AI Summary & Key Takeaways
AI InsightThe spatial characteristics of the site and their acoustical effects were not considered in the present approach (e.g. distance to road or buildings, road surface), hence, the results should be transferred to other sites with some caution.
📌 3 Key Takeaways
Özet
Urban road traffic is a major joint emission source for particles and ambient noise. This study explores the relationship between both environmental stressors at an urban traffic site and analyses the potential to model particle number size distributions (NSD) from measurements of ambient noise frequency levels. Thus, a measurement campaignwas conducted within an urban street canyon covering a period of 50 days. First, noise frequency levels were used to successfully model traffic intensity at the street canyon site on a half-hourly basis (R2 ¼ 0.78). Thereafter, two multiple linear regression models were built to calculate NSD using noise frequency levels in combination with meteorological quantities (wind speed and air temperature)and air pollutant data(NO2) asexplanatoryvariables. Implementationof meteorological quantities in Model 1 captured the diurnal variation of measured NSD. However, total particle number concentration (TNC) as derived from modelled NSD underestimated observed TNC. Implementation of NO2 led to higher model performance for TNC (R2 ¼ 0.57) but not for particle NSD. Detailed information about urban background particle concentrations as a proxy for local conditions and about boundary layer conditions (e.g. atmospheric stability, mixing layer height) might help improving the model. The spatial characteristics of the site and their acoustical effects were not considered in the present approach (e.g. distance to road or buildings, road surface), hence, the results should be transferred to other sites with some caution.