巴尤马斯县小学空气细菌预测因素分析2020

Tri Cahyono, Linda Restu Pamuji, Sukma Cantika Graha Putri
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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. 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引用次数: 0

摘要

学校和正规教育可能是由空气细菌引起的空气传播疾病的桥梁。空气细菌的测量结果显示,在puworkerto Selatan地区的SDN 5 Teluk, 4级(9482 CFU/m3)和5级(2371 CFU/m3)。Baturaden地区Karangmangu的平均空气宝石率为1685.33 CFU/m3。本研究的目的是分析Banyumas县公立小学空气细菌数量的预测因素。方法采用横断面分析法进行观察性研究。自变量或预测变量是温度、湿度、照明、占用密度、占用行为、清洁频率和通风面积。因变量是空气细菌的数量。样本量为46间教室。分析采用简单回归和多元回归。研究结果平均温度(29.9130℃)、湿度(74.087%)、照度(225.304 lux)、占用密度(2.050 m2 /人)、清洁次数(2.5次/天)、占用行为(53.470%主动)、通风面积(9171%)、空气细菌率(3425,130 CFU / m3)、风速(未检测工具)。用空气细菌数预测温度,Y = 1026.505 + 80.187 X, R = 0.169, p = 0.262。空气细菌数对湿度的预测,Y = 2719.038 + 9.531 X, R = 0.083, p = 0.585。空气细菌数对暴露的预测,Y = 3343.684 + 0.361 X, R = 0.059, p = 0.696。用空气细菌数预测占用密度,Y = 3959.041 + (-260.389) X, R = - 0.386, p = 0.008。用空气细菌数预测清洗次数,Y = 3204.664 + 88.187 X, R = 0.150, p = 0.320。空气细菌数对乘员行为的预测,Y = 3632.488 + (-3.878) X, R = - 0.160, p = 0.289。空气细菌数预测通风面积,Y = 3965.421 + (-58.911) X, R = -0.427, p = 0.003。同时用空气细菌数预测温度、湿度、光照、占用密度、清洁频率、占用者行为和通风面积,Y =(-1267.495) +(-194.907)(密度p = 0.049) +(-42.019)(通风p = 0.061) + 148.449(温度p = 0.072) + 90.826(清洁p = 0.379) + 12.187(湿度p = 0.543) +(-2.205)(行为p = 0.561) + 0.111(暴露p = 0.913), R = 0.5850。结论:居住密度、通风量和温度是预测细菌数量的重要预测因子。建议需要规范每个班级的学生人数,提供标准通风,并增加一个排气器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Air Germs Prediction Factors Analysis for Elementary School In Banyumas Regency 2020
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.
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