Quantifying Individual PM 2.5 Exposure with Human Mobility Inferred from Mobile Phone Data

Zhaoping Hu, Le Huang, Xi Zhai, Tao Yang, Yaohui Jin () and Yanyan Xu ()
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Zhaoping Hu: MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Le Huang: MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Xi Zhai: Shanghai Urban and Rural Construction and Traffic Development Research Institute, Shanghai 200240, China
Tao Yang: Shanghai Urban and Rural Construction and Traffic Development Research Institute, Shanghai 200240, China
Yaohui Jin: MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Yanyan Xu: MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China

Sustainability, 2023, vol. 16, issue 1, 1-15

Abstract: Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM 2.5 exposure by combining residents’ mobility traces and PM 2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM 2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM 2.5 concentration, we quantify individual PM 2.5 exposure and find that the average PM 2.5 exposure of residences in Shanghai is 60.37 ug·h·m − 3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM 2.5 exposure in Shanghai. Our PM 2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development.

Keywords: human mobility; mobile phone data; PM 2.5 exposure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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