Lagrangian Stochastic Dispersion Model Research Articles

QES-Plume v1.0: a Lagrangian dispersion model

Abstract. Low-cost simulations providing accurate predictions of transport of airborne material in urban areas, vegetative canopies, and complex terrain are demanding because of the small-scale heterogeneity of the features influencing the mean flow and turbulence fields. Common models used to predict turbulent transport of passive scalars are based on the Lagrangian stochastic dispersion model. The Quick Environmental Simulation (QES) tool is a low-computational-cost framework developed to provide high-resolution wind and concentration fields in a variety of complex atmospheric-boundary-layer environments. Part of the framework, QES-Plume, is a Lagrangian dispersion code that uses a time-implicit integration scheme to solve the generalized Langevin equations which require mean flow and turbulence fields. Here, QES-Plume is driven by QES-Winds, a 3D fast-response model that computes mass-consistent wind fields around buildings, vegetation, and hills using empirical parameterizations, and QES-Turb, a local-mixing-length turbulence model. In this paper, the particle dispersion model is presented and validated against analytical solutions to examine QES-Plume’s performance under idealized conditions. In particular, QES-Plume is evaluated against a classical Gaussian plume model for an elevated continuous point-source release in uniform flow, the Lagrangian scaling of dispersion in isotropic turbulence, and a non-Gaussian plume model for an elevated continuous point-source release in a power-law boundary-layer flow. In these cases, QES-Plume yields a maximum relative error below 6 % when compared with analytical solutions. In addition, the model is tested against wind-tunnel data for a uniform array of cubical buildings. QES-Plume exhibits good agreement with the experiment with 99 % of matched zeros and 59 % of the predicted concentrations falling within a factor of 2 of the experimental concentrations. Furthermore, results also emphasize the importance of using high-quality turbulence models for particle dispersion in complex environments. Finally, QES-Plume demonstrates excellent computational performance.

Heterogenous Canopy in a Lagrangian-Stochastic Dispersion Model for Particulate Matter from Multiple Sources over the Haifa Bay Area

The Haifa Bay area (HBA) is a major metropolitan area in Israel, which consists of high volume transportation routes, major industrial complexes, and the largest international seaport in Israel. These, which lie relatively near densely populated residential areas, result in a multitude of air pollution sources, many of whose emissions are in the form of particulate matter (PM). Previous studies have associated exposure to such PM with adverse health effects. This potential consequence serves as the motivation for this study whose aim is to provide a realistic and detailed three-dimensional concentration field of PM, originating simultaneously from multiple sources. The IIBR in-house Lagrangian stochastic pollutant dispersion model (LSM) is suitable for this endeavor, as it describes the dispersion of a scalar by solving the velocity fluctuations in high Reynolds number flows. Moreover, the LSM was validated in urban field experiments, including in the HBA. However, due to the fact that the multiple urban sources reside within the canopy layer, it was necessary to integrate into the LSM a realistic canopy layer model that depicts the actual effect of the roughness elements’ drag on the flow and turbulent exchange of the urban morphology. This was achieved by an approach which treats the canopy as patches of porous media. The LSM was used to calculate the three-dimensional fields of PM10 and PM2.5 concentrations during the typical conditions of the two workday rush-hour periods. These were compared to three air quality monitoring stations located downstream of the PM sources in the HBA. The LSM predictions for PM2.5 satisfy all acceptance criteria. Regarding the PM10 predictions, the LSM results comply with three out of four acceptance criteria. The analysis of the calculated concentration fields has shown that the PM concentrations up to 105 m AGL exhibit a spatial pattern similar to the ground level. However, it decreases by a factor of two at 45 m AGL, while, at 105 m, the concentration values are close to the background concentrations.

Evaluation of a Gaussian dispersion transformation technique for tomographic mapping of the concentration field of atmospheric chemicals using multi-path optical remote sensing.

