The aim of this experiment was to study the effect of air duct cleaning on the indoor air quality. Three buildings in the Helsinki metropolitan area were selected for the study. In two of the test buildings the ducts were cleaned using three different cleaning methods. The third building serves as a control where no cleaning was done. The air handling systems in the test buildings had operated 26 and 30 years without cleaning.
The aim of the experiment was to study the efficiency of three duct cleaning methods. The methods used were ( 1) rotating brushes, (2) compressed air cleaning, and (3) wiping by hand. The air handling systems under investigations had been in use 26 and 30 years after the construction phase and the systems had not been cleaned since buildings were completed. Accumulated amount of dust in the supply air duct was determined by BM-Dustdetector, tape method, and by visual inspection before and after cleaning. The amount of dust on the duct surface was decreased with all three cleaning methods.
Samples of surface dust were collected from ducts before and after an HV AC system cleaning project in an office complex in Canada. Dust levels were quantified gravimetrically and concentrations of viable fungi were determined (1) using a standard dilution plating method from vacuum-collected surface dust samples; and (2) by the collection of surface samples on commercially available agar contact slides.
The aim of this experiment was to compare three measuring methods to determine the level of dust in air ducts. Compared methods were a vacuum test, a tape method and an optical method. The dust samples were taken from the supply air ducts of new buildings. The paper presents and compares the results of the tests. The samples were taken from three day-care centers in the Helsinki area. The ducts were not cleaned after the manufacturing process or protected during the construction. Duct surfaces had also oil residues from the manufacturing process.
A series of experiments was carried out to study the effect of temperature and humidity on the perception of indoor air quality. The study included both laboratory and controlled field experiments using an untrained sensory panel to judge the air quality at different levels of temperature and humidity. Facial and whole-body exposure for a short term (up to 20 minutes) was used in the laboratory study, and long-term whole-body exposure (up to 4. 6 hours) was used in the field study. The study found a significant impact of temperature and humidity on the perception of indoor air quality.
Although a significant amount of work has been done to elucidate the conditions under which fungi will grow on the surfaces of materials, little information is available that quantitatively relates surface concentrations to airborne concentration and, ultimately, exposure. This paper discusses the impact of relative humidity (RJI), air velocity, and surface growth on the emission rates of fungal spores from the surface of contaminated material.
High outdoor ventilation air requirements can lead to significant increases in building energy use, thermal discomfort, indoor air quality problems, and litigation. Engineers often avoid ground-source heat pumps because of the perception that there are no acceptable methods for conditioning the ventilation air. However, this difficulty is currently a problem with all types of heating and cooling systems. Decisions may be based on system performance at design conditions without regard to seasonal energy consumption.
Part I of this paper discussed the theoretical considerations of creating a nonlinear black box model. In P art II, the constraints on the nonlinear model imposed by the application are discussed followed by presentation of the model structure, training method, input selection, and input transformation. The test results of applying the proposed model with the selected features to five test buildings are discussed next. One of the test buildings (Zachry Engineering C enter) selected for this study was also used in a previous study as a p art of energy prediction competition (Haber!
A building energy management system (BEMS) generally monitors and manages energy usage in commercial buildings. With the ability to monitor a plant and to recall the collected data at a later time, actual building energy performance can be measured and compared with the expected performance. The comparison will help in detecting possible abnormalities with the building energy usage and in identifying opportunities to optimize the building energy performance. In order to predict expected building energy performance, a reasonably accurate building energy model is needed.
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