Estimates of air pollution mitigation with green plants and green roofs using the UFORE model

The purpose of this study was to investigate the effect of green roofs and green walls on air pollution in urban Toronto. The research looked at the synergistic effects on air pollution mitigation of different combinations of vegetation by manipulating quantities of trees, shrubs, green roofs and green walls in the study area. The effects of these manipulations were simulated with the Urban Forest Effects (UFORE) model developed by the USDA Forest Service Northeastern Regional Station. While UFORE contains several modules, Module—D quantifies the levels of air pollution for contaminants such as NO₂, S0₂, CO, PM₁₀ and ozone as well as hourly pollution removal rates and the economic value of pollutant removal. Six vegetation scenarios were developed within the Toronto study area to compare different subsets of vegetation and their effect on air contaminants. Results of the study indicate that grass on roofs (extensive green roofs) could augment the effect of trees and shrubs in air pollution mitigation, placing shrubs on a roof (intensive green roofs) would have a more significant impact. By extension, a 10–20% increase in the surface area for green roofs on downtown buildings would contribute significantly to the social, financial and environmental health of all citizens.

Nonparametric inference and uniqueness for periodically observed progressive disease models

In many studies examining the progression of HIV and other chronic diseases, subjects are periodically monitored to assess their progression through disease states. This gives rise to a specific type of panel data which have been termed “chain-of-events data”; e.g. data that result from periodic observation of a progressive disease process whose states occur in a prescribed order and where state transitions are not observable. Using a discrete time semi-Markov model, we develop an algorithm for nonparametric estimation of the distribution functions of sojourn times in a J state progressive disease model. Issues of uniqueness for chain-of-events data are not well-understood. Thus, a main goal of this paper is to determine the uniqueness of the nonparametric estimators of the distribution functions of sojourn times within states. We develop sufficient conditions for uniqueness of the nonparametric maximum likelihood estimator, including situations where some but not all of its components are unique. We illustrate the methods with three examples.     

Qualitative exploration of the nature of grief-related beliefs and expectations

Societal expectations of grief impact the experience of bereavement. The congruence of societal expectations with current scientific understanding of grief is unknown. Therefore two qualitative studies explored community perceptions of grief. In study one, three small focus groups (N = 9) examined grief-related expectations associated with hypothetical scenarios of bereavement. In study two, the impact of grief-related perceptions on the lived experience of bereavement for 11 individuals was explored through semi-structured interviews. Across both studies, elements of a traditional stage model view of grief were evident, with participants viewing emotional expression of grief as important. An avoidant coping style in the bereaved was considered problematic. Findings of study two suggested that grief-related beliefs may impact the bereavement experience via appraisal of the grief response and willingness to support bereaved individuals. The studies suggested that stage model assumptions in the beliefs of the general population persist, although there was a recognition of diversity in the grief response.