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Enhanced Models of Chronic Human Exposure to Chemicals with Lognormal Distributions for the Duration

机译:Enhanced Models of Chronic Human Exposure to Chemicals with Lognormal Distributions for the Duration

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摘要

Abstract Probabilistic models are widely used to assess chronic human exposure to chemicals. However, the prevalent strategy (used by the vast majority of recent studies) directly applies probabilistic distributions of model inputs to generate the simulation results of a single-day exposure, which may fail to construct the exposure profile for chronic scenarios. To address this issue, we enhanced probabilistic exposure assessment models by including the period of exposure. Because lognormal distribution is often used in human chemical exposure assessment, a lognormal-based modeling approach was presented to execute the modeling exercise, which considered the entire exposure duration. Using the product theorem and the sum approximation of independent lognormal random variables, a four-step simulation approach was developed to determine the daily average exposure dose to chemicals. By setting the exposure duration to 1?day, the proposed lognormal model is transformable into the conventional technique (i.e., the common method). Using chronic dietary exposure to chlorpyrifos in crops as an illustration, simulation results suggested that the distribution of the daily average exposure dose was more centered for the lognormal method than for the common method. That could be explained by the central limit theorem. When the exposure assessment study concentrated on the results at the high end of the distribution (e.g., 95th and 97.5th percentiles), the proposed lognormal technique, taking the complete exposure duration into account, provided lower daily chemical exposure doses than the common method. Due to its simplicity and conservatism (with a focus on the top end of the distribution), the usual technique can be used to assess human exposure to chemicals; however, the proposed lognormal method, which takes the entire exposure duration into consideration, is better suitable for real-world applications.

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