Horizontal radial plume mapping is a cost-effective optical remote sensing method for sensitive mapping concentration distribution of atmospheric chemicals in real time. However, its sparse sampling poses challenges for reconstruction algorithms. Neither non-smooth nor smooth algorithms can recover the realistic plume shape. A new approach called Gaussian dispersion transformation (GDT) has been proposed. It first reconstructs the emission rates from unknown sources. Then concentrations are calculated through a transformation matrix defined by a Gaussian dispersion model. Smoothness regularization is also applied during the reconstruction. The method was evaluated by using randomly generated maps. It shows significant improvement over a reconstructed plume shape. The nearness shows 72%-117% better than the non-negative least-square (NNLS) algorithm and 15%-26% better than the low third derivative (LTD) algorithm. A controlled-release field experiment of methane was also conducted. The realistic concentration distribution was calculated by using a Lagrangian stochastic dispersion model. The GDT algorithm successfully recovered the realistic plume shape. The nearness shows approximately 16% better than the NNLS and the LTD algorithms. Finally, a sensitivity analysis shows that the wind direction and atmospheric stability are the main parameters that affect the performance of the GDT algorithm.

Comparison of forward and backward Lagrangian transport modelling to determine methane emissions from anaerobic digestion facilities

In biogas plants, where biological treatment of organic matter by anaerobic digestion (AD) is performed, as well as in plants for upgrading of biogas to biomethane, there might be emissions to air from different parts of the plants. Precise and comparable methods to quantify methane (CH 4 ) emissions from biogas plants are necessary to evaluate mitigation strategies and to generate more accurate emissions factors for national emission inventories in the context of the United Nations Framework Convention on Climate Change (UNFCCC) reporting. The main objective of this study was to provide recommendations for the measurement and modelling procedures of inverse dispersion modelling (IDM) to assure comparability of CH 4 emissions among diverse approaches and AD facilities. Two Lagrangian stochastic dispersion models were used operating in forward (LASAT) and backward (WindTrax) mode based on open-path concentration and meteorological data acquired by three international measurement teams at two biogas plants. The sensitivity analysis of the modelling systems regarding input parameters and setups (e.g. source configuration, terrain, sampling distance from the site, different combinations of wind data) showed that in WindTrax (backward mode) the measurement speed of meteorological parameters (via three-dimensional ultrasonic anemometer) had the largest influence on the calculated emission estimates, while in LASAT (forward mode) the assumption of the source configuration greatly affected the flux estimates. The used concentration data (e.g. location of observations, measurement mode of background concentration) was critically important to the success of IDM. The CH 4 emission rates from the AD facility in flat terrain (plant 1) calculated with both Lagrangian models by IDM showed good agreement with the DIAL (Differential Absorption Light Detection And Ranging) method and methane release tests (deviation in the range of 10–20%) when using best fit configurations (e.g. volume source in LASAT, area source in WindTrax, optimal measurement fetch). In moderately complex terrain (plant 2), the retrieval rates of controlled CH 4 releases, and thus the certainty of flux estimates of both dispersion models tended to decrease without the use of a terrain model. • First comparison of emission results inferred with LASAT and WindTrax. • Concentration and meteorological data obtained by three independent measurement teams. • Evaluation of sensitivity of the modelling system to input parameters and setups. • Recommendations for measurement and modelling procedure. • Uncertainty of ±10–20% for both models in flat terrains using best fit configurations.

Methane emissions from a waste treatment site: Numerical analysis of aircraft-based data

Methane (CH4) is an important greenhouse gas. Emissions from landfills and waste treatment sites are regarded as the third largest anthropogenic source in the world. Precise estimates of CH4 emissions depend on a well-designed sampling procedure and detailed analyses. This study estimates CH4 emissions from a waste treatment site by analyzing the data from a flight, which was carried out in Eastern Ontario, Canada, on May 6, 2011. The CH4 dispersion plume at about 140 m above ground level downwind of the source is analyzed with data from 14 flight tracks. The observation period of about 72 min is characterized by unstable boundary layer conditions, strong wind, and shifting wind direction. To replicate the boundary layer turbulence and the CH4 dispersion during the measurement period, a combined large-eddy simulation (LES) and Lagrangian stochastic (LS) particle dispersion modeling method is used. A preliminary simulation run with mean meteorological variables during the experiment's duration is carried out. The results reproduced the shifts in wind direction well, in addition to the dispersion plume, but in a smaller magnitude in comparison to the measured data. Therefore, two sets of LES runs are carried out to represent the boundary layer turbulence before and after the wind shift. Another deficiency in the LES runs is that the simulated turbulent velocity variances are systematically smaller than the observed counterparts. In response to this discrepancy, additional terms are incorporated into the LS dispersion model so as to represent the turbulence dispersion process better. With these treatments, the LES-LS modeling results replicated the dispersion plume both in lateral width and in concentration distribution shape. Then, by using the simulated relationship between source and concentrations/vertical fluxes in space, the CH4 emission rate of the site is estimated as 2393 (kg CH4 hr−1) / 2088 (kg CH4 hr−1) respectively.

