Inverse Dispersion Model Research Articles

Using open-path dual-comb spectroscopy to monitor methane emissions from simulated grazing cattle

Abstract. Accurate whole-farm or herd-level measurements of livestock methane emissions are necessary for anthropogenic greenhouse gas inventories and to evaluate mitigation strategies. A controlled methane (CH4) release experiment was performed to determine if dual-comb spectroscopy (DCS) can detect CH4 concentration enhancements produced by a typical herd of beef cattle in an extensive grazing system. Open-path DCS was used to measure downwind and upwind CH4 concentrations from 10 point sources of methane simulating cattle emissions. The CH4 mole fractions and wind velocity data were used to calculate CH4 flux using an inverse dispersion model, and the simulated fluxes were then compared to the actual CH4 release rate. For a source located 60 m from the downwind path, the DCS system detected 10 nmol mol−1 CH4 horizontal concentration gradient above the atmospheric background concentration with a precision of 6 nmol mol−1 in 15 min interval. A CH4 release of 3970 g d−1 was performed, resulting in an average concentration enhancement of 24 nmol mol−1 of CH4. The calculated CH4 flux was 4002 g d−1, showing good agreement with the actual CH4 release rate. Periodically altering the downwind path, which may be needed to track moving cattle, did not adversely affect the ability of the instruments to determine the CH4 flux. These results give us confidence that CH4 flux can be determined by grazing cattle with low disturbance and direct field-scale measurements.

Long-term trends of black carbon and particle number concentrations and their vehicle emission factors in Stockholm

Black carbon (BC) and particle number (PN) concentrations are usually high in cities due to traffic emissions. European mitigation policies, including Euro emission standards, have been implemented to curb these emissions. We analyzed BC and PN (particle diameter Dp > 4 nm) concentrations in Stockholm spanning the years 2013–2019 (BC) and 2009–2019 (PN) measured at street canyon and rooftop sites to assess the effectiveness of the implemented policies. Combining these data with inverse dispersion modeling, we estimated BC and PN emission factors (EFBC and EFPN) for the mixed fleet, reflecting real-world driving conditions. The pollutants showed decreasing trends at both sites, but PN concentrations remained high at the canyon site considering the World Health Organization (WHO) recommendations. BC concentrations declined more rapidly than PN concentrations, showing a −9.4% and −4.9% annual decrease at the canyon and −7.2% and −0.5% at the rooftop site in the years 2013–2019. The EFBC and EFPN trends showed that the mitigation strategies for reducing particulate emissions for on-road vehicles were successful over the study period. However, the introduction of biofuels in the vehicle fleet —ethanol and later rapeseed methyl ester (RME)— increased the concentrations of particles with Dp < 10 nm before the adoption of particulate filters in the exhausts. Stricter Euro emission regulations, especially with diesel particulate filters (DPF) in Euro 5, 6, and VI vehicles, led to 66% decrease in EFBC and 55% in EFPN. Real-world EFBC surpassed HBEFA (Handbook Emission Factors for Road Transport) database values by 2.4–4.8 times; however, direct comparisons between real-world and HBEFA EFPN are difficult due to differences in lower cut-off sizes and measurement techniques. Our results underscore the necessity for revising the HBEFA database, updating laboratory testing methods and portable emission measuring systems (PEMS) measurements to account for liquid condensate contributions to PN measurements.

Seasonal ammonia emissions from an intensive beef cattle feedlot in Victoria Australia

Ammonia (NH3) emitted from concentrated animal feeding operations can cause environmental and health problems, and indirectly contribute to greenhouse gas emissions. Cattle feedlots are known to be large sources of NH3, but few studies have documented seasonal emissions from Australian feedlots. We conducted two field campaigns to measure NH3 emissions from an intensive beef cattle feedlot in southeast Australia, and these results were combined with previous measurements at the same feedlot to document seasonal variations in emissions and to derive annual feedlot emission factors (EFs). Emission rates were calculated with an inverse dispersion modelling (IDM) technique, based on NH3 concentrations measured at the feedlot with open-path lasers (OPLs). The average area emission rates in spring, summer, autumn and winter were 90.5, 167.4, 96.2 and 86.8 μg NH3 m−2 s−1 from the cattle pens, and 22.5, 18.1, 7.7 and 20.7 μg NH3 m−2 s−1 from the manure stockpile area, respectively. The total per-animal EFs ranged from 126.0 (autumn) to 190.2 g NH3 animal−1 d−1 (summer), representing a loss of 47.5–64.6% of the fed N. Seasonal variations in emissions were related to air temperature. Slight changes in crude protein content of the cattle diet may also have impacted seasonal variability. Taking seasonal variations into consideration, the average feedlot EF was 160.4 g NH3 animal−1 d−1, with 90% of the emissions coming from the cattle pens. Extrapolating the EF to all feedlot cattle in the country, the direct NH3 emissions from Australian feedlots amount to 65.2 Gg NH3 annually, or 3.7% of the national total. Our study benchmarks seasonal and annual EFs and N losses for Australian commercial feedlots, and provides a baseline for extrapolating the impacts of mitigation efforts.

