, respectively, which represents a 12.8 kg N ha-1 of excess input (Katayanagi et al., 2013). However, quantitative information on N deposition and N inputs in irrigation are limited.

N deposition is divided into wet deposition, which represents N inputs in precipitation, and dry deposition, which represents inputs of gaseous and particulate Nr through interactions with soils, water, and plants. Nationwide monitoring by Japan’s Ministry of the Environment from 1983 to 2002 suggests that the annual mean wet N deposition rates at 23 remote, 29 rural, and 29 urban sites were 7.0, 7.9, and 8.2 kg N ha-1, respectively (Hayashi and Yan, 2010). These values might approximate the N input to Japanese rice paddies by wet deposition.

In contrast, dry deposition has several features that make its flux difficult to quantify: (1) a variety of chemical forms (gases and particles) and species (reduced and oxidized N) are independently or interactively (e.g., gas-particle interactions; Nemitz et al., 2004) involved in dry deposition; (2) deposition velocities of Nr, here, a flux is expressed as the product of a velocity by an air concentration of a target substance, are spatiotemporally variable and differ among chemical forms and species (e.g., Flechard et al., 2011); and (3) a counteracting process, surface emission, can occur for some chemical species. Major Nr species involved in dry deposition are NH3, nitric acid (HNO3), nitrous acid (HNO2), nitrogen oxide (NO), and nitrogen dioxide (NO2) as gases, and particulate ammonium (pNH4) and particulate nitrate (pNO3) as particles. To determine the dry deposition flux, monitoring at each target site is desirable because the flux is strongly influenced by surface conditions such as the aerodynamic roughness length and surface reaction properties (Erisman and Draaijers, 1995). However, limited information is available for dry N deposition at Japanese rice paddies (e.g., Hayashi et al., 2013a, b, 2012) in contrast to the recent progress on dry N deposition in Japanese natural ecosystems (e.g., Ban et al., 2016; Katata et al., 2013b; Yamaguchi et al., 2013; Hayashi et al., 2011a, 2009b). Of the studies mentioned above, Koyama (1981) did not account for the contribution of dry deposition, and Katayanagi et al. (2013) used a value of dry deposition obtained from the literature for a turf grassland.

We should be careful about surface emissions. Rice paddies can become emitters of atmospheric Nr, typically as nitrous oxide (N2O) accompanied by nitrification and denitrification (e.g., Hayashi et al., 2015; Akiyama et al., 2005), emissions of various gaseous and particulate forms of Nr induced by open-field burning of crop residues (e.g., Hayashi et al., 2014, 2013a), and NH3 volatilization loss after N fertilization (e.g., Hayashi et al., 2008, 2006). Hence, any measured flux denotes the “exchange”, which represents the difference between dry deposition and emission.

Given the large losses of N into bodies of water, some N will inevitably be present in irrigation water. The N input in irrigation depends on the quality and quantity of the irrigation water. Rivers, lakes, ponds, and groundwater are a major water source for irrigation. In Japan, groundwater is frequently contaminated with nitrate due to anthropogenic N loads, mainly produced from non-point sources. For example, at an experimental rice paddy in central Japan, irrigation water with a high concentration of nitrate (4 to 8 mg N L-1) resulted in a large total N input of 69 to 123 kg N ha-1 during the cropping season (Kyaw et al., 2005). Koyama (1981) estimated that irrigation provided 23 kg N ha-1 to Japanese rice paddies. The contribution of irrigation was not estimated in the case study of Katayanagi et al. (2013).

Accordingly, the goal of this study was to quantify the N inputs in a typical Japanese rice paddy area by providing previously unavailable data on N inputs via atmospheric deposition and irrigation with special emphasis on exchange of gaseous and particulate Nr.

2. 1 Study Site and Study Period

The study site was used for single cropping of paddy rice located in Tsukubamirai City, Ibaraki Prefecture, Japan (35°58ʹ27ʺN, 139°59ʹ32ʺE, 10 m A.S.L.), where Japan’s Institute for Agro-Environmental Sciences established a free-air CO2 enrichment (FACE) facility for paddy rice in 2009. The paddy area is in a typical rural area in this region, where the land represents a mosaic dominated by cropland (mostly rice paddies), small forests, and residential areas. The area had a trapezoidal shape, with a length of about 200 to 300 m for the east-west direction and about 600 m for the north-south direction; it consisted of many fields individually managed in terms of their irrigation and drainage. This area is bordered on the east by the Kokaigawa River, on the south and west by small forests and residential areas, and on the north by the Joban Expressway. Our atmospheric monitoring point was in the paddy area, at a minimum distance of about 400 m from the expressway. Our irrigation monitoring plots were adjacent to the atmospheric monitoring point.

The following were general agricultural practices in the paddy area. Basal fertilization was conducted by a single application of organic and inorganic mixed fertilizer containing 69% organic matter (Dokidoki Yuki Ippatsu, Co-op Chemical Co. Ltd., Tokyo, Japan; N-P-K: 12%-7%-5%) at an application rate of 50 to 60 kg N ha-1, followed by submersion and puddling in late April (the applied fertilizer was therefore incorporated into the soil). Rice seedlings were transplanted in early May. Mid-summer drainage was conducted in mid- to late June. Rice paddies were then re-submerged until drainage in mid- to late August. Paddy rice was harvested in early September. Rice paddies were entirely drained in a fallow season with a mostly bare soil surface with scattered rice residues until the next spring. Two or three tillage events with a mixing depth of approximately 15 cm were conducted in and at the end of the fallow season. A relatively low N fertilization rate is used to enhance the rice quality. The rice yield was approximately 4800 kg ha-1 as brown rice at a moisture content of 15%. The source of irrigated water was groundwater pumped up near the paddy area and provided to each paddy using pipelines.

We defined the cropping season as the period from submersion of the rice paddies (late April) to the completion of harvesting (mid-September). The study period was 2 years, from 15 September 2010 to 12 September 2012 (i.e., 104 weeks), and was divided into four periods: fallow season 1 (FS1) from 15 September 2010 to 27 April 2011 (32 weeks), cropping season 1 (CS1) from 27 April 2011 to 14 September 2011 (20 weeks), fallow season 2 (FS2) from 14 September 2011 to 25 April 2012 (32 weeks), and cropping season 2 (CS2) from 25 April 2012 to 12 September 2012 (20 weeks). The mean air temperatures were 9.6, 22.9, 9.3, and 22.6°C in FS1, CS1, FS2, and CS2, respectively.

