Temporal and spatial variations in IAQ and its association with building characteristics and human activities in tropical and subtropical areas

Jung, Chien‐Cheng; Hsu, Nai Yun; Su, Huey Jen

doi:10.1016/j.buildenv.2019.106249

Cited by 22 publications

(15 citation statements)

References 58 publications

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“…Moreover, Clark’s study indicated that forced air heating is a key variable in prediction models of indoor PM 2.5 concentrations. We did not find that heater use was associated with indoor PM 2.5 concentration, possibly because only 8% of occupants had heaters; moreover, the heaters were only operated 0.3 h/day during the winter in our previous study [ 21 ]. This indicates that heater use is not a critical variable in tropical and subtropical regions.…”

Section: Resultsmentioning

confidence: 66%

“…We speculate that outdoor air does not critically influence indoor air in different temperature regions because of doors and windows closing, particularly in the winter. In Taiwan, the frequency of window-opening behavior is high due to its warm climate [ 21 ]; thus, outdoor air considerably influences indoor air in Taiwan. Moreover, Clark’s study indicated that forced air heating is a key variable in prediction models of indoor PM 2.5 concentrations.…”

Section: Resultsmentioning

confidence: 99%

“…Studies have demonstrated that indoor air quality differs by ventilation type [ 18 , 19 , 20 ]. Our previous study observed that outdoor air more critically affects indoor air quality in tropical and subtropical regions than in cold or temperate regions [ 21 ], particularly during cold seasons. Outdoor air is a critical source of PM 2.5 in homes [ 13 , 22 ], and indoor sources, such as cigarettes, cooking, or coal smoke from home heating stoves, are major indoor sources of PM 2.5 [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Jung

Lin

Hsu

et al. 2020

IJERPH

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Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations.

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Section: Resultsmentioning

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Jung

Lin

Hsu

et al. 2020

IJERPH

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“…Indoor air pollution in a residential building is affected by many factors such as furniture, temperature and humidity, ventilation rate, indoor living activities, and adsorption effect (Jung et al, 2019). It has a more complex emission mechanism than the release characteristics seen in single materials (Gunschera et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Influence of the Building Material on the Indoor Air Quality in Apartment in Dubai

Arar

Jung

Qassimi

2022

Front. Built Environ.

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The residents of Dubai spend more than 90% of their time indoors and this lifestyle makes them easily exposed to Sick Building Syndrome (SBS). Even though Dubai Municipality strictly apply the IAQ (Indoor Air Quality) stipulation, indiscreet use of unproven finishing materials has been increased to deteriorate the health of residents in Dubai. The objective of this paper is to investigate the degree of influence of building material on indoor air pollutants concentration by measurement and prediction. As a methodology, indoor pollutants concentration was measured and investigated, variables were extracted through emission intensity experiments, and the indoor concentration was predicted by applying the double exponential decay model. The result had shown that electronic products, furniture, and textile products become new sources of indoor air pollution. The difference in emission patterns of wallpaper and flooring is confirmed via the emission rate test. It is statistically proven that Formaldehyde (CH2O) and VOCs showed a difference in the cumulative emission amount within 100 h but after that, it was confirmed that the difference in emission amount between materials became very small. In case of CH2O, the cumulative emission of the flooring material is greater than that of the wallpaper. This study will serve as a basic data to explore the cause of indoor air pollutants in daily life to reduce SBS symptoms in Dubai.

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“…Extreme temperature has a critical effect on human health [1], numerous studies have investigated the risk of extreme temperature exposure on respiratory diseases [2][3][4][5][6][7]. People spend over 80% of their time indoors [8,9], and several factors affect indoor temperature change, such as building materials, air conditioning usage, and human activities [10]. Therefore, indoor temperature may be a more decisive factor in the risk of respiratory diseases than outdoor temperature is.…”

Section: Introductionmentioning

confidence: 99%

Indoor temperature and respiratory disease–related emergency department visits: a study of cumulative effects among older adults in Taiwan

Chien-Cheng¹,

Hsia²,

Hsu³

et al. 2020

Preprint

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Background Studies have demonstrated that exposure to extreme outdoor temperatures increases respiratory diseases related mortality and morbidity, especially in older adults. However, older adults spend over 80% of their time indoors, and the cumulative effects of exposure to indoor temperature on the risk of respiratory diseases have not been investigated. The objective of this study was to study the cumulative effect of indoor temperature exposure on emergency department visit due to respiratory diseases among older adults. Methods Participant data were collected from the Longitudinal Health Insurance Database from 2000 to 2014 in Taiwan. The cumulative degree hour was used to reflect the cumulative effect of indoor temperature exposure. A distributed lag nonlinear model was used to analyze the association between cumulative degree hour and emergency department visit due to respiratory diseases. Results Our findings revealed a significant risk of emergency department visits due to respiratory diseases at 27, 28, 29, 30, and 31 °C when cooling cumulative degree hours exceeded 60, 30, 19, 1, and 1, respectively, during the hot season (May to October) and at 19, 20, 21, 22, and 23 °C when heating cumulative degree hours exceeded 1, 1, 1, 39, and 49, respectively, during the cold season (November to April). Conclusions We conclude that the cumulative effects of indoor temperature exposure should be considered to reduce respiratory disease risk under climate change.

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Temporal and spatial variations in IAQ and its association with building characteristics and human activities in tropical and subtropical areas

Cited by 22 publications

References 58 publications

Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Investigating the Influence of the Building Material on the Indoor Air Quality in Apartment in Dubai

Indoor temperature and respiratory disease–related emergency department visits: a study of cumulative effects among older adults in Taiwan

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