Abstract
Background Schools and formal education can be a bridge for airborne disease to spread caused by air germs. Measurement of air germs result, shows that class 4 (9482 CFU/m3) and class 5(2371 CFU/m3) in SDN 5 Teluk, Purwokerto Selatan district. The average air gems rate is 1685.33 CFU/m3 in SDN Karangmangu, Baturaden district. The aims of this study was to analyze predictive factors for air germs number in public elementary schools in Banyumas Regency. Methods This research is observational study with cross sectional analytic approach. The independent variables or predictive variables are temperature, humidity, lighting, occupancy density, occupant behavior, cleaning frequency, and ventilation area. The dependent variable is the number of air germs. The sample size was 46 classrooms. The analysis used simple and multiple regression. Research Resulth average temperature (29.9130C), humidity (74.087%), lighting (225.304 lux), occupancy density (2.050 m2 / person), cleaning frequency (2.5 times / day), occupant behavior (53.470% active), ventilation area (9,171%), air germ rate (3425,130 CFU / m3), wind speed (not detected by tools). Prediction of temperature with the number of air germs, Y = 1026.505 + 80.187 X, R = 0.169, p = 0.262. Prediction of humidity with the number of air germs, Y = 2719.038 + 9.531 X, R = 0.083, p = 0.585. Prediction of exposure with air germ count, Y = 3343.684 + 0.361 X, R = 0.059, p = 0.696. Prediction of occupancy density with air germ numbers, Y = 3959.041 + (-260.389) X, R = - 0.386, p = 0.008. Prediction of cleaning frequency with air germ count, Y = 3204.664 + 88.187 X, R = 0.150, p = 0.320. Prediction of occupant behavior with air germ count, Y = 3632.488 + (-3.878) X, R = - 0.160, p = 0.289. Prediction of ventilation area with air germ count, Y = 3965.421 + (-58.911) X, R = -0.427, p = 0.003. Simultaneously predict temperature, humidity, lighting, occupancy density, cleaning frequency, occupant behavior and ventilation area with air germ count, Y = (-1267.495) + (-194.907) (density p = 0.049) + (-42.019) ( Ventilation p = 0.061) + 148.449 (Temperature p = 0.072) + 90.826 (Cleaning p = 0.379) + 12.187 (Humidity p = 0.543) + (-2.205) (Behavior p = 0.561) + 0.111 (Exposure p = 0.913), R = 0.5850. Conclusion , predictive factors for occupancy density, ventilation and temperature are significant in predicting the number of airborne germs. Suggestions need to regulate the number of students in each class, the availability standard ventilation, and the addition of an Exhauster.
Highlights
Schools as a means of formal education in this country should be a comfortable place to study (Rr. Sumiyati, 2015, p. 2)
Predictions of Exposure to Air Germ Numbers The results of the analysis showed that the value of p = 0.696, it was stated that there was an insignificant relationship between lighting and the number of air germs
The prediction of exposure to air germ counts in this study shows that the higher the lighting in the classroom, the higher the air germ count in public elementary school classrooms
Summary
The independent variables or predictive variables are temperature, humidity, lighting, occupancy density, occupant behavior, cleaning frequency, and ventilation area. Research Resulth average temperature (29.9130C), humidity (74.087%), lighting (225.304 lux), occupancy density (2.050 m2 / person), cleaning frequency (2.5 times / day), occupant behavior (53.470% active), ventilation area (9,171%), air germ rate (3425,130 CFU / m3), wind speed (not detected by tools). Predict temperature, humidity, lighting, occupancy density, cleaning frequency, occupant behavior and ventilation area with air germ count, Y = (-1267.495) + (-194.907) (density p = 0.049) + (-42.019) ( Ventilation p = 0.061) + 148.449 (Temperature p = 0.072) + 90.826 (Cleaning p = 0.379) + 12.187 (Humidity p = 0.543) + (-2.205) (Behavior p = 0.561) + 0.111 (Exposure p = 0.913), R = 0.5850. Suggestions need to regulate the number of students in each class, the availability standard ventilation, and the addition of an Exhauster
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