Backward Lagrangian Stochastic Model Research Articles

Applicability of the inverse dispersion method to measure emissions from animal housings

Abstract. Emissions from agricultural sources substantially contribute to global warming. The inverse dispersion method (IDM) has been successfully used for emission measurements from various agricultural sources. The IDM has also been validated in multiple studies with artificial gas releases mostly in open fields. Release experiments from buildings have rarely been conducted and were partly affected by additional nearby sources of the target gas. Specific release studies for naturally ventilated animal housings are lacking. In this study, a known and predefined amount of methane (CH4) was released from an artificial source inside a barn that mimicked a naturally ventilated dairy housing, and IDM recovery rates, using a backward Lagrangian stochastic (bLS) model, were determined. For concentration measurements, open-path devices (OPs) with a path length of 110 m were placed in a downwind direction of the barn at fetches of 2.0h, 5.3h, 8.6h, and 12h (h equals the height of the highest obstacle), and a 3D ultrasonic anemometer (UA) was placed in the middle of the first three OP paths. Upwind of the barn, an additional OP and a UA were installed. The median IDM recovery rates determined with the UA placed upwind of the barn and the downwind OP ranged between 0.55–0.75. It is concluded that, for the present study case, the effect of the building and a tree in the main wind axis led to a systematic underestimation of the IDM-derived emission rate probably due to deviations in the wind field and turbulent dispersion from the underlying assumptions of the used dispersion model.

Effect of Covering Deep Litter Stockpiles on Methane and Ammonia Emissions Analyzed by an Inverse Dispersion Method

Ammonia (NH3) and methane (CH4) emission rates from cattle deep litter and horse manure deep litter in farm-scale stockpiles were measured using an inverse dispersion method based on the backward Lagrangian Stochastic (bLS) model. The aims of the study were to (i) evaluate the effects of timing and carefulness of covering a stockpile, (ii) measure emissions from handling and storing deep litter, (iii) compare emissions from manure stored in a container and stockpile, (iv) determine emissions from cattle and horse manure deep litter, and (v) evaluate potential effects of pile temperature on emissions. New emission data with relatively high temporal resolution are provided. High variability in CH4 emissions was observed with no clear effects on the timing of covering, even though the temperatures inside the piles were different. NH3 emissions occur immediately after pile establishment and the emissions were highly influenced by the timing of covering. In the first week of storage, covering reduced NH3 emissions by 92 ± 9% when comparing the emissions from a noncovered and totally covered stockpile. This demonstrates the effect of applying a cover immediately to avoid NH3 emissions. The long-term effects of covering strategies as well as the influence of temperature and season are also discussed. For horse manure deep litter, storage in a closed (covered) container instead of a covered stockpile was observed to reduce CH4 emissions by 50 ± 58% and NH3 emissions by 67 ± 37%.

Full-Scale Investigation of Methane and Ammonia Mitigation by Early Single-Dose Slurry Storage Acidification

The effects of early acidification with sulfuric acid of animal slurry in concrete storage tanks to pH 5 on methane and ammonia emissions have been investigated on two cattle and two pig slurry storage tanks in full scale. The tanks’ up- and down-wind concentrations were measured online by cavity ring-down spectroscopy (CRDS). The inverse-dispersion method (IDM), using a backward Lagrangian stochastic (bLS) model, was used to determine the emission rates. The results show that CH4 emissions after single-dose acidification of slurry tanks in the early summer were reduced by 95 ± 14% and 95% ± 6% for cattle and pig manure, respectively, compared to the emissions measured before acidification. The reductions occurred even though pH increased during the storage with and without addition of fresh slurry. After acidification, NH3 emissions were comparable to or lower than when a natural crust was present. A supplementary experiment on small-scale slurry containers was conducted to investigate the pH development when fresh slurry was added to the acidified slurry. The influence of pH development on the long-term emission dynamics of methane is discussed by comparing the large-scale and small-scale studies. The study presents the first full-scale documentation that slurry acidification is a promising technology for mitigation of methane from slurry storage.

