Research on risk scorecard of sick building syndrome based on machine learning

Fan, Lingxiao; Ding, Yong

doi:10.1016/j.buildenv.2021.108710

Cited by 15 publications

(8 citation statements)

References 48 publications

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“…Community follow-up work is the daily work of community doctors to carry out early screening and management of chronic diseases of diabetes. Through mining the big data of community follow-up, the establishment of diabetes risk score card ( Fan and Ding, 2022 , Wang et al, 2021 ) can help community doctors to assess the risk of diabetes by calculating community residents' scores through important life characteristics. According to the definition of diabetes (the fasting blood glucose of normal people is lower than 6.1 mmol/L, and the fasting blood glucose is greater than or equal to 7.0 mmol/L, which meets the diagnostic criteria of diabetes), we made classified statistics on the fasting blood glucose of the data set according to “<6.1″, ”>=6.1,<7.1″, “>=7.1″.…”

Section: Resultsmentioning

confidence: 99%

Diabetes risk prediction model based on community follow-up data using machine learning

Jiang,

Xia,

Zhu

et al. 2023

Preventive Medicine Reports

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Section: Resultsmentioning

confidence: 99%

Diabetes risk prediction model based on community follow-up data using machine learning

Jiang,

Xia,

Zhu

et al. 2023

Preventive Medicine Reports

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“…In the non-sealed building, fungi exposure was the unique significant risk factor for SBS’. Fan and Ding 56 investigated 2370 office buildings in Chongqing, China and found that the prevalence of mucosal irritation was 29%. The ratios of irritation of the nose or throat in Singapore and Japan were 39.4% and 25%, respectively.…”

Section: End-user Experience Resultsmentioning

confidence: 99%

Investigation of the indoor environment of a passive house office building under cold climate in China

Han

Zhao

et al. 2022

Indoor and Built Environment

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This study analyzed the applicability of the Passive House standard to office buildings in northern China from an indoor environment perspective. The case building has been auto-monitored continuously since 2017 and a survey for instantaneous and retrospective appraisal of indoor environmental conditions was administered in 2021 during the summer, winter and months in between, referred to as ‘transition months’. The results show that indoor environment quality was good, with high satisfaction levels and ultra-low energy consumption; overheating and the need for window opening were observed. The prevalence of sick building syndrome was reduced compared to conventional buildings. This work further determined how comfort parameters such as perceived thermal comfort, indoor air quality and acoustics could affect end-users’ appraisal. Findings show that indoor thermal comfort design parameters, particularly in summer, are critical factors affecting users' satisfaction in Passive Houses. We further examined the feasibility of the PMV-PPD index for predicting occupants’ perceived comfort and suggested deriving the preferred thermal comfort design parameters for the Chinese market, using long-term data and measurements. These findings provide insights into the localization of Passive Houses in China, thereby improving the acceptance of Passive Houses in what is expected to be a fast growing market.

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“…Excessive number of fungi in the indoor air may contribute to the development of the SBS (Sik Buildin Syndrome) phenomenon, which, together with chemical, physical and psychological factors, may have a negative impact on human health [4,5].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Indoor air Quality in Offices and Classrooms in Public Utility Building - A Case Study

Łuszczyńska

2022

Civil and Environmental Engineering Reports

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A microbiological analysis of indoor air quality in offices, lecture rooms, and hallways in a public utility building has been carried out in the Lubuskie Voivodship (Poland). In one of the storage rooms, the presence of fungi on the building partition was found in the place of water infiltration as a result of a technological failure in the building. A total of 17 non-residential interiors were examined. The total number of psychrophilic and mesophilic bacteria and the total number of fungi were sampled by the collision method with the air sampler. A qualitative analysis has been performed of fungi present in the indoor air and from building partition of the studied interiors. A total of 11 species of fungi have been specified. Relative humidity and air temperature were measured with a hygrometer. The studies showed that the number of psychrophilic bacteria in indoor air was in the range 0–730 CFU/m3, while the number of mesophilic bacteria was slightly higher (0 - 896 CFU/m3). A high level of contamination and a large number of fungi (734 CFU/m3) were found in the indoor air of storage room, where the problem of biodeterioration of building partitions occurred. In the remaining rooms, the level of microorganisms was low or moderate. Microbiological tests of the outdoor air (background) did not reveal an excessive number of microorganisms in the air.

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Research on risk scorecard of sick building syndrome based on machine learning

Cited by 15 publications

References 48 publications

Diabetes risk prediction model based on community follow-up data using machine learning

Diabetes risk prediction model based on community follow-up data using machine learning

Investigation of the indoor environment of a passive house office building under cold climate in China

Indoor air Quality in Offices and Classrooms in Public Utility Building - A Case Study

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