2. 2 Monitoring of Atmosphere-rice Paddy Exchanges

Target chemical species throughout the study period were NH3, HNO3, and HNO2 as gases and pNH4, pNO3, and particulate nitrite (pNO2) as particles. Their weekly mean air concentrations (μg N m-3 20℃, 1013 hPa), with separate day and night values, were measured at heights of 6 and 2 m above the surface using a filter-pack method with open-face filter holders (NLO, NILU). The day/night separation in the weekly measurements was intended to reduce the systematic error in flux calculation that results from the long averaging time of air concentrations (here, 1 week) particularly for chemical species such as NH3 and HNO3 that show large diurnal variation in their concentrations (Hayashi et al., 2013b). No size separation was conducted for the particles.

Three sets of filter packs were prepared at each height to collect the target chemical species: one during the day and one during the night, with active sampling, and one as a field blank (passive sampling, without suction). Active sampling using an air pump (APN-085, Iwaki) was performed for the day and night sampling lines, which were switched at sunrise and sunset. Each filter pack was covered with aluminum foil as a sunshade to reduce degradation of the sampled particles by heat. The amount of each chemical species collected by the filter pack was expressed as the difference in quantities between the exposed filters and the unused filters as a blank. An ion chromatograph (ICS-1600, Thermo Scientific Dionex) was used to quantify the collected amounts. Details of the method used to calculate the weekly mean air concentrations with separation between day and night data are provided by Hayashi et al. (2013b).

Air concentrations of NO and NO2 at the two heights were measured using a nitrogen oxides analyzer (42iTL, Thermo Scientific) from 15 June 2011 to 15 February 2012, and the difference between total nitrogen oxides and NO was defined as the NO2 concentration. The data were obtained at 30-minute intervals and converted into the weekly mean concentrations of NO and NO2, with separate day and night values.

The mean 1-week air concentration (without separation into day and night data) was also calculated for all the target substances as a weighted mean using the day and night lengths as a weighting factor.

Micrometeorological observations were conducted for the following factors. Air temperature and relative humidity were measured using a temperature-humidity sensor (HMP45A, Vaisala) (height of 4.8 m). Global solar radiation was measured using a pyranometer (CM3, Kipp and Zonen). Atmospheric pressure was measured using a barometer (PTB101B, Vaisala). Fluctuations in the three-dimensional wind velocity and virtual temperature at 10 Hz were measured (Model 81000 anemometer, Young) at a height of 6.0 m to determine the mean wind velocity, friction velocity, and Monin-Obukhov length. The 30-min means or sums of these factors were recorded using a data logger (CR1000, Campbell Scientific).

We applied a gradient method to determine the atmosphere-rice paddy exchange flux, in which the flux equals the difference in air concentrations between the two heights multiplied by the diffusion velocity between the two heights. The weekly mean diffusion velocity, with separate day and night values, was calculated by the method of Hayashi et al. (2013b). The weekly mean exchange flux, with separate day and night values, expressed as the flux at the standard temperature and atmospheric pressure was corrected based on the mean air temperature and atmospheric pressure during the week. The weekly mean exchange flux was converted into a weekly total flux, with separate day and night values, by multiplying the mean flux by the day and night length, respectively. The cumulative flux in each of four seasons was then calculated. For NO and NO2, for which 30-min air concentrations were available, the exchange flux was also calculated on a 30-min basis and then summed to provide the weekly and seasonal values.

To support a discussion of the fluxes determined by the gradient method, we applied the inferential method (Matsuda, 2008) to estimate the dry deposition flux. This method calculates the total resistance that a target substance experiences during its deposition from a target height to the ground surface. The reciprocal of the total resistance denotes the deposition velocity. The dry deposition flux is obtained by multiplying the deposition velocity at a given height by the air concentration at that height. Application of the inferential method to multiple Nr species is still being refined; for example, the calculated deposition velocities of an Nr species vary greatly depending on the chosen parameterization (Flechard et al., 2011). In this study, we calculated the deposition velocities for NH3. We used the surface resistance parameterization of Wesely (1989) except for the outer surface resistance of NH3. For the outer surface resistance of NH3, the non-stomatal resistance of Smith et al. (2000) was used. The weekly mean deposition velocity at a height of 6 m above the surface, with separate day and night data, was calculated. Note that the resistance model of Matsuda (2008) assumes no surface emission of target substances. The dry deposition flux expressed as the flux at the standard temperature and atmospheric pressure was corrected based on the mean air temperature and atmospheric pressure during the measurement.

2. 3 Monitoring of Wet Deposition

A wet-only sampler with refrigerated storage (US-330, Ogasawara Keiki) was installed at the study site. Precipitation (mostly rain, but also several snowfalls during the winter) was collected on a weekly basis. The concentrations of inorganic N (ammonium, NH4-N; nitrate, NO3-N; and nitrite, NO2-N) and total N dissolved in the precipitation were determined using an ion chromatograph (ICS-1600, Thermo Scientific Dionex) and a total organic carbon and total N analyzer (TOC-V/TNM-1, Shimadzu), respectively. The difference between the total and inorganic N was defined as organic N (Org-N). The weekly flux in the form of wet N deposition was obtained by multiplying the precipitation amount by the N concentration.

2. 4 Monitoring of irrigation

Monitoring of the irrigation water for NH4-N, NO3-N, NO2-N, and total N contents was conducted in a paddy field adjacent to the atmospheric monitoring point from 27 April to 19 August 2011 in CS1 and from 26 April to 27 August 2012 in CS2. The field was 32.6 m×100 m (0.326 ha) with two water inlets at the short southern side and one water outlet at the short northern side. This field was flooded continuously from the beginning of submergence to the final drainage. The water flows were recorded using a measuring weir with a water gauge (SE-TR/WT500, TruTrack) at each inlet and outlet. The water quality was monitored weekly by sampling and analyzing water at the inlets and outlet.

The inorganic N concentrations were determined using the abovementioned ICS-1600 ion chromatograph and the total N was determined using the abovementioned TOC-V/TNM-1 total organic carbon and total N analyzer. The input with irrigation and the output with outflow for each N species were calculated by multiplying the water flow rate by the concentration of each N species, with linear interpolation applied to account for temporal variation in the concentration of each N species. The N inflow and outflow were further divided by the area of the field (0.326 ha) to obtain the N flux per unit area. The mean water quality for the target substances throughout the irrigation period was also calculated by dividing the total flow of the substance by the total water flow.

3. 1 Atmosphere-rice paddy exchange

Fig. 1 shows the weekly mean air concentrations of Nr during the day and night, and Table 1 shows the corresponding seasonal mean concentrations. The following are results for the main target species, i.e., NH3, HNO3, pNH4, and pNO3. NH3 had the highest concentration among the target Nr, except for higher NO and NO2 concentrations in January 2012. The mean NH3 concentrations (6 m above the surface) during the study period were 3.8 and 3.1 μg N m-3, respectively, during the day and night. The NH3 concentrations were higher at a height of 2 m than at a height of 6 m, particularly in the day during CS1 and CS2. HNO3 showed very low concentrations, except during the day in the warm season (which mostly corresponds to CS1 and CS2); the mean HNO3 concentrations (6 m above the surface) were 0.41 and 0.08 μg N m-3, respectively, during the day and night (Fig. 1). Both pNH4 and pNO3 existed in the atmosphere, but at lower concentrations than those of NH3 (Fig. 1). The mean concentrations (6 m above the surface) were 1.1 and 1.3 μg N m-3 during the day and night, respectively, for pNH4, and were 1.3 and 1.3 μg N m-3, respectively, for pNO3. pNO2 was not detected.

