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Quantification and mapping of tyre wear emissions: from EU regional analysis to global projections
Summary
Researchers quantified and mapped tyre wear particle emissions across EU member states and developed global projections, finding substantial discrepancies in per capita emission estimates across different national methodologies and providing guidance for optimizing future emission estimations.
Tyre wear particle (TWP) emissions are a major source of microplastic pollution. This study analyzes the variability in national TWP emissions estimated through different methodologies and provides guidance for optimizing these estimations. Findings show substantial discrepancies in per capita emissions across European countries, with variations up to 2 kg/y*cap, particularly between Southern and Eastern European countries, as well as Estonia and Finland. In contrast, Western European countries exhibit minimal variation despite diverse methodologies. We predicted annual TWP loads reaching environmental compartments such as air, soil, sewers, and surface waters. Germany, France, and Italy were identified as major emitters, each exceeding 100,000 tons annually. Notably, only a small fraction of these emissions, approximately 13,000 tons per year, reaches surface waters due to varying efficiencies of wastewater treatment facilities. Based on the country-specific emission estimates, we developed predictive models for emission estimation based on socioeconomic variables such as Gross Domestic Product. While a simplified Ordinary Least Squares (OLS) model identified key linear drivers, a more advanced Random Forest (RF) model demonstrated significantly higher predictive accuracy (LOOCV R > 0.7 for major vehicle types), revealing the complex, non-linear nature of emission drivers. This dual-modeling approach provides a robust framework for global projections, highlighting Luxembourg and the United States as leaders in per capita emissions (3.56 and 3.12 kg/cap*y, respectively), while China and India exhibit the highest total emissions. Our predictive models reduce data requirements and facilitate a preliminary, data-driven estimation of the global distribution of TWP emissions.
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