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Integrated Modelling for Health and Environmental Impact Assessment of Air Pollution and Climate Change

By Stefan Reis, Tim Oxley and Ed Rowe

Abstract

Modelling the impacts of air pollution and climate change on human health and ecosystems\ud in integrated assessment models (IAMs) has emerged as a key tool to inform policy\ud decision making, where simplistic solutions are unlikely to deliver efficient and sustainable\ud pathways for future development.\ud Model integration is facing a complex set of challenges in different dimensions, as\ud integrated models have to be:\ud Spatially explicit and of sufficiently high spatial resolution for their respective domain,\ud with nesting approaches providing the integration across different spatial scales.\ud Temporally dynamic to model system responses and recovery e.g. pollutant\ud accumulation, time-lag (e.g. of measure implementation) and time-bomb effects. Due\ud to different temporal horizons for different processes (e.g. days-years for air pollution,\ud decades-centuries for climate change, centuries-millennia for accumulation of heavy\ud metals/POPs in soils), integrated models also need to nest models with different\ud temporal resolution.\ud Sectorally detailed to model trade-offs and synergies and to allow for the\ud representation of paradigm-shifts (e.g. in energy systems) and behavioural changes\ud (e.g. non-technical measures).\ud Accessible, providing clear illustrations of inter-sectoral synergies and tradeoffs (e.g.\ud ammonia emission reduction vs. nitrate leaching in agriculture) using visualisations\ud and multi-media.\ud In addition to the aforementioned requirements, integrated models need to be flexible and\ud scalable to be able to provide answers to varying problems. This paper discusses current\ud challenges faced by IAMs and emerging developments based on a literature review.\ud \u

Topics: Ecology and Environment, Atmospheric Sciences, Health
Publisher: International Environmental Modelling and Software Society
Year: 2010
OAI identifier: oai:nora.nerc.ac.uk:12946

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