Using mobile monitoring to visualise diurnal variation of traffic pollutants across two near-highway neighbourhoods

It is widely accepted that concentrations of primary traffic pollutants can vary substantially across relatively small urban areas. Fixed-site monitors have been shown to be largely inadequate for representing concentrations at nearby locations, resulting in the increasing use of spatial modelling or mobile sampling methods to achieve spatial saturation. In this study, we employ the use of a simple bicycle to sample concentrations of ultrafine particles (UFPs), carbon monoxide (CO) and particulate matter (PM10) at two small areas (<2.5 km2) in South Auckland, New Zealand. Portable instruments were mounted inside a custom-built casing at the front of the bicycle and every street within each study area was sampled in a grid-like fashion, at four times of day (07:00, 12:00, 17:00 and 22:00). Each area has a six-lane highway running through its centre and the core aim was to visualise and describe spatial variability of pollutant levels about the highway, main arterials and quieter streets, at periods of contrasting meteorological and traffic conditions. A total of 20 sampling runs in each area (five at each of the four timings) were conducted. Meteorological data were logged continuously at background sites within each study area. Results show that the influence of highway traffic (UFPs, CO) was strongest during the mornings and late evenings when wind speeds were low, while for the midday and afternoon timings, concentrations were highest at the arterial and shopping zones. Concentrations of PM10 appeared to be strongest in the residential areas during mornings and late evenings, suggesting an influence of wood burning for home heating. For all timings combined, for all three pollutants, it appears the arterial roads featuring shops and numerous intersections with traffic lights, had a stronger influence on concentrations than the busier but more free-flowing highways. This study provides not only an insight into microspatial hotspot variation across suburbs, but also how this variation shifts diurnally.