Schmitz Oliver, de Hoogh Kees, Probst-Hensch Nicole, Jeong Ayoung, Flückiger Benjamin, Lu Meng, Ndiaye Aisha, Vienneau Danielle, Hoek Gerard, Kyriakou Kalliopi, Vermeulen Roel C H, Karssenberg Derek
Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands.
Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
J Expo Sci Environ Epidemiol. 2025 Aug 2. doi: 10.1038/s41370-025-00799-7.
Agent-based assessment of long-term personal exposure to environmental factors accounts for spatio-temporal variation in exposures along daily activity tracks of individuals. Application up to nationwide study populations requires integration of large data sets on environmental factors, personal behavior, and socio-economic status, as well as propagating uncertainties in these inputs to personal exposure values.
To develop and illustrate a methodology and software framework for agent-based personal exposure assessment for large cohorts, including uncertainty assessment.
We design an agent-based methodology that addresses the sparse information on individual activity patterns available in large cohorts. This methodology was implemented in a Python-based open-source and reusable framework, which was subsequently applied to assess exposure to air pollution and noise for 626,381 residential addresses in the province of Utrecht, the Netherlands. Air pollution exposures were also assessed across all addresses in Switzerland and the EPIC-NL cohort in the Netherlands.
The designed framework aggregates time by divisions marked by a particular pattern in individual movement (e.g., weekdays, weekend days). Movement over a division is represented by a sequence of activities, each with a duration and spatial context, i.e., the geographical area where the activity takes place. Several activity types are included, each with a methodology to assess the spatial context, for instance, the route from home to work location. Uncertainty in inputs is defined by probability distributions constrained by observational data, if available, like statistics on origin and destination of trips, and propagated to calculated personal exposures through Monte Carlo simulation. The exposures assessed through our framework result in minor to moderate differences with those calculated using home-based exposure (for Utrecht an r of 0.79 for noise and 0.98 for nitrogen dioxide (NO) and particulate matter with aerodynamic diameters of 2.5 microns or smaller (PM), respectively), in particular leading to reduced contrast across the population in exposures.
Epidemiological studies on long-term effects of air pollution typically use a residential-based exposure assessment. However, it fails to account for individual mobility and spatial contrasts in environmental concentrations. While there is thus a need to investigate activity-based methods, their implementation is constrained by the lack of conceptual frameworks and software, particularly for large cohorts, which present unique demands regarding data inputs and computation. To address this gap, we introduce general concepts and a reusable, open-source software framework, designed for cluster computing, that can be applied consistently across a wide array of environmental factors and cohort studies.
基于主体的长期个人环境暴露评估考虑了个体日常活动轨迹中暴露的时空变化。要将其应用于全国范围的研究人群，需要整合有关环境因素、个人行为和社会经济地位的大量数据集，并将这些输入数据中的不确定性传播到个人暴露值中。
开发并阐述一种适用于大规模队列的基于主体的个人暴露评估方法及软件框架，包括不确定性评估。
我们设计了一种基于主体的方法，以解决大规模队列中个体活动模式信息稀疏的问题。该方法在一个基于Python的开源且可重复使用的框架中实现，随后应用于评估荷兰乌得勒支省626381个居住地址的空气污染和噪声暴露。还对瑞士所有地址以及荷兰的EPIC-NL队列中的空气污染暴露进行了评估。
所设计的框架按个体移动的特定模式（如工作日、周末）划分的时间段进行汇总。一个时间段内的移动由一系列活动表示，每个活动都有持续时间和空间背景，即活动发生的地理区域。包含了几种活动类型，每种类型都有评估空间背景的方法，例如从家到工作地点的路线。输入数据中的不确定性由观测数据（如有）约束的概率分布定义，如出行起点和终点的统计数据，并通过蒙特卡罗模拟传播到计算出的个人暴露值中。通过我们的框架评估的暴露结果与使用基于家庭的暴露计算出的结果存在轻微到中等程度的差异（对于乌得勒支，噪声的r值为0.79，二氧化氮（NO）和空气动力学直径小于等于2.5微米的颗粒物（PM）的r值分别为0.98），特别是导致人群中暴露的对比度降低。
关于空气污染长期影响的流行病学研究通常使用基于居住地的暴露评估。然而，它无法考虑个体流动性和环境浓度的空间差异。因此，虽然需要研究基于活动的方法，但其实施受到概念框架和软件的缺乏的限制，特别是对于大规模队列，这对数据输入和计算提出了独特的要求。为了填补这一空白，我们引入了通用概念和一个为集群计算设计的可重复使用的开源软件框架，该框架可在广泛的环境因素和队列研究中一致应用。
