Characteristics of indoor air quality at urban elementary schools in Seoul, Korea: Assessment of effect of surrounding environments

Yang, Jinho; Nam, In-Sick; Yun, Hyunjun; Kim, Jin Man; Sohn, Jong–Ryeul; Lee, Dohee; Jeon, Si Moon; Yoo, Seung Hun

doi:10.1016/j.apr.2015.06.009

Cited by 59 publications

(25 citation statements)

References 51 publications

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“…Like results from previous studies, this study also found that indoor VOCs and HCHO levels differed by construction year, with a cut-off year of 2000, a median of the 1978–2006 period [ 30 – 32 ]. Much higher levels of indoor air pollutants were found in newly-constructed buildings than older buildings.…”

Section: Discussionsupporting

confidence: 83%

Indoor levels of volatile organic compounds and formaldehyde from emission sources at elderly care centers in Korea

et al. 2018

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The objective of this study is to characterize indoor and outdoor levels of volatile organic compounds (VOCs) and formaldehyde (HCHO) and identify indoor emission sources in thirty elderly care centers (ECCs) located in the Seoul metropolitan city and Gyeonggi province in Korea. Air monitoring samples from indoor and outdoor environments were collected from January to December in 2007. Statistical analyses of indoor and outdoor VOCs and HCHO levels in three rooms (a bedroom, living, and dining rooms) of each ECC were performed, and these were compared to identify environmental factors associated with an increase of indoor pollution levels. Total volatile organic compounds (TVOC) levels were significantly (p<0.05) different between indoor (230.7±1.7 μg/m3) and outdoor (137.8±1.9 μg/m3) environments, with an I/O ratio of 1.67. The indoor HCHO level (20.1±1.6 μg/m3) was significantly (p<0.05) higher than the outdoor level (8.1±1.9 μg/m3), with an I/O ratio of 2.48. Indoor VOCs and HCHO levels in the bedrooms were significantly (p<0.05) higher than those in the living and dining rooms. Furthermore, indoor levels of VOCs and HCHO at ECCs were significantly (p<0.05) different depending on environmental factors such as the use of carpet, paint, and wooden furniture. In multiple regression analysis, indoor VOCs and HCHO levels at ECCs were significantly (p<0.05) correlated with two micro-environmental factors: the use of carpet and paint. This study confirmed that indoor VOCs and HCHO levels were significantly higher than those in outdoor environments. These air pollutants were mainly emitted from indoor sources, such as carpet, paint, and construction materials at the ECCs in Korea.

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Section: Discussionsupporting

confidence: 83%

Indoor levels of volatile organic compounds and formaldehyde from emission sources at elderly care centers in Korea

et al. 2018

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“…On the weekend the concentrations of both pollutants were relatively constant, and for TVOC they were very close to zero. In CR1_A, CR3_A and CR3_C it was possible to identify a similar pattern between the concentrations of CH 2 O and TVOC with a Spearman correlation coefficient between 0.717 and 0.836 ( p < 0.05), suggesting that these two pollutants were emitted simultaneously by the same source, in accordance with Yang, et al [ 33 ]. In CR1_A a peak concentration of both pollutants was detected during the occupation period, specifically during the sleeping/nap time, which seemed to be related to deep cleaning actions in the living and lunch rooms, while children were in the sleeping room (attached to the living and lunch rooms).…”

Section: Resultssupporting

confidence: 85%

“…Branco, et al [ 21 ] identified furniture as the main responsible for the indoor concentrations of CH 2 O in school ME, although reported maximum values of CH 2 O were lower (204 µg/m 3 ) than those found in the present study. In turn, Yang, et al [ 33 ] also identified emission from construction and furnishing materials as a possible cause for the increase of CH 2 O concentrations. A recent study carried out by Bradman, et al [ 58 ], in 40 early childhood education facilities in California serving children aged 6 years or less, also detected CH 2 O at all sampling sites with values lower (max = 48.8 µg/m 3 ) than those measured in the present study.…”

Section: Resultsmentioning

confidence: 99%

“…After observing high concentrations of indoor air pollutants, mainly particulate matter (PM), carbon dioxide (CO 2 ) and volatile organic compounds (VOC), several studies concluded that there is a pressing need to implement strategies to improve IAQ, through the implementation of measures to mitigate indoor air pollution (IAP): (i) the change of some behavioural habits, promoting efficient ventilation [ 8 , 12 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]; (ii) the improvement of cleaning actions [ 19 , 21 , 22 , 23 , 27 , 29 , 31 , 32 , 33 ]; (iii) the replacement of carpets and carpeted floors by smooth panels [ 27 , 33 ]; (iv) the adequacy of occupational density and promotion of more class breaks and outdoor activities [ 12 , 27 , 32 ]; and (v) structural measures such as the installation of air purifiers [ 33 ], the replacement of building materials, furniture and windows [ 25 , 34 ] and the replacement of the heating system [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Low-Cost Mitigation Measures Implemented to Improve Air Quality in Nursery and Primary Schools

Sá

Branco

Alvim-Ferraz

et al. 2017

IJERPH

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Indoor air pollution mitigation measures are highly important due to the associated health impacts, especially on children, a risk group that spends significant time indoors. Thus, the main goal of the work here reported was the evaluation of mitigation measures implemented in nursery and primary schools to improve air quality. Continuous measurements of CO2, CO, NO2, O3, CH2O, total volatile organic compounds (VOC), PM1, PM2.5, PM10, Total Suspended Particles (TSP) and radon, as well as temperature and relative humidity were performed in two campaigns, before and after the implementation of low-cost mitigation measures. Evaluation of those mitigation measures was performed through the comparison of the concentrations measured in both campaigns. Exceedances to the values set by the national legislation and World Health Organization (WHO) were found for PM2.5, PM10, CO2 and CH2O during both indoor air quality campaigns. Temperature and relative humidity values were also above the ranges recommended by American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). In general, pollutant concentrations measured after the implementation of low-cost mitigation measures were significantly lower, mainly for CO2. However, mitigation measures were not always sufficient to decrease the pollutants’ concentrations till values considered safe to protect human health.

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“…Concentrations of NO 2 are usually higher at night than those in the daytime. Therefore, it is reasonable that the NO emitted by anthropogenic sources in daytime was transformed into NO 2 and that NO 2 was accumulated under unfavorable diffusion conditions during night-time (Yang et al, 2015). Chai et al (2014) studied spatial and temporal variation of gaseous pollutants in 26 cities in China.…”

Section: Characteristics Of Gaseous Pollutantsmentioning

confidence: 99%

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models

Shi

Liu

Wang

et al. 2018

Environmental Pollution

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PM is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM were collected at Tianjin, a megacity in northern China. The ambient PM dataset, source information, and gas-to-particle ratios (such as SO/PM, CO/PM, and NOx/PM ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

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Characteristics of indoor air quality at urban elementary schools in Seoul, Korea: Assessment of effect of surrounding environments

Cited by 59 publications

References 51 publications

Indoor levels of volatile organic compounds and formaldehyde from emission sources at elderly care centers in Korea

Indoor levels of volatile organic compounds and formaldehyde from emission sources at elderly care centers in Korea

Evaluation of Low-Cost Mitigation Measures Implemented to Improve Air Quality in Nursery and Primary Schools

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models

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