The domestic and international movement to regulate harmful anthropogenic pollution like CFCs and carbon dioxide depends on our ability to accurately monitor spatiotemporal processes on a global scale. However, what is relevant on a global scale can be meaningless in the highly stratified socioeconomic landscape we inhabit, where parks and factories can exist in the same small city. Autonomous and semiautonomous vehicles mounted with analytical instrumentation are capable of sampling on spatiotemporal scales fine enough to inform industry regulation, public health initiatives, local climate models, and even environmental justice advocacy groups. With this in mind, we have developed a system comprising a semiautonomous car mounted with a mixing condensation particle counter (mCPC) that samples particulate matter (PM) with 1 Hz resolution. A spatial Kalman filter algorithm continually updates the rastered pollution map, and a sampling route is planned to maximize the specified objective function without deviating from the existing road system. Altering the prediction algorithm to include temporal variability and underlying deterministic processes helped more accurately predict the true spatiotemporal process. This work bridges the fields of atmospheric chemistry/air pollution and autonomous vehicles to improve sampling rate and spatial coverage.<br/>

Improving pollution mapping with autonomous and semi-autonoumous vehicles

255th National Meeting (2018)

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