CS Events

PhD Defense

Fine-grained Air Quality Nowcasting

 

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Tuesday, November 22, 2022, 09:30am - 12:30pm

 

Speaker: Srinivas Devarakonda

Location : CoRE 305

Committee

  • Prof. Badri Nath (Chair)
  • Prof. Desheng Zhang
  • Prof. Yongfeng Zhang
  • Prof. Yu Yang (External)

Event Type: PhD Defense

Abstract: Prolonged exposure to air pollution is a health hazard. Users could reduce pollution exposure by accessing an accurate air quality information stream. The current air quality information stream is coarse-grained (measurements spatially and temporally few and far between) and does not reflect the localized air quality variations that a fine-grained (spatially and temporally dense measurements) information stream could. This dissertation aims to fill this need by proposing new sensing and modeling recipes to achieve fine-grained information streams with pollution inventory. In the first part of the dissertation, we present two mobile sensing platforms for fine-grained real-time pollution measurements - a portable sensing platform deployable on public transportation infrastructure and a personal sensing device that can create a social pollution sensing network. We assemble mobile sensing platforms and deploy them on Rutgers campus buses for evaluation. We conclude that mobile sensing platforms deployed on public transportation infrastructure can help collect fine-grained pollution measurements. We also propose a new neural network architecture - "InsideOut," to infer Carbon Monoxide measurements outdoors based on in-vehicle Carbon Monoxide measurements collected by users monitoring their personal space, thus contributing to the fine-grained pollution inventory outdoors. In the second part of the dissertation, we propose "X-PoSuRe" - a neural network-based regression model for pollution super-resolution trained to infer fine-grained pollution information from coarse-grained pollution measurements akin to image super-resolution, where a neural network model creates high-resolution images from low-resolution images. The X-PoSuRe model uses Nitrogen Dioxide measurements and other air quality covariates for pollution super-resolution. The proposed X-PoSuRe model provides a promising new and novel method for pollution super-resolution from existing low-resolution data sources without the need for deploying expensive equipment over a large area. We further extend this model to infer fine-grained measurements for other pollutants.In the third and final step, we evaluate the benefits of a fine-grained pollution inventory by demonstrating on a neighborhood scale that a significant reduction in pollution exposure can be achieved by choosing a healthy way instead of the shortest or quickest way.Overall, our work pushes the frontiers of inference models in modeling and inferring a hard-to-measure entity using its easily measurable covariates.

Organization

Rutgers