第九届青年地学论坛

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⋅m^-2⋅μg⋅m^-3) and residential-commercial land (292.34 people⋅m^-2⋅μg⋅m^-3). Exposure risks of PM_2.5 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 influenced 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 characteristics 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 costeffective for sustainable urban planning and development.

Mobile monitoring,Geographically weighted regression,Particulate matter,Cell phone signaling data,Exposure risk