Reactive nitrogen fluxes over peatland and forest ecosystems using micrometeorological measurement techniques

Interactions of reactive nitrogen (Nr) compounds between the atmosphere and the earth’s surface play a key role in atmospheric chemistry and in understanding nutrient cycling of terrestrial ecosystems. While continuous observations of inert greenhouse gases through micrometeorological flux measurements have become a common procedure, information about temporal dynamics and longer-term budgets of Nr compounds is still extremely limited. Within the framework of the research projects NITROSPHERE and FORESTFLUX, field campaigns were carried out to investigate the biosphere-atmosphere 15 exchange of selected Nr compounds over different land surfaces. The aim of the campaigns was to test and establish novel measurement techniques in eddy-covariance setups for continuous determination of surface fluxes of ammonia (NH3) and total reactive nitrogen (Nr) using two different analytical devices. While high-frequency measurements of NH3 were conducted with a quantum cascade laser (QCL) absorption spectrometer, a custom-built converter called Total Reactive Atmospheric Nitrogen Converter (TRANC) connected and operated upstream of a chemiluminescence detector (CLD) was used for the 20 measurement of Nr. As high-resolution data of Nr surface-atmosphere exchange are still scarce, but highly desired for testing and validating local inferential and larger scale models, we provide access to campaign data including concentrations, fluxes, and ancillary measurements of meteorological parameters. Campaigns (n=4) were carried out in natural (forest) and seminatural (peatland) ecosystem types. The published datasets stress the importance of recent advancements in laser spectrometry and help improve our understanding of the temporal variability of surface-atmosphere exchange in different ecosystems, 25 thereby providing validation opportunities for inferential models simulating the exchange of reactive nitrogen. The dataset has been placed in the Zenodo repository (http://doi.org/10.5281/zenodo.4513854; Brümmer et al., 2021) and contains individual data files for each campaign. https://doi.org/10.5194/essd-2021-85 O pe n A cc es s Earth System Science Data D icu ssio n s Preprint. Discussion started: 6 July 2021 c © Author(s) 2021. CC BY 4.0 License.


Introduction
The term 'reactive nitrogen' (Nr) describes all forms of nitrogen that are biologically, photochemically, and radiatively active. 30 The most important substances are nitric oxide (NO), nitrogen dioxide (NO2), nitrate (NO3 − ), nitric acid (HNO3), ammonium (NH4 + ), and ammonia (NH3). Historically, their low availability had been the main limiting factor for the productivity of natural ecosystems. However, with the invention of the Haber-Bosch process in 1908, inexpensive industrial mass production of synthetic Nr fertilizers through synthesizing NH3 from di-nitrogen (N2) and hydrogen became viable, thereby inducing a number of positive and unintended negative consequences (Erisman et al., 2008;Sutton et al., 2011). A clear benefit was the 35 stabilization of the food supply for populations in developed countries, whereas substantial Nr losses into the environment caused a number of adverse effects for terrestrial and aquatic ecosystems. These were for example impacts on greenhouse gas exchange such as enhanced nitrous oxide (N2O) and NH3 emissions and inhibition of soil methane (CH4) oxidation. Other effects comprised direct toxicity of plants followed by biodiversity loss, while acidification and eutrophication of soils and inshore waters, respectively, were further observed (Galloway et al., 2003;Flechard et al., 2011). 40 Biosphere-atmosphere interactions of Nr compounds play a key role in atmospheric chemistry and in nutrient dynamics of terrestrial and aquatic ecosystems (Ollinger et al., 2002;Farmer et al., 2008). While cropland or intensively managed grasslands typically lose massive amounts of Nr via gaseous emissions to the atmosphere or via nitrate leaching into soil and water bodies, (semi-) natural ecosystems such as forests and peatlands receive crucial Nr input deposited from the atmosphere, thereby being a controlling factor for their productivity, species composition, and their biosphere-atmosphere exchange of greenhouse gases 45 (Fleischer et al., 2013;Hurkuck et al., 2014). Depending on regional emission sources and local surface characteristics, the ratio of dry to wet deposition may range from 1:2 to 2:1 (Simpson et al., 2006).
Micrometeorological flux measurements of non-reactive greenhouse gases using the eddy-covariance technique have become a common procedure for continuous observations of the biosphere-atmosphere exchange (Baldocchi, 2014). Hundreds of flux towers are nowadays organized in regional or continental-scale networks such as ICOS (Integrated Carbon Observation 50 System; Franz et al., 2018;Gielen et al., 2017) and follow standardized measurement protocols (Rebmann et al., 2018). Despite recent technological advances, particularly in the area of laser spectroscopy, flux measurements of Nr compounds are still challenging and have largely remained experimental with limited campaign durations mainly due to high costs for devices, maintenance, and operation (Flechard et al., 2011). Other aspects challenging Nr flux field campaigns are gas phase reactions (Meixner, 1994) and gas-aerosol particle interactions (Wolff et al., 2010) within the chemical mixture of Nr, thereby requiring 55 individual measurements of several parameters simultaneously. A comprehensive overview of commonly-used measurement techniques for a variety of Nr compounds is given in the introductory section of Marx et al. (2012). A general issue with flux measurements using the eddy-covariance technique is a potential flux loss occurring in the high-frequency range because of different types of setups and response characteristics of instruments, e.g., closed vs. open-path instruments. These losses are found to be significantly higher for Nr than for carbon dioxide (CO2) or water vapor flux measurements (cf. Fratini et al., 2012;60 https://doi.org/10.5194/essd-2021-85 Open Access Earth System Science Data Discussions Preprint. Discussion started: 6 July 2021 c Author(s) 2021. CC BY 4.0 License. Ibrom et al., 2007;Mamadou et al., 2016;Moravek et al., 2019;Wintjen et al., 2020a) and can be up to 35% of an individually measured flux rate.
Models simulating plant-soil-atmosphere interactions of Nr species are useful tools to investigate exchange patterns of different ecosystems. Land surface-atmosphere schemes (Massad et al., 2010;Wichink Kruit et al., 2010) can either be applied in localscale studies (e.g., Schrader et al., 2016) or within the framework of chemical transport models such as DEPAC (DEPosition 65 of Acidifying Compounds) (Erisman et al., 1994)