Using the inverse dispersion method to determine methane emissions from biogas plants and wastewater treatment plants with complex source configurations

Wastewater treatment plants (WWTPs) and biogas plants (BGPs) are significant sources of methane (CH4), with a combined share of around 40% within the waste sector of the Swiss national emission inventory. We conducted whole-plant CH4 emission measurements at two WWTPs and four agricultural BGPs in Switzerland using the inverse dispersion method (IDM). This involved open-path concentration measurements up- and downwind of the plant in combination with a backward Lagrangian stochastic (bLS) model. WWTPs in particular consist of multiple CH4 sources with different areas and emission strengths. For the combination of the individual emission sources in the bLS modelling, three different calculation approaches with different levels of detail were applied: (i) single source over enveloping polygon area, (ii) uniform emission density for all individual source areas, (iii) specified relative weighting of individual sources based on literature data. Average CH4 emissions for WWTP-1 and WWTP-2 were 0.82 kg h−1 and 0.61 kg h−1 and scaled to population equivalents (PE) 166 g PE−1 y−1 and 381 g PE−1 y−1, respectively. BGPs CH4 emissions varied between 0.39 kg h−1 and 2.22 kg h−1, corresponding to less than 5% of the plants’ CH4 production. The highest numbers were due to measurements during other than normal operating conditions. The emissions of WWTPs and BGPs comply with literature values. Approach (iii) with source weighting led to a difference of up to 43% for the two WWTPs compared to the assumption of uniform emissions. Furthermore, we demonstrate how multiple open-path concentration measurements can be combined and how the measurements can be corrected for nearby external CH4 sources not belonging to the investigated plants. The results of the present study contribute to improved emission data from the waste sector.

Assessment of the inverse dispersion method for the determination of methane emissions from a dairy housing

Methane (CH4) emissions from dairy housings, mainly originating from enteric fermentation of ruminating animals, are a significant source of greenhouse gases. The quantification of emissions from naturally ventilated dairy housings is challenging due to the spatial distribution of sources (animals, housing areas) and variable air exchange. The inverse dispersion method (IDM) is a promising option, which is increasingly used to determine gaseous emissions from stationary sources, as it offers high flexibility in the application at reasonable costs. We used a backward Lagrangian stochastic model combined with concentration measurements by open-path tunable diode laser spectrometers placed up- and downwind of a naturally ventilated housing with 40 dairy cows to determine the CH4 emissions. The average emissions per livestock unit (LU) were 317 (±44) g LU−1 d−1 and 267 (±43) g LU−1 d−1 for the first and second campaign, in September – October and November – December, respectively. For each campaign, inhouse tracer ratio measurements (iTRM) were conducted in parallel during two subperiods. For simultaneous measurements, IDM showed average emissions which were lower by 8% and 1% than that of iTRM, respectively, for the two campaigns. The differences are within the uncertainty range of any of the two methods. The IDM CH4 emissions were further analysed by wind direction and atmospheric stability and no differences in emissions were found. Overall, IDM showed its aptitude to accurately determine CH4 emissions from dairy housings or other stationary sources if the site allows adequate placement of sensors up- and downwind in the prevailing wind direction. To acquire reliable emission data, depending on the data loss during measurements due to quality filtering or instrument failure, a measuring time of at least 10 days is required.

Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Nitrous oxide (N2O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological methods has been limited. This study implemented a backward Lagrangian stochastic (bLS) technique to simultaneously and near-continuously measure N2O emissions from four adjacent fields of approximately 1 ha each. A scanning open-path Fourier-transform infrared spectrometer (OP-FTIR), edge-of-field gas sampling and measurement, locally measured turbulence, and bLS emissions modeling were integrated to measure N2O emissions from four adjacent fields of maize production using different management in 2015. The maize N management treatments consisted of 220 kg NH3-N ha−1 applied either as one application in the fall after harvest or spring before planting or split between fall after harvest and spring before planting. The field preparation treatments evaluated were no-till (NT) and chisel plow (ChP). This study showed that the OP-FTIR plus bLS method had a minimum detection limit (MDL) of ±1.2 µg m−2 s−1 (3σ) for multi-source flux measurements. The average N2O emission of the four treatments ranged from 0.1 to 2.3 µg m−2 s−1 over the study period of 01 May to 11 June after the spring fertilizer application. The management of the full-N rate applied in the fall led to higher N2O emissions than the split-N rates applied in the fall and spring. Based on the same N application, the ChP practice tended to increase N2O emissions compared with NT. Advection of N2O from adjacent fields influenced the estimated emissions; uncertainty (1σ) in emissions was 0.5 ± 0.3 µg m−2 s−1 if the field of interest received a clean measured upwind background air, but increased to 1.1 ± 0.5 µg m−2 s−1 if all upwind sources were advecting N2O over the field of interest. Moreover, higher short-period emission rates (e.g., half-hour) were observed in this study by a factor of 1.5~7 than other micrometeorological studies measuring N2O-N loss from the N-fertilized cereal cropping system. This increment was attributed to the increase in N fertilizer input and soil temperature during the measurement. We concluded that this method could make near-continuous “simultaneous” flux comparisons between treatments, but further studies are needed to address the discrepancies in the presented values with other comparable N2O flux studies.

Ammonia Emissions from a Western Open-Lot Dairy

Manure on dairies is the second largest agricultural source of ammonia emissions. Ammonia (NH3) emissions were measured at a 3400-cow open-lot dairy in Texas using a backward Lagrangian Stochastic model in combination with measurements from long-path tunable diode lasers and on-site sonic anemometers. Measurements were made for multiple weeks at a time for most seasons over two years. Both 30-min and daily average emissions were influenced by air temperature consistent with a van ’t Hoff equation. Emissions were also linearly related to saturation vapor density deficit. The influence of temperature on NH3 solubility, expressed by the van ’t Hoff temperature correction function, decreased as the water vapor deficit increased. The mean annual daily NH3 emissions at the farm was estimated at 82 g NH3 d−1 per animal (105 g NH3 AU−1 d−1, 1 AU = 500 kg) with mean emission during the summer of 124 g NH3 d−1 per animal (159 g NH3 AU−1 d−1). A distinct diurnal pattern in NH3 emissions was consistent with diurnal patterns in wind speed, saturation vapor density deficit and air temperature. The mean daytime emissions were twice the mean nighttime emissions. Additional studies are needed to evaluate the frequency of high emission days during the summer.

Uncertainty in using dispersion models to estimate methane emissions from manure lagoons in dairies

Manure lagoons in dairies make significant contributions to emissions of methane, a major greenhouse gas; however, there is a high level of uncertainty in these emissions. In this paper, we apply dispersion models in combination with a unique sampling strategy, which involves stationary measurements at multiple points around the lagoons to estimate methane emissions from manure lagoons located in two dairies, one in Southern California and the other in Central California. We then estimate the uncertainty associated with the results from this approach by interpreting our measurements with two dispersion models, a numerical Eulerian model (EN) and a backward Lagrangian stochastic (bLS) model. The range of emissions inferred from these two models is a measure of uncertainty related to differences in the formulation of these models. We also estimate 95% confidence intervals for the emission estimates from each of the models by bootstrapping the residuals between model estimates and measurements. Both models explain more than 85% of the variance of the methane concentrations measured at the two dairies. For the Southern California dairy (1066 milking cows), the 95% confidence interval of the emission rate inferred by the EN model is 282 kg/d to 482 kg/d. The corresponding interval for the bLS model is 174 kg/d to 246 kg/d. The best fit value from the EN model is about 1.9 times that from the bLS model. For the Central California dairy (3200 milking cows), the best emission rates from the two models differ by about 10%. The emission rate inferred by the EN model ranges from 3198 kg/d to 5312 kg/d, and that from the bLS ranges from 2943 kg/d to 4977 kg/d. Our results are consistent with methane emissions derived from information on dairy cow population and manure management practices at these two dairies. These results suggest this measurement technique is easily deployed and effective at quantifying uncertainties associated with methane emissions from manure lagoons.

