The influence of indoor air pollution sources on respiratory health of occupants in offices : a cross-sectional study at Suan Sunandha Rajabhat University, Bangkok, Thailand
Bibliographic record
Abstract
Background: Indoor air pollution contains several substances and can emanate from a range of sources. In particular importance might be substances known as volatile organic compounds and respirable particulates. The exposure to indoor air pollution can induce a wide range of acute and chronic respiratory health effects. The study’s aims were to identify the effect of indoor air pollution sources and concentrations in offices on respiratory health of occupants and identify potential factors that may be related to respiratory health problems. Methods: Fourteen offices were measured the concentration of PM2.5 and TVOC at 1.20 meters high for 8 hours and office characteristics such as building age, floor and furnishing materials, room volume were observed together with number of computers, printers and photocopiers. The 212 occupants in these offices were questioned and tested the lung function. The subjective respiratory symptoms were cough, phlegm, wheezing and short breathing. Focus group discussions was conducted with twelve occupants with abnormal lung function and symptoms Results: Two hundred and twelve occupants mostly are female, nonsmoker and age average 34.61 ± 7.501 years old. They mostly report no history in medical records and history in dusty job, gas or volatile job and fume job. Quarter of them have over ten year experience in current job and two third work more than eight hour a day. The age of office buildings are 8-26 years. Floor materials are tile, rubber, carpet and furnishing materials are MDF, cement, gypsum, glass and metal. The most number of computers, printer and photocopiers is 50, 28 and 3 sets respectively. The mean concentration of PM2.5 in these offices is 0.026 ± 0.006 mg/m3 and in range of 0.015 - 0.039 mg/m3. The mean concentration of TVOC in these offices is 156.38 ± 59.34 ppb and in range of 45.33 – 260.67 ppb. The prevalence of restrictive lung function, obstructive lung function and combined are 236, 28 and 28 cases per thousand persons respectively and the prevalence of cough, phlegm, wheezing and short breathing are 255, 160, 184 and 156 cases per thousand persons respectively. The logistic regression analysis shows that concentration of TVOC was significantly associated with FVC and wheeze symptoms (p-value < 0.05), concentration of PM2.5 was significantly associated with FEV1/FVC (p-value < 0.05) and smoking was significantly associated with cough (p-value < 0.05). The significantly association between history in gas/volatile job and phlegm, current work experience and FEV1/FVC were found. The odds of restrictive abnormal lung function were 9.289 times higher in high TVOC exposure and 0.110 times lower in the large office. The odds of obstructive abnormal lung function were 3.588 times higher in the high PM2.5 exposure and 3.407 times higher in longer experience in current job. The odds of cough were 2.438 times higher in smoker. The odds of phlegm were 4.184 times higher in former exposure in gas or volatile. The odds of wheezing were 3.196 times higher in the high TVOC exposure. The odds of short breathing were 2.791 times higher in female. Conclusion: Indoor air pollution exposure can risk the respiratory health effects. Smoking and work experience also affect to the respiratory health. The risk of occupational respiratory health may decrease if indoor air pollution become lower.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".