International Journal of Agronomy

International Journal of Agronomy / 2020 / Article
Special Issue

Effectiveness of Livestock Manure Fertilization and Nitrogen Losses Assessment

View this Special Issue

Review Article | Open Access

Volume 2020 |Article ID 8909784 | https://doi.org/10.1155/2020/8909784

Ester Scotto di Perta, Nunzio Fiorentino, Marco Carozzi, Elena Cervelli, Stefania Pindozzi, "A Review of Chamber and Micrometeorological Methods to Quantify NH3 Emissions from Fertilisers Field Application", International Journal of Agronomy, vol. 2020, Article ID 8909784, 16 pages, 2020. https://doi.org/10.1155/2020/8909784

A Review of Chamber and Micrometeorological Methods to Quantify NH3 Emissions from Fertilisers Field Application

Academic Editor: Wei Wu
Received26 Jul 2019
Revised01 Jul 2020
Accepted06 Jul 2020
Published01 Aug 2020

Abstract

Agriculture is mainly responsible for ammonia (NH3) volatilisation. A common effort to produce reliable quantifications, national emission inventories, and policies is needed to reduce health and environmental issues related to this emission. Sources of NH3 are locally distributed and mainly depend on farm building characteristics, management of excreta, and the field application of mineral fertilisers. To date, appropriate measurements related to the application of fertilisers to the field are still scarce in the literature. Proper quantification of NH3 must consider the nature of the fertiliser, the environmental variables that influence the dynamic of the emission, and a reliable measurement method. This paper presents the state of the art of the most commonly used direct methods to measure NH3 volatilisation following field application of fertilisers, mainly focusing on chamber method. The characteristics and the associated uncertainty of the measurement of the most widespread chamber types are discussed and compared to the micrometeorological methods.

1. Introduction

Agriculture represents the major emitter of ammonia (NH3) and is responsible for the 94% of total emission in EU-28 in 2016 [1]. Among all the agricultural activities, livestock breeding contributes considerably to anthropogenic NH3 emission in Europe [2, 3]. Even if each step of livestock manure management is characterised by a significant loss of ammonia [4, 5], the field application of slurry is responsible for 30–50% of total emissions [6, 7]. In recent years, an increase in animal manure use as fertiliser has been documented [8], with the aim of recovering manure nutrients to close the nutrient cycle of the agroecosystems and save fertilization costs [9]. Nevertheless, detailed knowledge of the amount of NH3 lost during the application of different manure types is still lacking. This threatens both air and ecosystem quality [10] and often causes important economic farm losses due to the misestimation of real available N to plants [11, 12].

In the light of this, stricter regulations on the use of N in agriculture have been introduced over time. The last one, the National Emission Ceilings (NEC) Directive [13], establishes new national emission ceilings in Europe for five pollutants (sulphur dioxide, SO2; nitrogen oxides, NOx; non-methane volatile organic compounds, NMVOC; ammonia NH3; and fine particulate matter (PM2,5)) and compiles and checks the national emission inventories to compile with 2020 and 2030 reduction commitments. A common effort has been made in all European countries to produce reliable ammonia emission inventories. Despite that, there is still a lack of data regarding specific fertilisers (i.e., buffalo manure [14]) as well as the reference in various pedoclimatic conditions. In addition, data collection is affected by the heterogeneity of measurement methods, with a reduction of the accuracy of the total ammonia emission assessment [15].

Ammonia release into the atmosphere, known as the process of gaseous NH3 transfer from the immediate surface of a solution with ammonium ion (), like slurry on soil surface, into a free airstream [11, 16], depends on several factors. First of all, it is affected by the concentration gradient of gaseous ammonia at the liquid surface and in the air boundary layer above it [17]. Thus, the greater the concentration of dissolved free ammonia NH3(aq) in a liquid solution, the higher the gaseous ammonia emission NH3(g). The total ammoniacal nitrogen (TAN) is the sum of NH3(aq) plus deriving from the hydrolysis of urea, according to the following dynamic equations of ionisation (equation (1)) and liquid–gas equilibrium (equation (2)):

NH3 emissions depend on the dissociation of [11, 18, 19] since only the free NH3 in the liquid (NH3(aq)) can directly volatilise into the atmosphere (equation (2); NH3(g)). The pH of the ammoniacal solution and the soil matrix can be considered the most important driving force for ammonia release into the atmosphere, followed by the air temperature, on which Kd (the equilibrium constant) and KH (the Henry law constant) depend [20]. Indeed, increasing pH in the ammoniacal solution moves the equilibrium to the right, thus increasing the concentration of NH3-N in the liquid solution [9, 21]. In most cases, the current weather conditions affect the NH3 emission rate as the air temperature which increases NH3 concentration in the solution, while the rainfall dilutes the TAN and favours a rapid infiltration of the solution (i.e., slurry) in porous media (i.e., soil). Moreover, wind speed and solar radiation influence the ammonia gas transfer, increasing the turbulent transport at the emission surface [11]. The dynamic of the land–atmosphere emission over time is an important issue, since the highest ammonia fluxes are recorded in the first hours after manure spreading [9, 22, 23]. The interactions between soil conditions, chemical composition of animal slurry, and/or fertilisers characteristics together with amendment spreading techniques significantly influence ammonia volatilisation [9, 11, 12, 18]. As suggested in [24], surface spreading causes the major ammonia-volatilised amount, compared with a narrowband application or shallow injection.

A proper assessment of the ammonia volatilisation under field conditions depends on the measuring methods [25, 26]. In general, two different groups of methods can be identified: micrometeorological and chamber (enclosure) method. Micrometeorological methods are used for large fields (>0.5 ha) to small- and medium-scale fields (20–50 m on the side), whereas enclosures cover a confined portion of the surface (∼0.1-2 m2) [9, 27]. Generally, the chamber method is recommended for comparison studies, since the microenvironment inside them could be different from the ambient conditions [28].

Over the years, several studies focused on ammonia volatilisation assessment under various conditions highlighting the strengths and the limitations of different measurement methods. The most appropriate measurement method should be chosen according to the specific field conditions, type of fertiliser, and the agronomic practice used for the application [29], since dissimilar results can be produced due to the variability of the abovementioned process.

With this in mind, in this paper, the state of the art of the most widespread direct methods is reported to assess NH3 emissions from fertiliser application to the field.

The characteristics and the uncertainties of the measurement techniques are considered and discussed through the results of the past 38 years literature (peer-reviewed papers from 1982 to 2020). Reviewed contributions have been selected among those who applied enclosure methods alone or in comparison with micrometeorological methods to assess NH3 emissions from fertilizer application to the field. This allowed highlighting the strength and weak points, as well as the latest developments of each approach.

2. Chamber Method

2.1. Description of Method

The operating principle of chamber method consists of measuring the NH3 that volatilises inside a hood, which is facing the emitting surface, during a given amount of time. Currently, different types of chambers, in terms of size and shape, have been used under both field condition and storage studies. In the present paper, only results from field trials were considered.

Compared to micrometeorological methods, chamber approach is simpler, as it allows replication and application to small experimental plots [27], as variety and agronomic trials. On the other hand, the shape of the chambers and the adopted operating conditions can introduce microclimate perturbation as radiation, evaporation, temperature, and wind speed, affecting transport of NH3 [30]. This is the reason why they have been used for relative comparison of NH3 emission from different fertilisation treatments. In fact, without an appropriate correction of collected data, these chambers could lead to inaccurate quantification of absolute field ammonia emissions [31, 32]. Nevertheless, the enclosure method is more flexible and easy to use for small-area sources compared to other methods; that is why more efforts have been made in the recent years to enhance the performance of this method and provide a suitable alternative to micrometeorological methods [32, 33].

Since the construction typologies of chambers have been classified in nonrigorous ways, to clarify and be effective, the classification operated by Matson and Harriss [34] was adopted. According to this, enclosures can be categorized by (i) operating conditions and (ii) construction (Figure 1). In the first case, it is possible to distinguish from “non-steady-state” and “steady-state” conditions belonging to static (or “closed”) and dynamic chambers, respectively. The main difference between these categories is that in the closed chambers ammonia concentration gradient decreases during the measurement (Figures 1(a)–1(c)), while in the dynamic chambers, being connected to the atmosphere and equipped with a pump for constant forced air circulation, the inner gas concentration is lower or equal compared to the outgoing air (Figure 1(d)).