Ammonia emissions from irrigated pastures on Solonetz in Victoria, Australia

This paper reports ammonia (NH3) emissions from irrigated, fertilized pastures grazed intensively by dairy cattle in Victoria, Australia. Irrigation bays (65 × 365 m) were grazed in rotation by 315 dairy cattle and daily emissions were measured in summer and autumn. After grazing for a few days, the bays were fertilized with urea at 50 kg N ha−1 and irrigated with 50 mm of water. Passive NH3 samplers were used to measure horizontal fluxes of NH3 from different sectors of the bays directly and the corresponding surface emissions were calculated from the sampler data using a backward Lagrangian stochastic (bLs) model of atmospheric dispersion. Ammonia emissions from the whole herd during the grazing phases were 14.8 kg NH3−N in summer, but only 2.0 kg NH3−N in autumn, corresponding to emission factors of 12% and 1% for an assumed annual input of nitrogen (N) in excreta of 150 kg N head−1 yr−1. Only 1% of the urea-N was lost through NH3 volatilization when irrigation followed fertilization immediately, but 6% was lost when irrigation was delayed for 3 days. The bLs model appears to be a useful tool for assessing gas emissions from dairy farm operations.

High temporal resolution measurements of ammonia emissions following different nitrogen application rates from a rice field in the Taihu Lake Region of China

Ammonia emission is one of the dominant pathways of nitrogen fertilizer loss from rice fields in China. It is difficult to measure ammonia emissions by high-frequency sampling with the chamber methods widely used in China, which is of great significance for investigating the environmental effects on the ammonia emissions. The chamber methods also can not accurately determine the ammonia emissions. In this study, the backward Lagrangian stochastic dispersion model, with ammonia concentrations continuously measured by the open-path tunable diode laser absorption spectroscopy technique, was used to determine ammonia emissions from a rice field after fertilizer application at excessive (270 kg N ha−1) and appropriate (210 kg N ha−1) rates in the Taihu Lake Region of China. High temporal resolution measurements of ammonia emissions revealed that high intraday fluctuations of ammonia emissions were significantly affected by the meteorological conditions. Multiple regression analysis showed a dominant solar radiation dependence of intraday ammonia emission cycles, especially during the rice panicle formation stage. The NH4+-N concentrations of the surface water of the rice field were found to be the decisive factor that influenced interday dynamics of ammonia emissions. Accurate quantifications of ammonia emissions indicated that the total ammonia losses under appropriate nitrogen application rate were 27.4 kg N ha−1 during the rice tillering stage and 11.2 kg N ha−1 during the panicle formation stage, which were 29.4% and 17.0% less than those under traditional excessive nitrogen application rate used by the local farmers, respectively. The ammonia loss proportions during the rice panicle formation stage were significantly lower than those of the tillering stage, which might be due to different nitrogen application rates and environmental effects during the two stages. This study indicated that the open-path tunable diode laser absorption spectroscopy technique could facilitate the investigation of high temporal resolution dynamic of ammonia emissions from farmland and the environmental influence on the ammonia emissions.

A fast forecasting method for PM2.5 concentrations based on footprint modeling and emission optimization