within LOTOS-EUROS (LOng Term Ozone Simulation -EURopean
Operational Smog) (van Zanten et al., 2010;Manders et al., 2017). Outputs of these models help understand ecosystem functioning and can further be used for gap filling in order to compile total nitrogen budgets, which form the basis of national inventories of air pollutants and their assessment reports. For a more robust model validation and to better facilitate investigations of the high degree of spatial and temporal variability of Nr fluxes, a close coupling of modeling and measurement 70 studies has been recently postulated (Schrader, 2019;Schrader et al., 2020). The latter studies also provide a state-of-the-art overview of current challenges and perspectives in Nr modelling focusing on biosphere-atmosphere exchange of ammonia.
The aim of this paper is the release and description of campaign datasets of Nr fluxes, which have been made publicly available in the Zenodo repository (http://doi.org/10.5281/zenodo.4513854). Four multi-months to multi-year field campaigns covering two different ecosystem types (peatland and forest) have been carried out within the research projects NITROSPHERE 75 (Brümmer et al., 2019) and FORESTFLUX (Brümmer et al., 2020). We used a quantum cascade laser (QCL) absorption spectrometer and a custom-built converter called Total Reactive Atmospheric Nitrogen Converter (TRANC) connected upstream to a chemiluminescence detector (CLD) for measuring ammonia and total reactive nitrogen (hereafter named Nr) fluxes, respectively. These datasets demonstrate the suitability of QCL and TRANC in eddy-covariance setups andas in-situ high-resolution data of surface-atmosphere fluxes of Nr compounds are still scarcehighlight potential applications such as 80 model validation, understanding temporal dynamics of Nr exchange, derivation of deposition velocities, and using model output as a gap-filling strategy for eddy-covariance datasets. In the here presented study, we follow the nomenclature of Marx et al. (2012), with the definition of Nr comprising all nitrogen-containing trace species, but excluding N2 and N2O as these are non-reactive in the lower troposphere.

Field campaign sites 85
Datasets of reactive nitrogen ecosystem-atmosphere fluxes presented in this paper were acquired during four field campaigns between October 2012 and June 2018. Measurements took place at two different sites representing a natural forest (FOR) and a semi-natural peatland (WET). At each of the sites, both high-frequency QCL and TRANC measurements of NH3 and Nr, respectively, were conducted in eddy-covariance setups. These micrometeorological measurements were accompanied by monthly integrated low-cost denuder and filter samplers for a variety of Nr compounds (Section 3.3). Further site information, 90 campaign numbers, individual campaign lengths, and site acronyms used in this paper are given in Tables 1 to 3.

Bourtanger Moor site (WET)
The Bourtanger Moor is a natural park in the border region between Northwest Germany and Northeast Netherlands. The area was a wide-ranging and intact raised bog ecosystem in central Europe before being drained and cultivated in the seventeenth century (Casparie, 1993). Before rewetting was considered, the site had been moderately drained since the 1950s and 95 constitutes nowadays one of the last protected semi-natural peatlands in the region, whereas the surrounding raised bog complex of an approx. size of 700 ha is characterized by extensive peat excavation with drainage ditches and water table lowering primarily for the purpose of oil and gas drilling. Maximum accumulated peat depth is roughly 4 m. Main vegetation components are bog heather (Erica tetralix), purple moor grass (Molinia caerulea), cotton grass (Eriophorum vaginatum and Eriophorum angustifolium), coppices with some solitary birches (Betula pubescens) and Scots pines (Pinus sylvestris), and 100 Sphagnum mosses. The Bourtanger Moor is surrounded by an area of intensive crop production and livestock breeding resulting in ~25 kg ha −1 atmospheric nitrogen deposition annually. The ratio of dry to wet deposition has been found to be ~2:3 (Hurkuck et al., 2014) thereby substantially exceeding the critical N load of 5 to 10 kg ha −1 yr −1 prescribed for semi-natural peatlands. More site-specific information about the Bourtanger Moor can be found in the publications by Hurkuck et al. (2015;2016) and references therein. Considerable seasonal variability in water table depth was observed with fully water-saturated 105 soil in December and January and up to 60 cm below surface in late summer and early autumn averaging annually at around 10 cm. Annual average air temperature and precipitation for the period 1981 to 2010 at a nearby weather station from the German Meteorological Service in Lingen was 10.0°C and 799 mm, respectively.