Experimental and model-based comparison of wind tunnel and inverse dispersion model measurement of ammonia emission from field-applied animal slurry

Volatilization of ammonia from field-applied animal slurry is a significant problem. Accurate emission measurements are needed for inventories and research, but are not provided by all measurement methods. Wind tunnels may give emission values substantially above or below micrometeorological results, which have been shown to be accurate. This limitation reduces the utility of wind tunnel results, which make up a large fraction of available measurements. The present work focused on understanding wind tunnel measurement error by comparing micrometeorological and wind tunnel measurements with the aid of a semi-empirical model. Ammonia loss from digestate after field application was measured in high time resolution in three field trials using wind tunnels and the backward Lagrangian stochastic (bLS) dispersion technique simultaneously. Differences in measured emission were interpreted using the ALFAM2 model, and measurements were used to evaluate the model. Results showed that wind tunnel and bLS methods provided different cumulative emission estimates, although there were similarities in measured emission dynamics. The ALFAM2 model was generally able to reproduce emission dynamics for both measurement methods, but only when differences in mass transfer between the two methods were incorporated in an experimental parameter set. This important result suggests that: 1) the simple structure of the ALFAM2 model captures the essential physical and chemical processes controlling emission and 2) the two measurement methods differ only (or mainly) through mass transfer above the slurry/soil surface and rain. Therefore, with careful selection of wind tunnel air flow it should be possible to approximately match emission that occurs under open-air conditions. But without temporal variation in air flow, actual emission dynamics cannot be captured. This work provides a template for integrating and comparing measurements from different methods, and suggests it is possible to use wind tunnel measurements for model evaluation and even parameter estimation.

Measurement of Long-Term CH4 Emissions and Emission Factors from Beef Feedlots in Australia

One of the major challenges for the Australian feedlot and meat sectors is to have accurate and robust long-term greenhouse gas (GHG) emissions data. Long-term measurements of methane (CH4) emissions (2015–2017) were made at two Australian beef feedlots having different climates, cattle types, and management practices. Emissions were measured using the inverse-dispersion model (IDM) micrometeorological technique, using CH4 concentrations measured at the feedlots with a closed-path Fourier transform infrared spectroscopic technique (CP-FTIR). The emissions data were used to evaluate methods used by the Department of Climate Change, Energy, the Environment and Water to estimate CH4 emissions from feedlots in Australian national inventory calculations. Expressed as a CH4 yield (emissions per unit dry matter intake, DMI), the two feedlots had emissions of 13.1 and 18.9 g CH4 kg−1 DMI. The lower-emitting feedlot had emissions that were 30% lower than the national inventory calculations based on feed intakes, while the second feedlot had emissions that were similar to the inventory calculations. The accurate quantification of emissions from feedlots, as demonstrated as part of this study, is important for validating the national accounting methods and therefore the sector’s GHG emissions profile.

Evaluation of open- and closed-path sampling systems for the determination of emission rates of NH3 and CH4 with inverse dispersion modeling

Abstract. The gas emission rates of ammonia (NH3) and methane (CH4) from an artificial source covering a surface area of 254 m2 were determined by inverse dispersion modeling (IDM) from point-sampling and line-integrated concentration measurements with closed- and open-path analyzers. Eight controlled release experiments were conducted with different release rates ranging from 3.8±0.21 to 17.4±0.4 mg s−1 and from 30.7±1.4 to 142.8±2.9 mg s−1 for NH3 and CH4, respectively. The distance between the source and concentration measurement positions ranged from 15 to 60 m. Our study consisted of more than 200 fluxes averaged over intervals of 10 or 15 min. The different releases cover a range of different climate conditions: cold (&lt;5 ∘C), temperate (&lt;13 ∘C), and warm (&lt;18 ∘C). As the average of all releases with all instrument types, the CH4 recovery rate QbLS/Q was 0.95±0.08 (n=19). There was much more variation in the recovery of NH3, with an average of 0.66±0.15 (n=10) for all the releases with the line-integrated system. However, with an improved sampling line placed close to the source an average recovery rate of 0.82±0.05 (n=3) was obtained for NH3. Under comparable conditions, the recovery rate obtained with an open-path analyzer was 0.91±0.07 (n=3). The effects of measurement distance, physical properties of the sampling line, and deposition are discussed.

