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- W2022341943 abstract "During the last decade, the assessment of concentration levels of chemical substances in the environment has become a major issue for defining actual exposure in risk evaluation processes. Currently, different approaches are proposed to achieve this assessment. The first one, widely used, relies on multi-media fate models. On the other hand, monitoring data begin also to be used for this purpose but, up to now, few statistical methods have been proposed to validate and summarize them adequately at a local or regional level. A crucial characteristic of monitoring data is that many observations stay below the detection limits of the measurement devices and therefore the data are highly censored. This article presents a practical methodology to estimate a regional distribution for the concentration of a chemical substance in the surface water of a given region. The estimate of the distribution is obtained from complete or summarized monitoring data collected over time in the region's sampling stations. From this estimate, we derive different statistical summaries, such as the concentration mean, the standard deviation and the percentiles. Two approaches have been developed and are compared on U.K. mercury data. A first non-parametric approach derives the regional distribution by aggregating the observed data without making any assumption on their statistical distribution. A second approach, which may be applied to complete or summarized local data, proceeds in two steps. First, a statistical distribution (e.g. lognormal, gamma,…) of concentration is fitted to the, possibly censored, data of each sampling station, and then these local distributions are aggregated at a regional level and statistics of interest are derived. Copyright © 2004 John Wiley & Sons, Ltd." @default.
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- W2022341943 date "2005-01-01" @default.
- W2022341943 modified "2023-09-27" @default.
- W2022341943 title "From monitoring data to regional distributions: a practical methodology applied to water risk assessment" @default.
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- W2022341943 doi "https://doi.org/10.1002/env.665" @default.
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