Secondary effects of catalytic diesel particulate filters: copper-induced formation of PCDD/Fs.

Potential risks of a secondary formation of polychlorinated dibenzodioxins/furans (PCDD/Fs) were assessed for two cordierite-based, wall-through diesel particulate filters (DPFs) for which soot combustion was either catalyzed with an iron- or a copper-based fuel additive. A heavy duty diesel engine was used as test platform, applying the eight-stage ISO 8178/4 C1 cycle. DPF applications neither affected the engine performance, nor did they increase NO, NO2, CO, and CO2 emissions. The latter is a metric for fuel consumption. THC emissions decreased by about 40% when deploying DPFs. PCDD/F emissions, with a focus on tetra- to octachlorinated congeners, were compared under standard and worst case conditions (enhanced chlorine uptake). The iron-catalyzed DPF neither increased PCDD/F emissions, nor did it change the congener pattern, even when traces of chlorine became available. In case of copper, PCDD/F emissions increased by up to 3 orders of magnitude from 22 to 200 to 12 700 pg I-TEQ/L with fuels of < 2, 14, and 110 microg/g chlorine, respectively. Mainly lower chlorinated DD/Fs were formed. Based on these substantial effects on PCDD/F emissions, the copper-catalyzed DPF system was not approved for workplace applications, whereas the iron system fulfilled all the specifications of the Swiss procedures for DPF approval (VERT).

Towards high-quality simultaneous EEG-fMRI at 7T: Detection and reduction of EEG artifacts due to head motion.

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.