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

Setting

Over 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.

Relevance

While 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.

Objectives

Following 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:

1. Emission and fate in catchments and cities
2. Accumulation in organisms
3. Effects on communities

Methods, Expected Results, and Impact

Based 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. This will cover, 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
2. Crucially reduces the data-hungriness of assessment tools.