Fig. 1. 
Weekly mean air concentrations of gaseous nitrogen (ammonia [NH3], nitric acid [HNO3], nitrous acid [HNO2], nitrogen oxide [NO], and nitrogen dioxide [NO2]) and particulate nitrogen (ammonium particles [pNH4] and nitrate particles [pNO3]) at two heights (6 and 2 m above the surface), with separate values for the day and night. FS and CS denote the fallow and cropping seasons, respectively; FS1, 32 weeks from 15 September 2010 to 27 April 2011; CS1, 20 weeks from 27 April to 14 September 2011; FS2, 32 weeks from 14 September 2011 to 25 April 2012; and CS2, 20 weeks from 25 April to 12 September 2012.

Table 1. 
Seasonal and annual mean concentrations of reactive N in the atmosphere (at 6 m above the surface), ND, not detected.
Season/Year Gas concentration
μg N m-3, 20℃, 1013 hPa
Particle concentration
μg N m-3, 20℃, 1013 hPa
FS1 Mean 3.68 0.12 0.69 - - 1.37 1.47 ND
Day 3.90 0.19 0.44 - - 1.25 1.52 ND
Night 3.50 0.07 0.89 - - 1.47 1.42 ND
CS1 Mean 3.22 0.51 0.64 1.07 4.16 0.75 1.15 ND
Day 3.75 0.76 0.57 1.22 4.01 0.81 1.25 ND
Night 2.51 0.14 0.74 0.88 4.36 0.71 1.11 ND
FS2 Mean 3.76 0.09 0.74 6.29 9.08 1.67 1.44 ND
Day 4.07 0.13 0.47 4.90 8.17 1.50 1.44 ND
Night 3.50 0.06 0.90 7.34 9.97 1.78 1.43 ND
CS2 Mean 2.85 0.42 0.47 - - 0.67 0.94 ND
Day 3.33 0.65 0.41 - - 0.69 0.98 ND
Night 2.18 0.09 0.55 - - 0.65 0.87 ND
Year 1
Mean 3.50 0.27 0.67 - - 1.13 1.34 ND
Day 3.83 0.45 0.50 - - 1.05 1.40 ND
Night 3.18 0.09 0.84 - - 1.22 1.32 ND
Year 2
Mean 3.40 0.22 0.61 - - 1.28 1.24 ND
Day 3.74 0.36 0.44 - - 1.14 1.23 ND
Night 3.07 0.07 0.79 - - 1.41 1.25 ND
Season: FS1, Fallow season 1 (32 weeks) from 15 September 2010 to 27 April 2011; CS1, Cropping season 1 (20 weeks) from 27 April to 14 September 2011; FS2, Fallow season 2 (32 weeks) from 14 September 2011 to 25 April 2012; and CS2, Cropping season 2 (20 weeks) from 25 April to 12 September 2012.
Duration of data: From 15 June to 14 September 2011 for CS1 and from 14 September 2011 to 15 February 2012 for FS2.

Fig. 4 shows the weekly exchange fluxes, with positive and negative values representing net deposition and net emission, respectively. Uncertainty in these calculated fluxes is discussed in Section 4.4. NH3 showed a tendency towards net deposition in the night, particularly in FS1 and FS2, and net emission in CS1 and CS2, particularly during the day; the net emission in CS1 and CS2 was occasionally large. The maximum weekly NH3 flux was net deposition of 0.45 kg N ha-1 wk-1 and net emission of 1.0 kg N ha-1 wk-1. HNO3 generally showed net deposition during the day in CS1 and CS2; the maximum flux was 0.17 kg N ha-1 wk-1 of deposition. pNH4 and pNO3 tended to show net deposition. The net emission that occasionally occurred during the day might be attributable to some data artifacts (Section 4.4), because there were no direct emission sources for particles at the paddy surface. The maximum weekly net deposition of pNH4 and pNO3 was 0.16 and 0.22 kg N ha-1 wk-1, respectively.

Table 2 shows the seasonal and annual exchange fluxes of gases and particles. The cumulative NH3 fluxes were net deposition in FS1 and FS2 (2.0 to 3.4 kg N ha-1) and net emission in CS1 and CS2 (1.7 to 3.2 kg N ha-1). The annual NH3 flux was 1.2 kg N ha-1 of net emission in year 1 and 1.7 kg N ha-1 of net deposition in year 2. HNO3 showed net deposition in every season, but the amount was negligibly small in FS1 and FS2. The annual HNO3 deposition flux in years 1 and 2 was 0.55 and 0.30 kg N ha-1, respectively. pNH4 showed net deposition in FS1 and FS2 (1.0 to 1.1 kg N ha-1) and net deposition in CS1 (0.35 kg N ha-1) or relatively small net emission in CS2 (0.16 kg N ha-1). The annual pNH4 flux showed net deposition (0.87 to 1.4 kg N ha-1). pNO3 showed net deposition in every season, and the annual pNO3 deposition flux in years 1 and 2 was 2.2 and 0.74 kg N ha-1, respectively. The seasonal exchange fluxes as Nr, excluding NO and NO2, showed net deposition in FS1 and FS2 (4.4 to 4.8 kg N ha-1) but net emission in CS1 and CS2 (1.2 kg N ha-1 in both cases) due to the high NH3 emissions; thus, the annual Nr exchange fluxes were net deposition (3.2 to 3.6 kg N ha-1).

Table 2. 
Seasonal and annual exchange fluxes of Nr in gaseous and particulate forms.
Season/Year Gas flux
kg N ha-1
Particle flux
kg N ha-1
kg N ha-1
NH3 HNO3 HNO2 NO NO2 pNH4 pNO3 Excl. NO+NO2 Incl. NO+NO2
FS1 2.00 0.01 - 0.14 - - 1.05 1.50 4.42 -
CS1 - 3.19 0.54 0.35 - 0.05 0.16 0.35 0.73 - 1.22 (- 1.11)
FS2 3.40 0.04 0.08 1.04 2.33 1.03 0.26 4.81 (8.18)
CS2 - 1.66 0.26 - 0.15 - - - 0.16 0.48 - 1.23 -
Year 1
- 1.19 0.55 0.21 - - 1.40 2.23 3.20 -
Year 2
1.74 0.30 - 0.07 - - 0.87 0.74 3.58 -
Season: FS1, Fallow season 1 (32 weeks) from 15 September 2010 to 27 April 2011; CS1, Cropping season 1 (20 weeks) from 27 April to 14 September 2011; FS2, Fallow season 2 (32 weeks) from 14 September 2011 to 25 April 2012; and CS2, Cropping season 2 (20 weeks) from 25 April to 12 September 2012.
Duration of data: From 15 June to 14 September 2011 for CS1 and from 14 September 2011 to 15 February 2012 for FS2.

