The Power of Size: How cross-disciplinary scaling transforms assessment and management of environmental risks

SettingOver the last decades, anthropogenic pressures on the environment have dramatically intensified and diversified. Nowadays, we have to deal with 100,000+ pollutants, species and sites.

To set the right priorities among these environmental problems and to select the best alternatives among sustainable solutions, proper assessment tools are urgently needed.RelevanceWhile several models are available, application to thousands of cases is severely limited by data gaps due to financial, ethical, disciplinary and other constraints.

As an alternative, missing information can be obtained by linking parameters to size (e.g., catchment area, organism weight).

While scaling has been proven valuable for a few parameters, relationships have been derived in a mono-disciplinary context only.ObjectivesFollowing the urgent scientific and societal needs, the overall aim of PowerOfSize is to obtain a cross-disciplinary suite of mechanistic and statistical relationships for environmentally relevant parameters (quantities) in assessment models as a function of size and other descriptors, underpinned by overarching scaling principles.

Based on research gaps and policy priorities, we focus on pollutants, covering emission and fate in catchments and cities, accumulation in organisms and effects on communities.Methods, expected results and impactBased on information from reviews, databases and articles, we will derive empirical and theoretical relationships for rate, time, density and other quantities Y (parameters) as a function of city, catchment, organism and community size X, covering, e.g., water in catchments, materials in cities, blood in organisms and biomass in communities as well as the pollutants they generate, carry and degrade.

Identifying overarching principles based on shared system characteristics (e.g., dimension), the integrated suite obtained 1) profoundly advances our understanding and 2) crucially reduces data-hungriness of assessment tools.