The development of the indoor air pollution index for office buildings.

摘要

The collection of environmental data is increasing continually. The public does not comprehend the magnitude of the problem(s) and trends of environmental quality degradation or improvement. Regulators are phased with difficult to understand and communicate scientific information. Environmental scientists have not devised a quantitative method that unifies the qualitative concepts of measuring all environmental components. Clearly, all environmental objectives cannot be put into mere numbers, yet on certain occasions the formulation of a number indicator is appropriate and may assist in defining the present environmental status and suggesting environmental endpoints. IAQ studies measure concentrations of one or more individual pollutants but do not estimate the IAQ of a building. The lack of an indoor air quality metric has caused communication concerns among building tenants (the public), building managers (decision-makers), and IAQ investigators. The Indoor Air Pollution Index (IAPI) is developed for office buildings to bridge this communication discord. The index, simple and easily understood, employs the range of pollutant concentrations found in the literature and concentrations in the subject building to estimate a unitless single number, the IAPI, between zero (lowest pollution level, best indoor air quality) and ten (highest pollution level, worst indoor air quality). A tree-structured method is employed in conjunction with the arithmetic mean as the aggregation function. The hierarchical structure of the method renders not only one index value, but several subindex values may also be provided for a more in-depth examination. Major contributing pollutant(s) to the IAPI are determined and ranked by decomposing the index through the tree structure.; In addition, the index ranks office indoor air pollution relative to the index distribution of the U.S. office building population. The index values for the population of the U.S. office buildings follow lognormal distribution. The index associates well with two of the occupant symptom metrics, Percent of Occupants with Symptoms (POS) and Maximum Occupant Symptom Score (MOSS). The association with the third symptom metric, Building Symptom Index (BSI), is not as strong as the former two, however, inclusion of building age as an independent variable with BSI formulated a statistically significant model with 63% of the variation in the index explained by the multiple linear regression model.