Health effects of multidimensional spatiotemporal environmental exposure

Personal exposure assessment is crucial for public health research, which requires multi-dimensional environmental data with high spatiotemporal resolution. Previous studies mainly relied on either stationary or mobile sensing methods to measure environmental indicators. However, these approaches faced limitations in spatial coverage or temporal continuity, which may lead to inadequate accuracy for high-resolution mapping. To address this gap, this paper proposes a "Stationary-mobile Sensing" paradigm that integrates stationary and mobile sensing through dynamic calibration and spatiotemporal fusion. Experiments were conducted within a 3-hectare dormitory area, utilizing six multi-sensor devices to collect various environmental data including temperature, noise, and PM2.5 data. By combining the temporal continuity of stationary sensing with the spatial granularity of mobile sampling, this integrated approach enabled 15m-resolution environmental mapping with temporal variation for each indicator. Results demonstrated that estimation error reductions of the proposed method reduced over 50% compared to single-mode approaches, achieving RMSE values of 0.61°C for temperature, 1.48 μg/m³ for PM2.5, and 0.90 dB for noise. This optimized sensing and mapping method can help to enhance the precision in individual exposure assessment, supporting targeted interventions to mitigate urban health risks.