3. 2 Other processes
3. 2. 1 Wet deposition

The dry winter is a typical feature in this region, where there is little or no precipitation from late December to January (Fig. 2). In contrast, this region sees much rain in early summer and mid-autumn, when typhoons occasionally bring heavy rain in the autumn (but infrequently in the summer). The annual precipitation totaled 1655 mm in the first year (FS1+CS1) and 1457 mm in the second year (FS2+CS2) (Table 3). The corresponding annual precipitation at the nearest official weather station (Tateno, Ibaraki Prefecture) totaled 1565 and 1411 mm, respectively, which was not significantly different from the mean precipitation in a decade from 2006 to 2015, 1404±148 (±standard deviation) mm.

Fig. 2. 
Precipitation (top) and wet deposition (bottom) of ammonium (NH4-N) and nitrate (NO3-N) as weekly values.

Table 3. 
Seasonal and annual wet N deposition, ND, not detected.
Season/Year Precipitation
Rain water concentration mg N L-1 Wet deposition kg N ha-1
NH4-N NO3-N NO2-N Org-N NH4-N NO3-N Total
FS1 948 0.22 0.19 ND ND 2.12 1.85 3.97
CS1 707 0.45 0.38 ND ND 3.16 2.68 5.84
FS2 748 0.31 0.24 ND ND 2.33 1.77 4.10
CS2 709 0.33 0.24 ND ND 2.36 1.68 4.04
Year 1
1655 0.32 0.27 ND ND 5.28 4.53 9.81
Year 2
1457 0.32 0.24 ND ND 4.69 3.45 8.14
Season: FS1, Fallow season 1 (32 weeks) from 15 September 2010 to 27 April 2011; CS1, Cropping season 1 (20 weeks) from 27 April to 14 September 2011; FS2, Fallow season 2 (32 weeks) from 14 September 2011 to 25 April 2012; and CS2, Cropping season 2 (20 weeks) from 25 April to 12 September 2012.

The annual wet N deposition was 9.8 and 8.1 kg N ha-1 yr-1 in years 1 and 2, respectively (Table 3), which were approximately 20% larger than and similar to (respectively) the mean wet N deposition at the rural sites monitored by Japan’s Ministry of the Environment, at 7.9 kg N ha-1 yr-1 (Hayashi and Yan, 2010).

3. 2. 2 Irrigation

The cumulative water inflows during the irrigation period in CS1 and CS2 were 1830 and 1484 mm, respectively, versus outflows of 706 and 434 mm, respectively (Table 4). The total water inputs, including the precipitation, during the irrigation period in CS1 and CS2 were 2486 and 2139 mm, respectively. The differences between the total water input and outflow in CS1 and CS2 (1780 and 1705 mm, respectively) were similar, and corresponded to the sum of evapotranspiration and vertical drainage.

Dissolved N at the inlet consisted mainly of NO3-N and Org-N (Fig. 3). The concentrations of NH4-N were significantly lower than these concentrations (Tukey’s multiple comparison test, p<0.05).The spikes of NH4-N and Org-N concentrations at the outlet in mid-May can be ascribed to the effects of N fertilization and puddling in an experimental plot embedded in the field. The volume-weighted mean water N contents were similar in the two cropping seasons (Table 4).

Fig. 3. 
Water fluxes by irrigation and outflows; water concentrations of ammonium (NH4-N), nitrate (NO3-N), and organic nitrogen (Org-N); and nitrogen fluxes in irrigation and outflow water, as weekly values.

Table 4. 
Seasonal properties of irrigation and outflow waters, ND, not detected.
Season Process Water volume
Water concentration mg N L-1 Flux per unit area kg N ha-1
NH4-N NO3-N NO2-N Org-N NH4-N NO3-N Org-N Total
CS1 Irrigation 1830 0.13 0.42 ND 0.24 2.47 7.62 4.41 14.50
Outflow 706 0.02 0.03 ND 0.49 - 0.12 - 0.19 - 3.48 - 3.79
CS2 Irrigation 1484 0.08 0.41 ND 0.25 1.19 6.16 3.78 11.13
Outflow 434 0.03 0.01 ND 0.48 - 0.12 - 0.06 - 2.07 - 2.25
Season: CS1, Cropping season 1 (20 weeks) from 27 April to 14 September 2011; CS2, Cropping season 2 (20 weeks) from 25 April to 12 September 2012.

For the cumulative fluxes (Table 4), the contribution of Nr species to the N input showed the following sequence in both CS1 and CS2; NO3-N>Org-N >NH4-N. The N outflow mostly consisted of Org-N in both seasons. The total N input from irrigation ranged from 11.1 to 14.5 kg N ha-1. The net N inputs, which equal the difference between irrigation and outflow, were 10.7 and 8.9 kg N ha-1 in CS1 and CS2, respectively.

3. 3 N budget of atmospheric deposition and irrigation

Table 5 summarizes the seasonal and annual N fluxes as wet deposition, gas and particle exchange, irrigation inputs, and outflows. In FS1 and FS2, the wet deposition (4.0 to 4.1 kg N ha-1) and the exchange as net dry deposition (4.4 to 4.8 kg N ha-1) were similar, totaling 8.4 to 8.9 kg N ha-1 of N deposition. In CS1 and CS2, irrigation inputs (11.1 to 14.5 kg N ha-1) were the largest component, followed by wet deposition (4.0 to 5.8 kg N ha-1), and the gas and particle exchange showed net emission (both 1.2 kg N ha-1); the sum of these inputs was 19.1 and 13.9 kg N ha-1 in CS1 and CS2 respectively. The annual N input in years 1 and 2 was 27.5 and 22.9 kg N ha-1, respectively. The contribution of each component to the annual N input was in the following sequence: irrigation>wet deposition> gas and particle exchange.

