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A platform for research: civil engineering, architecture and urbanism
Prediction models of bioaerosols inside office buildings: A field study investigation
https://orcid.org/0000-0003-2848-9146
Bioaerosols formed by microorganisms in the air directly affect people’s health. The air quality in an office building in Shenzhen, China, is investigated and pollutant levels measured on 36 occasions; six times for each of six indoor spaces. A relationship between indoor bioaerosols and environmental factors was determined using both linear regression analysis and Poisson regression analysis. Our results and analysis indicate that linear regression is a poor predictor for the concentration of bioaerosols based on a single indicator. In contrast, Poisson regression can better predict the concentration of bioaerosols, and PM10 may be the indicator with the greatest impact on bioaerosols. As a result, a simple, fast, and low-cost online monitoring method for monitoring indoor bioaerosols is developed and reported. Our paper provides first-hand basic data to predict the indoor bioaerosol concentration and helps to formulate appropriate monitoring guidelines. The proposed method offers more practical values compared to existing studies as our prediction model facilitates estimation of the concentration of bioaerosols at low cost. Additionally, due to the current maturity and low cost of indoor environmental sensors, the proposed method is suitable for large-scale deployment for most buildings.
Prediction models of bioaerosols inside office buildings: A field study investigation
https://orcid.org/0000-0003-2848-9146
Bioaerosols formed by microorganisms in the air directly affect people’s health. The air quality in an office building in Shenzhen, China, is investigated and pollutant levels measured on 36 occasions; six times for each of six indoor spaces. A relationship between indoor bioaerosols and environmental factors was determined using both linear regression analysis and Poisson regression analysis. Our results and analysis indicate that linear regression is a poor predictor for the concentration of bioaerosols based on a single indicator. In contrast, Poisson regression can better predict the concentration of bioaerosols, and PM10 may be the indicator with the greatest impact on bioaerosols. As a result, a simple, fast, and low-cost online monitoring method for monitoring indoor bioaerosols is developed and reported. Our paper provides first-hand basic data to predict the indoor bioaerosol concentration and helps to formulate appropriate monitoring guidelines. The proposed method offers more practical values compared to existing studies as our prediction model facilitates estimation of the concentration of bioaerosols at low cost. Additionally, due to the current maturity and low cost of indoor environmental sensors, the proposed method is suitable for large-scale deployment for most buildings.
Prediction models of bioaerosols inside office buildings: A field study investigation
https://orcid.org/0000-0003-2848-9146
Bioaerosols formed by microorganisms in the air directly affect people’s health. The air quality in an office building in Shenzhen, China, is investigated and pollutant levels measured on 36 occasions; six times for each of six indoor spaces. A relationship between indoor bioaerosols and environmental factors was determined using both linear regression analysis and Poisson regression analysis. Our results and analysis indicate that linear regression is a poor predictor for the concentration of bioaerosols based on a single indicator. In contrast, Poisson regression can better predict the concentration of bioaerosols, and PM10 may be the indicator with the greatest impact on bioaerosols. As a result, a simple, fast, and low-cost online monitoring method for monitoring indoor bioaerosols is developed and reported. Our paper provides first-hand basic data to predict the indoor bioaerosol concentration and helps to formulate appropriate monitoring guidelines. The proposed method offers more practical values compared to existing studies as our prediction model facilitates estimation of the concentration of bioaerosols at low cost. Additionally, due to the current maturity and low cost of indoor environmental sensors, the proposed method is suitable for large-scale deployment for most buildings.
Prediction models of bioaerosols inside office buildings: A field study investigation
Jiang, Dong (author) / Gong, Xiaoqiang (author) / Xu, Zhengsong (author) / Yuan, Kai (author) / Bu, Zengwen (author)
Building Services Engineering Research & Technology ; 44 ; 577-600
2023-09-01
24 pages
Article (Journal)
Electronic Resource
English
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