Development of a Methodology for the Identification of High Emitting Mobile Sources in Narrow and Deep Street Canyons

Abstract

In urban areas transport represents a significant source of atmospheric pollutants and greenhouse gases (GHG). In the case of vehicular transport, a significant contribution to total emissions is given by a category of vehicles with excessively high emissions of one or more pollutants defined as high emitting vehicles (high-emitters). High emitters can contribute a disproportionally way to total emissions of many airborne pollutants (NOx, COV, PM and GHGs). Remote sensing (RS) techniques have been developed with the aim to identify high emitterss but until now they have found only few practical applications. Among RS technologies, point sampling (PS) is the most promising for implementation in narrow and deep street canyons due to the limited impact on both pedestrians and architecture and the small space occupancy. In this paper we present results of preliminary monitoring campaigns carried out in a narrow and deep street canyon in Naples (Italy) in low-traffic conditions. Fine particles (FPs) concentration (20-1000 nm) were monitored using a condensation particle counter (CPC). Time patterns of FPs concentration have been analyzed by a code developed in MATLAB to identify FP concentration peaks and successively to attribute each identified peak to a specific vehicle. To study the effect of operating conditions (wind speed and direction) on the plume formed by vehicle exhausts, CFD simulations have been also carried out. Results show good performances of the code in the identification of FPs peaks and a limited effect of ambient parameters on the dispersion of the plumes inside the street canyon studied.