measurement

Building airtightness requirements are becoming more and more common in Europe (Leprince, Carrié, & Kapsalaki, 2017). However, airtight buildings require an efficient ventilation system to ensure good indoor air quality. In France, the inspection of ventilation system (Jobert, 2012) has revealed many noncompliance. They are mainly due to bad conception, poor implementation, and lack of maintenance. This often leads to reduced ventilation flowrates and poor indoor air quality. Leaky ductwork is one of the reasons for this noncompliance.

Since the turn of the century, alarming data produced by the Indoor Air Quality Observatory (OQAI) have led to changes in French legislation, including, most notably, the introduction of compulsory labelling for construction products (decree no. 2011-321 of 23 March 2011).

Particulate matter with a diameter of ≤2.5µm (PM2.5) has been shown to be present in many buildings at concentrations that are harmful to human health. Accordingly, they should be used as metrics of indoor air quality (IAQ) and included in standards or norms. This paper uses measurements of PM2.5 concentrations made in three different environments using three different devices to show that there are barriers that must be before they can be considered viable diagnostics. Optical particle counters (OPCs) are a common device used to measure temporal changes in PM2.5 concentration.

Since the 1970s, many authors have discussed the impact of poor airtightness on building energy use, indoor air quality, building damage, or noise transmission (Carrié and Rosenthal, 2008) (Tamura, 1975) (Sherman and Chan, 2006) (Orr and Figley, 1980). Nowadays, because poor airtightness affects significantly the energy performance of buildings, and even more significantly with low-energy targets, many countries include requirements for building airtightness in their national regulations or energy-efficiency programs.

This paper analyses the contribution of a steady wind to the uncertainties in building pressurisation tests, using the approach developed in another paper (Carrié and Leprince, 2016). The uncertainty due to wind is compared to the uncertainties due to other sources of uncertainty (bias, precision and deviation of flow exponent).
The main results of this study are:

As UK homes are insulated and draught proofed in an attempt to reduce wintertime heating demand they become more airtight. Any reduction in infiltration could have a detrimental effect on indoor air quality. Controllable background ventilation provided by trickle vents is one method of maintaining indoor air quality.

This paper aims to present the elaboration of a device able to modify the airtightness of a test House. This project has been conducted with three Master student groups in the framework of their research projects from 2015 to 2017. The TIPEE IEQ test House, comprising of two floors and eight rooms, is dedicated to the study of Indoor Air Quality (IAQ), thermal comfort and energy consumption. Its envelope has been designed to reach an airtightness target slightly lower than the French Energy Efficiency Standard RT2012 requirement for dwellings i.e.

Existing protocols for the inspection of mechanical residential systems poorly address both the assessment of uncertainties and recommendations or specifications for measurement methods and devices to be used to guarantee low measurement uncertainties. This paper gives the major elements of a new protocol developed within the Promevent project to overcome this problem. We have analyzed results from 180 airflow measurements performed in laboratory conditions in accordance with this protocol.

Demand controlled ventilation (DCV) can reduce the energy use significantly compared to a constant air volume (CAV) system. However, there is still a large uncertainty about the real energy savings and the ventilation efficiency. Furthermore, control and operation of the system are more complex. To formulate answers to these questions, measurements on a DCV system in a university building in Ghent, Belgium provide insight in the system operation and performance and the air distribution in the classrooms. Monitoring is carried out in March and May 2015.

We have analysed the steady wind model error based on a simplified building model with one leak on the windward side and one on the leeward side of the building. Our model gives an analytical expression of this error that depends on the leakage distribution and pressure coefficients. Using a test pressure of 50 Pa in this model, standard measurement protocol constraints contain the steady wind model error within about 3% and 11% with wind speeds below 6 m s-1 and 10 m s-1, respectively. At 10 Pa, the error is in the range of 35% and 60% at 6 m s-1 and 10 m s-1, respectively.