EANM practical guidance on uncertainty analysis for molecular radiotherapy absorbed dose calculations.

A framework is proposed for modelling the uncertainty in the measurement processes constituting the dosimetry chain that are involved in internal absorbed dose calculations. The starting point is the basic model for absorbed dose in a site of interest as the product of the cumulated activity and a dose factor. In turn, the cumulated activity is given by the area under a time-activity curve derived from a time sequence of activity values. Each activity value is obtained in terms of a count rate, a calibration factor and a recovery coefficient (a correction for partial volume effects). The method to determine the recovery coefficient and the dose factor, both of which are dependent on the size of the volume of interest (VOI), are described. Consideration is given to propagating estimates of the quantities concerned and their associated uncertainties through the dosimetry chain to obtain an estimate of mean absorbed dose in the VOI and its associated uncertainty. This approach is demonstrated in a clinical example.

Assessing the performance of standard methods to predict the standard uncertainty of air quality data having incomplete time coverage.

As a result of the complex nature of operating multi-station national air quality networks it is rare that complete data sets are produced from these networks. The reliance of most air quality legislation on the assessment of measured annual average concentrations against target or limit concentrations necessitates the use of methods to calculate an annual average value and the uncertainty in this value in the absence of a complete data set for the year in question. Standard procedures exist for performing these calculations, but it is not clear how effective these are when data having low time resolution are collected and missing data accounts for large periods of the year. This paper investigates the influence of these deficiencies using data from UK air quality networks in the form of monthly average concentrations for polycyclic aromatic hydrocarbons and for metals in the PM10 phase of ambient air. Whilst the standard methods currently employed produce good results on average, for individual cases the uncertainty in the annual average calculated when data is missing may be appreciably different from that obtained when full knowledge of the distribution of the data is known. These effects become more apparent as the quantity of missing data increases.

Improved strategies for calculating annual averages of ambient air pollutants in cases of incomplete data coverage.

The consequences of missing data during air quality monitoring activities and the calculation of the annual average mass concentration of ambient pollutants are discussed. Possible strategies for mitigating totally and partially missing data during given measurement periods are presented and evaluated. A mathematical description of a preferred method for the determination of annual average concentration using the simple mean, and not using time weighting to account for missing data, is justified. It is hoped this discussion paper will provoke debate in the air quality community about the best way to assess measured concentrations of ambient pollutants against legislative values.