Bavarian Forest site (FOR)
The Bavarian Forest site represents a remote location some tens of kilometres away from rather small anthropogenic emission 110 sources providing the opportunity to study background concentration variability and natural flux exchange of a mixed-forest ecosystem. The site is located in the 'Forellenbach' catchment in the Bavarian Forest National Park, thereby being unmanaged and a tree composition of ~80% spruce (Picea abies) and ~20% beech (Fagus sylvatica) in the flux tower footprint. The forest was subjected to a massive bark beetle outbreak between 1995 and 2000 and is still recovering from the disturbance (Beudert et al., 2014). Stand height during the campaign was up to 20 m. Mean annual air temperature and precipitation according to 115 records from the Bavarian Forest National Park Administration from the years 1978 to 2017 is 6.6°C and 1563 mm, respectively.

TRANC -Total Reactive Atmospheric Nitrogen Converter
The detection of concentrations of total reactive nitrogen (Nr) were conducted with a manufactured in-house device named 125 TRANC connected upstream to a chemiluminescence detector (CLD, model 780TR, ECO PHYSICS AG, Dürnten, Switzerland). The housing of the TRANC has an outer diameter of 12 cm and is 71 cm long. In all field campaigns the sonic anemometer was positioned upwind of the TRANC from the main wind direction, thereby keeping unintended disturbance of the natural micro-turbulence as low as possible. The measurement concept is based on two sequential conversion paths with the first one being a thermal step and the second one being a catalytic step including the addition of carbon monoxide (CO) as 130 a reducing agent. Reaction temperatures are 870 °C and 300 °C in an actively heated iron-nickel-chrome (FeNiCr) tube and a passively heated gold tube, respectively. Within the two paths, all reactive nitrogen compounds are converted to nitric oxide (NO), which is analysed in a downstream connected CLD at a frequency of 10 Hz (FOR site) or 20 Hz (WET site). Flow rate was kept constant at ~2.0 L min −1 depending on site setup (cf. Table 2) by using a dry vacuum scroll pump (BOC Edwards XDS10, Sussex, UK) connected to the CLD. Pressure reduction was assured through a critical orifice upstream of the CLD 135 ensuring the required regime of approx. 14 mbar needed for the operation of the reaction chamber. The conversion efficiency of the TRANC under both laboratory conditions and in a long-term field experiment was tested by Marx et al. (2012). In the laboratory, the authors examined recovery rates for the gases NO2 and NH3, which were found to be 99%and 95%, respectively.

QCL -Quantum Cascade Laser Spectrometer
Ammonia concentration measurements were conducted with a quantum cascade laser absorption spectrometer, model mini QC-TILDAS-76, Aerodyne Research, Inc., Billerica, MA, USA. The QCL was connected to a dry vacuum scroll pump (TriScroll 600, Agilent Technologies, Santa Clara, USA) maintaining a flow rate through the system of approx. 17 L min −1 . 145 Ammonia concentrations were measured in a 0.5 L multi-pass adsorption cell (76 m path length) at a frequency of 10 Hz. The signal was corrected for water vapor dilution using an online implementation, thereby providing mixing ratios of mol NH3 per mol dry air. The QCL has a detection limit in the sub-ppb range (cf. McManus et al., 2008). Precision of the instrument is 0.042, 0.021, 0.016, and 0.010 ppb over 1, 10, 20, and 60 s, respectively. A specifically designed 'inertial inlet' box by the manufacturer was installed upstream of the QCL using a 3.5 m heated (40 °C) anti-adhesive perfluoroalkoxy (PFA) tube to 150 avoid unintended interactions of NH3 with other gases, particles, and surfaces. More than 50% of the aerosols (>300 nm) were removed inside the inlet box through a split in the incoming air and a forced 180° turn of the air sample (Ellis et al., 2010;Ferrara et al., 2012;von Bobrutzki et al., 2010;Zöll et al., 2016). Additionally, an "active passivation" system injecting a surfactant vapor (perfluorooctylamine) to the inlet of the instrument to continuously coat the inlet walls was used for response time improvements and minimizing wall losses (cf. Roscioli et al., 2016). A critical glass orifice maintained an operating 155 pressure range of 4.6 to 6 kPa. Glass parts of the inlet box were cleaned prior to each campaign and at least once a month during field operation. The system was calibrated internally by the laser itself through alignment of the sampled NH3 absorption peak to the standard of the HITRAN database (Rothman et al., 2009). Other than measurement height (cf. Table 2), the setup for ammonia concentration measurements at each campaign was identical to the one reported in Zöll et al. (2016), where also details about instrument performance can be found. 160

