Suchergebnisse

Thermal comfort and indoor air quality (IAQ) of educational buildings can affect students' academic performance and well-being and are closely related to ventilation energy consumption. Demands of the indoor environmental quality within the classroom generally vary with the education levels and result in ventilation energy consumption accounting for a considerable proportion of the total energy use in bulk educational buildings. Its huge energy-saving potential is attracting worldwide attention from scholars and governments. Therefore, appropriate operation strategies of ventilation systems should be adopted to effectively reduce energy consumption without sacrificing thermal comfort and IAQ. However, the absence of relevant standards and guidelines for designing a quality classroom environment considering the special features of educational buildings remains an important research question. This study conducts a comprehensive review to determine research gaps and identify future directions for the interaction between thermal comfort, IAQ and ventilation energy consumption for educational buildings. The review results show that: (1) The thermal comfort prediction model should consider the influences of genders, ages and socioeconomic backgrounds; (2) The mixed-mode ventilation coupling the natural and mechanical approaches is preferred given its advantage of lower energy consumption and improved thermal comfort, but its control strategies need further exploration; (3) Optimizing passive design parameters of buildings (e.g., window to wall ratios, window orientations and sun shading installations) can significantly reduce the ventilation demands while maintaining indoor thermal comfort; (4) More studies are required for investigating thermal comfort in educational buildings during the heating period; and (5) IAQ of university buildings clearly requires further studies, especially on bacterial and fungal aerosol pollutants, for a more comprehensive assessment of the built environment.

BACKGROUND: The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments' policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory. METHODS: Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government's response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases. RESULTS: Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage—from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments' responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R(2) = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R(2) = 0.42) days earlier to the peak number of daily new case. CONCLUSIONS: Early start of a high-level response to COVID-19 ...

Mountain farming systems rely on both empirical and academic knowledge. Their sustainability depends on how effectively diverse knowledge is used for solution-oriented decision making. For mountains, decisions must be conducive to rural farmers whose livelihoods depend on agriculture and related activities. Adopting transdisciplinary research approach, we define a composite Sustainability Space indicator that will help decision makers better understand the ingredients for sustainability, and formulate policy and management decisions to reinforce on-the-ground sustainability. Sustainability Space was derived through analysis of the positive and negative impact factors co-defined by community and disciplinary experts, and visualized through a radar diagram. We used Principal Component Analysis to understand relationships between factors. The results on Sustainability Spaces for eight cases of farming systems from the far-Eastern Himalayas indicated that the sustainability of farming systems is strengthened if decisions holistically cater to (i) geophysical pre-requisites, (ii) ecological foundations, (iii) integrated processes and practices, (iv) resources, knowledge, and value systems, (v) stakeholders' development and economic aspirations, (vi) well-being of farming communities, and (vii) government support mechanisms. More equitable the attention to these seven components, the higher the sustainability of farming systems in this region could be.