We propose a method for fast forecasting of PM2.5 concentrations in the North China Plain based on footprint (source–receptor relationship) modeling and emission inventory inversion. A backward Lagrangian stochastic particle dispersion model was employed to derive the footprint, using meteorological fields and boundary layer parameters provided by the WRF model. An analytical Bayesian inversion model was used to optimize existing emission inventories using long-term, multi-site PM2.5 monitoring data. The fast simulation of PM2.5 concentrations was based on the optimized inventory and the footprint results. Two-year simulations were carried out for six cities (Baoding, Beijing, Dezhou, Shijiazhuang, Tianjin, and Tangshan), with model establishment and emission inversion in the first year (2015) and test forecasting in the second year (2016). Promising simulation results were obtained even when using the primary emission inventory. For all six cities, the fractions of simulations of measurements within a factor of two ranged from 0.49 to 0.68, and the correlation coefficients ranged from 0.40 to 0.56 in 2015. The model also well reproduced temporal variations in PM2.5 concentrations in Beijing during severe haze episodes in the winter of 2015. Great improvement was achieved for the simulations by using the optimized emission inventory. The proportion of samples that met the PM model criteria increased from 88% to 97%, and the proportion that achieved the modeling goal increased from 25% to 44%. This method maintained its high forecasting skill in 2016, with 92% and 46% of samples meeting the PM model criteria and achieving the modeling goal, respectively. However, the corresponding values were86% and 39% if emission optimization was not applied.

Comparison of backward Lagrangian stochastic model with micrometeorological mass balance method for measuring ammonia emissions from rice field

A field experiment was conducted to assess the accuracy of the backward Lagrangian stochastic (BLS) model combined with an open-path tunable diode laser (OPTDL) system for estimating ammonia emissions from rice field by comparing with the micrometeorological mass balance (MMB) method. During the experimental period, the average height of rice was 0.65 m, and the average height of laser path was 1.3 m. The results showed that there was no significant difference between the ammonia emission rates estimated by the two methods, indicating no significant effect of tall rice on the accuracy of the BLS model. The high temporal resolution data showed that ammonia emissions had a dramatic diurnal cycle: rise at daytime and fall at night. Although the OPTDL technique was not able to measure ammonia concentrations at low emission rates due to relatively high detection limit, the total ammonia loss estimated by the BLS model was not significant different from that of the MMB method. The results demonstrate the effectiveness of the BLS model with the OPTDL technique for estimating ammonia emissions from farmland covered with tall vegetation. Within the acceptable accuracy, the relatively low laser path height is recommended for farmland with tall vegetation due to relatively high detection limit of the OPTDL technique and low ammonia concentration of farmland.

Ammonia Emissions Measured Using Two Different GasFinder Open-Path Lasers

The challenges of accurately measuring in situ ammonia (NH3) losses from agricultural systems are well known. Using an open path laser coupled with a backward Lagrangian stochastic dispersion model is a promising approach for quantifying both point- and area-sources; however, this approach requires the open path laser to detect low NH3 concentrations and small concentration differences. In this study, we compared the new GasFinder3 open path laser (Boreal laser Inc., Edmonton, Canada) with the GasFinder2 sensor, the previous version. The study took place at two locations: an outdoor open-air manure compost site, and a field of wheat stubble which was fertilized with urea ammonium nitrate. Results showed the two lasers reported similar concentrations during three days of measurements at the compost site, but differed at the field site, where concentrations were close to the minimum detection limit. The GasFinder3 had a lower standard deviation under all conditions, especially with low wind speed and high relative humidity.

Grazing-related nitrous oxide emissions: from patch scale to field scale

Abstract. Grazed pastures are strong sources of the greenhouse gas nitrous oxide (N2O). The quantification of N2O emissions is challenging due to the strong spatial and temporal variabilities of the emission sources and so N2O emission estimates are very uncertain. This study presents N2O emission measurements from two grazing systems in western Switzerland over the grazing season of 2016. The 12 dairy cows of each herd were kept in an intensive rotational grazing management. The diet for the two herds of cows consisted of different protein-to-energy ratios (system G: grass only diet; system M: grass with additional maize silage) resulting in different nitrogen (N) excretion rates. The N in the excretion was estimated by calculating the animal nitrogen budget taking into account the measurements of feed intake, milk yield, and body weight of the cow herds. Directly after the rotational grazing phases, background and urine patches were identified based on soil electric conductivity measurements while fresh dung patches were identified visually. The magnitude and temporal pattern of these different emission sources were measured with a fast-box (FB) chamber and the field-scale fluxes were quantified using two eddy covariance (EC) systems. The FB measurements were finally upscaled to the field level and compared to the EC measurements for quality control by using EC footprint estimates of a backward Lagrangian stochastic dispersion model. The comparison between the two grazing systems was performed during emission periods that were not influenced by fertilizer applications. This allowed the calculation of the excreta-related N2O emissions per cow and grazing hour and resulted in considerably higher emissions for system G compared to system M. Relating the found emissions to the excreta N resulted in excreta-related emission factors (EFs) of 0.74±0.26 % for system M and 0.83±0.29 % for system G. These EF values were thus significantly smaller compared to the default EF of 2 % provided by the IPCC guidelines for cattle excreta deposited on pasture. The measurements showed that urine patch emission dominated the field-scale fluxes (57 %), followed by significant background emissions (38 %), and only a small contribution of dung patch emission (5 %). The resulting source-specific EFs exhibited a clear difference between urine (1.12±0.43 %) and dung (0.16±0.06 %), supporting a disaggregation of the grazing-related EFs by excreta type in emission inventories. The study also highlights the advantage of a N-optimized diet, which resulted in reduced N2O emissions from animal excreta.

