Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands.
Department of Geography and Resource Management and Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China.
J Expo Sci Environ Epidemiol. 2023 Nov;33(6):954-962. doi: 10.1038/s41370-023-00527-z. Epub 2023 Feb 14.
Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects.
We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations.
We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM), noise, green space, and blue space.
Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space.
For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM exposure.
准确量化人们的户外环境暴露情况对于识别疾病风险因素非常重要。目前已经存在几种基于活动空间的暴露评估方法，这些方法可能会导致不同的暴露估计值，并且尚未考虑个人出行方式或与暴露相关的距离衰减效应。
我们旨在（1）开发一种基于活动空间的暴露评估方法，该方法包括出行方式和与暴露相关的距离衰减效应，以及（2）比较这些方法得出的活动空间大小和暴露估计值在常用的活动空间操作定义中的差异。
我们使用了 269 名荷兰成年人为期 7 天的基于全球定位系统（GPS）的智能手机追踪数据。将人们的 GPS 轨迹点分为被动和主动出行方式。通过线性、指数和高斯衰减函数来模拟与暴露相关的距离衰减效应。我们对这三种功能衰减模型以及一个无权重模型与四种活动空间模型（即基于家庭的缓冲区、最小凸多边形、两个标准偏差椭圆和时间加权的基于 GPS 的缓冲区）进行了交叉比较。我们应用非参数 Kruskal-Wallis 检验、两两 Wilcoxon 符号秩检验和 Spearman 相关系数来评估活动空间范围的均值差异以及与颗粒物（PM）、噪声、绿地和蓝地暴露的相关性。
参与者平均有 42%的日常生活时间在户外。我们发现，将出行方式纳入活动空间划定会导致活动空间明显更加紧凑。PM 和蓝地的暴露估计值在考虑或不考虑出行方式的情况下有显著差异（p<0.05），而噪声和绿地的暴露估计值则没有显著差异。虽然纳入距离衰减效应会显著影响噪声和绿地暴露评估，但应用的衰减函数似乎对结果没有影响。我们发现，住宅暴露估计值可作为人们日常生活中 PM 暴露总量的代理值，而基于 GPS 的评估值则适用于噪声、绿地和蓝地。
对于某些暴露，虽然经过测试的活动空间定义显著相关，但基于是否包含出行方式或距离衰减效应，其暴露估计值结果存在差异。结果仅支持使用基于家庭的缓冲区值作为个体日常短期 PM 暴露的代理值。
