Correlation analysis of noise and ultrafine particle counts in a street canyon

Ultrafine particles (UFP, diameter < 100 nm) are very likely to negatively affect human health, as underlined by some epidemiological studies. Unfortunately, further investigation and monitoring are hindered by the high cost involved in measuring these UFP. Therefore we investigated the possibility to correlate UFP counts with data coming from low-cost sensors, most notably noise sensors. Analyses are based on an experiment where UFP counts, noise levels, traffic counts, nitrogen oxide (NO, NO 2 and their combination NO x) concentrations, and meteorological data were collected simultaneously in a street canyon with a traffic intensity of 3200 vehicles/day, over a 3-week period during summer. Previous reports that NO x concentrations could be used as a proxy to UFP monitoring were verified in our setup. Traffic intensity or noise level data were found to correlate with UFP to a lesser degree than NO x did. This can be explained by the important influence of meteorological conditions (mainly wind and humidity), influencing UFP dynamics. Although correlations remain moderate, sound levels are more correlated to UFP in the 20–30 nm range. The particles in this size range have indeed rather short atmospheric residence times, and are thus more closely short-term traffic-related. Finally, the UFP estimates were significantly improved by grouping data with similar relative humidity and wind conditions. By doing this, we were able to devise noise indicators that correlate moderately with total particle counts, reaching a Spearman correlation of R = 0.62. Prediction with noise indicators is even comparable to the more-expensive-to-measure NO x for the smallest UFP, showing the potential of using microphones to estimate UFP counts. ► Correlations between noise, traffic, UFP and NO x are studied in a street canyon. ► Correlations strongly depend on the size of particles. ► NO x correlates more with UFP than noise and traffic flow rates do. ► Noise correlates with UFP after grouping data based on meteorological conditions.