Comparisons of forward-in-time and backward-in-time Lagrangian stochastic dispersion models for micro-scale atmospheric dispersion

ABSTRACTLagrangian stochastic dispersion models are sometimes run in backward mode to estimate air emissions from different types of sources including area sources. The forward-in-time and backward-in-time Lagrangian stochastic (fLS and bLS) dispersion models may not result in the same estimates. The two models were compared under different atmospheric conditions in micro-scale applications. They are equivalent in a steady-state and horizontally homogeneous atmosphere in many features including estimating concentration at a point, using surface receptor, and prerunning the models. Although bLS shows better computational efficiency, it has a larger uncertainty in results due to the use of surface receptors. In a non-steady-state wind field, the two models show opposite transition trends when the wind fields experience a step change. Under sinusoidal-varying winds, the two models show different shapes of the predicated concentration curves. The normalized differences of the mean concentrations mainly increase with the receptor height when the source-receptor distance is fixed. A controlled methane release experiment was conducted to investigate the behaviors of the two models driven by real wind fields. The correlation coefficient between model-predicted concentrations is 0.95. The model-predicted (forward model) and measured concentrations show similar trend with a correlation coefficient of 0.70. The bLS model estimates larger peak concentrations than that fLS model under the same emission rate. The best-fitted results of the fLS and bLS models give recovery ratios of 1.1558 and 0.9675, respectively, which are better than that using a constant 15-min averaged wind (0.7922).Implications: There are large uncertainties and difficulties in quantification of fugitive air emissions from area sources such as landfills, agriculture, and industry sections. Lagrangian stochastic dispersion model is a versatile tool for these applications with the capability of near-field description and good efficiency. Backward models are usually used to estimate emission rates from area sources due to high computing efficiencies. But they may not result in the same estimate as the forward models due to factors involving model realization and input parameters. It is necessary to investigate the discrepancies to select the best model with minimal uncertainty in the results.

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.

A very-high resolution (20m) measurement-based dust emissions and dispersion modeling approach for the Oceano Dunes, California

This study shows the results from very high-resolution (20 m) dust emissions and transport simulations for the Oceano Dunes State Vehicular Recreation Area (ODSVRA), a coastal sand dune complex located in San Luis Obispo County, California. Field data from an enhanced observation period carried out in May–July 2013 helped estimate the emissions and flow conditions over the dune field. Emissions are based on a comprehensive emissions grid developed from in-situ measurements using the Portable In-Situ Wind ERosion Lab (PI-SWERL). PI-SWERL estimates the potential for a soil surface to produce PM10 dust emissions for a range of wind speeds. This approach provided a well-determined PM10 emissions field as a function of time and space. Wind and turbulence fields were estimated using the CALMET diagnostic meteorological model constrained with surface stations, upper air soundings, buoys, and the North American Reanalysis data. Hourly, three-dimensional wind flow and instability objective analysis fields were developed at 20 m resolution in order to consider the complex flow over realistic dune morphology, land use/land cover and terrain characteristics over and around the Oceano Dunes. The dust dispersion simulations were performed using a computationally efficient and vectorized Lagrangian Stochastic Particle Dispersion Model driven by the CALMET output and the PI-SWERL time-space variable emissions. The dispersion model is based on the Langevin formulation and includes the turbulent diffusion and stochastic particle motion (of millions of particles) in the inertial sub-range, and assuming particles as discrete units neglecting deposition. The model estimates diffusion of particles from an initial particle releases that scale according to the PI-SWERL time-variable emissions estimates. Results were then tested at two independent-downwind locations, with positive correlations for flow conditions (R2 = 0.89) and similar receptor PM10 concentrations (R2 = 0.85). Evaluations against those observations during mean flow conditions as well as for elevated dust events suggest that the model framework can capture the spatial and temporal characteristics of mean day-to-day and diurnal PM10 variability. In this study we describe the details of the model framework and its performance as well as its implementation to locate the dust sources that have the strongest impact in the receptor sites and to evaluate the impact of different dust reduction strategies used at the ODSVRA to mitigate PM10 at downwind receptors.

