Identification of the Sources and Geographic Origins of Black Carbon using Factor Analysis at Paired Rural and Urban sites

Black carbon particles, composed of forms of elemental carbon (EC), contribute significantly to regional and global warming. The origins of EC were examined in southeastern Canada as part of a source apportionment study using positive matrix factorization (PMF), performed on long-term PM_2.5 chemical speciation data collected at two paired rural and urban sites. Comparisons of the urban and rural sites revealed a previously unrecognized EC-rich factor that accounted for 41–56% of the total EC in this region. This factor was characterized by the more thermally stable EC fractions that exhibit strong light absorption characteristics. While these EC fractions are often attributed to local diesel emissions, this interpretation was rejected for several reasons. The EC-rich factor was present in similar temporal patterns at both the high-traffic urban and low-traffic rural sites across this 600 km region. The geographic origins of the EC-rich factor were found to be Ohio and Western Pennsylvania regions with heavy industry and multiple coal-based electrical generating stations. The direct radiative forcing due to this EC-rich factor was roughly estimated to be +0.2 W m^–2, which represented a substantial portion of the aerosol induced warming in the region. Thus, this region was impacted by an important unidentified source of EC associated with long-range transport

Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability Using Emission Factors

A daily integrated emission factor (EF) method was applied to data from three near-road monitoring sites to identify variables that impact traffic related pollutant concentrations in the near-road environment. The sites were operated for 20 months in 2015–2017, with each site differing in terms of design, local meteorology, and fleet compositions. Measurement distance from the roadway and local meteorology were found to affect pollutant concentrations irrespective of background subtraction. However, using emission factors mostly accounted for the effects of dilution and dispersion, allowing intersite differences in emissions to be resolved. A multiple linear regression model that included predictor variables such as fraction of larger vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature accounted for intersite variability of the fleet average NO, NO_<i>x</i>, and particle number EFs (R²:0.50–0.75), with lower model performance for CO and black carbon (BC) EFs (R²:0.28–0.46). NO_<i>x</i> and BC EFs were affected more than CO and particle number EFs by the fraction of larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs also varied with ambient temperature and because there were little seasonal changes in fleet composition, this was attributed to changes in fuel composition and/or post-tailpipe transformation of pollutants