Non-steady-state and steady-state chambers are discussed in the following paragraph, while specific types of dynamic chambers are described in separate paragraphs later: Dräger-Tube method and wind tunnel.

2.2. Non-Steady-State and Steady-State Chambers

Among non-steady-state chambers, the nonvented or “static chambers” (Figure 1(a)) are characterised from no forced air circulation in which the accumulation of ammonia emitted [35] is monitored, according to the variation of concentration within specific time intervals [5]. Static chambers prove to be the easiest and cheapest option to investigate the relative differences among different treatments [27]. Nômmik [36] used a simple static chamber, consisting in a metal cylinder with a 245 mm diameter and 150 mm height, for comparing emissions from different urea prills sizes (Figure 2). Two polyurethane plastic foam discs, previously treated with a solution of H3PO4 and glycerol, were placed at two different heights from the soil within each chamber, in order to absorb volatilized ammonia. The amount of ammonia trapped was then determined by titration and the cumulative emission was monitored replacing disks at scheduled time intervals during the sampling period. This simple system allowed comparing more treatments at the same time with low economic and labour costs, even if measured fluxes were affected by nonnegligible perturbation of soil temperature and moisture content due to the obstruction of the surface-to-atmosphere exchange.

On the basis of Nômmik [36], other studies have been conducted, adapting the construction material and the design to the circumstances. Grant et al. [42] and Rawluk et al. [43] used polyvinyl chloride cylinders with a diameter of 150 mm and a height of 200 mm, equipped with two ammonia absorbers polyfoam disc; these materials were tested in comparative field trials. Thereafter, Smith et al. [37] modified material and dimension of the closed static device using plexiglass 400 mm high and 200 mm wide. In this case, foam absorbers were placed in each chamber to discriminate between different ammonia sources: one was placed on the base of the chamber to monitor NH3 volatilised from the soil, while the second was placed on a support device above the previous absorber to protect it from atmospheric NH3, rainfall, and dust. Balsari et al. [38] used a PVC funnel covering 0.138 m2 area, placed above the emitting surface. This system is usually equipped with a trap containing 1% boric acid solution to fix ammonia standing in the air over the funnel, during a fixed period of time (usually 24 h). Ammonia volatilisation is estimated by quantifying of NH3 accumulated in the acid trap. This type of chamber is generally cheap and easy to manage. Nevertheless, “funnel system” is the less accurate method because of the slow accumulation of ammonia in the inner air within the chamber, due to a lowered emission rate [35] as a consequence of the small sampling area and the modifications of the boundary conditions [15]. To overcome the time resolution of measurements, but not the limits of this type of chamber, Verdi et al. [39] designed a circular PVC static chamber with a 20 cm diameter and 30 cm high headspace above soil, coupled with a portable gas analyser.

Vented chambers (Figure 1(b)) are not completely closed, since they allow an air exchange with the atmosphere through a pressure vent. Wang et al. [41] used the chambers described by Liao [44] made of a PVC tube with a diameter of 150 mm and a height of 100 mm, which contains two treated sponges, placed in two different positions, having the same functions of those described in the Nômmik [36] device, with the difference that a porous foam was adopted to allow the ventilation toward the atmosphere. Wang et al. [41] found that this system proved to be more reliable than static chambers in terms of ammonia emission assessment (about 30% bias). Steady-state flow-through and vented chambers were typically used in laboratory application, both applied to acid traps [45] and photoacoustic multigas [46] and portable analysers [47, 48] for comparison studies.

More efficient than previous chambers, closed-loop chambers (Figure 1(c)) are characterised by the circulation of the inner air containing emitted NH3 within the inner space [35]. This type of chamber is generally characterised by a closed plastic container, which has one entry and one exit for headspace air. The exit is connected by means of Teflon tube to an acid trap, a flow meter to regulate the flow rate, and a vacuum pump to pull air through the system. Closed-loop chambers are used in many laboratory applications to simulate storage conditions or the spreading of fertilisers to the soil [40, 4952]. Thanks to their construction features, they can offer the possibility to measure small variations in gas concentration [53]. In recent years, this type of chamber has been applied both in laboratory and field studies to compare anaerobic digestion and solid separation on ammonia emissions from stored and land applied dairy manure, as reported by Neerackal et al. [54]. The authors found significant differences between the two treatments using closed-loop chambers. Holly et al. [53] used an analogous closed-loop system for greenhouse gas and ammonia emission assessment from storage and field application of digested and separated dairy manure. They also found that closed-loop chambers can underestimate the cumulative NH3 emissions after field application when TAN content in the fertilizer is low and the measurement period is too short.

Among field applications, Yang et al. [55] use a steady-state flow-through and vented chamber (Figure 1(d)) on rice and wheat fields fertilised with urea. The shape of the chambers was a polymethylmethacrylate cylinder of 200 mm of diameter and 400 mm height. NH3 was detected via a portable gas analyser. The authors compared the abovementioned chamber design with other construction types, finding an underestimation of the fluxes, as discussed below in the text. In summary, chamber types analysed are reported in Table 1.


Operating conditionsConstructionMeasurement surface area (cm2)Chamber characteristicsPros and consReferences

Non-steady stateNon-flow-throughNonvented314.2Cylindric, PVC, portable gas analyserPros:
 (i) Multiple treatments
 (ii) Low economic cost
 (iii) Reduced field labour
Cons:
 (i) Serious perturbation of boundary conditions
 (ii) Limited spatial representativeness
 (iii) “Memory effects” on the chamber walls
Verdi et al. [39]
314.2Cylindric, plexiglass, acid trapSmith et al. [37]
176.7Cylindric, polyvinyl, acid trapRawluk et al. [43]
176.7Cylindric, polyvinyl, acid trapGrant et al. [42]
1380.0Funnel shape, PVC, acid trapBalsari et al. [38]
471.4Cylindric, metal body, acid trapNômmik [36]
Cylindric, PVC, acid trapWang et al. [41]
3000.0Cylindric, IR spectroscopyHolly et al. [53]
324.0Cylindric, IR spectroscopyNeerackal et al. [54]

Steady stateFlow-throughVented314.2Cylindric, polymethylmethacrylate, portable gas analyserYang et al. [55]

2.3. Dräger-Tubes

Dräger-tube method (DTM) [5659] uses a different type of chamber for the monitoring of NH3 volatilisation in field conditions, characterised by four chambers placed onto the emitting surface. It can be considered as a modified dynamic chamber, where air is sucked by means of a pump and the NH3 concentration measured by a Dräger gas-analysis detector tube. The NH3 flux is corrected by means of two calibration equations, for summer and winter experiments, to overcome the problem of the low air-exchange rate within the chambers (Table 2).


Chamber methodMeasurement surface area (m)Airspeed (m s−1)Chamber characteristicsPros and consReference

0.252Variable (0.07 max)It consisted of a polycarbonate chamber (50 cm by 50 cm) open to one side and the bottom.(i) Minimizes the temperature and wind speed differences with outside.
(ii) Simulates the natural wind speed.
(iii) Condensation on the internal walls during the night.
Vallis et al. [60]

120.04–3.77Wind tunnel made of 2 parts: a tunnel formed from a transparent polycarbonate sheet and a steel circular duct, connected with the fan.(i) Provides natural sward condition inside it.
(ii) Obtains internal airspeed similar to outside one.
(iii) Condensation inside of the tunnel occurs.
Lockyer [61]

220.3–3.5Wind tunnel characterised by 6 following chambers: inlet, calming section, testing section, mixing section, 2 axial fans section, and outlet.(i) Alteration of microclimatic conditions inside the chambers is avoided by automatic adjusting of inside air.
(ii) The testing section is covered by a transparent foil to not alter the irradiation.
Braschkat et al. [62]

0.3220.33Wind tunnel based on Lindvall [63] hood consists of an emission chamber 25 cm high, situated between a divergent diffuser and a convergent duct, respectively, 50 cm and 15 cm long.(i) Aerodynamic disadvantages of the primal geometries are corrected, introducing some flow devices (flat vanes, perforated baffle, and extension duct).Jiang et al. [64]

4152VariableDynamic chamber characterised by 4 chambers placed onto the emitting surface. Air is sucked from them simultaneously by a pump and the ammonia concentrations are measured by a Dräger tube.(i) High reliability of this method for comparative studies.
(ii) No electricity and laboratory analysis.
(iii) Low air exchange rate could lead to an underestimation of flow rate.
Roelcke et al. [56]

2.4. Wind Tunnels (WT)

Wind tunnels are the enclosure technique generally preferred in field application for assessing fluxes from small emitting surface [65]. They are constituted by a chamber covering small area in which a fan forces an airflow inside them. The main advantage of this method is the opportunity to reproduce the field wind conditions, known as one of the main drivers affecting ammonia volatilisation. In these chambers, the emission rate is governed by the air velocity selected throughout the measurements and can be assessed as the product of the flow rate and the concentration of volatilized ammonia under the shelter, in which the aerodynamics and flow rates are controlled [64].

