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. 2021 May 30;18(11):5884.
doi: 10.3390/ijerph18115884.

Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing

Junli Liu  1 , Panli Cai  1 , Jin Dong  1 , Junshun Wang  1 , Runkui Li  1   2   3 , Xianfeng Song  1   2   4   5
Affiliations

Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing

Junli Liu et al. Int J Environ Res Public Health. .

Abstract

The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 μg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 μg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.

Keywords: activity pattern; cell phone; exposure assessment; fine particulate matter; land use regression model.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Some of the prepared land use variables. (a) building; (b) major road; (c) terrain slope; and (d) distance to the south boundary.
Figure 2
Figure 2
Average PM2.5 concentration map and base station distribution, with the cell phone number of each base station at 15:00.
Figure 3
Figure 3
Diurnal change of PM2.5 in different seasons.
Figure 4
Figure 4
Average hourly variation of PM2.5 exposure from 2013 to 2015. The blue line represented the hourly variation solely caused by the commute of the population, and the black line represented the total variation caused by both the commute of the population and the hourly change of PM2.5 concentrations.
Figure 5
Figure 5
Hourly variation of PM2.5 exposure for different seasons after subtracting their mean values. (a) the hourly variation of exposure solely caused by the commute of the population, (b) the hourly total variation of exposure caused by both the commute of population and the hourly change of PM2.5 concentration.
Figure 6
Figure 6
The change in the exposure curve due to the commute of the population. The line at 2:00 represents people at home addresses, and the line at 15:00 represents the working addresses after commuting.
See this image and copyright information in PMC

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