Study On The Basis Of Identification And Control Of Microbial Pollution Sources In High-humidity Buildings

The wall of a high-humidity building is prone to condensation due to prolonged exposure to a high-humidity environment,which may induce the growth of microorganisms such as wall mold.The presence of mold on the wall surface affects indoor air quality(IAQ),leading to Sick Building Syndrome(SBS),which poses a potential hazard to human health.There is therefore a need for an efficient method or means to help identify potential sources of contamination with limited remote sensor data inputs.Therefore,this paper takes the high-humidity buildings in Shenyang as the research object,and uses the methods of experimental sampling,theoretical analysis,numerical simulation and computational verification to propose a method for identifying the limited sudden microbial pollution sources in the high-humidity building space.This paper first introduces the mold breeding prediction model and the microbial pollution source identification method.In the identification of pollution source location,the optimization method and probability method are adopted.The Pearson correlation coefficient method is proposed to qualitatively identify the pollution source.At the same time,the probability of fuzzy set theory is used to realize the qualitative location identification of the pollution source.By simulating the pollution sources of 8 groups of points to be measured in 8 areas of the room,the three sensors arranged at special points in the indoor space realized accurate source identification within 200s,and the recognition result rate reached more than 90%after 300s..The correctness of the Pearson correlation coefficient method is verified.In terms of the identification of pollution source intensity,it is inferred that there is a proportional relationship between the source intensity of the pollution source of the point to be measured and the source intensity of the body point of the hypothetical area,and the proportional coefficient is the slope of the fitted curve of the two.The inference proposes that the source strength characteristics of the pollution source emission point can be accurately predicted within a limited time when the pollution source is unknown.In the optimization of multi-space overall sensor layout,the source identification effects of sensors ?1,?2 and ?3 at three locations are compared and analyzed from two aspects:source recognition duration and recognition score rate.The scores obtained by the time-lapse period are standard.The comparison shows that in the mixed ventilation room,the exhaust vent is the best position for arranging the sensor,followed by the position of the air vent,and the worst recognition effect is the geometric center position of the room.Taking the public bath office hall of a university in Shenyang as the research object of high-humidity building,the sampling experiment of mold type was carried out.Subsequently,using CHAMPS wall simulation software and WUFI-Bio mold growth simulation software to simulate mold growth,the mold growth rate curve and mold growth index curve were obtained.Through the analysis of the sensitivity of the factors affecting the mold,it is found that the factors affecting the growth of mold are,from large to small,humidity,mold growth material and temperature.Based on the identification results,the critical line of indoor temperature and humidity of mold growth risk in the wall surface was obtained,and a series of preventive measures for mold growth of high-humidity building walls were proposed.The research results of this subject will realize the identification of limited sudden microbial pollution sources in high-humidity buildings,thereby achieving the purpose of reducing building energy consumption,enhancing wall durability and improving IAQ,and forming high-efficiency indoor pollutants in high-humidity buildings.Control strategy to solve the major livelihood problems of deteriorating air quality in high-humidity buildings.