Impacts of distinct travel behaviors on potential air pollution exposure measurement error

Lu, Yougeng; Habre, Rima

doi:10.1016/j.atmosenv.2023.119820

Cited by 1 publication

(7 citation statements)

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“…Previous studies have also found that the work activity played an important role in assessing personal exposure to air pollution. 7,16,47,48 In addition, for work actives with commuting distances below 10 km, we found that hourly residence-based PM 2.5 exposures were underestimated by 1%; this underestimation reached 10% for commuting distances over 30 km. This dependency of exposure bias on distance was also observed in previous studies.…”

Section: Discussionmentioning

confidence: 68%

“…This difference is consistent with previous studies. 7,16 For example, Lu 7 found that the mean static PM 2.5 exposure was 0.05 μg∕m 3 lower than dynamic estimates for 100,784 individuals in Los Angeles County; further analysis revealed that this difference was larger for workers (0.28 μg∕m 3 ) with long commuting distance and was close to 0 μg∕m 3 for students. 16 This study had two extreme cases in term of the difference between daily static and dynamic estimates.…”

Section: Discussionmentioning

confidence: 98%

“…7,16 For example, Lu 7 found that the mean static PM 2.5 exposure was 0.05 μg∕m 3 lower than dynamic estimates for 100,784 individuals in Los Angeles County; further analysis revealed that this difference was larger for workers (0.28 μg∕m 3 ) with long commuting distance and was close to 0 μg∕m 3 for students. 16 This study had two extreme cases in term of the difference between daily static and dynamic estimates. For the first case, which corresponded an airport worker who stayed at the airport for 24 h on the case day, the daily static exposure was lower by 4.90 μg∕m 3 compared with the dynamic one.…”

Section: Discussionmentioning

confidence: 98%

“…Mobility sources for this field typically include global positioning system (GPS)-based monitoring measurements, [10][11][12] mobile phone location data, 6,[13][14][15] and travel model data. 7,16 These data sources, however, have some limitations. Specifically, GPS data usually contain a limited sample size due to participant burden and device cost.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of personal air pollution exposure, one challenge is the lack of human mobility data. Mobility sources for this field typically include global positioning system (GPS)-based monitoring measurements, 10 – 12 mobile phone location data, 6 , 13 – 15 and travel model data 7 , 16 . These data sources, however, have some limitations.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of personal exposure using movement trajectory and hourly 1-km PM2.5 concentrations

Bai,

Song,

et al. 2023

J. Appl. Rem. Sens.

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Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM 2.5 concentrations, with a good performance for out-of-station cross validation (correlation coefficient of 0.89, root-mean-square error of 5.60 μg∕m 3 , and mean absolute error (MAE) of 4.04 μg∕m 3 ). Based on these PM 2.5 concentrations and location data, PM 2.5 exposures considering individual movement were calculated and further compared with residence-based exposures. Our results showed that daily residence-based exposures had an MAE of 0.19 μg∕m 3 and were underestimated by <1% overall. For hourly residence-based exposures, the MAE exhibited a diurnal variation: it decreased from 0.58 μg∕m 3 at 09:00 to 0.44 μg∕m 3 at 12:00 and then continuously increased to 0.74 μg∕m 3 at 17:00. The biases also depended on activity types and distances from home to activity locations. Specifically, the largest MAE (3.86 μg∕m 3 ) occurred in visits that were among the top four types of activity other than being at home. As distances changed from <10 to >30 km, the degree of underestimation for hourly residence-based exposures increased from 1% to 6%. This trend was more obvious for work activities, suggesting that personal exposure assessments should consider individual movement for work cases with long commuting distances.

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Section: Discussionmentioning

confidence: 68%