Ammonia emission measurements of an intensively grazed pasture

Abstract. The quantification of ammonia (NH3) emissions is still a challenge and the corresponding emission factor for grazed pastures is uncertain. This study presents NH3 emission measurements of two pasture systems in western Switzerland over the entire grazing season 2016. During the measurement campaign, each pasture system was grazed by 12 dairy cows in an intensive rotational management. The cow herds on the two pastures differed in the energy to protein balance of the diet. NH3 concentrations were measured upwind and downwind of a grazed subplot with line-integrating open path instruments that were able to retrieve small horizontal concentration differences (< 0.2 µg NH3 m−3). The NH3 emission fluxes were calculated by applying a backward Lagrangian stochastic (bLS) dispersion model to the difference of paired concentration measurements and ranged from 0 to 2.5 µg N–NH3 m−2 s−1. The fluxes increased steadily during a grazing interval from previous non-significant values to reach maximum emissions at the end of the grazing interval. Afterwards they decreased exponentially to near zero-values within 3–5 days. A default emission curve was calculated for each of the two systems and adopted to each rotation in order to account for missing data values and to estimate inflow disturbances due to grazing on upwind paddocks. Dung and cow location were monitored to account for the non-negligible inhomogeneity of cow excreta on the pasture. The average emission (± SD of individual rotation values) per grazing hour was calculated as 0.64±0.11 g N–NH3 cow−1 h−1 for the herd with the N-balanced diet (system M) and 1.07±0.06 g N–NH3 cow−1 h−1 for the herd with the protein-rich grass-only diet (system G). Surveys of feed intake, body weight and milk yield of the cow herds were used to estimate the nitrogen (N) excretion by an animal N budget model. Based on that, mean relative emission factors of 6.4±2.0 % and 8.7±2.7 % of the applied urine N were found for the systems M and G, respectively. The results can be used to validate the Swiss national emission inventory and demonstrate the positive effect of an N-balanced diet on pasture NH3 emissions.

Comparing methane emissions estimated using a backward-Lagrangian stochastic model and the eddy covariance technique in a beef cattle feedlot

Accurate methodologies to measure emissions of greenhouse gases (GHG) from livestock systems are necessary to improve the emission coefficients used in national GHG inventories and to evaluate mitigation strategies. The objective of this study was to compare methane (CH4) emissions estimated using the eddy covariance (EC) technique and a backward-Lagrangian stochastic (bLS) model. A closed-path EC system was used to measure CH4 fluxes in a commercial beef cattle feedlot. The EC fluxes were scaled from the feedlot to the animal scale using a footprint analysis. The EC measurements of CH4 concentration and wind data were used with the bLS model to infer CH4 emissions. The average CH4 emissions (±standard deviation) during the experiment were 87 (±30) g animal−1 d−1 and 85 (± 27) g animal−1 d−1 for EC and bLS techniques, respectively. These values are consistent with the results from previous studies with similar animal and feed characteristics. Both techniques were able to capture a pronounced daytime and nighttime variation in CH4 emissions, with higher CH4 emissions during the day and lower emissions at night. Our results indicate that the eddy covariance technique combined with footprint models can be successfully used to accurately measure enteric CH4 from cattle.

