However, limited by the cost of data transmission and storage, most mobile phone data are only collected when the individual made a phone call or sent a message. It has the advantages of massive users, wide spatial coverage and passive acquisition capability, which has the potential for long-term and well-represented exposure estimation. With the development of information and communication technology (ICT) and the ubiquity of mobile phones, mobile phone data have become an emerging dataset to measure human mobility. In terms of social media data-based methods, representativeness is still problematic, as the data collected only reflects the individual movement characteristics of people who used the application at a specific time. As for the survey data-based methods, the cost of personal monitoring and sampling processes limits the number of samples, thereby reducing the persuasiveness of the estimation results. Although existing literatures have applied survey data, social media data and mobile phone data to record human movement behaviors, several limitations remain. To address this problem, more detailed spatiotemporal human movement information is required. In the field of human exposure estimation, the importance of human mobility has long been recognized. To solve this problem, a better understanding of the negative health effects of air pollution is a prerequisite, which leads to an increased demand of accurate individual air pollution exposure estimates in public health studies. According to the European Society of Cardiology, 8.8 million extra deaths can be linked to air pollution worldwide each year. Air pollution has been proven to be associated with higher health risk of respiratory infection, chronic obstructive pulmonary disease, stroke, heart disease, and lung cancer, among others. With the acceleration of urbanization and industrialization in the past few years, ubiquitous and unavoidable air pollution has become a widespread health problem in many developing countries. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes.

Air media server app dynamic