Measured and modelled concentrations and vertical profiles of airborne particulate matter within the boundary layer of a street canyon

Concentrations and vertical profiles of various fractions of airborne particulate matter (suspended particulate matter (SPM), PM10 and PM2.5) have been measured over the first three metres from ground in a street canyon. Measurements were carried out using automated near rear-time apparatus called the Kinetic Sequential Sampling (KSS) system. KSS system is essentially an electronically-controlled lift carrying a real-time particle monitor for sampling air sequentially, at different heights within the breathing zone, which includes all heights within the surface layer of a street canyon at which people may breathe. Data is automatically logged at the different receptor levels, for the determination of the average vertical concentration profile of airborne particulate matter. For measuring the airborne particle concentration, a Grimm Dust Monitor 1.104/5 was used. The recorded data also allows for time series analysis of airborne particulate matter concentration at different heights. Time series data and hourly-average vertical concentration profiles in the boundary layer of the confines of a street are thought to be mainly determined by traffic emissions and traffic associated processes. Hence the measured data were compared with results of a street canyon emission-dispersion model in time and space. This Street Level Air Quality (SLAQ) model employs the plume-box technique and includes modules for simulating vehicle-generated effects such as thermally- and mechanically-generated turbulence and resuspension of road dust. Environmental processes, such as turbulence resulting from surface sensible heat and the formation of sulphate aerosol from sulphur dioxide exhaust emissions, are taken into account. The paper presents an outline description of the measuring technique and model used, and a comparison of the measured and modelled data. (C) 1999 Elsevier Science B.V. All rights reserved.