Previous researches have shown several examples of portable wind tunnels. Vallis et al.’s [60] study was the first to propose a wind tunnel characterised by a clear plastic cover 0.25 m2 base and 150 mm height, open at one end.

The wind tunnel by Lindvall et al. [63] consisted of a rectangular measurement section, with contraction and expansion sections. Afterward, Lockyer [61] proposed a wind tunnel, 1 m2 base and 450 mm height, made assembling two components: a tunnel made of transparent polycarbonate sheet and a steel circular duct, connected with an electrically powered fan.

All the other tunnel systems that have been used in later years were inspired by these two. The main chamber types studied over the years are summarised and reported in Table 2.

Bearing in mind that the tunnel system is constituted to reproduce the influence of environmental conditions, numerous issues emerged from monitoring campaigns in the literature. Table 3 summarizes the main studies focused on dynamic chamber method improvements.


Lockyer (1984)Jiang et al. [64]Roelcke et al. [56]Study conditionsAimImportant improvementsReference

X(i) CO2 was used instead of NH3
(ii) 3 trials were carried out: two of them in a greenhouse and the other in the field
Testing the reliability of the conventional sampling system.Introduction of 20 sampling points on 4 branches, to avoid underestimation of the actual gas flux.Loubet et al. [7]
X(i) CO was used as a gas tracer
(ii) It was introduced below a water surface, using a single point or a linear manifold
Determination and improvement of gas recovery rate.The recovery rate was improved up to 92–102%, using a modified sampling chamber and tube configuration.Wang et al. [66]

X(i) 2 indoor experiments conducted at constant wind speeds of 0.5 and 1.0 m·s−1
(ii) An alkaline solution (3 L) containing ammonium sulphate was used as trap for each tunnel
Design, construction, and calibration of a revised wind tunnelA new arrangement that allows each tunnel to be an independent unit, with an adjustable speed motor and a continuous air sampler.Meisinger et al. [67]
X(i) 5 field experiments were carried out measuring NH3 volatilisation with IHF and DTM, in winter and summer season
(ii) Urea was used as fertiliser
Calibration of DTM by means of comparison with IHF results.Two different calibration equations:
ln (NH3fluxIHF)=0.444 ln (NH3fluxDTM) + 0.590 ln (v2m) (winter season)
ln (NH3fluxIHF)=0.456 ln (NH3fluxDTM) + 0.745 ln (v2m) − 0.280 ln (v0.2m) (summer season.)
Pacholski et al. [57]

X(i) Laboratory experiments were conducted with an NH3 source tank
(ii) Mean wind speed of 0.1–0.4 m·s−1, while turbulence intensities of 11–33%
Studying and modelling the NH3 mass transfer in the wind tunnel.NH3 mass transfer coefficient was modelled statistically, depending on wind velocity and turbulence intensity.Saha et al. [68]

X(i) 5 wind tunnel sizes were simulated using CFD
(ii) Inlet air velocity range is 0.1–0.6 m·s−1
Studying the effect of wind tunnel sizes on NH3 emissions.The effects of wind tunnel size were evaluated. In particular, wind tunnel height affects both velocity and concentration boundary layer thickness.Saha et al. [69]
X(i) 4 flow distribution devices were designed and compared using CFD
(ii) Inlet air velocities used were 1, 2.5, and 5 m·s−1
Assessment of the best aerodynamic performances with different WT configurations.The problem of air stagnation and flow recirculation inside the chamber could be solved introducing particular flow distribution devices.Scotto di Perta et al. [70]

Notes. IHF = integrated horizontal flux; DTM = Dräger tube method; WT = wind tunnel; CFD = computational fluid dynamics.

Lockyer [61] highlighted that although his configuration system allowed for realistic wind speed conditions, condensation on the inner surface cover of the tunnel occurred during the night.

Many studies were conducted to assess the effects of the different tunnel geometries, since making a direct comparison among several emission rates measured by wind tunnels with different shapes’ result is not easily practicable [69]. To this purpose, Saha et al. [69] showed that wind tunnel dimension and mainly chamber’s height significantly affect ammonia emission. Smaller wind tunnels gave higher emission rate than the bigger ones, due to the different internal air velocity and turbulence profiles that are generated. Other studies [7] showed that during open-field monitoring, a higher air turbulence occurred in the first part of the tunnel due to the external wind action related to a wide inlet tunnel section.

Nevertheless, hood from Lindvall et al. [63] was tested in a research [64] who observed a rotation airflow generating around vertical axis. This phenomenon was called “jet effect” and it is due to the specific shape of the tunnel. In the same study, flow distribution devices were suggested to minimize this problem.

Since the aerodynamic performance of the tunnel is considered a critical parameter [64], in recent years few studies have been carried out to assess the airflow conditions inside the tunnels and how much they affect ammonia emission rate. The most recent papers dealing with this topic involve the Computational Fluid Dynamics (CFD) simulation model and investigate the airflow characteristics above ammonia-emitting surfaces to better understand what is the effect of wind tunnel dimensions and shape on ammonia emission and the mass transfer process [6871].

3. Micrometeorological Methods

Micrometeorological methods are generally preferred compared to enclosure one when the aim is to assess NH3 volatilisation under medium and field scale conditions and over short-to-long integration time. Compared to chamber method, this approach limits the uncertainty in the measurement of NH3 emissions since it is nonintrusive and barely disturbs the natural exchange between land surface and atmosphere [30, 7274].

Moreover, these methods provide an integrated measure over the study plot area, resulting more representative of real conditions. In spite of that, micrometeorological methods suffer from many limitations due to the need of large, homogeneous monitoring areas as well as the great number of samples and analyses required [33].

Micrometeorological techniques include eddy covariance (EC), aerodynamic gradient method (AGM), inverse dispersions modelling (IDM), and mass balance techniques [74].

3.1. Eddy Covariance

Eddy covariance technique measures the turbulent transfer within the atmospheric boundary layer and it is considered the most direct and least error-prone approach for flux determination [73, 74]. In particular, this technique evaluates the gaseous exchange rate across the interface between the atmosphere and the emitting surface by measuring the covariance between fluctuations in vertical wind velocity and NH3 mixing ratio. Indeed, it is considered that ammonia transport is given by eddying motion in the boundary layer over an extensive and uniform surface [27].

The requirement is to sample each eddy of air that contributes to the flux so that a fast instrument response time is necessary, typically 10 to 20 Hz [35, 74]; otherwise, fluxes can be underestimated [27]. The mean vertical flux density of the NH3 is given bywhere is the instantaneous vertical velocity and is the instantaneous fluctuation of the NH3 concentration of each eddy. The bar denotes an average across a sampling period of usually 30 minutes [75], in order to consider all eddy fluctuations affecting the flux [73]. The advantage of this technique is to perform continuous measurements over large areas, although it needs expensive equipment and some nonnegligible correction as a function of the source strength.

3.2. Aerodynamic Gradient Method

The aerodynamic gradient is a technique related to the concept that NH3 emitted from a surface moves along the mean concentration gradient, thanks to the simultaneous presence of two processes, considered in the same way: turbulent transport and molecular diffusion. Moreover, the horizontal concentration gradient is assumed negligible with regard to vertical one, hypothesising a horizontal airflow uniformity and a constant vertical flux with height.

The aerodynamic gradient is one of the most commonly used techniques nowadays to measure ammonia emission, but it is a technique sensitive to advection of NH3 affecting the flux measurement and requires sensors with high resolution. The most limiting parameter of this method is the possibility of having an undisturbed flow to avoid flux underestimation [27, 74].

Ammonia flux is calculated as follows:where K (m2 s−1) is assumed to be equal to the eddy diffusivity for heat or transport coefficient of ammonia in atmosphere and z (m) is the height above the emitting surface at which concentration c (µg·m−3) is measured.

