Distribution patterns and influencing factors of population exposure risk to particulate matters based on cell phone signaling data

In this study, spatial-temporal characteristics of particular matter (PM) exposure risk in Shenyang were analyzed with landscape patterns using data from land use, cell phone signaling, and PM mobile monitoring. Pollution surfaces were established with geographically weighted regression models and impact factors analysis was implemented by boosted regression tree models. The results showed that weekdays and weekends had different spatial distributions of PM, and the exposure risk was lower on weekends. High exposure risks of PM10 were concentrated in the first ring zone (76.53 people center dot m(-2)center dot mu g center dot m(-3)) and residential-commercial land (292.34 people center dot m(-2)center dot mu g center dot m(-3)). Exposure risks of PM2.5 were most affected by residential-commercial land and fourth-class (relative contribution: 59.69 and 8.88, respectively). However, the exposure risks of PM10 were more influ-enced by first-class roads (relative contribution: 2.01). The results indicated that independent modeling analysis of different types of PM and periods contribute to more detailed studies of spatial-temporal variation of PM. For human activity studies, cell phone signaling data can effectively distinguish spatial-temporal distribution char-acteristics of the population on weekdays and weekends. Multi-source big data combined with mobile monitoring and model simulations were used to make population exposure risk studies more accessible, real-time, and cost-effective for sustainable urban planning and development.

Automated summary

In this study, the spatial-temporal characteristics of PM exposure risk in Shenyang were analyzed using landscape patterns and data from land use, cell phone signaling, and PM mobile monitoring. The results showed different spatial distributions of PM on weekdays and weekends, with lower exposure risk on weekends. The study also identified the key factors influencing PM exposure risks.