1993 Volume 43 Issue 3 Pages 331-344
The aim of this study was to identify dental hospital air pollution and environmental problems. In a previous study, climatic factors (temperature, humidity, air flow) and environmental factors (number of persons, opened windows, outlets of functioning air-conditioner) were measured for 2 years in 4 clinics at the Asahi University dental hospital. Airborne bacteria were detected with a SY type pin hole sampler (SY method) and Koch's dropping method (Koch method) at the time of measuring the climatic and environmental factors described above.
Analyses were performed regarding three points, namely, chronological changes in number of airborne bacteria colonies caused by the clinic, evaluation of correlations between the number of airborne bacteria by the SY method and by the Koch method, and the number of airborne bacteria outside and inside the clinics, and multiple regression analysis of the climatic and emvironmental factors affecting airborne bacterial colonies in the clinic, to estimate the equation of multiple regression.
The following conclusions were obtained,
1. The airborne bacterial count was from 0.05 to 1.20 colony forming units (CFU) per liter with the SY method and 0.48 to 4.23 with the Koch method in the middle-sized clinics, and from 0.05 to 0.58 CFU per liter with the SY method and 0.47 to 2.32 with the Koch method in the large clinics.
2. The operation of the air-conditioner and the number of persons affected the chronological changes in number of airborne bacterial colonies in the clinics.
3. The correlation between the number of airborne bacteria detected with the SY method and that detected with the Koch method had a high coefficient of 0.644 (the middle-sized clinics) and 0.600 (the large clinics).
4. The correlation between the number of airborne bacteria outdoors and that inside the clinics was not significant.
5. The degree of influence of climatic and environmental factors on the number of airborne bacteria was quantified by the multiple regression analysis. We believe that the linear multiple regression equation can be used to estimate the number of airborne bacteria in the dental clinic on real time.
