Evaluation of the performance of different short-range atmospheric dispersion models for the monitoring of CH4 emissions from industrial facilities

<p>Methane (CH<sub>4</sub>) is a powerful greenhouse gas which plays a major role in climate change. The accurate monitoring of emissions from industrial facilities is needed to ensure efficient emission mitigation strategies. Local-scale atmospheric inversions are increasingly being used to provide estimates of the rates and/or locations of CH<sub>4</sub> sources from industrial sites. They rely on local-scale atmospheric dispersion models, CH<sub>4</sub> measurements and inversion approaches. Gaussian plume models have often been used for local-scale atmospheric dispersion modelling and inversions of emissions, because of their simplicity and good performance when used in a flat terrain and relatively constant mean wind conditions. However, even in such conditions, failure to account for wind and mole fraction variability can limit the ability to exploit the full potential of these measurements at high frequency.</p><p>We study whether the accuracy of inversions can be increased by the use of more complex dispersion models. Our assessments are based on the analysis of 25 to 75-min CH<sub>4 </sub>controlled releases during a one-week campaign in October 2019 at the TOTAL’s TADI operative platform in Lacq, France (in a flat area). During this campaign, for each controlled release, we conducted near-surface in situ measurements of CH<sub>4</sub> mole fraction from both a mobile vehicle and a circle of fixed points around the emission area. Our inversions based on a Gaussian model and either the mobile or fixed-point measurements both provided estimates of the release rates with 20-30% precision.  </p><p>Here we focus on comparisons between modeling and inversion results when using this Gaussian plume model, a Lagrangian model “GRAL” and a Gaussian puff model. The parameters for the three models are based on high-frequency meteorological values from a single stationary 3D sonic anemometer. GRAL should have relatively good skills under low-wind speed conditions. The Gaussian puff is a light implementation of time-dependent modeling and can be driven by high-frequency meteorological data. The performance of these dispersion models is evaluated with various metrics from the observation field that are relevant for the inversion. These analyses lead to the exploration of new types of definitions of the observational constraint for the inversions with the Gaussian puff model, when using the timeseries from fixed measurement points. The definitions explore a range of metrics in the time domain as well as in the frequency domain.</p><p>Eventually, the Lagrangian model does not outperform the Gaussian plume model in these experiments, its application being notably limited by the short scales of the transport characteristics. On the other hand, the Gaussian puff model provides promising results for the inversion, in particular, in terms of comparison between the simulated and observed timeseries for fixed stations. Its performance when driven by a spatially uniform wind field is an incentive to explore the use of meteorological data from several sonic stations to parameterize its configuration. The fixed-point measurements are shown to allow for more robust inversions of the source location than the mobile measurements, with an average source localization error of the order of 10 m.</p>

Research on Optimization of Ingestion Emergency Planning Zone Sizing

Food contamination has aroused public concern since Fukushima accident. As emergency preparedness is often viewed as an important approach to protect staff working on site and public around the site, ingestion emergency planning zone (EPZ) is applied to protect public from the exposure of contaminated food. Ingestion EPZ is one of the technical foundations for nuclear emergency preparedness, which will be influenced by design features of plant and characteristics of the site. This paper is devoted to the research on the optimization of ingestion EPZ sizing from the view of the atmospheric dispersion model and the food chain model, which are crucial points for the sizing of ingestion EPZ. Compared to the traditional straight-line Gaussian plume model with a quite conservative assumption that plume segments always transport in the downwind direction, the Lagrangian Gaussian puff model considers the swing of wind direction over time, which makes the simulation more realistic. With the results of radionuclide concentrations evaluated by the dispersion model, the transportation of the radionuclides in food is simulated by the food chain model. The traditional food chain model is essentially a static model with no consideration that food contamination level has a strong dependence on the accident date, which may overstate the risk from nuclear plant accidents and result in unfounded fear of public. The dynamic food chain model, which takes daily changes of plant biomass, or livestock feeding periods in consideration, has been developed to estimate radionuclide concentrations in different foodstuffs. On basis of the study of the dispersion models and food chain models above, we evaluate the ingestion EPZ size of Tianwan NPP by choosing the comparatively realistic ones from them. In the scenario considered in this paper, the simulation domain of Tianwan NPP within 80km-range and hourly time-step is applied, and meteorological conditions are carefully set according to observation data in recent years. Results show that there is significant margin and conservatism in the traditional ingestion EPZ sizing. Radionuclide concentrations predicted by the Lagrangian Gaussian puff model is almost an order of magnitude lower than the Gaussian plume model. Moreover, the dynamic food chain model considers the seasonal effect that simulation results of radionuclide concentrations in foodstuffs are significantly higher in summer than in winter, which helps to make a more realistic consideration of ingestion pathway. This research gives an example of the application of new models for the optimization of ingestion EPZ sizing, which may contribute to strengthen public confidence in nuclear safety and emergency preparedness.

