Literature DB >> 28741740

Ultrafine, fine, and black carbon particle concentrations in California child-care facilities.

F W Gaspar1, R Maddalena2, J Williams3, R Castorina1, Z-M Wang4, K Kumagai2,4, T E McKone2, A Bradman1.   

Abstract

Although many U.S. children spend time in child care, little information exists on exposures to airborne particulate matter (PM) in this environment, even though PM may be associated with asthma and other respiratory illness, which is a key concern for young children. To address this data gap, we measured ultrafine particles (UFP), PM2.5 , PM10 , and black carbon in 40 California child-care facilities and examined associations with potential determinants. We also tested a low-cost optical particle measuring device (Dylos monitor). Median (interquartile range) concentrations for indoor UFP, gravimetric PM2.5 , real-time PM2.5 , gravimetric PM10 , and black carbon over the course of a child-care day were 14 000 (11 000-29 000) particles/cm3 , 15 (9.6-21) μg/m3 , 15 (11-23) μg/m3 , 48 (33-73) μg/m3 , and 0.43 (0.25-0.65) ng/m3 , respectively. Indoor black carbon concentrations were inversely associated with air exchange rate (Spearman's rho = -.36) and positively associated with the sum of all Gaussian-adjusted traffic volume within a one-kilometer radius (Spearman's rho = .45) (P-values <.05). Finally, the Dylos may be a valid low-cost alternative to monitor PM levels indoors in future studies. Overall, results indicate the need for additional studies examining particle levels, potential health risks, and mitigation strategies in child-care facilities.
© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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Keywords:  daycare; early childhood education; indoor air; predictors

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Year:  2017        PMID: 28741740     DOI: 10.1111/ina.12408

Source DB:  PubMed          Journal:  Indoor Air        ISSN: 0905-6947            Impact factor:   5.770


  1 in total

1.  Assessing and Validating the Ability of Machine Learning to Handle Unrefined Particle Air Pollution Mobile Monitoring Data Randomly, Spatially, and Spatiotemporally.

Authors:  Asmaa Alazmi; Hesham Rakha
Journal:  Int J Environ Res Public Health       Date:  2022-08-16       Impact factor: 4.614

  1 in total

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