Accounting for Field-Scale Dry Deposition in Backward Lagrangian Stochastic Dispersion Modelling of NH3 Emissions

A controlled ammonia (NH3) release experiment was performed at a grassland site. The aim was to quantify the effect of dry deposition between the source and the receptors (NH3 measurement locations) on emission rate estimates by means of inverse dispersion modelling. NH3 was released for three hours at a constant rate of Q = 6.29 mg s−1 from a grid of 36 orifices spread over an area of 250 m2. The increase in line-integrated NH3 concentration was measured with open-path optical miniDOAS devices at different locations downwind of the artificial source. Using a backward Lagrangian stochastic (bLS) dispersion model (bLSmodelR), the fraction of the modelled release rate to the emitted NH3 ( Q bLS / Q ) was calculated from the measurements of the individual instruments. Q bLS / Q was found to be systematically lower than 1, on average between 0.69 and 0.91, depending on the location of the receptor. We hypothesized that NH3 dry deposition to grass and soil surfaces was the main factor responsible for the observed depletion of NH3 between source and receptor. A dry deposition algorithm based on a deposition velocity approach was included in the bLS modelling. Model deposition velocities were evaluated from a ‘big-leaf’ canopy resistance analogy. Canopy resistances (generally termed R c ) that provided Q bLS / Q = 1 ranged from 75 to 290 s m−1, showing that surface removal of NH3 by dry deposition can plausibly explain the original underestimation of Q bLS / Q . The inclusion of a dry deposition process in dispersion modelling is crucial for emission estimates, which are based on concentration measurements of depositing tracers downwind of homogeneous area sources or heterogeneously-distributed hot spots, such as, e.g., urine patches on pastures in the case of NH3.

Evaluation of a Simple, Small-Plot Meteorological Technique for Measurement of Ammonia Emission: Feasibility, Costs, and Recommendations

Abstract. Ammonia emission reduces the reliability and nitrogen (N) fertilizer efficiency of animal manure and mineral fertilizers applied to fields. The loss of ammonia to the atmosphere is frequently compensated for by costly over-application of N fertilizers. New technologies to reduce ammonia emission are regularly developed, and their efficacy needs to be tested using accurate methods. To date, a major obstacle to many available emission measurement techniques is the requirement of large plot sizes of homogeneous surface characteristics, which particularly is a challenge to the number of plot-level replicates that can be carried out on a field providing uniform surface characteristics throughout. The objectives of this research were to test three different methods for measuring NH3 flux when applied to small plots (<315 m2) by comparison with conventional micrometeorological methods and to determine the labor intensity and expenses related to the respective methods in their entirety. The integrated horizontal flux (IHF) method and the ZINST method were used with passive flux Leuning samplers as micrometeorological reference methods. As examples of conventional small-plot emission measurement techniques, wind tunnels measuring gas-phase ammonia using ALPHA passive diffusion samplers and a flux chamber method using Dräger tubes for measurements of ammonia concentration (DTM) were used. As an inexpensive alternative small-plot method, we studied the feasibility of applying ALPHA passive diffusion samplers and battery-driven cup anemometers at ZINST height on small source areas (<315 m2), coupled with a backward Lagrangian stochastic (bLS) dispersion model to calculate emission fluxes (referred to as the AbLS method). When exposure duration was appropriate and weather conditions were not extreme, tests showed no significant difference in NH3 emission fluxes measured with AbLS, compared to those obtained with IHF and ZINST using Leuning samplers. However, the AbLS method did not give reliable emission measurements in periods with high wind speeds and heavy rain. It was also shown that the AbLS method provided valid results when reducing the plot radius from the standard 20 m to 10 m, or even 5 m, provided that the ALPHA samplers were exposed for at least 5 or 6 h. Emission from 200 kg urea-N ha-1 was between 20 and 30 kg N ha-1 in the two trials. The cost for one study running for one week using the ZINST or bLS methodology, including equipment for four plots and eight measurement intervals, was $2785 if horizontal fluxes were measured using the ALPHA samplers, compared to $12,301 using the Leuning samplers and $13,928 using gas washing bottles. Using the DTM flux chamber method once is a little more expensive than using the AbLS method, but less expensive if the cost of purchasing the equipment is distributed over five studies in five years. Using wind tunnels is as costly as measuring emissions with the Leuning samplers or gas washing bottles using the bLS or ZINST method. Keywords: ALPHA samplers, Ammonia emission, AbLS, bLS method, DTM method, IHF method, Labor cost, Passive ammonia samplers, Wind tunnels.

Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling

Abstract. Ammonia (NH3) fluxes were estimated from a field being grazed by dairy cattle during spring by applying a backward Lagrangian stochastic model (bLS) model combined with horizontal concentration gradients measured across the field. Continuous concentration measurements at field boundaries were made by open-path miniDOAS (differential optical absorption spectroscopy) instruments while the cattle were present and for 6 subsequent days. The deposition of emitted NH3 to clean patches on the field was also simulated, allowing both net and gross emission estimates, where the dry deposition velocity (vd) was predicted by a canopy resistance (Rc) model developed from local NH3 flux and meteorological measurements. Estimated emissions peaked during grazing and decreased after the cattle had left the field, while control on emissions was observed from covariance with temperature, wind speed and humidity and wetness measurements made on the field, revealing a diurnal emission profile. Large concentration differences were observed between downwind receptors, due to spatially heterogeneous emission patterns. This was likely caused by uneven cattle distribution and a low grazing density, where hotspots of emissions would arise as the cattle grouped in certain areas, such as around the water trough. The spatial complexity was accounted for by separating the model source area into sub-sections and optimising individual source area coefficients to measured concentrations. The background concentration was the greatest source of uncertainty, and based on a sensitivity/uncertainty analysis the overall uncertainty associated with derived emission factors from this study is at least 30–40 %.Emission factors can be expressed as 6 ± 2 g NH3 cow−1 day−1, or 9 ± 3 % of excreted urine-N emitted as NH3, when deposition is not simulated and 7 ± 2 g NH3 cow−1 day−1, or 10 ± 3 % of excreted urine-N emitted as NH3, when deposition is included in the gross emission model. The results suggest that around 14 ± 4 % of emitted NH3 was deposited to patches within the field that were not affected by urine or dung.

Evaluation of a backward Lagrangian stochastic model for determining surface ammonia emissions

The rate of ammonia emissions from a small circular plot of maize was estimated by three procedures: (i) by inverse dispersion (based on upwind and downwind laser concentration-detectors interpreted using a backward Lagrangian stochastic model); (ii) by the height-integrated horizontal flux measured by point flux-detectors arrayed along the axis of the plot (i.e., the integrated horizontal flux method, IHF); and (iii) by extrapolation from static flux chambers (SC). The results indicated that the estimates made by the open-path tunable diode laser (OPTDL) system combined with the backward Lagrangian stochastic (BLS) model were statistically equivalent to those made by the IHF method. The ammonia fluxes estimated by the OPTDL-BLS technique were only 2.3% higher than those from the IHF method. Although the OPTDL technique failed to monitor concentration differences at low ammonia fluxes due to its detection limit, the OPTDL-BLS technique estimated the total ammonia loss to be only 10.9% less than the IHF results. The SC method was found to underestimate ammonia emissions and cumulative ammonia loss significantly compared with both the OPTDL-BLS and IHF methods Although the ammonia emissions estimated by the OPTDL-BLS technique showed a similar emission pattern to those estimated by the IHF method, the former provided an opportunity to estimate the diurnal pattern of ammonia emissions and to understanding the primary driving factors. A clear diurnal cycle and a dominant net solar radiation dependence in ammonia emissions were found.