Uncertainties in atmospheric dispersion modelling during nuclear accidents.

Predictions of the atmospheric dispersion of radionuclides accidentally released from a nuclear power plant are influenced by two large sources of uncertainty: one associated with the meteorological data employed, and one with the source term, i.e. the temporal evolution of the amount and physical and chemical properties of the release. A methodology is presented for quantitative estimation of the variability of the prediction of atmospheric dispersion resulting from both sources of uncertainty. The methodology, which allows for efficient calculation, and thus is well suited for real-time assessment, is applied to a hypothetical accidental release of radionuclides.

Method for source localization proposed and applied to the October 2017 case of atmospheric dispersion of Ru-106.

An efficient numerical method for the temporal and spatial localization of an unknown point source of an atmospheric tracer is proposed. The method, which is based on inverse modelling techniques, employs available data from a network of ground-level stations on the condition that the measurements represent the plume geographically and temporally. The method, which employs a level-of-agreement approach, is applied to the October 2017 air concentration measurements of Ru-106 in Europe.

Microclimatic temperatures at Danish cattle farms, 2000-2016: quantifying the temporal and spatial variation in the transmission potential of Schmallenberg virus.

BACKGROUND: Microclimatic temperatures provide better estimates of vector-borne disease transmission parameters than standard meteorological temperatures, as the microclimate represent the actual temperatures to which the vectors are exposed. The objectives of this study were to quantify farm-level geographic variations and temporal patterns in the extrinsic incubation period (EIP) of Schmallenberg virus transmitted by Culicoides in Denmark through generation of microclimatic temperatures surrounding all Danish cattle farms. METHODS: We calculated the hourly microclimatic temperatures at potential vector-resting sites within a 500 m radius of 22,004 Danish cattle farms for the months April to November from 2000 to 2016. We then modeled the daily EIP of Schmallenberg virus at each farm, assuming vectors choose resting sites either randomly or based on temperatures (warmest or coolest available) every hour. The results of the model output are presented as 17-year averages. RESULTS: The difference between the warmest and coolest microhabitats at the same farm was on average 3.7 °C (5th and 95th percentiles: 1.0 °C to 7.8 °C). The mean EIP of Schmallenberg virus (5th and 95th percentiles) for all cattle farms during spring, summer, and autumn was: 23 (18-33), 14 (12-18) and 51 (48-55) days, respectively, assuming Culicoides select resting sites randomly. These estimated EIP values were considerably shorter than those estimated using standard meteorological temperatures obtained from a numerical weather prediction model for the same periods: 43 (39-52), 21 (17-24) and 57 (55-58) days, respectively. When assuming that vectors actively select the coolest resting sites at a farm, the EIP was 2.3 (range: 1.1 to 4.1) times longer compared to that of the warmest sites at the same farm. CONCLUSIONS: We estimated a wide range of EIP in different microclimatic habitats surrounding Danish cattle farms, stressing the importance of identifying the specific resting sites of vectors when modeling vector-borne disease transmission. We found a large variation in the EIP among different farms, suggesting disease transmission may vary substantially between regions, even within a small country. Our findings could be useful for designing risk-based surveillance, and in the control and prevention of emerging and re-emerging vector-borne diseases.

Microclimatic temperatures increase the potential for vector-borne disease transmission in the Scandinavian climate.

We quantified the difference between the meteorological temperature recorded by the Danish Meteorological Institute (DMI) weather stations and the actual microclimatic temperatures at two or three different heights at six potential insect habitats. We then compared the impact of the hourly temperature on the extrinsic incubation period (EIP) of six pathogens. Finally, we developed a regression model, enabling us to predict the microclimatic temperatures of different habitats based on five standard meteorological parameters readily available from any meteorological institution. Microclimatic habitats were on average 3.5-5 °C warmer than the DMI recorded temperatures during midday and 1-3 °C cooler at midnight. The estimated EIP for five of the six microclimatic habitats was shorter than the estimates based on DMI temperatures for all pathogens studied. The microclimatic temperatures also predicted a longer season for virus development compared to DMI temperatures. Based on DMI data of hourly temperature, solar radiation, wind speed, rain and humidity, we were able to predict the microclimatic temperature of different habitats with an R2 of 0.87-0.96. Using only meteorological temperatures for vector-borne disease transmission models may substantially underestimate both the daily potential for virus development and the duration of the potential transmission season.

The Danish emergency response model of the atmosphere (DERMA).

The three-dimensional atmospheric dispersion model DERMA is of Lagrangian type making use of a hybrid stochastic particle-puff diffusion description. It is currently capable of describing plumes at downwind distances greater than about 20km and up to the global scale. The model employs aerosol-size dependent dry and wet deposition parameterisations. DERMA is developed and used mainly for nuclear emergency preparedness purposes, and it has recently become integrated with the ARGOS nuclear decision-support system.

Urban meteorological modelling for nuclear emergency preparedness.

The main objectives of the current EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure" (FUMAPEX) are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction (NWP) models to urban air pollution and population dose models, the building of improved urban air quality information and forecasting systems, and their application in cities in various European climates. In addition to the forecast of the worst air-pollution episodes in large cities, the potential use of improved weather forecasts for nuclear emergency management in urban areas, in case of hazardous releases from nuclear accidents or terror acts, is considered. Such use of NWP data is tested for the Copenhagen metropolitan area and the Øresund region. The Danish Meteorological Institute (DMI) is running an experimental version of the HIRLAM NWP model over Zealand including the Copenhagen metropolitan area with a horizontal resolution of 1.4km, thus approaching the city-scale. This involves 1-km resolution physiographic data with implications for the urban surface parameters, e.g. surface fluxes, roughness length and albedo. For the city of Copenhagen, the enhanced high-resolution NWP forecasting will be provided to demonstrate the improved dispersion forecasting capabilities of the Danish nuclear emergency preparedness decision-support system, the Accident Reporting and Guidance Operational System (ARGOS), used by the Danish Emergency Management Agency (DEMA). Recently, ARGOS has been extended with a capability of real-time calculation of regional-scale atmospheric dispersion of radioactive material from accidental releases. This is effectuated through on-line interfacing with the Danish Emergency Response Model of the Atmosphere (DERMA), which is run at DMI. For local-scale modelling of atmospheric dispersion, ARGOS utilises the Local-Scale Model Chain (LSMC), which makes use of high-resolution DMI-HIRLAM NWP model data provided to DEMA by DMI four times a day under operational surveillance and covering Denmark and surroundings. The integration of DERMA in ARGOS is effectuated through automated on-line digital communication and exchange of data. The calculations are carried out in parallel for each NWP model to which DMI has access, thereby providing a mini-ensemble of dispersion forecasts for the emergency management.