Atmospheric dispersion using a Lagrangian stochastic approach: Application to an idealized urban area under neutral and stable meteorological conditions

We present an adaptation of the Lagrangian stochastic dispersion model of the computational fluid dynamics (CFD) open source code Code_Saturne to simulate atmospheric dispersion of pollutants in complex urban geometries or around industrial plants. The wind is modeled within the same code with an Eulerian RANS (Reynolds-averaged Navier-Stokes equations) approach and thus involves the solution for the ensemble-mean velocity field and turbulent moments, using eddy viscosity or Reynolds stress turbulence models adapted to the atmosphere and complex geometries. The Lagrangian stochastic model used for the dispersion of the particles within this flow field is the simplified Langevin model, which pertains to the approaches referred to as PDF (Probability Density Function) methods. This formulation of model has not been widely used in atmospheric applications, despite interesting theoretical and computational benefits. Therefore, its usage must be validated on different atmospheric cases. In this paper, we present the validation of the model with a field experiment, considering atmospheric stratification and buildings: the MUST (Mock Urban Setting Test) campaign, conducted in Utah’s desert, USA.

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.

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.

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle.

Grazing systems represent a significant source of enteric methane (CH), but available techniques for quantifying herd scale emissions are limited. This study explores the capability of an eddy covariance (EC) measurement system for long-term monitoring of CH emissions from grazing cattle. Measurements were made in two pasture settings: in the center of a large grazing paddock, and near a watering point where animals congregated during the day. Cattle positions were monitored through time-lapse images, and this information was used with a Lagrangian stochastic dispersion model to interpret EC fluxes and derive per-animal CH emission rates. Initial grazing paddock measurements were challenged by the rapid movement of cattle across the measurement footprint, but a feed supplement placed upwind of the measurements helped retain animals within the footprint, allowing emission estimates for 20% of the recorded daytime fluxes. At the water point, >50% of the flux measurement periods included cattle emissions. Overall, cattle emissions for the paddock site were higher (253 g CH m adult equivalent [AE] d, SD = 75) and more variable than emissions at the water point (158 g CH AE d, SD = 34). Combining results from both sites gave a CH production of 0.43 g kg body weight, which is in range of other reported emissions from grazing animals. With an understanding of animal behavior to allow the most effective use of tower placement, the combination of an EC measurement platform and a Lagrangian stochastic model could have practical applications for long-term monitoring of fluxes in grazing environments.

Impact Assessment of Pollutant Emissions in the Atmosphere from a Power Plant over a Complex Terrain and under Unsteady Winds

The development of a natural gas-fired tri-generation power plant (520 MW Combined Cycle Gas Turbines + 58 MW Tri-generation) in the Republic of San Marino, a small independent country in Northern Italy, is under assessment. This work investigates the impact of atmospheric emissions of NOx by the plant, under the Italian and European regulatory framework. The impact assessment was performed by the means of the Aria Industry package, including the 3D Lagrangian stochastic particle dispersion model SPRAY, the diagnostic meteorological model SWIFT, and the turbulence model SURFPRO (Aria Technologies, France, and Arianet, Italy). The Republic of San Marino is almost completely mountainous, 10 km west of the Adriatic Sea and affected by land-sea breeze circulation. SPRAY is suitable for simulations under non-homogenous and non-stationary conditions, over a complex topography. The emission scenario included both a worst-case meteorological condition and three 10-day periods representative of typical atmospheric conditions for 2014. The simulated NOx concentrations were compared with the regulatory air quality limits. Notwithstanding the high emission rate, the simulation showed a spatially confined environmental impact, with only a single NOx peak at ground where the plume hits the hillside of the Mount Titano (749 m a.s.l.), 5 km west of the future power plant.