Passive samplers
During each of the campaigns, passive samplers of the IVL type (named after the Swedish Environmental Research Institute; Ferm, 1991) were used for monthly integrated NH3 concentration measurements. IVL samplers had been satisfactorily tested in past comparison studies (Dämmgen et al., 2010;Kirchner et al., 1999;Zimmerling et al., 2000). The sampler consists of a 165 lens tube (length: 10 mm; opening: 20 mm; material: PP (Polypropylene)) that is attached vertically to the exposure. The upper tube opening is closed with a snap cover (material: PE (Polyethylene)) attached to a coated filter (company: Sartorius, Göttingen, Germany; material: cellulose; pore size: 0.45 μm). The lower opening is closed by a PTFE filter (company: Millipore, Darmstadt, Germany; diameter: 25 mm; pore size: 1.0 μm), which is stabilized on both sides with a stainless-steel mesh (mesh size: 0.125 mm; wire diameter: 0.08 mm). PTFE membrane and steel mesh are pressed onto the tube using a 170 second snap cover (material: PE), which has a punched opening of 20 mm and is the sample opening of the passive collector.
For NH3 separation, the cellulose filter was coated with citric acid. The mass of NH3 being deposited on the coated filter depends on the molecular diffusion coefficient, exposure period, and design of the sampler (Dämmgen et al., 2010;Ferm, 1991). During laboratory analysis, impregnated filters were extracted with 5 ml ultrapure water using a shaking apparatus for 1 h before applying either segmented flow analysis or ion chromatography for NH3 detection. 175

DELTA denuder and filter samplers
Monthly integrated air concentrations of NH3, HONO, HNO3, particulate NH4, and particulate NO3 were measured with a custom-built DELTA (DEnuder for Long-Term Atmospheric sampling) system. A comprehensive description of the measurement principle is given in Sutton et al. (2001) and Tang et al. (2009). The system consists of four cylindrical denuders, two of which are for the determination of acidic gases and two for ammonia. A filter pack captures nitrogen species in 180 particulate matter. Air flow was recorded with a standard gas meter and was usually averaging 0.5 m 3 d −1 . In each of the campaigns, sampling height of the DELTA system was equal to the measurement height of the sonic anemometer (cf. Table   2). Basic denuders were coated with sodium carbonate and glycerol dissolved in water and methanol, whereas acidic denuders were coated with citric acid and glycerol and also being dissolved in water and methanol. The filter pack was a two-stage holder using round cellulose filter (

Instrumentation for meteorological measurements
At all campaign sites, the same ultrasonic anemometer model (R3, Gill Instruments, Lymington, UK) was installed for the 190 measurement of the 3-dimensional wind components (u, v, and w). From these signals wind speed, wind direction, friction velocity ( * ), and atmospheric stability were calculated. Measurement heights above ground were adapted to the respective vegetation type and are listed in Table 2. In every setup, both QCL inlet box and TRANC were mounted on a separate boom at the north side of the anemometer, which was in all cases an infrequent (<10%) wind direction. The position of the sample air inlets was approx. 10 cm below and 30 cm north of the centre of the sonic anemometer array. Air temperature (Ta) and 195 relative humidity (RH) were measured with a HC2S3 probe (Campbell Scientific, Logan, Utah, USA). Global radiation (Rg), i.e. total incoming shortwave radiation, was recorded by the upward facing pyranometer from a CNR4 net radiometer (Kipp&Zonen B.V., Delft, The Netherlands). An internal Pt-100 and a thermistor temperature sensor were used to correct for instrument heating. Precipitation was recorded with an automatic tipping-bucket rain gauge (Thies Clima, Göttingen, Germany). During all campaigns, meteorological instruments were installed at the approx. same height as the sonic 200 anemometer. Further details can be taken from the individual campaign publications listed in Table 2.