3.3. Inverse Dispersion Modelling

Inverse dispersion modelling relates one or more concentrations measured in the plume to the atmospheric turbulent characteristics to obtain the emission rate of the corresponding source. The underlying hypotheses are that the studied tracer should be conservative over the measurement integration time and the volatilisation flux should be spatially homogeneous [76]. This technique provides a prediction of emitted ammonia from a surface of any geometry and size. Ammonia emission, in a single source configuration, is determined as follows:where C and Cbgd are, respectively, the concentrations (µg·m−3) measured downwind from the source and the background; D is the transfer coefficient (m·s−1) calculated by the dispersion model from the turbulence parameters.

The most common dispersion models used to estimate NH3 emission in short range are the backward Lagrangian stochastic (bLS) [77] and the Eulerian [78].

The advantage of this method is the independence from any confined surface geometry and the reduced number of inputs required. Another limitation is linked to the time resolution and the sensitivity of the concentration measurement downwind of an emitting surface [27, 74, 77, 79].

Recently, Loubet et al. [78] adopted this method to monitor multisource experimental units, as agronomic plots (25 to 200 m side), having several and simultaneously small- and medium-size emitting sources. This method consists in the measure of concentration with time-averaged acid traps and the study of the turbulence parameters with a three-dimensional ultrasonic anemometer. This nonintrusive application is a low-cost solution to estimate NH3 emissions that does not bias volatilisation estimates, with an uncertainty less than 10%. IDM accuracy has been confirmed for short times measurement (e.g., 30 min) [31].

3.4. Mass Balance or “Integrated Horizontal Flux” Method

Conversely to the above-described methods, the integrated horizontal flux (IHF) technique requires a small experimental circular area with fetch ranging from 15 to 20 and up to 50 m, as long as there are almost uniform wind conditions. For this reason, IHF method is commonly adopted [30], being applicable for measuring gas emission from a spatially inhomogeneous nonplanar source. Due to its flexibility, it is considered the most representative technique and, for this reason, it is the reference method to validate new methods for assessing ammonia emission from the field [27, 31, 80].

It allows the calculation of vertical flux from measurements of horizontal fluxes across downwind and upwind boundaries of the emitting source. The technique is robust and needs no further chemical or physical assumption for the estimation of vertical fluxes.

Based on the conservation of mass, the general method equates the vertical ammonia flux emitted from the treated plot with the net horizontal flux at a known downwind distance.

The horizontal flux density at any height is the product of horizontal wind speed u and gas concentration cg. The total horizontal flux is obtained by integrating that product over the depth of the modified layer z. The average surface flux density is given bywhere x is the radius of the circular source (m). The integration is calculated over 0, that is the roughness length (height where the wind speed is 0) and z that corresponds to the maximum height of the emission plume where the concentration equals cupwind.

Concentrations are measured by means of a mast placed in the centre of the source, or multiple masts upwind or downwind from the source; each mast is equipped with air samplers disposed to different heights [35]. In particular, among the various types of NH3 samplers and analytical techniques studied, the most used are “Leuning et al.’s samplers” [81] and glass tubes [82].

The IHF system proposed by Leuning et al. [81] is equipped with passive NH3 samplers consisting of a cone and a pipe made with PVC, able to point always toward the wind direction. The airstream enters in the device through an orifice and leaves it from the bottom. Inside each sampler, there is a stainless complex surface coated with a thin film of oxalic acid, which traps ammonia contained in the airstream. In this context, a number of samplers are mounted on a measurement mast that is placed in the centre of the treated plot to sample air at different heights (usually 5) and obtain the vertical profile of the horizontal ammonia flux [83].

The IHF system proposed by Schjoerring et al. [82] uses passive flux samplers consisting of two pairs of glass tubes (each tube 100 mm long, 10 mm outer diameter, and 7 mm inner diameter) with a coating of oxalic acid on their inner surfaces. Two tubes are connected by means of a piece of silicone tubing. One side of the tube is connected to a steel disc with a hole, in which the airstream enters. These devices are nonrotating samplers so that two units of samplers must be mounted at four heights on four masts placed on the perimeter of the circular plot to trap ammonia in the four wind directions.

Compared with Leuning et al.’s samplers, the glass tubes are easier to manage and cheaper. The sole disadvantage is the need of a great number of glass tubes. To solve this problem, an improved glass tube method was proposed by Wood et al. [84]. Instead of using four masts, a rotating mast centred in the circular plot was associated with the glass tubes. This system allowed reducing cost, labour, and analytical requirement considering the qualities of the previous flux methods. Moreover, results showed that the improved method increased the accuracy of ammonia volatilisation measurement. The ZINST method [85] is a particular case of IHF, where a single measurement of u and is required to estimate the emission. This measurement height represents the point where the ratio of horizontal to vertical fluxes are relatively unaffected by atmospheric stability conditions. ZINST, as well as IHF, requires flat and uniform areas to be applied, but with the advantage of further reducing costs due to a single measurement point [80].

Recently, IHF method has been recently questioned [86] for systematic overestimation of the flux, since in theory it does not consider the turbulent horizontal transport (uc′, or the fast fluctuating components around that average value). Sintermann et al. [6] suggested that this correction could vary between 5 and 20% depending on atmosphere stability, except for samplers like “Leuning et al.’s samplers” [81] and glass tubes [82], which captured NH3 proportional to the horizontal wind speed.

4. Comparison of Ammonia Fluxes Measurement Methods

Several studies reported results of ammonia volatilisation from field experiment by using and comparing enclosure and micrometeorological methods; thus, it is possible to make a cross-comparison among them in the various situations (see Table 4).


Ammonia cumulative emission kg·N·ha−1 (% applied N)Source typeReference cropImportant findingsReference
Micrometeorological methodsChambers methods

49.1f(24.55%)f30.2h (15.1%)hExp 1 (1 m·s−1)
 200 kg·Urea-N·ha−1
Cut swardRain leads to overestimating the NH3 losses with the wind tunnel.Ryden et al. [87]
96.9f(48.45%)f101h (50.5%)hExp 2 (1–3 m·s−1)
 200 kg Urea-N·ha−1
Wind tunnel efficiency could enhance with automatic control of airspeed inside the tunnel, according to ambient wind speed.
10.8f(41.7%)f, +10.7g (41.4%)gPig and cattle slurry
 24 kg TAN·ha−1
Bare soilGood accordance in the results between both methods under standard conditions in field applications.Mannheim et al. [88]
15.6f(77.4%)f, +15.2g (74.4%)g12.3 kg TAN·ha−1
3.4f(27.2%)f,+4.3g (35.2%)g20.4 kg TAN·ha−1
1.9f(7.3%)f, +11.2g (42.1%)g26.6 kg TAN·ha−1
(75%)a,(71%)hCattle slurry:
 127.25 kg·N·ha−1
Bare soilWind tunnels are preferred to make small plot comparative studies.Misselbrook et al. [89]
(54%)a,(21%)hPoultry manure:
 613.74 kg·N·ha−1
(29%)a,(39%)hPoultry wetted manure:
 316.2 kg·N·ha−1
32.7a(43.6%)a45.6c (60.8%)c26.8–30.6d (35.5%)d75 kg Urea-N·ha−1Bare soilIHF(GT) tends to underestimate or overestimate ammonia flux (12.5 to 64%), while dynamic chambers and IHF(L) have a similar ammonia loss kinetic.Pacholski et al. [58]
21.6a(1.8%)a8.2c (4.1%)c22.2d (11.1%)d200 kg Urea-N ha−1
23.9a(19.9%)a21c (17.5%)c25–29.8d (20.8%)d120 kg Urea-N ha−1
18.8a(12.5%)a8.6c (5.7%)c51–59.8d (34%)d150 kg Urea-N ha−1
9.9 (4.9%)b7.4 (3.7%)mUrea:
 200 kg·N·ha−1
Bare soilWT measurements are affected by frequent sampling activities, but that correlation between WT and IHF method could be improved with 3 h of minimum sampler exposition time.Scotto di Perta et al. [14]
46.8 (11.7%)b26.5 (6.63%)mBuffalo slurry:
 400 kg·N·ha−1
49.2 (27.95%)b26.4 (15%)mBuffalo digestate:
 176 kg·N·ha−1

Notes. Data in round brackets “( )”are expressed in % applied N. IHF = integrated horizontal flux; IHF(GT) = integrated horizontal flux with glass tubes, IHF(L) = integrated horizontal flux with Leuning et al.’s samplers, DTM = Dräger tube method; WT = wind tunnel; TAN = total ammoniacal-N; UAN = uric acid and ammoniacal-N. aIHF method by Leuning et al. [81]; bIHF method by Wood et al. [84]; cIHF method by Schjoerring et al. [82]; dDTM; eZINST; fIHF method by Denmean [90]; gWT by Braschkat et al. [62]; hWT by Lockyer [61]; mWT by Jiang et al. [64]. +As % of applied TAN; as % of applied of UAN.