Table 5. 
Nitrogen budget at the paddy field in the present study based on atmosphere-rice paddy exchanges, irrigation inputs, and outflow losses.
Season/Year Atmosphere-rice paddy exchange Irrigation (I)
kg N ha-1
Outflow (O)
kg N ha-1
Input total
kg N ha-1
Net total
kg N ha-1
deposition (W)
kg N ha-1
Gas particle
exchange (E)
kg N ha-1
total (W+E)
kg N ha-1
FS1 3.97 4.42 8.39 - - 8.39 8.39
CS1 5.84 - 1.22 4.62 14.50 - 3.79 19.12 15.33
FS2 4.10 4.81 8.91 - - 8.91 8.91
CS2 4.04 - 1.23 2.81 11.13 - 2.25 13.94 11.69
Year 1
9.81 3.20 13.01 14.50 - 3.79 27.51 23.72
Year 2
8.14 3.58 11.72 11.13 - 2.25 22.85 20.60
Season: FS1, Fallow season 1 (32 weeks) from 15 September 2010 to 27 April 2011; CS1, Cropping season 1 (20 weeks) from 27 April to 14 September 2011; FS2, Fallow season 2 (32 weeks) from 14 September 2011 to 25 April 2012; and CS2, Cropping season 2 (20 weeks) from 25 April to 12 September 2012.
Gas and particle exchange corresponds to the net dry deposition based on the difference between the gross dry deposition and emissions. NO and NO2 are excluded from this calculation due to the limited duration of their data.

4. 1 Rice paddies as an emitter of NH3

In the present study, NH3 emissions were occasionally observed during the day in mid-summer (Fig. 4), i.e., the cropping seasons with well-grown rice plants. Here, we discuss possible causes of the NH3 emissions. One possible cause is emissions from the surface of floodwater in rice paddies, and the other is emissions from rice plants.

Fig. 4. 
Exchange fluxes of gaseous nitrogen (ammonia [NH3], nitric acid [HNO3], nitrous acid [HNO2], nitrogen oxide [NO], and nitrogen dioxide [NO2]) and of particulate nitrogen (ammonium particles [pNH4] and nitrate particles [pNO3]) as weekly values, with separate values for the day and night. The positive and negative values represent dry deposition and emission, respectively. FS and CS denote the fallow and cropping seasons, respectively; FS1, 32 weeks from 15 September 2010 to 27 April 2011; CS1, 20 weeks from 27 April to 14 September 2011; FS2, 32 weeks from 14 September 2011 to 25 April 2012; and CS2, 20 weeks from 25 April to 12 September 2012.

Paddy rice prefers to use ammonium as an N source (Ishiyama et al., 2004). Fertilizers applied to rice paddies, therefore, consist mainly of ammoniacal N, including urea, whose hydrolysis releases NH3. Surface broadcast of N fertilizers leads to a large NH3 volatilization loss from rice paddies, whereas the incorporation of N fertilizers into the soil greatly reduces NH3 volatilization (e.g., Hayashi et al., 2008, 2006). According to Hayashi et al. (2008), surface broadcast of urea with a rate of 30 kg N ha-1 showed 20.9% of volatilization loss as NH3, whereas the incorporation of urea with a rate of 50 kg N ha-1 into the plowed layer resulted in 2.1% of volatilization loss. The incorporation of controlled-release fertilizers in the study area would inhibit NH3 volatilization more effectively by reducing the excess NH4-N in the soil solution. In fact, no remarkable NH3 emissions were detected in late April after the fertilization at that time (Fig. 4).

Stomata of plant leaves are a channel of atmosphereplant exchange of gases, e.g., carbon dioxide, water vapor, and so on including NH3. Stomatal NH3 emissions to the atmosphere occur when the stomatal cavity’s NH3 concentration becomes higher than the NH3 concentration in the ambient air. In the opposite case, stomatal NH3 absorption from the atmosphere takes place, which represents a form of dry deposition. The threshold concentration between emission and deposition can be defined as the NH3 compensation point (Farquhar et al., 1980). It is known that graminoid upland crops such as wheat and barley emit NH3 to the atmosphere at a rate that depends on their N nutrition status and plant phenology (e.g., Schjoerring and Mattsson, 2001; Husted et al., 1996).

In the present study, no supplemental fertilization was conducted in mid-summer when large NH3 emissions were occasionally observed (Fig. 4). In this case, the surface of rice paddies was hard to be a strong NH3 emitter. Therefore, it is expected that rice plant emission is the most possible pathway of NH3 loss into the atmosphere. Some field data (Hayashi et al., 2011b, 2008) supports the possibility of NH3 emission from rice plants. Meanwhile, a laboratory experiment (Miyazawa et al., 2014) showed that the NH3 compensation point of rice (cv. Koshihikari), which ranged from 0.4 to 0.6 nmol mol-1, was one order of magnitude lower than those of upland crops; therefore, rice leaves were not an active NH3 emitter. This range corresponds to 0.23 to 0.35 μg N m-3 of NH3, which was less than one-tenth of the mean NH3 concentrations in the day during the cropping season at times when large NH3 emissions occurred (Fig. 1); thus, NH3 emission from rice leaves does not appear to be important in the study area. Further research will be needed to unravel the cause(s) of the NH3 emissions during the day in the cropping seasons.

4. 2 Importance of exchange in terms of N input

Among the input processes, irrigation accounted for the largest annual N input, followed by wet deposition (Table 5). However, the gas and particle exchange, which represents the difference between the gross dry deposition and emissions, was also significant. The tendency towards NH3 emission, particularly in CS1 and CS2 (Fig. 4, Table 2), masks the NH3 input by dry deposition. As shown in FS1 and FS2, the NH3 exchange fluxes were similar quantity of wet NH4-N deposition (Tables 2 and 3). For reference, the annual dry NH3 deposition estimated by the inferential method was 3.6 and 3.4 kg N ha-1 in years 1 and 2, respectively. These values were larger than the annual NH3 exchange fluxes (Table 2) by 4.8 and 1.7 kg N ha-1 in years 1 and 2, respectively. Therefore, dry deposition is an important process that quantity is comparable to wet deposition. Atmospheric deposition, as the sum of wet and dry deposition, can therefore be an N source larger than irrigation water, depending on the local water and air quality.

Large exchange fluxes as net deposition, comparable to NH3, were observed for NO and NO2 in FS2, particularly at night (Fig. 4), though these gases were excluded from our evaluation of the annual N input due to the limited duration of their monitoring. The air concentrations of NO and NO2 were also higher in FS2 than in CS1 (Fig. 1), which implies frequent inflows of these gases into the paddy area. The expressway running from east to west at least 400 m north of the atmospheric monitoring point is a possible major source of the NO and NO2. Fine weather with relatively strong northwesterly winds prevails in this region in winter. The frequency of west-northwest and northwest winds at the atmospheric monitoring point was 28% in FS1 and 30% in FS2, versus 5% in CS1 and 6% in CS2. Consequently, the large dry deposition of NO and NO2 might be caused by the following mechanism: the near-surface atmospheric layer becomes stable on clear nights in the fallow season due to radiative cooling, which restricts near-surface convection so that plumes containing vehicle exhaust from the expressway can be transported downwind directly to the atmospheric monitoring point, leading to high rates of dry deposition. We should therefore pay attention to such local Nr sources and the balance between convection and advection conditions when we calculate the N budget for a site.

