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Highly spatially- and seasonally-resolved predictive contamination maps for persistent organic pollutants:
Cristiano Ballabio1, Niccoló Guazzoni, Roberto Comolli
1European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra (VA), Italy. cristiano.ballabio@jrc.ec.europa.eu
The Science of the Total Environment
|May 28, 2013
Summary
This study developed high-resolution maps to track persistent organic pollutants (POPs) in Alpine soils. These models accurately predict POPs accumulation and distribution, aiding in environmental monitoring and risk assessment.
Area of Science:
- Environmental Science
- Soil Science
- Geospatial Analysis
Background:
- Persistent organic pollutants (POPs) pose environmental risks, necessitating accurate spatial and temporal assessments.
- Soil properties significantly influence POPs accumulation, exhibiting high variability across space and time.
- Existing methods often lack the resolution to capture this complex variability for effective burden and flux studies.
Purpose of the Study:
- To develop predictive contamination maps for soil accumulation capacity of POPs at a 1x1m resolution.
- To model the spatial, vertical, and seasonal distribution of Polychlorinated Biphenyls (PCBs) contamination potential in Alpine soils.
- To create dynamic models for seasonal variations in soil POPs concentrations.
Main Methods:
- Utilized physical algorithms to estimate soil temperature and organic carbon profiles.
- Developed predictive accumulation capacity (Ksa) maps for PCBs.
- Cross-validated model predictions with independent PCB contamination data.
- Employed regression analysis to map soil contamination, incorporating soil characteristics and temperature.
- Developed a dynamic model for seasonal PCB concentration variations based on fitted rate parameters.
Main Results:
- Generated highly resolved (1x1m, daily) predictive contamination maps for soil accumulation capacity.
- Achieved excellent agreement between predicted Ksa maps and experimental PCB data (e.g., R²=0.80 for CB-153).
- Successfully mapped soil contamination across the study area, considering soil properties and temperature.
- Developed a dynamic model partially explaining seasonal variations in PCB soil concentrations, including summer discharge and autumn recharge phases.
Conclusions:
- The developed predictive maps provide a powerful tool for assessing POPs burden and fluxes with high spatial and temporal detail.
- The models accurately capture the spatial and temporal variability of POPs accumulation in soils.
- Understanding seasonal dynamics is crucial for interpreting POPs behavior in soil environments.
