利用随机人体暴露和剂量模拟模型建立车内 PM(2.5) 暴露模型。

Xiaozhen Liu, H Christopher Frey, Ye Cao, Bela Deshpande
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引用次数: 0

摘要

确定并评估了影响车内 PM(2.5) 暴露的因素。回顾了当前版本的颗粒物随机暴露和剂量模拟模型(SHEDS-PM)中用于计算车内 PM(2.5) 浓度的方法,并确定和评估了替代建模方法。SHEDS-PM 采用线性回归模型,根据环境 PM(2.5) 浓度(如固定地点监测仪(FSM)或空气质量模型得出的网格单元平均浓度估计值)估算车内 PM(2.5) 浓度。车内浓度与 FSM 浓度的比率因地点、车辆类型和其他因素的不同而有很大差异。SHEDS-PM用于估算北卡罗来纳州维克县1%居民的PM(2.5)暴露,以评估车内暴露的重要性。对某些人来说,车内 PM(2.5) 暴露可能高达总暴露量的一半,这取决于就业状况和通勤期间在车内花费的时间。本文探讨了一种替代建模方法,该方法的基础是使用基于 FSM 数据的扩散模型来估算近路 PM(2.5) 浓度,以及使用质量平衡模型来估算车内浓度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling of In-vehicle PM(2.5) Exposure Using the Stochastic Human Exposure and Dose Simulation Model.

Factors that influence in-vehicle PM(2.5) exposure are indentified and assessed. The methodology used in the current version of Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) for in-vehicle PM(2.5) concentration is reviewed, and alternative modeling approaches are identified and evaluated. SHEDS-PM uses a linear regression model to estimate in-vehicle PM(2.5) concentration based on ambient PM(2.5) concentration, such as from a fixed site monitor (FSM) or a grid cell average concentration estimate from an air quality model. The ratio of in-vehicle to FSM concentration varies substantially with respect to location, vehicle type and other factors. SHEDS-PM was used to estimate PM(2.5) exposure for 1% of people living in Wake County, NC in order to assess the importance of in-vehicle exposures. In-vehicle PM(2.5) exposure can be as much as half of the total exposure for some individuals, depending on employment status and the time spent in-vehicle during commuting. An alternative modeling approach is explored based on the use of a dispersion model to estimate near-road PM(2.5) concentration based on FSM data and a mass balance model for estimating in-vehicle concentration.Recommendations for updating the input data to the existing model, and implementation of the alternative modeling approach are made.

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