Improved quantification of CO2 emission at Campi Flegrei by combined Lagrangian Stochastic and Eulerian dispersion modelling

This study investigates fumarolic CO2 emissions at Campi Flegrei (Southern Italy) and their dispersion in the lowest atmospheric boundary layer. We innovatively utilize a Lagrangian Stochastic dispersion model (WindTrax) combined with an Eulerian model (DISGAS) to diagnose the dispersion of diluted gas plumes over large and complex topographic domains. New measurements of CO2 concentrations acquired in February and October 2014 in the area of Pisciarelli and Solfatara, the two major fumarolic fields of Campi Flegrei caldera, and simultaneous measurements of meteorological parameters are used to: 1) test the ability of WindTrax to calculate the fumarolic CO2 flux from the investigated sources, and 2) perform predictive numerical simulations to resolve the mutual interference between the CO2 emissions of the two adjacent areas. This novel approach allows us to a) better quantify the CO2 emission of the fumarolic source, b) discriminate “true” CO2 contributions for each source, and c) understand the potential impact of the composite CO2 plume (Pisciarelli “plus” Solfatara) on the highly populated areas inside the Campi Flegrei caldera.

Evaluating an eddy covariance technique to estimate point-source emissions and its potential application to grazing cattle

Measurement of gas emissions from grazing cattle presents a challenging application of the eddy-covariance (EC) technique. A cattle herd represents point sources on the landscape, violating the assumptions of spatial homogeneity made in typical EC applications. A proper evaluation of EC fluxes in this case requires an analysis based on the overlap between the EC flux footprint and animal positions. A controlled gas release study was conducted to evaluate the potential of a Lagrangian stochastic (LS) dispersion model to interpret EC fluxes and estimate emissions from point sources. Methane (CH4) gas was released from eight fixed points within a confined area (representing animals in a paddock) while two EC systems monitored CH4 fluxes at two distances downwind of the source area (a near and far tower). Overall accuracy was greater at the far tower location with estimates within 3% of the actual emission rate. The near tower overestimated total emissions by 16%. Deviations from the true emission rate were greatest for night-time and morning periods and least for mid-afternoon to early evening periods when neutral stability and favorable wind directions prevailed. We also investigated the effect of treating the simulated paddock as a homogeneous area emission source. The near tower emission estimate improved with the area source approach (9% overestimation). The far tower suffered a loss of accuracy (17% underestimation), but this was substantially improved (7% underestimation) by reducing the source area to the minimum required to contain the eight release points. Our study suggests that EC can be used to measure animal emissions from grazing cattle on pasture with a level of accuracy similar to other micrometeorological approaches.

Numerical Considerations for Lagrangian Stochastic Dispersion Models: Eliminating Rogue Trajectories, and the Importance of Numerical Accuracy

When Lagrangian stochastic models for turbulent dispersion are applied to complex atmospheric flows, some type of ad hoc intervention is almost always necessary to eliminate unphysical behaviour in the numerical solution. Here we discuss numerical strategies for solving the non-linear Langevin-based particle velocity evolution equation that eliminate such unphysical behaviour in both Reynolds-averaged and large-eddy simulation applications. Extremely large or ‘rogue’ particle velocities are caused when the numerical integration scheme becomes unstable. Such instabilities can be eliminated by using a sufficiently small integration timestep, or in cases where the required timestep is unrealistically small, an unconditionally stable implicit integration scheme can be used. When the generalized anisotropic turbulence model is used, it is critical that the input velocity covariance tensor be realizable, otherwise unphysical behaviour can become problematic regardless of the integration scheme or size of the timestep. A method is presented to ensure realizability, and thus eliminate such behaviour. It was also found that the numerical accuracy of the integration scheme determined the degree to which the second law of thermodynamics or ‘well-mixed condition’ was satisfied. Perhaps more importantly, it also determined the degree to which modelled Eulerian particle velocity statistics matched the specified Eulerian distributions (which is the ultimate goal of the numerical solution). It is recommended that future models be verified by not only checking the well-mixed condition, but perhaps more importantly by checking that computed Eulerian statistics match the Eulerian statistics specified as inputs.