4. 3 Contributions of atmospheric deposition and irrigation to N input

First, the magnitude of atmospheric N deposition in the present study is compared with other data. Ban et al. (2016) evaluated atmospheric N deposition at 8 remote sites in Japan from 2013 to 2012. Target N species in their study were NH4-N and NO3-N for wet deposition, and NH3, HNO3, pNH4 and pNO3 for dry deposition. The total N deposition at the 6 of 8 remote sites ranged from 9.7 to 12.9 kg N ha-1 yr-1, except Rishiri, an island in the northernmost Hokkaido (7.0 kg N ha-1 yr-1) and Ogasawara, remote ocean islands (3.0 kg N ha-1 yr-1). This range is similar to our data, 11.7-13.0 kg N ha-1 yr-1 as the sum of wet deposition and gas particle exchange (Table 5). It is noted that our data denote the exchange fluxes as the difference between dry deposition and emission; where, the gross dry deposition fluxes were unknown. Air concentration measurements with fine time resolution for major Nr species is needed to evaluate gross rates of both dry deposition and emission.

A question in this study is how the N input to rice paddies via atmospheric deposition and irrigation compares with the N fertilization rate and the N demand of the rice. We used the exchange fluxes to evaluate the contribution of dry deposition. Accordingly, the N input is expressed as the sum of wet deposition, gas and particle exchange, and irrigation fluxes. The atmosphere-rice paddy exchange of NO and NO2 was excluded from these fluxes due to the limited data duration. The 2-year monitoring showed a total N input of 22.8 to 27.5 kg N ha-1 yr-1 (Table 5), which corresponds to 38 to 55% of the N fertilizer application rates (50 to 60 kg N ha-1). This N input also corresponded to 53 to 67% of the N uptake in brown rice for cv. Koshihikari (a staple cultivar in this region), which ranged from 41 to 43 kg N ha-1 these values were calculated from the mean yield of farmers in the study area (4800 kg ha-1, at 15% moisture content) and the brown rice N contents in the same area (1.01 to 1.06% w/w) (Zhang et al., 2013). Thus, N deposition and irrigation contributed greatly to the N inputs.

4. 4 Uncertainties in gas and particle exchange fluxes

Uncertainty of the determined fluxes in the gas and particle exchange results from errors in measurements and in the flux calculations. The measurement errors can be divided into two categories: air concentrations and diffusion velocities. The flux calculation errors can also be divided into two categories: the averaging time used to determine air concentration differences and the methodological assumptions.

In terms of the precision of the filter packs and their ability to determine air concentrations, the coefficients of variation (CV) of NH3 and pNH4 at an upland field in a previous study were 7.1 and 15.1%, respectively (Hayashi et al., 2009a); there are comparable to the CVs of HNO3, pNH4, and pNO3 at four CASTNet sites, with values of 6.2, 3.0, and 11.4%, respectively (Sickles et al., 1999). The filter pack method has a possible artifact that results from the evaporation of particles trapped on the filter in the first stage of a filter pack (Keck and Wittmaack, 2005; Pathak and Chan, 2005; Anlauf et al., 1985). This can result from heating of filter packs by sunlight and reactions of the trapped particles with acidic gases (e.g., NaNO3+HCl→NaCl+HNO3). In the present study, wrapping the filter pack with aluminum foil to prevent the direct effect of sunlight should have reduced the magnitude of this artifact. Pathak and Chan (2005) compared a filter pack with an annular denuder, and found that although the latter method is relatively complicated, it eliminates this artifact; the loss of acidity in the first stage of the filter that trapped particles under ammonium-rich conditions (with an ammonium/sulfate ratio greater than 1.5) and re-evaporation of pNO3 and particulate chloride also occurred in the filter pack method.

For errors in the diffusion velocity determined by micrometeorological measurements, Miyata (2001) found that the maximum error of the diffusion velocity was 20% in the day and 100% at night. Hayashi et al. (2013b) evaluated that the maximum errors of weekly mean fluxes determined by the gradient method were 131% for NH3 and 124% for HNO3 due to the combined effects of the CV of the filter packs and the maximum error of the diffusion velocity.

The problem of the averaging time originates from a correlation between the air concentration differences and the diffusion velocities. The diffusion velocity usually exhibits a daily pattern, with large values during the day and small values at night (Hayashi et al., 2013a). When the air concentration difference has a daily pattern, the flux (which equals the mean concentration difference multiplied by the mean diffusion velocity) induces a systematic error; that is, the method will underestimate the flux of a substance with larger concentration differences during the day than at night. Our separation of the day and night values in the air concentration measurements should reduce this systematic error in the weekly monitoring; our previous research suggests that this reduced the overestimation of NH3 fluxes for the gradient method by 121%±128% at the study site during the cropping season (Hayashi et al., 2013b).

In terms of assumptions, the gradient method assumes that there are no changes in the chemical form of a target substance between the two measurement heights, but gas-to-particle conversions (e.g., NH3+H2SO4→NH4HSO4) and gas-particle interconversions (e.g., NH3+HNO3↔NH4NO3) occur in the air (Nemitz et al., 2004). These reactions induce errors in the calculated flux via their effects on the air concentration differences between the two measurement heights. The remarkable emission fluxes of pNH4 and pNO3 that we occasionally observed in the daytime (Fig. 4) might be partly attributable to these effects, but may also be an artifact of the evaporation of particles trapped on the filters, because direct surface emissions of particles should rarely occur. Even so, the gradient method offers an advantage, namely that the effect of the changes in the forms of the species on the flux does not affect the flux as the sum of relevant gas and particle fluxes (e.g., ammoniacal N, NH3+pNH4), because the diffusion velocity, a function of aerodynamics, is the same regardless of the chemical form.

Enhancing the temporal resolution of the air concentration data can effectively reduce the uncertainty of the determined fluxes. Obtaining flux data with fine temporal resolution can also approximate the gross flux of dry deposition or emission. Continuous monitoring of multiple Nr species, including gaseous and particulate forms, would be expensive, and accurate in-situ measurements would be challenging at rice paddies, particularly in hot and humid summers. Such measurements are, however, worth trying, given the importance of atmosphere-rice paddy Nr exchanges in ecosystem N budgets.

Fluxes derived from the gradient and inferential methods can be compared. Ideally, the fluxes derived from the two methods should be the same for a substance with no surface emissions, such as sulfur dioxide (SO2). The ratios of SO2 fluxes obtained using the gradient method to those obtained using the inferential method at our study site were 1.05 (day) and 1.22 (night) during the cropping season and 1.79 (day) and 2.84 (night) during the fallow season (Hayashi et al., 2013b). This result suggests that the gradient method overestimates or the inferential method underestimates SO2 fluxes (or a combination of both), particularly during the fallow season. Estimated deposition velocities in the inferential method differ greatly among parameterizations (Flechard et al., 2011); thus, the parameterization used in the present study might have underestimated the deposition velocities. This result does not allow the emission flux to be approximated by subtracting the exchange flux estimated using the gradient method from the dry deposition flux estimated using the inferential method. A study that compares a gradient method with an inferential method would be effective and important, since the accuracy of the gradient method could be enhanced by measurements with high temporal resolution, and the parameterization of the deposition velocity of Nr species in the inferential method could be improved by data with high temporal resolution from the gradient method.


