How self-awareness is connected to less experience of action crises in personal goal pursuit.

In this research, we applied a differential perspective to the study of action crises, i.e., being in an intra-psychic decisional conflict whether to pursue or abandon a goal once difficulties in its pursuit arise. In two studies, we investigated the role of individuals' levels of self-awareness when experiencing such action crises. Both among professional ballet dancers (daily diary, Study 1) and university undergraduates (preregistered experience sampling, Study 2), individuals with greater levels of (dispositional and situational) self-awareness showed an adaptive, that is, problem-solving oriented way of dealing with difficulties in the pursuit of their (training or study) goals. As a consequence, self-awareness contributed to less experience of action crisis during goal pursuit and, as a result, led to better goal performance. Supplementary Information: The online version contains supplementary material available at 10.1007/s11031-022-09942-5.

A spatiotemporal land-use regression model of winter fine particulate levels in residential neighbourhoods.

Residential wood burning can be a significant wintertime source of ambient fine particles in urban and suburban areas. We developed a statistical model to predict minute (min) levels of particles with median diameter of <1 µm (PM1) from mobile monitoring on evenings of winter weekends at different residential locations in Quebec, Canada, considering wood burning emissions. The 6 s PM1 levels were concurrently measured on 10 preselected routes travelled 3 to 24 times during the winters of 2008-2009 and 2009-2010 by vehicles equipped with a GRIMM or a dataRAM sampler and a Global Positioning System device. Route-specific and global land-use regression (LUR) models were developed using the following spatial and temporal covariates to predict 1-min-averaged PM1 levels: chimney density from property assessment data at sampling locations, PM2.5 "regional background" levels of particles with median diameter of <2.5 µm (PM2.5) and temperature and wind speed at hour of sampling, elevation at sampling locations and day of the week. In the various routes travelled, between 49% and 94% of the variability in PM1 levels was explained by the selected covariates. The effect of chimney density was not negligible in "cottage areas." The R(2) for the global model including all routes was 0.40. This LUR is the first to predict PM1 levels in both space and time with consideration of the effects of wood burning emissions. We show that the influence of chimney density, a proxy for wood burning emissions, varies by regions and that a global model cannot be used to predict PM in regions that were not measured. Future work should consider using both survey data on wood burning intensity and information from numerical air quality forecast models, in LUR models, to improve the generalisation of the prediction of fine particulate levels.