Agent-based Modeling of Human Exposure to Urban Environmental Stressors – A Docking Study

Abstract

<p>The goal of environmental exposure modelling is to link fundamental human activities with stress via the environment. Stress is here defined as environmental conditions negatively affecting human health and well-being. Especially in urban areas, humans can be exposed to multiple stressors such as air pollution, noise (e.g. traffic), and heat. The importance of being able to predict the exposure level in urban areas is increasing due to ongoing urbanization and global climate change. For instance, in Germany annual Greenhouse Gas (GHG) emissions have been reduced by 28% from 1990 to 2014 but contributions by the transport sector have been quite stable (from 0.163 GtCO2Equivalents in 1990 to 0.160 GtCO2Equivalents in 2014 (Umweltbundesamt, 2016). Yang et al. (2018) provides a stylized agent-based model of human exposure to environmental stressors (heat, rain, NO2) for Hamburg, Germany. Within this ABM, the changing exposure to environmental stressors is analyzed for citizens as a function of time and location. The population is classified into different archetypes; they range from young, single students to families with children to old, rich and single persons. While their choice of transportation is a function of exposure, commuting time and costs, each agent has different preferences and different rates to adapt to changing environmental conditions. The agents are moving in multiple layers of housing (e.g. residential buildings) and infrastructure (e.g. streets, subway). Depending on the agent types, bike, car or public transport is chosen as the preferred mean of transport. However, Yang et al. (2018) consider stylized agent-based dynamics without any interaction among the agents. We provide a multi-agent docking study of human exposure to environmental stressors implemented in Netlogo and find distributional and relational equivalence (Axtell et al., 1996, Hokamp et al. 2018) to Yang et al. (2018). To put it differently, we analyze interacting individual heterogeneous agents in an actual urban environment. Results give information about the mean of transportation with the lowest exposure and how very low costs for public transport affect choices of transportation and so the road traffic. Further, the results may be used by policy makers and citizens (e.g. via mobile devices using an app) to improve environmental quality of life.</p><p><br>References</p><p>Axtell, R., Axelrod, R., Epstein, J.M., and Cohen, M.D. (1996) Aligning simulation models: a case study and results. Computational & Mathematical Organization Theory, 1 (2), 123–141.</p><p>Hokamp, S., Gulyas, L., Koehler, M. and Wijesinghe, S. (2018), Agent-based Modelling and Tax Evasion: Theory and Application, 3-35, Hoboken, NJ, John Wiley & Sons Ltd.</p><p>Umweltbundesamt (2014) Submission under the United Nations Framework Convention on Climate Change and the Kyoto Protocol 2016 – National Inventory Report for the German Greenhouse Gas Inventory 1990-2014.</p><p>Yang, L. E., Hoffmann, P., Scheffran, J. , Rühe, S. , Fischereit, J. and Gasser, I. (2018), An Agent-Based Modeling Framework for Simulating Human Exposure to Environmental Stresses in Urban Areas, Urban Science, 2, 36.</p>