A 2-year monitoring of atmospheric deposition and irrigation on a weekly basis was conducted to measure the N input to a single-cropping rice paddy area in central Japan, in which gas and particle exchange of Nr was of interest. The rice paddy occasionally showed large NH3 emissions during the day in the cropping seasons; here, these emissions were independent of the N fertilization conducted in the late April. Atmospheric deposition was an important N source for rice paddies in our study area. This is true both for wet deposition and dry deposition. However, surface emission of a given substance can counter the input by dry deposition; this was particularly true for NH3. Thus, the atmosphere-rice paddy exchange can result in a net loss of an N species in some situations. Rice plant canopy might contribute to the NH3 emissions in the cropping season, possible mechanism of which should be unraveled in future study. The N input via atmospheric deposition and irrigation, 22.8 to 27.5 kg N ha-1 yr-1, corresponded to 38% to 55% of the N fertilizer application rate and 53% to 67% of the brown rice N uptake, which was obtained at one site for two years. Further data accumulation is needed to validate and achieve the temporal and spatial representativeness within the area, district, and country.

In the present study, the cause(s) of the occasional NH3 emissions during the day in the cropping seasons remain unidentified. It is needed to unravel the unknown emitters. The atmosphere-rice paddy exchange of gases and particles is regulated by surface conditions including interactions between rice plant canopy and soil or water surface. For example, a study of NH3 exchange at a rice paddy combining observation and numerical modeling (Katata et al., 2013a) concluded that canopy structure was important to determine canopy exchange of NH3. Under a dense canopy, in-canopy flow was decoupled with above-canopy turbulence and much of NH3 volatilized from floodwater was recaptured, which strongly reduced volatilization loss of NH3 to the atmosphere. Plant phenology strongly regulates the land surface conditions. Future studies to link changes in rice plant phenology and exchange fluxes of Nrelated gases and particles would provide further understanding of the N dynamics in rice paddy ecosystems.


We thank Messrs. Hirofumi Nakamura of Taiyo Keiki, Japan, for his support during the monitoring of atmospheric deposition; Hironori Wakabayashi and Terushi Kamata, Institute for Agro-Environmental Sciences, NARO (NIAES), Japan, for their support during the monitoring of irrigation; and Ms. Yuu Hashimoto, NIAES, for her support during the sample analysis. This study was supported by Grant-in-Aid for Scientific Research (Nos. 22248026 and 26252061) provided by the Japan Society for the Promotion of Science. The Tsukuba FACE facility was established and maintained by the project ‘‘Development of technologies for mitigation and adaptation to climate change in Agriculture, Forestry and Fisheries’’, which was funded by the Ministry of Agriculture, Forestry and Fisheries, Japan.