Stationary and drone-assisted methane plume localization with dispersion spectroscopy

This work presents stationary and mobile retroreflector-based remote sensing techniques for methane leak localization and quantification using chirped laser dispersion spectroscopy equipped with a custom laser transceiver capable of continuous tracking of a flying drone and coupled with inverse atmospheric gas dispersion modeling. The techniques demonstrate the ability to localize leaks as low as 0.13 g CH4·s−1, which are up to 25 times smaller than those typically observed at natural gas facilities, as well as actively track a moving retroreflector mounted on a lightweight (∼250 g) drone to enable spatial plume reconstruction. This system exhibited a 2.3 ppm-m sensitivity over pathlengths of 40–150 m. Source localization to within ±7 m is demonstrated using a modified horizontal radial plume mapping technique with a stationary retroreflector grid. Meanwhile, the mobile system utilizing a drone-mounted retroreflector is able to localize a controlled release within ±1 m of its source location and estimate leak rates using inversion techniques assuming type B Gaussian plume stability class within ±30% error with respect to the actual low flow rate releases.

Quantifying Emissions from Fugitive Area Sources Using a Hybrid Method of Multi-Path Optical Remote Sensing and Tomographic Inverse-Dispersion Techniques

Reducing methane (CH4) emissions from anthropogenic activities is critical to climate change mitigation efforts. However, there is still considerable uncertainty over the amount of fugitive CH4 emissions due to large-scale area sources and heterogeneous emission distributions. To reduce the uncertainty and improve the spatial and temporal resolutions, a new hybrid method was developed combining optical remote sensing (ORS), computed tomography (CT), and inverse-dispersion modeling techniques on the basis of which a multi-path scanning system was developed. It uses a horizontal radial plume mapping path configuration and adapts a Lagrangian stochastic dispersion mode into CT reconstruction. The emission map is finally calculated by using a minimal curvature tomographic reconstruction algorithm, which introduces smooth constraints at each pixel. Two controlled-release experiments of CH4 were conducted with different configurations, showing relative errors of only 2% and 3%. Compared with results from the single-path inverse-dispersion method (5–175%), the new method can not only derive the emission distribution but also obtain a more accurate emission rate. The outcome of this research would bring broad application of the ORS-CT and inverse-dispersion techniques to other gases and sources.

Determination of methane emissions from biogas plants, using different quantification methods

Reliable and comparable quantification methods are needed for assessing the effectiveness of the biogas production and utilisation process in mitigating methane (CH4) emissions as well as improving the database for emission inventories. The objective of this study was to compare and validate CH4 emissions quantified at two agricultural biogas plants, for up to three days, using diverse on-site (two teams) and off-site methods (three teams), including differential absorption lidar (DIAL), tracer gas dispersion (TDM) and inverse dispersion modelling (IDM). For plant 1, with a constant combined heat and power (CHP) load, the average emission factor varied from 0.3% CH4 (on-site approaches) to 1.2% CH4 (off-site approaches). On-site approaches underestimated overall emissions due to many small (unquantified) CH4 leakages. All methods observed comparable average emission factors for plant 2, ranging between 1.9 and 2.2% CH4. In this case, the majority of emissions emanated from just a few sources. However, correcting the significant influence of the varying CHP load during the measurement campaign revealed significant differences between TDM and IDM (DIAL did not participate). It was demonstrated that TDM and IDM could recover the emission rate from a known point source (controlled release of CH4 via a small diffuser) within an accuracy of 93 ± 15% (TDM) and 92 ± 17% (IDM) under favourable and similar conditions.

