Source reconstruction of airborne toxics based on acute health effects information.

The intentional or accidental release of airborne toxics poses great risk to the public health. During these incidents, the greatest factor of uncertainty is related to the location and rate of released substance, therefore, an information of high importance for emergency preparedness and response plans. A novel computational algorithm is proposed to estimate, efficiently, the location and release rate of an airborne toxic substance source based on health effects observations; data that can be readily available, in a real accident, contrary to actual measurements. The algorithm is demonstrated by deploying a semi-empirical dispersion model and Monte Carlo sampling on a simplified scenario. Input data are collected at varying receptor points for toxics concentrations (C; standard approach) and two new types: toxic load (TL) and health effects (HE; four levels). Estimated source characteristics are compared with scenario values. The use of TL required the least number of receptor points to estimate the release rate, and demonstrated the highest probability (>90%). HE required more receptor points, than C, but with lesser deviations while probability was comparable, if not better. Finally, the algorithm assessed very accurately the source location when using C and TL with comparable confidence, but HE demonstrated significantly lower confidence.

Comparisons between a new point kernel-based scheme and the infinite plane source assumption method for radiation calculation of deposited airborne radionuclides from nuclear power plants.

Radiation from the deposited radionuclides is indispensable information for environmental impact assessment of nuclear power plants and emergency management during nuclear accidents. Ground shine estimation is related to multiple physical processes, including atmospheric dispersion, deposition, soil and air radiation shielding. It still remains unclear that whether the normally adopted "infinite plane" source assumption for the ground shine calculation is accurate enough, especially for the area with highly heterogeneous deposition distribution near the release point. In this study, a new ground shine calculation scheme, which accounts for both the spatial deposition distribution and the properties of air and soil layers, is developed based on point kernel method. Two sets of "detector-centered" grids are proposed and optimized for both the deposition and radiation calculations to better simulate the results measured by the detectors, which will be beneficial for the applications such as source term estimation. The evaluation against the available data of Monte Carlo methods in the literature indicates that the errors of the new scheme are within 5% for the key radionuclides in nuclear accidents. The comparisons between the new scheme and "infinite plane" assumption indicate that the assumption is tenable (relative errors within 20%) for the area located 1 km away from the release source. Within 1 km range, the assumption mainly causes errors for wet deposition and the errors are independent of rain intensities. The results suggest that the new scheme should be adopted if the detectors are within 1 km from the source under the stable atmosphere (classes E and F), or the detectors are within 500 m under slightly unstable (class C) or neutral (class D) atmosphere. Otherwise, the infinite plane assumption is reasonable since the relative errors induced by this assumption are within 20%. The results here are only based on theoretical investigations. They should be further thoroughly evaluated with real measurements in the future.

Modeling Short-Term Maximum Individual Exposure from Airborne Hazardous Releases in Urban Environments. Part I: Validation of a Deterministic Model with Field Experimental Data.

The release of airborne hazardous substances in the atmosphere has a direct effect on human health as, during the inhalation, an amount of concentration is inserted through the respiratory system into the human body, which can cause serious or even irreparable damage in health. One of the key problems in such cases is the prediction of the maximum individual exposure. Current state of the art methods, which are based on the concentration cumulative distribution function and require the knowledge of the concentration variance and the intermittency factor, have limitations. Recently, authors proposed a deterministic approach relating maximum individual exposure to parameters such as the fluctuation intensity and the concentration integral time scale. The purpose of the first part of this study is to validate the deterministic approach with the extensive dataset of the MUST (Mock Urban Setting Test) field experiment. This dataset includes 81 trials, which practically cover various atmospheric conditions and stability classes and contains in total 4004 non-zero concentration sensor data with time resolutions of 0.01-0.02 s. The results strengthen the usefulness of the deterministic model in predicting short-term maximum individual exposure. Another important output is the estimation of the methodology uncertainty involved.

Modelling Short-Term Maximum Individual Exposure from Airborne Hazardous Releases in Urban Environments. Part ΙI: Validation of a Deterministic Model with Wind Tunnel Experimental Data.

The capability to predict short-term maximum individual exposure is very important for several applications including, for example, deliberate/accidental release of hazardous substances, odour fluctuations or material flammability level exceedance. Recently, authors have proposed a simple approach relating maximum individual exposure to parameters such as the fluctuation intensity and the concentration integral time scale. In the first part of this study (Part I), the methodology was validated against field measurements, which are governed by the natural variability of atmospheric boundary conditions. In Part II of this study, an in-depth validation of the approach is performed using reference data recorded under truly stationary and well documented flow conditions. For this reason, a boundary-layer wind-tunnel experiment was used. The experimental dataset includes 196 time-resolved concentration measurements which detect the dispersion from a continuous point source within an urban model of semi-idealized complexity. The data analysis allowed the improvement of an important model parameter. The model performed very well in predicting the maximum individual exposure, presenting a factor of two of observations equal to 95%. For large time intervals, an exponential correction term has been introduced in the model based on the experimental observations. The new model is capable of predicting all time intervals giving an overall factor of two of observations equal to 100%.