1% had symptoms of GAD. Women who experienced low mental health, moderate levels of symptoms of PPD and/or GAD were more likely to report thoughts of self-harm. LIMITATIONS As thoughts of self-harm and aspects of mental health are self-reported, there is the potential for social desirability bias and underreporting. The cross-sectional survey design did not allow the reporting of thoughts of self-harm at different time points. DISCUSSION The high proportion of postpartum women in Canada reporting thoughts of self-harm and strong associations with aspects of maternal mental health highlight the need for effective supports during postpartum. V.Food production and consumption are major drivers of global environmental change, endangering the safe operating space of many environmental areas. Globally, there has been a growing trend of dining out, termed food away from home (FAFH) here, but its environmental sustainability has received insufficient attention. In this review, we examine studies quantifying the life-cycle environmental impacts of FAFH and identify mitigation strategies across the food supply chain. Overall, previous life cycle assessment (LCA) studies focused on the composition of FAFH meals and pre-use life cycle stages, especially food production. Greenhouse gas (GHG) emissions of FAFH meals range from 0.134 kg CO2 e/meal to 13.2 kg CO2 e/meal for school canteen meals, and from 0.60 kg CO2 e/meal to 9.6 kg CO2 e/meal for other catering services. Meat ingredients are the dominant source in a variety of environmental impact categories, and the food production stage usually accounts for over half of the total GHG emissions in the FAFH life cycle. Supply side mitigation strategies include advancing farming practices, updating cold transportation technology, and improving building energy efficiency. Demand side mitigation focuses on dietary change towards meals with less meat ingredients, with nudging and sustainable menu-designing as the two primary groups of strategies. Areas of focus for LCA include improving modeling of building energy consumption related to food consumption, advancing uncertainty characterization of life cycle results, and capturing geographical variations in food production. The urban heat island (UHI) effect is an increasingly consequential problem that confronts cities. The accurate characterization and quantification of UHI are crucial for sustainable urban development. Few UHI studies, however, compare data source, spatio-temporal variations, and indicators for the same city in parallel. This study uses Changchun, a snow climate city in China, as an example and compares five different indicators of the UHI based on land surface temperature (LST) derived from Landsat 8 TIRS and hourly air temperature (AT) collected from 41 meteorological weather stations to conduct a more comprehensive comparative study of the UHI. The results show the following. (1) The relationships between LST and AT are all statistically significant, and the surface urban heat island (SUHI) intensity characterized by the LST is considerably stronger than that of AT both in summer and winter. (2) The SUHI intensity is significantly stronger in summer (6.83 °C) than in winter (1.55 °C) based on the morning LST, whereas the UHI intensity (0.27 °C in summer and 0.40 °C in winter) that is simultaneously quantified by the AT has an opposite result. The mean whole-day and daytime UHI intensity difference, which is quantified hourly by the AT between summer and winter, is not significant. The difference between nighttime and daytime UHI intensities is evident in both summer (1.26 °C) and winter (0.76 °C). Additionally, the high temperatures for both LST and AT have a more concentrated distribution in winter than in summer. (3) The values of UHI/SUHI intensity considerably vary based on different indicators. The different choices among land covers to represent "urban" and "rural" areas would significantly affect the values of UHI/SUHI intensity. The selection of appropriate indicators and data sources to quantify the UHI remains a problem that has to be resolved in future studies. Mixtures with high fatty acid content are produced during vegetable oil and animal fat purification and paper production. check details These waste fractions can be converted into alternative fuels through several steps. The co-hydrogenation of waste polypropylene thermal cracked fraction or waste fatty acid mixture with unrefined gas oils is a potential solution for their conversion into hydrocarbons. The co-processing of these three different fractions was not yet investigated in these ratios. So the aim of the research work was to produce high quality diesel fuels and to study the occurring reactions and the interaction among these different compounds. The catalytic conversion of the mixture of unrefined gas oil, waste polypropylene cracked fraction (20 wt %) and waste fatty acid mixture (10, 20 and 30 wt %) was carried out on a commercial sulphided nickel-molybdenum-alumina catalyst. The effect of the feedstock compositions and the process parameters on the quantity and quality of the products was studied. The favourable process conditions to produce high quality diesel fuel blending components were selected (e.g., 10 wt % fatty acid waste, 360 °C temperature, 1.0 h-1 liquid hourly space velocity). The performance properties of this fuel were better than the conventional diesels', so their usage can be more environmentally friendly and lead to lower pollutant emission. Accurate estimations of flood waste generation are a crucial issue in disaster waste management. Multilinear regression of related parameters has been recognized as a promising technique for flood waste estimation. There are two types of flood waste estimation methods pre-event predictions using factors related to regional properties and rainfall hazards, and post-event predictions using damage variables due to floods, such as the number of damaged buildings. Previous attempts to establish these models used deterministic approaches; however, probabilistic methods have never been applied. Considering the large degrees of uncertainty in waste generation from floods, a probabilistic approach can provide a more accurate model compared to models developed by the conventional deterministic approach. This study applied Bayesian inference to develop a flood waste regression model in South Korea. The aims of the study are as follows (1) to analyze the characteristics of coefficients estimated by the Bayesian approach; (2) evaluate the performance of the prediction model by Bayesian inference; and (3) assess the effectiveness of Bayesian updating in a flood waste estimation.