Assessing human exposures to environmental risk factors at continental-scale: accounting for short range variation in environmental factors and human activity patterns

Evaluating human interaction with environmental health determinants in space and time is fundamental to estimate personal environmental exposures. The increasing demand in an exposure assessment of entire populations requires to combine environmental variables at high resolution on large spatial extent, e.g. at nationwide or continental scale, with the space-time activity pattern of each individual in a study population.

Modelling population health and citizens' exposures is a complex process involving multiple procedural steps. One major step is to generate spatio-temporal information on environmental factors, either considered as beneficial for human wellbeing, for example, accessibility to green space or blue space, or considered as having negative health impacts such as the existence of air pollution, noise or heat. To capture the spatial variability these datasets need to be generated at high resolution. To allow for studies comparing cities, regions or countries, a geographical extent of subnational or larger size is required. In addition, data can be temporal to cover diurnal or seasonal variation of an environmental variable. Another major step is to use the environmental factors to as input to models calculating exposures for entire study populations, ranging from a few hundred participants up to millions of citizen. Here, socio-economic variables, mobility, different travel modes, and other daily activities with accompanying location changes need to be considered to mimic the space-time paths of each participant of a study population. These tasks require sufficient flexibility in both constructing environmental models as well as executing those eventually on HPC systems to break computational barriers of common workstations.

We present a computational framework for implementing both procedural steps and show the development of two European scale raster maps on a 25m grid and their subsequent usage to estimate human exposures to greenness visibility and noise. The maps were created with LUE (https://lue.computationalgeography.org/), an open-source modelling framework providing a Python package with currently 115 general-purpose operations for the construction of spatio-temporal simulation models. We implemented two custom focal operations that make use of the LUE framework. The first focal operation calculates for each raster cell the visible green area within a particular buffer size (c.f. Labib 2021, https://doi.org/10.1016/j.scitotenv.2020.143050). The second focal operation aggregates traffic-related noise within a particular buffer size, considering attenuation due to geometric divergence, atmospheric absorption, ground effects and diffraction.

We calculated visible green within a radius of 800m and noise within 1500m radius using 768 CPUs on eight HPC cluster nodes, and then used Campo (https://campo.computationalgeography.org/) for activity-based exposure assessment. The obtained exposure estimates can show considerable differences for different typical human activity patterns, such as homemaker or commuter, as well as a high spatial variability.