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Transport most likely to cause air pollution peak exposures in everyday life: Evidence from over 2000 days of personal monitoring.

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Title: Transport most likely to cause air pollution peak exposures in everyday life: Evidence from over 2000 days of personal monitoring.
Authors: Dons, Evi1,2 (AUTHOR) evi.dons@uhasselt.be, Laeremans, Michelle2,3 (AUTHOR), Orjuela, Juan Pablo4 (AUTHOR), Avila-Palencia, Ione5,6,7 (AUTHOR), de Nazelle, Audrey4 (AUTHOR), Nieuwenhuijsen, Mark5,6,7 (AUTHOR), Van Poppel, Martine2 (AUTHOR), Carrasco-Turigas, Glòria5,6,7 (AUTHOR), Standaert, Arnout2 (AUTHOR), De Boever, Patrick1,2 (AUTHOR), Nawrot, Tim1,8 (AUTHOR), Int Panis, Luc2,3 (AUTHOR)
Source: Atmospheric Environment. Sep2019, Vol. 213, p424-432. 9p.
Subject Terms: *Air pollution, *Air quality standards, *Carbon-black, Cycling, Everyday life
Geographic Terms: Belgium, Spain
Abstract: Air quality standards are typically based on long term averages – whereas a person may encounter exposure peaks throughout the day. Exposure peaks may contribute meaningfully to health impacts beyond their contribution to long term averages, and therefore should be considered alongside longer-term exposures. We aim to define and explain peak exposure to black carbon air pollution and look at the relationship between short peak exposures and longer term personal exposure. A peak detection algorithm was applied to pooled data from two independent studies. High-resolution personal black carbon monitoring was performed in 175 healthy adult volunteers for a minimum of two 24-h periods per person. At the same time, we retrieved information on the time-activity pattern. Data covered Belgium, Spain, and the United Kingdom. In total, 2053 monitoring days were included. Exposure profiles revealed 2.8 ± 1.6 (avg ± SD) peaks per person per day. The average black carbon concentration during a peak was 4206 ng/m³. On 5.5% of the time participants were exposed to peak concentrations, but this contributed to 21.0% of their total exposure. The short time in transport (8%), was responsible for 32.7% of the peaks. 24.1% of the measurements in transport were categorized as peak exposure; while sleeping this was only 0.9%. When considering transport modes, participants were most likely to encounter peaks while cycling (34.0%). Most peaks were encountered at rush hour, from Monday through Friday, and in the cold season. Gender and age had no impact on the presence of peaks. Daily average black carbon exposure showed only a moderate correlation with peak frequency (r = 0.44). This correlation coefficient increased when considering longer term exposure to r > 0.60 from 10 days onward. The occurrence of peaks varied substantially over time, across microenvironments and transport modes. Daily average exposure was moderately correlated with peak frequency. Real-time air pollution alerting systems may use the peak detection algorithm to support citizens in self-management of air pollution health effects. • Over 2000 days of personal monitoring of black carbon with the microAeth AE51. • Exposure profiles revealed 2.8 peaks per person per day using our peak detection algorithm. • Peaks contributed to 21% of total daily exposure to black carbon. • Participants most likely to encounter peaks while being in transport, and specifically bicycling. • Peak frequency and average exposure were only moderately correlated in a 24-h period. [ABSTRACT FROM AUTHOR]
Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Transport most likely to cause air pollution peak exposures in everyday life: Evidence from over 2000 days of personal monitoring.
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  Data: <searchLink fieldCode="AR" term="%22Dons%2C+Evi%22">Dons, Evi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> evi.dons@uhasselt.be</i><br /><searchLink fieldCode="AR" term="%22Laeremans%2C+Michelle%22">Laeremans, Michelle</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Orjuela%2C+Juan+Pablo%22">Orjuela, Juan Pablo</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Avila-Palencia%2C+Ione%22">Avila-Palencia, Ione</searchLink><relatesTo>5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22de+Nazelle%2C+Audrey%22">de Nazelle, Audrey</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nieuwenhuijsen%2C+Mark%22">Nieuwenhuijsen, Mark</searchLink><relatesTo>5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Van+Poppel%2C+Martine%22">Van Poppel, Martine</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Carrasco-Turigas%2C+Glòria%22">Carrasco-Turigas, Glòria</searchLink><relatesTo>5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Standaert%2C+Arnout%22">Standaert, Arnout</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22De+Boever%2C+Patrick%22">De Boever, Patrick</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nawrot%2C+Tim%22">Nawrot, Tim</searchLink><relatesTo>1,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Int+Panis%2C+Luc%22">Int Panis, Luc</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Atmospheric+Environment%22">Atmospheric Environment</searchLink>. Sep2019, Vol. 213, p424-432. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Air+pollution%22">Air pollution</searchLink><br />*<searchLink fieldCode="DE" term="%22Air+quality+standards%22">Air quality standards</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon-black%22">Carbon-black</searchLink><br /><searchLink fieldCode="DE" term="%22Cycling%22">Cycling</searchLink><br /><searchLink fieldCode="DE" term="%22Everyday+life%22">Everyday life</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Belgium%22">Belgium</searchLink><br /><searchLink fieldCode="DE" term="%22Spain%22">Spain</searchLink>
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  Data: Air quality standards are typically based on long term averages – whereas a person may encounter exposure peaks throughout the day. Exposure peaks may contribute meaningfully to health impacts beyond their contribution to long term averages, and therefore should be considered alongside longer-term exposures. We aim to define and explain peak exposure to black carbon air pollution and look at the relationship between short peak exposures and longer term personal exposure. A peak detection algorithm was applied to pooled data from two independent studies. High-resolution personal black carbon monitoring was performed in 175 healthy adult volunteers for a minimum of two 24-h periods per person. At the same time, we retrieved information on the time-activity pattern. Data covered Belgium, Spain, and the United Kingdom. In total, 2053 monitoring days were included. Exposure profiles revealed 2.8 ± 1.6 (avg ± SD) peaks per person per day. The average black carbon concentration during a peak was 4206 ng/m³. On 5.5% of the time participants were exposed to peak concentrations, but this contributed to 21.0% of their total exposure. The short time in transport (8%), was responsible for 32.7% of the peaks. 24.1% of the measurements in transport were categorized as peak exposure; while sleeping this was only 0.9%. When considering transport modes, participants were most likely to encounter peaks while cycling (34.0%). Most peaks were encountered at rush hour, from Monday through Friday, and in the cold season. Gender and age had no impact on the presence of peaks. Daily average black carbon exposure showed only a moderate correlation with peak frequency (r = 0.44). This correlation coefficient increased when considering longer term exposure to r > 0.60 from 10 days onward. The occurrence of peaks varied substantially over time, across microenvironments and transport modes. Daily average exposure was moderately correlated with peak frequency. Real-time air pollution alerting systems may use the peak detection algorithm to support citizens in self-management of air pollution health effects. • Over 2000 days of personal monitoring of black carbon with the microAeth AE51. • Exposure profiles revealed 2.8 peaks per person per day using our peak detection algorithm. • Peaks contributed to 21% of total daily exposure to black carbon. • Participants most likely to encounter peaks while being in transport, and specifically bicycling. • Peak frequency and average exposure were only moderately correlated in a 24-h period. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.atmosenv.2019.06.035
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      – Code: eng
        Text: English
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        StartPage: 424
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      – SubjectFull: Air pollution
        Type: general
      – SubjectFull: Air quality standards
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      – SubjectFull: Carbon-black
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      – SubjectFull: Cycling
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      – SubjectFull: Everyday life
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      – SubjectFull: Belgium
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      – SubjectFull: Spain
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