Acquisition and flux calculation
The procedure of data collection was nearly the same at all sites. The software EddyMeas, a program of the software package EddySoft (Kolle and Rebmann, 2007), was used for half-hourly recording of the measurements at all sites but with different 205 time resolutions. In campaign 1 (WET-TRANC, Table 1), the sampling frequency was 20 Hz, whereas the data acquisition of sonic anemometer and respective gas analysers was set to a frequency of 10 Hz during campaigns 2 to 4 (WET-QCL, FOR-TRANC, FOR-QCL). Analog signals of the CLD, QCL, and anemometer were recorded at the sonic's interface and joined to a common data stream. Only at WET, NH3 measurements were logged separately on the onboard computer of the QCL. Thus, data from the QCL and the sonic had to be interpolated to a reference timestamp (cf. Zöll et al., 2016). Before calculating eddy-210 covariance fluxes (Aubinet et al. 2012;Burba, 2013), periods of maintenance, instrument malfunction, and obvious outliers were flagged based on visually checking instrumental performance and parameters such as flow rate, laser operating temperature, absorption cell pressure and temperature, and TRANC heating temperature. The software EddyPro (https://www.licor.com/env/support/EddyPro/topics/introduction.html) was used for eddy flux calculation. Raw data time series were accounted for spikes after Vickers and Mahrt (1997), a 2-D rotation of the wind vector components was applied 215 (Wilczak et al., 2001), and block averaging was performed. Half-hourly fluxes were calculated using the eddy-covariance method. The time series of vertical wind speed and gas concentration are shifted against each other until the covariance is maximized. The number of samples a time series has to be shifted corresponds to the time lag. The physical time lag is the elapsed duration from a point in time the measurement of an air sample is done by the sonic anemometer until the gas concentration of the same sample is recorded by the gas analyser after moving through the sample tubes. We used the option 220 "covariance maximization with default" for determining time lags and their corresponding fluxes in the respective half hour.
The default values, which are given in Table 2, as well as the range around the physical lag were based on theoretical considerations. Ranges were adapted to the highest time lag density and were mostly ±3 s around the default value. For quantitative investigation of the fluxes, several quality flagging criteria were applied. These were (i) removing flux and concentration outliers, (ii) excluding values where w, T, or Nr concentration were larger than the mean plus 3×1.96σ with σ 225 being the standard deviation of the variances, and (iii) discarding fluxes, which were attributed to very stable atmospheric conditions, i.e. * < 0.1 m s −1 (cf. Zöll et al., 2019). Furthermore, (iv) only fluxes with flag "0" and flag "1" after Mauder and Foken (2006) were used. The contribution of flag "0" and flag "1" to available fluxes after application of visual screening of outliers (i), exclusions based on variances (ii) and low turbulence (iii) is given in Table 3. After application of all quality criteria, 48% of the Nr fluxes were left for further flux analysis. 230

High-frequency damping corrections
High-frequency signals measured by any eddy-covariance setup are influenced by chemical, physical, or sampling processes leading to noise and to a reduced intensity in high or low-frequency parts of the signal. Thus, fluxes calculated from these filtered signals are attenuated by certain factor compared to signals, which would be measured by an ideal instrument, especially in the high-frequency range. These losses are relatively large for closed-path instruments designed for measuring 235 Nr compounds, since these gases are highly reactive and some of them 'sticky' compared to inert gases like CO2, CH4, or N2O.
As chemical and physical characteristics of inert compounds are different from those of reactive gases, formerly developed high-frequency correction methods for inert gases are not necessarily suitable for Nr compounds like NH3 and Nr.

Gap filling
Every set of eddy-covariance measurements usually reveals data gaps of various lengths after rigorous quality control, irrespective of the species of interest. These gaps may originate from situations where the basic assumptions of EC theory fail, e.g. under non-turbulent conditions or when fluxes represent an area (footprint) that is non-uniform or non-horizontal. Further 250 violations arise when the average of fluctuations does not equal zero or when significant density fluctuations are observed (Aubinet et al., 2012). Other reasons for data gaps commonly include instrument failure or implausible spikes of unknown origin. With the here presented datasets we provide gap-filled time series of NH3 and Nr to better facilitate estimations of the sites' nitrogen budgets.

Data-driven gap-filling 255
A common approach for gap filling is the usage of statistical methods such as Look-Up Tables (LUT), Mean Diurnal Variation (MDV), or Non-Linear Regression (NLR) (Falge et al., 2001). These methods were originally developed for fluxes of carbon dioxide and/or water vapor and require short-term stability of exchange patterns for a sufficient quality of the gap-filled flux values. In case of Nr and NH3, the exchange pattern can heavily vary over different time scales as these species usually exhibit lower autocorrelation and higher non-stationarity than for example carbon dioxide or water vapor. Thus, the application of 260 data-driven methods to Nr fluxes is recommended for data gaps spanning only over a few days. LUT require that dependencies of the gas of interest on several parameters such as temperature, relative humidity, radiation, concentration, or turbulencerelated parameters are well defined. Applying LUT on Nr becomes complicated since Nr describes the sum of several Nr compounds. These gases and particles differ significantly in physical and chemical properties and in their contribution to Nr.
The ratio of the compounds varies throughout the annual cycle  and further depends on site 265 characteristics and surrounding conditions. Little is known about the overall dependency of Nr on the climatic parameters.
We therefore decided to use MDV as gap-filling technique. In our dataset, the window for calculating the filled flux was set to 5 days before and 5 days after the gap in the original time series.