Emergency Planning Zones Estimation for Karachi-2 and Karachi-3 Nuclear Power Plants using Gaussian Puff Model

Emergency planning zones (PAZ and UPZ) around the Karachi-2 and Karachi-3 nuclear power plants (K-2/K-3 NPPs) have been realistically determined by employing Gaussian puff model and Gaussian plume model together for atmospheric transport, diffusion, and deposition of radioactive material using onsite and regional data related to meteorology, topography, and land-use along with latest IAEA Post-Fukushima Guidelines. The analysis work has been carried out using U.S.NRC computer code RASCAL 4.2. The assumed environmental radioactive releases provide the sound theoretical and practical bases for the estimation of emergency planning zones covering most expected scenario of severe accident and most recent multiunit Fukushima Accident. Sheltering could be used as protective action for longer period of about 04 days. The area about 3 km of K-2/K-3 NPPs site should be evacuated and an iodine thyroid blocking agent should be taken before release up to about 14 km to prevent severe deterministic effects. Stochastic effects may be avoided or minimized by evacuating the area within about 8 km of the K-2/K-3 NPPs site. Protective actions may become more effective and cost beneficial by using current methodology as Gaussian puff model realistically represents atmospheric transport, dispersion, and disposition processes in contrast to straight-line Gaussian plume model explicitly in study area. The estimated PAZ and UPZ were found 3 km and 8 km, respectively, around K-2/K-3 NPPs which are in well agreement with IAEA Post-Fukushima Study. Therefore, current study results could be used in the establishment of emergency planning zones around K-2/K-3 NPPs.

Development of a plume-in-grid model for industrial point and volume sources: application to power plant and refinery sources in the Paris region

Plume-in-grid (PinG) models incorporating a host Eulerian model and a subgrid-scale model (usually a Gaussian plume or puff model) have been used for the simulations of stack emissions (e.g., fossil fuel-fired power plants and cement plants) for gaseous and particulate species such as nitrogen oxides (NOx), sulfur dioxide (SO2), particulate matter (PM) and mercury (Hg). Here, we describe the extension of a PinG model to study the impact of an oil refinery where volatile organic compound (VOC) emissions can be important. The model is based on a reactive PinG model for ozone (O3), which incorporates a three-dimensional (3-D) Eulerian model and a Gaussian puff model. The model is extended to treat PM, with treatments of aerosol chemistry, particle size distribution, and the formation of secondary aerosols, which are consistent in both the 3-D Eulerian host model and the Gaussian puff model. Furthermore, the PinG model is extended to include the treatment of volume sources to simulate fugitive VOC emissions. The new PinG model is evaluated over Greater Paris during July 2009. Model performance is satisfactory for O3, PM2.5 and most PM2.5 components. Two industrial sources, a coal-fired power plant and an oil refinery, are simulated with the PinG model. The characteristics of the sources (stack height and diameter, exhaust temperature and velocity) govern the surface concentrations of primary pollutants (NOx, SO2 and VOC). O3 concentrations are impacted differently near the power plant than near the refinery, because of the presence of VOC emissions at the latter. The formation of sulfate is influenced by both the dispersion of SO2 and the oxidant concentration; however, the former tends to dominate in the simulations presented here. The impact of PinG modeling on the formation of secondary organic aerosol (SOA) is small and results mostly from the effect of different oxidant concentrations on biogenic SOA formation. The investigation of the criteria for injecting plumes into the host model (fixed travel time and/or puff size) shows that a size-based criterion is recommended to treat the formation of secondary aerosols (sulfate, nitrate, and ammonium), in particular, farther downwind of the sources (beyond about 15 km). The impacts of PinG modeling are less significant in a simulation with a coarse grid size (10 km) than with a fine grid size (2 km), because the concentrations of the species emitted from the PinG sources are relatively less important compared to background concentrations when injected into the host model with a coarser grid size.