Dynamic chambers together with micrometeorological methods have been used in several studies (Table 4) using different fertilisers under different pedoclimatic conditions.

Compared to the chamber method, wind tunnels proved to be the best approach to minimize the discrepancy between the environmental conditions from inside to outside the chamber [25]. As a consequence, in the studies which compared NH3 emissions from static and dynamic chambers, those measured using wind tunnels are always higher. Balsari et al. [91] found that NH3 losses measured with the funnel-shaped static chamber, after manual application of raw cattle slurry to alfalfa grassland, is about 16% lower than those measured by wind tunnels (with an air velocity of 0.6 m·s−1), both during summer and autumn. Moreover, both methods proved to be useful in comparing different fertilisers; indeed, they were sensitive to treatments and temperature variation of the season.

Unlike dynamic chambers, static ones are associated with a general underestimation of the emissions due to the higher resistance to atmospheric vertical transfer in absence or under low headspace air turbulence [92]. Miola et al. [65] compared NH3 emission measured by static chambers and wind tunnel after field application of different manures. They found a large underestimation of the static chambers up to 80% (23% on average), regardless of the source strength, motivating this discrepancy as a consequence of low air movement that increases the resistance to NH3 atmospheric transfer in static chambers. Furthermore, they found an indirect and time-related bias linked to the impact of chamber environment on the ammonification of organic N supplied by “manure amendment.”

With regard to comparison between static and dynamic systems, as also suggested by Balsari et al. [2], NH3 emission measurements performed on the same source and environmental conditions with the “funnel system” and wind tunnel were significantly different. The main reason for this difference is the constant airflow recirculation inside the wind tunnel over the emitting surface and the absence of this in the “funnel system.” In particular, the ammonia emission rate evaluated with the wind tunnel was higher than the one measured by means of the “funnel system.” Thus, this static chamber did not allow obtaining comparable data to those of real environmental conditions, but it can be used only as comparison system. Instead, the results obtained by the wind tunnel can be considered closer to the real emission phenomenon.

Yang et al. [55] compared different chamber types, a steady-state flow-through and vented chambers, with a vented and a closed chamber in a lab experiment, finding a severe underestimation of NH3 quantification with all the chamber designs, due to large and negative variances, as also found by Wang et al. [41]. According to these results, the authors proposed that all the researchers adopting chamber methods declare the underestimation without applying any empirical correction of measured emissions, which can be source-strength dependent.

Finally, other studies, such as that of Pacholski et al. [58], reported the comparison of micrometeorological methods and dynamic chamber methods on urea emissions (Table 4). The authors used an IHF method equipped with Leuning et al.’s [81] passive samplers (IHF(L)) and an IHF equipped with glass tubes [82] (IHF(GT)) and a DTM. The results showed that IHF(GT) tends to underestimate or overestimates ammonia losses probably due to the different responsiveness of the samplers to the wind speed or the choosing of a smaller diameter pot (12.5 m), as well as the introduction of plastic-cover roof for the rain. On the other hand, DTM presented a good agreement with IHF(L) results in terms of ammonia loss kinetic, since only a qualitative comparison could be made.

Another comparison between static chambers and IHF method proposed by Bittman et al. [93] and Shah et al. [94] confirms the underestimation of static chambers such as those reported by Verdi et al. [39], Smith et al. [37], Rawluk et al. [43], Grant et al. [42], Balsari et al. [38], Nômmik [36], and Wang et al. [41], compared to the micrometeorological method. In addition, static chambers should not be chosen to perform ammonia emission measurements in field application of fertilisers because the enclosure affects heat transfer inside the chamber, whereas wind tunnels better mimic natural airflow. In most parts of them, except for Mannheim et al. [88], wind tunnels underestimate NH3 emissions if compared with IHF method. In particular, the main parameters affecting the wind tunnel efficiency is the air velocity inside the dynamic chamber [87]. Indeed, as reported by Misselbrook et al. [89], comparable results with the IHF method can be achieved when the inner air velocity corresponds with the ambient wind speed.

In conclusion, a nonnegligible aspect in the selection of the proper measurement method is the consideration of many factors, including the resources and objective of the research. To this purpose, some parameters (e.g., replication, land area requirement, labour costs, analytical costs, reliability of technique, duration of measurement, and intrusiveness) should be taken into account. [89, 94].

5. Conclusions

Different aspects of ammonia measurement methods have been considered and discussed. Overall, the chambers method can be a viable option when it is not possible to apply micrometeorological methods. IHF micrometeorological technique is considered as a reference for quantifying NH3 emission after manure field application, even if some corrections have been lately proposed. Compared to chamber method, wind tunnels proved to be the most suitable technique to mimic wind conditions, thus reducing the uncertainty with ammonia fluxes, as supported by the latest improvements on this technique. Finally, this literature review reported the strength and the weak points of the method nowadays used to assess ammonia emission in the field. The conclusion is that enclosure methods, as well as the dynamic chambers like the wind tunnels, are a reliable tool for a relative comparison of the emissions, when their limits and uncertainties are considered to choose the most suitable technique for specific experimental conditions.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was realized under the projects RiAGRI-Sele and SOS_AGRI funded by the Rural Development Program for 2014–2020 of Campania Region.