Model-based gap-filling (FOR-Nr only)
Regular MDV gap-filling cannot account for long gaps due to a limited averaging window size. However, filling these gaps is 270 a necessary step before calculating long-term budgets, which was one of the main goals of the measurement campaign at FOR.
We therefore used a dry deposition inferential model as an additional gap-filling method during this campaign. The computer code DEPAC (DEposition of Acidifying Compounds; Van Zanten et al., 2010) version 3.21 was used in a stand-alone version, i.e., with input data measured at the site instead of modelled inside a chemistry transport model (CTM). We here used the variant of DEPAC that is used within the chemistry transport model LOTOS-EUROS (Manders et al., 2017) (1977) and Hummelshoj (1995, 1997), respectively, using stability corrections following Webb (1970) and Paulson 280 (1970).
An approximation to the dry deposition of Nr that was used to fill gaps was calculated as the sum of the modelled dry deposition fluxes of NH3, NO, NO2, and HNO3. Particle deposition was not modelled. Input data were available on a half-hour basis for most variables. These include temperature, relative humidity, global radiation, ambient pressure, and friction velocity, as well as concentrations of the individual chemical compounds. Monthly DELTA-Denuder concentration measurements were 285 used for HNO3, and, during times of QCL malfunction, NH3.

Dry deposition inferential modelling (WET-NH3 only)
A dry deposition inferential model was used as an additional plausibility check in our first NH3-QCL campaign at WET (Zöll et al., 2016). We used the parameterisation of a bi-directional canopy compensation point model of Massad et al. (2010), following the works of Nemitz et al. (2001), in a single-layer big-leaf configuration (i.e., without explicitly modelling exchange 290 with the ground-layer). The stomatal resistance was modelled using the approach of Wesely (1989), who implemented a dependency on temperature and radiation with a minimum stomatal resistance for H2O of 200 s m −1 ; all other exchange resistances and parameters were set following the recommendations of Massad et al. (2010) with default parameters for seminatural vegetation described therein. Exchange parameters at the leaf-layer (e.g., stomatal and non-stomatal resistance) were calculated using temperature and relative humidity at the mean notional height of trace gas exchange (cf. Nemitz et al., 2009). 295

Uncertainty estimation
While understanding and reporting of uncertainty is not consistent across disciplines (Alekandrov, 2001), describing the quality of measurements is necessary to understand the level of confidence in any observation (Csavina et al., 2017). The eddy flux community usually distinguishes between random and systematic errors in tower-based measurements. Random errors may arise from a variety of sources including (i) turbulence sampling errors, e.g., due to incomplete sampling of large eddies and 300 the associated uncertainty in the calculated covariance between vertical wind velocity (w) and the scalar of interest (c), (ii) instrument errors in the measurement of w and c, and (iii) uncertainty attributable to changes in wind direction and velocity which influence the footprint (Aubinet et al., 2012). Systematic errors originate from unmet assumptions and methodological challenges such as advectionpreferentially over non-flat terrainor errors resulting from instrument calibration and design including high-frequency losses (Wintjen et al., 2020a) and additionally from those errors associated with data processing 305 (e.g., through detrending, coordinate rotation, gap filling). While for some sources of uncertainty a reliable quantification is impossible without further observations, e.g., in the case of advective fluxes, other procedures mentioned above have been For the here presented datasets we provide uncertainty estimates as follows: • Is the originally measured half-hour flux value kept, i.e. has a quality flag of "0" or "1" been assigned based on the criteria of Mauder and Foken (2006), we calculated random uncertainty following Finkelstein and Sims (2001).
• When short gaps are filled with the MDV method, we use the standard error of the mean from the half hours in the respective calculation window. 315 • In the FOR TRANC dataset, larger gaps were filled by DEPAC model estimates (Section 4.3.2). For those half hours, the uncertainty is given by ,

