Rapid estimation of tire-wear particle concentration in road dust using PM10 and traffic data in a ternary plot

Air pollution, a pressing global issue, is significantly exacerbated by airborne particulate matter (PM), affecting air quality and human health. Urban vehicular activities majorly contribute to PM rise through both exhaust and non-exhaust emissions. Despite strides in managing exhaust emissions, non-exhaust particles, such as tire wear particles (TWP) remain under-addressed. This research proposes a method for estimating TWP concentrations using PM10 data and traffic activity, which could offer a valuable tool for controlling roadside fine particles and TWP. This paper introduces a ternary plotting technique and step-by-step procedure to estimate TWP levels in road dust using only PM10 and traffic data. Traditional analysis of TWP via pyrolysis-gas chromatography-mass spectrometry is complex and time-consuming. Hence, our proposed approach presents an alternate method that leverages readily accessible PM and traffic data, providing critical information for road management interpretation. The triangular plot analysis demonstrated a linear correlation: [log(Traffic) + 2]-[250,000/TWP-13]- 0.18PM10. While the resulting correlation may vary based on specific road conditions, the method can be tailored to different regions, offering insights into efficient estimation of TWP concentrations and promoting improved roadside pollution management.

Automated summary

This research proposes a method for estimating tire wear particle (TWP) concentrations using PM10 data and traffic activity, providing a valuable tool for controlling roadside fine particles and TWP. The study introduces a ternary plotting technique and procedure to estimate TWP levels in road dust.