References

  1. C. Guerreiro, A. G. Ortiz, F. M. de LeeuwViana, and J. Horálek, “Air quality in Europe-2016 report,” Tech. Rep., Publications Office of the European Union, Brussels, Belgium, 2016. View at: Google Scholar
  2. P. Balsari, G. Airoldi, E. Dinuccio, and F. Gioelli, “Ammonia emissions from farmyard manure heaps and slurry stores-effect of environmental conditions and measuring methods,” Biosystems Engineering, vol. 97, no. 4, pp. 456–463, 2007. View at: Publisher Site | Google Scholar
  3. L. Clarisse, C. Clerbaux, F. Dentener, D. Hurtmans, and P.-F. Coheur, “Global ammonia distribution derived from infrared satellite observations,” Nature Geoscience, vol. 2, no. 7, pp. 479–483, 2009. View at: Publisher Site | Google Scholar
  4. S. Pindozzi, S. Faugno, C. Okello, and L. Boccia, “Measurement and prediction of buffalo manure evaporation in the farmyard to improve farm management,” Biosystems Engineering, vol. 115, no. 2, pp. 117–124, 2013. View at: Publisher Site | Google Scholar
  5. J. E. Olesen and S. G. Sommer, “Modelling effects of wind speed and surface cover on ammonia volatilization from stored pig slurry,” Atmospheric Environment. Part A. General Topics, vol. 27, no. 16, pp. 2567–2574, 1993. View at: Publisher Site | Google Scholar
  6. J. Sintermann, A. Neftel, C. Ammann et al., “Are ammonia emissions from field-applied slurry substantially over-estimated in European emission inventories?” Biogeosciences, vol. 9, no. 5, pp. 1611–1632, 2012. View at: Publisher Site | Google Scholar
  7. B. Loubet, P. Cellier, D. Flura, and S. Génermont, “An evaluation of the wind-tunnel technique for estimating ammonia volatilization from land: Part 1. Analysis and improvement of accuracy,” Journal of Agricultural Engineering Research, vol. 72, no. 1, pp. 71–81, 1999. View at: Publisher Site | Google Scholar
  8. FAOSTAT, FAO Statistics, Food and Agriculture Organization of the United Nations, FAOSTAT, Rome, Italy, 2019, http://www.fao.org/faostat/en/#data.
  9. M. Rostami, S. Monaco, D. Sacco, C. Grignani, and E. Dinuccio, “Comparison of ammonia emissions from animal wastes and chemical fertilizers after application in the soil,” International Journal of Recycling of Organic Waste in Agriculture, vol. 4, no. 2, pp. 127–134, 2015. View at: Publisher Site | Google Scholar
  10. C. A. Rotz, F. Montes, S. D. Hafner, A. J. Heber, and R. H. Grant, “Ammonia emission model for whole farm evaluation of dairy production systems,” Journal of Environmental Quality, vol. 43, no. 4, pp. 1143–1158, 2014. View at: Publisher Site | Google Scholar
  11. S. G. Sommer and N. J. Hutchings, “Ammonia emission from field applied manure and its reduction-invited paper,” European Journal of Agronomy, vol. 15, no. 1, pp. 1–15, 2001. View at: Publisher Site | Google Scholar
  12. J. Martínez-Lagos, F. Salazar, M. Alfaro, and T. Misselbrook, “Ammonia volatilization following dairy slurry application to a permanent grassland on a volcanic soil,” Atmospheric Environment, vol. 80, pp. 226–231, 2013. View at: Publisher Site | Google Scholar
  13. EC, Directive 2016/2284 of the European Parliament and of the Council of 14 December 2016 on the reduction of national emissions of certain atmospheric pollutants, amending directive 2003/35/EC and repealing directive 2001/81/EC, EC Official Journal of the European Union L. 344 of 17.12.2016, 2016.
  14. E. Scotto di Perta, N. Fiorentino, L. Gioia, E. Cervelli, S. Faugno, and S. Pindozzi, “Prolonged sampling time increases correlation between wind tunnel and integrated horizontal flux method,” Agricultural and Forest Meteorology, vol. 265, pp. 48–55, 2019. View at: Publisher Site | Google Scholar
  15. S. Minoli, M. Acutis, and M. Carozzi, “NH3 emissions from land application of manures and N-fertilisers: a review of the Italian literature,” Italian Journal of Agrometeorology, vol. 3, pp. 5–24, 2015. View at: Google Scholar
  16. J. Ni, “Mechanistic models of ammonia release from liquid manure: a review,” Journal of Agricultural Engineering Research, vol. 72, no. 1, pp. 1–17, 1999. View at: Publisher Site | Google Scholar
  17. Z. Ye, G. Zhang, B. Li, J. Søberg Strøm, G. Tong, and P. J. Dahl, “Influence of airflow and liquid properties on the mass transfer coefficient of ammonia in aqueous solutions,” Biosystems Engineering, vol. 100, no. 3, pp. 422–434, 2008. View at: Publisher Site | Google Scholar
  18. S. Génermont and P. Cellier, “A mechanistic model for estimating ammonia volatilization from slurry applied to bare soil,” Agricultural and Forest Meteorology, vol. 88, no. 1–4, pp. 145–167, 1997. View at: Publisher Site | Google Scholar
  19. S. D. Hafner, F. Montes, and C. Alan Rotz, “The role of carbon dioxide in emission of ammonia from manure,” Atmospheric Environment, vol. 66, pp. 63–71, 2013. View at: Publisher Site | Google Scholar
  20. D. Beutier and H. Renon, “Representation of NH3-H2S-H2O, NH3-CO2-H2O, and NH3-SO2-H2O vapor-liquid equilibria,” Industrial & Engineering Chemistry Process Design and Development, vol. 17, no. 3, pp. 220–230, 1978. View at: Publisher Site | Google Scholar
  21. V. K. Vaddella, P. M. Jiang, and A. Jiang, “An empirical model of ammonium ion dissociation in liquid dairy manure,” Transactions of the ASABE, vol. 54, no. 3, pp. 1119–1126, 2011. View at: Publisher Site | Google Scholar
  22. Z. Yang, H. Niimi, K.-i. Kanda, and Y. Suga, “Measurement of ammonia volatilization from a field, in upland Japan, spread with cattle slurry,” Environmental Pollution, vol. 121, no. 3, pp. 463–467, 2003. View at: Publisher Site | Google Scholar
  23. M. Carozzi, R. M. Ferrara, M. Fumagalli et al., “Field-scale ammonia emissions from surface spreading of dairy slurry in Po Valley,” Italian Journal of Agrometeorology, vol. 3, pp. 15–24, 2012. View at: Google Scholar
  24. J. F. M. Huijsmans, J. M. G. Hol, and M. M. W. B. Hendriks, “Effect of application technique, manure characteristics, weather and field conditions on ammonia volatilization from manure applied to grassland,” NJAS-Wageningen Journal of Life Sciences, vol. 49, no. 4, pp. 323–342, 2001. View at: Publisher Site | Google Scholar
  25. H. T. Søgaard, S. G. Sommer, N. J. Hutchings et al., “Ammonia volatilization from field-applied animal slurry—the ALFAM model,” Atmospheric Environment, vol. 36, no. 20, pp. 3309–3319, 2002. View at: Google Scholar
  26. P. Gostelow, P. Longhurst, S. A. Parsons, and R. M. Stuetz, “Sampling for measurement of odours,” Tech. Rep., IWA Publishing, London, UK, 2003, Scientific and Technical Report No. 17. View at: Google Scholar
  27. L. A. Harper, “Ammonia: measurement issues,” in Micrometeorology in Agricultural Systems, vol. 47, pp. 345–379, ASA, CSSA, and SSSA, Madison, WI, USA, 2005. View at: Google Scholar
  28. S. G. Sommer and T. H. Misselbrook, “A review of ammonia emission measured using wind tunnels compared with micrometeorological techniques,” Soil Use and Management, vol. 32, pp. 101–108, 2016. View at: Publisher Site | Google Scholar
  29. M. Ferm, B. Galle, L. Klemedtsson, A. Kasimir-Klemedtsson, and D. W. T. Griffith, Comparison of different Techniques to Measure Ammonia Emission after Manure Application, Swedish Water and Air Pollution Research Laboratory-Publications-IVL B, Gothenburg, Sweden, 2000.
  30. J. S. Schepers and W. Raun, Eds.in Nitrogen in Agricultural Systems. No. 49, ASA-CSSA-SSSA, Madison, WI, USA, 2008.
  31. S. G. Sommer, S. M. McGinn, and T. K. Flesch, “Simple use of the backwards Lagrangian stochastic dispersion technique for measuring ammonia emission from small field-plots,” European Journal of Agronomy, vol. 23, no. 1, pp. 1–7, 2005. View at: Publisher Site | Google Scholar
  32. D. Parker, J. Ham, B. Woodbury et al., “Standardization of flux chamber and wind tunnel flux measurements for quantifying volatile organic compound and ammonia emissions from area sources at animal feeding operations,” Atmospheric Environment, vol. 66, pp. 72–83, 2013. View at: Publisher Site | Google Scholar
  33. R. R. Sharpe and L. A. Harper, “Soil, plant and atmospheric conditions as they relate to ammonia volatilization,” in Nitrogen Economy in Tropical Soils, pp. 149–158, Springer Netherlands, Dordrecht, Netherlands, 1996. View at: Google Scholar