Data description 325
The presentation and structure of the published campaign datasets were attempted to be as self-explanatory as possible. Tables 1 to 3 containing information on site description, technical details with data descriptors, and column explanations were published alongside the data time series in the Zenodo repository (http://doi.org/10.5281/zenodo.4513854). In the following, we briefly highlight key data characteristics.
Campaign time series and mean diurnal courses of both concentrations and fluxes are shown in Fig. 3. The considerably 330 different pollution climate between the peatland (WET), which is subjected to intensive agricultural land use in close vicinity to the measurement location (cf. Hurkuck et al., 2014), and the forest (FOR) within a protected national park, becomes obvious.
While at WET mean campaign values of Nr and NH3 were 21.7 and 15.1 ppb, respectively, forest campaign means were 5.2 ppb for Nr and 1.8 ppb for NH3 (Fig. 4). At all campaigns, clear diurnal concentration patterns with unexpectedly low noise were observed. Surprisingly, WET NH3 and FOR Nrtwo different sites and two different analysersrevealed an almost 335 similar bimodal distribution with one peak around noon and an even larger peak in the late afternoon. Lowest numbers were found during the night. The only apparent difference between these campaigns were the mean daily amplitude. While concentrations at WET NH3 stretched over ~7 ppb, the daily averaged span at FOR Nr was within 1 ppb. The diurnal concentration pattern at FOR NH3 was similar to that of FOR Nr and WET NH3, but it was showing only one distinct peak in the late afternoon. Different diurnal concentration dynamics were found at WET Nr. While the averaged daily amplitude 340 was ~4 ppb, the peak was found in the early morning hours, whereas lowest values were observed shortly after midday.
During all campaigns both emission and deposition of nitrogen could be detected on a half-hourly time resolution (Fig. 3 and   5). Mean campaign fluxes at WET Nr, WET NH3, and FOR Nr were −5, −17, and −14 ng N m −2 s −1 , respectively. Negative numbers indicate an overall net deposition to the respective ecosystem. After rigorous QA/QC, we concluded that a reliable calculation of NH3 fluxes at FOR site could not be achieved. Due to the very low concentration level close to the detection 345 limit of the analyser (cf. Zöll et al., 2016), we observed insufficient variability in NH3 concentrations leading to a low signalto-noise ratio making a covariance detection between vertical wind and NH3 concentration impossible.
As for the concentration regime, we found clear diurnal flux patterns with relatively low noise in the signal, at least at WET NH3 and FOR Nr. At WET Nr, no average net nitrogen exchange was observed in the afternoon, while Nr uptake was relatively stable around −7 ng N m −2 s −1 during the night. The flux pattern for NH3 at WET was similar, but with near-neutral 350 exchange occurring a few hours earlier in the afternoon. Night-time NH3 uptake was stronger around ~20 to 25 ng N m −2 s −1 .
The diurnal flux pattern of Nr at FOR was approximately inverted to those at the WET site. Highest average uptake around 18 ng N m −2 s −1 was found between 9:00 and 15:00, whereas night-time uptake of Nr was considerably lower at around 10 ng N m −2 s −1 .
The frequency distribution of concentrations and fluxes is shown in Fig. 5. While relatively narrow peaks with >50 % of the 355 overall data in only 2 to 3 concentration and flux bins were found at FOR campaigns, a wide distribution spanning over a large range of bins was observed during the WET campaigns. At both sites NH3, was peaking at the respective lower concentration end, i.e. at 7 and 1 ppb for WET and FOR, respectively, whereas Nr was more broadly distributed towards higher concentrations with less distinctive peaks. This could be expected as NH3 is part of the Nr signal, thereby making the shape of the frequency distribution a matter of the contribution strength of other Nr compounds to the Nr signal. The different site-360 specific shares are presented alongside the flux dataset in the form of monthly integrated HONO, HNO3, aerosol NH4, and aerosol NO3 measured by DELTA denuder and filter samplers. Further details for the WET and FOR sites can be found in the publications of Hurkuck et al. (2014) and Wintjen et al. (2020b), respectively. In more than a third (38.3 %) of the campaign time, net emission of Nr was observed at the peatland site. For NH3, the distribution was shifted towards larger and more frequent deposition (83.7 %). With 88.1 % of all fluxes being recorded as net reactive nitrogen uptake by the ecosystem, the 365 forest site revealed the most dominant deposition regime.
As campaign lengths differed substantially, total cumulative N exchange of the campaigns cannot be directly compared.
However, their sums and evolution offer a useful opportunity to assess atmospheric N loads over longer time spans (see Section 6). In the nine weeks of QCL measurements, 892 g N ha −1 in the form of NH3 were taken up by the peatland. At the same site, net Nr uptake was even less (830 g N ha −1 ) indicating both bidirectionality of Nr compounds (e.g., NO is usually emitted, 370 NO2 deposited, NH3 can be emitted or deposited) and most likely a tendency towards N saturation at the site due to the historic land use in the region over the past decades (cf. Hurkuck et al., 2014). The forest site was a clear sink for Nr with 10.6 kg N ha −1 being recorded after 2.5 years of measurements.

Potential applications
Various useful applications arise from the datasets presented in this study. Information on nitrogen exchange has remained 375 highly speculative or had to undergo comprehensive process-based or statistical modelling based on a number of vague assumptions in the past before a clear picture could be derived. Through technological advancements given and detailed in the introductory and methodological section above, the long-term continuous character in combination with a much higher temporal resolution, mainly led to novel opportunities understanding the functioning of ecosystems and their associated land surface-atmosphere interactions. These are briefly outlined in the following paragraph. 380

Improving process understanding through high temporal resolution
• While information on sub-daily Nr exchange had been challenging to get due to metrological limitations (cf. , half-hour fluxes from eddy-covariance measurements allow for investigating the mechanisms governing the surface-atmosphere exchange on different temporal scales. For example, high temporal resolution 385 helps to identify the bidirectional exchange of NH3 Zhu et al., 2015;Schulte et al., 2021) and reveals phases of net emission vs. net deposition (McGinn et al., 2016;Zöll et al, 2016;Wintjen et al, 2020b).
A clearer picture can be derived about the interplay of stomatal (Geßler et al., 2002) and non-stomatal pathways (Schrader et al., 2016), the exchange between soil and the atmosphere, seasonal site-specific conditions, e.g., through vegetation senescence and decomposition of fallen leaves (Hansen et al., 2013) or periods of elevated 390 evaporation from either water droplets on leaves (Hales and Drewes, 1979) or whole water bodies. Furthermore, although not presented in this study, short-term temporal dynamics of Nr after field management like crop or grassland fertilization usually leading to immediate emission peaks (Martins et al., 2017) can be used for better quantification of nitrogen losses to the atmosphere (Brümmer et al., 2013;Ma et al., 2020).

