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Introduction:Traditional methods to estimate exposure to PM_2.5(particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure. Methods:This study aims to improve traditional exposure assessment methods for PM_2.5by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US. Results:Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM_2.5concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM_2.5concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM_2.5concentrations when developing exposure reduction strategies. Discussion:This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution. 

Occupations, like many other social systems, are hierarchical. They evolve with other elements within the work ecosystem including technology and skills. This paper investigates the relationships among these elements using an approach that combines network theory and modular systems theory. A new method of using work related data to build occupation networks and theorize occupation evolution is proposed. Using this technique, structural properties of occupations are discovered by way of community detection on a knowledge network built from labor statistics, based on more than 900 occupations and 18,000 tasks. The occupation networks are compared across the work ecosystem as well as over time to understand the interdependencies between task components and the coevolution of occupation, tasks, technology, and skills. In addition, a set of conjectures are articulated based on the observations made from occupation structure comparison and change over time.