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Summary: The material intensity (MI) of the economy remains among the most widely cited indicators in international statistics and reports, evaluating the efficient use and productivity of natural resources in the economic process. In the context of the contemporary economy‐wide material flow accounting framework, the material intensity of a country is evaluated through the estimation of the ratio of the domestic material consumption (DMC) to the gross domestic product (GDP) index (DMC/GDP). Indeed, the essential contribution of natural resources to the economic process requires the establishment of reliable projections of this intricate relationship to the future. These projections may provide critical information to policy makers and practitioners in order to evaluate the future dynamics of the efficient use of natural resources in the production process. Toward this objective, the present study evaluates and proposes an alternative novel methodology for MI statistical projections, based on the beta distribution, by using a deterministic model for predicting the maximum expected values. The parameters of the deterministic model are calculated from the estimated MI of the global economy. The evaluation of the model is then performed by using MI estimates from 107 individual countries. The agreement between the model and the estimates is very good. The proposed method's merit is its simplicity, as by using two statistics of the material intensity (mean and variance) and an integral time scale, it is feasible to calculate the probabilities of the MI of any country with a high degree of confidence. [ABSTRACT FROM AUTHOR]

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%.

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.