Integrated view of total Nr dynamics through TRANC technology
• While in the past, a laborious combination of several methods was required, TRANC data have the advantage that the sum of all relevant reactive nitrogen compounds is ready to use for further analysis by the measurement of a single signal. This provides a simple integrated overview representing the internally complex interactions of Nr speciessuch as NO emission from soil, atmospheric NO2 or HNO3 deposition, bidirectional NH3 exchange 400 or gas-particle interactions (Nemitz et al., 2004;Wolff et al., 2010), amongst othersall incorporated in one single number. Although TRANC data do not reveal single compound patterns, applying the TRANC technology An exceedance of a certain deposition threshold (e.g., 5 kg N ha −1 yr −1 for bogs) may result in an invasion by more nitrophilous grasses and trees (e.g. Molinia caerulea, Betula pubescens) and a decline in the native ecosystem-specific species (Tomassen et al., 2003;Hurkuck et al., 2014). Fig. 4 and 6 highlight the potential of campaign data being used in local validations of possible critical level and critical load exceedances. In our cases, 415 we found that ambient concentrations at a peatland site are similar to those at highly fertilized arable land (cf. Brümmer et al., 2013). Furthermore, unmanaged remote forest sites (like FOR) some tens of kilometres away from emission sources offer an opportunity to investigate the natural exchange mechanisms of trees with the atmosphere.

Determination of controlling factors
• A further potential application is the analysis of controlling factors of reactive nitrogen exchange. Fluxes have been shown to be highly non-linear (e.g. Flechard et al., 2013) in space and time, which complicates the development of surface-atmosphere exchange schemes. On the basis of the FOR dataset, Wintjen et al. (2020b) demonstrate that mean diurnal flux courses stratified by concentration, temperature, moisture or surface wetness 425 reveal the strongest dependence on the actual Nr concentration regardless of flux direction, i.e. situations of deposition or emission. Deposition velocity (vd), however, was invariant with changing concentration, temperature, and relative humidity under dry leaf conditions, whereas wet leaf surfaces led to increased atmospheric resistances (Ra) and decreased vd, thereby reducing the Nr flux magnitudes.

Verification of annual Nr budgets and management strategies
• Long-term data on average exchange fluxes help establish nitrogen budgets and provide an essential piece in the nitrogen cycle, particularly with regard to its temporal resolution. Cumulating all half-hourly fluxes like in Fig.   6 showcase a robust way to quantify total nitrogen input or loss from an ecosystem over a certain time span. A number of applications can be drawn from this information, e.g. annual nitrogen budgets. Furthermore, management practices like proper timing of fertilization events with their respective amounts of nitrogen content can be derived. In Brümmer et al. (2013) the authors conclude that the last of three fertilizer inputs at a Thuringian crop site was likely to be unnecessary as much of the added nitrogen was not properly taken up by already senescing wheat plants and was immediately lost to the atmospheric, thereby causing an additional yet avoidable burden to local air quality. 440

Investigation of Nr deposition effects on greenhouse gas exchange and carbon storage capacity of ecosystems
• Finally, these datasets can be used to analyse interactive effects with other components of biogeochemical cycles. Particular interest is placed on how nitrogen deposition may alter the carbon sink strength over different landuse types. While ecosystems such as forests may benefit from an increased nitrogen input from the atmosphere 445 (de Vries et al., 2009;Flechard et al., 2020;Fleischer et al., 2013;Bala et al., 2013), others like nitrogen-limited peatlands were shown to respond with inhibited carbon uptake as a consequence of surplus available nitrogen (Limpens et al., 2011). The here presented datasets may offer the opportunity to validate those results and help find potentialyet unknowneffects on shorter time scales.

Conclusions and outlook 450
Our datasets demonstrate the suitability of QCL (Quantum Cascade Laser) and TRANC (Total Reactive Atmospheric Nitrogen Converter) to measure eddy-covariance fluxes of NH3 and Nr, respectively. In terms of stability, practicality and ease of operation, the standardization of field setups and data post-processing of reactive nitrogen measurements still have a highly experimental character, thereby being two decades behind those of inert greenhouse gas measurements, which are nowadays organized in continental-scale flux networks like for example ICOS in Europe (Heiskanen et al., 2021)  be useful for estimating ammonia dry deposition, which would in turn offer a potential validation tool for regional to nationalscale deposition maps.

Data availability and structure
The presented datasets have been placed into the Zenodo data repository and are accessible under 470 http://doi.org/10.5281/zenodo.4513854. The structure of the dataset is given in Table 3 of this manuscript. Beside auxiliary meteorological data like air temperature, relative humidity, global radiation, wind speed, wind direction, friction velocity, atmospheric stability, and precipitation, we make field campaign measurements and model output of concentrations and fluxes of total reactive nitrogen and ammonia on half-hourly basis publicly available.