寒冷地区旅馆客房内CO_(2)浓度变化及其相关性分析

Change and correlation analysis of CO_(2) concentration in hotel rooms in cold area

为研究寒冷地区旅馆客房在外界因素条件(入住人数、入住时间、是否开窗、体重、性别、温湿度)改变时,室内CO_(2)浓度变化情况及各因素之间的相关性,对典型地区天津市的旅馆客房内CO_(2)浓度进行实地测试,采用逐步回归方法进行分析与预测,使用修正后的CO_(2)浓度富集模型表征客房内CO_(2)理论值.结果表明,旅馆客房内CO_(2)污染严重,各因素均对CO_(2)浓度存在不同程度影响.回归分析结果表明,最优回归预测模型为模型3,入住人数、入住时间、是否开窗与客房内CO_(2)浓度呈极显著正相关,各因素对CO_(2)浓度的影响从大到小依次为是否开窗、入住人数、入住时间;体重、湿度、温度、性别与CO_(2)浓度呈微弱正相关.修正后的CO_(2)浓度富集模型得到的理论值与实测值、预测值之间的误差较小,该模型可适用于计算旅馆客房内CO_(2)浓度.研究结果有助于随时随地了解客房CO_(2)浓度,对客房内CO_(2)的控制提供理论依据,对以后安装新风系统进行风量优化设计时提供数据支撑.

The condition of external factors for the study of cold region hotel rooms(registration number,check-in time,whether to open the window,weight,gender,temperature and humidity)change,the indoor CO_(2) concentration changes and the correlation between the various factors,the typical area of tianjin hotel guest room CO_(2) concentration field tests,using stepwise regression method to analysis and forecast,The revised CO_(2) concentration enrichment model was used to characterize the theoretical value of CO_(2) in the guest room.The results showed that CO_(2) pollution in hotel rooms was serious,and all factors had different effects on CO_(2) concentration.The results of regression analysis show that the optimal regression prediction model is Model 3.The number of guests,check-in time,window opening and CO_(2) concentration in the guest room were significantly positively correlated.The influence of each factor on CO_(2) concentration was whether to open the window,number of guests and check-in time in descending order.Bodyweight,humidity,temperature and gender were weakly positively correlated with CO_(2) concentration.The error between the theoretical value,the measured value and the predicted value of the revised CO_(2) concentration enrichment model is small,and the model can be used to calculate the CO_(2) concentration in hotel rooms.The research results are helpful to understand the CO_(2) concentration of guest rooms anytime and anywhere,provide theoretical basis for the control of CO_(2) in guest rooms,and provide data support for the air volume optimization design of the installation of fresh air system in the future.