Elucidating multipollutant exposure across a complex metropolitan area by systematic deployment of a mobile laboratory(43 page pdf, I. Levy, C. Mihele, G. Lu, J. Narayan, N. Hilker, and J. R. Brook, Atmos. Chem. Phys. Discuss., Dec.7, 2012)
Today we review a description of one of the world’s leading mobile labs taking measurements of air quality in the large city of Montreal where vehicle emissions is among the leading pollution sources in addition to industrial sources and the heating of buildings in winter. The validity of a few regional ambient air quality stations to describe the state of the air across this city (and one imagines in many other cities) was challenged by differences of 200-300% between the ambient measurements and those taken with the mobile lab in and near traffic. The importance of using monitors such as this for assessing health impacts near roads is clear.
“Mobile measurements of 23 air pollutants were taken at high resolution in Montreal, Quebec, Canada, and examined with respect to space, time and their interrelationships…taken by Environment Canada’s mobile lab: Canadian Regional and Urban Investigation System for Environmental Research (CRUISER).”
“Pollution sources on the island besides traffic include a variety of industrial activities, oil and gas refining, storage and distribution facilities, petrochemicals, metal refining, light manufacturing, multiple port areas, as well as heating (in the winter)”
“While AQ monitoring networks represent long term conditions, they cannot account for the spatial variability existing in the urban environment”
“NO2 measurements next to a main road microenvironment are shown to be 210-265% higher than levels measured at a nearby urban background monitoring site, while black carbon is higher by 180-200% and ultrafine particles are 300% higher.”
“Existing methods for predicting exposure surfaces at intra-urban scales all have inherent limitations. While AQ monitoring networks are limited to a finite number of point meteorology and emissions and empirical models are dependent upon the number of sites and the conditions they reflect during short term saturation monitoring campaigns, as well as on the ability of the predictors to capture the full variability that exists at the scales of interest”