Calculating gas emissions from open-pit mines using inverse dispersion modelling: A numerical evaluation using CALPUFF and CFD-LS

Inverse Dispersion Modelling (IDM) establishes a relationship between an air pollutant concentration downstream of a source and the strength of an emission source by reliance on an air dispersion model. Thus, ideally measuring pollutant concentration downstream is sufficient to infer the emission source strength. However, the accuracy of IDM relies on the accuracy of the underlying air dispersion model. Diagnostic dispersion models face difficulty when applied to complex terrains of open-pit mines. To elucidate such difficulties and their causes, the diagnostic CALifornia PUFF (CALPUFF) model is compared to a Computational Fluid Dynamics-Lagrangian Stochastic (CFD-LS) model for quantifying the short-range dispersion of fugitive gases released from a synthetic open-pit mine. Two mine depths (100–500 m) and three thermal stability conditions (stable-neutral-unstable) are investigated. In all cases the surface concentration predicted by the two models are in disagreement, regardless of CALPUFF model setup. Overall, less than 30% of receptor points predict the concentration within a factor of two of CFD-LS simulations (FAC2 < 0.3). Model differences appear to be related to the internal algorithms of the CALPUFF model to predict the wind field appropriately. The results should caution practitioners considering diagnostic models for IDM analysis over complex terrain.

Estimating oil sands emissions using horizontal path-integrated column measurements

Abstract. Improved technologies and approaches to reliably measure and quantify fugitive greenhouse gas emissions from oil sands operations are needed to accurately assess emissions and develop mitigation strategies that minimize the cost impact of future production. While several methods have been explored, the spatial and temporal heterogeneity of emissions from oil sand mines and tailings ponds suggests an ideal approach would continuously sample an area of interest with spatial and temporal resolution high enough to identify and apportion emissions to specific areas and locations within the measurement footprint. In this work we demonstrate a novel approach to estimating greenhouse gas emissions from oil sands tailings ponds and open-pit mines. The approach utilizes the GreenLITE™ gas concentration measurement system, which employs a laser-absorption-spectroscopy-based, open-path, integrated column measurement in conjunction with an inverse dispersion model to estimate methane (CH4) emission rates from an oil sands facility located in the Athabasca region of Alberta, Canada. The system was deployed for extended periods of time in the summer of 2019 and spring of 2020. CH4 emissions from a tailings pond were estimated to be 7.2 metric tons per day (t/d) for July–October 2019, and 5.1 t/d for March–July 2020. CH4 emissions from an open-pit mine were estimated to be 24.6 t/d for September–October 2019. Uncertainty in retrieved emission for the tailings pond in March–July 2020 is estimated to be 2.9 t/d. Descriptions of the measurement system, measurement campaigns, emission retrieval scheme, and emission results are provided.

Emission Determination by Three Remote Sensing Methods in Two Release Trials

Concentrations of greenhouse gases such as carbon dioxide (CO2), nitrous dioxide (N2O) and methane (CH4) in the atmosphere are rising continuously. The first step to reduce emissions from landfills is to gain better knowledge about the quantities emitted. There are several ways to quantify CH4 emissions at landfills. Comprehensive quality analyses of individual methods for emission rate quantification at landfills are few to date. In the present paper, the authors conducted two field trials with three different remote sensing methods to gain more knowledge about the possibilities and challenges in quantification of CH4 emissions from landfills. One release trial was conducted with released N2O as tracer and CH4 for quality assessment of the methods. In the second trial, the N2O tracer was released on a landfill to gain experience under field conditions. The well-established inverse dispersion modelling method (IDMM) was used based on concentration data of TDLAS (Tunable Diode Laser Absorption Spectroscopy)-instruments and on concentration data of a partly drone based Fourier-Transformation-Infrared-Spectroscopy (FTIR)-instrument. Additionally, a tracer-method with N2O-tracer and FTIR measurements was conducted. In both trials, IDMM based on TDLAS data and FTIR data provided the best results for high emission rates (15% deviation) and low emission rates (47% deviation). However, both methods have advantages, depending on the field of application. IDMM based on TDLAS measurements is the best choice for long-term measurements over several hours with constant wind conditions (8% deviation). The IDMM based on drone based FTIR measurements is the means of choice for measurements under changing wind conditions and where no linear measurement distances are possible.

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.