  34. P. A. Matson and R. C. Harriss, Biogenic Trace Gases: Measuring Emissions from Soil and Water, John Wiley & Sons, Hoboken, NJ, USA, 2009.
  35. FAO, Global Estimates of Gaseous Emissions of NH3, NO and N2O from Agricultural Land, International Fertilizer industry Association/Food and Agricultural Organization of the United Nations, Rome, Italy, 2001.
  36. H. Nômmik, “The effect of pellet size on the ammonia loss from urea applied to forest soil,” Plant and Soil, vol. 39, no. 2, pp. 309–318, 1973. View at: Google Scholar
  37. E. Smith, R. Gordon, C. Bourque, and A. Campbell, “Comparison of three simple field methods for ammonia volatilization from manure,” Canadian Journal of Soil Science, vol. 87, no. 4, pp. 469–477, 2007. View at: Publisher Site | Google Scholar
  38. P. Balsari, G. Magrini, and R. Pons, “Ammonia losses from pig slurry storage: first results of field tests,” in Proceedings of the 5th Technical consultation on the ESCORENA, Rome, Italy, 1994. View at: Google Scholar
  39. L. Verdi, P. J. Kuikman, S. Orlandini, M. Mancini, M. Napoli, and A. Dalla Marta, “Does the use of digestate to replace mineral fertilizers have less emissions of N2O and NH3?” Agricultural and Forest Meteorology, vol. 269-270, pp. 112–118, 2019. View at: Publisher Site | Google Scholar
  40. P. M. Ndegwa, V. K. Vaddella, A. N. Hristov, and H. S. Joo, “Measuring concentrations of ammonia in ambient air or exhaust air stream using acid traps,” Journal of Environmental Quality, vol. 38, no. 2, pp. 647–653, 2009. View at: Publisher Site | Google Scholar
  41. Z. H. Wang, X. J. Liu, X. T. Ju, F. S. Zhang, and S. S. Malhi, “Ammonia volatilization loss from surface-broadcast urea: comparison of vented-and closed-chamber methods and loss in winter wheat–summer maize rotation in North China Plain,” Communications in Soil Science and Plant Analysis, vol. 35, no. 19-20, pp. 2917–2939, 2004. View at: Publisher Site | Google Scholar
  42. C. A. Grant, K. R. Brown, L. D. Bailey, and S. Jia, “Short communication: volatile losses of NH3 from surface-applied urea and urea ammonium nitrate with and without the urease inhibitors NBPT or ammonium thiosulphate,” Canadian Journal of Soil Science, vol. 76, no. 3, pp. 417–419, 1996. View at: Publisher Site | Google Scholar
  43. C. D. L. Rawluk, C. A. Grant, and G. J. Racz, “Ammonia volatilization from soils fertilized with urea and varying rates of urease inhibitor NBPT,” Canadian Journal of Soil Science, vol. 81, no. 2, pp. 239–246, 2001. View at: Publisher Site | Google Scholar
  44. X. L. Liao, “The methods of research of gaseous loss of nitrogen fertilizer,” Progress in Soil Science, vol. 11, pp. 49–55, 1983. View at: Google Scholar
  45. S. Monaco, D. Sacco, S. Pelissetti et al., “Laboratory assessment of ammonia emission after soil application of treated and untreated manures,” The Journal of Agricultural Science, vol. 150, no. 1, pp. 65–73, 2012. View at: Publisher Site | Google Scholar
  46. E. Dinuccio, W. Berg, and P. Balsari, “Gaseous emissions from the storage of untreated slurries and the fractions obtained after mechanical separation,” Atmospheric Environment, vol. 42, no. 10, pp. 2448–2459, 2008. View at: Publisher Site | Google Scholar
  47. E. Scotto di Perta, E. Cervelli, S. Faugno, and S. Pindozzi, “Monitoring of NH3 and CH4 emissions from dairy cows under storage conditions,” in Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 35–39, Naples, Italy, October 2019. View at: Google Scholar
  48. E. Scotto di Perta, A. Mautone, M. Oliva, E. Cervelli, and S. Pindozzi, “Influence of treatments and covers on NH3 emissions from dairy cow and buffalo manure storage,” Sustainability, vol. 12, no. 7, pp. 1–11, 2020. View at: Publisher Site | Google Scholar
  49. D. R. Chadwick, J. Martinez, C. Marol, and F. Béline, “Nitrogen transformations and ammonia loss following injection and surface application of pig slurry: a laboratory experiment using slurry labelled with 15N-ammonium,” The Journal of Agricultural Science, vol. 136, no. 2, pp. 231–240, 2001. View at: Publisher Site | Google Scholar
  50. Y. Shi, D. B. Parker, N. A. Cole, B. W. Auvermann, and J. E. Mehlhorn, “Surface amendments to minimize ammonia emissions from beef cattle feedlots,” Transactions of the ASAE, vol. 44, no. 3, p. 677, 2001. View at: Publisher Site | Google Scholar
  51. T. H. Misselbrook, J. M. Powell, G. A. Broderick, and J. H. Grabber, “Dietary manipulation in dairy cattle: laboratory experiments to assess the influence on ammonia emissions,” Journal of Dairy Science, vol. 88, no. 5, pp. 1765–1777, 2005. View at: Publisher Site | Google Scholar
  52. V. K. Vaddella, P. M. Ndegwa, and H. Joo, “Ammonia loss from simulated post-collection storage of scraped and flushed dairy-cattle manure,” Biosystems Engineering, vol. 110, no. 3, pp. 291–296, 2011. View at: Publisher Site | Google Scholar
  53. M. A. Holly, R. A. Larson, J. M. Powell, M. D. Ruark, and H. Aguirre-Villegas, “Greenhouse gas and ammonia emissions from digested and separated dairy manure during storage and after land application,” Agriculture, Ecosystems & Environment, vol. 239, pp. 410–419, 2017. View at: Publisher Site | Google Scholar
  54. G. M. Neerackal, P. M. Ndegwa, H. S. Joo et al., “Effects of anaerobic digestion and solids separation on ammonia emissions from stored and land applied dairy manure,” Water, Air, & Soil Pollution, vol. 226, no. 9, p. 301, 2015. View at: Publisher Site | Google Scholar
  55. Y. Yang, X. Ni, B. Liu et al., “Measuring field ammonia emissions and canopy ammonia fluxes in agriculture using portable ammonia detector method,” Journal of Cleaner Production, vol. 216, pp. 542–551, 2019. View at: Publisher Site | Google Scholar
  56. M. Roelcke, S. X. Li, X. H. Tian, Y. J. Gao, and J. Richter, “In situ comparisons of ammonia volatilization from N fertilizers in Chinese loess soils,” Nutrient Cycling in Agroecosystems, vol. 62, no. 1, pp. 73–88, 2002. View at: Publisher Site | Google Scholar
  57. A. Pacholski, G. Cai, R. Nieder et al., “Calibration of a simple method for determining ammonia volatilization in the field-comparative measurements in Henan province, China,” Nutrient Cycling in Agroecosystems, vol. 74, no. 3, pp. 259–273, 2006. View at: Publisher Site | Google Scholar
  58. A. Pacholski, G.-X. Cai, X.-H. Fan et al., “Comparison of different methods for the measurement of ammonia volatilization after urea application in Henan province, China,” Journal of Plant Nutrition and Soil Science, vol. 171, no. 3, pp. 361–369, 2008. View at: Publisher Site | Google Scholar
  59. D. Gericke, A. Pacholski, and H. Kage, “Measurement of ammonia emissions in multi-plot field experiments,” Biosystems Engineering, vol. 108, no. 2, pp. 164–173, 2011. View at: Publisher Site | Google Scholar
  60. I. Vallis, L. Harper, V. Catchpoole, and K. Weier, “Volatilization of ammonia from urine patches in a subtropical pasture,” Australian Journal of Agricultural Research, vol. 33, no. 1, pp. 97–107, 1982. View at: Publisher Site | Google Scholar
  61. D. R. Lockyer, “A system for the measurement in the field of losses of ammonia through volatilisation,” Journal of the Science of Food and Agriculture, vol. 35, no. 8, pp. 837–848, 1984. View at: Publisher Site | Google Scholar
  62. J. Braschkat, T. Mannheim, D. Horlacher, and H. Marschner, “Measurement of ammonia emissions after liquid manure application: I. Construction of a windtunnel system for measurements under field conditions,” Zeitschrift für Pflanzenernährung und Bodenkunde, vol. 156, no. 5, pp. 393–396, 1993. View at: Publisher Site | Google Scholar
  63. T. Lindvall, O. Thyselius, and L. Thyselius, “Odor reduction for liquid manure systems,” Transactions of the ASAE, vol. 17, no. 3, pp. 508–512, 1974. View at: Publisher Site | Google Scholar
  64. K. Jiang, P. J. Bliss, and T. J. Schulz, “The development of a sampling system for determining odor emission rates from areal surfaces: Part I. Aerodynamic performance,” Journal of the Air & Waste Management Association, vol. 45, no. 11, pp. 917–922, 1995. View at: Publisher Site | Google Scholar
