Methane flux measurements were carried out at a temperate forest (Haliburton Forest and Wildlife Reserve) in central Ontario (45°17´11´´ N, 78°32´19´´ W) from June to October 2011. Continuous measurements were made by an off-axis integrated cavity output spectrometer that measures methane (CH₄) at 10 Hz sampling rates. Fluxes were calculated from the gas measurements in conjunction with wind data collected by a 3-D sonic anemometer using the eddy covariance (EC) method. Observed methane fluxes showed net uptake of CH₄ over the measurement period with an average uptake flux (±standard deviation of the mean) of −2.7 ± 0.13 nmol m⁻² s⁻¹. Methane fluxes showed a seasonal progression with average rates of uptake increasing from June through September and remaining high in October. This pattern was consistent with a decreasing trend in soil moisture content at the monthly timescale. On the diurnal timescale, there was evidence of increased uptake during the day, when the mid-canopy wind speed was at a maximum. These patterns suggest that substrate supply of CH₄ to methanotrophs, and in certain cases hypoxic soil conditions supporting methanogenesis in low-slope areas, drives the observed variability in fluxes. A network of soil static chambers used at the tower site showed reasonable agreement with the seasonal trend and overall magnitude of the eddy covariance flux measurements. This suggests that soil-level microbial processes, and not abiological leaf-level CH₄ production, drive overall CH₄ dynamics in temperate forest ecosystems such as Haliburton Forest.

An automated identification and integration method has been developed for in-use vehicle emissions under real-world conditions. This technique was applied to high-time-resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada, during four seasons, through month-long campaigns in 2013–2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number; black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline-dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg fuel⁻¹ and 7.5 × 10¹⁴ # kg fuel⁻¹, respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (< 25 %) contributed significantly to total fleet emissions: 100, 100, 81, and 77 % for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter; however, regulatory strategies to more efficiently target multi-pollutant mixtures may be better developed by considering the co-emitted pollutants as well.