AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
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Jiayu Li, PhD/Postdoc/Low-cost Sensor and Aerosol Engineering
JIAYU LI, Washington University in St Louis
Abstract Number: 358 Working Group: Meet the Job Seekers
Abstract My Ph.D. work is related to design, calibration, and application of low-cost PM sensors. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. We have deployed sensor network in a woodworking shop for spatiotemporal pollution mapping. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The integration of data sets was also a way to enhance the overall data quality and accuracy of these low-cost sensors. The maps created from these three data sources demonstrate an approximate 30-fold synergistic improvement in the spatial resolution of PM mapping, with minimal bias. This method will greatly assist the validation of PM transport models and enhance the accuracy of exposure estimations.