Development of a plume-in-grid model for industrial point and volume sources: application to power plant and refinery sources in the Paris region

Plume-in-grid (PinG) models incorporating a host Eulerian model and a subgrid-scale model (usually a Gaussian plume or puff model) have been used for the simulations of stack emissions (e.g., fossil fuel-fired power plants and cement plants) for gaseous and particulate species such as nitrogen oxides (NOx), sulfur dioxide (SO2), particulate matter (PM) and mercury (Hg). Here, we describe the extension of a PinG model to study the impact of an oil refinery where volatile organic compound (VOC) emissions can be important. The model is based on a reactive PinG model for ozone (O3), which incorporates a three-dimensional (3-D) Eulerian model and a Gaussian puff model. The model is extended to treat PM, with treatments of aerosol chemistry, particle size distribution, and the formation of secondary aerosols, which are consistent in both the 3-D Eulerian host model and the Gaussian puff model. Furthermore, the PinG model is extended to include the treatment of volume sources to simulate fugitive VOC emissions. The new PinG model is evaluated over Greater Paris during July 2009. Model performance is satisfactory for O3, PM2.5 and most PM2.5 components. Two industrial sources, a coal-fired power plant and an oil refinery, are simulated with the PinG model. The characteristics of the sources (stack height and diameter, exhaust temperature and velocity) govern the surface concentrations of primary pollutants (NOx, SO2 and VOC). O3 concentrations are impacted differently near the power plant than near the refinery, because of the presence of VOC emissions at the latter. The formation of sulfate is influenced by both the dispersion of SO2 and the oxidant concentration; however, the former tends to dominate in the simulations presented here. The impact of PinG modeling on the formation of secondary organic aerosols (SOA) is small and results mostly from the effect of different oxidant concentrations on biogenic SOA formation. The investigation of the criteria for injecting plumes into the host model (fixed travel time and/or puff size) shows that a size-based criterion is recommended to treat the formation of secondary aerosols (sulfate, nitrate, and ammonium), in particular, farther downwind of the sources (from about 15 km). The impacts of the PinG modeling are less significant in a simulation with a coarse grid size (10 km) than with a fine grid size (2 km), because the concentrations of the species emitted from the PinG sources are relatively less important compared to background concentrations when injected into the host model.

Modelling aerosol concentration distributions from transient (puff) sourcesA paper submitted to the Journal of Environmental Engineering and Science.

A field trial involving 50 separate releases of inert aerosol (kaolin) was conducted to determine the concentration distribution within aerosol puffs resulting from near-instantaneous releases. Atmospheric conditions during the trial fell within Pasquill stability classes A and B (very and moderately unstable, respectively). Aerosol concentration measurements were made using a scanning lidar system operating at 1.06 μm. Artificial neural network (ANN) models were developed using the data to predict concentration distributions, given a number of meteorological parameters. The ANN predictions were compared to those from traditional Gaussian puff models, and provided better predictions than the Gaussian model parameterizations examined. The ANN models were also used to develop Gaussian fitting parameters to replace traditional Pasquill and Slade dispersion coefficients. The ANN-derived dispersion coefficients provided better predictions of measured puff concentration distributions than either the Pasquill or Slade parameterizations, though the full multi-input ANN models provided even better predictions than the Gaussian puff model using ANN-derived dispersion coefficients.