Short-term tracking of people's daily movements may systematically overstate environmental inequality, according to a study that used a full year of anonymized mobile signaling records from 110,000 Shenzhen residents. The research formalizes what its authors call the temporal exposure averaging problem (TEAP): the assumption that a few days or weeks of GPS or mobile data can stand in for long-term exposure to greenspace and air pollution.
The work, published (DOI: 10.1016/j.ese.2026.100782) in Environmental Science and Ecotechnology, was conducted by researchers at Hong Kong University of Science and Technology (Guangzhou), The Chinese University of Hong Kong, Tongji University, and Peking University Shenzhen Graduate School.
Prior mobility-based exposure studies have often relied on observation windows of days or weeks and implicitly treated them as representative of long-term conditions. The new analysis, covering March 2023 to February 2024, mapped residents' daily locations to 250 m or 500 m grid cells and linked them to monthly greenspace data and concentrations of fine particulate matter, sulfur dioxide, carbon monoxide, and nitrogen dioxide.
When the team compared monthly mobility-based exposure with year-long exposure, they found that monthly coefficients of variation consistently exceeded annual ones. Individuals with low monthly exposure tended to have higher annual exposure, and vice versa—a bidirectional compression toward the mean. As observation windows lengthened, both intraindividual deviation and interindividual variability declined, converging toward full-year values.
The effect held across age and gender groups, though it was weakest among middle-aged adults and stronger among younger and older groups and among females. Seasonally stratified four-month sampling produced results nearly identical to unrestricted four-month sampling, suggesting that extending observation duration matters more than selecting nominally representative months.
The authors emphasize that TEAP is not random measurement noise but a structural bias that reshapes how exposure inequality appears. Short windows can make environmental disparities look sharper than they are, while longer windows reveal convergence toward population averages.
The implications for environmental justice and urban planning are direct. Exposure disparities estimated from short or seasonally constrained data may misidentify disadvantaged groups or places, whereas long-term, mobility-informed estimates offer a more reliable basis for identifying persistent inequality. At the same time, planners should not rely only on long-term averages: rush-hour pollution peaks, heat waves, and other acute episodes require high-frequency monitoring and targeted interventions.
The authors propose a tolerance-based approach for determining minimum observation windows and call for model-based temporal extrapolation, adaptive sampling, and bias-correction methods when full-year data are unavailable. Integrating longitudinal mobility data into environmental monitoring can help distinguish persistent disparities from short-term fluctuations.
