Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.

BACKGROUND: A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. OBJECTIVE: In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. METHOD: We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. RESULTS: The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. SIGNIFICANCE: Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.

Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models.

Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research to reduce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect to their applicability to human exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency's Stochastic Human Exposure and Dose Simulation (SHEDS) model. The methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol's method. The first five methods are known as "sampling-based" techniques, wheras the latter two methods are known as "variance-based" techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol's method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs; however, Sobol's method and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the sampling-based methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.