Using Mobile Noise Measurements to Build Spatiotemporal Models for In-Traffic and Roadside Exposure to BC and PNC: a Pilot Study in Bangalore India

Abstract

Background: Traffic simultaneously emits noise and particulate air pollution. Previously we built an instantaneous spatiotemporal exposure model for bicyclists based on spectral noise exposure parameters, winds speed and a background concentration adjustment. This model provided the proof-of-concept to use in-traffic noise exposure assessment as a proxy for BC exposure. Aim: The aim is to validate the noise based exposure model for Black carbon and PNC in another country and to test the potential of stationary roadside noise measurements to predict particle matter exposure. In addition a low support and automatic cheap measurement setup is tested for international deployment. Methods: A pilot project is set up in Bangalore, India. Both in-traffic and short-term roadside stationary measurements are performed. Simultaneous measurements of noise, Black Carbon and PNC are available at a temporal resolution of 10 seconds. Background levels are measured at a dwelling in Bangalore city. Results: BC and PNC were successfully predicted from spectral noise measurements. In less than 5 passages a good characterization of the local traffic conditions can be achieved. The highly exposed background measurement station reduces the quality of the additive model which reconstructs the total exposure as a local traffic component plus a background contribution. The model therefore fails to predict the low exposure conditions and locations in Bangalore city due to the location and quality of the background station. Conclusions: The modeling technique is applied successfully in the new location for both BC and PNC. Standardized background measurement setups will be necessary in the developing countries to improve the spatial resolution of the models. The roadside measurements show the potential to use stationary noise measurements as a proxy for particulate matter exposure but full diurnal patterns should be measured to further validate this technique.