An Agent-Based Modeling Framework for Simulating Human Exposure to Environmental Stresses in Urban Areas

Yang, Liang Emlyn; Hoffmann, Peter; Scheffran, Jürgen; Rühe, Sven; Fischereit, Jana; Gasser, Ingenuin

doi:10.20944/preprints201802.0174.v1

2018

DOI: 10.20944/preprints201802.0174.v1

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An Agent-Based Modeling Framework for Simulating Human Exposure to Environmental Stresses in Urban Areas

Liang Emlyn Yang¹,

Peter Hoffmann²,

Jürgen Scheffran³

et al.

Abstract: Abstract:The importance of predicting the exposure to environmental hazards is highlighted by 16 issues like global climate change, public health problems caused by environment stresses, and 17property damages and depreciations. Several approaches have been used to assess potential 18 exposure and achieve optimal results under various conditions, for example, for different scales,

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Cited by 5 publications

References 42 publications

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Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

Ramacher

Karl

2020

IJERPH

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To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen’s health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from −12% to +7% for NO2 and PM2.5. The developed “dynamic transport approach” is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

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Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

Ramacher

Karl

2020

IJERPH

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Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg

Lenfers

Ahmady-Moghaddam

Glake

et al. 2021

Land

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Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

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Response of Global Air Pollutant Emissions to Climate Change and Its Potential Effects on Human Life Expectancy Loss

Cheng

et al. 2019

Sustainability

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Geographical environment and climate change are basic factors for spatial fluctuations in the global distribution of air pollutants. Against the background of global climate change, further investigation is needed on how meteorological characteristics and complex geographical environment variations can drive spatial air pollution variations. This study analyzed the response of air pollutant emissions to climate change and the potential effects of air pollutant emissions on human health by integrating the air pollutant emission simulation model (GAINS) with 3 versions and CMIP5. The mechanism by which meteorological characteristics and geographical matrices can drive air pollution based on monitoring data at the site-scale was also examined. We found the total global emission of major air pollutants increased 1.32 times during 1970–2010. Air pollutant emissions will increase 2.89% and 4.11% in China and developed countries when the scenario of only maximum technically feasible reductions is performed (V4a) during 2020–2050. However, it will decrease 19.33% and 6.78% respectively by taking the V5a climate scenario into consideration, and precipitation variation will contribute more to such change, especially in China. Locally, the air circulation mode that is dominated by local geographical matrices and meteorological characteristics jointly affect the dilution and diffusion of air pollutants. Therefore, natural conditions, such as climate changes, meteorological characteristics and topography, play an important role in spatial air pollutant emissions and fluctuations, and must be given more attention in the processes of air pollution control policy making.

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An Agent-Based Modeling Framework for Simulating Human Exposure to Environmental Stresses in Urban Areas

Cited by 5 publications

References 42 publications

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg

Response of Global Air Pollutant Emissions to Climate Change and Its Potential Effects on Human Life Expectancy Loss

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