  65. E. C. C. Miola, C. Aita, P. Rochette et al., “Static chamber measurements of ammonia volatilization from manured soils: impact of deployment duration and manure characteristics,” Soil Science Society of America Journal, vol. 79, no. 1, pp. 305–313, 2015. View at: Publisher Site | Google Scholar
  66. X. Wang, J. Jiang, and R. Kaye, “Improvement of a wind-tunnel sampling system for odour and VOCs,” Water Science and Technology, vol. 44, no. 9, pp. 71–77, 2001. View at: Publisher Site | Google Scholar
  67. J. J. Meisinger, A. M. Lefcourt, and R. B. Thompson, “Construction and validation of small mobile wind tunnels for studying ammonia volatilization,” Applied Engineering in Agriculture, vol. 17, no. 3, pp. 375–381, 2001. View at: Publisher Site | Google Scholar
  68. C. K. Saha, G. Zhang, and J.-Q. Ni, “Airflow and concentration characterisation and ammonia mass transfer modelling in wind tunnel studies,” Biosystems Engineering, vol. 107, no. 4, pp. 328–340, 2010. View at: Publisher Site | Google Scholar
  69. C. K. Saha, W. Wu, G. Zhang, and B. Bjerg, “Assessing effect of wind tunnel sizes on air velocity and concentration boundary layers and on ammonia emission estimation using computational fluid dynamics (CFD),” Computers and Electronics in Agriculture, vol. 78, no. 1, pp. 49–60, 2011. View at: Publisher Site | Google Scholar
  70. E. Scotto di Perta, M. A. Agizza, G. Sorrentino, L. Boccia, and S. Pindozzi, “Study of aerodynamic performances of different wind tunnel configurations and air inlet velocities, using computational fluid dynamics (CFD),” Computers and Electronics in Agriculture, vol. 125, pp. 137–148, 2016. View at: Publisher Site | Google Scholar
  71. L. Rong, B. Elhadidi, H. E. Khalifa, P. V. Nielsen, and G. Zhang, “Validation of CFD simulation for ammonia emissions from an aqueous solution,” Computers and Electronics in Agriculture, vol. 75, no. 2, pp. 261–271, 2011. View at: Publisher Site | Google Scholar
  72. M. Ferm, J. K. Schjørring, S. G. Sommer, and S. B. Nielsen, “Field investigation of methods to measure ammonia volatilisation,” Odour and Ammonia Emissions from Livestock Farming, Elsevier Science Publishers Ltd., Barking, Essex, UK, 1991. View at: Google Scholar
  73. T. P. Meyers and D. D. Baldocchi, “Current micrometeorological flux methodologies with applications in agriculture,” Micrometeorology in Agricultural Systems, vol. 47, pp. 381–396, 2005. View at: Google Scholar
  74. A. N. Hristov, M. Hanigan, A. Cole et al., “Review: ammonia emissions from dairy farms and beef feedlots,” Canadian Journal of Animal Science, vol. 91, no. 1, pp. 1–35, 2011. View at: Publisher Site | Google Scholar
  75. R. M. Ferrara, G. Rana, and N. Martinelli, “Caso studio: approccio micrometeorologico per il monitoraggio continuo dei flussi di ammoniaca in ambiente semi-arido,” in Proceedings of the XII National Conference of Agrometeorology, Climate and Agriculture: Adaptation and Mitigation Strategies, pp. 134-135, Sassari, Italy, 2009. View at: Google Scholar
  76. M. Carozzi, B. Loubet, M. Acutis, G. Rana, and R. M. Ferrara, “Inverse dispersion modelling highlights the efficiency of slurry injection to reduce ammonia losses by agriculture in the Po Valley (Italy),” Agricultural and Forest Meteorology, vol. 171-172, pp. 306–318, 2013. View at: Publisher Site | Google Scholar
  77. T. K. Flesch, J. D. Wilson, and E. Yee, “Backward-time Lagrangian stochastic dispersion models and their application to estimate gaseous emissions,” Journal of Applied Meteorology, vol. 34, no. 6, pp. 1320–1332, 1995. View at: Publisher Site | Google Scholar
  78. B. Loubet, M. Carozzi, P. Voylokov, J.-P. Cohan, R. Trochard, and S. Génermont, “Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots,” Biogeosciences, vol. 15, no. 11, pp. 3439–3460, 2018. View at: Publisher Site | Google Scholar
  79. P. Robin, G. Amand, C. Aubert et al., Reference Procedures for the Measurement of Gaseous Emissions from Livestock Houses and Stores of Animal Manure, Institut National de la Recherche Agronomique (INRA), Paris, France, 2010, https://www6.rennes.inra.fr/umrsas/content/download/4310/46810/version/1/file/rapport_final_ADEME_AnimalEmissionProcedures2010.pdf.
  80. S. V. Pedersen, E. Scotto di Perta, S. D. Hafner, A. S. Pacholski, and S. G. Sommer, “Evaluation of a simple, small-plot meteorological technique for measurement of ammonia emission: feasibility, costs, and recommendations,” Transactions of the ASABE, vol. 61, no. 1, pp. 103–115, 2018. View at: Google Scholar
  81. R. Leuning, J. R. Freney, O. T. Denmead, and J. R. Simpson, “A sampler for measuring atmospheric ammonia flux,” Atmospheric Environment, vol. 19, no. 7, pp. 1117–1124, 1967. View at: Google Scholar
  82. J. K. Schjoerring, S. G. Sommer, and M. Ferm, “A simple passive sampler for measuring ammonia emission in the field,” Water, Air, and Soil Pollution, vol. 62, no. 1-2, pp. 13–24, 1992. View at: Publisher Site | Google Scholar
  83. J. Laubach, A. Taghizadeh-Toosi, R. R. Sherlock, and F. M. Kelliher, “Measuring and modelling ammonia emissions from a regular pattern of cattle urine patches,” Agricultural and Forest Meteorology, vol. 156, pp. 1–17, 2012. View at: Publisher Site | Google Scholar
  84. C. W. Wood, S. B. Marshall, and M. L. Cabrera, “Improved method for field‐scale measurement of ammonia volatilization,” Communications in Soil Science and Plant Analysis, vol. 31, no. 5-6, pp. 581–590, 2000. View at: Publisher Site | Google Scholar
  85. J. Lavrsen Kure, J. Krabben, S. Vilms Pedersen, M. Carozzi, and S. G. Sommer, “An assessment of low-cost techniques to measure ammonia emission from multi-plots: a case study with urea fertilization,” Agronomy, vol. 8, no. 11, p. 245, 2018. View at: Publisher Site | Google Scholar
  86. A. Hensen, A. Neftel, D. Famulari et al., in Proceedings of the International Workshop on Ammonia Measurements (IWAM), Ras al Khaimah, UAE, 2015.
  87. J. C. Ryden and D. R. Lockyer, “Evaluation of a system of wind tunnels for field studies of ammonia loss from grassland through volatilisation,” Journal of the Science of Food and Agriculture, vol. 36, no. 9, pp. 781–788, 1985. View at: Publisher Site | Google Scholar
  88. T. Mannheim, J. Braschkat, and H. Marschner, “Measurement of ammonia emission after liquid manure application: II. Comparison of the wind tunnel and the IHF method under field conditions,” Zeitschrift für Pflanzenernährung und Bodenkunde, vol. 158, no. 3, pp. 215–219, 1995. View at: Publisher Site | Google Scholar
  89. T. H. Misselbrook, F. A. Nicholson, B. J. Chambers, and R. A. Johnson, “Measuring ammonia emissions from land applied manure: an intercomparison of commonly used samplers and techniques,” Environmental Pollution, vol. 135, no. 3, pp. 389–397, 2005. View at: Publisher Site | Google Scholar
  90. O. T. Denmead, “Micrometeorological methods for measuring gaseous losses of nitrogen in the field,” in Gaseous loss of Nitrogen from Plant-Soil Systems, pp. 133–157, Springer, Dordrecht, Netherlands, 1983. View at: Google Scholar
  91. P. Balsari, E. Dinuccio, E. Santoro, and F. Gioelli, “Ammonia emissions from rough cattle slurry and from derived solid and liquid fractions applied to alfalfa pasture,” Australian Journal of Experimental Agriculture, vol. 48, no. 2, pp. 198–201, 2008. View at: Publisher Site | Google Scholar
  92. K. Jiang and R. Kaye, “Comparison study on portable wind tunnel system and isolation chamber for determination of VOCs from areal sources,” Water Science and Technology, vol. 34, no. 3-4, pp. 583–589, 1996. View at: Publisher Site | Google Scholar
  93. S. Bittman, L. J. P. van Vliet, C. G. Kowalenko, S. McGinn, D. E. Hunt, and F. Bounaix, “Surface-banding liquid manure over aeration slots: a new low-disturbance method for reducing ammonia emissions and improving yield of perennial grasses,” Agronomy Journal, vol. 97, no. 5, pp. 1304–1313, 2005. View at: Publisher Site | Google Scholar
  94. S. B. Shah, P. W. Westerman, and J. Arogo, “Measuring ammonia concentrations and emissions from agricultural land and liquid surfaces: a review,” Journal of the Air & Waste Management Association, vol. 56, no. 7, pp. 945–960, 2006. View at: Publisher Site | Google Scholar

Copyright © 2020 Ester Scotto di Perta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views354
Downloads339
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.