1. Akiyama, H., Yagi, K., Yan, X.Y., (2005), Direct N2O emissions from rice paddy fields: summary of available data, Global Biogeochemical Cycles, 19, GB1005.
2. Anlauf, K.G., Fellin, P., Wiebe, H.A., Schiff, H.I., Mackay, G.I., Braman, R.S., Gilbert, R., (1985), A comparison of three methods for measurement of atmospheric nitric acid and aerosol nitrate and ammonium, Atmospheric Environment, 19, p325-333.
3. Ban, S., Matsuda, K., Sato, K., Ohizumi, T., (2016), Longterm assessment of nitrogen deposition at remote EANET sites in Japan, Atmospheric Environment, 146, p70-78.
4. Erisman, J.W., Draaijers, G.P.J., (1995), Atmospheric Deposition in Relation to Acidification and Eutrophication, Series in Environmental Sciences 63, Elsevier, Amsterdam, p405.
5. FAO, (2015), FAOSTAT via website (, Food and Agriculture Organization of the United Nations.
6. Farquhar, G.D., Firth, P.M., Wetselaar, R., Weir, B., (1980), On the gaseous exchange of ammonia between leaves and the environment: determination of the ammonia compensation point, Plant Physiology, 66, p710-714.
7. 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, p2703-2728.
8. Hayashi, K., Koga, N., Yanai, Y., (2009a), Effects of fieldapplied composted cattle manure and chemical fertilizer on ammonia and particulate ammonium exchanges at an upland field, Atmospheric Environment, 43, p5702-5707.
9. Hayashi, K., Takagi, K., Noguchi, I., Fukuzawa, K., Takahashi, H., Fukazawa, T., Shibata, H., Fujinuma, Y., (2009b), Ammonia emission from a young larch ecosystem afforested after clear-cutting of a pristine forest in northernmost Japan, Water, Air, and Soil Pollution, 200, p33-46.
10. Hayashi, K., Matsuda, K., Takahashi, A., Nakaya, K., (2011a), Atmosphere-forest exchanges of ammonia and particulate ammonium in a cool-temperate deciduous forest in central Japan during a summer week, Asian Journal of Atmospheric Environment, 5, p134-143.
11. Hayashi, K., Mano, M., Ono, K., Takimoto, T., Miyata, A., (2013a), Four-year monitoring of atmospheric ammonia using passive samplers at a single-crop rice paddy field in central Japan, Journal of Agricultural Meteorology, 69, p229-241.
12. Hayashi, K., Matsuda, K., Ono, K., Tokida, T., Hasegawa, T., (2013b), Amelioration of the reactive nitrogen flux calculation by a day/night separation in weekly mean air concentration measurements, Atmospheric Environment, 79, p462-471.
13. Hayashi, K., Nishimura, S., Yagi, K., (2006), Ammonia volatilization from the surface of a Japanese paddy field during rice cultivation, Soil Science and Plant Nutrition, 52, p545-555.
14. Hayashi, K., Nishimura, S., Yagi, K., (2008), Ammonia volatilization from a paddy field following applications of urea: Rice plants are both an absorber and an emitter for atmospheric ammonia, Science of the Total Environment, 390, p485-494.
15. Hayashi, K., Ono, K., Kajiura, M., Sudo, S., Yonemura, S., Fushimi, A., Saito, K., Fujitani, Y., Tanabe, K., (2014), Trace gas and particle emissions from open burning of three cereal crop residues: Increase in residue moistness enhances emissions of carbon monoxide, methane, and particulate organic carbon, Atmospheric Environment, 95, p36-44.
16. Hayashi, K., Ono, K., Tokida, T., Mano, M., Takimoto, T., Miyata, A., Matsuda, K., (2012), Atmosphere-rice paddy exchanges of inorganic aerosols and relevant gases during a winter and a summer weeks, Journal of Agricultural Meteorology, 68, p55-68.
17. Hayashi, K., Tokida, T., Hasegawa, T., (2011b), Potential ammonia emission from flag leaves of paddy rice (Oryza sativa L. cv. Koshihikari), Agriculture, Ecosystems and Environment, 144, p117-123.
18. Hayashi, K., Tokida, T., Kajiura, M., Yanai, Y., Yano, M., (2015), Cropland soil-plant systems control production and consumption of methane and nitrous oxide and their emissions to the atmosphere, Soil Science and Plant Nutrition, 61, p2-33.
19. Hayashi, K., Yan, X., (2010), Airborne nitrogen load in Japanese and Chinese agroecosystems, Soil Science and Plant Nutrition, 56, p2-18.
20. Husted, S., Mattsson, M., Schjoerring, J.K., (1996), Ammonia compensation points in two cultivars of Hordeum vulgare L. during vegetative and generative growth, Plant, Cell and Environment, 19, p1299-1306.
21. Ishiyama, K., Inoue, E., Tabuchi, M., Yamaya, T., Takahashi, T., (2004), Biochemical background and compartmentalized functions of cytosolic glutamine synthetase for active ammonium assimilation in rice roots, Plant and Cell Physiology, 45, p1640-1647.
22. Katata, G., Hayashi, K., Ono, K., Nagai, H., Miyata, A., Mano, M., (2013a), Coupling atmospheric ammonia exchange process over a rice paddy field with a multilayer atmosphere-soil-vegetation model, Agricultural and Forest Meteorology, 180, p1-21.
23. Katata, G., Yamaguchi, T., Sato, H., Watanabe, Y., Noguchi, I., Hara, H., Nagai, H., (2013b), Aerosol deposition and behavior on leaves in cool-temperate deciduous forests. Part 3: Estimation of fog deposition onto cooltemperate deciduous forest by the inferential method, Asian Journal of Atmospheric Environment, 7, p17-24.
24. Katayanagi, N., Ono, K., Fumoto, T., Mano, M., Miyata, A., Hayashi, K., (2013), Validation of the DNDC-Rice model to discover problems in evaluating the nitrogen balance at a paddy field for single-cropping of rice, Nutrient Cycling in Agroecosystems, 95, p255-268.
25. Keck, L., Wittmaack, K., (2005), Effect of filter type and temperature on volatilization losses from ammonium salts in aerosol matter, Atmospheric Environment, 39, p4093-4100.
26. Koyama, T., (1981), The transformations and balance of nitrogen in Japanese paddy fields, Fertilizer Research, 2, p261-278.
27. Kyaw, K.M., Toyota, K., Okazaki, M., Motobayashi, T., Tanaka, H., (2005), Nitrogen balance in a paddy field planted with whole crop rice (Oryza sativa cv. Kusahonami) during two rice-growing seasons, Biology and Fertility of Soils, 42, p72-82.
28. Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., Garnier, J., (2014), 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland, Environmental Research Letters, 9, p105011.
29. Matsuda, K., (2008), Estimation of dry deposition for sulfur and nitrogen compounds in the atmosphere: Updated parameterization of deposition velocity, Journal of Japan Society for Atmospheric Environment, 43, p332-339, (in Japanese with English abstract).
30. Miyata, A., (2001), Observational study on methane exchange between wetland ecosystems and the atmosphere, Bulletin of National Institute for Agro-Environmental Sciences, 19, p61-183.
31. Miyazawa, S., Hayashi, K., Nakamura, H., Hasegawa, T., Miyao, M., (2014), Elevated CO2 decreases the photorespiratory NH3 production but does not decrease the NH3 compensation point in rice leaves, Plant and Cell Physiology, 55, p1582-1591.
32. Nemitz, E., Sutton, M.A., Wyers, G.P., Jongejan, P.A.C., (2004), Gas-particle interactions above a Dutch heathland: I. Surface exchange fluxes of NH3, SO2, HNO3 and HCl, Atmospheric Chemistry and Physics, 4, p989-1005.
33. Pathak, R.K., Chan, C.K., (2005), Inter-particle and gas-particle interactions in sampling artifacts of PM 2.5 in filter-based samplers, Atmospheric Environment, 39, p1597-1607.
34. Schjoerring, J.K., Mattsson, M., (2001), Quantification of ammonia exchange between agricultural cropland and the atmosphere: measurements over two complete growth cycles of oilseed rape, wheat, barley and pea, Plant and Soil, 228, p105-115.
35. Sickles II, J.E., Hodson, L.L., Vorburger, L.M., (1999), Evaluation of the filter pack for long-duration sampling of ambient air, Atmospheric Environment, 33, p2187-2202.
36. Smith, R.I., Fowler, D., Sutton, M.A., Flechard, C., Coyle, M., (2000), Regional estimation of pollutant gas dry deposition in the UK: model description, sensitivity analyses and outputs, Atmospheric Environment, 34, p3757-3777.
37. Steffen, W., Richardson, K., Rockström, J., Cornell, S.E., Fetzer, I., Bennett, E.M., Biggs, R., Carpenter, S.R., de Vries, W., de Wit, C.A., Folke, C., Gerten, D., Heinke, J., Mace, G.M., Persson, L.M., Ramanathan, V., Reyers, B., Sörlin, S., (2015), Planetary boundaries: Guiding human development on a changing planet, Science, 347, p1259855.
38. Sutton, M.A., Howard, C.M., Erisman, J.W., Bealey, W.J., Billen, G., Bleeker, A., Bouwman, A.F., Grennfelt, P., van Grinsven, H., Grizzetti, B., (2011), The challenge to integrate nitrogen science and policies: the European Nitrogen Assessment approach (Sutton, M.A., Howard, C.M., Erisman, J.W., Billen, G., Bleeker, A., Grennfelt, P., van Grinsven, H., Grizzetti, B. Eds.), The European Nitrogen Assessment: Source, Effects and Policy Perspectives, Cambridge University Press, Cambridge, UK, p82-96.
39. Wesely, M.L., (1989), Parameterization of surface resistance to gaseous dry deposition in regional scale, numerical models, Atmospheric Environment, 23, p1293-1304.
40. Yamaguchi, T., Noguchi, I., Watanabe, Y., Katata, G., Sato, H., Hara, H., (2013), Aerosol deposition and behavior on leaves in cool-temperate deciduous forests. Part 2: Characteristics of fog water chemistry and fog deposition in northern Japan, Asian Journal of Atmospheric Environment, 7, p8-16.
41. Zhang, G., Sakai, H., Tokida, T., Usui, Y., Zhu, C., Nakamura, H., Yoshimoto, M., Fukuoka, M., Kobayashi, K., Hasegawa, T., (2013), The effects of free-air CO2 enrichment (FACE) on carbon and nitrogen accumulation in grains of rice (Oryza sativa L.), Journal of Experimental Botany, 64, p3179-3188.