Evaluation of ammonia emissions from filtration of digestate used for fertigation

Fertigation can be a suitable technique for utilizing digestate, minimizing nitrogen losses, and contributing to circularity within a farming system. For this purpose, digestate usually is first processed with a screw-press separator. However, further filtration is required to remove particles that could clog the nozzles of drip or sprinkling irrigation systems. Advanced filtration can be obtained using mechanical separation with screens having openings of 100- 300 μm. This operation can be another source of ammonia emission, but this aspect has not been adequately investigated. This study aimed to address this knowledge gap by evaluating the emissions from three different filtration systems for digestate. The study was conducted in three different farms located in Lombardy (Italy) using digestate to fertigate maize by drip irrigation (two farms) and pivot irrigation (one farm). Ammonia emissions were measured with passive samplers and the fluxes were examined using an inverse dispersion model implemented in Windtrax software. The emissions were measured both when the filtration systems were in operation and when they were switched off. Ammonia emissions (mean values between 375 and 876 μg NH3/m2/s) tended to increase during operation of the filtration systems. However, no significant differences were found in the emissions from active and inactive equipment on any of the farms. The emissions from the filtration systems were higher than from a storage tank (22-67 μg NH3/m2/s). However, the mean emissions amounted to only 0.3% of the nitrogen content of the digestate. These emissions can be considered irrelevant in the context of the whole management scheme for digestate. This work provides a first insight on ammonia emissions arising from advanced filtration of digestate, with specific reference to Po Valley farming systems. Further studies are required to improve knowledge about emissions from the entire digestate management process, including the treatments required for specific application techniques.

Long-term trends in nitrogen oxides concentrations and on-road vehicle emission factors in Copenhagen, London and Stockholm

Road transport is the main anthropogenic source of NOx in Europe, affecting human health and ecosystems. Thus, mitigation policies have been implemented to reduce on-road vehicle emissions, particularly through the Euro standard limits. To evaluate the effectiveness of these policies, we calculated NO2 and NOx concentration trends using air quality and meteorological measurements conducted in three European cities over 26 years. These data were also employed to estimate the trends in NOx emission factors (EFNOx, based on inverse dispersion modeling) and NO2:NOx emission ratios for the vehicle fleets under real-world driving conditions. In the period 1998–2017, Copenhagen and Stockholm showed large reductions in both the urban background NOx concentrations (−2.1 and −2.6% yr−1, respectively) and EFNOx at curbside sites (68 and 43%, respectively), proving the success of the Euro standards in diminishing NOx emissions. London presented a modest decrease in urban background NOx concentrations (−1.3% yr−1), while EFNOx remained rather constant at the curbside site (Marylebone Road) due to the increase in public bus traffic. NO2 primary emissions —that are not regulated— increased until 2008–2010, which also reflected in the ambient concentrations. This increase was associated with a strong dieselization process and the introduction of new after-treatment technologies that targeted the emission reduction of other species (e.g., greenhouse gases or particulate matter). Thus, while regulations on ambient concentrations of specific species have positive effects on human health, the overall outcomes should be considered before widely adopting them. Emission inventories for the on-road transportation sector should include EFNOx derived from real-world measurements, particularly in urban settings.

Towards robust on-site ammonia emission measuring techniques based on inverse dispersion modeling

Ammonia is recognized as one of the major atmospheric pollutants affecting air and ecosystem quality. The application of N fertilizers is a major source of NH3 emissions. It is necessary to develop simple, accurate and low cost measurement techniques to obtain representative data for a wide range of regions and agricultural practices. This information would improve national inventories and support decision-making processes regarding strategies for NH3 emission abatement. Measurement techniques can be complex, adjusted to specific conditions, labor-intensive and costly. This work analyses different methods aimed at reaching a balance between the accuracy and precision required for measurements and their complexity. Three techniques were tested under: semiopen passive chambers (SOCs) and an inverse dispersion model (IDM) combined with two different NH3 air concentration measuring techniques: ALPHA® passive samplers and acid bubblers. Different setups were evaluated testing different heights over the emitting surface. These techniques were assessed using micrometeorological mass balance integrated horizontal flux (IHF) with passive flux samplers as a reference method. The SOC results showed a close linear relationship with the IHF results (R2=0.784, p<0.01), although emissions after 16 days were 10.6% higher. The bLS IDM with acid bubblers showed promising results, although they were labor-demanding and required a power supply. The IDM with ALPHA® samplers placed at a 1.25-m height was demonstrated to embrace precision and close agreement with the IHF observations in our experimental conditions (R2=0.768, p<0.01; no difference from line 1:1; mean bias: 0.041 kg N ha−1h−1 and rRMSE: 46.1%). However, these data require special attention for periods of drastically changing weather, when the NH3 fluxes determined with air concentration samplers moved away from the overall emission pattern. Longer sampling intervals and the assumption of neutral atmospheric conditions in IDM may decrease costs, simplify the procedure and provide a cost-efficient alternative to the IHF method.

Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R2 from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R2 values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R2 for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.

Beef cattle methane emissions measured with tracer-ratio and inverse dispersion modelling techniques

Abstract. The development and validation of management practices to mitigate greenhouse gas (GHG) emissions from livestock require accurate emission measurements. This study assessed the accuracy of a practical inverse dispersion modelling (IDM) technique to quantify methane (CH4) emitted from a small cattle herd (16 animals) confined to a 63 m × 60 m experimental pen. The IDM technique calculates emissions from the increase in the CH4 concentration measured downwind of the animals. The measurements were conducted for 7 d. Two types of open-path (OP) gas sensors were used to measure concentration in the IDM calculation: a Fourier transform infrared spectrometer (IDM-FTIR) or a CH4 laser (IDM-Laser). The actual cattle emission rate was measured with a tracer-ratio technique using nitrous oxide (N2O) as the tracer gas. We found very good agreement between the two IDM emission estimates (308.1 ± 2.1 – mean ± SE – and 304.4 ± 8.0 g CH4 head−1 d−1 for the IDM-FTIR and IDM-Laser respectively) and the tracer-ratio measurements (301.9 ± 1.5 g CH4 head−1 d−1). This study suggests that a practical IDM measurement approach can provide an accurate method of estimating cattle emissions.

Ammonia, nitrous oxide, carbon dioxide, and water vapor fluxes after green manuring of faba bean under Mediterranean climate

Green manuring of legume crops has increased in interest in recent years because of environmental and economic costs of synthetic fertilizers and need of sustainable cropping systems. Climatic conditions can reduce the agronomic and environmental effectiveness of green manuring promoting reactive nitrogen losses, or fostering soil heterotrophic respiration. This study aims to quantify ammonia and nitrous oxide losses, as well as carbon dioxide exchanges following green manuring of faba bean (Vicia faba spp. minor L.) under semi-arid climate conditions, typical of the Mediterranean basin. Ammonia emissions were measured by means of an aerodynamic gradient approach equipped with a multi-channel wet-denuder system (ROSAA), alongside with a concentration-based inverse dispersion modelling assessment. Nitrous oxide losses were measured with an automated chamber system, whereas carbon dioxide and water vapor exchanges were monitored jointly by means of eddy covariance technique. Ammonia volatilization lasted for three weeks and was triggered by the increase of soil water content due to rainfall, occurring 5 days after green manuring. Total emissions were 0.31% of the nitrogen supplied by green manuring (230 kg N ha−1). Nitrous oxide emissions from soil were of low intensity during the measurement period and were directly corelated to the water filled pore space. The biogeochemical model CERES-EGC was validated with the measurements of nitrous oxide, then used to reproduce the dynamic until the sowing of the succeeding crop. Cumulated emissions of nitrous oxide were 1% of the total nitrogen supplied. Soil became a net source both of carbon dioxide and water vapor soon after green manuring, with an emission of 900 kg C-CO2 ha−1 by heterotrophic respiration (23% of added carbon). In terms of water balance, 19 mm of water evaporated from soil surface, originated from the addition of fresh biomass. Green manuring proved to be a significant agronomic strategy to improve soil fertility, limiting reactive nitrogen and carbon losses to the atmosphere.

Methane emissions from an oil sands tailings pond: a quantitative comparison of fluxes derived by different methods

Abstract. Tailings ponds in the Alberta oil sands region are significant sources of fugitive emissions of methane to the atmosphere, but detailed knowledge on spatial and temporal variabilities is lacking due to limitations of the methods deployed under current regulatory compliance monitoring programs. To develop more robust and representative methods for quantifying fugitive emissions, three micrometeorological flux methods (eddy covariance, gradient, and inverse dispersion) were applied along with traditional flux chambers to determine fluxes over a 5-week period. Eddy covariance flux measurements provided the benchmark. A method is presented to directly calculate stability-corrected eddy diffusivities that can be applied to vertical gas profiles for gradient flux estimation. Gradient fluxes were shown to agree with eddy covariance within 18 %, while inverse dispersion model flux estimates were 30 % lower. Fluxes were shown to have only a minor diurnal cycle (15 % variability) and were weakly dependent on wind speed, air, and water surface temperatures. Flux chambers underestimated the fluxes by 64 % in this particular campaign. The results show that the larger footprint together with high temporal resolution of micrometeorological flux measurement methods may result in more robust estimates of the pond greenhouse gas emissions.