IAQ 2016 Defining Indoor Air Quality: Policy, Standards and Best Practices Co-Organized by ASHRAE and AIVC

In order to achieve nearly net zero energy use, both new and energy refurbished existing buildings will in the future need to be still more efficient and optimized. As such buildings can be expected to be already well insulated, airtight, and have heat recovery systems installed, one of the next focal points to limiting energy consumption for thermally conditioning the indoor environment will be to possibly reducing the ventilation rate, or making it in a new way demand controlled. However, this must be done such that it does not have adverse effects on indoor air quality (IAQ). Annex 68, Indoor Air Quality Design and Control in Low Energy Residential Buildings, is a project under IEA’s Energy Conservation in Buildings and Communities Program (EBC), which will endeavor to investigate how future residential buildings are able to have very high energy performance whilst providing comfortable and healthy indoor environments. New paradigms for demand control of ventilation will be investigated, which consider the pollution loads and occupancy in buildings. As well the thermal and moisture conditions of such advanced building shall be considered because of interactions between the hygrothermal parameters, the chemical conditions, ventilation and the wellbeing of occupants. The project is divided into the five subtasks: 1. Defining the metrics, 2. Pollutant loads in residential buildings, 3. Modelling - review, gap, analysis and categorization, 4. Strategies for design and control of buildings, 5. Field measurements and case studies. A flagship outcome of the project is anticipated to be a guidebook on design and operation of ventilation in residential buildings to achieve high IAQ with smallest possible energy consumption The paper illustrates the working program of each of these activities, and the presentation of the Annex project at the conference shall foster some interest and discussion about its work items.

The quality of the environment inside in buildings matters as much as the quality of the environment outdoors. Actually even more, as the health and comfort of the building occupants depend on it. Indoor air quality (IAQ) and thermal comfort conditions (ITQ) as well as hygrometric aspect are concerned to define indoor climate quality (ICQ) which is just part of Indoor Environmental Quality (IEQ). An enlarged concept of IEQ comprises four more quality aspects; sound (ISQ), lighting (ILQ), odor (IOQ), and vibration (IVQ). A high indoor environmental quality can increase health, wellbeing and productivity of building occupants, but also decrease costs for energy and building maintenance. This paper presents materials and methods used on indoor environmental research. In the studies of our research group measured parameters incorporate outdoor climate conditions, indoor thermal, hygrometric, and air quality aspects as well as energy consumption readings including electricity, district heating, and water. Data analysis were performed using computational, visualization and grouping artificial neural network methods. Furthermore, additional external study cases utilizing artificial neural networks (ANN), model-based control, and big data analysis are presented and compared. In this paper, in all 16 examples of intelligent data analysis and knowledge deployment are reviewed in the sense of the benefits of methods used.

High levels of carbon dioxide (CO₂) in classrooms can affect students’ ability to concentrate on academic tasks. CO₂ concentration in an indoor environment is commonly used as a metric for measuring air quality. Although this metric does not reflect all air containments, a high level of CO₂concentration can indicate towards insufficient ventilation of indoor space. In order to mitigate the impacts of climate change, governments in major economies have updated and/or developed building regulations to improve the thermal performance of building fabric, with a view to reduce heating and cooling energy consumption in buildings. With the sustained reduction in heating/cooling energy demand, the share of energy used for artificial lighting increases. For example, artificial lighting accounts for 25-40% of the total building energy consumption in the USA. In addition to the physical factors such as building form, orientation, glazing characteristics and location, energy use for artificial lighting depends on behavioural and psycho-physiological factors of occupants. Previous research suggests that sub-optimal operation of movable insulation such as blinds and curtains have an impact on artificial lighting use and occupant comfort (thermal and visual) with a resulting impact on energy use. This research is aimed at developing a method for optimising the operation of window opening to facilitate natural ventilation and window blinds to reduce energy consumption in a low energy educational building (rated BREEAM excellent ≈ LEED platinum). The research employs model-based optimization using daylight-coupled thermal model in EnergyPlus to model the interrelationships between blind positions; window opening; lighting and heating/cooling energy consumption; and thermal comfort.

In 2006, the voters from the state of California approved the Proposition 1D, which provided $100 million in an incentive grant. Knowing as High Performance Incentive (HPI), this supplemental grant is intended to promote the using of high performance attributes in new and modernization project for K-12 schools. This paper examines the differences on the average amount of change of student achievement results between before and after the completion of high performance attributes for selected funding grantee schools. From the grant description, high performance attributes are designing and using materials that improve indoor environmental quality (IEQ) of schools. Examples include promoting energy and water efficiency, maximizing the use of natural lighting, improving indoor air quality, utilizing materials that emit a minimal amount of toxic substances, and employing acoustics that are conducive to teaching and learning. Criteria from each aspect form the predictors, which used to compare the achievement results built based on the California Standardized Testing and Results Program (STAR) and schools’ Academic Performance Index (API). The general linear mixed linear model (GLMLM) is utilized to analyze the predictors and achievement results. Academic achievement results include several years of results before the project start and the year after the project completion. Average difference of the before and after achievement results is then evaluated based on varied levels of certain predictors.